autonomous vehicles

203 results back to index


Autonomous Driving: How the Driverless Revolution Will Change the World by Andreas Herrmann, Walter Brenner, Rupert Stadler

Airbnb, Airbus A320, algorithmic bias, augmented reality, autonomous vehicles, blockchain, call centre, carbon footprint, clean tech, computer vision, conceptual framework, congestion pricing, connected car, crowdsourcing, cyber-physical system, DARPA: Urban Challenge, data acquisition, deep learning, demand response, digital map, disruptive innovation, driverless car, Elon Musk, fault tolerance, fear of failure, global supply chain, industrial cluster, intermodal, Internet of things, Jeff Bezos, John Zimmer (Lyft cofounder), Lyft, manufacturing employment, market fundamentalism, Mars Rover, Masdar, megacity, Pearl River Delta, peer-to-peer rental, precision agriculture, QWERTY keyboard, RAND corporation, ride hailing / ride sharing, self-driving car, sensor fusion, sharing economy, Silicon Valley, smart cities, smart grid, smart meter, Steve Jobs, Tesla Model S, Tim Cook: Apple, trolley problem, uber lyft, upwardly mobile, urban planning, Zipcar

Others focus on urban use and the integration of autonomous cars with public transport. Autonomous mobility offers the opportunity to link up various modes of transportation intelligently (see Figure 1.4). One application of self-driving vehicles could be to transport travellers on the last mile from the train station to their homes, for example. It is to be expected, however, that not just one, but three types of autonomous vehicles will emerge in the coming years [24, 113, 115]. Robo-cars are revolutionary because they have been conceived as autonomous vehicles right from the start. These vehicles will operate in cities at low speeds, in exactly defined areas and on previously programmed routes.

This page intentionally left blank INDEX A9 autobahn in Germany, 134, 135, 407 ACCEL, 324 Accelerating, 8, 22, 27, 59, 78, 91, 122, 295, 296 Access Economy, 344 Acoustic signals, 108 Ad-hoc mobility solutions, 354 Ad-hoc networks, 133 Adaptive cruise control, 4, 51, 72 74, 78, 86, 96, 113, 116, 289, 297, 333 Aerospace industry, 153 Agenda for auto industry culture change, 396 increasing speed, 398 service-oriented business model, 397 398 V-to-home and V-to-business applications, 399 Agile operating models, 330 Agriculture, 154 productivity, 155 sector, 154 157 Air pollution, 27 AirBnB, 311 Airplane electronics, 144 Aisin, 9 Albert (head of design at Yahoo), 228 Alexandra (founder and owner of Powerful Minds), 228 Alibaba Alipay payment system, 372 Alternative fuels, autonomous vehicles enabling use of, 305 Altruistic mode (a-drive mode), 252 Amazon, 138, 141, 311 American Trucking Association, 68 Android operating system, 327 Anthropomorphise products, 290 Appel Logistics transports, 167 Apple, 9, 138, 327 CarPlay, 285 Apple Mac OS, 247 Apple-type model, 323 Application layer, 119 software, 118 Artificial intelligence, 115, 255, 291, 332 333 Artificial neuronal networks, 114 115 Asia projects, 371 374 Assembly Row, 386 Assessment of Safety Standards for Automotive Electronic Control Systems, 144 Assistance systems, 71 77 Audi, 5, 130, 134, 137, 179, 211, 301, 318, 322, 398 Driverless Race Car, 5 piloted driving, 286 piloted-parking technology, 386 387 Audi A7, 44, 198, 282 427 428 Audi A8 series-car, 79, 180 Audi AI traffic jam pilot, 79 Audi Fit Driver service, 318 319 Audi piloted driving lab, 227, 229 Audi Q7, 74 assistance systems in, 75 Audi RS7, 43, 44, 79 autonomous racing car, 179 driverless, 227 Audi TTS, 43 Audi Urban Future Initiative, 384 386, 406 Augmented reality, 279 vision and example, 279 280 Authorities and cities, 171 173 Auto ISAC, 146 Autolib, 317, 344 Autoliv, 285 Automakers’ bug-bounty programs, 146 Automated car, 233, 246, 264, 289, 384 Automated driving division of labour between driver and driving system, 48 examples, 51 53 image, 177 levels of, 47 51 scenarios for making use of travelling time, 52 strategies, 53 56 technology, 160 Automated vehicles, 9, 174, 246 Automated Vehicles Index, 367 368 Automatic car, 233, 244 Automatic pedestrian highlighting, 78 Automation ironies of, 76 responsibility with increasing, 235 Automobile, 3, 21 locations, 405 manufacturers, 311 Index Automotive design, 265 266 Automotive Ethernet, 126 Automotive incumbents operate, 330 Automotive industry, 332 335, 367, 379, 397 Automotive technology, 327 328 AutoNet2030 project, 369 Autonomous buses, 14, 81, 158, 159, 175, 302 Autonomous cars, 25, 126, 197, 205 206, 233, 244, 270 expected worldwide sales of, 85 savings effects from, 67 68 Autonomous driving, 3, 8, 39, 62, 94, 111, 116, 120, 121 123, 141, 160 162, 171, 173, 207 208, 217, 247, 252, 266, 332 333, 379 applications, 10 12, 160 aspects for, 93 Audi car, 5 autonomous Audi TTS on Way to Pikes Peak, 43 in combination with autonomous loading hubs, 166 driving to hub, 213 ecosystem, 18 20, 131 element, 243 facts about, 306 functions, 74 impression, 40 industry, 16 18 living room in Autonomous Mercedes F015, 44 milestones of automotive development, 4 NuTonomy, 6 projects, 41 45 real-world model of, 92 scenarios, 211 215 science fiction, 39 41 technology, 9 10, 92 Index time management, 215 218 vehicles, 12 16 See also Human driving Autonomous driving failure, 221 consequence, 221 222 decision conflict in autonomous car, 223 design options, 222 223 influencer, 223 224 Autonomous Mercedes F015, living room in, 44 Autonomous mobility, 12, 13, 16 17, 172, 405 establishment as industry of future, 404 405 resistance to, 171 172 Autonomous Robocars, 81 Autonomous sharp, 274 ‘Autonomous soft’ mode, 274 Autonomous trucks, 161 from Daimler, 163 savings effects from, 68 69 Autonomous vehicles, 26, 81, 99, 138, 155, 182, 221, 238, 249, 255, 353 354 enabling use of alternative fuels, 305 integration in cities, 406 promoting tests with, 407 uses, 153 AutoVots fleet, 350 Backup levels, 127 Baidu apps, 338, 372 Base layer, 119 Becker, Jan, 42 43 Behavioural law, 234 Being driven, 61, 63, 78, 342 343 Ben-Noon, Ofer, 142, 143, 145 Benz, Carl, 3, 4 Bertha (autonomous research vehicle), 42 Big data, 313, 332 333 BlaBlaCar, 359 429 Blackfriars bridge, lidar print cloud of, 104 Blind-spot detection, 78 Bloggers, 225 227 Blonde Salad, The, 226 Bluetooth, 130, 142, 154 BMW, 6, 130, 137, 174, 180, 316, 320, 322, 332 333, 372, 398 3-series cars, 338 BMW i3, 27 holoactive touch, 285 Boeing 777 development, 243 Boeing, 787, 261 Bosch, 9, 181 182 Bosch, Robert, 333 Bosch suppliers, 315 BosWash, metropolitan region, 384 Budii car, 272 273 Business models, 311, 353 355 automobile manufacturers, 311 content creators, 319 320 data creators, 320 322 examples, 312 hardware creators, 314 315 options, 312 314 passenger looks for new products, 321 passenger visits website, 321 service creators, 316 319 software creators, 315 316 strategic mix, 322 323 Business vehicle, 15 Business-to-consumer car sharing, 342 343 Cadillac, 180 California PATH Research Reports, 298 299 Cambot, 290 Cameras, 111, 126 CAN bus, 126, 143 Capsule, 33 Car and ride sharing, studies on, 348 430 Car dealers, repair shops and insurance companies, 173 174 Car manufacturers, 328, 396 397 business model, 312 Car-pooling efforts, 364 365 Car-sharing programs, 364 365 service, 383 Car-sharing, 206 Car2Go, 317, 345 Casey Neistat, 226 Castillo, Jose, 364 365 Celebrities and bloggers, 225 227 Central driver assistance control unit, 124 Central processing unit, 96, 124 zFAS, 125 Centre for Economic and Business Research in London, 189 Chevrolet, 40 app from General Motors, 316 Spark EV, 27 Cisco, 41 CityMobil project, 369, 406 CityMobil2, 14, 157 Cognitive distraction, 287 Coherent European framework, 246 Committee on Autonomous Road Transport for Singapore, 347 Communication, 198 200 investing in communication infrastructure, 403 404 technology, 261 Community, 341 detection algorithms, 389 Companion app, 316 Compelling force, 223 Competitiveness Iain Forbes, 368 369 projects in Asia, 371 374 Index projects in Europe and United States, 369 371 projects in Israel, 374 375 Computer operating systems, 247 Computer-driven driving, 108 Computerised information processing, 109 Congestion pricing, 296 Connected car, 129 ad-hoc networks, 133 connected driving, 137 138 connected mobility, 138 development of mobile communication networks, 130 digital ecosystems, 138 eCall, 136 137 online services, 136 137 permanent networks, 130 statement by telecommunications experts, 132 133 V-to-I communication, 134 135 V-to-V communication, 133 134 V-to-X communication, 135 136 See also Digitised car Connected mobility, 129, 138 Connected vehicles, 138 vulnerability of, 142 Connected-car services, 313 Connectivity of vehicles, 147 Consumer-electronics companies, 285 Container Terminal, 159 Content creators, 319 320 Continental (automotive suppliers), 9, 284, 315 Continuous feedback, 281 Convenience, 302 304, 306 Conventional breakthrough approach, 332 Index Conventional broadband applications, 132 Conventional car manufacturing, 10 Cook, Tim, 182 Cooperative intelligent transport system (C-ITS), 369 370 Corporate Average Fuel Economy standard, 297 Cost(s), 187 192, 295 autonomous vehicles enabling use of alternative fuels, 305 fuel economy, 297 299 intelligent infrastructures, 299 301 land use, 304 operating costs, 301 302 relationship between road speed and road throughput, 296 vehicle throughput, 295 297 Croove app, 318 Culture, 330 change, 396 differences, 195 197 and organisational transformation, 395 Curtatone, Joseph, 387 Customers’ expectations attitudes, 204 207 incidents, 203 204 interview with 14 car dealers, 207 persuasion, 207 208 statements by two early adopters, 205 Cyber attacks, 141 Cyber hacking or failures in algorithms, 354 Cyber security, 141 146 Cyber-physical systems, 9 Daimler, 130 Data, 121 categories in vehicle, 147 creators, 320 322 431 from passengers, 94 95 privacy, 147 148 processing, 91 protection principles, 148 recorders, 239 Data-capturing technology, 103 Data-protection issues, 239 Database, 98 Decelerating, 91, 122 Decision-making mechanism, 369 Declaration of Amsterdam, 246 247 Deep learning, 115 Deep neural networks, 115 116 Deere, John, 154, 155 Deere, John, 154, 155, 263 Defense Advanced Research Project Agency (DARPA), 41 Degree of autonomous driving, 53 Degree of autonomy, 262 Degree of market penetration, 84 Degree of not-invented-here arrogance, 332 Degree of vehicle’s automation, 233 234 Delhi municipal government, 21 22 Delphi, 9, 181 Delphi Automotive Systems, 6 Demise of Kodak, 111 Denner, Volkmar, 333 334 Denso, 9 Depreciation, 345 Destination control, 299, 300 Digital company development, 395 396 Digital economy, 225 Digital ecosystems, 138 Digital light-processing technology, 277, 279 Digital maps, 101 Digital products, 267 Digitised car algorithms, 113 117 432 backup levels, 127 car as digitised product, 111 112 data, 121 drive recorder, 125 126 drive-by-wire, 122 over-provisioning, 127 processor, 122 125 software, 117 121 See also Connected car Digitising and design of vehicle, 265 267 Dilemma situations, 61 Direct attacks, 141 Direct connectivity of vehicle, 130 Disruptions in mobility, 31, 34 arguments, 34 35 history, 32 33 OICA, 34 Disruptive technologies, 221, 223, 402 Document operation-relevant data, 263 Doll, Claus, 166 Dongles, 142 Drees, Joachim, 165 ‘Drive boost’ mode, 274 “Drive me” project, 370 Drive recorder, 125 126 ‘Drive relax’ mode, 274 Drive-by-wire, 122 DriveNow, 317, 345 Driver, 235 role, 235 238 Driver distraction, 55 causes and consequences, 278 Driver-assistance systems, 53, 71, 160, 174, 222, 298, 333, 353 Driverless cars, 3, 7, 27 28, 222, 233, 244 taxis, 302 vans, 406 vehicles, 168 Index Driverless Audi RS7, 227 229 Driverless Race Car of Audi, 5 Driving manoeuvres, 91 modes, 107 oneself, 342 343 Drunk driving, 303 Dvorak keyboard, 242 Dynamic patterns of movement in city of London, 390 eCall.

Google is involved in the development of fleets of autonomous vehicles that are mainly designed for use in urban traffic (see Figure 6.3). It will be possible to reserve these robo-cars via smartphone and to be picked up by them at certain places in city centres. It is not yet clear who will operate these fleets, but they will certainly compete with public transport, which is why railroad and bus companies are interested in such fleets of self-driving cars. Will this make Google into an automobile manufacturer? Probably not; it is more likely that Google will become a supplier of software for controlling autonomous vehicles, which can be offered to all automobile manufacturers.


pages: 472 words: 80,835

Life as a Passenger: How Driverless Cars Will Change the World by David Kerrigan

3D printing, Airbnb, airport security, Albert Einstein, autonomous vehicles, big-box store, Boeing 747, butterfly effect, call centre, car-free, Cesare Marchetti: Marchetti’s constant, Chris Urmson, commoditize, computer vision, congestion charging, connected car, DARPA: Urban Challenge, data science, deep learning, DeepMind, deskilling, disruptive innovation, Donald Shoup, driverless car, edge city, Elon Musk, en.wikipedia.org, fake news, Ford Model T, future of work, General Motors Futurama, hype cycle, invention of the wheel, Just-in-time delivery, Lewis Mumford, loss aversion, Lyft, Marchetti’s constant, Mars Rover, megacity, Menlo Park, Metcalfe’s law, Minecraft, Nash equilibrium, New Urbanism, QWERTY keyboard, Ralph Nader, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Rodney Brooks, Sam Peltzman, self-driving car, sensor fusion, Silicon Valley, Simon Kuznets, smart cities, Snapchat, Stanford marshmallow experiment, Steve Jobs, technological determinism, technoutopianism, TED Talk, the built environment, Thorstein Veblen, traffic fines, transit-oriented development, Travis Kalanick, trolley problem, Uber and Lyft, Uber for X, uber lyft, Unsafe at Any Speed, urban planning, urban sprawl, warehouse robotics, Yogi Berra, young professional, zero-sum game, Zipcar

You need to tread carefully about this because if in writing some article that is negative you are effectively dissuading people from using autonomous vehicles, you are killing people” Elon Musk, CEO, Tesla Media coverage is under the spotlight worldwide as never before after allegations of fake news, echo bubbles and social media complicity in spreading misleading information. It is against this backdrop that the emergence of driverless cars is taking place. What I’ve seen so far regarding driverless cars has often been sensationalist (both negative and positive) rather than genuinely analytical or even just informative. I don’t think the fact that the robocars (which are by all accounts still years away from use) needed occasional intervention is worthy of a headline like the BBC used: “Google’s self-drive cars had to be stopped from crashing” as they reported[352] in January 2016 on Google’s publication of data related to disengagements.

MOD=AJPERES [348] https://www.automotiveisac.com/best-practices/ [349] https://techcrunch.com/2015/10/23/connected-car-security-separating-fear-from-fact/ [350] http://www.raymondloewy.com/about.html [351] https://www.ft.com/content/97a04f76-3494-11e7-99bd-13beb0903fa3 [352] http://www.bbc.com/news/technology-35301279 [353] https://static.nhtsa.gov/odi/inv/2016/INCLA-PE16007-7876.PDF [354] http://www.reuters.com/investigates/special-report/autos-driverless/ [355] https://en.wikipedia.org/wiki/Stanford_marshmallow_experiment [356] https://www.scribd.com/document/333075344/Apple-Comments-on-Federal-Automated-Vehicles-Policy [357] Remarks at Infrastructure Week, May 2017 [358] Technological Revolutions and Financial Capital, Carlota Perez, 2002 [359] https://www.washingtonpost.com/news/innovations/wp/2014/10/14/move-over-humans-the-robocars-are-coming/ [360] https://www.cnet.com/uk/news/a-brief-history-of-the-qwerty-keyboard/ [361] http://www.wsj.com/articles/could-self-driving-cars-spell-the-end-of-ownership-1448986572 [362] https://www.morganstanley.com/articles/autonomous-cars-the-future-is-now [363] https://www.wired.com/2016/04/american-cities-nowhere-near-ready-self-driving-cars/ [364] Machiavelli, The Prince, Chapter 6 [365] http://time.com/4236980/against-human-driving/ [366] https://www.ft.com/content/e961f914-6ba3-11e6-ae5b-a7cc5dd5a28c [367] Paul Roberts, The Impulse Society: What's Wrong With Getting What We Want, 2014 [368] http://content.time.com/time/magazine/article/0,9171,2033076,00.html [369] http://www.ft.com/intl/cms/s/0/da5d033c-8e1c-11e1-bf8f-00144feab49a.html#axzz1t4qPww6r [370] http://www.wbur.org/bostonomix/2016/04/29/traffic-future-driverless-cars

The starting point was to require special driver’s license endorsements for anyone operating an autonomous or semi-autonomous car requiring, that the driver take lessons and tests on the capabilities and limits of the autonomous vehicle, as well as how to take over control in an emergency. Other recommendations include state limitations on where and in what types of conditions autonomous cars can operate on public roads, requirements that the cars operate for a certain number of miles on private roads before use on public roads, and a system to record and report any failures or accidents of autonomous cars. The document concluded with a realisation that further research and updates would be required; “As innovation in this area continues and the maturity of self-driving technology increases, we will reconsider our present position on this issue”.


pages: 175 words: 54,755

Robot, Take the Wheel: The Road to Autonomous Cars and the Lost Art of Driving by Jason Torchinsky

autonomous vehicles, barriers to entry, call centre, commoditize, computer vision, connected car, DARPA: Urban Challenge, data science, driverless car, Elon Musk, en.wikipedia.org, interchangeable parts, job automation, Philippa Foot, ransomware, self-driving car, sensor fusion, side project, Tesla Model S, trolley problem, urban sprawl

Also, in the early stages, it may prove helpful for robotic vehicles to make their presence very obvious. With this in mind, something like a federally mandated external robotic/autonomous vehicle warning lamp could prove effective. It may sound a bit like I’m paranoid about a massive robo-car uprising and I want to be able to see potential uprisers, but that’s not it at all. I think being able to visually see where the autonomous cars are will help them be accepted into mainstream traffic, and provide tools to study their interactions with other autonomous cars, meat-driven cars, pedestrians, and all the other chaos of life on the roads. I also think nervous human drivers might feel more comfortable if there’s an easy way to identify which cars are machine-driven, so, if they choose, they can safely avoid those cars.

., Dynamic Machine Vision, http://www.dyna-vision.de/. 22 “DARPA Announces Third Grand Challenge,” May 1, 2006, https://www.grandchallenge.org/grandchallenge/docs/PR_UC_Announce_Update_12_06.pdf. 23 Tartan Racing, http://www.tartanracing.org/. Chapter 3 How Do They Work, Anyway? If we’re going to talk and think about autonomous cars, self-­driving cars, robo-cars, drive-o-droids or whatever the hell we want to call these things, we should get a sense of exactly what they do and how they do it. Those of us who drive may want to think back to a time before driving was all ingrained muscle memory, something we now do almost automatically, except when we’re trying, like idiots, to drive while texting someone something super important about this hilarious drunken fight we think we saw behind a supermarket.

⁴⁸ Here Asimov accurately describes the very simple operation of an autonomous car (punch in a destination, let it go), but also acknowledges that such cars will likely be much more expensive than conventional cars, and suggests that ownership of private autonomous cars will be limited to the rich, with less wealthy folk using the “omnibus-automatics,” where you “call a company and have one stop at your door in a matter of minutes and take you where you wanted to go.” That sounds a hell of a lot like Uber, which, of course, is conducting a lot of research into autonomous vehicles. The emphasis on safety as the primary justification for autonomous car development is something we see a lot of today.


pages: 245 words: 83,272

Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard

"Susan Fowler" uber, 1960s counterculture, A Declaration of the Independence of Cyberspace, Ada Lovelace, AI winter, Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, autonomous vehicles, availability heuristic, barriers to entry, Bernie Sanders, Big Tech, bitcoin, Buckminster Fuller, Charles Babbage, Chris Urmson, Clayton Christensen, cloud computing, cognitive bias, complexity theory, computer vision, Computing Machinery and Intelligence, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data science, deep learning, Dennis Ritchie, digital map, disruptive innovation, Donald Trump, Douglas Engelbart, driverless car, easy for humans, difficult for computers, Electric Kool-Aid Acid Test, Elon Musk, fake news, Firefox, gamification, gig economy, global supply chain, Google Glasses, Google X / Alphabet X, Greyball, Hacker Ethic, independent contractor, Jaron Lanier, Jeff Bezos, Jeremy Corbyn, John Perry Barlow, John von Neumann, Joi Ito, Joseph-Marie Jacquard, life extension, Lyft, machine translation, Mark Zuckerberg, mass incarceration, Minecraft, minimum viable product, Mother of all demos, move fast and break things, Nate Silver, natural language processing, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, One Laptop per Child (OLPC), opioid epidemic / opioid crisis, PageRank, Paradox of Choice, payday loans, paypal mafia, performance metric, Peter Thiel, price discrimination, Ray Kurzweil, ride hailing / ride sharing, Ross Ulbricht, Saturday Night Live, school choice, self-driving car, Silicon Valley, Silicon Valley billionaire, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, TechCrunch disrupt, Tesla Model S, the High Line, The Signal and the Noise by Nate Silver, theory of mind, traumatic brain injury, Travis Kalanick, trolley problem, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, We are as Gods, Whole Earth Catalog, women in the workforce, work culture , yottabyte

Programmers don’t even like to type; it’s hard to imagine them being so detail-oriented that they voluntarily comply with fifty-plus different state traffic schemas and then manage to communicate these different operating procedures to each customer who buys an autonomous car. The communication problem surfaces again when we talk about self-driving cars. The National Highway Traffic Safety Association (NHTSA), the government agency in charge of motor vehicle and highway safety, had to come up with a complex scale to describe autonomous driving so we could talk about it. For a long time, programmers and executives used the term self-driving car without defining specifically what they meant. Again—normal for language, problematic for policy. In an effort to wrangle the Wild West of autonomous vehicles, the NHTSA published a set of categories for autonomous vehicles.

The lidar guidance system in an autonomous car works by bouncing laser beams off nearby objects. It estimates how far the objects are by measuring the reflection time. In the rain or snow or dust, the beams bounce off the particles in the air instead of bouncing off obstacles like bicyclists. One self-driving car was spotted going the wrong way down a one-way street. The software apparently didn’t reflect that the street was one way. The cars are easy to confuse because they rely on the same mediocre image recognition algorithms that mislabel pictures of black people as gorillas.13 Most autonomous vehicles use algorithms called deep neural networks, which can be confused by simply putting a sticker or graffiti on a stop sign.14 GPS hacking is a very real danger for autonomous vehicles as well.

The story of the race to build a self-driving car is a story about the fundamental limits of computing. Looking at what worked—and what didn’t—during the first decade of autonomous vehicles is a cautionary tale about how technochauvinism can lead to magical thinking about technology and can create a public health hazard. My first ride happened on an autonomous vehicle test track: the weekend-empty parking lot of the Boeing factory in South Philadelphia. The Ben Franklin Racing Team, a group of engineering students at the University of Pennsylvania, was building an autonomous vehicle for a competition. I was writing a story about them for the University of Pennsylvania alumni magazine.


pages: 265 words: 74,807

Our Robots, Ourselves: Robotics and the Myths of Autonomy by David A. Mindell

Air France Flight 447, air gap, Apollo 11, Apollo 13, Apollo Guidance Computer, autonomous vehicles, Beryl Markham, Boeing 747, Captain Sullenberger Hudson, Charles Lindbergh, Chris Urmson, digital map, disruptive innovation, driverless car, drone strike, Easter island, en.wikipedia.org, Erik Brynjolfsson, fudge factor, Gene Kranz, human-factors engineering, index card, John Markoff, low earth orbit, Mars Rover, Neil Armstrong, ride hailing / ride sharing, Ronald Reagan, self-driving car, Silicon Valley, telepresence, telerobotics, trade route, US Airways Flight 1549, William Langewiesche, zero-sum game

John Markoff, “Google Cars Drive Themselves, in Traffic,” New York Times, October 9, 2010, http://www.nytimes.com/2010/10/10/science/10google.html. The Google car’s successful driving tests: Mark Harris, “How Google’s Autonomous Car Passed the First U.S. State Self-Driving Test,” IEEE Spectrum Online, September 10, 2014, http://spectrum.iee.org. Idem., “These Are the Secrets Google Wanted to Keep about Its Self-Driving Cars,” Quartz, http://qz.com/252817/these-are-the-secrets-google-wanted-to-keep-about-its-self-driving-cars/, accessed November 18, 2014. Mark Harris, “How Much Training Do You Need to Be a Robocar Test Driver? It Depends On Whom You Work For,” IEEE Spectrum Cars That Think, February 24, 2015, http://spectrum.ieee.org/cars-that-think/transportation/human-factors/how-much-training-do-you-need-to-be-a-robocar-test-driver-it-depends-on-whom-you-work-for.

Or a person on the surface might even teleoperate the vehicle when it’s in optical range, then let it do more on its own when out of range or if the optical link is lost. Autonomy then becomes a function of position and bandwidth. Overall, the lines between the human, remote, and autonomous vehicles undersea are blurring. Engineers now envision an ocean with many vehicles working in concert. Some may contain people, others will be remote or autonomous, all will be capable of shifting modes at different times. The recently upgraded Alvin has software originally designed for autonomous vehicles; one day it may connect to the surface with an optical fiber. One day it might even operate unmanned. The challenges are to coordinate all of these machines, keep the humans informed, and ensure the robots’ actions reflect human intentions.

It Depends On Whom You Work For,” IEEE Spectrum Cars That Think, February 24, 2015, http://spectrum.ieee.org/cars-that-think/transportation/human-factors/how-much-training-do-you-need-to-be-a-robocar-test-driver-it-depends-on-whom-you-work-for. He put a video camera on the dashboard of his car: John Leonard, “Conversations on Autonomy,” presentation, MIT, March 13, 2014. John Markoff, “Police, Pedestrians and the Social Ballet of Merging: The Real Challenges for Self-Driving Cars,” Bits Blog, http://bits.blogs.nytimes.com/2014/05/29/police-bicyclists-and-pedestrians-the-real-challenges-for-self-driving-cars/, accessed July 10, 2014. We know that driverless cars will be susceptible: John Markoff, “Collision in the Making Between Self-Driving Cars and How the World Works,” New York Times, January 23, 2012, http://www.nytimes.com/2012/01/24/technology/googles-autonomous-vehicles-draw-skepticism-at-legal-symposium.html.


pages: 386 words: 113,709

Why We Drive: Toward a Philosophy of the Open Road by Matthew B. Crawford

1960s counterculture, Airbus A320, airport security, augmented reality, autonomous vehicles, behavioural economics, Bernie Sanders, Big Tech, Boeing 737 MAX, British Empire, Burning Man, business logic, call centre, classic study, collective bargaining, confounding variable, congestion pricing, crony capitalism, data science, David Sedaris, deskilling, digital map, don't be evil, Donald Trump, driverless car, Elon Musk, emotional labour, en.wikipedia.org, Fellow of the Royal Society, Ford Model T, gamification, gentrification, gig economy, Google Earth, Great Leap Forward, Herbert Marcuse, hive mind, Ian Bogost, income inequality, informal economy, Internet of things, Jane Jacobs, labour mobility, Lyft, mirror neurons, Network effects, New Journalism, New Urbanism, Nicholas Carr, planned obsolescence, Ponzi scheme, precautionary principle, Ralph Nader, ride hailing / ride sharing, Ronald Reagan, Sam Peltzman, security theater, self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, smart cities, social graph, social intelligence, Stephen Hawking, surveillance capitalism, tacit knowledge, tech worker, technoutopianism, the built environment, The Death and Life of Great American Cities, the High Line, time dilation, too big to fail, traffic fines, Travis Kalanick, trolley problem, Uber and Lyft, Uber for X, uber lyft, Unsafe at Any Speed, urban planning, Wall-E, Works Progress Administration

Ian Bogost offers a convincing thought experiment about the terms on which the public will have access to roads as public infrastructure comes to be financed and planned by partnerships between municipalities and tech companies. “It’s easy to imagine that cross-town transit might soon require acceptance of non-negotiable terms of service that would allow your robocar provider to aggregate and sell where you go, when, with whom, and for what purpose.” One can imagine the removal of street signs, those eyesores that aren’t needed by autonomous vehicles, tipping us further into dependence on the cartel. Bogost writes that “other, stranger realities are possible. Imagine if walking across the street required a microtransaction to insure safe passage. Violations might be subject to tickets or fines—although more likely, your local transit vendor would already know where you are thanks to your smartphone, and just debit your metered service plan accordingly.”7 Something about this picture sits ill with our liberal political traditions, but its wrongness goes deeper still.

In November 2017 in Automotive News, Bob Lutz, the head of GM, suggested that driving will be outlawed in twenty years. Bob Lutz, “Kiss the Good Times Goodbye,” Automotive News, November 5, 2017, http://www.autonews.com/apps/pbcs.dll/article?AID=/20171105/INDUSTRY_REDESIGNED/171109944/industry-redesigned-bob-lutz. 7.Ian Bogost, “Will Robocars Kick Humans off City Streets?” Atlantic, June 23, 2016, https://www.theatlantic.com/technology/archive/2016/06/robocars-only/488129/. 8.A. M. Glenberg and J. Hayes, “Contribution of Embodiment to Solving the Riddle of Infantile Amnesia,” Frontiers in Psychology 7 (2016), as characterized in M. R. O’Connor, “For Kids, Learning Is Moving,” Nautilus, September 22, 2016, http://nautil.us/issue/40/learning/for-kids-learning-is-moving.

At each stage, remaining pockets of human judgment and discretion appear as bugs that need to be solved. Put more neutrally, human intelligence and machine intelligence have a hard time sharing control. This becomes evident in the problems posed by partially autonomous cars, and is evident also in the problems posed when fully autonomous cars have to share the road with human drivers. Driverless cars are programmed to follow traffic rules to the letter and err on the side of caution, making them an awkward fit with other cars piloted by humans. The New York Times reports that one Google car “couldn’t get through a four-way stop because its sensors kept waiting for other (human) drivers to stop completely and let it go.


Driverless: Intelligent Cars and the Road Ahead by Hod Lipson, Melba Kurman

AI winter, Air France Flight 447, AlphaGo, Amazon Mechanical Turk, autonomous vehicles, backpropagation, barriers to entry, butterfly effect, carbon footprint, Chris Urmson, cloud computing, computer vision, connected car, creative destruction, crowdsourcing, DARPA: Urban Challenge, deep learning, digital map, Donald Shoup, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, General Motors Futurama, Geoffrey Hinton, Google Earth, Google X / Alphabet X, Hans Moravec, high net worth, hive mind, ImageNet competition, income inequality, industrial robot, intermodal, Internet of things, Jeff Hawkins, job automation, Joseph Schumpeter, lone genius, Lyft, megacity, Network effects, New Urbanism, Oculus Rift, pattern recognition, performance metric, Philippa Foot, precision agriculture, RFID, ride hailing / ride sharing, Second Machine Age, self-driving car, Silicon Valley, smart cities, speech recognition, statistical model, Steve Jobs, technoutopianism, TED Talk, Tesla Model S, Travis Kalanick, trolley problem, Uber and Lyft, uber lyft, Unsafe at Any Speed, warehouse robotics

Brad Templeton “California DMV Regulations May Kill the State’s Robocar Lead,” 4brad.com, December 17, 2015, http://ideas.4brad.com/california-dmv-regulations-may-kill-states-robocar-lead 18. Grace Meng, “H.R.3876—Autonomous Vehicle Privacy Protection Act of 2015,” Congress.gov, https://www.congress.gov/bill/114th-congress/house-bill/3876/text 8 Rise of the Robots Modern driverless cars began to emerge from the labs of robotics researchers in the final decades of the twentieth century. Throughout the 1980s and 1990s, German autonomous-vehicle pioneer Ernst Dickmanns built several prototypes that used sensors and intelligent software to steer themselves.

In general, car companies and transportation officials take a longer view, estimating that driverless cars will be a mainstay on public roads sometime after the year 2025. According to the automotive market research firm IHS, the first sales of autonomous vehicles will begin around the year 2025.14 IHS analysts estimate that by the year 2035, roughly 10 percent of new cars sold will be autonomous, a total number of 11.8 million cars each year. After 2050, IHS predicts that almost all new vehicles sold will be autonomous. Car companies have preferred a staged, gradual approach to autonomous driving, another factor that makes it impossible to pin down an exact date for the adoption of driverless cars.

. … Anticipation of green phases of stoplights could navigate vehicles through an urban area on a “green wave” with the appropriate engine performance and minimized fuel consumption.14 Perhaps someday, autonomous vehicles will communicate with one another over some kind of network. Even if this day arrives, however, it’s unlikely V2X-equipped cars of the future will use the same costly infrastructure of short-wave radio technology that figures in current USDOT strategy. Once the majority of cars on the road are fully self-driving, by then, it’s highly likely that connectedness will be achieved for a lower price. Fully autonomous cars will be able to communicate with one another and with roadside traffic-management servers using cheaper forms of networks such as the existing cellphone infrastructure.


pages: 362 words: 97,288

Ghost Road: Beyond the Driverless Car by Anthony M. Townsend

A Pattern Language, active measures, AI winter, algorithmic trading, Alvin Toffler, Amazon Robotics, asset-backed security, augmented reality, autonomous vehicles, backpropagation, big-box store, bike sharing, Blitzscaling, Boston Dynamics, business process, Captain Sullenberger Hudson, car-free, carbon footprint, carbon tax, circular economy, company town, computer vision, conceptual framework, congestion charging, congestion pricing, connected car, creative destruction, crew resource management, crowdsourcing, DARPA: Urban Challenge, data is the new oil, Dean Kamen, deep learning, deepfake, deindustrialization, delayed gratification, deliberate practice, dematerialisation, deskilling, Didi Chuxing, drive until you qualify, driverless car, drop ship, Edward Glaeser, Elaine Herzberg, Elon Musk, en.wikipedia.org, extreme commuting, financial engineering, financial innovation, Flash crash, food desert, Ford Model T, fulfillment center, Future Shock, General Motors Futurama, gig economy, Google bus, Greyball, haute couture, helicopter parent, independent contractor, inventory management, invisible hand, Jane Jacobs, Jeff Bezos, Jevons paradox, jitney, job automation, John Markoff, John von Neumann, Joseph Schumpeter, Kickstarter, Kiva Systems, Lewis Mumford, loss aversion, Lyft, Masayoshi Son, megacity, microapartment, minimum viable product, mortgage debt, New Urbanism, Nick Bostrom, North Sea oil, Ocado, openstreetmap, pattern recognition, Peter Calthorpe, random walk, Ray Kurzweil, Ray Oldenburg, rent-seeking, ride hailing / ride sharing, Rodney Brooks, self-driving car, sharing economy, Shoshana Zuboff, Sidewalk Labs, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, SoftBank, software as a service, sovereign wealth fund, Stephen Hawking, Steve Jobs, surveillance capitalism, technological singularity, TED Talk, Tesla Model S, The Coming Technological Singularity, The Death and Life of Great American Cities, The future is already here, The Future of Employment, The Great Good Place, too big to fail, traffic fines, transit-oriented development, Travis Kalanick, Uber and Lyft, uber lyft, urban planning, urban sprawl, US Airways Flight 1549, Vernor Vinge, vertical integration, Vision Fund, warehouse automation, warehouse robotics

., User Perspectives on Autonomous Driving: A Use-Case-Driven Study in Germany (Berlin, Germany: DLR Institute of Transport Research, 2016), 13; Chris Tennant et al., Executive Summary, Autonomous Vehicles—Negotiating a Place on the Road (London, UK: London School of Economics, 2016), 1–10. 31“Why do all of these interior designs”: Alanis King, “Autonomous Cars Aren’t Even Here Yet and I’m Already Bored with Them,” Jalopnik, September 11, 2017, https://jalopnik.com/autonomous-cars-arent-even-here-yet-and-im-already-bore-1803756153. 31Audi has recruited Disney: Reese Counts, “We Try Audi and Disney’s New In-Car Entertainment System on the Track,” Autoblog, January 9, 2019, https://www.autoblog.com/2019/01/09/audi-disney-holoride-car-vr-entertainment/. 31Kia built a concept car: Laura Bliss, “The ‘Driverless Experience’ Looks Awfully Distracting,” CityLab, January 11, 2019, https://www.citylab.com/transportation/2019/01/self-driving-car-technology-consumer-electronics-show/580027/. 31bigger . . . than the entire auto industry today: Lanctot, Accelerating the Future, 5. 31serve up precision-targeted media: Joann Muller, “One Big Thing: What Your Car Will Know about You,” Axios, May 10, 2019, https://www.axios.com/newsletters/axios-autonomous-vehicles-7b382e7a-e9f1-466b-9c7b-33e4aadc03f4.html; sensors. . . uniquely identify your heartbeat: “Goode Intelligence Forecasts That Biometrics Market for the Connected Car Will Be Just under $1bn by 2023,” Goode Intelligence, November 13, 2017, https://www.goodeintelligence.com/wp-content/uploads/2017/11/Goode-Intelligence-Biometrics-for-the-Connected-Car_Nov17_-news_release-13112017.pdf. 32GM already tracks: Jamie LaReau, “GM Tracked Radio Listening Habits for 3 Months: Here’s Why,” Detroit Free Press, October 1, 2018, https://www.freep.com/story/money/cars/general-motors/2018/10/01/gm-radio-listening-habits-advertising/1424294002/. 32“We know how long they’ve lived”: Phoebe Wall Howard, “Data Could Be What Ford Sells Next as It Looks for New Revenue,” Detroit Free Press, November 13, 2018, https://www.freep.com/story/money/cars/2018/11/13/ford-motor-credit-data-new-revenue/1967077002/. 33odds of a crash instantly double: National Highway Traffic Safety Administration, Overview of the National Highway Traffic Safety Administration’s Driver Distraction Program, DOT HS 811 299, April 2010, https://www.nhtsa. gov/sites/nhtsa.dot.gov/files/811299.pdf. 33road crashes in the US declined: Wikipedia, s.v.

Even as this self-driving paradise takes shape, it becomes a killing field. In a gruesome twist, a cocaine-tainted batch of gasoline turns the robocars against their human masters. “There seemed to be a personal devil in each of them, directing their actions,” Keller writes. “Cars, without control, coursed the public highways, chasing pedestrians, killing little children, smashing fences. . . . Fifty million machines were on a wild riot of uncontrolled destruction.” The renegade robocars even discover how to gas themselves up at service stations, perpetuating the reign of terror indefinitely. For all its B-movie sensationalism, the lessons of “The Living Machine” remain fresh and relevant today.

Then I Tried One,” Opinion, New York Times, October 22, 2017, https://www.nytimes.com/2017/10/22/opinion/driverless-cars-test-drive.html. 960 million people were killed: Death: A Self-Portrait, 2012, Richard Harris Collection, London, UK: Wellcome Collection, exhibition. 9time wasted in traffic: “INRIX Global Traffic Scorecard,” INRIX, accessed February 15, 2018, http://inrix.com/scorecard. 1025 million people have disabilities that limit travel: Stephen Brumbaugh, Travel Patterns of Americans with Disabilities (Washington, DC: Bureau of Transportation Statistics, 2018), https://www.bts.gov/sites/bts.dot.gov/files/docs/explore-topics-and-geography/topics/passenger-travel/222466/travel-patterns-american-adults-disabilities-9-6-2018_1.pdf. 10By 2030 . . . tens of millions: BlackRock Investment Group, Future of the Vehicle: Winners and Losers: From Cars and Cameras to Chips (BlackRock Investment Institute, 2017), 8. 10two billion human-driven cars and trucks: Daniel Sperling and Deborah Gordon, Two Billion Cars: Driving toward Sustainability (New York: Oxford University Press, 2009). 10“The future is already here”: Marianne Trench, Cyberpunk (New York: Intercon Production, 1990), YouTube video. 11a mix of both worlds: Bern Grush and John Niles, The End of Driving: Transportation Systems and Public Policy Planning for Autonomous Vehicles (Cambridge, MA: Elsevier, 2018). 11Half will end up in China: “Autonomous Vehicle Sales to Surpass 33 Million Annually in 2040, Enabling New Autonomous Mobility in More Than 26 Percent of New Car Sales, IHS Markit Says,” IHS Markit, January 2, 2018, https://technology.ihs.com/599099/autonomous-vehicle-sales-to-surpass-33-million-annually-in-2040-enabling-new-autonomous-mobility-in-more-than-26-percent-of-new-car-sales-ihs-markit-says. 11$2 trillion global auto-manufacturing industry: Roger Lanctot, Accelerating the Future: The Economic Impact of the Emerging Passenger Economy (Strategy Analytics, June 2017), https://newsroom. intel. com/newsroom/wp-content/uploads/sites/11/2017/05/passenger-economy.pdf; roughly the size of the entire EU economy: “The Economy,” European Union (website), accessed April 11, 2019, https://europa.eu/european-union/about-eu/figures/economy_en. 11capture a $1.7 trillion annual share by 2030: Author’s calculation based on Peter Campbell, “Waymo Forecast to Capture 60% of Driverless Market,” Financial Times, May 10, 2018, https://www.ft.com/content/3355f5b0-539d-11e8-b24e-cad6aa67e23e. 11of many shapes and sizes will have replaced them: Scott Corwin et al., “The Future of Mobility: What’s Next?”


pages: 181 words: 52,147

The Driver in the Driverless Car: How Our Technology Choices Will Create the Future by Vivek Wadhwa, Alex Salkever

23andMe, 3D printing, Airbnb, AlphaGo, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, benefit corporation, Bernie Sanders, bitcoin, blockchain, clean water, correlation does not imply causation, CRISPR, deep learning, DeepMind, distributed ledger, Donald Trump, double helix, driverless car, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, gigafactory, Google bus, Hyperloop, income inequality, information security, Internet of things, job automation, Kevin Kelly, Khan Academy, Kickstarter, Law of Accelerating Returns, license plate recognition, life extension, longitudinal study, Lyft, M-Pesa, Mary Meeker, Menlo Park, microbiome, military-industrial complex, mobile money, new economy, off-the-grid, One Laptop per Child (OLPC), personalized medicine, phenotype, precision agriculture, radical life extension, RAND corporation, Ray Kurzweil, recommendation engine, Ronald Reagan, Second Machine Age, self-driving car, seminal paper, Silicon Valley, Skype, smart grid, stem cell, Stephen Hawking, Steve Wozniak, Stuxnet, supercomputer in your pocket, synthetic biology, Tesla Model S, The future is already here, The Future of Employment, Thomas Davenport, Travis Kalanick, Turing test, Uber and Lyft, Uber for X, uber lyft, uranium enrichment, Watson beat the top human players on Jeopardy!, zero day

Someone is sitting in the passenger seat, but no one seems to be sitting in the driver seat. How odd, I think. And then I realize I am looking at a Google car. The technology giant is headquartered in Mountain View, and the company is road-testing its diminutive autonomous cars there. This is my first encounter with a fully autonomous vehicle on a public road in an unstructured setting. The Google car waits patiently as a pedestrian passes in front of it. Another car across the intersection signals a left-hand turn, but the Google car has the right of way. The automated vehicle takes the initiative and smoothly accelerates through the intersection.

This paradigm shift will not be without costs or controversies. For sure, widespread adoption of autonomous vehicles will eliminate the jobs of the millions of Americans whose living comes of driving cars, trucks, and buses (and eventually all those who pilot planes and ships). We will begin sharing our cars, in a logical extension of Uber and Lyft. But how will we handle the inevitable software faults that result in human casualties? And how will we program the machines to make the right decisions when faced with impossible choices—such as whether an autonomous car should drive off a cliff to spare a busload of children at the cost of killing the car’s human passenger?

When parents can call a Google car and put their children in the back seat for a ride to soccer practice, that increases autonomy. When an elderly person who can no longer drive can call an autonomous vehicle for a lift to the supermarket or to the art museum, that increases autonomy. When all of this is affordable—so affordable that anyone can pay for it—it will have brought about a massive net increase in autonomy for all and an important increase in equity. Yes, we will be dependent on autonomous cars, but we have always been dependent. Here, the dependency is actually replaced with something more reliable. The child always needs to get to soccer practice, whether a parent or neighbor or a Google car is providing the transportation.


pages: 257 words: 64,285

The End of Traffic and the Future of Transport: Second Edition by David Levinson, Kevin Krizek

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, American Society of Civil Engineers: Report Card, autonomous vehicles, barriers to entry, Bay Area Rapid Transit, big-box store, bike sharing, carbon tax, Chris Urmson, collaborative consumption, commoditize, congestion pricing, crowdsourcing, DARPA: Urban Challenge, dematerialisation, driverless car, Dutch auction, Elon Musk, en.wikipedia.org, Ford Model T, Google Hangouts, high-speed rail, Induced demand, intermodal, invention of the printing press, jitney, John Markoff, labor-force participation, Lewis Mumford, lifelogging, Lyft, means of production, megacity, Menlo Park, Network effects, Occam's razor, oil shock, place-making, pneumatic tube, post-work, printed gun, Ray Kurzweil, rent-seeking, ride hailing / ride sharing, Robert Gordon, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, tacit knowledge, techno-determinism, technological singularity, Tesla Model S, the built environment, The future is already here, Thomas Kuhn: the structure of scientific revolutions, transaction costs, transportation-network company, Uber and Lyft, Uber for X, uber lyft, urban renewal, women in the workforce, working-age population, Yom Kippur War, zero-sum game, Zipcar

Widely called the 'sharing economy' or 'collaborative consumption,' its manifestations in transport: carsharing and ridesharing are viable if not widespread. Couple these technologies with autonomous vehicles discussed in the previous chapter, and one arrives at what we term 'cloud commuting' — the convergence of ridesharing, carsharing, and autonomous vehicles.211 More formally, this range of options can be termed Mobility-as-a-Service (MaaS). While nascent today, clearly big players are placing big bets that this will be a big change in how people travel. It is this which explains Uber's $62.5 Billion valuation.212 A vehicle from a giant pool of autonomous cars operated by organizations based 'in the cloud' would be dispatched to a customer on-demand and in short order, and then would deliver the customer to her destination (be it work or otherwise) before moving on to the next customer.

In short: speed decentralizes.248 And, some places, including suburbs, exurbs, and rural areas will likely see increased decentralization. But in cities, there is increasing evidence to suggest the reverse. These phenomena will likely be context dependent. Autonomous vehicles will likely be faster, particularly on freeways, especially after widespread deployment once either human drivers are banned or a network of separate lanes are designated for autonomous cars. Coupling with just the faster speed, the fully autonomous vehicle lowers the cognitive burden on the former driver/now passenger. Modes with lower cognitive burden tend to have longer trip durations. Time matters. What you does with that time (the quality of the experience) also matters.

Thus, capacity at bottlenecks should increase, both in throughput per lane and the number of lanes per unit road width. These cars still need to go somewhere, so auto-mobility still requires some capacity on city streets as well as freeways, but ubiquitous adoption of autonomous vehicles would save space on parking and lane width. Cars without people. Autonomous cars can drive without people at all. They can be used for pickup and delivery, in addition to the dead-heading from drop-off to parking, or from drop-off of one passenger to pick-up of another, or for recharging or refueling. All of this can increase total travel on the road.


pages: 688 words: 147,571

Robot Rules: Regulating Artificial Intelligence by Jacob Turner

"World Economic Forum" Davos, Ada Lovelace, Affordable Care Act / Obamacare, AI winter, algorithmic bias, algorithmic trading, AlphaGo, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, autonomous vehicles, backpropagation, Basel III, bitcoin, Black Monday: stock market crash in 1987, blockchain, brain emulation, Brexit referendum, Cambridge Analytica, Charles Babbage, Clapham omnibus, cognitive dissonance, Computing Machinery and Intelligence, corporate governance, corporate social responsibility, correlation does not imply causation, crowdsourcing, data science, deep learning, DeepMind, Demis Hassabis, distributed ledger, don't be evil, Donald Trump, driverless car, easy for humans, difficult for computers, effective altruism, Elon Musk, financial exclusion, financial innovation, friendly fire, future of work, hallucination problem, hive mind, Internet of things, iterative process, job automation, John Markoff, John von Neumann, Loebner Prize, machine readable, machine translation, medical malpractice, Nate Silver, natural language processing, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, nudge unit, obamacare, off grid, OpenAI, paperclip maximiser, pattern recognition, Peace of Westphalia, Philippa Foot, race to the bottom, Ray Kurzweil, Recombinant DNA, Rodney Brooks, self-driving car, Silicon Valley, Stanislav Petrov, Stephen Hawking, Steve Wozniak, strong AI, technological singularity, Tesla Model S, The Coming Technological Singularity, The Future of Employment, The Signal and the Noise by Nate Silver, trolley problem, Turing test, Vernor Vinge

Ngo, “Redesign of the Vehicle Bonnet Structure for Pedestrian Safety”, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, Vol. 226, No. 1 (2012), 70–84. 109Many commentators have pointed out the applicability of the Trolley Problem to self-driving cars, but beyond articulating the issue, few have actually suggested a legal or moral answer. See, for example, Matt Simon, “To Make Us All Safer, Robocars Will Sometimes Have to Kill”, Wired, 17 March 2017, https://​www.​wired.​com/​2017/​03/​make-us-safer-robocars-will-sometimes-kill/​, accessed 1 June 2018; Alex Hern, “Self-Driving Cars Don’t Care About Your Moral Dilemmas”, The Guardian, 22 August 2016, https://​www.​theguardian.​com/​technology/​2016/​aug/​22/​self-driving-cars-moral-dilemmas, accessed 1 June 2018; Jean-François Bonnefon, Azim Shariff, and Iyad Rahwan, “The Social Dilemma of Autonomous Vehicles”, Science, Vol. 352, No. 6293 (2016), 1573–1576; Noah J. Goodall, “Machine Ethics and Automated Vehicles”, in Road Vehicle Automation, edited by Gereon Meyer and Sven Beiker (New York: Springer, 2014), 93–102. 110“Ethics Commission at the German Ministry of Transport and Digital Infrastructure”, 5 June 2017, https://​www.​bmvi.​de/​SharedDocs/​EN/​Documents/​G/​ethic-commission-report.​pdf?​

The term does not include an active safety system or a system for driver assistance, including without limitation, a system to provide electronic blind spot detection, crash avoidance, emergency braking, parking assistance, adaptive cruise control, lane keeping assistance, lane departure warning, or traffic jam and queuing assistance, unless any such system, alone or in combination with any other system, enables the vehicle on which the system is installed to be driven without the active control or monitoring of a human operator (Added to NRS by 2013, 2009). Chapter 482A—Autonomous Vehicles, https://​www.​leg.​state.​nv.​us/​NRS/​NRS-482A.​html, accessed 1 June 2018. 45Ryan Calo, “Nevada Bill Would Pave the Road to Autonomous Cars”, Centre for Internet and Society Blog, 27 April 2011, http://​cyberlaw.​stanford.​edu/​blog/​2011/​04/​nevada-bill-would-pave-road-autonomous-cars, accessed 1 June 2018. 46Will Knight, “Alpha Zero’s “Alien” Chess Shows the Power, and the Peculiarity, of AI”, MIT Technology Review, https://​www.​technologyreview​.​com/​s/​609736/​alpha-zeros-alien-chess-shows-the-power-and-the-peculiarity-of-ai/​, accessed 1 June 2018.

Kazuo Yano, “Enterprises of the Future Will Need Multi-purpose AIs”, Hitachi Website, http://​www.​hitachi.​co.​jp/​products/​it/​it-pf/​mag/​special/​2016_​02th_​e/​interview_​ky_​02.​pdf, accessed 1 June 2018. 41UK Department of Transport, “The Pathway to Driverless Cars: Detailed Review of Regulations for Automated Vehicle Technologies”, UK Government Website, February 2015, https://​www.​gov.​uk/​government/​uploads/​system/​uploads/​attachment_​data/​file/​401565/​pathway-driverless-cars-main.​pdf, accessed 1 June 2018. 42When in 2017 the UK’s House of Lords Science and Technology Select Committee published a report entitled “Connected and Autonomous Vehicles: The Future?”, it concentrated solely on land-based vehicles. House of Lords, Science and Technology Select Committee, “Connected and Autonomous Vehicles: The Future?”, 2nd Report of Session 2016–17, HL Paper 115 (15 March 2017). The Report expressly says at para. 23: “We have not considered remote control vehicles (RCV) or drones (unmanned aerial vehicles) in this report”.


pages: 237 words: 64,411

Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence by Jerry Kaplan

Affordable Care Act / Obamacare, Amazon Web Services, asset allocation, autonomous vehicles, bank run, bitcoin, Bob Noyce, Brian Krebs, business cycle, buy low sell high, Capital in the Twenty-First Century by Thomas Piketty, combinatorial explosion, computer vision, Computing Machinery and Intelligence, corporate governance, crowdsourcing, driverless car, drop ship, Easter island, en.wikipedia.org, Erik Brynjolfsson, estate planning, Fairchild Semiconductor, Flash crash, Gini coefficient, Goldman Sachs: Vampire Squid, haute couture, hiring and firing, income inequality, index card, industrial robot, information asymmetry, invention of agriculture, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, Kiva Systems, Larry Ellison, Loebner Prize, Mark Zuckerberg, mortgage debt, natural language processing, Nick Bostrom, Own Your Own Home, pattern recognition, Satoshi Nakamoto, school choice, Schrödinger's Cat, Second Machine Age, self-driving car, sentiment analysis, short squeeze, Silicon Valley, Silicon Valley startup, Skype, software as a service, The Chicago School, The Future of Employment, Turing test, Vitalik Buterin, Watson beat the top human players on Jeopardy!, winner-take-all economy, women in the workforce, working poor, Works Progress Administration

Transport Times, June, 2014, http://www.transporttimes.co.uk/Admin/uploads/64165-Transport-Times_A-2050-Vision-for-London_AW-WEB-READY.pdf; http://emarketing.pwc.com/reaction/images/AutofactsAnalystNoteUS(Feb2013)FINAL.pdf 7. According to Brad Templeton, autonomous car consultant to Google, “In Los Angeles, it is estimated that over half of all real estate is devoted to cars (roads and environs, driveways, parking),” personal blog, accessed November 29, 2014, http://www.templetons.com/brad/robocars/numbers.html. 8. Transportation Energy Data Book, table 8.5, Center for Transportation Analysis, Oak Ridge National Laboratory, accessed November 29, 2014, http://cta.ornl.gov/data/chapter8.shtml. 9.

Shaw and Company, 53, 95, 96, 97 DIDO (distributed input, distributed output), 127 digital recording, 193 Dijkstra, Edgar, 3 dishwashing, 145 Disneyland VIP tour option, 165 doctors. See medical care Dow Jones Industrial Average, 8–9, 61–63 driverless cars. See autonomous vehicles drones, 43, 44 duty-based normative ethics, 82 “Easterlin Paradox,” 225n31 economic system, 7, 10–15 absolute vs. reactive needs and, 186 asset-based, 14–15, 175–87 autonomous vehicles’ effects on, 195, 196 class and, 115, 116, 118 competitive advantage and, 102, 103, 106, 161–65, 181, 186, 187 expansion of, 15, 165 incentives and, 176, 177 inequality and, 12–15, 117–18, 165–66, 174–76 inflation rate, 173, 175 innovation and, 158, 161–64, 186–87 Silicon Valley disruption of, 16 synthetic intellect takeover of, 201–2.

., 200 Rocket Fuel, 64–65, 67–71, 136 founding/current worth of, 72 Rolling Stone (magazine), 170 Roosevelt, Franklin Delano, 170 Rosenblatt, Frank, 24 Rothschild, Nathan Mayer, 58 R202 (mechanical factotum), 40 Rutter, Brad, 150 SAAS (software-as-a-service), 43 safety: autonomous vehicles, 89, 142, 195 commercial pilots, 151 highway, 44–45, 142, 178 traffic, 195 workplace, 37–38, 44–45 salaries, 116, 120, 145, 172 salespeople, 139 S&P 500 E-mini, 62 San Francisco State University, 121, 158 sanitation, 169 savings. See assets ownership Scheinman, Victor, 35 schools. See education system Schrodinger’s cat, 213n9 science, ix, 114 science fiction, ix–x, xii Seattle, 114 SEC (Securities and Exchange Commission), 8, 61–62, 63 segregation, 168, 222n10 self-driving vehicles. See autonomous vehicles self stocking, 40 sensors, 194, 205 applications of, 4, 5 network of, 42–43, 44 recognition by, 39 sex workers and toys, 144–45 Shaw, Dave (King Quant), 51–53, 58, 95, 96, 97, 103 shipping, 39 costs of, 100, 101 delivery and, 141–42, 177 “free,” 101 warehouse stacking and, 144 ShotSpotter system, 43 Silicon Valley startups, x–xi, 64–65, 95–96, 127, 144, 223–24n15 disruption of industries by, 16 personal wealth from, 109 restricted stock vesting by, 184 Simon, Paul, 112 simulated intelligence.


pages: 296 words: 78,631

Hello World: Being Human in the Age of Algorithms by Hannah Fry

23andMe, 3D printing, Air France Flight 447, Airbnb, airport security, algorithmic bias, algorithmic management, augmented reality, autonomous vehicles, backpropagation, Brixton riot, Cambridge Analytica, chief data officer, computer vision, crowdsourcing, DARPA: Urban Challenge, data science, deep learning, DeepMind, Douglas Hofstadter, driverless car, Elon Musk, fake news, Firefox, Geoffrey Hinton, Google Chrome, Gödel, Escher, Bach, Ignaz Semmelweis: hand washing, John Markoff, Mark Zuckerberg, meta-analysis, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, pattern recognition, Peter Thiel, RAND corporation, ransomware, recommendation engine, ride hailing / ride sharing, selection bias, self-driving car, Shai Danziger, Silicon Valley, Silicon Valley startup, Snapchat, sparse data, speech recognition, Stanislav Petrov, statistical model, Stephen Hawking, Steven Levy, systematic bias, TED Talk, Tesla Model S, The Wisdom of Crowds, Thomas Bayes, trolley problem, Watson beat the top human players on Jeopardy!, web of trust, William Langewiesche, you are the product

Because there’s another layer of difficulty to contend with when trying to build that sci-fi fantasy of a go-anywhere, do-anything, steering-wheel-free driverless car, and it’s one that goes well beyond the technical challenge. A fully autonomous car will also have to deal with the tricky problem of people. Jack Stilgoe, a sociologist from University College London and an expert in the social impact of technology, explains: ‘People are mischievous. They’re active agents, not just passive parts of the scenery.’38 Imagine, for a moment, a world where truly, perfectly autonomous vehicles exist. The number one rule in their on-board algorithms will be to avoid collisions wherever possible. And that changes the dynamics of the road.

‘It’s about keeping the world as it is but making and allowing a robot to just be as good as and then better than a human at navigating it. And I think that’s stupid.’ But hang on, some of you may be thinking. Hasn’t this problem already been cracked? Hasn’t Waymo, Google’s autonomous car, driven millions of miles already? Aren’t Waymo’s fully autonomous cars (or at least, close to fully autonomous cars) currently driving around on the roads of Phoenix, Arizona? Well, yes. That’s true. But not every mile of road is created equally. Most miles are so easy to drive, you can do it while daydreaming. Others are far more challenging. At the time of writing, Waymo cars aren’t allowed to go just anywhere: they’re ‘geo-fenced’ into a small, pre-defined area.

They, more than anyone, know how far away we are from having to worry about the trolley problem as a reality. Breaking the rules of the road Bayes’ theorem and the power of probability have driven much of the innovation in autonomous vehicles ever since the DARPA challenge. I asked Paul Newman, professor of robotics at the University of Oxford and founder of Oxbotica, a company that builds driverless cars and tests them on the streets of Britain, how his latest autonomous vehicles worked, and he explained as follows: ‘It’s many, many millions of lines of code, but I could frame the entire thing as probabilistic inference. All of it.’36 But while Bayesian inference goes some way towards explaining how driverless cars are possible, it also explains how full autonomy, free from any input by a human driver, is a very, very difficult nut to crack.


pages: 307 words: 90,634

Insane Mode: How Elon Musk's Tesla Sparked an Electric Revolution to End the Age of Oil by Hamish McKenzie

Airbnb, Albert Einstein, augmented reality, autonomous vehicles, barriers to entry, basic income, Bay Area Rapid Transit, Ben Horowitz, business climate, car-free, carbon footprint, carbon tax, Chris Urmson, Clayton Christensen, clean tech, Colonization of Mars, connected car, crony capitalism, Deng Xiaoping, Didi Chuxing, disinformation, disruptive innovation, Donald Trump, driverless car, Elon Musk, Fairchild Semiconductor, Ford Model T, gigafactory, Google Glasses, Hyperloop, information security, Internet of things, Jeff Bezos, John Markoff, low earth orbit, Lyft, Marc Andreessen, margin call, Mark Zuckerberg, Max Levchin, megacity, Menlo Park, Nikolai Kondratiev, oil shale / tar sands, paypal mafia, Peter Thiel, ride hailing / ride sharing, Ronald Reagan, self-driving car, Shenzhen was a fishing village, short selling, side project, Silicon Valley, Silicon Valley startup, Snapchat, Solyndra, South China Sea, special economic zone, stealth mode startup, Steve Jobs, tech worker, TechCrunch disrupt, TED Talk, Tesla Model S, Tim Cook: Apple, Tony Fadell, Uber and Lyft, uber lyft, universal basic income, urban planning, urban sprawl, Zenefits, Zipcar

The development of autonomous vehicles goes hand in hand with the development of electric vehicles, because self-driving cars are best controlled by drive-by-wire systems, in which electrical signals and digital controls, rather than mechanical functions, operate a car’s core systems, such as steering, acceleration, and braking. The absence of a large engine block, too, opens up more design possibilities for electric vehicles, so autonomous cars could come in more varied shapes and sizes, as small as a covered Segway or as large as a double-decker bus. But to the extent that the spread of autonomous vehicles depends on electric vehicles, so, too, must they depend on the expansion of electric vehicle infrastructure, especially the proliferation of charging stations. And who wouldn’t want a car that could refuel itself?

Level 5 was the highest, at which a car would have no controls for human drivers whatsoever. At that point, you could read a book, take a nap, or watch a movie while the car drove itself. Google has tested fully autonomous vehicles to a Level 5 designation, meaning the cars could perform all “safety-critical driving functions and monitor roadway conditions for an entire trip,” but they haven’t yet left the test circuit. The development of autonomous vehicles goes hand in hand with the development of electric vehicles, because self-driving cars are best controlled by drive-by-wire systems, in which electrical signals and digital controls, rather than mechanical functions, operate a car’s core systems, such as steering, acceleration, and braking.

In April 2016, as NHTSA held public hearings about self-driving cars, a group that included Ford, Google, Uber, Lyft, and Volvo announced the formation of the Self-Driving Coalition for Safer Streets, led by David Strickland, a former NHTSA administrator. The group has been advocating for a clear set of federal standards for autonomous vehicles in the United States. In June 2016, the National Association of City Transportation Officials, a coalition of officials from dozens of large North American cities, published a policy statement that included a series of safety- and civic-minded recommendations, such as capping inner-city speeds for autonomous vehicles at twenty-five miles an hour and offering federal and state incentives to cities that prioritize self-driving electric cars that can be shared.


pages: 350 words: 98,077

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

Ada Lovelace, AI winter, Alignment Problem, AlphaGo, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, backpropagation, Bernie Sanders, Big Tech, Boston Dynamics, Cambridge Analytica, Charles Babbage, Claude Shannon: information theory, cognitive dissonance, computer age, computer vision, Computing Machinery and Intelligence, dark matter, deep learning, DeepMind, Demis Hassabis, Douglas Hofstadter, driverless car, Elon Musk, en.wikipedia.org, folksonomy, Geoffrey Hinton, Gödel, Escher, Bach, I think there is a world market for maybe five computers, ImageNet competition, Jaron Lanier, job automation, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, license plate recognition, machine translation, Mark Zuckerberg, natural language processing, Nick Bostrom, Norbert Wiener, ought to be enough for anybody, paperclip maximiser, pattern recognition, performance metric, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Rodney Brooks, self-driving car, sentiment analysis, Silicon Valley, Singularitarianism, Skype, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, tacit knowledge, tail risk, TED Talk, the long tail, theory of mind, There's no reason for any individual to have a computer in his home - Ken Olsen, trolley problem, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, world market for maybe five computers

Sizing up a complex situation and making split-second assessments of who is likely to jaywalk, dart across the street to run for a bus, turn abruptly without signaling, or stop in a crosswalk to adjust a broken high-heeled shoe—this is second nature to most human drivers, but not yet to self-driving cars. Another looming problem for autonomous vehicles is the potential for malicious attacks of various kinds. Computer-security experts have shown that even many of the nonautonomous cars we drive today—which are increasingly controlled by software—are vulnerable to hacking via their connection to wireless networks, including Bluetooth, cell phone networks, and internet connections.2 Because autonomous cars will be completely controlled by software, they will potentially be even more vulnerable to malicious hacking. In addition, as I described in chapter 6, machine-learning researchers have demonstrated possible “adversarial attacks” on computer-vision systems of self-driving cars—some as simple as putting inconspicuous stickers on stop signs that make the car classify them as speed-limit signs.

Cars with partial autonomy exist now, but are dangerous because the humans driving them don’t always pay attention. The most likely solution to this dilemma is to change the definition of full autonomy: allowing autonomous cars to drive only in specific areas—those that have created the infrastructure to ensure that the cars will be safe. A common version of this solution goes by the name “geofencing.” Jackie DiMarco, former chief engineer for autonomous vehicles at Ford Motor Company, explained geofencing this way: When we talk about level 4 autonomy, it’s fully autonomous within a geofence, so within an area where we have a defined high definition map.

The self-driving car industry desperately wants to produce and sell fully autonomous vehicles (that is, level 5); indeed, full autonomy is what we, the consumers, have long been promised in all the buzz around self-driving cars. What are the obstacles to getting to true autonomy in our cars? The primary obstacles are the kinds of long-tail situations (“edge cases”) that I described in chapter 6: situations that the vehicle was not trained on, and that might individually occur rarely, but that, taken together, will occur frequently when autonomous vehicles are widespread. As I described, human drivers deal with these events by using common sense—particularly the ability to understand and make predictions about novel situations by analogy to situations the driver already understands.


pages: 165 words: 45,397

Speculative Everything: Design, Fiction, and Social Dreaming by Anthony Dunne, Fiona Raby

3D printing, Adam Curtis, Anthropocene, augmented reality, autonomous vehicles, behavioural economics, Berlin Wall, Boeing 747, Buckminster Fuller, capitalist realism, Cass Sunstein, computer age, corporate governance, David Attenborough, en.wikipedia.org, Fall of the Berlin Wall, game design, General Motors Futurama, global village, Google X / Alphabet X, haute couture, Herman Kahn, intentional community, life extension, machine readable, Mark Zuckerberg, mouse model, New Urbanism, Peter Eisenman, RAND corporation, Richard Thaler, Ronald Reagan, self-driving car, Silicon Valley, social software, synthetic biology, systems thinking, technoutopianism, Wall-E

Timothy Mitchell, "Hydrocarbon Utopia," in Utopia/Dystopia: Conditions of Historical Possibility, ed. Michael D.Gordin, Helen Tilley, and Gyan Prakish (Princeton, NJ: Princeton University Press, 2010), 118. 27. For more about how the design of cars can change to due to robocars, see Brad Templeton, "New Design Factors for Robot Cars." Available at http:// www.templetons.com/brad/robocars/design-change.html. Accessed December 23, 2012. 28. Each micro-kingdom represents a different combination of technology and political ideology. For example, if biotechnology were part of the digitarian world, it would be shaped by market mechanisms and lead to a "bio-tarian" culture.


pages: 1,172 words: 114,305

New Laws of Robotics: Defending Human Expertise in the Age of AI by Frank Pasquale

affirmative action, Affordable Care Act / Obamacare, Airbnb, algorithmic bias, Amazon Mechanical Turk, Anthropocene, augmented reality, Automated Insights, autonomous vehicles, basic income, battle of ideas, Bernie Sanders, Big Tech, Bill Joy: nanobots, bitcoin, blockchain, Brexit referendum, call centre, Cambridge Analytica, carbon tax, citizen journalism, Clayton Christensen, collective bargaining, commoditize, computer vision, conceptual framework, contact tracing, coronavirus, corporate social responsibility, correlation does not imply causation, COVID-19, critical race theory, cryptocurrency, data is the new oil, data science, decarbonisation, deep learning, deepfake, deskilling, digital divide, digital twin, disinformation, disruptive innovation, don't be evil, Donald Trump, Douglas Engelbart, driverless car, effective altruism, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, fake news, Filter Bubble, finite state, Flash crash, future of work, gamification, general purpose technology, Google Chrome, Google Glasses, Great Leap Forward, green new deal, guns versus butter model, Hans Moravec, high net worth, hiring and firing, holacracy, Ian Bogost, independent contractor, informal economy, information asymmetry, information retrieval, interchangeable parts, invisible hand, James Bridle, Jaron Lanier, job automation, John Markoff, Joi Ito, Khan Academy, knowledge economy, late capitalism, lockdown, machine readable, Marc Andreessen, Mark Zuckerberg, means of production, medical malpractice, megaproject, meta-analysis, military-industrial complex, Modern Monetary Theory, Money creation, move fast and break things, mutually assured destruction, natural language processing, new economy, Nicholas Carr, Nick Bostrom, Norbert Wiener, nuclear winter, obamacare, One Laptop per Child (OLPC), open immigration, OpenAI, opioid epidemic / opioid crisis, paperclip maximiser, paradox of thrift, pattern recognition, payday loans, personalized medicine, Peter Singer: altruism, Philip Mirowski, pink-collar, plutocrats, post-truth, pre–internet, profit motive, public intellectual, QR code, quantitative easing, race to the bottom, RAND corporation, Ray Kurzweil, recommendation engine, regulatory arbitrage, Robert Shiller, Rodney Brooks, Ronald Reagan, self-driving car, sentiment analysis, Shoshana Zuboff, Silicon Valley, Singularitarianism, smart cities, smart contracts, software is eating the world, South China Sea, Steve Bannon, Strategic Defense Initiative, surveillance capitalism, Susan Wojcicki, tacit knowledge, TaskRabbit, technological solutionism, technoutopianism, TED Talk, telepresence, telerobotics, The Future of Employment, The Turner Diaries, Therac-25, Thorstein Veblen, too big to fail, Turing test, universal basic income, unorthodox policies, wage slave, Watson beat the top human players on Jeopardy!, working poor, workplace surveillance , Works Progress Administration, zero day

She also concludes that China is in the middle of the two paradigms, with ongoing political and cultural debate on the fundamental premises of human-robot interaction. As these distinctive approaches evolve, the economics profession should not presume to put a finger on the scale in favor of substitutive automation. It can just as readily prescribe human-led care. THE POLITICAL ECONOMY OF ROBO-CARE Larger trends in workforce participation and demand also matter. Robotic caregiving makes far more sense in a society where the adult children of the elderly are under constant pressure to work more; an overburdened “sandwich generation” has to sacrifice somewhere. If, by contrast, productivity gains were better distributed, demand for robots in elder care would likely diminish.

Legislators are already grappling with the question of whether to require such vehicles to revert control to a person upon request or to give that control to police when they demand it.63 I used the ambiguous term “person” in the last sentence because we still do not have a good term for occupants of a semi-autonomous vehicle. Both law and norms will shape that new identity over time.64 None of these decisions should be made solely—or even predominantly—by the programmers and corporations developing algorithms for self-driving cars. They involve governance and participation by a much wider range of experts, ranging from urban-studies scholars to regulators to police and attorneys.

But to deploy such powerful technology to ticket speeders, ferret out benefits fraud, or catch petty thieves is like using a sledgehammer to kill a fly.28 Stark’s proposal is particularly insightful because it extends a widely agreed logic for limiting the power of machines: restricting violence. No one should be able to buy or equip an autonomous vehicle with machine guns on its hood without some kind of license; there’s simply a common sense that tanks are for war, not personal armies. Many uses of facial recognition technology portend structural violence: systematic efforts to typecast individuals, to keep them monitored in their place, or to ransack databases for ways to manipulate them.


pages: 717 words: 150,288

Cities Under Siege: The New Military Urbanism by Stephen Graham

"hyperreality Baudrillard"~20 OR "Baudrillard hyperreality", addicted to oil, airport security, Alan Greenspan, Anthropocene, anti-communist, autonomous vehicles, Berlin Wall, call centre, carbon footprint, clean tech, clean water, congestion charging, creative destruction, credit crunch, DARPA: Urban Challenge, defense in depth, deindustrialization, digital map, disinformation, Dr. Strangelove, driverless car, edge city, energy security, European colonialism, export processing zone, failed state, Food sovereignty, gentrification, Gini coefficient, global supply chain, Global Witness, Google Earth, illegal immigration, income inequality, knowledge economy, late capitalism, Lewis Mumford, loose coupling, machine readable, market fundamentalism, mass incarceration, McMansion, megacity, military-industrial complex, moral panic, mutually assured destruction, Naomi Klein, New Urbanism, offshore financial centre, one-state solution, pattern recognition, peak oil, planetary scale, post-Fordism, private military company, Project for a New American Century, RAND corporation, RFID, Richard Florida, Scramble for Africa, Seymour Hersh, Silicon Valley, SimCity, smart transportation, surplus humans, The Bell Curve by Richard Herrnstein and Charles Murray, urban decay, urban planning, urban renewal, urban sprawl, Washington Consensus, white flight, white picket fence

Eleven fully robotized SUVs and other cars had to navigate a simulated urban course completely autonomously. 9.10 Estimates for the future introduction of fully autonomous military and civilian vehicles from the Urban Challenge presentations of Stanford University’s entry. Whilst driverless cars are unlikely to become available to consumers until 2030 at the earliest, the Urban Challenge robocars are already being displayed at car shows, billed as a way to ‘fortify road safety and eliminate driver error as the most common cause of crashes’.131 The already strong links between militarized robotic combat vehicles (Figure 9.10) and an increasingly militarized society where cars become increasingly automated and surveilled, will likely intensify.

Widespread campaigns, drawing on a long history of such activism, have targeted the militarized R&D that is carried on in US universities and so firmly underpins securocratic war, ubiquitous bordering, and the Long War.57 Two of the main centres for work on the robotization of weapons–the Robotics Institute and its commercial arm, the National Robotics Engineering Center (NREC) – are at Carnegie Mellon University in Pittsburgh, and both have been the target of a jamming campaign (Figure 10.13). (In Chapter 9 we already encountered NREC: its ‘robocar’ was the winner of DARPA’s 2007 Urban Challenge competition.) The Carnegie Mellon campaign, labelled ‘Barricade the War Machine’, is challenging the take-over of engineering sciences in the university and the local economy by military-robotics research in the service of the military-industrial-academic complex.

For example, in an attempt to stimulate further development of robotic ground vehicles for use in both the US military and on the streets of US cities, the Pentagon’s high-tech R&D arm, the Defense Advanced Research Projects Agency (DARPA), has initiated a series of high-profile rob otic-vehicle competitions. The agency stressed that the aim of the 2007 competition, called ‘Urban Challenge’, was to develop ‘technology that will keep warfighters off the battlefield and out of harm’s way’.127 It was ‘the first time in history that truly autonomous vehicles met and (mostly) avoided each other on the open road’.128 The event required that competing teams build vehicles capable of driving autonomously in traffic, relying entirely on on-board sensors, cameras, radars, computers and GPS systems. These vehicles had to perform turns, mergers, overtaking, and passing, and had to negotiate junctions within a cordoned-off sixty-mile ‘urban’ course in and around a former military base in Victorville, California.


pages: 368 words: 96,825

Bold: How to Go Big, Create Wealth and Impact the World by Peter H. Diamandis, Steven Kotler

3D printing, additive manufacturing, adjacent possible, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Apollo 11, augmented reality, autonomous vehicles, Boston Dynamics, Charles Lindbergh, cloud computing, company town, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deal flow, deep learning, dematerialisation, deskilling, disruptive innovation, driverless car, Elon Musk, en.wikipedia.org, Exxon Valdez, fail fast, Fairchild Semiconductor, fear of failure, Firefox, Galaxy Zoo, Geoffrey Hinton, Google Glasses, Google Hangouts, gravity well, hype cycle, ImageNet competition, industrial robot, information security, Internet of things, Jeff Bezos, John Harrison: Longitude, John Markoff, Jono Bacon, Just-in-time delivery, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, loss aversion, Louis Pasteur, low earth orbit, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Mars Rover, meta-analysis, microbiome, minimum viable product, move fast and break things, Narrative Science, Netflix Prize, Network effects, Oculus Rift, OpenAI, optical character recognition, packet switching, PageRank, pattern recognition, performance metric, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, Ray Kurzweil, recommendation engine, Richard Feynman, ride hailing / ride sharing, risk tolerance, rolodex, Scaled Composites, self-driving car, sentiment analysis, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart grid, SpaceShipOne, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, Stuart Kauffman, superconnector, Susan Wojcicki, synthetic biology, technoutopianism, TED Talk, telepresence, telepresence robot, Turing test, urban renewal, Virgin Galactic, Wayback Machine, web application, X Prize, Y Combinator, zero-sum game

Chapter Three: Five to Change the World 1 Adrian Kingsley-Hughes, “Mobile gadgets driving massive growth in touch sensors,” ZDNet, June 18, 2013, http://www.zdnet.com/mobile-gadgets-driving-massive-growth-in-touch-sensors-7000016954/. 2 Peter Kelly-Detwiler, “Machine to Machine Connections—The Internet of Things—And Energy,” Forbes, August 6, 2013, http://www.forbes.com/sites/peterdetwiler/2013/08/06/machine-to-machine-connections-the-internet-of-things-and-energy/. 3 See http://www.shotspotter.com. 4 Clive Thompson, “No Longer Vaporware: The Internet of Things Is Finally Talking,” Wired, December 6, 2012, http://www.wired.com/2012/12/20-12-st_thompson/. 5 Brad Templeton, “Cameras or Lasers?,” Templetons, http://www.templetons.com/brad/robocars/cameras-lasers.html. 6 See http://en.wikipedia.org/wiki/Passenger_vehicles_in_the_United_States. 7 Commercial satellite players include: PlanetLabs (already launched), Skybox (launched and acquired by Google), Urthecast (launched), and two still-confidential companies still under development (about which Peter Diamandis has firsthand knowledge). 8 Stanford University, “Need for a Trillion Sensors Roadmap,” Tsensorsummit.org, 2013, http://www.tsensorssummit.org/Resources/Why%20TSensors%20Roadmap.pdf. 9 Rickie Fleming, “The battle of the G networks,” NCDS.com blog, June 28, 2014, http://www.ncds.com/ncds-business-technology-blog/the-battle-of-the-g-networks. 10 AI with Dan Hesse, 2013–14. 11 Unless otherwise noted, all IoT information and Padma Warrior quotes come from an AI with Padma, 2013. 12 Cisco, “2013 IoE Value Index,” Cisco.com, 2013, http://internetofeverything.cisco.com/learn/2013-ioe-value-index-whitepaper. 13 NAVTEQ, “NAVTEQ Traffic Patterns,” Navmart.com, 2008, http://www.navmart.com/pdf/NAVmart_TrafficPatterns.pdf. 14 Juho Erkheikki, “Nokia to Buy Navteq for $8.1 Billion, Take on TomTom (Update 7),” Bloomberg, October 1, 2007, http://www.bloomberg.com/apps/news?

As Arduino hacker Charalampos Doukas says, as sensor prices crash downward, “The only limit is your imagination.” To look at this from a more expansive angle, consider that we now live in a world where Google’s autonomous car can cruise our streets safely because of a rooftop sensor called LIDAR—a laser-based sensing device that uses sixty-four eye-safe lasers to scan 360 degrees while concurrently generating 750 megabytes of image data per second to help with navigation.5 Pretty soon, though, we’ll live in a world with, say, two million autonomous cars on our roads (not much of a stretch, as that’s less than one percent of cars currently registered in the United States),6 seeing and recording nearly everything they encounter, giving us near-perfect knowledge of the environment they observe.

To look at this from a more expansive angle, consider that we now live in a world where Google’s autonomous car can cruise our streets safely because of a rooftop sensor called LIDAR—a laser-based sensing device that uses sixty-four eye-safe lasers to scan 360 degrees while concurrently generating 750 megabytes of image data per second to help with navigation.5 Pretty soon, though, we’ll live in a world with, say, two million autonomous cars on our roads (not much of a stretch, as that’s less than one percent of cars currently registered in the United States),6 seeing and recording nearly everything they encounter, giving us near-perfect knowledge of the environment they observe. What’s more, ubiquitous imaging doesn’t stop there. 360-degree LIDAR imaging in Google’s driverless car Source: http://people.bath.ac.uk/as2152/cars/lidar.jpg In addition to these autonomous cars scanning the roadside, by 2020, an estimated five privately owned low-Earth-orbiting satellite constellations will be imaging every square meter of the Earth’s surface in resolutions ranging from 0.5 to 2 meters.7 Simultaneously, we’re also about to see an explosion of AI-operated microdrones buzzing around our cities and taking images down in the centimeter range.


pages: 235 words: 62,862

Utopia for Realists: The Case for a Universal Basic Income, Open Borders, and a 15-Hour Workweek by Rutger Bregman

"World Economic Forum" Davos, Alan Greenspan, autonomous vehicles, banking crisis, Bartolomé de las Casas, basic income, Berlin Wall, Bertrand Russell: In Praise of Idleness, Branko Milanovic, cognitive dissonance, computer age, conceptual framework, credit crunch, David Graeber, Diane Coyle, driverless car, Erik Brynjolfsson, everywhere but in the productivity statistics, Fall of the Berlin Wall, Ford Model T, Francis Fukuyama: the end of history, Frank Levy and Richard Murnane: The New Division of Labor, full employment, George Gilder, George Santayana, happiness index / gross national happiness, Henry Ford's grandson gave labor union leader Walter Reuther a tour of the company’s new, automated factory…, income inequality, invention of gunpowder, James Watt: steam engine, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, Kodak vs Instagram, low skilled workers, means of production, megacity, meta-analysis, microcredit, minimum wage unemployment, Mont Pelerin Society, Nathan Meyer Rothschild: antibiotics, Occupy movement, offshore financial centre, Paul Samuelson, Peter Thiel, post-industrial society, precariat, public intellectual, radical decentralization, RAND corporation, randomized controlled trial, Ray Kurzweil, Ronald Reagan, Rutger Bregman, Second Machine Age, Silicon Valley, Simon Kuznets, Skype, stem cell, Steven Pinker, TED Talk, telemarketer, The future is already here, The Future of Employment, The Spirit Level, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thomas Malthus, Thorstein Veblen, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, wage slave, War on Poverty, We wanted flying cars, instead we got 140 characters, wikimedia commons, women in the workforce, working poor, World Values Survey

Or compare it to electricity: All the major technological innovations happened in the 1870s, but it wasn’t until around 1920 that most factories actually switched to electric power.25 Fast forward to today, and chips are doing things that even ten years ago were still deemed impossible. In 2004 two prominent scientists authored a chapter suggestively titled “Why People Still Matter.”26 Their argument? Driving a car is something that could never be automated. Six years later, Google’s robo-cars had already covered a million miles without a mishap. Okay, one mishap – when a human decided to take the wheel. Futurologist Ray Kurzweil is convinced that by 2029 computers will be just as intelligent as people. In 2045 they might even be a billion times smarter than all human brains put together.


pages: 533 words: 125,495

Rationality: What It Is, Why It Seems Scarce, Why It Matters by Steven Pinker

affirmative action, Albert Einstein, autonomous vehicles, availability heuristic, Ayatollah Khomeini, backpropagation, basic income, behavioural economics, belling the cat, Black Lives Matter, butterfly effect, carbon tax, Cass Sunstein, choice architecture, classic study, clean water, Comet Ping Pong, coronavirus, correlation coefficient, correlation does not imply causation, COVID-19, critical race theory, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, data science, David Attenborough, deep learning, defund the police, delayed gratification, disinformation, Donald Trump, Dr. Strangelove, Easter island, effective altruism, en.wikipedia.org, Erdős number, Estimating the Reproducibility of Psychological Science, fake news, feminist movement, framing effect, George Akerlof, George Floyd, germ theory of disease, high batting average, if you see hoof prints, think horses—not zebras, index card, Jeff Bezos, job automation, John Nash: game theory, John von Neumann, libertarian paternalism, Linda problem, longitudinal study, loss aversion, Mahatma Gandhi, meta-analysis, microaggression, Monty Hall problem, Nash equilibrium, New Journalism, Paul Erdős, Paul Samuelson, Peter Singer: altruism, Pierre-Simon Laplace, placebo effect, post-truth, power law, QAnon, QWERTY keyboard, Ralph Waldo Emerson, randomized controlled trial, replication crisis, Richard Thaler, scientific worldview, selection bias, social discount rate, social distancing, Social Justice Warrior, Stanford marshmallow experiment, Steve Bannon, Steven Pinker, sunk-cost fallacy, TED Talk, the scientific method, Thomas Bayes, Tragedy of the Commons, trolley problem, twin studies, universal basic income, Upton Sinclair, urban planning, Walter Mischel, yellow journalism, zero-sum game

The human visual system is one of the wonders of the world. It is a precision instrument that can detect a single photon, recognize thousands of shapes, and negotiate rocky trails and high-speed autobahns. It outperforms our best artificial vision systems, which is why at the time of this writing autonomous vehicles have not been loosed on city streets despite decades of R&D. The vision modules of the robocars are apt to mistake a tractor trailer for a billboard, or a traffic sign plastered with stickers for a refrigerator filled with food.60 The shape and shading illusions are not bugs but features. The goal of the visual system is to provide the rest of the brain with an accurate description of the 3-D shapes and material composition of the objects in front of us.61 This is a hard problem because the information coming into the brain from the retina doesn’t reflect reality directly.


pages: 309 words: 91,581

The Great Divergence: America's Growing Inequality Crisis and What We Can Do About It by Timothy Noah

air traffic controllers' union, Alan Greenspan, assortative mating, autonomous vehicles, Bear Stearns, blue-collar work, Bonfire of the Vanities, Branko Milanovic, business cycle, call centre, carbon tax, collective bargaining, compensation consultant, computer age, corporate governance, Credit Default Swap, David Ricardo: comparative advantage, Deng Xiaoping, easy for humans, difficult for computers, Erik Brynjolfsson, Everybody Ought to Be Rich, feminist movement, Ford Model T, Frank Levy and Richard Murnane: The New Division of Labor, Gini coefficient, government statistician, Gunnar Myrdal, income inequality, independent contractor, industrial robot, invisible hand, It's morning again in America, job automation, Joseph Schumpeter, longitudinal study, low skilled workers, lump of labour, manufacturing employment, moral hazard, oil shock, pattern recognition, Paul Samuelson, performance metric, positional goods, post-industrial society, postindustrial economy, proprietary trading, purchasing power parity, refrigerator car, rent control, Richard Feynman, Ronald Reagan, shareholder value, Silicon Valley, Simon Kuznets, Stephen Hawking, Steve Jobs, subprime mortgage crisis, The Spirit Level, too big to fail, trickle-down economics, Tyler Cowen, Tyler Cowen: Great Stagnation, union organizing, upwardly mobile, very high income, Vilfredo Pareto, War on Poverty, We are the 99%, women in the workforce, Works Progress Administration, Yom Kippur War

In 2011 their MIT colleagues Erik Brynjolfsson and Andrew McAfee of the Sloan School of Management wrote that this conclusion had become obsolete by the end of 2010. In October of that year Google automated a fleet of Toyota Priuses and put them on the road (with human drivers behind the wheel as safety backups). The robocars navigated from Google’s Mountain View, California, headquarters to its Santa Monica office, taking a detour along the way to wind down San Francisco’s Lombard Street (“the crookedest street in the world”). The cars made the 350-mile trip with only a few minor human interventions. “Levy and Murnane were correct that automatic driving on populated roads is an enormously difficult task,” Brynjolfsson and McAfee conclude, “and it’s not easy to build a computer that can substitute for human perception and pattern matching in this domain.


pages: 412 words: 128,042

Extreme Economies: Survival, Failure, Future – Lessons From the World’s Limits by Richard Davies

Abraham Maslow, agricultural Revolution, air freight, Anton Chekhov, artificial general intelligence, autonomous vehicles, barriers to entry, big-box store, cashless society, clean water, complexity theory, deindustrialization, digital divide, eurozone crisis, failed state, financial innovation, Ford Model T, Garrett Hardin, gentleman farmer, Global Witness, government statistician, illegal immigration, income inequality, informal economy, it's over 9,000, James Hargreaves, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, joint-stock company, large denomination, Livingstone, I presume, Malacca Straits, mandatory minimum, manufacturing employment, means of production, megacity, meta-analysis, new economy, off grid, oil shale / tar sands, pension reform, profit motive, randomized controlled trial, rolling blackouts, school choice, school vouchers, Scramble for Africa, side project, Silicon Valley, Simon Kuznets, Skype, spinning jenny, subscription business, The Chicago School, the payments system, trade route, Tragedy of the Commons, Travis Kalanick, uranium enrichment, urban planning, wealth creators, white picket fence, working-age population, Y Combinator, young professional

The staff carefully match players of a similar cognitive function and on many tables the players are so focused that the carers are hardly needed at all. Kazuko Kikuchi’s table is bubbling away and as I say goodbye the octogenarian mah-jong master summons me to her table, looking over her glasses: ‘Tell me: if the elderly in the UK don’t have Las Vegas, what on earth do they do?’ ROBO-CARER Life enters its last phases eventually, even in Japan, leaving many unable to attend day-care, and needing full-time personal nursing and observation instead. Here, the country faces another crunch. Late-stage care often requires one-on-one tasks such as feeding patients and lifting them from bed to bath.


Driverless Cars: On a Road to Nowhere by Christian Wolmar

Airbnb, autonomous vehicles, Beeching cuts, bitcoin, Boris Johnson, BRICs, carbon footprint, Chris Urmson, cognitive dissonance, congestion charging, connected car, deskilling, Diane Coyle, don't be evil, driverless car, Elon Musk, gigafactory, high net worth, independent contractor, RAND corporation, ride hailing / ride sharing, self-driving car, Silicon Valley, smart cities, technological determinism, Tesla Model S, Travis Kalanick, wikimedia commons, Zipcar

Without proper security systems in place, it is feasible that people could commandeer self-driving vehicles to do their bidding, which could be malicious or simply just for the thrill. This issue has been of great concern to developers of autonomous cars, and an article in the MIT Technology Review outlined details of various forms of hacking that could disrupt autonomous vehicle use: [Autonomous] vehicles will have to anticipate and defend against a full spectrum of malicious attackers wielding both traditional cyberattacks and a new generation of attacks based on so-called adversarial machine learning. 42 The author points out that one possible motive, apart from terrorism, for cyber attacks on autonomous cars would 72 Bumps in the road be anger over the widespread loss of jobs that would result from their introduction (an issue that we discuss in the next chapter).

But politicians need to be properly informed about the risks and the potential. One potential negative is if legislators and regulators feel they have to create a framework that is favourable to autonomous cars, in the same way as happened in the early days of the motor car. There is a contradiction at the heart of the happy talk from Silicon Valley about autonomous vehicles. The introduction of autonomous cars is often presented as a liberating force, providing people with greater mobility and wider access to all kinds of services. In fact, it is obvious from all the above that any move towards universal autonomy would require radical intervention from government.

Volvo has attempted to pre-empt the situation by accepting liability for any collisions involving its autonomous vehicles. This is an easy promise to make when there are no cars on the road but it might be far more difficult if there were a series of incidents that could bankrupt the company. Hacking could also cause accidents, adding complexity to the question of liability. Will autonomous cars require regular ‘patches’ to make up for security or other flaws? And if so, whose fault will it be if the patch is not installed? Even a small-scale trial involving three autonomous vehicles (with a ‘driver’ aboard) on fifteen miles of road in an older citizens’ residential village in San Jose, California was nearly killed off because of insurance concerns.


pages: 328 words: 90,677

Ludicrous: The Unvarnished Story of Tesla Motors by Edward Niedermeyer

autonomous vehicles, barriers to entry, Bear Stearns, bitcoin, business climate, call centre, carbon footprint, Clayton Christensen, clean tech, Colonization of Mars, computer vision, crowdsourcing, disruptive innovation, Donald Trump, driverless car, Elon Musk, en.wikipedia.org, facts on the ground, fake it until you make it, family office, financial engineering, Ford Model T, gigafactory, global supply chain, Google Earth, housing crisis, hype cycle, Hyperloop, junk bonds, Kaizen: continuous improvement, Kanban, Kickstarter, Lyft, Marc Andreessen, Menlo Park, minimum viable product, new economy, off grid, off-the-grid, OpenAI, Paul Graham, peak oil, performance metric, Ponzi scheme, ride hailing / ride sharing, risk tolerance, Sand Hill Road, self-driving car, short selling, short squeeze, side project, Silicon Valley, Silicon Valley startup, Skype, smart cities, Solyndra, stealth mode startup, Steve Jobs, Steve Jurvetson, tail risk, technoutopianism, Tesla Model S, too big to fail, Toyota Production System, Uber and Lyft, uber lyft, union organizing, vertical integration, WeWork, work culture , Zipcar

CNBC, May 19, 2017. https://www.cnbc.com/2017/05/19/tesla-stock-valuation-driverless-taxi.html 175Gartner released its latest “hype cycle” analysis: Mike Ramsey. “Autonomous Vehicles Fall Into the Trough of Disillusionment . . . But That’s Good.” Forbes, August 14, 2018. https://www.forbes.com/sites/enroute/2018/08/14/autonomous-vehicles-fall-into-the-trough-of-disillusionment-but-thats-good/#3b5a3c6e7b5a 176John Krafcik publicly admitted that autonomous vehicles might never work in all locations: Sam Abuelsamid. “Transition to Autonomous Cars Will Take Longer Than You Think, Waymo CEO Tells Governors.” Forbes, July 20, 2018. https://www.forbes.com/sites/samabuelsamid/2018/07/20/waymo-ceo-tells-governors-av-time-will-be-longer-than-you-think/#277b432cd7da 178reports of heavy discounts during the third quarter of 2016: Liane Yvkoff.

“[It is] something that I think does not reflect well upon the media. It really doesn’t. Because, and really you need to think carefully about this, because if, in writing some article that’s negative, you effectively dissuade people from using an autonomous vehicle, you’re killing people.” Musk was correct in the sense that the media’s Autopilot coverage had contributed to a death. By encouraging the hands-free use of Autopilot and heralding it as the first (sort of) autonomous car when it launched, the media had helped create the overconfidence in Autopilot’s abilities that ultimately killed Joshua Brown. If anything, the wave of negative coverage that followed Brown’s death was a long-overdue examination of the risks inherent to Tesla’s Autopilot strategy, which should have been given a more thorough airing at launch.

Increasingly, the entire goal of a Level 5 vehicle, capable of full autonomy anywhere, was being dismissed in favor of Level 4 vehicles, which operate fully autonomously but only in a limited “geofenced” area. By restricting the operational design domain of an autonomous car to a specific thoroughly mapped area and optimizing the system for the conditions found there, you could reduce the possible scope of edge cases to a manageable level. This retreat from the hype of 2016 isolated Tesla in an awkward contrast. Companies developing autonomous vehicles that had vastly more sophisticated sensor and compute systems than Tesla’s were saying that Level 5 was a fantasy . . . while Tesla continued to collect payments for a Level 5 system.


pages: 290 words: 85,847

A Brief History of Motion: From the Wheel, to the Car, to What Comes Next by Tom Standage

accelerated depreciation, active transport: walking or cycling, autonomous vehicles, back-to-the-city movement, bike sharing, car-free, carbon footprint, Cesare Marchetti: Marchetti’s constant, Chris Urmson, City Beautiful movement, Clapham omnibus, congestion charging, coronavirus, COVID-19, deep learning, Didi Chuxing, Donald Shoup, driverless car, Elaine Herzberg, Elon Musk, flex fuel, Ford Model T, Ford paid five dollars a day, garden city movement, General Motors Futurama, Ida Tarbell, Induced demand, interchangeable parts, invention of the wheel, James Watt: steam engine, Jane Jacobs, jitney, Joan Didion, John Zimmer (Lyft cofounder), Lewis Mumford, lockdown, Lyft, Marshall McLuhan, minimum wage unemployment, oil shock, Own Your Own Home, peak oil, prompt engineering, Ralph Nader, Richard Florida, ride hailing / ride sharing, Rosa Parks, safety bicycle, self-driving car, social distancing, Steve Jobs, streetcar suburb, tech bro, The Death and Life of Great American Cities, trade route, Travis Kalanick, Uber and Lyft, uber lyft, unbiased observer, Unsafe at Any Speed, Upton Sinclair, urban planning, urban sprawl, Victor Gruen, W. E. B. Du Bois, walkable city, white flight, wikimedia commons, Yom Kippur War, Zipcar

“Drivers could bowl along with their hands off the wheel, free to enjoy the scenery or hug their girls.” With self-driving cars there would be no traffic and no accidents—claims that are once again being made by proponents of autonomous vehicles today. Are they right? We may soon find out because driverless cars are no longer the figment of a designer’s imagination. Fittingly, just as the modern automotive era began with the Paris–Rouen race of 1894, recent efforts to build autonomous cars also began with an unusual competition. Known as the Grand Challenge, it was organized by DARPA, America’s main military-research agency, and took place in March 2004 in the Mojave Desert.

But many engineers working on self-driving cars believe that semiautonomous systems are inherently dangerous because of the risk that drivers will come to trust them too much and fail to supervise them. Opponents of semiautonomy, including John Krafcik, the chief executive of Waymo, believe that it is safer to focus on building fully autonomous vehicles where passengers are never required to intervene, as his firm is doing. But such vehicles need to be able to handle absolutely any situation without human intervention, and that is a tall order. By 2020 Waymo’s fleet of autonomous cars had driven more than 20 million miles on public roads in twenty-five cities, and billions more miles in simulation. The company says its vehicles drove 1.45 million miles in California in 2019, with a disengagement rate of 0.076 per 1,000 miles (equivalent to one disengagement every thirteen thousand miles).

Building one that can handle that final 10 percent has turned out to be far more difficult than expected. HOW AUTONOMOUS CARS WORK Building a fully autonomous car can be broken down into three separate problems: perception (figuring out what is going on in the world), prediction (determining what will happen next), and driving policy (steering left or right, accelerating or braking). The last part, driving policy, is considered relatively simple. Perception and prediction pose the biggest challenges. Autonomous cars perceive the world using a combination of sensors including cameras, radar, and lidar—a radar-like technique that uses invisible pulses of light, rather than radio waves, to create a high-resolution 3D map of the surroundings.


pages: 413 words: 119,587

Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots by John Markoff

A Declaration of the Independence of Cyberspace, AI winter, airport security, Andy Rubin, Apollo 11, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, basic income, Baxter: Rethink Robotics, Bill Atkinson, Bill Duvall, bioinformatics, Boston Dynamics, Brewster Kahle, Burning Man, call centre, cellular automata, Charles Babbage, Chris Urmson, Claude Shannon: information theory, Clayton Christensen, clean water, cloud computing, cognitive load, collective bargaining, computer age, Computer Lib, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data acquisition, Dean Kamen, deep learning, DeepMind, deskilling, Do you want to sell sugared water for the rest of your life?, don't be evil, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dr. Strangelove, driverless car, dual-use technology, Dynabook, Edward Snowden, Elon Musk, Erik Brynjolfsson, Evgeny Morozov, factory automation, Fairchild Semiconductor, Fillmore Auditorium, San Francisco, From Mathematics to the Technologies of Life and Death, future of work, Galaxy Zoo, General Magic , Geoffrey Hinton, Google Glasses, Google X / Alphabet X, Grace Hopper, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, Hans Moravec, haute couture, Herbert Marcuse, hive mind, hype cycle, hypertext link, indoor plumbing, industrial robot, information retrieval, Internet Archive, Internet of things, invention of the wheel, Ivan Sutherland, Jacques de Vaucanson, Jaron Lanier, Jeff Bezos, Jeff Hawkins, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, John von Neumann, Kaizen: continuous improvement, Kevin Kelly, Kiva Systems, knowledge worker, Kodak vs Instagram, labor-force participation, loose coupling, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, military-industrial complex, Mitch Kapor, Mother of all demos, natural language processing, Neil Armstrong, new economy, Norbert Wiener, PageRank, PalmPilot, pattern recognition, Philippa Foot, pre–internet, RAND corporation, Ray Kurzweil, reality distortion field, Recombinant DNA, Richard Stallman, Robert Gordon, Robert Solow, Rodney Brooks, Sand Hill Road, Second Machine Age, self-driving car, semantic web, Seymour Hersh, shareholder value, side project, Silicon Valley, Silicon Valley startup, Singularitarianism, skunkworks, Skype, social software, speech recognition, stealth mode startup, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Strategic Defense Initiative, strong AI, superintelligent machines, tech worker, technological singularity, Ted Nelson, TED Talk, telemarketer, telepresence, telepresence robot, Tenerife airport disaster, The Coming Technological Singularity, the medium is the message, Thorstein Veblen, Tony Fadell, trolley problem, Turing test, Vannevar Bush, Vernor Vinge, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, We are as Gods, Whole Earth Catalog, William Shockley: the traitorous eight, zero-sum game

A young Google engineer, Anthony Levandowski, routinely commuted from Berkeley to Mountain View, a distance of fifty miles, in one of the Priuses, and Thrun himself would let a Google car drive him from Mountain View to his vacation home in Lake Tahoe on weekends. Today, partially autonomous cars are already appearing on the market, and they offer two paths toward the future of transportation—one with smarter and safer human drivers and one in which humans will become passengers. Google had not disclosed how it planned to commercialize its research, but by the end of 2013 more than a half-dozen automakers had already publicly stated their intent to offer autonomous vehicles. Indeed, 2014 was the year that the line was first crossed commercially when a handful of European car manufacturers including BMW, Mercedes, Volvo, and Audi announced an optional feature—traffic jam assist, the first baby step toward autonomous driving.

Stanley wasn’t driving down the freeway, so as the desert scenery slid by, it seemed increasingly unnecessary to wear crash helmets for what was more or less a Sunday drive in the country. The car was in training to compete in the Pentagon’s second Grand Challenge, an ambitious autonomous vehicle contest intended to jump-start technology planned for future robotic military vehicles. At the beginning of the twenty-first century, Congress instructed the U.S. military to begin designing autonomous vehicles. Congress even gave the Pentagon a specific goal: by 2015, one-third of the army’s vehicles were supposed to go places without human drivers present. The directive wasn’t clear as to whether both autonomous and remotely teleoperated vehicles would satisfy the requirement.

Because 90 percent of road accidents result from driver error, it is likely that a transition to autonomous vehicles will result in a dramatic drop in the overall number of injuries and deaths. So, clearly the greater good would be served even though there will still be a small number of accidents purely due to technological failures. In some respects, the automobile industry has already agreed with this logic. Air bags, for example, save more lives than are lost due to faulty air bag deployments. Secondly, the narrow focus of the question ignores how autonomous vehicles will probably operate in the future, when it is highly likely that road workers, cops, emergency vehicles, cars, pedestrians, and cyclists will electronically signal their presence to each other, a feature that even without complete automation should dramatically increase safety.


pages: 501 words: 114,888

The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives by Peter H. Diamandis, Steven Kotler

Ada Lovelace, additive manufacturing, Airbnb, Albert Einstein, AlphaGo, Amazon Mechanical Turk, Amazon Robotics, augmented reality, autonomous vehicles, barriers to entry, Big Tech, biodiversity loss, bitcoin, blockchain, blood diamond, Boston Dynamics, Burning Man, call centre, cashless society, Charles Babbage, Charles Lindbergh, Clayton Christensen, clean water, cloud computing, Colonization of Mars, computer vision, creative destruction, CRISPR, crowdsourcing, cryptocurrency, data science, Dean Kamen, deep learning, deepfake, DeepMind, delayed gratification, dematerialisation, digital twin, disruptive innovation, Donald Shoup, driverless car, Easter island, Edward Glaeser, Edward Lloyd's coffeehouse, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Ethereum, ethereum blockchain, experimental economics, fake news, food miles, Ford Model T, fulfillment center, game design, Geoffrey West, Santa Fe Institute, gig economy, gigafactory, Google X / Alphabet X, gravity well, hive mind, housing crisis, Hyperloop, impact investing, indoor plumbing, industrial robot, informal economy, initial coin offering, intentional community, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the telegraph, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Kickstarter, Kiva Systems, late fees, Law of Accelerating Returns, life extension, lifelogging, loss aversion, Lyft, M-Pesa, Mary Lou Jepsen, Masayoshi Son, mass immigration, megacity, meta-analysis, microbiome, microdosing, mobile money, multiplanetary species, Narrative Science, natural language processing, Neal Stephenson, Neil Armstrong, Network effects, new economy, New Urbanism, Nick Bostrom, Oculus Rift, One Laptop per Child (OLPC), out of africa, packet switching, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, Peter Thiel, planned obsolescence, QR code, RAND corporation, Ray Kurzweil, RFID, Richard Feynman, Richard Florida, ride hailing / ride sharing, risk tolerance, robo advisor, Satoshi Nakamoto, Second Machine Age, self-driving car, Sidewalk Labs, Silicon Valley, Skype, smart cities, smart contracts, smart grid, Snapchat, SoftBank, sovereign wealth fund, special economic zone, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Jurvetson, Steven Pinker, Stewart Brand, supercomputer in your pocket, supply-chain management, tech billionaire, technoutopianism, TED Talk, Tesla Model S, Tim Cook: Apple, transaction costs, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, urban planning, Vision Fund, VTOL, warehouse robotics, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, X Prize

The big deal here is both safety and a nearly unassailable market advantage. All that data puts Waymo far ahead of the competition. It means that our autonomous car transition hasn’t really begun and already traditional insurance companies are years behind the curve. When we combine autonomous vehicle technology with smart traffic systems and sensor-embedded roads—two developments that have already begun rolling out—transit risks don’t just plummet, they mutate. For instance, if the LIDAR sensor that’s helping steer an autonomous car goes on the blink and causes an accident, who do you blame? Not the passenger. Maybe the carmaker. Maybe the LIDAR supplier.

With this many cars, Waymo intends to deliver a million trips per day in 2020 (this might be ambitious but Uber currently delivers 15 million rides a day). To understand the importance of this figure or anything close to it, consider that the more miles an autonomous car drives, the more data it gathers—and data is the gasoline of the driverless world. Since 2009, Waymo’s vehicles have logged over 10 million miles. By 2020, with twenty thousand Jaguars doing hundreds of thousands of daily trips, they’ll be adding an extra million miles or so every day. All of those miles matter. As autonomous vehicles drive, they gather information: positions of traffic signs, road conditions, and the like. More information equals smarter algorithms equals safer cars—and this combination is the very edge needed for market domination.

But what happens over the next decade, when autonomous vehicles take to the road and change every aspect of that calculation? Right now, human error sits at the center of auto insurance. People—distractible, emotional, and sometimes irrational—are responsible for 90 percent of the 1.2 million traffic fatalities a year. Yet, without humans in the driver seat, 90 percent of that danger gets removed. For an insurance industry built on assessing risk, this alone is a huge change. Now take it a step further. Today, we insure the stuff we own. But autonomous cars shift us from car-as-property to car-as-service, removing the need for consumer-facing auto insurance altogether.


Succeeding With AI: How to Make AI Work for Your Business by Veljko Krunic

AI winter, Albert Einstein, algorithmic trading, AlphaGo, Amazon Web Services, anti-fragile, anti-pattern, artificial general intelligence, autonomous vehicles, Bayesian statistics, bioinformatics, Black Swan, Boeing 737 MAX, business process, cloud computing, commoditize, computer vision, correlation coefficient, data is the new oil, data science, deep learning, DeepMind, en.wikipedia.org, fail fast, Gini coefficient, high net worth, information retrieval, Internet of things, iterative process, job automation, Lean Startup, license plate recognition, minimum viable product, natural language processing, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, six sigma, smart cities, speech recognition, statistical model, strong AI, tail risk, The Design of Experiments, the scientific method, web application, zero-sum game

Those considerations are that AI could cause physical harm and that collecting data for training and improving AI needs to be done differently than when you have a nonphysical system. This section elaborates on those considerations. For example, AI is applied in physical systems to autonomous vehicles [38] and the Internet of Things (IoT) [46]. Companies ranging from Google to Tesla and Uber have autonomous driving projects. Autonomous cars aren’t possible without AI, and a fully autonomous vehicle requires capabilities superior to what AI can provide today. AI is also used to analyze data harvested from IoT devices (an example being smart thermostats such as Nest [36]). Let’s take a look at some of the related concerns. 8.2.1 First, do no harm When you’re controlling heavy metal objects moving with substantial speed, it’s imperative to keep people safe—humans are vulnerable in collisions with metal objects.

For example, in the case of the AI-powered autonomous vehicle [38], you’d ship the vehicle itself. Once the vehicle is delivered, an additional capability could be added to it as a software upgrade. But you’re stuck with the sensors and effectors (engine, brakes, steering mechanisms, horn, signal lights, headlights, and so forth) that are shipped with the car. Once you distribute physical systems to your customers/users, it’s often impossible (or expensive) to add the capacity to perform new actions that you didn’t envision at design time. Whatever autonomous cars we have in the future, it’s a safe bet that some of their capabilities will be fixed at the time the car is manufactured and will be difficult to change later. 2.5.3 Using AI to automate part of the business process One of the uses of AI that’s getting increasing attention in both industry and the popular press is the use of AI to perform actions that previously required humans.

Even then, there are significant limitations on how much the algorithm can change. While the previous approach might be acceptable if you’re building an AI for research purposes (such as an AI that learns to play old computer games better than a human [137]), such cavalier treatment of human safety doesn’t work when applied to autonomous vehicles. You can’t let an autonomous car learn to drive by letting it drive in the physical world and drive itself right off a cliff—or worse! Local vs. global models One taxonomy of AI methods divides models between those that are global versus those that are local. In the global models, there’s a limited capacity to instruct the algorithm to change the results for only a single input without affecting the results for other inputs (more on global models in section 8.5).


pages: 274 words: 63,679

Right of Way: Race, Class, and the Silent Epidemic of Pedestrian Deaths in America by Angie Schmitt

active transport: walking or cycling, autonomous vehicles, car-free, congestion pricing, COVID-19, crossover SUV, desegregation, Donald Trump, Elaine Herzberg, gentrification, global pandemic, high-speed rail, invention of air conditioning, Lyft, megacity, move fast and break things, off-the-grid, Ralph Nader, Richard Florida, Ronald Reagan, self-driving car, Silicon Valley, Skype, subprime mortgage crisis, super pumped, Uber and Lyft, uber lyft, Unsafe at Any Speed, urban planning, urban sprawl, white flight, wikimedia commons

Simon Romero, “Wielding Rocks and Knives, Arizonans Attack Self-Driving Cars,” New York Times, December 31, 2018, https://www.nytimes.com/2018/12/31/us/waymo-self-driving-cars-arizona-attacks.html. 8. Governors Highway Safety Association, “Autonomous Vehicles,” accessed February 29, 2020, https://www.ghsa.org/state-laws/issues/autonomous%20vehicles. 9. Kyle Wiggers, “Waymo’s Autonomous Cars Have Driven 20 Million Miles on Public Roads,” VentureBeat, January 6, 2020, https://venturebeat.com/2020/01/06/waymos-autonomous-cars-have-driven-20-million-miles-on-public-roads/. 10. Amri Efrati, “The Uber Whistleblower’s Email,” The Information, December 10, 2018, https://www.theinformation.com/articles/the-uber-whistleblowers-email. 11.

Governors like Ducey, for their part, have been relatively enthusiastic about inviting the companies to test on public streets. Autonomous car testing offers government officials an aura of tech and business friendliness—and perhaps some jobs—usually without requiring any upfront public investment. Twelve US states currently allow companies to test or market vehicles that operate without a driver.8 Google’s self-driving car operation, Waymo, claimed to have driven twenty million miles on public roads as of January 2020.9 That company already offers driverless taxi service to a small group of screened riders, who sign nondisclosure agreements, in Arizona. Uber and other autonomous vehicle companies like Arizona in part because environments like Mill Avenue—where Herzberg was struck—are so hostile to pedestrians.

This kind of technology could, for example, prevent red-light-running crashes by sending a signal to the vehicle and instructing it to brake after the traffic light turns red. The NHTSA has estimated that vehicle-to-vehicle communication alone could save 1,366 lives annually and prevent 615,000 injuries.44 V2V and V2I safety systems have a big potential benefit over autonomous vehicles as well: they can be installed retroactively in used cars. A safety improvement based on autonomous vehicles, by contrast, would require waiting perhaps fifteen years for the US vehicle fleet to entirely turn over. For a time a few years ago, it looked as if this kind of safety tech—V2V, V2I—was inevitable. In late 2016, the Obama administration proposed adding DSRC to all new vehicles.


pages: 339 words: 88,732

The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson, Andrew McAfee

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, Alan Greenspan, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, American Society of Civil Engineers: Report Card, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, basic income, Baxter: Rethink Robotics, Boston Dynamics, British Empire, business cycle, business intelligence, business process, call centre, carbon tax, Charles Lindbergh, Chuck Templeton: OpenTable:, clean water, combinatorial explosion, computer age, computer vision, congestion charging, congestion pricing, corporate governance, cotton gin, creative destruction, crowdsourcing, data science, David Ricardo: comparative advantage, digital map, driverless car, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, Fairchild Semiconductor, falling living standards, Filter Bubble, first square of the chessboard / second half of the chessboard, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, full employment, G4S, game design, general purpose technology, global village, GPS: selective availability, Hans Moravec, happiness index / gross national happiness, illegal immigration, immigration reform, income inequality, income per capita, indoor plumbing, industrial robot, informal economy, intangible asset, inventory management, James Watt: steam engine, Jeff Bezos, Jevons paradox, jimmy wales, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Khan Academy, Kiva Systems, knowledge worker, Kodak vs Instagram, law of one price, low skilled workers, Lyft, Mahatma Gandhi, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Mars Rover, mass immigration, means of production, Narrative Science, Nate Silver, natural language processing, Network effects, new economy, New Urbanism, Nicholas Carr, Occupy movement, oil shale / tar sands, oil shock, One Laptop per Child (OLPC), pattern recognition, Paul Samuelson, payday loans, post-work, power law, price stability, Productivity paradox, profit maximization, Ralph Nader, Ray Kurzweil, recommendation engine, Report Card for America’s Infrastructure, Robert Gordon, Robert Solow, Rodney Brooks, Ronald Reagan, search costs, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Simon Kuznets, six sigma, Skype, software patent, sovereign wealth fund, speech recognition, statistical model, Steve Jobs, Steven Pinker, Stuxnet, supply-chain management, TaskRabbit, technological singularity, telepresence, The Bell Curve by Richard Herrnstein and Charles Murray, the Cathedral and the Bazaar, the long tail, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Vernor Vinge, warehouse robotics, Watson beat the top human players on Jeopardy!, winner-take-all economy, Y2K

Thomas Whelan et al., “Kintinuous: Spatially Extended KinectFusion,” n.d., http://dspace.mit.edu/bitstream/handle/1721.1/71756/MIT-CSAIL-TR-2012-020.pdf?sequence=1. 24. Brett Solomon, “Velodyne Creating Sensors for China Autonomous Vehicle Market,” Technology Tell, July 5, 2013, http://www.technologytell.com/in-car-tech/4283/velodyne-creating-sensors-for-china-autonomous-vehicle-market/. Chapter 4 THE DIGITIZATION OF JUST ABOUT EVERYTHING 1. Nick Wingfield and Brian X. Chen, “Apple Keeps Loyalty of Mobile App Developers,” New York Times, June 10, 2012, http://www.nytimes.com/2012/06/11/technology/apple-keeps-loyalty-of-mobile-app-developers.html. 2.

This same period is called by others the Second Industrial Revolution, which is how we’ll refer to it in later chapters. “Any sufficiently advanced technology is indistinguishable from magic.” —Arthur C. Clarke IN THE SUMMER OF 2012, we went for a drive in a car that had no driver. During a research visit to Google’s Silicon Valley headquarters, we got to ride in one of the company’s autonomous vehicles, developed as part of its Chauffeur project. Initially we had visions of cruising in the back seat of a car that had no one in the front seat, but Google is understandably skittish about putting obviously autonomous autos on the road. Doing so might freak out pedestrians and other drivers, or attract the attention of the police.

DARPA, the Defense Advanced Research Projects Agency, was founded in 1958 (in response to the Soviet Union’s launch of the Sputnik satellite) and tasked with spurring technological progress that might have military applications. In 2002 the agency announced its first Grand Challenge, which was to build a completely autonomous vehicle that could complete a 150-mile course through California’s Mojave Desert. Fifteen entrants performed well enough in a qualifying run to compete in the main event, which was held on March 13, 2004. The results were less than encouraging. Two vehicles didn’t make it to the starting area, one flipped over in the starting area, and three hours into the race only four cars were still operational.


pages: 288 words: 86,995

Rule of the Robots: How Artificial Intelligence Will Transform Everything by Martin Ford

AI winter, Airbnb, algorithmic bias, algorithmic trading, Alignment Problem, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, Automated Insights, autonomous vehicles, backpropagation, basic income, Big Tech, big-box store, call centre, carbon footprint, Chris Urmson, Claude Shannon: information theory, clean water, cloud computing, commoditize, computer age, computer vision, Computing Machinery and Intelligence, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, data is the new oil, data science, deep learning, deepfake, DeepMind, Demis Hassabis, deskilling, disruptive innovation, Donald Trump, Elon Musk, factory automation, fake news, fulfillment center, full employment, future of work, general purpose technology, Geoffrey Hinton, George Floyd, gig economy, Gini coefficient, global pandemic, Googley, GPT-3, high-speed rail, hype cycle, ImageNet competition, income inequality, independent contractor, industrial robot, informal economy, information retrieval, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jeff Bezos, job automation, John Markoff, Kiva Systems, knowledge worker, labor-force participation, Law of Accelerating Returns, license plate recognition, low interest rates, low-wage service sector, Lyft, machine readable, machine translation, Mark Zuckerberg, Mitch Kapor, natural language processing, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, Ocado, OpenAI, opioid epidemic / opioid crisis, passive income, pattern recognition, Peter Thiel, Phillips curve, post scarcity, public intellectual, Ray Kurzweil, recommendation engine, remote working, RFID, ride hailing / ride sharing, Robert Gordon, Rodney Brooks, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, Silicon Valley startup, social distancing, SoftBank, South of Market, San Francisco, special economic zone, speech recognition, stealth mode startup, Stephen Hawking, superintelligent machines, TED Talk, The Future of Employment, The Rise and Fall of American Growth, the scientific method, Turing machine, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, universal basic income, very high income, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y Combinator

Major manufacturers including Toyota and Nissan likewise promised self-driving vehicles by 2020.56 All those predictions have now been rolled back. Urmson remains confident and said in 2019 that he expects at least “hundreds” of fully autonomous vehicles to be deployed on public roads within five years,57 and that there may be 10,000 or more such cars operating within ten years.58 My own view is that even those predictions could well turn out to be optimistic. I’d say there’s a real danger that truly autonomous cars are going to remain five years in the future for many years to come. The reality is that the routine operation of autonomous cars on both highways and in more urban environments—in other words, situations where things work more or less as expected—has largely been solved.

A connection achieved through eye contact, a wave of a hand, pausing midstride to wait for a driver’s acknowledgement and numerous other nuanced behaviors make up a kind of unspoken language that is somehow understood by nearly everyone who shares the road. I think it is quite possible that it may turn out that negotiating these types of interactions is simply beyond the capabilities of today’s deep learning systems. In other words, truly autonomous cars may require technology much further along the path toward general machine intelligence, and that could be a long wait. Many analysts believe that, given the difficulties faced by autonomous cars in urban settings, the first truly practical driverless vehicles to appear on our roads will be long-haul trucks. Driving on highways, after all, is a problem that has largely already been solved by systems like Tesla’s autopilot.

To address these issues and eventually achieve the kind of productivity increases that will finally rein in healthcare’s cost disease, I think we will have little choice except to rely far more heavily on medical machine intelligence. SELF-DRIVING CARS AND TRUCKS: A LONGER THAN EXPECTED WAIT Elon Musk’s promise of a million robotic taxis operating on roads by the end of 2020 is only the most recent example of overexuberance in the autonomous vehicle industry. Perhaps because of the centrality of the automobile to our way of life, especially in the United States, no application of artificial intelligence has been subject to as much hype and hyperbolic enthusiasm as the self-driving car. Since the industry’s emergence following the Defense Advanced Research Projects Agency (DARPA) grand challenges in 2004 and 2005, the technology has achieved astonishing progress while at the same time regularly falling short of overinflated expectations.


pages: 340 words: 92,904

Street Smart: The Rise of Cities and the Fall of Cars by Samuel I. Schwartz

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, active transport: walking or cycling, Affordable Care Act / Obamacare, American Society of Civil Engineers: Report Card, autonomous vehicles, bike sharing, car-free, City Beautiful movement, collaborative consumption, congestion charging, congestion pricing, crowdsourcing, desegregation, Donald Shoup, driverless car, Enrique Peñalosa, Ford Model T, Ford paid five dollars a day, Frederick Winslow Taylor, high-speed rail, if you build it, they will come, Induced demand, intermodal, invention of the wheel, lake wobegon effect, Lewis Mumford, Loma Prieta earthquake, longitudinal study, Lyft, Masdar, megacity, meta-analysis, moral hazard, Nate Silver, oil shock, parking minimums, Productivity paradox, Ralph Nader, rent control, ride hailing / ride sharing, Rosa Parks, scientific management, self-driving car, skinny streets, smart cities, smart grid, smart transportation, TED Talk, the built environment, the map is not the territory, transportation-network company, Uber and Lyft, Uber for X, uber lyft, Unsafe at Any Speed, urban decay, urban planning, urban renewal, walkable city, Wall-E, white flight, white picket fence, Works Progress Administration, Yogi Berra, Zipcar

This is a nontrivial point: a system that is “only” driverless on expressways, for example, isn’t going to change behavior in large ways, since most trips are less than ten miles in length. And don’t get me started on trying to figure out who gets sued in the event of a collision between autonomous cars. Maybe more plausibly, others have wondered whether autonomous cars, by reducing the pain and misery associated with driving, will therefore make it more appealing—so appealing, in fact, as to reverse the centripetal phenomenon that is now drawing more and more people back into densely populated cities from the sprawling suburbs that attracted their parents and grandparents after the Second World War.

They will aid him in passing through intersections without slowing down or causing anyone else to do so and without endangering himself or others. For the next five decades, companies like RCA, General Motors, Mercedes-Benz, and others worked to bring Bel Geddes’s vision to life. For most of that time, autonomous vehicles were conceived as part of a system that traveled on dedicated roads or tracks, rather than streets, and went by the name of Personal Rapid Transit, or PRT. PRT is generally used to describe a network of small, driverless electrical vehicles—pod cars—traveling on elevated guidewaysh containing sensors and switches that can, in combination, offer point-to-point travel nearly as flexibly as an automobile, but as safely and efficiently as a subway or streetcar.

Google’s versions of the driverless car—refitted Toyotas, Audis, and Lexuses—combine real-time access to all that data with a laser rangefinder that creates and refreshes three-dimensional maps of the area immediately around the car. It has so far succeeded in a dozen different road tests, comprising more than seven hundred thousand autonomous miles without a single self-caused problem (one car did get rear-ended; not, one hopes, by another autonomous vehicle). Though the company admits to a number of limitations to the existing technology, including bad weather, the Google car has done a spectacular job promoting the potential of autonomous driving. For people who believe in the never-ending bounty of digital improvement it seems only a matter of a few years before Google solves the remaining technical obstacles in the path of truly autonomous driving.j (At that point, Google, which invested more than $250 million in Uber back in 2013, will be able to launch its new subsidiary, which I call Goober.)


pages: 385 words: 111,113

Augmented: Life in the Smart Lane by Brett King

23andMe, 3D printing, additive manufacturing, Affordable Care Act / Obamacare, agricultural Revolution, Airbnb, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, Apollo 11, Apollo Guidance Computer, Apple II, artificial general intelligence, asset allocation, augmented reality, autonomous vehicles, barriers to entry, bitcoin, Bletchley Park, blockchain, Boston Dynamics, business intelligence, business process, call centre, chief data officer, Chris Urmson, Clayton Christensen, clean water, Computing Machinery and Intelligence, congestion charging, CRISPR, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, deskilling, different worldview, disruptive innovation, distributed generation, distributed ledger, double helix, drone strike, electricity market, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, fiat currency, financial exclusion, Flash crash, Flynn Effect, Ford Model T, future of work, gamification, Geoffrey Hinton, gig economy, gigafactory, Google Glasses, Google X / Alphabet X, Hans Lippershey, high-speed rail, Hyperloop, income inequality, industrial robot, information asymmetry, Internet of things, invention of movable type, invention of the printing press, invention of the telephone, invention of the wheel, James Dyson, Jeff Bezos, job automation, job-hopping, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, Kim Stanley Robinson, Kiva Systems, Kodak vs Instagram, Leonard Kleinrock, lifelogging, low earth orbit, low skilled workers, Lyft, M-Pesa, Mark Zuckerberg, Marshall McLuhan, megacity, Metcalfe’s law, Minecraft, mobile money, money market fund, more computing power than Apollo, Neal Stephenson, Neil Armstrong, Network effects, new economy, Nick Bostrom, obamacare, Occupy movement, Oculus Rift, off grid, off-the-grid, packet switching, pattern recognition, peer-to-peer, Ray Kurzweil, retail therapy, RFID, ride hailing / ride sharing, Robert Metcalfe, Salesforce, Satoshi Nakamoto, Second Machine Age, selective serotonin reuptake inhibitor (SSRI), self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart grid, smart transportation, Snapchat, Snow Crash, social graph, software as a service, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, synthetic biology, systems thinking, TaskRabbit, technological singularity, TED Talk, telemarketer, telepresence, telepresence robot, Tesla Model S, The future is already here, The Future of Employment, Tim Cook: Apple, trade route, Travis Kalanick, TSMC, Turing complete, Turing test, Twitter Arab Spring, uber lyft, undersea cable, urban sprawl, V2 rocket, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, white picket fence, WikiLeaks, yottabyte

However, once manufacturers demonstrate the safety of self-driving car systems, it is far more likely that both passengers and legislators will start to opt in voluntarily. Some legislators will insist that autonomous vehicles must have the option for a human to “take over”, and there will no doubt be purists who try to hack around autonomous routines in some way. Moreover, we can expect lobby groups of manufacturers who fall behind in autonomous technology to attempt to muddy the waters with figures around safety. The first death of a passenger or a pedestrian by an autonomous vehicle will be a watershed moment. It is unlikely, however, to stop autonomous cars from dominating our future. Interestingly, the CEO of Volvo, Håkan Samuelsson, has already said that Volvo will accept liability when a self-driving car is involved in an accident.7 That is a big deal!

Other data showed that the autonomous software was much better and more consistent at maintaining a safe distance from the vehicle ahead. “We’re spending less time in near-collision states,” said Chris Urmson, the leader of Google’s autonomous car project at a robotics conference in 2013. “Our car is driving more smoothly and more safely than our trained professional drivers.” Google’s self-driving car has the most data publicly available about this incredible autonomous capability, but other car manufacturers like Tesla, Audi, BMW, Mercedes and Volvo all say similar things about the future of driving. Autonomous vehicles will most likely be significantly safer than those driven by humans within a decade or so. Giving further insight into this technology, Google has disclosed that the sensors on the Google self-driving car capture nearly 1 gigabyte (GB) of sensor data every second, and subsequently process that information to identify risks or anticipate issues that it may need to react to.

It is estimated that iRobot sold more than 100,000 home robots in 2015, with the Roomba 800/900 vacuum cleaner being the most popular.6 The Federal Aviation Administration (FAA) estimated that over 1 million drones were sold over the 2015 Christmas period alone.7 It is likely that we added close to 10 million robots to the global robot population just in 2015, if you include industrial robots, household robots and military application. But there are some big outliers coming in the next five to ten years, including autonomous vehicles. By 2025, it is estimated that between 15 and 20 million autonomous vehicles could be sold annually.8 By 2025, more than 1.5 billion robots will be operating on the planet, and we’ll be seeing that exponential growth curve exhibited with that number doubling every few years. By the early 2030s, robots are likely to outnumber humans.


AI 2041 by Kai-Fu Lee, Chen Qiufan

3D printing, Abraham Maslow, active measures, airport security, Albert Einstein, AlphaGo, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, augmented reality, autonomous vehicles, basic income, bitcoin, blockchain, blue-collar work, Cambridge Analytica, carbon footprint, Charles Babbage, computer vision, contact tracing, coronavirus, corporate governance, corporate social responsibility, COVID-19, CRISPR, cryptocurrency, DALL-E, data science, deep learning, deepfake, DeepMind, delayed gratification, dematerialisation, digital map, digital rights, digital twin, Elon Musk, fake news, fault tolerance, future of work, Future Shock, game design, general purpose technology, global pandemic, Google Glasses, Google X / Alphabet X, GPT-3, happiness index / gross national happiness, hedonic treadmill, hiring and firing, Hyperloop, information security, Internet of things, iterative process, job automation, language acquisition, low earth orbit, Lyft, Maslow's hierarchy, mass immigration, mirror neurons, money: store of value / unit of account / medium of exchange, mutually assured destruction, natural language processing, Neil Armstrong, Nelson Mandela, OpenAI, optical character recognition, pattern recognition, plutocrats, post scarcity, profit motive, QR code, quantitative easing, Richard Feynman, ride hailing / ride sharing, robotic process automation, Satoshi Nakamoto, self-driving car, seminal paper, Silicon Valley, smart cities, smart contracts, smart transportation, Snapchat, social distancing, speech recognition, Stephen Hawking, synthetic biology, telemarketer, Tesla Model S, The future is already here, trolley problem, Turing test, uber lyft, universal basic income, warehouse automation, warehouse robotics, zero-sum game

Yang Juan pulled out her phone and passed it to Chamal. “Read this. How do you feel about your new title, hmm?” The headline said, “The Holy Driver: Sri Lankan Boy Saves 11 Lives Piloting Autonomous Vehicle.” The picture below was of Chamal’s silhouette, wearing his iconic helmet. ANALYSIS AUTONOMOUS VEHICLES, FULL AUTONOMY AND SMART CITIES, ETHICAL AND SOCIAL ISSUES From Knight Rider to Minority Report, science fiction has presented the arrival of autonomous vehicles (AVs) as a foregone conclusion. But AVs are actually one of the Holy Grails of artificial intelligence. Driving is a complex task with many subtasks and inputs, as well as the potential for uncertain environments and unlikely events.

—JIMI HENDRIX, QUOTED IN JIMI HENDRIX: A BROTHER’S STORY, BY LEON HENDRIX NOTE FROM KAI-FU: Set in Sri Lanka, “The Holy Driver” imagines a society two decades from now that is in the midst of transitioning from human drivers to autonomous driving by AI. In the story, a talented young gamer is recruited for a mysterious project—one that reveals the capacity for both humans and AI to make mistakes, but very different ones. In my commentary, I will describe how autonomous vehicles work, and how and when fully autonomous vehicles will emerge. THE WRISTWATCH BUZZED, its face blinking with an urgent red flash. It was time for Chamal to race. A regular at the VR Café, he had developed a full pre-race routine. His expression solemn, Chamal would dress himself in the skintight haptic suit, carefully comb his hair, and then place the conch-shaped helmet on his head.

Other questions include: How should we balance the livelihoods of millions of truck drivers against the millions of hours saved by autonomous vehicles? Is it acceptable to have interim AI that makes mistakes that human drivers would not make if, in five years, the total number of fatalities can be halved because the AI will improve after learning from billions of miles of experience? And the most fundamental question: Should we ever let a machine make decisions that may harm human lives? If the answer is no, it would put an end to autonomous vehicles. Since lives are at stake, every company must proceed with caution. There are two distinct approaches to take, each with different merits.


pages: 410 words: 119,823

Radical Technologies: The Design of Everyday Life by Adam Greenfield

3D printing, Airbnb, algorithmic bias, algorithmic management, AlphaGo, augmented reality, autonomous vehicles, bank run, barriers to entry, basic income, bitcoin, Black Lives Matter, blockchain, Boston Dynamics, business intelligence, business process, Californian Ideology, call centre, cellular automata, centralized clearinghouse, centre right, Chuck Templeton: OpenTable:, circular economy, cloud computing, Cody Wilson, collective bargaining, combinatorial explosion, Computer Numeric Control, computer vision, Conway's Game of Life, CRISPR, cryptocurrency, David Graeber, deep learning, DeepMind, dematerialisation, digital map, disruptive innovation, distributed ledger, driverless car, drone strike, Elon Musk, Ethereum, ethereum blockchain, facts on the ground, fiat currency, fulfillment center, gentrification, global supply chain, global village, Goodhart's law, Google Glasses, Herman Kahn, Ian Bogost, IBM and the Holocaust, industrial robot, informal economy, information retrieval, Internet of things, Jacob Silverman, James Watt: steam engine, Jane Jacobs, Jeff Bezos, Jeff Hawkins, job automation, jobs below the API, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, John von Neumann, joint-stock company, Kevin Kelly, Kickstarter, Kiva Systems, late capitalism, Leo Hollis, license plate recognition, lifelogging, M-Pesa, Mark Zuckerberg, means of production, megacity, megastructure, minimum viable product, money: store of value / unit of account / medium of exchange, natural language processing, Network effects, New Urbanism, Nick Bostrom, Occupy movement, Oculus Rift, off-the-grid, PalmPilot, Pareto efficiency, pattern recognition, Pearl River Delta, performance metric, Peter Eisenman, Peter Thiel, planetary scale, Ponzi scheme, post scarcity, post-work, printed gun, proprietary trading, RAND corporation, recommendation engine, RFID, rolodex, Rutger Bregman, Satoshi Nakamoto, self-driving car, sentiment analysis, shareholder value, sharing economy, Shenzhen special economic zone , Sidewalk Labs, Silicon Valley, smart cities, smart contracts, social intelligence, sorting algorithm, special economic zone, speech recognition, stakhanovite, statistical model, stem cell, technoutopianism, Tesla Model S, the built environment, The Death and Life of Great American Cities, The Future of Employment, Tony Fadell, transaction costs, Uber for X, undersea cable, universal basic income, urban planning, urban sprawl, vertical integration, Vitalik Buterin, warehouse robotics, When a measure becomes a target, Whole Earth Review, WikiLeaks, women in the workforce

In his successful 2014 attempt to eliminate subsidies for mass transit in his district, Brandes argued that it was futile to invest in mass transit when an age of autonomous vehicles was dawning upon us: “It’s like they’re designing the Pony Express in the world of the telegraph.”77 Never mind that Pony Express riders historically delivered mail, packages and other things the telegraph could not have; the argument from technological inevitability is a vivid and compelling one, especially for Americans nurtured practically from birth on the belief in a gleaming technological future. If autonomous cars really are just a year or two away, why invest in modes of public transit that would surely be rendered obsolete before they even entered service?

The software controlling a moving vehicle must integrate in real time a highly unstable environment, engine conditions, changes in weather, and the inherently unpredictable behavior of animals, pedestrians, bicyclists, other drivers and random objects it may encounter.8 (Now the significance of those reports you may have encountered of Google pre-driving nominally autonomous vehicles through the backstreets of its Peninsular domain becomes clearer: its engineers are training their guidance algorithm in what to expect from its first environment.) For autonomous vehicles, drones, robots and other systems intended to reckon with the real world in this way, then, the grail is unsupervised deep learning. As the name implies, the algorithms involved are neither prompted nor guided, but are simply set loose on vast fields of data.

Or a global collective of human journalists and autonomous search agents working stories together, linked in a comradely fashion by a collaboration platform that lets them render findings vital to the public interest permanently visible. We might even join Bitcoin core developer Mike Hearn in dreaming a transhuman future in which sovereign autonomous vehicles own themselves, lease themselves to users, and transact with a marketized grid for the power they need.37 It’s not unreasonable to be intrigued by these possibilities, whatever your own politics. But if the collapse of The DAO holds any lesson for us, it’s that any envelope of potentials in which these things are possible must also necessarily contain monsters.


pages: 190 words: 62,941

Wild Ride: Inside Uber's Quest for World Domination by Adam Lashinsky

"Susan Fowler" uber, "World Economic Forum" Davos, Airbnb, always be closing, Amazon Web Services, asset light, autonomous vehicles, Ayatollah Khomeini, Benchmark Capital, business process, Chuck Templeton: OpenTable:, cognitive dissonance, corporate governance, DARPA: Urban Challenge, Didi Chuxing, Donald Trump, driverless car, Elon Musk, Erlich Bachman, gig economy, Golden Gate Park, Google X / Alphabet X, hustle culture, independent contractor, information retrieval, Jeff Bezos, John Zimmer (Lyft cofounder), Lyft, Marc Andreessen, Mark Zuckerberg, megacity, Menlo Park, multilevel marketing, new economy, pattern recognition, price mechanism, public intellectual, reality distortion field, ride hailing / ride sharing, Salesforce, San Francisco homelessness, Sand Hill Road, self-driving car, side hustle, Silicon Valley, Silicon Valley billionaire, Silicon Valley startup, Skype, Snapchat, South of Market, San Francisco, sovereign wealth fund, statistical model, Steve Jobs, super pumped, TaskRabbit, tech worker, Tony Hsieh, transportation-network company, Travis Kalanick, turn-by-turn navigation, Uber and Lyft, Uber for X, uber lyft, ubercab, young professional

In the summer of 2013, with his business booming yet barely three years old, Travis Kalanick confronted Uber’s own potential disruption: autonomous vehicles, also known as self-driving cars. The development was so potentially game-changing that it could eliminate the one area where Uber still relied on people. These vehicles would use rapid advancement of sensors and artificial intelligence to “see” all the obstacles and guideposts that drivers need to navigate. In theory, autonomous vehicles would be far safer, given that robots aren’t susceptible to drowsiness or distraction. While radical, there was ample precedent for the technology.

He lets on that this six-mile walk, always including the In-N-Out Burger stop, has become a summer-evening routine and that he typically walks it with one person he won’t identify. I later learn his walking partner is Anthony Levandowski, the ex-Google autonomous vehicles engineer who went on to found Otto, the self-driving truck company. Uber will purchase Otto just a few weeks after our walk, and Kalanick tells me he used his time with Levandowski to absorb the technology and business-plan vision for autonomous vehicles. Having spent so much time discussing Kalanick’s entrepreneurial days, I want to know how he views the bigger, more established company Uber has become. His answers betray a reluctance to think of the company that way.

Where the company has sufficient scale, in all its major cities, the system is so efficient that power users have begun to think the unthinkable: car ownership isn’t so necessary anymore. As well, Uber’s carpooling aspirations portend to eliminate congestion itself. If people don’t own cars and ride in them with other people more, at least in theory there would be fewer cars on the street. Should the vision of autonomous vehicles become a reality—and Uber is investing heavily in the technology—roads may become less crowded for the first time since the invention of roads. That said, it’s easy to get carried away with Uber’s promise—and Uber frequently does. Car ownership hasn’t yet declined in the United States as the result of the advent of ridesharing or for any other reason.


pages: 307 words: 88,180

AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee

"World Economic Forum" Davos, AI winter, Airbnb, Albert Einstein, algorithmic bias, algorithmic trading, Alignment Problem, AlphaGo, artificial general intelligence, autonomous vehicles, barriers to entry, basic income, bike sharing, business cycle, Cambridge Analytica, cloud computing, commoditize, computer vision, corporate social responsibility, cotton gin, creative destruction, crony capitalism, data science, deep learning, DeepMind, Demis Hassabis, Deng Xiaoping, deskilling, Didi Chuxing, Donald Trump, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, fake news, full employment, future of work, general purpose technology, Geoffrey Hinton, gig economy, Google Chrome, Hans Moravec, happiness index / gross national happiness, high-speed rail, if you build it, they will come, ImageNet competition, impact investing, income inequality, informal economy, Internet of things, invention of the telegraph, Jeff Bezos, job automation, John Markoff, Kickstarter, knowledge worker, Lean Startup, low skilled workers, Lyft, machine translation, mandatory minimum, Mark Zuckerberg, Menlo Park, minimum viable product, natural language processing, Neil Armstrong, new economy, Nick Bostrom, OpenAI, pattern recognition, pirate software, profit maximization, QR code, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, risk tolerance, Robert Mercer, Rodney Brooks, Rubik’s Cube, Sam Altman, Second Machine Age, self-driving car, sentiment analysis, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, SoftBank, Solyndra, special economic zone, speech recognition, Stephen Hawking, Steve Jobs, strong AI, TED Talk, The Future of Employment, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, urban planning, vertical integration, Vision Fund, warehouse robotics, Y Combinator

For starters, taxi, truck, bus, and delivery drivers will be largely out of luck in a self-driving world. There will also inevitably be malfunctions in autonomous vehicles that cause crashes. There will be circumstances that force an autonomous vehicle to make agonizing ethical decisions, like whether to veer right and have a 55 percent chance of killing two people or veer left and have a 100 percent chance of killing one person. Every one of these downside risks presents thorny ethical questions. How should we balance the livelihoods of millions of truck drivers against the billions of dollars and millions of hours saved by autonomous vehicles? What should a self-driving car “optimize for” in situations where it is forced to choose which car to crash into?

What should a self-driving car “optimize for” in situations where it is forced to choose which car to crash into? How should an autonomous vehicle’s algorithm weigh the life of its owner? Should your self-driving car sacrifice your own life to save the lives of three other people? These are the questions that keep ethicists up at night. They’re also questions that could hold up the legislation needed for autonomous-vehicle deployment and tie up AI companies in years of lawsuits. They may well lead American politicians, ever fearful of interest groups and attack ads, to pump the brakes on widespread self-driving vehicle deployment. We’ve already seen early signs of this happening, with unions representing truck drivers successfully lobbying Congress in 2017 to exclude trucks from legislation aimed at speeding up autonomous-vehicle deployment.

In just six months, Tesla had accumulated 47 million miles. Google and Tesla are now inching toward one another in terms of approach. Google—perhaps feeling the heat from Tesla and other rivals—accelerated deployment of fully autonomous vehicles, piloting a program with taxi-like vehicles in the Phoenix metropolitan area. Meanwhile, Tesla appears to have pumped the brakes on its rapid rollout of fully autonomous vehicles, a deceleration that followed a May 2016 crash that killed a Tesla owner who was using autopilot. But the fundamental difference in approach remains, and it presents a real tradeoff. Google is aiming for impeccable safety, but in the process it has delayed deployment of systems that could likely already save lives.


pages: 179 words: 43,441

The Fourth Industrial Revolution by Klaus Schwab

"World Economic Forum" Davos, 3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Anthropocene, augmented reality, autonomous vehicles, barriers to entry, Baxter: Rethink Robotics, bitcoin, blockchain, Buckminster Fuller, call centre, circular economy, clean water, collaborative consumption, commoditize, conceptual framework, continuous integration, CRISPR, cross-border payments, crowdsourcing, digital divide, digital twin, disintermediation, disruptive innovation, distributed ledger, driverless car, Edward Snowden, Elon Musk, epigenetics, Erik Brynjolfsson, future of work, global value chain, Google Glasses, hype cycle, income inequality, Internet Archive, Internet of things, invention of the steam engine, job automation, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, life extension, Lyft, Marc Benioff, mass immigration, megacity, meta-analysis, more computing power than Apollo, mutually assured destruction, Narrative Science, Network effects, Nicholas Carr, nuclear taboo, OpenAI, personalized medicine, precariat, precision agriculture, Productivity paradox, race to the bottom, randomized controlled trial, reshoring, RFID, rising living standards, Sam Altman, Second Machine Age, secular stagnation, self-driving car, sharing economy, Silicon Valley, smart cities, smart contracts, social contagion, software as a service, Stephen Hawking, Steve Jobs, Steven Levy, Stuxnet, supercomputer in your pocket, synthetic biology, TaskRabbit, The Future of Employment, The Spirit Level, total factor productivity, transaction costs, Uber and Lyft, uber lyft, Watson beat the top human players on Jeopardy!, Wayback Machine, WikiLeaks, winner-take-all economy, women in the workforce, working-age population, Y Combinator, Zipcar

All three are deeply interrelated and the various technologies benefit from each other based on the discoveries and progress each makes. 2.1.1 Physical There are four main physical manifestations of the technological megatrends, which are the easiest to see because of their tangible nature: – autonomous vehicles – 3D printing – advanced robotics – new materials Autonomous vehicles The driverless car dominates the news but there are now many other autonomous vehicles including trucks, drones, aircrafts and boats. As technologies such as sensors and artificial intelligence progress, the capabilities of all these autonomous machines improve at a rapid pace. It is only a question of a few years before low-cost, commercially available drones, together with submersibles, are used in different applications.

Consider the unlimited possibilities of having billions of people connected by mobile devices, giving rise to unprecedented processing power, storage capabilities and knowledge access. Or think about the staggering confluence of emerging technology breakthroughs, covering wide-ranging fields such as artificial intelligence (AI), robotics, the internet of things (IoT), autonomous vehicles, 3D printing, nanotechnology, biotechnology, materials science, energy storage and quantum computing, to name a few. Many of these innovations are in their infancy, but they are already reaching an inflection point in their development as they build on and amplify each other in a fusion of technologies across the physical, digital and biological worlds.

The speed of innovation in terms of both its development and diffusion is faster than ever. Today’s disruptors – Airbnb, Uber, Alibaba and the like – now household names - were relatively unknown just a few years ago. The ubiquitous iPhone was first launched in 2007. Yet there were as many as 2 billion smart phones at the end of 2015. In 2010 Google announced its first fully autonomous car. Such vehicles could soon become a widespread reality on the road. One could go on. But it is not only speed; returns to scale are equally staggering. Digitization means automation, which in turn means that companies do not incur diminishing returns to scale (or less of them, at least). To give a sense of what this means at the aggregate level, compare Detroit in 1990 (then a major centre of traditional industries) with Silicon Valley in 2014.


pages: 339 words: 92,785

I, Warbot: The Dawn of Artificially Intelligent Conflict by Kenneth Payne

Abraham Maslow, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, AlphaGo, anti-communist, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, Asperger Syndrome, augmented reality, Automated Insights, autonomous vehicles, backpropagation, Black Lives Matter, Bletchley Park, Boston Dynamics, classic study, combinatorial explosion, computer age, computer vision, Computing Machinery and Intelligence, coronavirus, COVID-19, CRISPR, cuban missile crisis, data science, deep learning, deepfake, DeepMind, delayed gratification, Demis Hassabis, disinformation, driverless car, drone strike, dual-use technology, Elon Musk, functional programming, Geoffrey Hinton, Google X / Alphabet X, Internet of things, job automation, John Nash: game theory, John von Neumann, Kickstarter, language acquisition, loss aversion, machine translation, military-industrial complex, move 37, mutually assured destruction, Nash equilibrium, natural language processing, Nick Bostrom, Norbert Wiener, nuclear taboo, nuclear winter, OpenAI, paperclip maximiser, pattern recognition, RAND corporation, ransomware, risk tolerance, Ronald Reagan, self-driving car, semantic web, side project, Silicon Valley, South China Sea, speech recognition, Stanislav Petrov, stem cell, Stephen Hawking, Steve Jobs, strong AI, Stuxnet, technological determinism, TED Talk, theory of mind, TikTok, Turing machine, Turing test, uranium enrichment, urban sprawl, V2 rocket, Von Neumann architecture, Wall-E, zero-sum game

DARPA was back in the hunt for autonomous vehicles. Its ‘Grand Challenge’ required vehicles to navigate 142 miles of off-road course, entirely without human input, within ten hours. There was a $1million prize, and fifteen competitors vying for it on the starting line. But no winners. The furthest anyone got that first year was 7.5 miles. Undeterred, DARPA relaunched the competition, and scheduled a new race eighteen months later. This time there was a winner: Stanford’s ‘Stanley’, a modified VW Tuareg, led home a field of five finishers, jump-starting the modern race for commercially viable autonomous cars.2 It was a powerful demonstration of the American approach to AI—once again harnessing universities and hobbyists to the defence complex.

Ground combat is particularly hard for AI not just because of awkward terrain, but because it involves other humans. I’ll save a fuller discussion of the human dimension until the next chapter, when we consider the connection between autonomous cars and strategy, but for now it’s worth flagging. The major problem with autonomy for car manufacturers isn’t navigating the physical landscape but knowing how the humans in it will interact with your machine. In autonomous cars that’s an ethical issue. It is too in combat—a warbot must understand who is a combatant and who is on which side. But it’s also essential for fighting power. The machine needs to gauge how much effort is needed to win any engagement; for now it lacks that contextual understanding.

Another warbot commissioned in the 1970s that came to maturity in this period was the Tomahawk cruise missile, launched from the sea and able to hug terrain, before arriving precisely on target over a thousand miles distant. Still another was the land-based Patriot missile interceptor, which like the Tomahawk entered public consciousness in the 1990/1 Gulf War. All suggested highly refined autonomy—but not much intelligence. But DARPA’s lumbering effort at an autonomous vehicle pointed to the stark limits of AI in the 1980s. It was rather closer to Shakey than KITT, its fictional counterpart in the Knight Rider television series. Precision-guided missiles like Aegis and Tomahawk were polished versions of the proximity fused artillery shell—a very limited sort of intelligence.


pages: 431 words: 107,868

The Great Race: The Global Quest for the Car of the Future by Levi Tillemann

Affordable Care Act / Obamacare, An Inconvenient Truth, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, banking crisis, Bear Stearns, car-free, carbon footprint, clean tech, creative destruction, decarbonisation, deindustrialization, demand response, Deng Xiaoping, Donald Trump, driverless car, electricity market, Elon Musk, en.wikipedia.org, energy security, factory automation, Fairchild Semiconductor, Ford Model T, foreign exchange controls, gigafactory, global value chain, high-speed rail, hydrogen economy, index card, Intergovernmental Panel on Climate Change (IPCC), joint-stock company, Joseph Schumpeter, Kanban, Kickstarter, manufacturing employment, market design, megacity, Nixon shock, obamacare, off-the-grid, oil shock, planned obsolescence, Ralph Nader, RFID, rolodex, Ronald Reagan, Rubik’s Cube, self-driving car, shareholder value, Shenzhen special economic zone , short squeeze, Silicon Valley, Silicon Valley startup, skunkworks, smart cities, Solyndra, sovereign wealth fund, special economic zone, Steve Jobs, Tesla Model S, too big to fail, Unsafe at Any Speed, zero-sum game, Zipcar

But as it approached the sharp off-ramp toward Treasure Island, Levandowski’s autonomous car slowly smooshed into a concrete barrier. The car ground to a halt, unable to move. Levandowski jumped into the car, disengaged the twitching steering column, and set it back on course for delivery. There were high-fives all around, but in reality it was a mixed success. Driving Blind But five years later, the awkward robot had transformed into a sleek, formidable, and truly autonomous driving machine. In the interim, Google had acquired the autonomous vehicles company, and management at the cash-flush Silicon Valley giant had been so impressed by the team’s results that they eventually gave them a virtually unlimited development budget.

For academics and policy makers it was suddenly legitimate, reasonable, and even necessary to start pondering the implications of a future with cars that drove themselves and what this might mean for automakers, cities, and countries around the world. Different people used different terminologies with various meanings to describe these robotic cars—self-driving cars, robot cars, automated vehicles, and autonomous vehicles, to name a few. However, the ultimate goal was generally understood to be a car that could drive itself. Although theirs was not the first autonomous car, Google broke the logjam. And that is why one might say that automation is not simply the next frontier, but the finishing line in the Great Race. For in some profound sense, automated cars cease to be cars. They are, instead, robots.

Inevitably people will worry about the safety of these robots. But it seems quite likely that autonomous vehicles will in fact be fundamentally safer. Unlike humans, they won’t get distracted by their kids fighting, or vacation plans, or a pretty face on the corner, or job stress. That’s because the car’s full-time job will be to get you to where you are going and to keep you safe. Insurance companies have already expressed interest in lowering their rates for autonomous vehicles. In fact, autonomous vehicles may end up being the most significant safety innovation since seat belts. How many uneasy teenagers have found themselves in a vehicle with an unsettlingly intoxicated classmate or friend?


pages: 416 words: 112,268

Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell

3D printing, Ada Lovelace, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Alfred Russel Wallace, algorithmic bias, AlphaGo, Andrew Wiles, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, basic income, behavioural economics, Bletchley Park, blockchain, Boston Dynamics, brain emulation, Cass Sunstein, Charles Babbage, Claude Shannon: information theory, complexity theory, computer vision, Computing Machinery and Intelligence, connected car, CRISPR, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deep learning, deepfake, DeepMind, delayed gratification, Demis Hassabis, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ernest Rutherford, fake news, Flash crash, full employment, future of work, Garrett Hardin, Geoffrey Hinton, Gerolamo Cardano, Goodhart's law, Hans Moravec, ImageNet competition, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the wheel, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Nash: game theory, John von Neumann, Kenneth Arrow, Kevin Kelly, Law of Accelerating Returns, luminiferous ether, machine readable, machine translation, Mark Zuckerberg, multi-armed bandit, Nash equilibrium, Nick Bostrom, Norbert Wiener, NP-complete, OpenAI, openstreetmap, P = NP, paperclip maximiser, Pareto efficiency, Paul Samuelson, Pierre-Simon Laplace, positional goods, probability theory / Blaise Pascal / Pierre de Fermat, profit maximization, RAND corporation, random walk, Ray Kurzweil, Recombinant DNA, recommendation engine, RFID, Richard Thaler, ride hailing / ride sharing, Robert Shiller, robotic process automation, Rodney Brooks, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, smart cities, smart contracts, social intelligence, speech recognition, Stephen Hawking, Steven Pinker, superintelligent machines, surveillance capitalism, Thales of Miletus, The Future of Employment, The Theory of the Leisure Class by Thorstein Veblen, Thomas Bayes, Thorstein Veblen, Tragedy of the Commons, transport as a service, trolley problem, Turing machine, Turing test, universal basic income, uranium enrichment, vertical integration, Von Neumann architecture, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, web application, zero-sum game

In 1987, Ernst Dickmanns demonstrated a self-driving Mercedes van on the autobahn in Germany; it was capable of staying in lane, following another car, changing lanes, and overtaking.3 More than thirty years later, we still don’t have a fully autonomous car, but it’s getting much closer. The focus of development has long since moved from academic research labs to large corporations. As of 2019, the best-performing test vehicles have logged millions of miles of driving on public roads (and billions of miles in driving simulators) without serious incident.4 Unfortunately, other autonomous and semi-autonomous vehicles have killed several people.5 Why has it taken so long to achieve safe autonomous driving? The first reason is that the performance requirements are exacting.

Forecast of economic effects of automation on transportation costs: Adele Peters, “It could be 10 times cheaper to take electric robo-taxis than to own a car by 2030,” Fast Company, May 30, 2017. 8. The impact of accidents on the prospects for regulatory action on autonomous vehicles: Richard Waters, “Self-driving car death poses dilemma for regulators,” Financial Times, March 20, 2018. 9. The impact of accidents on public perception of autonomous vehicles: Cox Automotive, “Autonomous vehicle awareness rising, acceptance declining, according to Cox Automotive mobility study,” August 16, 2018. 10. The original chatbot: Joseph Weizenbaum, “ELIZA—a computer program for the study of natural language communication between man and machine,” Communications of the ACM 9 (1966): 36–45. 11.

Some projects are trying more direct approaches based on reinforcement learning (mainly in simulation, of course) and supervised learning from recordings of hundreds of human drivers, but these approaches seem unlikely to reach the required level of safety. The potential benefits of fully autonomous vehicles are immense. Every year, 1.2 million people die in car accidents worldwide and tens of millions suffer serious injuries. A reasonable target for autonomous vehicles would be to reduce these numbers by a factor of ten. Some analyses also predict a vast reduction in transportation costs, parking structures, congestion, and pollution. Cities will shift from personal cars and large buses to ubiquitous shared-ride, autonomous electric vehicles, providing door-to-door service and feeding high-speed mass-transit connections between hubs.7 With costs as low as three cents per passenger mile, most cities would probably opt to provide the service for free—while subjecting riders to interminable barrages of advertising.


System Error by Rob Reich

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 2021 United States Capitol attack, A Declaration of the Independence of Cyberspace, Aaron Swartz, AI winter, Airbnb, airport security, Alan Greenspan, Albert Einstein, algorithmic bias, AlphaGo, AltaVista, artificial general intelligence, Automated Insights, autonomous vehicles, basic income, Ben Horowitz, Berlin Wall, Bernie Madoff, Big Tech, bitcoin, Blitzscaling, Cambridge Analytica, Cass Sunstein, clean water, cloud computing, computer vision, contact tracing, contact tracing app, coronavirus, corporate governance, COVID-19, creative destruction, CRISPR, crowdsourcing, data is the new oil, data science, decentralized internet, deep learning, deepfake, DeepMind, deplatforming, digital rights, disinformation, disruptive innovation, Donald Knuth, Donald Trump, driverless car, dual-use technology, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, Fairchild Semiconductor, fake news, Fall of the Berlin Wall, Filter Bubble, financial engineering, financial innovation, fulfillment center, future of work, gentrification, Geoffrey Hinton, George Floyd, gig economy, Goodhart's law, GPT-3, Hacker News, hockey-stick growth, income inequality, independent contractor, informal economy, information security, Jaron Lanier, Jeff Bezos, Jim Simons, jimmy wales, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, Lean Startup, linear programming, Lyft, Marc Andreessen, Mark Zuckerberg, meta-analysis, minimum wage unemployment, Monkeys Reject Unequal Pay, move fast and break things, Myron Scholes, Network effects, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, NP-complete, Oculus Rift, OpenAI, Panopticon Jeremy Bentham, Parler "social media", pattern recognition, personalized medicine, Peter Thiel, Philippa Foot, premature optimization, profit motive, quantitative hedge fund, race to the bottom, randomized controlled trial, recommendation engine, Renaissance Technologies, Richard Thaler, ride hailing / ride sharing, Ronald Reagan, Sam Altman, Sand Hill Road, scientific management, self-driving car, shareholder value, Sheryl Sandberg, Shoshana Zuboff, side project, Silicon Valley, Snapchat, social distancing, Social Responsibility of Business Is to Increase Its Profits, software is eating the world, spectrum auction, speech recognition, stem cell, Steve Jobs, Steven Levy, strong AI, superintelligent machines, surveillance capitalism, Susan Wojcicki, tech billionaire, tech worker, techlash, technoutopianism, Telecommunications Act of 1996, telemarketer, The Future of Employment, TikTok, Tim Cook: Apple, traveling salesman, Triangle Shirtwaist Factory, trolley problem, Turing test, two-sided market, Uber and Lyft, uber lyft, ultimatum game, union organizing, universal basic income, washing machines reduced drudgery, Watson beat the top human players on Jeopardy!, When a measure becomes a target, winner-take-all economy, Y Combinator, you are the product

In the context of autonomous cars, the problem asks whether a vehicle should be programmed to endanger or sacrifice the life of its sole passenger by running off the road in order to avoid potentially hitting five pedestrians crossing the road. As a society, we might prefer that the vehicle pick the option that saves the most lives. Accordingly, most participants in a 2016 survey preferred that such vehicles be programmed to minimize casualties on the road generally. But the same survey showed that when participants were asked to be a passenger—that is, potential purchaser—of an autonomous vehicle, “they would themselves prefer to ride in AVs that protect their passengers at all costs.”

Reflecting on the race, Thrun crowed, “It’s a no-brainer that 50 to 60 years from now, cars will drive themselves.” His estimate was far too pessimistic. In 2020, at least thirty different countries were testing autonomous vehicles on their roads. California alone licensed more than fifty companies and more than five hundred autonomous vehicles, logging more than 2 million miles. One of the primary motivations for the commercial development of autonomous vehicles is their potential for improving road safety. The World Health Organization estimates that there were 1.25 million road traffic deaths globally in 2013. In 2017, 37,133 people were killed in motor vehicle crashes in the United States alone, and more than 90 percent of those crashes involved human error.

The upshot is the common conviction that technological progress is rolling over us with the wheels of inevitability. Ordinary people can’t undo technological discoveries and innovations, and they can’t shape the products we buy or the effects of technology across society. What can a truck driver whose job may be replaced by autonomous vehicles do? What can a parent do about the apps a child is transfixed by, short of taking the phone out of the child’s hands? What can an employee do about the deployment of facial recognition at the office? What can a citizen do about the spread of disinformation on the platforms that deliver us news and information?


pages: 586 words: 186,548

Architects of Intelligence by Martin Ford

3D printing, agricultural Revolution, AI winter, algorithmic bias, Alignment Problem, AlphaGo, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, Big Tech, bitcoin, Boeing 747, Boston Dynamics, business intelligence, business process, call centre, Cambridge Analytica, cloud computing, cognitive bias, Colonization of Mars, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, CRISPR, crowdsourcing, DARPA: Urban Challenge, data science, deep learning, DeepMind, Demis Hassabis, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, driverless car, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, fake news, Fellow of the Royal Society, Flash crash, future of work, general purpose technology, Geoffrey Hinton, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, Hans Rosling, hype cycle, ImageNet competition, income inequality, industrial research laboratory, industrial robot, information retrieval, job automation, John von Neumann, Large Hadron Collider, Law of Accelerating Returns, life extension, Loebner Prize, machine translation, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, Mustafa Suleyman, natural language processing, new economy, Nick Bostrom, OpenAI, opioid epidemic / opioid crisis, optical character recognition, paperclip maximiser, pattern recognition, phenotype, Productivity paradox, radical life extension, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, seminal paper, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, sparse data, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, synthetic biology, systems thinking, Ted Kaczynski, TED Talk, The Rise and Fall of American Growth, theory of mind, Thomas Bayes, Travis Kalanick, Turing test, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, women in the workforce, working-age population, workplace surveillance , zero-sum game, Zipcar

How far off are they really? Imagine you’re in a city and you’re going to call for a fully autonomous car that will take you from one random location to another. What’s the time frame for when you think that becomes a widely available service? ANDREW NG: I think that self-driving cars in geofenced regions will come relatively soon, possibly by the end of this year, but that self-driving cars in more general circumstances will be a long way off, possibly multiple decades. MARTIN FORD: By geofenced, you mean autonomous cars that are running essentially on virtual trolley tracks, or in other words only on routes that have been intensively mapped?

We’re not aware of doing much randomization in our normal day-to-day lives, even though—for sure—the world is full of agents; so it ought to be game-theoretic, and yet we’re not aware of randomizing very much in our day-to-day lives. MARTIN FORD: Self-driving cars are one of the highest-profile applications of AI. What is your estimate for when fully autonomous vehicles will become a truly practical technology? Imagine you’re in a random place in Manhattan, and you call up an Uber, and it’s going to arrive with no one in it, and then it will take you to another random place that you specify. How far off is that realistically, do you think? STUART J. RUSSELL: Yes, the timeline for self-driving cars is a concrete question, and it’s also an economically important question because companies are investing a great deal in these projects.

In fact in the last year we’ve started to get a ton of inbound interest from the automotive industry. It’s really exciting because it’s a major market opportunity for Affectiva and we’re solving two interesting problems for the car industry. In the cars of today, where there is an active driver, safety is a huge issue. And safety will continue to be an issue, even when we have semi-autonomous vehicles like Tesla that can drive themselves for a while but do still need a co-pilot to be paying attention. Using Affectiva software, we’re able to monitor the driver or the co-pilot for things like drowsiness, distraction, fatigue and even intoxication. In the case of intoxication, we would alert the driver or also even potentially have the car intervene.


pages: 304 words: 90,084

Net Zero: How We Stop Causing Climate Change by Dieter Helm

3D printing, autonomous vehicles, Berlin Wall, biodiversity loss, blockchain, Boris Johnson, carbon credits, carbon footprint, carbon tax, clean water, congestion charging, coronavirus, COVID-19, CRISPR, decarbonisation, deindustrialization, demand response, Deng Xiaoping, Donald Trump, electricity market, Extinction Rebellion, fixed income, food miles, Ford Model T, Francis Fukuyama: the end of history, general purpose technology, Great Leap Forward, green new deal, Greta Thunberg, Haber-Bosch Process, high-speed rail, hydrogen economy, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jevons paradox, lockdown, market design, means of production, microplastics / micro fibres, North Sea oil, ocean acidification, off grid, off-the-grid, oil shale / tar sands, oil shock, peak oil, planetary scale, precautionary principle, price mechanism, quantitative easing, remote working, reshoring, rewilding, Ronald Reagan, smart meter, South China Sea, sovereign wealth fund, statistical model, systems thinking, Thomas Malthus

People rarely worry what type of car a taxi or Uber driver uses when summoning one, and when calling up an autonomous vehicle on an app, programming it to take you from A to B, you may not really care what sort of battery it has got. The market may respond to this. Suppose what matters to you for this A to B trip is the price. An autonomous vehicle provider (and not you) will own the car and will have powerful incentives to standardise the technologies of its fleet. It may see an advantage in battery swapping back at the autonomous car depot, or perhaps as part of the optimised journey timing and use. This is what the car companies fear. They may not be able to extract the economic rents that go with advertising and branding. They may simply sell thousands of identical cars to a car pool company, with the capital provided by an infrastructure fund, and then the economic value comes in the convenience and frequency of the autonomous vehicle’s availability, and not from packing it with all sorts of extras that the customer might not need.

It looks like we are going down the route of a mix of home charging and rapid charging, and the result will be a much greater cost to decarbonisation. Choice comes at a price. It might improve as autonomous vehicles come onto the road systems. If the car is autonomous and guided by smart systems, it may be that there is a shift in our relationship with the car, from the huge variety of styles, engines, colours, designs and interiors, towards seeing it as merely a way of getting from A to B. People rarely worry what type of car a taxi or Uber driver uses when summoning one, and when calling up an autonomous vehicle on an app, programming it to take you from A to B, you may not really care what sort of battery it has got.

The reason this smart technology is not in place is because the communications infrastructure is not up to the job, and nor will it be for the whole country for perhaps another decade. You cannot run a smart meter or enable your smart devices unless you have good internet and mobile connectivity. The road system is designed entirely around petrol and diesel vehicles. It is anything but smart, and incapable of supporting the roll-out of smart cars and autonomous vehicles. Charging points for electric vehicles are still notable by their absence even in major conurbations. Where they are available, the roads are often so congested that getting to a charge point can be a challenge in itself. The oil companies have not developed a retail petrol and diesel network designed around the electricity grid, for the very good reason that it has been irrelevant.


pages: 278 words: 91,332

Carmageddon: How Cars Make Life Worse and What to Do About It by Daniel Knowles

active transport: walking or cycling, autonomous vehicles, Bandra-Worli Sea Link, bank run, big-box store, bike sharing, Boeing 747, Boris Johnson, business cycle, car-free, carbon footprint, congestion charging, congestion pricing, coronavirus, COVID-19, Crossrail, decarbonisation, deindustrialization, Detroit bankruptcy, Donald Shoup, Donald Trump, driverless car, Elaine Herzberg, Elon Musk, first-past-the-post, Ford Model T, Frank Gehry, garden city movement, General Motors Futurama, gentrification, ghettoisation, high-speed rail, housing crisis, Hyperloop, Induced demand, James Watt: steam engine, Jane Jacobs, Jeremy Corbyn, Jevons paradox, Lewis Mumford, lockdown, Lyft, megacity, megastructure, New Urbanism, Northern Rock, parking minimums, Piers Corbyn, Richard Florida, ride hailing / ride sharing, safety bicycle, self-driving car, Silicon Valley, Southern State Parkway, Steve Jobs, TED Talk, Tesla Model S, The Death and Life of Great American Cities, the High Line, Traffic in Towns by Colin Buchanan, Uber and Lyft, uber lyft, upwardly mobile, urban planning, urban renewal, walkable city, white flight, white picket fence, Yom Kippur War, young professional

In that Wall Street Journal interview, in which he described automated cars as one of the most transformational technologies human civilization is likely to invent, he went on to argue that they will also force America to come up with “something to deal with extreme traffic,” he said. “As autonomous vehicles come to the fore, and it’s easier to drive without going through the pain of having to drive yourself . . . there will be more cars on the road and the traffic will get much worse.” He was quite right. And not even alone among car executives in realizing it. Bill Ford, the executive chairman of the Ford Motor Company, of all people, has also warned that autonomous vehicles could create “global gridlock” by 2050. “Our infrastructure cannot support such a large volume of vehicles without creating massive congestion,” he wrote in a newspaper opinion piece in 2014.

Already, this is used as an argument for why investment in new public transport, such as trains, is redundant. In Britain Matt Ridley, a Conservative member of the House of Lords who was chairman of Northern Rock, the only British bank to suffer a bank run in more than a century, is among those who reckon that HS2, Britain’s new high-speed railway, will quickly be made redundant by autonomous vehicles. So too do the Taxpayers’ Alliance, a group that opposes any government spending on anything. In the immediate term, the problem with this is that despite decades of development, autonomous driving technology has scarcely improved enough to navigate normal suburban streets at 30 miles per hour.

Sam Schwartz imagines businessmen taking meetings in Manhattan sending their cars to drive around in circles rather than find a parking space. As the technology gets cheaper, more and more people will buy them, and use them for ever more outlandish things. It will be chaos. Jevons paradox will win again. And yet, inevitably perhaps, given he is a car executive, Musk does not think that this means we should not all buy autonomous vehicles. Rather, the solution, apparently, is “some combination of tunnels and double-deckering freeways.” He did admit that “flying cars” are not the answer, because people do “not want the skies to be swarming with helicopters.” On that he is right, despite the vast sums of money being poured into the development of “flying cars” using the sort of quadcopter technology now used for smaller drones.


pages: 389 words: 119,487

21 Lessons for the 21st Century by Yuval Noah Harari

"World Economic Forum" Davos, 1960s counterculture, accounting loophole / creative accounting, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, algorithmic trading, augmented reality, autonomous vehicles, Ayatollah Khomeini, basic income, behavioural economics, Bernie Sanders, bitcoin, blockchain, Boris Johnson, Brexit referendum, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, carbon tax, carbon-based life, Charlie Hebdo massacre, cognitive dissonance, computer age, computer vision, cryptocurrency, cuban missile crisis, decarbonisation, DeepMind, deglobalization, disinformation, Donald Trump, Dr. Strangelove, failed state, fake news, Filter Bubble, Francis Fukuyama: the end of history, Freestyle chess, gig economy, glass ceiling, Google Glasses, illegal immigration, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invisible hand, job automation, knowledge economy, liberation theology, Louis Pasteur, low skilled workers, Mahatma Gandhi, Mark Zuckerberg, mass immigration, means of production, Menlo Park, meta-analysis, Mohammed Bouazizi, mutually assured destruction, Naomi Klein, obamacare, pattern recognition, post-truth, post-work, purchasing power parity, race to the bottom, RAND corporation, restrictive zoning, Ronald Reagan, Rosa Parks, Scramble for Africa, self-driving car, Silicon Valley, Silicon Valley startup, TED Talk, transatlantic slave trade, trolley problem, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, uranium enrichment, Watson beat the top human players on Jeopardy!, zero-sum game

Fagnant and Kara Kockelman, ‘Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations’, Transportation Research Part A: Policy and Practice 77 (2015), 167–81; for a general worldwide survey, see, for example: OECD/ITF, Road Safety Annual Report 2016 (Paris: OECD, 2016). 8 Kristofer D. Kusano and Hampton C. Gabler, ‘Safety Benefits of Forward Collision Warning, Brake Assist, and Autonomous Braking Systems in Rear-End Collisions’, IEEE Transactions on Intelligent Transportation Systems 13:4 (2012), 1546–55; James M. Anderson et al., Autonomous Vehicle Technology: A Guide for Policymakers (Santa Monica: RAND Corporation, 2014), esp. 13–15; Daniel J.

., Autonomous Vehicle Technology: A Guide for Policymakers (Santa Monica: RAND Corporation, 2014), esp. 13–15; Daniel J. Fagnant and Kara Kockelman, ‘Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations’, Transportation Research Part A: Policy and Practice 77 (2015), 167–81; Jean-François Bonnefon, Azim Shariff and Iyad Rahwan, ‘Autonomous Vehicles Need Experimental Ethics: Are We Ready for Utilitarian Cars?’, arXiv (2015), 1–15. For suggestions for inter-vehicle networks to prevent collision, see: Seyed R. Azimi et al., ‘Vehicular Networks for Collision Avoidance at Intersections’, SAE International Journal of Passenger Cars – Mechanical Systems 4:1 (2011), 406–16; Swarun Kumar et al., ‘CarSpeak: A Content-Centric Network for Autonomous Driving’, SIGCOM Computer Communication Review 42:4 (2012), 259–70; Mihail L.

Gabler, ‘Safety Benefits of Forward Collision Warning, Brake Assist, and Autonomous Braking Systems in Rear-End Collisions’, IEEE Transactions on Intelligent Transportation Systems 13:4 (2012), 1546–55; James M. Anderson et al., Autonomous Vehicle Technology: A Guide for Policymakers (Santa Monica: RAND Corporation, 2014), esp. 13–15; Daniel J. Fagnant and Kara Kockelman, ‘Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations’, Transportation Research Part A: Policy and Practice 77 (2015), 167–81. 20 Tim Adams, ‘Job Hunting Is a Matter of Big Data, Not How You Perform at an Interview’, Guardian, 10 May 2014. 21 For an extremely insightful discussion, see Cathy O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (New York: Crown, 2016).


pages: 295 words: 81,861

Road to Nowhere: What Silicon Valley Gets Wrong About the Future of Transportation by Paris Marx

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, A Declaration of the Independence of Cyberspace, Airbnb, An Inconvenient Truth, autonomous vehicles, back-to-the-land, Berlin Wall, Bernie Sanders, bike sharing, Californian Ideology, car-free, carbon credits, carbon footprint, cashless society, clean tech, cloud computing, colonial exploitation, computer vision, congestion pricing, corporate governance, correlation does not imply causation, COVID-19, DARPA: Urban Challenge, David Graeber, deep learning, degrowth, deindustrialization, deskilling, Didi Chuxing, digital map, digital rights, Donald Shoup, Donald Trump, Douglas Engelbart, Douglas Engelbart, driverless car, Elaine Herzberg, Elon Musk, energy transition, Evgeny Morozov, Extinction Rebellion, extractivism, Fairchild Semiconductor, Ford Model T, frictionless, future of work, General Motors Futurama, gentrification, George Gilder, gig economy, gigafactory, global pandemic, global supply chain, Google Glasses, Google X / Alphabet X, green new deal, Greyball, high-speed rail, Hyperloop, independent contractor, Induced demand, intermodal, Jane Jacobs, Jeff Bezos, jitney, John Perry Barlow, Kevin Kelly, knowledge worker, late capitalism, Leo Hollis, lockdown, low interest rates, Lyft, Marc Benioff, market fundamentalism, minimum viable product, Mother of all demos, move fast and break things, Murray Bookchin, new economy, oil shock, packet switching, Pacto Ecosocial del Sur, Peter Thiel, pre–internet, price mechanism, private spaceflight, quantitative easing, QWERTY keyboard, Ralph Nader, Richard Florida, ride hailing / ride sharing, Ronald Reagan, safety bicycle, Salesforce, School Strike for Climate, self-driving car, Sidewalk Labs, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Silicon Valley startup, smart cities, social distancing, Southern State Parkway, Steve Jobs, Stewart Brand, Stop de Kindermoord, streetcar suburb, tech billionaire, tech worker, techlash, technological determinism, technological solutionism, technoutopianism, the built environment, The Death and Life of Great American Cities, TikTok, transit-oriented development, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, Unsafe at Any Speed, urban planning, urban renewal, VTOL, walkable city, We are as Gods, We wanted flying cars, instead we got 140 characters, WeWork, Whole Earth Catalog, Whole Earth Review, work culture , Yom Kippur War, young professional

Duhigg’s report appeared in the New Yorker in 2018, at a time when the shine was already coming off the dream of autonomous vehicles and the hype machine was in free fall. But had that information been in the public domain years earlier when Brin was building the hype in the first place, the perception of autonomous vehicles and their capabilities could have been very different. Instead, we were treated to years of news cycles that helped create the myth that ubiquitous self-driving vehicles would solve all our transport problems, to such a degree that libertarian groups funded by the billionaire Koch brothers used the promise of autonomous vehicles to attack plans to expand public transit systems in the United States.

In the same way that automobiles required a social reconstruction in addition to a physical reconstruction, so too will autonomous vehicles—and some people involved with them have already admitted it. As the date when autonomous vehicles were supposed to arrive came and went, and the challenges facing the technology became apparent both to those in the industry who were trying to make progress with their driving systems and to the public who began to see a growing number of stories about autonomous vehicles crashing in troubling ways, some experts started to discuss how more than just a smart AI would be necessary to bring their fantasies to life.

Safety isn’t just about the quality of the AI technology.”15 What Ng described was a far cry from what people like Brin or Musk were saying autonomous vehicles would do. Instead of detecting anything around them, Ng’s response suggested that social norms and pedestrian behavior would once again have to be altered to make way for autonomous vehicles. The police will be necessary to ensure that people follow the new rules, reflecting how enforcing regulations on automobiles vastly expanded the powers of the police in the twentieth century. In the aftermath, there were autonomous vehicle advocates who rejected Ng’s comments, but his admission is a more realistic assessment of what will be required to make self-driving cars “work” in any meaningful way than the much more common utopian visions.


pages: 352 words: 104,411

Rush Hour: How 500 Million Commuters Survive the Daily Journey to Work by Iain Gately

Albert Einstein, Alvin Toffler, autonomous vehicles, Beeching cuts, blue-collar work, Boris Johnson, British Empire, business intelligence, business process, business process outsourcing, California high-speed rail, call centre, car-free, Cesare Marchetti: Marchetti’s constant, Clapham omnibus, cognitive dissonance, congestion charging, connected car, corporate raider, DARPA: Urban Challenge, Dean Kamen, decarbonisation, Deng Xiaoping, Detroit bankruptcy, don't be evil, driverless car, Elon Musk, extreme commuting, Ford Model T, General Motors Futurama, global pandemic, Google bus, Great Leap Forward, Henri Poincaré, high-speed rail, Hyperloop, Jeff Bezos, lateral thinking, Lewis Mumford, low skilled workers, Marchetti’s constant, planned obsolescence, postnationalism / post nation state, Ralph Waldo Emerson, remote working, safety bicycle, self-driving car, Silicon Valley, social distancing, SpaceShipOne, stakhanovite, Steve Jobs, Suez crisis 1956, telepresence, Tesla Model S, Traffic in Towns by Colin Buchanan, urban planning, éminence grise

According to its twelfth five-year plan for transportation, it will spend US$787.4 billion on building roads between 2011 and 2015, or about the same as the GDP of Holland. While commuting by motorcar looks set to stay for the foreseeable future, there may be significant changes in the way it’s carried out. Although more and more people will be using cars to get to work, it may be as passengers rather than drivers. Driverless or autonomous vehicles have been on futurologists’ radars for longer than telecommuting. The first recorded example was the Achen Motor Company’s ‘phantom motor car’, which it promised to drive around the streets of Milwaukee by radio control in December 1926. The Milwaukee Sentinel waxed lyrical over the ‘ghost’: ‘Driverless, it will start its own motor, throw its clutch, twist its steering wheel, toot its horn, and it may even “sass” the policeman at the corner.’

Their business model relied on people loving driving themselves, if nothing else for the freedom it offered, and they were happy to let the idea of driverless vehicles sleep. However, it was revived by the American military after the 2003 Iraq War. Why send a soldier as well as a vehicle through a minefield? The Defense Advanced Research Projects Agency (DARPA), part of the US Department of Defense – hoping to inspire the creation of autonomous vehicles that might have martial applications – staged the DARPA Grand Challenges of 2004 and 2005, and the Urban Challenge of 2007, which offered million-dollar prizes and grants to teams who could create effective driverless cars. Various universities built entries, and while none met the challenge in 2004, four succeeded in 2005 and there were as many winners in the urban event.

The World Health Organization estimates that 1.3 million people die in traffic accidents every year, and a further 50 million are maimed or crippled. Most accidents are caused by human error. If motorcars could detect each other, could communicate among themselves, and might be programmed to avoid collisions, then rush hours would be far safer. Google, which is leading research in autonomous vehicles, is also motivated by safety. Its informal corporate motto is ‘Don’t be Evil’, and it believes that driverless cars will end the global carnage on the roads that claims more victims each year than warfare. In the same speech in which CFO Patrick Pichette dismissed telecommuting, he also stated that, in an ideal world, ‘nobody should be driving cars… Look at factorial math and probabilities of everything that could go wrong, times the number of cars out there… That’s why you have gridlock… It makes no sense to make people drive cars.’


pages: 477 words: 75,408

The Economic Singularity: Artificial Intelligence and the Death of Capitalism by Calum Chace

"World Economic Forum" Davos, 3D printing, additive manufacturing, agricultural Revolution, AI winter, Airbnb, AlphaGo, Alvin Toffler, Amazon Robotics, Andy Rubin, artificial general intelligence, augmented reality, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Berlin Wall, Bernie Sanders, bitcoin, blockchain, Boston Dynamics, bread and circuses, call centre, Chris Urmson, congestion charging, credit crunch, David Ricardo: comparative advantage, deep learning, DeepMind, Demis Hassabis, digital divide, Douglas Engelbart, Dr. Strangelove, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Fairchild Semiconductor, Flynn Effect, full employment, future of work, Future Shock, gender pay gap, Geoffrey Hinton, gig economy, Google Glasses, Google X / Alphabet X, Hans Moravec, Herman Kahn, hype cycle, ImageNet competition, income inequality, industrial robot, Internet of things, invention of the telephone, invisible hand, James Watt: steam engine, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kiva Systems, knowledge worker, lifelogging, lump of labour, Lyft, machine translation, Marc Andreessen, Mark Zuckerberg, Martin Wolf, McJob, means of production, Milgram experiment, Narrative Science, natural language processing, Neil Armstrong, new economy, Nick Bostrom, Occupy movement, Oculus Rift, OpenAI, PageRank, pattern recognition, post scarcity, post-industrial society, post-work, precariat, prediction markets, QWERTY keyboard, railway mania, RAND corporation, Ray Kurzweil, RFID, Rodney Brooks, Sam Altman, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, SoftBank, software is eating the world, speech recognition, Stephen Hawking, Steve Jobs, TaskRabbit, technological singularity, TED Talk, The future is already here, The Future of Employment, Thomas Malthus, transaction costs, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, universal basic income, Vernor Vinge, warehouse automation, warehouse robotics, working-age population, Y Combinator, young professional

It argued instead for the etymologically purer “motor-car”.[clxxxi] Perhaps we will contract the phrase “autonomous vehicle”, and call them “autos”. Some people are going to hate self-driving cars, whatever they are called: petrol-heads like Jeremy Clarkson are unlikely to be enthusiastic about the objects of their devotion being replaced by machines with all the romance of a horizontal elevator. Some people are already describing a person who has been relegated from driver to chaperone as a “meat puppet”.[clxxxii] The US Department of Transport draws a distinction between (partly) autonomous cars and (fully) self-driving cars.[clxxxiii] The former still have steering wheels, and require a human driver to take over when they encounter a tricky situation.

utm_source=Twitter&utm_medium=tweet&utm_campaign=@KyleSGibson [clxxvi] The Flynn Effect: http://www.bbc.co.uk/news/magazine-31556802 [clxxvii] WHO "Global Status Report on Road Safety 2013: supporting a decade of action [clxxviii] http://www.japantimes.co.jp/news/2015/11/15/business/tech/human-drivers-biggest-threat-developing-self-driving-cars/#.Vo7D5fmLRD8 [clxxix] http://www.theatlantic.com/business/archive/2013/02/the-american-commuter-spends-38-hours-a-year-stuck-in-traffic/272905/ [clxxx] http://www.reinventingparking.org/2013/02/cars-are-parked-95-of-time-lets-check.html [clxxxi] http://www.etymonline.com/index.php?term=autocar [clxxxii] http://www.digitaltrends.com/cars/audi-autonomous-car-prototype-starts-550-mile-trip-to-ces/ [clxxxiii] http://www.nhtsa.gov/About+NHTSA/Press+Releases/U.S.+Department+of+Transportation+Releases+Policy+on+Automated+Vehicle+Development [clxxxiv] http://www.reuters.com/investigates/special-report/autos-driverless/ [clxxxv] http://www.wired.com/2015/04/delphi-autonomous-car-cross-country/ [clxxxvi] http://recode.net/2015/03/17/google-self-driving-car-chief-wants-tech-on-the-market-within-five-years/ [clxxxvii] http://techcrunch.com/2015/12/22/a-new-system-lets-self-driving-cars-learn-streets-on-the-fly/ [clxxxviii] http://cleantechnica.com/2015/10/12/autonomous-buses-being-tested-in-greek-city-of-trikala/ [clxxxix] http://www.bloomberg.com/news/articles/2015-12-16/google-said-to-make-driverless-cars-an-alphabet-company-in-2016 [cxc] http://electrek.co/2015/12/21/tesla-ceo-elon-musk-drops-prediction-full-autonomous-driving-from-3-years-to-2/ [cxci] http://venturebeat.com/2016/01/10/elon-musk-youll-be-able-to-summon-your-tesla-from-anywhere-in-2018/ [cxcii] https://www.washingtonpost.com/news/the-switch/wp/2016/01/11/elon-musk-says-teslas-autopilot-is-already-probably-better-than-human-drivers/ [cxciii] http://electrek.co/2016/04/24/tesla-autopilot-probability-accident/ [cxciv] http://www.bbc.co.uk/news/technology-35280632 [cxcv] http://www.zdnet.com/article/ford-self-driving-cars-are-five-years-away-from-changing-the-world/ [cxcvi] http://www.reuters.com/investigates/special-report/autos-driverless/ [cxcvii] http://www.wired.com/2015/12/californias-new-self-driving-car-rules-are-great-for-texas/ [cxcviii] http://www.reuters.com/investigates/special-report/autos-driverless/ [cxcix] It has been suggested that electric cars should make noises so that people don’t step off the pavement in front of them.

[cxcii] In April 2016 he went further, claiming that Tesla’s autopilot system was already reducing the number of accidents by 50% - where an accident meant an incident where an airbag was deployed.[cxciii] Ford reported success in January 2016 with tests of its self-driving car in snowy conditions. Unable to determine its location by the obscured road markings, it navigates by using buildings and other above-ground features.[cxciv] In May 2016 an executive in Ford’s autonomous vehicle team estimated that the remaining technological hurdles would be overcome within five years, although adoption would of course take longer. He said the amount of computing power each car currently required was “about the equivalent of five decent laptops.”[cxcv] At the time of writing, the only accident which a Google self-driving car might be blamed for happened in February 2016.


The New Map: Energy, Climate, and the Clash of Nations by Daniel Yergin

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", 3D printing, 9 dash line, activist fund / activist shareholder / activist investor, addicted to oil, Admiral Zheng, Albert Einstein, American energy revolution, Asian financial crisis, autonomous vehicles, Ayatollah Khomeini, Bakken shale, Bernie Sanders, BRICs, British Empire, carbon tax, circular economy, clean tech, commodity super cycle, company town, coronavirus, COVID-19, decarbonisation, deep learning, Deng Xiaoping, Didi Chuxing, disruptive innovation, distributed generation, Donald Trump, driverless car, Edward Snowden, Elon Musk, energy security, energy transition, failed state, Ford Model T, geopolitical risk, gig economy, global pandemic, global supply chain, green new deal, Greta Thunberg, hydraulic fracturing, Indoor air pollution, Intergovernmental Panel on Climate Change (IPCC), inventory management, James Watt: steam engine, John Zimmer (Lyft cofounder), Kickstarter, LNG terminal, Lyft, Malacca Straits, Malcom McLean invented shipping containers, Masayoshi Son, Masdar, mass incarceration, megacity, megaproject, middle-income trap, Mikhail Gorbachev, mutually assured destruction, new economy, off grid, oil rush, oil shale / tar sands, oil shock, open economy, paypal mafia, peak oil, pension reform, power law, price mechanism, purchasing power parity, RAND corporation, rent-seeking, ride hailing / ride sharing, rolling blackouts, Ronald Reagan, Russian election interference, self-driving car, Silicon Valley, smart cities, social distancing, South China Sea, sovereign wealth fund, Suez crisis 1956, super pumped, supply-chain management, TED Talk, trade route, Travis Kalanick, Twitter Arab Spring, Uber and Lyft, uber lyft, ubercab, UNCLOS, UNCLOS, uranium enrichment, vertical integration, women in the workforce

Burns reflected that “there haven’t really been any disruptive innovations in that time.” And he kept thinking, “That’s true of very few industries.” Autonomous cars could be a big part of the answer to Wagoner’s question—if they could work. Burns was also gripped by what he saw as the “most important sustainability issue faced by automobiles”—not energy or emissions, but a deadly epidemic in which 1.2 million people a year globally died in auto accidents. Autonomous vehicles might be able to virtually eliminate crashes. That was one of the main reasons why Burns hooked GM up with Whittaker and the robotics group at Carnegie Mellon.6 And in this third Grand Challenge, held on the deserted air base in Victorville, Carnegie Mellon won—by twenty minutes.

He had gone on to pioneer what became known as “probabilistic robotics,” which combines statistics to deal with the uncertainty that robots face in real-world settings. Thrun’s goal now was to create a vehicle with “the brainpower to make all the decisions along the way.” His interest in autonomous vehicles arose, he said, because he was “curious about human intelligence.” He also once explained that his “passion for cars that drive themselves” arose from traffic jams. “I feel I’ve lost a year or two just in traffic,” he explained. Thrun’s quest for an autonomous vehicle that would make driving safer had a personal dimension; his best friend had been killed in a car crash at age eighteen.3 Carnegie Mellon fielded two burly military-style vehicles.

Many things were thrown overboard, including, at GM, the joint research program with Carnegie Mellon. But there were others, across the country in Silicon Valley, who were not short of money. Google was already at work on autonomous vehicles, with Sebastian Thrun in the lead. Google’s effort—“Project Chauffeur”—was housed in a separate building. “No one at Google had a clue we existed for a year and a half,” said Thrun. As part of their work on autonomous vehicles, they posted 360-degree cameras on the roofs of the cars. This generated the idea for Google Street View, with the ambition of photographing every street in the world. In 2010, with a blog post from Sebastian Thrun—“we have developed technology for cars that can drive themselves”—Google went public with the stunning news that it was working on the autonomous car.


pages: 242 words: 73,728

Give People Money by Annie Lowrey

Abraham Maslow, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, Airbnb, airport security, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, bitcoin, Black Lives Matter, carbon tax, clean water, collective bargaining, computer age, crowdsourcing, cryptocurrency, deindustrialization, desegregation, Donald Trump, driverless car, Edward Glaeser, Elon Musk, ending welfare as we know it, everywhere but in the productivity statistics, full employment, gender pay gap, gentrification, gig economy, Google Earth, Home mortgage interest deduction, income inequality, indoor plumbing, information asymmetry, Jaron Lanier, jitney, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kickstarter, Kodak vs Instagram, labor-force participation, late capitalism, Lyft, M-Pesa, Mahatma Gandhi, Mark Zuckerberg, mass incarceration, McMansion, Menlo Park, mobile money, Modern Monetary Theory, mortgage tax deduction, multilevel marketing, new economy, obamacare, opioid epidemic / opioid crisis, Overton Window, Peter Thiel, post scarcity, post-work, Potemkin village, precariat, public intellectual, randomized controlled trial, ride hailing / ride sharing, Robert Bork, Robert Solow, Ronald Reagan, Rutger Bregman, Sam Altman, self-driving car, Silicon Valley, single-payer health, Steve Jobs, TaskRabbit, tech billionaire, The future is already here, The Future of Employment, theory of mind, total factor productivity, Turing test, two tier labour market, Uber and Lyft, uber lyft, universal basic income, uranium enrichment, War on Poverty, warehouse robotics, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, women in the workforce, working poor, World Values Survey, Y Combinator

Add in enough of those options, along with some advanced sensors and thousands of lines of code, and you end up with an autonomous car that can pilot itself from origin to destination. Soon enough, cars, trucks, and taxis might be able to do so without a driver in the vehicle at all. This technology has gone from zero to sixty—forgive me—in only a decade and a half. Back in 2002, the Defense Advanced Research Projects Agency, part of the Department of Defense and better known as DARPA, announced a “grand challenge,” an invitation for teams to build autonomous vehicles and race one another on a 142-mile desert course from Barstow, California, to Primm, Nevada.

Analysts have thus excitedly described this new technological frontier as a “gold rush” for the industry. Autonomous cars are expected to considerably expand the global market, with automakers anticipating selling 12 million vehicles a year by 2035 for some $80 billion in revenue. Yet to many, the driverless car boom does not seem like a stimulus, or the arrival of a long-awaited future. It seems like an extinction-level threat. Consider the fate of workers on industrial sites already using driverless and autonomous vehicles, watching as robots start to replace their colleagues. “Trucks don’t get pensions, they don’t take vacations.

in part because Americans were driving less: Elisabeth Rosenthal, “The End of Car Culture,” New York Times, June 29, 2013. young folks…were still so cash-strapped: Jordan Weissmann, “Why Don’t Young Americans Buy Cars?,” Atlantic, Mar. 25, 2012. 12 million vehicles a year by 2035: “Autonomous Vehicle Adoption Study,” Boston Consulting Group, Jan. 2015, https://www.bcg.com/​en-us/​industries/​automotive/​autonomous-vehicle-adoption-study.aspx. “Trucks don’t get pensions”: Kim Trynacity, “Oilsands Workers Worry Driverless Trucks Will Haul Away Their Jobs,” CBC News, Nov. 3, 2016. between 2.2 and 3.1 million jobs: Executive Office of the President, Artificial Intelligence, Automation, and the Economy (Washington, DC, Dec. 2016).


pages: 308 words: 84,713

The Glass Cage: Automation and Us by Nicholas Carr

Airbnb, Airbus A320, Andy Kessler, Atul Gawande, autonomous vehicles, Bernard Ziegler, business process, call centre, Captain Sullenberger Hudson, Charles Lindbergh, Checklist Manifesto, cloud computing, cognitive load, computerized trading, David Brooks, deep learning, deliberate practice, deskilling, digital map, Douglas Engelbart, driverless car, drone strike, Elon Musk, Erik Brynjolfsson, Evgeny Morozov, Flash crash, Frank Gehry, Frank Levy and Richard Murnane: The New Division of Labor, Frederick Winslow Taylor, future of work, gamification, global supply chain, Google Glasses, Google Hangouts, High speed trading, human-factors engineering, indoor plumbing, industrial robot, Internet of things, Ivan Sutherland, Jacquard loom, James Watt: steam engine, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Kelly, knowledge worker, low interest rates, Lyft, machine readable, Marc Andreessen, Mark Zuckerberg, means of production, natural language processing, new economy, Nicholas Carr, Norbert Wiener, Oculus Rift, pattern recognition, Peter Thiel, place-making, plutocrats, profit motive, Ralph Waldo Emerson, RAND corporation, randomized controlled trial, Ray Kurzweil, recommendation engine, robot derives from the Czech word robota Czech, meaning slave, scientific management, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley ideology, software is eating the world, Stephen Hawking, Steve Jobs, systems thinking, tacit knowledge, TaskRabbit, technological determinism, technological solutionism, technoutopianism, TED Talk, The Wealth of Nations by Adam Smith, turn-by-turn navigation, Tyler Cowen, US Airways Flight 1549, Watson beat the top human players on Jeopardy!, William Langewiesche

Until such thorny questions get sorted out, fully automated cars are unlikely to grace dealer showrooms. Progress will sprint forward nonetheless. Much of the Google test cars’ hardware and software will come to be incorporated into future generations of cars and trucks. Since the company went public with its autonomous vehicle program, most of the world’s major carmakers have let it be known that they have similar efforts under way. The goal, for the time being, is not so much to create an immaculate robot-on-wheels as to continue to invent and refine automated features that enhance safety and convenience in ways that get people to buy new cars.

What if there was an oncoming vehicle in the other lane? What if that vehicle was a school bus? Isaac Asimov’s first law of robot ethics—“a robot may not injure a human being, or, through inaction, allow a human being to come to harm”1—sounds reasonable and reassuring, but it assumes a world far simpler than our own. The arrival of autonomous vehicles, says Gary Marcus, the NYU psychology professor, would do more than “signal the end of one more human niche.” It would mark the start of a new era in which machines will have to have “ethical systems.”2 Some would argue that we’re already there. In small but ominous ways, we have started handing off moral decisions to computers.

Within a week, a consortium of other Wall Street firms bailed Knight out to avoid yet another disaster in the financial industry. Technology improves, of course, and bugs get fixed. Flawlessness, though, remains an ideal that can never be achieved. Even if a perfect automated system could be designed and built, it would still operate in an imperfect world. Autonomous cars don’t drive the streets of utopia. Robots don’t ply their trades in Elysian factories. Geese flock. Lightning strikes. The conviction that we can build an entirely self-sufficient, entirely reliable automated system is itself a manifestation of automation bias. Unfortunately, that conviction is common not only among technology pundits but also among engineers and software programmers—the very people who design the systems.


When Computers Can Think: The Artificial Intelligence Singularity by Anthony Berglas, William Black, Samantha Thalind, Max Scratchmann, Michelle Estes

3D printing, Abraham Maslow, AI winter, air gap, anthropic principle, artificial general intelligence, Asilomar, augmented reality, Automated Insights, autonomous vehicles, availability heuristic, backpropagation, blue-collar work, Boston Dynamics, brain emulation, call centre, cognitive bias, combinatorial explosion, computer vision, Computing Machinery and Intelligence, create, read, update, delete, cuban missile crisis, David Attenborough, DeepMind, disinformation, driverless car, Elon Musk, en.wikipedia.org, epigenetics, Ernest Rutherford, factory automation, feminist movement, finite state, Flynn Effect, friendly AI, general-purpose programming language, Google Glasses, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, industrial robot, Isaac Newton, job automation, John von Neumann, Law of Accelerating Returns, license plate recognition, Mahatma Gandhi, mandelbrot fractal, natural language processing, Nick Bostrom, Parkinson's law, patent troll, patient HM, pattern recognition, phenotype, ransomware, Ray Kurzweil, Recombinant DNA, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, sorting algorithm, speech recognition, statistical model, stem cell, Stephen Hawking, Stuxnet, superintelligent machines, technological singularity, Thomas Malthus, Turing machine, Turing test, uranium enrichment, Von Neumann architecture, Watson beat the top human players on Jeopardy!, wikimedia commons, zero day

This is one reason why manufactured goods have become much less expensive in recent years. Over the next few years we will see robots begin to leave the factories and enter less structured, more natural environments. An important and recent achievement is the development of cars that that can effectively drive themselves. The 2005 DARPA Grand Challenge had fully autonomous vehicles slowly drive for 11 km over a very rough and winding desert track. More recently, Google and others have successfully driven fully automated vehicles on ordinary roads. Negotiating suburban roads with normal traffic and pedestrians is much more difficult than driving down a freeway or traversing a Martian landscape.

Robotic vs cognitive intelligence In order to discuss these issues, it is useful to roughly classify intelligent programs as being either robotic or cognitive. Robotic programs are concerned with sensing the world using techniques such as vision, and then interacting with it by mechanical means. Autonomous vehicles mainly use robotic intelligence. Cognitive intelligence involves higher-level thinking that is abstracted from the real world. Watson and chess programs are examples of cognitive applications. Currently these are normally built using quite different technologies. Robotic intelligence requires many floating point calculations that measure and predict their environment, whereas cognitive applications tend to work with higher-level symbol manipulation.

Robotic surveillance and control is also becoming more sophisticated. Having large numbers of troops in places like Afghanistan, where they can be picked off by snipers or blown up by mines, is grossly inefficient and politically unpalatable. So armies are keen to augment and perhaps ultimately replace human soldiers with small semi-autonomous vehicles that can be conveniently controlled from far away. As the machines become more intelligent they will need fewer people to control them. And computer-based monitoring systems will make it easier for the authorities to control the controllers. This means that a smaller number of active personnel could more effectively control a large civilian population, even in rugged country such as Afghanistan.


pages: 339 words: 94,769

Possible Minds: Twenty-Five Ways of Looking at AI by John Brockman

AI winter, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Alignment Problem, AlphaGo, artificial general intelligence, Asilomar, autonomous vehicles, basic income, Benoit Mandelbrot, Bill Joy: nanobots, Bletchley Park, Buckminster Fuller, cellular automata, Claude Shannon: information theory, Computing Machinery and Intelligence, CRISPR, Daniel Kahneman / Amos Tversky, Danny Hillis, data science, David Graeber, deep learning, DeepMind, Demis Hassabis, easy for humans, difficult for computers, Elon Musk, Eratosthenes, Ernest Rutherford, fake news, finite state, friendly AI, future of work, Geoffrey Hinton, Geoffrey West, Santa Fe Institute, gig economy, Hans Moravec, heat death of the universe, hype cycle, income inequality, industrial robot, information retrieval, invention of writing, it is difficult to get a man to understand something, when his salary depends on his not understanding it, James Watt: steam engine, Jeff Hawkins, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kickstarter, Laplace demon, Large Hadron Collider, Loebner Prize, machine translation, market fundamentalism, Marshall McLuhan, Menlo Park, military-industrial complex, mirror neurons, Nick Bostrom, Norbert Wiener, OpenAI, optical character recognition, paperclip maximiser, pattern recognition, personalized medicine, Picturephone, profit maximization, profit motive, public intellectual, quantum cryptography, RAND corporation, random walk, Ray Kurzweil, Recombinant DNA, Richard Feynman, Rodney Brooks, self-driving car, sexual politics, Silicon Valley, Skype, social graph, speech recognition, statistical model, Stephen Hawking, Steven Pinker, Stewart Brand, strong AI, superintelligent machines, supervolcano, synthetic biology, systems thinking, technological determinism, technological singularity, technoutopianism, TED Talk, telemarketer, telerobotics, The future is already here, the long tail, the scientific method, theory of mind, trolley problem, Turing machine, Turing test, universal basic income, Upton Sinclair, Von Neumann architecture, Whole Earth Catalog, Y2K, you are the product, zero-sum game

The robot has an end user (or perhaps a few, like a personal robot caring for a family, a car driving a few passengers to different destinations, or an office assistant for an entire team); it has a designer (or perhaps a few); and it interacts with society—the autonomous car shares the road with pedestrians, human-driven vehicles, and other autonomous cars. How to combine these people’s values when they might be in conflict is an important problem we need to solve. AI research can give us the tools to combine values in any way we decide but can’t make the necessary decision for us. In short, we need to enable robots to reason about us—to see us as something more than obstacles or perfect game players.

Computer science has a long history—going back to before there even was computer science—of implementing neural networks, but for the most part these have been simulations of neural networks by digital computers, not neural networks as evolved in the wild by nature herself. This is starting to change: from the bottom up, as the threefold drivers of drone warfare, autonomous vehicles, and cell phones push the development of neuromorphic microprocessors that implement actual neural networks, rather than simulations of neural networks, directly in silicon (and other potential substrates); and from the top down, as our largest and most successful enterprises increasingly turn to analog computation in their infiltration and control of the world.

The Industrial Revolution did trigger enormous social change of this kind, including a shift to universal education. But it will not happen unless we make it happen: This is essentially about power, agency, and control. What’s next for, say, the forty-year-old taxi driver or truck driver in an era of autonomous vehicles? One idea that has been touted is that of a universal basic income, which will allow citizens to pursue their interests, retrain for new occupations, and generally be free to live a decent life. However, market economies, which are predicated on growing consumer demand over all else, may not tolerate this innovation.


pages: 72 words: 21,361

Race Against the Machine: How the Digital Revolution Is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy by Erik Brynjolfsson

Abraham Maslow, Amazon Mechanical Turk, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, business cycle, business process, call centre, combinatorial explosion, corporate governance, creative destruction, crowdsourcing, David Ricardo: comparative advantage, driverless car, easy for humans, difficult for computers, Erik Brynjolfsson, factory automation, first square of the chessboard, first square of the chessboard / second half of the chessboard, Frank Levy and Richard Murnane: The New Division of Labor, general purpose technology, hiring and firing, income inequality, intangible asset, job automation, John Markoff, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Khan Academy, Kickstarter, knowledge worker, Loebner Prize, low skilled workers, machine translation, minimum wage unemployment, patent troll, pattern recognition, Paul Samuelson, Ray Kurzweil, rising living standards, Robert Gordon, Robert Solow, self-driving car, shareholder value, Skype, the long tail, too big to fail, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, wealth creators, winner-take-all economy, zero-sum game

IBM is working with Columbia University Medical Center and the University of Maryland School of Medicine to adapt Watson to the work of medical diagnosis, announcing a partnership in that area with voice recognition software maker Nuance. And the Nevada state legislature directed its Department of Motor Vehicles to come up with regulations covering autonomous vehicles on the state’s roads. Of course, these are only a small sample of the myriad IT-enabled innovations that are transforming manufacturing, distribution, retailing, media, finance, law, medicine, research, management, marketing, and almost every other economic sector and business function. Where People Still Win (at Least for Now) Although computers are encroaching into territory that used to be occupied by people alone, like advanced pattern recognition and complex communication, for now humans still hold the high ground in each of these areas.

In Domain After Domain, Computers Race Ahead Just six years later, however, real-world driving went from being an example of a task that couldn’t be automated to an example of one that had. In October of 2010, Google announced on its official blog that it had modified a fleet of Toyota Priuses to the point that they were fully autonomous cars, ones that had driven more than 1,000 miles on American roads without any human involvement at all, and more than 140,000 miles with only minor inputs from the person behind the wheel. (To comply with driving laws, Google felt that it had to have a person sitting behind the steering wheel at all times).

Bureau of Economic Analysis added “Information Technology” as a category of business investment in 1958, so let’s use that as our starting year. And let’s take the standard 18 months as the Moore’s Law doubling period. Thirty-two doublings then take us to 2006 and to the second half of the chessboard. Advances like the Google autonomous car, Watson the Jeopardy! champion supercomputer, and high-quality instantaneous machine translation, then, can be seen as the first examples of the kinds of digital innovations we’ll see as we move further into the second half—into the phase where exponential growth yields jaw-dropping results. Computing the Economy: The Economic Power of General Purpose Technologies These results will be felt across virtually every task, job, and industry.


pages: 349 words: 95,972

Messy: The Power of Disorder to Transform Our Lives by Tim Harford

affirmative action, Air France Flight 447, Airbnb, airport security, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, assortative mating, Atul Gawande, autonomous vehicles, banking crisis, Barry Marshall: ulcers, Basel III, Berlin Wall, Bletchley Park, British Empire, Broken windows theory, call centre, Cass Sunstein, Chris Urmson, cloud computing, collateralized debt obligation, Computing Machinery and Intelligence, crowdsourcing, deindustrialization, Donald Trump, Erdős number, experimental subject, Ferguson, Missouri, Filter Bubble, financial engineering, Frank Gehry, game design, global supply chain, Googley, Guggenheim Bilbao, Helicobacter pylori, high net worth, Inbox Zero, income inequality, industrial cluster, Internet of things, Jane Jacobs, Jeff Bezos, Loebner Prize, Louis Pasteur, machine readable, Marc Andreessen, Mark Zuckerberg, Menlo Park, Merlin Mann, microbiome, out of africa, Paul Erdős, Richard Thaler, Rosa Parks, self-driving car, side project, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, Steven Levy, Stewart Brand, Susan Wojcicki, tacit knowledge, TED Talk, telemarketer, the built environment, The Death and Life of Great American Cities, the strength of weak ties, Turing test, Tyler Cowen, urban decay, warehouse robotics, William Langewiesche

There’s a revealing implication in that target: that unlike a plane’s autopilot, a self-driving car will never need to cede control to a human being. True to form, Google’s autonomous vehicles have no steering wheel, though one hopes there will be some way to jump out if they start heading for the ocean.19 Not everyone thinks it is plausible for cars to be completely autonomous—or, at least, not soon enough for Urmson junior. Raj Rajkumar, an autonomous-driving expert at Carnegie Mellon University, thinks completely autonomous vehicles are ten to twenty years away. Until then, we can look forward to a more gradual process of letting the car drive itself in easier conditions, while humans take over at more challenging moments.

Anuj Prajan has floated the idea that humans should have to acquire several years of manual experience before they are allowed to supervise an autonomous car. But it is hard to see how this solves the problem. No matter how many years of experience a driver has, her skills will slowly erode if she lets the computer take over. Prajan’s proposal gives us the worst of both worlds: we let teenage drivers loose in manual cars, when they are most likely to have accidents. And even when they’ve learned some road craft, it won’t take long being a passenger in a usually reliable autonomous car before their skills begin to fade. Recall that Earl Wiener said, “Digital devices tune out small errors while creating opportunities for large errors.”21 In the case of autopilots and autonomous vehicles, we might add that it’s because digital devices tidily tune out small errors that they create the opportunities for large ones.

Recall that Earl Wiener said, “Digital devices tune out small errors while creating opportunities for large errors.”21 In the case of autopilots and autonomous vehicles, we might add that it’s because digital devices tidily tune out small errors that they create the opportunities for large ones. Deprived of any awkward feedback, any modest challenges that might allow us to maintain our skills, when the crisis arrives we find ourselves lamentably unprepared. • • • Every application of Wiener’s insight about large and small errors involves a trade-off. The GPS routinely saves me the minor hassle of planning before a trip, but at the cost of occasionally sending me scuttling apologetically into a rural church just ahead of the bridal procession.


pages: 590 words: 152,595

Army of None: Autonomous Weapons and the Future of War by Paul Scharre

"World Economic Forum" Davos, active measures, Air France Flight 447, air gap, algorithmic trading, AlphaGo, Apollo 13, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Black Monday: stock market crash in 1987, brain emulation, Brian Krebs, cognitive bias, computer vision, cuban missile crisis, dark matter, DARPA: Urban Challenge, data science, deep learning, DeepMind, DevOps, Dr. Strangelove, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, fail fast, fault tolerance, Flash crash, Freestyle chess, friendly fire, Herman Kahn, IFF: identification friend or foe, ImageNet competition, information security, Internet of things, Jeff Hawkins, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Korean Air Lines Flight 007, Loebner Prize, loose coupling, Mark Zuckerberg, military-industrial complex, moral hazard, move 37, mutually assured destruction, Nate Silver, Nick Bostrom, PalmPilot, paperclip maximiser, pattern recognition, Rodney Brooks, Rubik’s Cube, self-driving car, sensor fusion, South China Sea, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Ballmer, Steve Wozniak, Strategic Defense Initiative, Stuxnet, superintelligent machines, Tesla Model S, The Signal and the Noise by Nate Silver, theory of mind, Turing test, Tyler Cowen, universal basic income, Valery Gerasimov, Wall-E, warehouse robotics, William Langewiesche, Y2K, zero day

UNLEASHING MAYHEM: THE CYBER GRAND CHALLENGE DARPA tackles only the most difficult research problems, “DARPA hard” problems that others might deem impossible. DARPA does this every day, but when a technical problem is truly daunting even for DARPA, the organization pulls out its big guns in a Grand Challenge. The first DARPA Grand Challenge was held in 2004, on autonomous vehicles. Twenty-one research teams competed to build a fully autonomous vehicle that could navigate a 142-mile course across the Mojave Desert. It was truly a “DARPA hard” problem. The day ended with every single vehicle broken down, overturned, or stuck. The furthest any car got was 7.4 miles, only 5 percent of the way through the course.

The human’s ability to actually regain control of the system in real time depends heavily on the speed of operations, the amount of information available to the human, and any time delays between the human’s actions and the system’s response. Giving a driver the ability to grab the wheel of an autonomous vehicle traveling at highway speeds in dense traffic, for example, is merely the illusion of control, particularly if the operator is not paying attention. This appears to have been the case in a 2016 fatality involving a Tesla Model S that crashed while driving on autopilot. For fully autonomous systems, the human is out of the loop and cannot intervene at all, at least for some period of time.

This time, it was a resounding success. Twenty-two vehicles beat the previous year’s distance record and five cars finished the entire course. In 2007, DARPA hosted an Urban Challenge for self-driving cars on a closed, urban course complete with traffic and stop signs. These Grand Challenges matured autonomous vehicle technology in leaps and bounds, laying the seeds for the self-driving cars now in development at companies like Google and Tesla. DARPA has since used the Grand Challenge approach as a way to tackle other truly daunting problems, harnessing the power of competition to generate the best ideas and launch a technology forward.


pages: 523 words: 61,179

Human + Machine: Reimagining Work in the Age of AI by Paul R. Daugherty, H. James Wilson

3D printing, AI winter, algorithmic management, algorithmic trading, AlphaGo, Amazon Mechanical Turk, Amazon Robotics, augmented reality, autonomous vehicles, blockchain, business process, call centre, carbon footprint, circular economy, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, data science, deep learning, DeepMind, digital twin, disintermediation, Douglas Hofstadter, driverless car, en.wikipedia.org, Erik Brynjolfsson, fail fast, friendly AI, fulfillment center, future of work, Geoffrey Hinton, Hans Moravec, industrial robot, Internet of things, inventory management, iterative process, Jeff Bezos, job automation, job satisfaction, knowledge worker, Lyft, machine translation, Marc Benioff, natural language processing, Neal Stephenson, personalized medicine, precision agriculture, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, robotic process automation, Rodney Brooks, Salesforce, Second Machine Age, self-driving car, sensor fusion, sentiment analysis, Shoshana Zuboff, Silicon Valley, Snow Crash, software as a service, speech recognition, tacit knowledge, telepresence, telepresence robot, text mining, the scientific method, uber lyft, warehouse automation, warehouse robotics

FUSION SKILL #2: Responsible Normalizing Definition: The act of responsibly shaping the purpose and perception of human-machine interaction as it relates to individuals, businesses, and society. It’s surprising how quickly you can get used to riding in an autonomous car. The first time you see its wheel turn on its own, you might shudder, but by the second right turn, it all starts to feel normal. Many people who have ridden in autonomous cars quickly conclude that driving is far too complex and dangerous a task for people to do. Unfortunately, autonomous cars are not yet widely distributed, and in many places, they’re still misunderstood. There’s a gap between the use of AI technologies and the wide acceptance and understanding of them.

But just as important is fostering positive experiences with AI augmentation. Make it clear to employees that you are using AI to replace tasks and reimagine processes. Demonstrate that AI tools can augment employees and make their day-to-day work less tedious and more engaging. Meanwhile, though, here’s what businesses are facing. When discussing the safety of autonomous vehicles, Gill Pratt, chief executive of the Toyota Research Institute, told lawmakers on Capitol Hill in 2017 that people are more inclined to forgive mistakes that humans make than those by machines.7 Research confirms the inconsistency and ambiguity with which we trust machines. A 2009 paper reported that when people thought their stock reports were coming from a human expert, their price estimates were more likely to be swayed than if they thought the information came from a statistical forecasting tool.

In this case, people’s driving skills—at scale—are crucial in the training of the system. AI has allowed Tesla to rethink its fundamental R&D processes and, along the way, speed up the development of its system. This reconsideration of how it conducts R&D is positioning Tesla to be a leader in autonomous cars. Tesla isn’t the only one using AI to rethink its R&D processes, using both machines and people in new, innovative ways. This chapter explores the way that AI enables experimentation within companies and how it’s shaking up business processes, especially those that involve customers, medical patients, and others who provide useful data.


pages: 535 words: 149,752

After Steve: How Apple Became a Trillion-Dollar Company and Lost Its Soul by Tripp Mickle

"World Economic Forum" Davos, Airbnb, airport security, Apple II, Apple's 1984 Super Bowl advert, augmented reality, autonomous vehicles, banking crisis, Boeing 747, British Empire, business intelligence, Carl Icahn, Clayton Christensen, commoditize, coronavirus, corporate raider, COVID-19, desegregation, digital map, disruptive innovation, Donald Trump, Downton Abbey, driverless car, Edward Snowden, Elon Musk, Frank Gehry, General Magic , global pandemic, global supply chain, haute couture, imposter syndrome, index fund, Internet Archive, inventory management, invisible hand, John Markoff, Jony Ive, Kickstarter, Larry Ellison, lateral thinking, Mark Zuckerberg, market design, megacity, Murano, Venice glass, Ralph Waldo Emerson, self-driving car, Sheryl Sandberg, Silicon Valley, skeuomorphism, Stephen Fry, Steve Jobs, Steve Wozniak, Steven Levy, stock buybacks, Superbowl ad, supply-chain management, thinkpad, Tim Cook: Apple, Tony Fadell, Travis Kalanick, turn-by-turn navigation, Wayback Machine, WikiLeaks, Y2K

Software development for the fully driverless car he had imagined was lagging behind because of a lack of data and the complexity of building an autonomous system from the ground up. The hardware effort was making progress but trailing the company’s ambitious timeline. There was no way a fully autonomous car would be ready by the self-imposed deadline of 2019. Ive erupted. It was clear to everyone involved that the project was suffering under the weight of its ambitions. Ive’s vision for a fully autonomous vehicle had contributed to the build-out of a massive team of programmers and sensor experts, while hardware chief Dan Riccio’s focus on creating an electric vehicle had led to the development of a massive team of battery and automotive experts.

The project had three leaders who appeared more focused on building out their corporate fiefdom than on moving a unified project forward. The challenges were reminiscent of the corporate infighting that plagued the Apple Watch. Lavish spending compounded the woes. The project costs had ballooned to a staggering $1 billion a year. Project Titan leaders had hired autonomous-vehicle researchers at $10 million each and invested in the development of lasers to aim into passengers’ eyes in a bid to reduce the motion sickness caused by a car’s abrupt movements. Apple’s R-and-D expenses mushroomed, nearly doubling to $8.1 billion by the end of 2015. It was loose change for a company with $200 billion in cash, but the engineers viewed the warehouse as the latest example of a Silicon Valley giant spending big with nothing to show for it.

The heavyset engineer with close-cropped hair had a background in semiconductors and had risen to the top of Apple by delivering breakthroughs on products such as the MacBook Air. In Apple’s hierarchical structure, he commanded respect. Mansfield addressed the group bluntly, making clear what most people in the room already knew: the project was a mess. Though he acknowledged that he didn’t fully appreciate the technical challenges of autonomous vehicles, he planned to use a Thor’s hammer approach to get the work back on track. He announced that there would be layoffs—some two hundred of the staff would be let go—as he streamlined the operation and shifted its focus. It was clear to him that Apple had no business plowing ahead with the construction of a car until it determined how to structure the underlying software that would enable it to navigate roads without a driver.


pages: 269 words: 70,543

Tech Titans of China: How China's Tech Sector Is Challenging the World by Innovating Faster, Working Harder, and Going Global by Rebecca Fannin

"World Economic Forum" Davos, Adam Neumann (WeWork), Airbnb, augmented reality, autonomous vehicles, Benchmark Capital, Big Tech, bike sharing, blockchain, call centre, cashless society, Chuck Templeton: OpenTable:, clean tech, cloud computing, computer vision, connected car, corporate governance, cryptocurrency, data is the new oil, data science, deep learning, Deng Xiaoping, Didi Chuxing, digital map, disruptive innovation, Donald Trump, El Camino Real, electricity market, Elon Musk, fake news, family office, fear of failure, fulfillment center, glass ceiling, global supply chain, Great Leap Forward, income inequality, industrial robot, information security, Internet of things, invention of movable type, Jeff Bezos, Kickstarter, knowledge worker, Lyft, Mark Zuckerberg, Mary Meeker, megacity, Menlo Park, money market fund, Network effects, new economy, peer-to-peer lending, personalized medicine, Peter Thiel, QR code, RFID, ride hailing / ride sharing, Sand Hill Road, self-driving car, sharing economy, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart transportation, Snapchat, social graph, SoftBank, software as a service, South China Sea, sovereign wealth fund, speech recognition, stealth mode startup, Steve Jobs, stock buybacks, supply-chain management, tech billionaire, TechCrunch disrupt, TikTok, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, urban planning, Vision Fund, warehouse automation, WeWork, winner-take-all economy, Y Combinator, young professional

Michael Dunne, “Driving the Future of US-China Relations: China’s Global Automotive Push,” Asia Society Northern California, February 27, 2019; asiasociety.org/video/driving-future-us-china-relations-chinas-global-automotive-push. 2. China Passenger Car Association, broker reports. Accessed February 28, 2019. 3. Luca Pizzuto et al., “How China Will Help Fuel the Revolution in Autonomous Vehicles,” McKinsey & Co., January 2019; mckinsey.com/industries/automotive-and-assembly/our-insights/how-china-will-help-fuel-the-revolution-in-autonomous-vehicles?reload. 4. Dana Hull and Peter Blumberg, “Tesla Joins Apple in Trade Secret Cases Tied to Xpeng, Bloomberg, March 21, 2019; bloomberg.com/news/articles/2019-03-21/tesla-sues-rival-zoox-claiming-ex-workers-stole-trade-secrets. 5.

Now back in the driver’s seat at Baidu, the challenge for Li is keeping a big-picture vision while running day-to-day operations and juggling both AI and search businesses. Next for Baidu is making money from its suite of AI products, branded Baidu Brain; voice-assisted DuerOS lights, speakers, and smartphone chargers, which have surpassed 200 million users; and self-driving technology Apollo, which has 50 municipal licenses in China to test autonomous vehicles on open roads. “If anyone starts to be able to generate meaningful revenue (in AI), we will be the first to achieve that goal,” Li told analysts on a recent earnings call, while acknowledging that Apollo is at a “very early stage.”8 Raymond Feng, an analyst at market research company Pacific Epoch in Shanghai, predicts that Baidu will start making money on driverless vehicle technology by 2020, providing improved AI services to vehicle manufacturers and drivers.

Since the early 1980s, China has wanted to have its own powerful automobile industry, but it’s not been until now that the nation can realize that goal through its leading technology companies, notes Michael Dunne, CEO of Hong Kong–based auto tech advisory firm ZoZo Go. Now these homegrown tech-oriented startups are powering up with electric and autonomous vehicles that can make China proud. With no legacy of gas-powered engines or Chinese state-owned, auto-making enterprises, they have an open road to roam in China. “China has the potential to make world-class vehicles with their smart and cashed-up tech companies,” said consultant Dunne, speaking at an Asia Society meeting in Northern California.


pages: 240 words: 65,363

Think Like a Freak by Steven D. Levitt, Stephen J. Dubner

Albert Einstein, Anton Chekhov, autonomous vehicles, Barry Marshall: ulcers, behavioural economics, call centre, carbon credits, Cass Sunstein, colonial rule, Donald Shoup, driverless car, Edward Glaeser, Everything should be made as simple as possible, fail fast, food miles, gamification, Gary Taubes, Helicobacter pylori, income inequality, information security, Internet Archive, Isaac Newton, medical residency, Metcalfe’s law, microbiome, prediction markets, randomized controlled trial, Richard Thaler, Scramble for Africa, self-driving car, Silicon Valley, sunk-cost fallacy, Tony Hsieh, transatlantic slave trade, Wayback Machine, éminence grise

(Once journalists stopped getting it, they stopped writing about it—but the problem persists, especially among blue-collar workers.) Some questions were existential: What makes people truly happy? Is income inequality as dangerous as it seems? Would a diet high in omega-3 lead to world peace? People wanted to know the pros and cons of: autonomous vehicles, breast-feeding, chemotherapy, estate taxes, fracking, lotteries, “medicinal prayer,” online dating, patent reform, rhino poaching, using an iron off the tee, and virtual currencies. One minute we’d get an e-mail asking us to “solve the obesity epidemic” and then, five minutes later, one urging us to “wipe out famine, right now!”

If you paper over the shortcomings of your plan, that only gives your opponent reason to doubt the rest of it. Let’s say you’ve become a head-over-heels advocate for a new technology you think will change the world. Your argument goes like this: The era of the self-driving car—a.k.a. the driverless car, or autonomous vehicle—is just around the corner, and we should embrace it as vigorously as possible. It will save millions of lives and improve just about every facet of our society and economy. You could go on and on. You could talk about how the toughest challenge—the technology itself—has largely been conquered.

—are the result of driver error, the driverless car may be one of the biggest lifesavers in recent history. Unlike humans, a driverless car won’t drive drowsy or drunk, or while texting or applying mascara; it won’t change lanes while putting ketchup on french fries or turn around to smack its kids in the backseat. Google has already driven its fleet of autonomous cars more than 500,000 miles on real roads throughout the United States without causing an accident.* But safety isn’t the only benefit. Elderly and handicapped people wouldn’t have to drive themselves to the doctor (or, if they prefer, to the beach). Parents wouldn’t have to worry about their reckless teenagers getting behind the wheel.


pages: 245 words: 64,288

Robots Will Steal Your Job, But That's OK: How to Survive the Economic Collapse and Be Happy by Pistono, Federico

3D printing, Albert Einstein, autonomous vehicles, bioinformatics, Buckminster Fuller, cloud computing, computer vision, correlation does not imply causation, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Firefox, future of work, gamification, George Santayana, global village, Google Chrome, happiness index / gross national happiness, hedonic treadmill, illegal immigration, income inequality, information retrieval, Internet of things, invention of the printing press, Jeff Hawkins, jimmy wales, job automation, John Markoff, Kevin Kelly, Khan Academy, Kickstarter, Kiva Systems, knowledge worker, labor-force participation, Lao Tzu, Law of Accelerating Returns, life extension, Loebner Prize, longitudinal study, means of production, Narrative Science, natural language processing, new economy, Occupy movement, patent troll, pattern recognition, peak oil, post scarcity, QR code, quantum entanglement, race to the bottom, Ray Kurzweil, recommendation engine, RFID, Rodney Brooks, selection bias, self-driving car, seminal paper, slashdot, smart cities, software as a service, software is eating the world, speech recognition, Steven Pinker, strong AI, synthetic biology, technological singularity, TED Talk, Turing test, Vernor Vinge, warehouse automation, warehouse robotics, women in the workforce

The initial investment is very low, and the distributed nature of computation allows costs to increase incrementally as the business expands. We are about to experience tremendous changes in such technologies, the consequences of which are unthinkable for us at the moment. Just as cavemen could not think of the complex cities and societies we live in today, so do we compared to what is about to come. 7.7 Autonomous Vehicles Often people say that something is either obvious and everything will change, or that it will never happen. It turns out that things are not quite that simple. Societies are multi-faceted, complex, evolving organisms, with many variables, and a certain degree of unpredictability. Technicians often fail to take into account the human factor, the psychology of the masses, and how events unfold accordingly.

Being in a car now became a whole different experience; it could be a truly social event. Given the situation, one would expect every car, bus, truck, and taxi to run autonomously by now. It would certainly have been the right choice: more efficient, less accidents, no traffic jams, cheaper and more reliable than human drivers…having autonomous vehicles would be logical. But things do not always go according to what is logical. They follow complex dynamics that have to do with society, group thinking and complex dynamics that have little to do with technology and what is good; and a lot to do with politics, marketing, emotional attachments, old habit, delusions, beliefs, and what appears to be good.

The classical “Turing test approach” has been largely abandoned as a realistic research goal, and is now just an intellectual curiosity (the annual Loebner prize for realistic chattiest81), but helped spawn the two dominant themes of modern cognition and artificial intelligence: calculating probabilities and producing complex behaviour from the interaction of many small, simple processes. As of today (2012), we believe these represent more closely what the human brain does, and they have been used in a variety of real-world applications: Google’s autonomous cars, search results, recommendation systems, automated language translation, personal assistants, cybernetic computational search engines, and IBM’s newest super brain Watson. Natural language processing was believed to be a task that only humans could accomplish. A word can have different meanings depending on the context, a phrase could not mean what it says if it is a joke or a pun.


Robot Futures by Illah Reza Nourbakhsh

3D printing, autonomous vehicles, Burning Man, business logic, commoditize, computer vision, digital divide, Mars Rover, Menlo Park, phenotype, Skype, social intelligence, software as a service, stealth mode startup, strong AI, telepresence, telepresence robot, Therac-25, Turing test, Vernor Vinge

Last year, following encouragement from Google, the State of Nevada enacted legislation to soon make it legal for autonomous vehicles to drive on the state’s highway system (State of Nevada 2011). To date Google’s autonomous driving machines have already covered more than 200,000 miles in California, where there are no laws explicitly forbidding robotically driven vehicles. And yet the diversity of ways in which legal boundaries, human behavior, and robot cars on the road will intersect are not predicted by Nevada’s legislation or by existing bodies of law. In August 2011 the automotive blog jalopnik broke the news of a fender bender caused by one of Google’s autonomous cars. Google issued a statement explaining that the accident was caused by the human in the Google car, since he was driving manually at the time.

See Motor Empathy, 54, 55, 114 Ethics, 25, 26, 55, 60–62, 101, 117, 118 Euclid Elements, 12 Index human, xvi, market, 6, 12, 43 Joints, 27, 29, 31, 32, 95, 123 Kinect, 36 Face tracking, 39 Fan out, 76–78, 81, 82 Flash, 26 Flying robots, xv, 29, 30, 47 Funding, 95, 96, 111–113 Fundraising. See Funding Gladwell, Malcolm, 82 Google Android operating system, 40 autonomous vehicles (see Driverless vehicle) Robo-Google, 43 Hacking, 22–24, 37 Human–robot interaction (HRI) adjustable autonomy, 45, 46, 77, 80, 102, 103, 121 ethics of, 54, 101, 104, 110 nanorobot coupling, 97–100 peer-to-peer, xix, 44, 45 psychological experiments, 54 Hyperactive Bob, 11 Identity, 62, 100, 103–107, 117 Intel IETF, 38 OpenCV, 39–41 Intelligence artificial (see Artificial Intelligence) Laser cutting, 28, 122 Machine learning, 98, 122 Manipulation, 29, 33, 40, 41, 123 Microsoft Kinect (see Kinect) robotics studio, 39 Moore’s Law, 31, 33 Motor, xv, xvii, 28, 29, 31–33, 38 Nanorobot, 89–94, 97–99, 106 NASA, 44, 45, 113 National Science Foundation, 112, 113 Nielsen, 5, 13 O’Terrill’s, 24, 25 Particulates.


pages: 424 words: 119,679

It's Better Than It Looks: Reasons for Optimism in an Age of Fear by Gregg Easterbrook

affirmative action, Affordable Care Act / Obamacare, air freight, Alan Greenspan, Apollo 11, autonomous vehicles, basic income, Bernie Madoff, Bernie Sanders, Black Lives Matter, Boeing 747, Branko Milanovic, Brexit referendum, business cycle, Capital in the Twenty-First Century by Thomas Piketty, carbon tax, clean tech, clean water, coronavirus, Crossrail, David Brooks, David Ricardo: comparative advantage, deindustrialization, Dissolution of the Soviet Union, Donald Trump, driverless car, Elon Musk, Exxon Valdez, factory automation, failed state, fake news, full employment, Gini coefficient, Google Earth, Home mortgage interest deduction, hydraulic fracturing, Hyperloop, illegal immigration, impulse control, income inequality, independent contractor, Indoor air pollution, interchangeable parts, Intergovernmental Panel on Climate Change (IPCC), invisible hand, James Watt: steam engine, labor-force participation, liberal capitalism, longitudinal study, Lyft, mandatory minimum, manufacturing employment, Mikhail Gorbachev, minimum wage unemployment, Modern Monetary Theory, obamacare, oil shale / tar sands, Paul Samuelson, peak oil, plant based meat, plutocrats, Ponzi scheme, post scarcity, purchasing power parity, quantitative easing, reserve currency, rising living standards, Robert Gordon, Ronald Reagan, self-driving car, short selling, Silicon Valley, Simon Kuznets, Slavoj Žižek, South China Sea, Steve Wozniak, Steven Pinker, supervolcano, The Chicago School, The Rise and Fall of American Growth, the scientific method, There's no reason for any individual to have a computer in his home - Ken Olsen, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, transaction costs, Tyler Cowen, uber lyft, universal basic income, War on Poverty, Washington Consensus, We are all Keynesians now, WikiLeaks, working poor, Works Progress Administration

If future autonomous cars really don’t have pedals and steering, just seats for passengers, a cyberattack that cripples GPS signals will bring society to a halt. Buses and trucks will be driven by computers too. Long-distance truck drivers often are overworked and sleepy; far more people die in collisions caused by trucks than in airline crashes, and the rate of truck-caused fatalities has been rising in recent years, even as other forms of unnatural death moderate. For most families, the chief benefit of autonomous vehicles will be convenience. For trucking companies, the autonomous vehicle will replace wages, benefits, and workplace litigation with a capital cost that can be depreciated.

IF PEOPLE ARE RESISTING MORE sensible cars, let’s get people out of the loop. Before too long, the autonomous car is likely. Computer-driven cars will reduce accidents: the computer won’t get drowsy or try to cut another computer off. They will reduce traffic jams: traffic would flow more smoothly if cars employed uniform speeds and didn’t make needless lane changes, jockeying to get a few seconds ahead. Studies by researchers at the Massachusetts Institute of Technology suggest that an all-autonomous-vehicle system would reduce traffic jams 80 percent—traffic would flow freely even in the Manhattan tunnels.

The horsepower arms race will end, since a car that refuses to violate the speed limit—this is going to please some people while driving others to distraction—would not benefit from prodigious power output. Automakers have opened research offices in and around Palo Alto, California, seeking techie-wizard input into self-driving designs. Ford Motors expects by 2021 to be selling fully autonomous cars—no steering wheel—designed for group ownership. This would seem the fulfillment of a Summer-of-Love hippie whimsy were it not the marketing strategy of a Fortune 500 firm. Once cars become safer through computer control, more affordable through group ownership, cleaner through lower oil consumption, and less of a source of urban headaches through the end of the rush-hour traffic jam—then we’ll never be rid of car culture, which will exist for decades or centuries to come, if not until the sun explodes.


pages: 282 words: 93,783

The Future Is Analog: How to Create a More Human World by David Sax

Alvin Toffler, augmented reality, autonomous vehicles, Bernie Sanders, big-box store, bike sharing, Black Lives Matter, blockchain, bread and circuses, Buckminster Fuller, Cal Newport, call centre, clean water, cognitive load, commoditize, contact tracing, contact tracing app, COVID-19, crowdsourcing, cryptocurrency, data science, David Brooks, deep learning, digital capitalism, Donald Trump, driverless car, Elon Musk, fiat currency, Francis Fukuyama: the end of history, future of work, gentrification, George Floyd, indoor plumbing, informal economy, Jane Jacobs, Jaron Lanier, Jeff Bezos, Kickstarter, knowledge worker, lockdown, Lyft, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Minecraft, New Urbanism, nuclear winter, opioid epidemic / opioid crisis, Peter Thiel, RAND corporation, Ray Kurzweil, remote working, retail therapy, RFID, Richard Florida, ride hailing / ride sharing, Saturday Night Live, Shoshana Zuboff, side hustle, Sidewalk Labs, Silicon Valley, Silicon Valley startup, Skype, smart cities, social distancing, sovereign wealth fund, Steve Jobs, Superbowl ad, supply-chain management, surveillance capitalism, tech worker, technological singularity, technoutopianism, TED Talk, The Death and Life of Great American Cities, TikTok, Uber and Lyft, uber lyft, unemployed young men, urban planning, walkable city, Y2K, zero-sum game

In the century since, smart city designs have become more digitally driven but no less idealistic. Each promises to solve the pesky troubles of a city, from traffic and pollution to economic opportunity and citizen safety, by unleashing the latest in digital technology—computers and smart phones, cameras and sensors, flying robots and autonomous vehicles—and feeding reams of data into a central brain of computers that will use statistics and machine learning to solve the intractable problems all cities face. Smart cities would be cleaner, safer, more democratic, more equal, and brimming with the sexy digital innovation that would drive economic growth, jobs, and investment.

“A combination of technologies, thoughtfully applied and integrated, can fundamentally alter nearly every dimension of quality of life in an urban environment,” Doctoroff said in an article he wrote for the consulting firm McKinsey’s website after the partnership was announced. “We’re convinced that by implementing a set of technologies—autonomous vehicles, modular building construction, or new infrastructure systems—we can, for example, reduce cost of living by 15 percent. With new mobility services and radical mixed-use development that brings homes near work, we can give people back an hour in their day.” At the launch event, which included representatives from every level of government, including Canadian prime minister Justin Trudeau, Sidewalk Toronto laid out its promise to build a new kind of mixed-use urban community, applying digital technology to create “people-centered neighborhoods.”

On the one hand you have an issue like housing affordability, which requires years of study, politically risky debate, and complex policy interventions, from zoning alterations to tax breaks and subsidized apartment construction, with no guarantee of success and the certainty of pissing someone off. On the other hand you say, “Oh, don’t worry, I’ll give you a gadget!” Saxe joked, “Well, people say, ‘Thank God! I’ll take the gadget, because I don’t have to do things that are hard… If we can do the same thing and insert a sexy new technology, then great! Why do you think autonomous cars are so attractive? You don’t have to stop driving. You don’t have to change roads. It doesn’t cost governments a cent. We can still drive, and the government says, ‘Look, all our problems are better!’” The fundamental problem with pegging the future of cities to digital projects like Sidewalk Toronto is confusing invention and innovation.


pages: 385 words: 112,842

Arriving Today: From Factory to Front Door -- Why Everything Has Changed About How and What We Buy by Christopher Mims

air freight, Airbnb, Amazon Robotics, Amazon Web Services, Apollo 11, augmented reality, autonomous vehicles, big-box store, blue-collar work, Boeing 747, book scanning, business logic, business process, call centre, cloud computing, company town, coronavirus, cotton gin, COVID-19, creative destruction, data science, Dava Sobel, deep learning, dematerialisation, deskilling, digital twin, Donald Trump, easy for humans, difficult for computers, electronic logging device, Elon Musk, Frederick Winslow Taylor, fulfillment center, gentrification, gig economy, global pandemic, global supply chain, guest worker program, Hans Moravec, heat death of the universe, hive mind, Hyperloop, immigration reform, income inequality, independent contractor, industrial robot, interchangeable parts, intermodal, inventory management, Jacquard loom, Jeff Bezos, Jessica Bruder, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, Kaizen: continuous improvement, Kanban, Kiva Systems, level 1 cache, Lewis Mumford, lockdown, lone genius, Lyft, machine readable, Malacca Straits, Mark Zuckerberg, market bubble, minimum wage unemployment, Nomadland, Ocado, operation paperclip, Panamax, Pearl River Delta, planetary scale, pneumatic tube, polynesian navigation, post-Panamax, random stow, ride hailing / ride sharing, robot derives from the Czech word robota Czech, meaning slave, Rodney Brooks, rubber-tired gantry crane, scientific management, self-driving car, sensor fusion, Shenzhen special economic zone , Shoshana Zuboff, Silicon Valley, six sigma, skunkworks, social distancing, South China Sea, special economic zone, spinning jenny, standardized shipping container, Steve Jobs, supply-chain management, surveillance capitalism, TED Talk, the scientific method, Tim Cook: Apple, Toyota Production System, traveling salesman, Turing test, two-sided market, Uber and Lyft, Uber for X, uber lyft, Upton Sinclair, vertical integration, warehouse automation, warehouse robotics, workplace surveillance

Getting through this light and the one just past it, which has funny timing and gates the entrance of vehicles onto the overpass proper, requires that the truck keep rolling even when it might normally stop. This situation is a good illustration of the frequently imperfect and entirely handcrafted world that autonomous vehicles must navigate. We all know that one intersection in our neighborhood where a bush obscures our view of traffic crossing our path, or the one where there should be a light, or that other one where people tend to take the curve too quickly and accidents happen all the time. To make it safe for autonomous vehicles to operate on our strange and inconsistent roads, we have to teach them not just how to drive under optimal conditions but how to handle the peculiarities of our built environment, just as we might teach a teenager just learning to drive.

(Please do: This is a long book, and it’s my hope that some of its sections will be worth the price of entry for those of you with narrower interests than the entire saga it chronicles.) You will learn what technology your cell phone has in common with spacecraft, cruise missiles, and the navigation systems of Polynesian explorers. You’ll also find a (hopefully) accessible explanation of the “thinking” process of the AI (artificial intelligence) that drives an autonomous vehicle. You’ll learn why automated warehouses are like microchips that process stuff instead of bits, and how the two were designed with the same principles in mind. You will be introduced to “Bezosism,” that braiding together of management practices, AI, workplace surveillance, robots, and hard automation that is the engine of Amazon’s success, and possibly the future of all low-skilled labor.

Then, with only a minimum of human intervention, it drove to us, never faster than four miles an hour. But to accomplish this sequence of events tens of thousands of times a week, in dozens of cities all over the world, in every kind of weather, and to make delivery via robot into a going concern—a real business that can eventually make money—is the real trick. While fully autonomous vehicles remain mostly a science project, as well as a potentially risky ongoing experiment on America’s roads, and drone delivery awaits both a workable business model and final approval by the FAA, Starship is the one company on the planet that, as of this writing, anyway, is doing autonomous delivery at scale.


pages: 285 words: 58,517

The Network Imperative: How to Survive and Grow in the Age of Digital Business Models by Barry Libert, Megan Beck

active measures, Airbnb, Amazon Web Services, asset allocation, asset light, autonomous vehicles, big data - Walmart - Pop Tarts, business intelligence, call centre, Clayton Christensen, cloud computing, commoditize, crowdsourcing, data science, disintermediation, diversification, Douglas Engelbart, Douglas Engelbart, future of work, Google Glasses, Google X / Alphabet X, independent contractor, Infrastructure as a Service, intangible asset, Internet of things, invention of writing, inventory management, iterative process, Jeff Bezos, job satisfaction, John Zimmer (Lyft cofounder), Kevin Kelly, Kickstarter, Larry Ellison, late fees, Lyft, Mark Zuckerberg, Mary Meeker, Oculus Rift, pirate software, ride hailing / ride sharing, Salesforce, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, six sigma, software as a service, software patent, Steve Jobs, subscription business, systems thinking, TaskRabbit, Travis Kalanick, uber lyft, Wall-E, women in the workforce, Zipcar

—Richard Branson, The Virgin Group EVEN A HUNDRED-YEAR-OLD ORGANIZATION CAN INNOVATE ITS BUSINESS MODEL. General Motors (GM) is coming enthusiastically, albeit a bit late, to the innovative ride-sharing market with a $500 million investment in Lyft as part of Lyft’s latest $1 billion venture financing round. Although a shift from car ownership to car sharing, and even further to autonomous vehicles, could be a risky disruption to their market, GM’s leaders have decided to embrace the changing business model landscape in transportation and innovate what they do and how they do it. Daniel Ammann, GM’s president, said, “We think there’s going to be more change in the world of mobility in the next five years than there has been in the last 50,” and GM is getting ready for that change.1 From that perspective, Lyft is an excellent partner who will help GM turn their views of the market upside down.

Daniel Ammann, GM’s president, said, “We think there’s going to be more change in the world of mobility in the next five years than there has been in the last 50,” and GM is getting ready for that change.1 From that perspective, Lyft is an excellent partner who will help GM turn their views of the market upside down. Lyft’s president John Zimmer stated, “We strongly believe that autonomous vehicle go-to-market strategy is through a network, not through individual car ownership.” According to executives at both GM and Lyft, they will start work on developing a network of self-driving vehicles—a challenge to Google, Tesla, and Uber, which are also devoting resources to this innovation.2 Openness Makes Space for Ongoing Change Will GM’s self-driving-car aspiration create value for the firm?

Founder of Angie’s List Angie Hicks connected homeowners to share reviews of local businesses and service providers, creating enormous value over traditional listings like the Yellow Pages. But don’t assume that only start-ups can embrace new mental models and core beliefs. General Motors is demonstrating that it too can shift its core beliefs about value with a $500 million investment in the ride-sharing start-up Lyft and a commitment to build digital networks and autonomous vehicles. Take It Outside Your Mind and into the Real World As you update your mental model, you need to take reinforcing actions to help realize the change. Here’s what we recommend. TAKE IN NEW INFORMATION, NEW DATA, AND NEW IDEAS. As a leader in business, you probably already keep up with the latest news and the ways others are thinking, but, as we’ve said, all of us naturally have a bias toward perspectives like our own.


pages: 444 words: 127,259

Super Pumped: The Battle for Uber by Mike Isaac

"Susan Fowler" uber, "World Economic Forum" Davos, activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, always be closing, Amazon Web Services, Andy Kessler, autonomous vehicles, Ayatollah Khomeini, barriers to entry, Bay Area Rapid Transit, Benchmark Capital, Big Tech, Burning Man, call centre, Cambridge Analytica, Chris Urmson, Chuck Templeton: OpenTable:, citizen journalism, Clayton Christensen, cloud computing, corporate governance, creative destruction, data science, Didi Chuxing, don't be evil, Donald Trump, driverless car, Elon Musk, end-to-end encryption, fake news, family office, gig economy, Google Glasses, Google X / Alphabet X, Greyball, Hacker News, high net worth, hockey-stick growth, hustle culture, impact investing, information security, Jeff Bezos, John Markoff, John Zimmer (Lyft cofounder), Kevin Roose, Kickstarter, Larry Ellison, lolcat, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Masayoshi Son, mass immigration, Menlo Park, Mitch Kapor, money market fund, moral hazard, move fast and break things, Network effects, new economy, off grid, peer-to-peer, pets.com, Richard Florida, ride hailing / ride sharing, Salesforce, Sand Hill Road, self-driving car, selling pickaxes during a gold rush, shareholder value, Shenzhen special economic zone , Sheryl Sandberg, side hustle, side project, Silicon Valley, Silicon Valley startup, skunkworks, Snapchat, SoftBank, software as a service, software is eating the world, South China Sea, South of Market, San Francisco, sovereign wealth fund, special economic zone, Steve Bannon, Steve Jobs, stock buybacks, super pumped, TaskRabbit, tech bro, tech worker, the payments system, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, ubercab, union organizing, upwardly mobile, Vision Fund, WeWork, Y Combinator

He spent his undergrad years in the East Bay, at the University of California, Berkeley, where as an industrial engineer he built one of his first robots—a Lego-constructed machine that could pick up and sort Monopoly money. Soon he convinced his classmates to enter the DARPA Robotics Challenge with him, a program put on by the Department of Defense in which competitors would build autonomous cars and race them across the Mojave Desert. They entered with high hopes, but the autonomous vehicle they built, a motorcycle nicknamed “Ghostrider,” ended up crashing within seconds of beginning the race.†††† The loss deflated him; Levandowski liked to win almost as much as he liked building his robots. He scored a post-collegiate job at Google working on the company’s Street View project.

Google was terrified to approve what Levandowski really wanted; true, open-road testing of autonomous vehicles. Aside from the ever-present concern about negative public opinion, the nonsensical design of San Francisco’s traffic-clogged grid presented an absurdly thorny engineering problem. The smallest error risked a dangerous accident. Naysayers imagined a video of a Google-branded SUV wrapped around the mangled chassis of another car—or worse, the mangled body of a pedestrian. But Levandowski knew Google needed real-world testing to get autonomous vehicles out of the conceptual phase. Levandowski imagined a future without automobile deaths or congestion, where carpooling was automatic and simple.

While Google was careful and methodical, employees saw Levandowski as corner-cutting and occasionally reckless. Without telling his bosses, Levandowski hired an outside lobbyist in Nevada to write a new law that allowed autonomous vehicles to operate in the state without a backup safety driver. Google executives were furious, yet the law passed statewide in 2011. Levandowski’s divisive methods earned him enemies. When he made a play to become leader of the Google X autonomous vehicle unit, a group of employees staged a mutiny, requiring Page himself to step in and name Chris Urmson, a rival of Levandowski’s, the head of the self-driving division. Levandowski was crushed and made no attempt to hide it; at one point, he stopped coming into work entirely.


pages: 411 words: 98,128

Bezonomics: How Amazon Is Changing Our Lives and What the World's Best Companies Are Learning From It by Brian Dumaine

activist fund / activist shareholder / activist investor, AI winter, Airbnb, Amazon Robotics, Amazon Web Services, Atul Gawande, autonomous vehicles, basic income, Bernie Sanders, Big Tech, Black Swan, call centre, Cambridge Analytica, carbon tax, Carl Icahn, Chris Urmson, cloud computing, corporate raider, creative destruction, Danny Hillis, data science, deep learning, Donald Trump, Elon Musk, Erik Brynjolfsson, Fairchild Semiconductor, fake news, fulfillment center, future of work, gig economy, Glass-Steagall Act, Google Glasses, Google X / Alphabet X, income inequality, independent contractor, industrial robot, Internet of things, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Kevin Roose, Lyft, Marc Andreessen, Mark Zuckerberg, military-industrial complex, money market fund, natural language processing, no-fly zone, Ocado, pets.com, plutocrats, race to the bottom, ride hailing / ride sharing, Salesforce, Sand Hill Road, self-driving car, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Snapchat, speech recognition, Steve Jobs, Stewart Brand, supply-chain management, TED Talk, Tim Cook: Apple, too big to fail, Travis Kalanick, two-pizza team, Uber and Lyft, uber lyft, universal basic income, warehouse automation, warehouse robotics, wealth creators, web application, Whole Earth Catalog, work culture

Bezos will in all likelihood use the savings to drop prices for his customers, which will in turn attract more sellers, which will lower costs and attract more customers. And his AI flywheel will spin faster and faster. With visions of those savings dangling before him, Bezos has jumped headlong into the autonomous vehicle race. Amazon’s vast computing power and machine-learning expertise make it a potentially formidable player in the field. In 2016, the company earned a patent for a system that helps autonomous cars figure out which direction traffic is traveling in any particular lane to help a vehicle safely enter the proper lane. In its partnership with Toyota, Amazon is developing a self-driving concept vehicle called the e-Palette, a minivan that can move people or packages, and the two companies plan to unveil it at the 2020 summer Olympic games in Tokyo.

Anderson ran Tesla’s autopilot program, Bagnell headed the autonomy and perception team at Uber, and Urmson was the former head of Google’s self-driving project, which has morphed into one of the leading self-driving car companies: Waymo. Aurora will not build cars but is developing the AI brains behind autonomous vehicles and plans to partner with retailers like Amazon and major automakers to create state-of-the-art autonomous vehicles. Amazon is far from alone in the race for self-driving vehicles. According to the research firm CB Insights, at least forty-six companies around the world are working on self-driving vehicle technology. The ranks include major automakers such as GM, Ford, BMW, and Audi; tech companies such as Alphabet, Baidu, Microsoft, and Cisco; Internet car services such as Uber and Didi in China; retailers such as Walmart, Kroger, and Alibaba; and a slew of start-ups like Aurora and Udelv.

The ranks include major automakers such as GM, Ford, BMW, and Audi; tech companies such as Alphabet, Baidu, Microsoft, and Cisco; Internet car services such as Uber and Didi in China; retailers such as Walmart, Kroger, and Alibaba; and a slew of start-ups like Aurora and Udelv. One thing that’s almost certain is that when autonomous vehicles do first appear in significant numbers, they’ll be delivery vans. That’s because carrying packages rather than humans greatly reduces the risk posed by self-driving vehicles. If an order of Dr. Bronner’s castile soap gets crushed in a fender bender, that’s unfortunate but not a tragedy. In an accident, the vans will be programmed to self-sacrifice themselves to avoid harm to pedestrians, bicyclists, or drivers of other vehicles.


pages: 380 words: 109,724

Don't Be Evil: How Big Tech Betrayed Its Founding Principles--And All of US by Rana Foroohar

"Susan Fowler" uber, "World Economic Forum" Davos, accounting loophole / creative accounting, Airbnb, Alan Greenspan, algorithmic bias, algorithmic management, AltaVista, Andy Rubin, autonomous vehicles, banking crisis, barriers to entry, behavioural economics, Bernie Madoff, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, book scanning, Brewster Kahle, Burning Man, call centre, Cambridge Analytica, cashless society, clean tech, cloud computing, cognitive dissonance, Colonization of Mars, computer age, corporate governance, creative destruction, Credit Default Swap, cryptocurrency, data is the new oil, data science, deal flow, death of newspapers, decentralized internet, Deng Xiaoping, digital divide, digital rights, disinformation, disintermediation, don't be evil, Donald Trump, drone strike, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Etonian, Evgeny Morozov, fake news, Filter Bubble, financial engineering, future of work, Future Shock, game design, gig economy, global supply chain, Gordon Gekko, Great Leap Forward, greed is good, income inequality, independent contractor, informal economy, information asymmetry, intangible asset, Internet Archive, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, junk bonds, Kenneth Rogoff, life extension, light touch regulation, low interest rates, Lyft, Mark Zuckerberg, Marshall McLuhan, Martin Wolf, Menlo Park, military-industrial complex, move fast and break things, Network effects, new economy, offshore financial centre, PageRank, patent troll, Paul Volcker talking about ATMs, paypal mafia, Peter Thiel, pets.com, price discrimination, profit maximization, race to the bottom, recommendation engine, ride hailing / ride sharing, Robert Bork, Sand Hill Road, search engine result page, self-driving car, shareholder value, sharing economy, Sheryl Sandberg, Shoshana Zuboff, side hustle, Sidewalk Labs, Silicon Valley, Silicon Valley startup, smart cities, Snapchat, SoftBank, South China Sea, sovereign wealth fund, Steve Bannon, Steve Jobs, Steven Levy, stock buybacks, subscription business, supply-chain management, surveillance capitalism, TaskRabbit, tech billionaire, tech worker, TED Talk, Telecommunications Act of 1996, The Chicago School, the long tail, the new new thing, Tim Cook: Apple, too big to fail, Travis Kalanick, trickle-down economics, Uber and Lyft, Uber for X, uber lyft, Upton Sinclair, warehouse robotics, WeWork, WikiLeaks, zero-sum game

But as autonomous driving and digital apps become a bigger deal, that ratio is expected to shift dramatically. Morgan Stanley predicts that in autonomous vehicles, 40 percent of the value of an automobile will come from hardware, 40 percent from software, and 20 percent from the content that streams into the vehicle.35 That would include things like games, advertisements, and news enabled by the software. This shift is partly driven—no pun intended—by the fact that millennials want their cars souped-up with all their favorite apps. But it also reflects another idea: When you are in an autonomous vehicle, brand identity disappears. “If you take away control of the steering wheel, consumers are much less likely to care what type of car they are in,” says Nick Johnson, principal at the consultancy Applico, who has advised major automakers on the shift.

The ubiquitous ride-hailing business he founded had been drawing criticism from municipal lawmakers and union activists—particularly in large cities like New York and San Francisco—for years, but their PR crisis reached a boiling point following a series of scandals that started with a blog post from a former engineer, Susan Fowler, alleging harassment and rampant sexism at the company. That news went viral in the same month that Waymo, an autonomous vehicle unit owned by Google’s parent company, Alphabet, filed a federal lawsuit against the ridesharing company alleging that a software engineer had stolen its trade secrets and taken them to Uber, which is developing its own autonomous vehicles. This was followed only five days later by a shocking video showing the CEO himself blowing up at an Uber driver who deigned to complain about the company’s payment system.1 Uber’s own dashcam recorded the interaction, in which the driver claimed to have gone bankrupt after investing $97,000 in a high-end car in order to drive for uberBLACK, only to find that rates began falling and the service was being dropped in favor of cheaper cars.

It’s a question that faces any number of industries, from retail (which has already been decimated by Amazon) to healthcare (under competitive threat from both Amazon and Google), finance (which is under threat from fintech, the merging of tech platform technology and banking), manufacturing, and so on. The businesses that have the best technology in automotive software and apps are, unsurprisingly, technology companies such as Google, Apple, and China’s Baidu, all of which are pouring money into autonomous vehicle technology and platforms to support it. Right now, motorists can mainly stream music, GPS information, and whatever other data they can access on their phone via such systems. But once the platforms are embedded more deeply in vehicles, customers will be able to tap into everything from fluid levels and engine temperatures to safety information.


pages: 300 words: 76,638

The War on Normal People: The Truth About America's Disappearing Jobs and Why Universal Basic Income Is Our Future by Andrew Yang

3D printing, Airbnb, assortative mating, augmented reality, autonomous vehicles, basic income, Bear Stearns, behavioural economics, Ben Horowitz, Bernie Sanders, call centre, corporate governance, cryptocurrency, data science, David Brooks, DeepMind, Donald Trump, Elon Musk, falling living standards, financial deregulation, financial engineering, full employment, future of work, global reserve currency, income inequality, Internet of things, invisible hand, Jeff Bezos, job automation, John Maynard Keynes: technological unemployment, Khan Academy, labor-force participation, longitudinal study, low skilled workers, Lyft, manufacturing employment, Mark Zuckerberg, megacity, meritocracy, Narrative Science, new economy, passive income, performance metric, post-work, quantitative easing, reserve currency, Richard Florida, ride hailing / ride sharing, risk tolerance, robo advisor, Ronald Reagan, Rutger Bregman, Sam Altman, San Francisco homelessness, self-driving car, shareholder value, Silicon Valley, Simon Kuznets, single-payer health, Stephen Hawking, Steve Ballmer, supercomputer in your pocket, tech worker, technoutopianism, telemarketer, The future is already here, The Wealth of Nations by Adam Smith, traumatic brain injury, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, unemployed young men, universal basic income, urban renewal, warehouse robotics, white flight, winner-take-all economy, Y Combinator

“This will 100 percent happen.” It is obvious that Tesla trucks will eventually have the same self-driving capabilities as their cars. Other autonomous vehicle companies report similar timelines, with 2020 being the first year of mass adoption. And it’s not just those driving trucks who are at risk. A senior official at one of the major ride-sharing companies told me that their internal projections are that half of their rides will be given by autonomous vehicles by 2022. This has the potential to affect about 300,000 Uber and Lyft drivers in the United States. The replacement of drivers will be one of the most dramatic, visible battlegrounds between automation and the human worker.

Companies can eliminate the jobs of call center workers, retail clerks, fast food workers, and the like with minimal violence and fuss. Truck drivers will be different. Right now, the federal government has said that it will allow autonomous vehicles in any states that permit them. One industry report noted that “the [U.S.] Department of Transportation is throwing its full support behind development of autonomous vehicles as a way to improve safety on our roadways.” In 2016 the trucking industry spent $9.1 million on lobbying, and the Ohio government has already committed $15 million to set up a 35-mile stretch of highway outside Columbus for testing self-driving trucks.

… about half of the 310,000 residents who left the workforce in Michigan between 2003 and 2013 went on disability: Chad Halcon, “Disability Rolls Surge in State: One in 10 Workers in Michigan Collecting Checks,” Crain’s Detroit Business, June 26, 2015. The average age of truck drivers is 49…: Sean Kilcarr, “Demographics Are Changing Truck Driver Management,” FleetOwner, September 20, 2017. Morgan Stanley estimated the savings of automated freight delivery…: Autonomous Cars: Self-Driving the New Auto Industry Paradigm, Morgan Stanley Blue Paper, November 6, 2013. Crashes involving large trucks killed 3,903 people…: Olivia Solon, “Self-Driving Trucks: What’s the Future for America’s 3.5 Million Truckers?” The Guardian, June 17, 2016. … 88 percent of drivers have at least one risk factor for chronic disease: W.


Work in the Future The Automation Revolution-Palgrave MacMillan (2019) by Robert Skidelsky Nan Craig

3D printing, Airbnb, algorithmic trading, AlphaGo, Alvin Toffler, Amazon Web Services, anti-work, antiwork, artificial general intelligence, asset light, autonomous vehicles, basic income, behavioural economics, business cycle, cloud computing, collective bargaining, Computing Machinery and Intelligence, correlation does not imply causation, creative destruction, data is the new oil, data science, David Graeber, David Ricardo: comparative advantage, deep learning, DeepMind, deindustrialization, Demis Hassabis, deskilling, disintermediation, do what you love, Donald Trump, driverless car, Erik Brynjolfsson, fake news, feminist movement, Ford Model T, Frederick Winslow Taylor, future of work, Future Shock, general purpose technology, gig economy, global supply chain, income inequality, independent contractor, informal economy, Internet of things, Jarndyce and Jarndyce, Jarndyce and Jarndyce, job automation, job polarisation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, knowledge economy, Loebner Prize, low skilled workers, Lyft, Mark Zuckerberg, means of production, moral panic, Network effects, new economy, Nick Bostrom, off grid, pattern recognition, post-work, Ronald Coase, scientific management, Second Machine Age, self-driving car, sharing economy, SoftBank, Steve Jobs, strong AI, tacit knowledge, technological determinism, technoutopianism, TED Talk, The Chicago School, The Future of Employment, the market place, The Nature of the Firm, The Wealth of Nations by Adam Smith, Thorstein Veblen, Turing test, Uber for X, uber lyft, universal basic income, wealth creators, working poor

., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T., & Hassabis, D. (2018). Mastering the Game of Go Without Human Knowledge. Nature, 550, 354–359. Tschangho, J. K. (2018). Automated Autonomous Vehicles: Prospects and Impacts on Society. Journal of Transportation Technologies, 8, 137–150. Walsh, T. (2018). Machines That Think: The Future of Artificial Intelligence. Prometheus Books. Part V Work in the Digital Economy 13 Work in the Digital Economy Daniel Susskind In this talk, I want to do two things.

Colton To summarize so far, I believe that any technical limitations on the abilities of AI systems to become intelligent enough for employment in workplaces instead of people are not in terms of fundamental, theoretical issues, but rather in terms of the speed to make scientific discoveries about computational intelligence, and in terms of engineering systems to take advantage of these breakthroughs. While it would not surprise me if we saw autonomous cars routinely on our streets in 10 years’ time, it would also not surprise me if it took another 50 years for this to happen (Tschangho 2018). The other limitations on the usage of AI in the workplace, will I hope, be self-imposed, as society in general responds to automation. Slowly but surely, AI systems will gain abilities to take on the duties of people undertaking intelligent tasks.

However, quite the opposite seems to be happening. Some taxi companies for example seem to be using human labour purely as a stop-­ gap to raise venture capital for research and development, so that in the 12 Possibilities and Limitations for AI: What Can’t Machines Do? 115 longer-term they can roll-out a fleet of autonomous cars, eventually putting their entire human workforce out of a job (Price 2019). I hold quite a utopian view that automation can free humanity from the drudgery of meaningless toil—which, taking a sincere look at the world of work—is what many people in paid employment are asked, or indeed forced via circumstance, to do.


pages: 271 words: 77,448

Humans Are Underrated: What High Achievers Know That Brilliant Machines Never Will by Geoff Colvin

Ada Lovelace, autonomous vehicles, Baxter: Rethink Robotics, behavioural economics, Black Swan, call centre, capital asset pricing model, commoditize, computer age, corporate governance, creative destruction, deskilling, driverless car, en.wikipedia.org, flying shuttle, Freestyle chess, future of work, Google Glasses, Grace Hopper, Hans Moravec, industrial cluster, industrial robot, interchangeable parts, job automation, knowledge worker, low skilled workers, Marc Andreessen, meta-analysis, Narrative Science, new economy, rising living standards, self-driving car, sentiment analysis, Silicon Valley, Skype, social intelligence, Steve Jobs, Steve Wozniak, Steven Levy, Steven Pinker, theory of mind, Tim Cook: Apple, transaction costs, Tyler Cowen

If the driver is spacing out, it could jar him back to attention by making the steering wheel vibrate. Most intriguingly (or hilariously), the researchers even used special thermochromatic paint to make a car change its external color based on the driver’s emotional state, as a signal to other drivers. Whether this technology has time to be commercialized before the arrival of autonomous vehicles makes it irrelevant is a separate question. The power of computers to sense human emotions means, inevitably, that a machine can outdo us even in detecting our own emotions. It’s surely tempting to suppose that I possess a sense of my own emotional state that no entity standing outside of me, human or electronic, could ever reach.

Watson could do it at light speed with an electronic signal, so the developers interposed a delay to level the playing field. Otherwise I’d never have a prayer of winning, even if we both knew the correct response. But, of course, even with the delay, I lost. So let’s confront reality: Watson is smarter than I am. In fact, I’m surrounded by technology that’s better than I am at sophisticated tasks. Google’s autonomous car is a better driver than I am. The company has a whole fleet of vehicles that have driven hundreds of thousands of miles with only one accident while in autonomous mode, when one of the cars was rear-ended by a human driver at a stoplight. Computers are better than humans at screening documents for relevance in the discovery phase of litigation, an activity for which young lawyers used to bill at an impressive hourly rate.

An example illustrates the gap in abilities: In 1997 a computer could beat the world’s greatest chess player yet could not physically move the pieces on the board. But again the technology needed only time, a few more doublings of power. The skills of physical work are also not immune to the advance of infotech. Google’s autonomous cars are an obvious and significant example—significant because the number one job among American men is truck driver. Many more examples are appearing. You can train a Baxter robot (from Rethink Robotics) to do all kinds of things—pack or unpack boxes, take items to or from a conveyor belt, fold a T-shirt, carry things around, count them, inspect them—just by moving its arms and hands (“end-effectors”) in the desired way.


pages: 244 words: 66,977

Subscribed: Why the Subscription Model Will Be Your Company's Future - and What to Do About It by Tien Tzuo, Gabe Weisert

3D printing, Airbnb, airport security, Amazon Web Services, augmented reality, autonomous vehicles, Big Tech, bike sharing, blockchain, Brexit referendum, Build a better mousetrap, business cycle, business intelligence, business process, call centre, cloud computing, cognitive dissonance, connected car, data science, death of newspapers, digital nomad, digital rights, digital twin, double entry bookkeeping, Elon Musk, factory automation, fake news, fiat currency, Ford Model T, fulfillment center, growth hacking, hockey-stick growth, Internet of things, inventory management, iterative process, Jeff Bezos, John Zimmer (Lyft cofounder), Kevin Kelly, Lean Startup, Lyft, manufacturing employment, Marc Benioff, Mary Meeker, megaproject, minimum viable product, natural language processing, Network effects, Nicholas Carr, nuclear winter, pets.com, planned obsolescence, pneumatic tube, profit maximization, race to the bottom, ride hailing / ride sharing, Salesforce, Sand Hill Road, shareholder value, Silicon Valley, skunkworks, smart meter, social graph, software as a service, spice trade, Steve Ballmer, Steve Jobs, subscription business, systems thinking, tech worker, TED Talk, Tim Cook: Apple, transport as a service, Uber and Lyft, uber lyft, WeWork, Y2K, Zipcar

Since GM and Chrysler emerged from bankruptcy in 2009, the Big Three have invested more than $30 billion in new jobs and facilities. The American automobile industry spends $18 billion a year on research and development, focusing on fuel-efficient, electric, and autonomous vehicles. GM CEO Mary Barra says that her company is “quarters, not years” away from deploying fully autonomous vehicles at scale. These car companies have spent decades crafting their vehicles and their brands, and as a result enjoy some forbidding advantages, but there is a red flag—if they don’t know their drivers by the time autonomy and access-based consumption roll around, they will lose out to a competitor.

All due respect to other potential ecommerce vendors, but Amazon has my business, in no small part due to Amazon Prime—they hooked me with the free shipping, and now I’ve got music, movies, and all sorts of other services. I’m not going anywhere. Uber and Lyft are both vying for that same lock-in effect by offering discounted services around consistent consumption patterns—in other words, they’re going after my commute. As Lyft president John Zimmer, anticipating fully autonomous vehicles, told The New York Times: “The cost of owning a car is $9,000 a year. Let’s say we offer a $500 monthly plan in which you can tap a button and get access to transportation whenever you want it, and you get to choose your room-on-wheels experience. Maybe you want a cup of coffee on your way to work, or you want to watch the Warriors game later, so you’re in what’s basically a sports bar, with a bartender.”

You Will Subscribe to It,”Slate, December 2, 2017, www.slate.com/articles/technology/technology/2017/12/car_subscriptions_ford_volvo_porsche_and_cadillac_offer_lease_alternative.html. a sports bar, with a bartender “The Rev-Up: Imagining a 20% Self Driving World,” The New York Times, November 8, 2017, www.nytimes.com/interactive/2017/11/08/magazine/tech-design-future-autonomous-car-20-percent-sex-death-liability.html?_r=0. 250 million connected cars on the road by 2020 “Gartner Says by 2020, a Quarter Billion Connected Vehicles Will Enable New In-Vehicle Services and Automated Driving Capabilities,” January 26, 2015, www.gartner.com/newsroom/id/2970017. Without control over the platform, PC hardware Horace Dediu, “IBM and Apple: Catharsis,” July 15, 2014, www.asymco.com/2014/07/15/catharsis.


pages: 260 words: 67,823

Always Day One: How the Tech Titans Plan to Stay on Top Forever by Alex Kantrowitz

accounting loophole / creative accounting, Albert Einstein, AltaVista, Amazon Robotics, Amazon Web Services, Andy Rubin, anti-bias training, augmented reality, Automated Insights, autonomous vehicles, Bernie Sanders, Big Tech, Cambridge Analytica, Clayton Christensen, cloud computing, collective bargaining, computer vision, Donald Trump, drone strike, Elon Musk, fake news, Firefox, fulfillment center, gigafactory, Google Chrome, growth hacking, hive mind, income inequality, Infrastructure as a Service, inventory management, iterative process, Jeff Bezos, job automation, Jony Ive, Kiva Systems, knowledge economy, Lyft, Mark Zuckerberg, Menlo Park, new economy, Nick Bostrom, off-the-grid, Peter Thiel, QR code, ride hailing / ride sharing, robotic process automation, Salesforce, self-driving car, Sheryl Sandberg, Silicon Valley, Skype, Snapchat, SoftBank, Steve Ballmer, Steve Jobs, Steve Wozniak, super pumped, tech worker, Tim Cook: Apple, uber lyft, warehouse robotics, wealth creators, work culture , zero-sum game

“Apple Delaying HomePod Smart Speaker Launch until next Year.” 9to5Mac, November 17, 2017. https://9to5mac.com/2017/11/17/homepad-delay/. Apple moved two hundred employees off its struggling Project Titan: Kolodny, Lora, Christina Farr, and Paul A. Eisenstein. “Apple Just Dismissed More than 200 Employees from Project Titan, Its Autonomous Vehicle Group.” CNBC. CNBC, January 24, 2019. https://www.cnbc.com/2019/01/24/apple-lays-off-over-200-from-project-titan-autonomous-vehicle-group.html. “How is the work culture”: “How Is the Work Culture at the IS&T Division of Apple?” Quora. https://www.quora.com/How-is-the-work-culture-at-the-IS-T-division-of-Apple. fifteen-dollar-per-hour wage floor: Salinas, Sara.

When you look into the Black Mirror, happy endings are hard to come by. From Doomsday to Disneyland? Linda, an accountant at a midsize financial services firm, gets made fun of by her husband and two kids as she makes them breakfast and sees them off for the day. A tear runs down her cheek as she takes an autonomous vehicle to work. When Linda arrives at the office, she’s approached by a consultant who tells her she’s going to wear a recording device at all times. The company has already automated her entire department, so she understands what’s coming. One month later, after the device has fully recorded her work, Linda’s self-driving car plunges into a lake.

And it’s made daily life better for iPhone owners with features like Face ID and Apple Pay (both delightful). Few companies get more out of their existing assets than Apple. Inventing beyond these devices, however, is another story. Apple’s bets to create ambitious new products—like the HomePod and its own autonomous car—are failing. And Apple’s refinement culture, a relic of the Jobs era, is to blame. The Refiner’s Mindset In place of Jobs, six Apple executives drive the company today, delivering ideas that the rest of the company executes. They are: Tim Cook, the unassuming CEO with an operations background.


pages: 424 words: 114,905

Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again by Eric Topol

"World Economic Forum" Davos, 23andMe, Affordable Care Act / Obamacare, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic bias, AlphaGo, Apollo 11, artificial general intelligence, augmented reality, autism spectrum disorder, autonomous vehicles, backpropagation, Big Tech, bioinformatics, blockchain, Cambridge Analytica, cloud computing, cognitive bias, Colonization of Mars, computer age, computer vision, Computing Machinery and Intelligence, conceptual framework, creative destruction, CRISPR, crowdsourcing, Daniel Kahneman / Amos Tversky, dark matter, data science, David Brooks, deep learning, DeepMind, Demis Hassabis, digital twin, driverless car, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, fake news, fault tolerance, gamification, general purpose technology, Geoffrey Hinton, George Santayana, Google Glasses, ImageNet competition, Jeff Bezos, job automation, job satisfaction, Joi Ito, machine translation, Mark Zuckerberg, medical residency, meta-analysis, microbiome, move 37, natural language processing, new economy, Nicholas Carr, Nick Bostrom, nudge unit, OpenAI, opioid epidemic / opioid crisis, pattern recognition, performance metric, personalized medicine, phenotype, placebo effect, post-truth, randomized controlled trial, recommendation engine, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, Skinner box, speech recognition, Stephen Hawking, techlash, TED Talk, text mining, the scientific method, Tim Cook: Apple, traumatic brain injury, trolley problem, War on Poverty, Watson beat the top human players on Jeopardy!, working-age population

., “How to Regulate Artificial Intelligence,” New York Times. 2017; Simonite, T., “Do We Need a Speedometer for Artificial Intelligence?” Wired. 2017. 59. Bonnefon, J. F., A. Shariff, and I. Rahwan, “The Social Dilemma of Autonomous Vehicles.” Science, 2016. 352(6293): pp. 1573–1576. 60. Bonnefon, Shariff, and Rahwan, “The Social Dilemma of Autonomous Vehicles.” 61. Bonnefon, Shariff, and Rahwan, “The Social Dilemma of Autonomous Vehicles.” 62. Road traffic injuries, ed. World Health Organization. 2018. 63. Howard, B., “Fatal Arizona Crash: Uber Car Saw Woman, Called It a False Positive,” Extreme Tech. 2018. 64. AI for Healthcare: Balancing Efficiency and Ethics, ed.

There is no correct answer, with the conflicts of moral values, cultural norms, and personal self-interest, but the majority of respondents did not go for the “greater good” choice of sacrificing themselves. Clearly, trying to deal with these issues in the design of an algorithm to control an autonomous vehicle will be formidable60 and has been labeled as one of “the thorniest challenges in artificial intelligence today.”61 Another layer of this dilemma is who should be involved in algorithmic design—consumers, manufacturers, government? As you might anticipate, companies are not enthusiastic about government regulation; many firms, including Microsoft and Google, have set up their own internal ethics boards, arguing that regulatory involvement might be counterproductive, delaying the adoption of self-driving cars over fringe issues when it already seems clear that autonomous vehicles will reduce traffic fatalities overall.

As you might anticipate, companies are not enthusiastic about government regulation; many firms, including Microsoft and Google, have set up their own internal ethics boards, arguing that regulatory involvement might be counterproductive, delaying the adoption of self-driving cars over fringe issues when it already seems clear that autonomous vehicles will reduce traffic fatalities overall. But we don’t think of it in the big picture way. More than 1.25 million people are killed by human drivers each year, most by human error, but we as a society don’t bat an eye at the situation.62 The introduction of computers into the mix sets up a cognitive bias, not acknowledging the net benefit.


pages: 252 words: 74,167

Thinking Machines: The Inside Story of Artificial Intelligence and Our Race to Build the Future by Luke Dormehl

"World Economic Forum" Davos, Ada Lovelace, agricultural Revolution, AI winter, Albert Einstein, Alexey Pajitnov wrote Tetris, algorithmic management, algorithmic trading, AlphaGo, Amazon Mechanical Turk, Apple II, artificial general intelligence, Automated Insights, autonomous vehicles, backpropagation, Bletchley Park, book scanning, borderless world, call centre, cellular automata, Charles Babbage, Claude Shannon: information theory, cloud computing, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, deep learning, DeepMind, driverless car, drone strike, Elon Musk, Flash crash, Ford Model T, friendly AI, game design, Geoffrey Hinton, global village, Google X / Alphabet X, Hans Moravec, hive mind, industrial robot, information retrieval, Internet of things, iterative process, Jaron Lanier, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, life extension, Loebner Prize, machine translation, Marc Andreessen, Mark Zuckerberg, Menlo Park, Mustafa Suleyman, natural language processing, Nick Bostrom, Norbert Wiener, out of africa, PageRank, paperclip maximiser, pattern recognition, radical life extension, Ray Kurzweil, recommendation engine, remote working, RFID, scientific management, self-driving car, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, social intelligence, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, tech billionaire, technological singularity, The Coming Technological Singularity, The Future of Employment, Tim Cook: Apple, Tony Fadell, too big to fail, traumatic brain injury, Turing machine, Turing test, Vernor Vinge, warehouse robotics, Watson beat the top human players on Jeopardy!

Not coincidentally, the finance sector quickly joined the video game business as an industry ready to throw money at AI researchers. The age of algorithmic trading had begun. Another eye-catching application of neural nets during this time was the invention of the self-driving car. Autonomous vehicles had been a long-time dream of technologists. In 1925, the inventor Francis Houdina demonstrated a radio-controlled car, which he drove through the streets of Manhattan without anyone at the steering wheel. Later, autonomous vehicle tests used guidewires and on-board sensors to follow painted white lines on the road or seek out the alternating current of buried cables. In 1969, John McCarthy came closest to describing modern self-driving vehicles when he wrote an essay with the provocative title, ‘Computer-Controlled Cars’.

On the street, by far the biggest visible change likely to happen in the next several decades will be the mass arrival of self-driving cars. Following on from the work of Dean Pomerleau, as described in the last chapter, both Google and Apple have invested in this field and look set to play a key role in bringing autonomous vehicles to the mainstream. Self-driving cars won’t only affect us on an individual level, but also collectively by helping to reduce traffic congestion in cities. The data that they gather will be vital to town planners as cities continue to expand. We are already starting to see how this may work.

Weighing up the pros and cons of stopping its mission to administer aid, potentially administering pain relief by applying traction in the field, and other conundrums are all complex issues for a human to navigate – let alone a machine. Issues like this will become ever more prevalent. Consider what would happen if a company that builds autonomous cars decides, in order to protect its driver, that it will make its vehicles swerve out of the way if they detect an imminent collision. This makes perfect sense, and is exactly what most of us would do if we were driving. However, what if your car is stopped at a red traffic light when it detects another vehicle coming up fast behind you?


Human Frontiers: The Future of Big Ideas in an Age of Small Thinking by Michael Bhaskar

"Margaret Hamilton" Apollo, 3D printing, additive manufacturing, AI winter, Albert Einstein, algorithmic trading, AlphaGo, Anthropocene, artificial general intelligence, augmented reality, autonomous vehicles, backpropagation, barriers to entry, basic income, behavioural economics, Benoit Mandelbrot, Berlin Wall, Big bang: deregulation of the City of London, Big Tech, Bletchley Park, blockchain, Boeing 747, brain emulation, Brexit referendum, call centre, carbon tax, charter city, citizen journalism, Claude Shannon: information theory, Clayton Christensen, clean tech, clean water, cognitive load, Columbian Exchange, coronavirus, cosmic microwave background, COVID-19, creative destruction, CRISPR, crony capitalism, cyber-physical system, dark matter, David Graeber, deep learning, DeepMind, deindustrialization, dematerialisation, Demis Hassabis, demographic dividend, Deng Xiaoping, deplatforming, discovery of penicillin, disruptive innovation, Donald Trump, double entry bookkeeping, Easter island, Edward Jenner, Edward Lorenz: Chaos theory, Elon Musk, en.wikipedia.org, endogenous growth, energy security, energy transition, epigenetics, Eratosthenes, Ernest Rutherford, Eroom's law, fail fast, false flag, Fellow of the Royal Society, flying shuttle, Ford Model T, Francis Fukuyama: the end of history, general purpose technology, germ theory of disease, glass ceiling, global pandemic, Goodhart's law, Google Glasses, Google X / Alphabet X, GPT-3, Haber-Bosch Process, hedonic treadmill, Herman Kahn, Higgs boson, hive mind, hype cycle, Hyperloop, Ignaz Semmelweis: hand washing, Innovator's Dilemma, intangible asset, interchangeable parts, Internet of things, invention of agriculture, invention of the printing press, invention of the steam engine, invention of the telegraph, invisible hand, Isaac Newton, ITER tokamak, James Watt: steam engine, James Webb Space Telescope, Jeff Bezos, jimmy wales, job automation, Johannes Kepler, John von Neumann, Joseph Schumpeter, Kenneth Arrow, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, Large Hadron Collider, liberation theology, lockdown, lone genius, loss aversion, Louis Pasteur, Mark Zuckerberg, Martin Wolf, megacity, megastructure, Menlo Park, Minecraft, minimum viable product, mittelstand, Modern Monetary Theory, Mont Pelerin Society, Murray Gell-Mann, Mustafa Suleyman, natural language processing, Neal Stephenson, nuclear winter, nudge unit, oil shale / tar sands, open economy, OpenAI, opioid epidemic / opioid crisis, PageRank, patent troll, Peter Thiel, plutocrats, post scarcity, post-truth, precautionary principle, public intellectual, publish or perish, purchasing power parity, quantum entanglement, Ray Kurzweil, remote working, rent-seeking, Republic of Letters, Richard Feynman, Robert Gordon, Robert Solow, secular stagnation, shareholder value, Silicon Valley, Silicon Valley ideology, Simon Kuznets, skunkworks, Slavoj Žižek, sovereign wealth fund, spinning jenny, statistical model, stem cell, Steve Jobs, Stuart Kauffman, synthetic biology, techlash, TED Talk, The Rise and Fall of American Growth, the scientific method, The Wealth of Nations by Adam Smith, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, TikTok, total factor productivity, transcontinental railway, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, uranium enrichment, We wanted flying cars, instead we got 140 characters, When a measure becomes a target, X Prize, Y Combinator

After decades of incrementalism, we are starting to see the outlines of something new. Electric cars are becoming ubiquitous; the reign of the internal combustion engine is ending. Likewise an even more fundamental shift in the nature of transport is undergoing a colossal research effort: autonomy. Arguably, autonomous vehicles are already the most promising and advanced breakthrough, in that a fleet of them, working as one great hive mind, would herald a revolution in the transport system. It may seem that supersonic travel is for the history books. Yet aeroplanes are subject to possible reinvention, with companies exploring speeds of up to Mach 5 – London to New York in ninety minutes.

To reach this point has already taken volumes of R&D capital massively in excess of those deployed for the creation of previous technologies. As we saw, it did not take teams of PhDs to build the Flyer. We have never invested so much in cutting-edge modes of transport since the high days of the space race. Private and public capital is pouring into areas like space, drones and autonomous vehicles; the big incumbents like Boeing and Volkswagen are finally waking up to the challenge and putting serious efforts into radical change. The potential is clear; but it's not unreasonable to wonder why it's not translating. Medicine and transport affect everyone. They are global priorities connecting scientific and engineering frontiers with politics and policy.

Impact reduces. If you spent life struggling with a horse and cart over mud-bathed dirt tracks or walking miles to get water, the transition to paved roads, a car and running water is almost inconceivably large. For us, moving from comfortable petrol-powered cars with cruise control to electric and autonomous vehicles, the shift is, while significant, smaller. We shouldn't be particularly surprised by this as nothing experiential can keep accelerating or escalating forever. In the words of the Oxford geographer Danny Dorling: ‘Sex, drugs, rock’n’roll, schools, jobs, homes, health, beliefs, views, experiences and travel – they cannot always be more different for the next generation than the last.’ 50 Science fiction writers have conjured worlds and technologies at the edge of what could ever be possible, and we've grown up comfortable in their vast imaginations.


pages: 626 words: 167,836

The Technology Trap: Capital, Labor, and Power in the Age of Automation by Carl Benedikt Frey

3D printing, AlphaGo, Alvin Toffler, autonomous vehicles, basic income, Bernie Sanders, Branko Milanovic, British Empire, business cycle, business process, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, Charles Babbage, Clayton Christensen, collective bargaining, computer age, computer vision, Corn Laws, Cornelius Vanderbilt, creative destruction, data science, David Graeber, David Ricardo: comparative advantage, deep learning, DeepMind, deindustrialization, demographic transition, desegregation, deskilling, Donald Trump, driverless car, easy for humans, difficult for computers, Edward Glaeser, Elon Musk, Erik Brynjolfsson, everywhere but in the productivity statistics, factory automation, Fairchild Semiconductor, falling living standards, first square of the chessboard / second half of the chessboard, Ford Model T, Ford paid five dollars a day, Frank Levy and Richard Murnane: The New Division of Labor, full employment, future of work, game design, general purpose technology, Gini coefficient, Great Leap Forward, Hans Moravec, high-speed rail, Hyperloop, income inequality, income per capita, independent contractor, industrial cluster, industrial robot, intangible asset, interchangeable parts, Internet of things, invention of agriculture, invention of movable type, invention of the steam engine, invention of the wheel, Isaac Newton, James Hargreaves, James Watt: steam engine, Jeremy Corbyn, job automation, job satisfaction, job-hopping, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kickstarter, Kiva Systems, knowledge economy, knowledge worker, labor-force participation, labour mobility, Lewis Mumford, Loebner Prize, low skilled workers, machine translation, Malcom McLean invented shipping containers, manufacturing employment, mass immigration, means of production, Menlo Park, minimum wage unemployment, natural language processing, new economy, New Urbanism, Nick Bostrom, Norbert Wiener, nowcasting, oil shock, On the Economy of Machinery and Manufactures, OpenAI, opioid epidemic / opioid crisis, Pareto efficiency, pattern recognition, pink-collar, Productivity paradox, profit maximization, Renaissance Technologies, rent-seeking, rising living standards, Robert Gordon, Robert Solow, robot derives from the Czech word robota Czech, meaning slave, safety bicycle, Second Machine Age, secular stagnation, self-driving car, seminal paper, Silicon Valley, Simon Kuznets, social intelligence, sparse data, speech recognition, spinning jenny, Stephen Hawking, tacit knowledge, The Future of Employment, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Malthus, total factor productivity, trade route, Triangle Shirtwaist Factory, Turing test, union organizing, universal basic income, warehouse automation, washing machines reduced drudgery, wealth creators, women in the workforce, working poor, zero-sum game

A survey prepared for the National Highway Transportation Safety Administration of car crashes found that human error was responsible for 92.6 percent of them.29 And the number of casualties are many: just in 2013, 1.25 million people died in car accidents globally and 32,000 in the United States alone.30 Thus, autonomous cars do not need to be perfect to be justifiable. Human drivers are certainly not. There are still situations that autonomous vehicles struggle to handle, especially in crowded cities where pedestrians and cyclists provide additional complicating elements. In Singapore, autonomous taxis have a safety driver in them who takes over in emergencies, to minimize the possibility of accidents.

But by storing records from the last time that snow fell, AI can now handle this problem.21 AI researchers have shown that algorithmic drivers now are able to identify major changes in the environment in which they operate, such as roadwork.22 In a major study, my Oxford engineering colleagues Bonolo Mathibela, Paul Newman, and Ingmar Posner concluded: “A vehicle can therefore prepare for the possibility of encountering humans on the road, or areas where [the vehicle] may not be stationary—thus gaining a dynamic sense of situational awareness, like a human.”23 While it is still early days, autonomous vehicles are being deployed in a number of settings. Some agricultural vehicles, forklifts, and cargo-handling vehicles are already autonomous; and in recent years hospitals have begun to use autonomous robots to transport food, prescriptions, and samples.24 In 2017, Rio Tinto, an Anglo-Australian metals and mining giant, announced that it will expand its fleet of autonomous hauling trucks in its Pilbara mine by 50 percent by 2019, making operations fully autonomous.25 But so far, the adoption of autonomous vehicles has mostly been limited to relatively structured environments like warehouses, hospitals, factories, and mines.

Today, the largest single occupation in most American states is that of the truck driver (figure 20). It is true, as the economist Austan Goolsbee has pointed out, that if all 3.5 million truck, bus, and taxi drivers lose their jobs to autonomous vehicles over a fifteen-year period, that would amount to just over nineteen thousand per month: in 2017, 5.1 million Americans were separated from their jobs on a monthly basis, while 5.3 million jobs were generated on average. In this scenario, autonomous vehicles would increase the separation rate by less than four-tenths of a percent.111 And this would be very unlikely to happen over a fifteen-year period. Technology adoption is never frictionless, and it will take much longer for taxis to become fully autonomous than long-haul trucks.


pages: 374 words: 111,284

The AI Economy: Work, Wealth and Welfare in the Robot Age by Roger Bootle

"World Economic Forum" Davos, 3D printing, agricultural Revolution, AI winter, Albert Einstein, AlphaGo, Alvin Toffler, anti-work, antiwork, autonomous vehicles, basic income, Ben Bernanke: helicopter money, Bernie Sanders, Bletchley Park, blockchain, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, Carl Icahn, Chris Urmson, computer age, Computing Machinery and Intelligence, conceptual framework, corporate governance, correlation does not imply causation, creative destruction, David Ricardo: comparative advantage, deep learning, DeepMind, deindustrialization, Demis Hassabis, deskilling, Dr. Strangelove, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, facts on the ground, fake news, financial intermediation, full employment, future of work, Future Shock, general purpose technology, Great Leap Forward, Hans Moravec, income inequality, income per capita, industrial robot, Internet of things, invention of the wheel, Isaac Newton, James Watt: steam engine, Jeff Bezos, Jeremy Corbyn, job automation, job satisfaction, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, Kevin Kelly, license plate recognition, low interest rates, machine translation, Marc Andreessen, Mark Zuckerberg, market bubble, mega-rich, natural language processing, Network effects, new economy, Nicholas Carr, Ocado, Paul Samuelson, Peter Thiel, Phillips curve, positional goods, quantitative easing, RAND corporation, Ray Kurzweil, Richard Florida, ride hailing / ride sharing, rising living standards, road to serfdom, Robert Gordon, Robert Shiller, Robert Solow, Rutger Bregman, Second Machine Age, secular stagnation, self-driving car, seminal paper, Silicon Valley, Silicon Valley billionaire, Simon Kuznets, Skype, social intelligence, spinning jenny, Stanislav Petrov, Stephen Hawking, Steven Pinker, synthetic biology, technological singularity, The Future of Employment, The Wealth of Nations by Adam Smith, Thomas Malthus, trade route, universal basic income, US Airways Flight 1549, Vernor Vinge, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, wealth creators, winner-take-all economy, world market for maybe five computers, Y2K, Yogi Berra

This is such a problem that some tech companies are trying to make driverless cars less robotic, even inducing them to cut corners, be aggressive, and inch forward at junctions. In fact, things aren’t quite so simple as even this might seem to imply. For all the boasts about what their autonomous vehicles can do and the reports that their vehicles have passed so many tests with flying colors, the claims of the manufacturers and developers of autonomous vehicles cannot be taken seriously. For these tests are usually conducted in secret and without independent verification. We do not know – and we are not allowed to know – the road and weather conditions that the vehicles were subjected to, nor how far they were dependent on any human intervention.

In the UK the Chancellor of the Exchequer, Philip Hammond, told the BBC that he aimed to have “fully driverless cars” in use by 2021. About 50 companies, including Alphabet, Apple, Ford, GM, Toyota, and Uber, are already testing self-driving cars in California. Indeed, more than a hundred trials of autonomous vehicles are currently taking place around the world. Moreover, according to the companies developing them, the performance of self-driven cars is already impressive and is improving all the time. All these companies have invested huge sums and clearly believe that driverless vehicles are the future.

Extremist groups have started to employ “virtual planner” models of terrorism, as a way of managing lone attackers. Top operatives are able to recruit members, coordinate the target and timing of attacks, and provide assistance on topics like bomb-making, without being detected. Furthermore, there are fears that terrorists will acquire autonomous vehicles and drones to perform attacks. Naturally, just as in conventional warfare, these dangers have led to a corresponding increase in defensive activities. Facebook has counterterrorism detection systems that operate using AI. It says that it removed or flagged 1.9 million pieces of content linked to terrorism in the first part of 2018, nearly a twofold increase over the previous quarter.


pages: 562 words: 201,502

Elon Musk by Walter Isaacson

4chan, activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, AltaVista, Apollo 11, Apple II, Apple's 1984 Super Bowl advert, artificial general intelligence, autism spectrum disorder, autonomous vehicles, basic income, Big Tech, blockchain, Boston Dynamics, Burning Man, carbon footprint, ChatGPT, Chuck Templeton: OpenTable:, Clayton Christensen, clean tech, Colonization of Mars, computer vision, Computing Machinery and Intelligence, coronavirus, COVID-19, crowdsourcing, cryptocurrency, deep learning, DeepMind, Demis Hassabis, disinformation, Dogecoin, Donald Trump, Douglas Engelbart, drone strike, effective altruism, Elon Musk, estate planning, fail fast, fake news, game design, gigafactory, GPT-4, high-speed rail, hiring and firing, hive mind, Hyperloop, impulse control, industrial robot, information security, Jeff Bezos, Jeffrey Epstein, John Markoff, John von Neumann, Jony Ive, Kwajalein Atoll, lab leak, large language model, Larry Ellison, lockdown, low earth orbit, Marc Andreessen, Marc Benioff, Mars Society, Max Levchin, Michael Shellenberger, multiplanetary species, Neil Armstrong, Network effects, OpenAI, packet switching, Parler "social media", paypal mafia, peer-to-peer, Peter Thiel, QAnon, Ray Kurzweil, reality distortion field, remote working, rent control, risk tolerance, Rubik’s Cube, Salesforce, Sam Altman, Sam Bankman-Fried, San Francisco homelessness, Sand Hill Road, Saturday Night Live, self-driving car, seminal paper, short selling, Silicon Valley, Skype, SpaceX Starlink, Stephen Hawking, Steve Jobs, Steve Jurvetson, Steve Wozniak, Steven Levy, Streisand effect, supply-chain management, tech bro, TED Talk, Tesla Model S, the payments system, Tim Cook: Apple, universal basic income, Vernor Vinge, vertical integration, Virgin Galactic, wikimedia commons, William MacAskill, work culture , Y Combinator

Musk would go into the design studio on Fridays, pull out his phone, and take pictures of the different mock-ups. “This is where the world is going,” he said at one session. “Let’s push ourselves there.” Every year, he had regularly predicted in public that a fully autonomous car was just a year or so away. Except that it wasn’t. Full autonomy continued to be a receding mirage, always a year or so away. Nevertheless, Musk concluded that the best way to raise more funding was to hold a dramatic demonstration showing that autonomous vehicles were the way that the company would become phenomenally profitable. He was convinced that his team could put on a demo—even show off a credible prototype—of what the future would be.

“Together, this system provides a view of the world that a driver alone cannot access, seeing in every direction simultaneously and on wavelengths that go far beyond the human senses,” the Tesla website announced in 2016. But even as Musk made this concession, it was clear that he would not give up pushing to make a camera-only system work. Accidents As Musk pursued his autonomous-vehicle ideas, he stubbornly and repeatedly exaggerated the Autopilot capability of Tesla cars. That was dangerous; it led some drivers to think they could ride in a Tesla without paying much attention. Even as Musk was making his grand promises in 2016, Tesla was being dropped by one of its camera suppliers, Mobileye.

“In just weeks, we were able to make it do seven difficult turns.” In his Autonomy Day presentation, Musk mixed, as he often did, vision and hype. Even in his own head, he blurred the line between what he believed and what he wanted to believe. Tesla, he said yet again, was within a year of creating a fully autonomous vehicle. At that point the company would deploy a million Robotaxis that people could summon for rides. In its story, CNBC reported that Musk “presented bold, visionary promises that only his most loyal followers would take at face value.” Nor did Musk fully impress major investors. “We asked a lot of tough questions when we had an analysts call with him afterwards,” says Joe Fath, the investment manager at T.


Innovation and Its Enemies by Calestous Juma

3D printing, additive manufacturing, agricultural Revolution, Asilomar, Asilomar Conference on Recombinant DNA, autonomous vehicles, behavioural economics, big-box store, biodiversity loss, business cycle, Cass Sunstein, classic study, clean water, collective bargaining, colonial rule, computer age, creative destruction, CRISPR, Daniel Kahneman / Amos Tversky, deskilling, disruptive innovation, driverless car, electricity market, energy transition, Erik Brynjolfsson, fail fast, financial innovation, global value chain, Honoré de Balzac, illegal immigration, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of movable type, invention of the printing press, Joseph Schumpeter, knowledge economy, loss aversion, Marc Andreessen, means of production, Menlo Park, mobile money, New Urbanism, Nicholas Carr, pensions crisis, phenotype, precautionary principle, Ray Kurzweil, Recombinant DNA, refrigerator car, Second Machine Age, self-driving car, smart grid, smart meter, stem cell, Steve Jobs, synthetic biology, systems thinking, tacit knowledge, technological singularity, The Future of Employment, Thomas Kuhn: the structure of scientific revolutions, Travis Kalanick

These operator requirements create the safeguard of a driver who is capable of taking control of the vehicle when needed.” California Department of Motor Vehicles, “Summary of Draft Autonomous Vehicles Deployment Regulations,” December 16, 2015: 2. 30. Bob Sorokanich, “California Proposes Tightened Regulations on Autonomous Cars,” Roadandtrack.com, December 17, 2015. 31. R. Rycroft and D. Kash, “Path Dependence and the Modernization of Agriculture: A Case Study of Aragon, 1955–1985,” Technology Analysis and Strategic Management 14, no. 1 (2002): 21–35. 32. Marie-Laure Djelic and Sigird Quack, “Overcoming Path Dependency: Path Generation in Open Systems,” Theory and Society 36, no. 2 (2007): 161–186. 33.

For a discussion on ideas, see Bernard Barber, “Resistance by Scientists to Scientific Discovery,” Science 134, no. 3479 (1961): 596–602. 29. The bill read, “Autonomous vehicle operators must be a licensed driver who possesses an autonomous vehicle operator certificate issued by the DMV. The operator will be responsible for monitoring the safe operation of the vehicle at all times, and must be capable of taking over immediate control in the event of an autonomous technology failure or other emergency. In addition, operators will be responsible for all traffic violations that occur while operating the autonomous vehicle. These operator requirements create the safeguard of a driver who is capable of taking control of the vehicle when needed.”

Computer-aided diagnosis, robotic surgery, and myriad medical devices are already changing the role of doctors and how medical care is provided.7 Artificial intelligence and computer algorithms are influencing the way basic decisions are made. Battlefields are being automated with drones and other autonomous vehicles doing the work that used to be performed by a wide range of military personnel. Some of the advances are already shifting the locus of product development. Data-based firms such as Google and IBM are moving into pharmaceutical research. Sharing services such as Uber are acquiring robotics and other engineering capabilities.


pages: 667 words: 149,811

Economic Dignity by Gene Sperling

active measures, Affordable Care Act / Obamacare, antiwork, autism spectrum disorder, autonomous vehicles, basic income, behavioural economics, benefit corporation, Bernie Sanders, Big Tech, Cass Sunstein, collective bargaining, company town, corporate governance, cotton gin, David Brooks, desegregation, Detroit bankruptcy, disinformation, Donald Trump, Double Irish / Dutch Sandwich, driverless car, Elon Musk, employer provided health coverage, Erik Brynjolfsson, Ferguson, Missouri, fulfillment center, full employment, gender pay gap, ghettoisation, gig economy, Gini coefficient, green new deal, guest worker program, Gunnar Myrdal, housing crisis, Ida Tarbell, income inequality, independent contractor, invisible hand, job automation, job satisfaction, labor-force participation, late fees, liberal world order, longitudinal study, low skilled workers, Lyft, Mark Zuckerberg, market fundamentalism, mass incarceration, mental accounting, meta-analysis, minimum wage unemployment, obamacare, offshore financial centre, open immigration, payday loans, Phillips curve, price discrimination, profit motive, race to the bottom, RAND corporation, randomized controlled trial, Richard Thaler, ride hailing / ride sharing, Ronald Reagan, Rosa Parks, Second Machine Age, secular stagnation, shareholder value, Sheryl Sandberg, Silicon Valley, single-payer health, speech recognition, stock buybacks, subprime mortgage crisis, tech worker, TED Talk, The Chicago School, The Future of Employment, The Wealth of Nations by Adam Smith, Toyota Production System, traffic fines, Triangle Shirtwaist Factory, Uber and Lyft, uber lyft, union organizing, universal basic income, W. E. B. Du Bois, War on Poverty, warehouse robotics, working poor, young professional, zero-sum game

Erik Brynjolfsson and Andrew McAfee also highlight 99Degrees Custom, an apparel maker in Lawrence, Massachusetts, as an example of how technology can generate jobs. “99Degrees Custom embraces a highly engineered, partially automated production line to make highly customized textile products.”70 That approach has allowed 99Degrees Custom to create new jobs that are “more varied, more highly skilled, and better paid” than “the old [textile] factory jobs.”71 The Massachusetts Executive Office of Housing and Economic Development gave the company a $2.8 million tax credit provided that the company hire 350 additional workers by 2023.72 Why shouldn’t all states provide tax credits for such companies that marry dignified labor and new technology? While most of the focus on autonomous vehicles and job loss has been on truck drivers and taxicab drivers, about six hundred thousand bus driver jobs—more than twice the number of taxi driver jobs—are at risk.73 Bus drivers typically work for the government and are disproportionately people of color and women.74 Instead of simply pocketing a reduction in labor costs due to autonomous vehicles, local governments could use extra tax dollars to reimagine how public transportation works for its citizens. Bus drivers are already “highly skilled customer-service” workers who manage conflicts and help people with directions, among other things.75 If we come to a day when fewer bus drivers are needed full-time, rather than just eliminate jobs, cities could train them and future “bus managers” to monitor and address bullying among schoolchildren, help older citizens explore their city, and better accommodate those with disabilities.

What is rarely noted is that the short run can be a lifetime.”7 Or three. Today, there are new looming threats that could cause even bigger upheavals than the automobile in the twentieth century—and they are occurring during an era of globalization that puts more pressure on jobs in the United States. Artificial intelligence (AI), robots, and autonomous vehicle technology are among the new technological advances that threaten major economic disruption. A quarter of American adults say the possibility that robots and computers could do many of the jobs done by humans makes them feel “very worried.”8 A widely cited study by Frey put the number of U.S. jobs at high risk of being automated in the next decade or two due to advances in AI and robots at 47 percent.9 According to a Brookings Institution study, thirty-six million jobs “will face high exposure to automation in the coming decades.”10 Some experts project up to three million jobs could be at risk due to self-driving trucks and cars.11 Martin Ford, author of Rise of the Robots, believes that artificial intelligence “could very well end up in a future with significant unemployment . . . maybe even declining wages . . .

Yet, as currently designed, these reforms turn back the clock from the protections for negative dignity in the private sector that grew out of the Progressive Era and were designed to ensure that the most vulnerable workers were not overwhelmed by the market power of employers. A NARROW VISION OF PEOPLE SUFFERING Some Dignity of Work Conservatives focus on the real policy neglect that many—particularly working-class white males—have felt in especially hard-hit manufacturing and coal communities, or could feel through technological disruptions like autonomous vehicles. They are certainly correct to draw attention to such neglect. But too often, their deployment of the need for “dignity” seems reserved for this limited segment of the population. And, having raised the sense of neglect, they call for precious few tangible policies that could remedy such dignity gaps.


Industry 4.0: The Industrial Internet of Things by Alasdair Gilchrist

3D printing, additive manufacturing, air gap, AlphaGo, Amazon Web Services, augmented reality, autonomous vehicles, barriers to entry, business intelligence, business logic, business process, chief data officer, cloud computing, connected car, cyber-physical system, data science, deep learning, DeepMind, deindustrialization, DevOps, digital twin, fault tolerance, fulfillment center, global value chain, Google Glasses, hiring and firing, industrial robot, inflight wifi, Infrastructure as a Service, Internet of things, inventory management, job automation, low cost airline, low skilled workers, microservices, millennium bug, OSI model, pattern recognition, peer-to-peer, platform as a service, pre–internet, race to the bottom, RFID, Salesforce, Skype, smart cities, smart grid, smart meter, smart transportation, software as a service, stealth mode startup, supply-chain management, The future is already here, trade route, undersea cable, vertical integration, warehouse robotics, web application, WebRTC, Y2K

By using a strategy of sensors, remote communication, and Big Data analytics, Thames Water can anticipate equipment failures and respond quicker to any critical situation that may arise due to inclement weather. However, other industries have other tactical priorities when deploying IIoT, one being health and safety. Here we have seen some innovative projects from using drones and autonomous vehicles to inspect Oil and Gas lines in inhospitable areas to using autonomous mining equipment. Indeed Schlumberger is currently using an autonomous underwater vehicle to inspect sub-sea conditions. The unmanned vehicle travels around the ocean floor and monitors conditions for anything up to a year powered only by wave motion, which makes deployment in remote ocean locations possible, as they are both autonomous and self-sufficient requiring no local team support.

Ideally, a forklift would communicate with other forklifts, ensuring they were aware of one another to take avoiding action, such as slowing or stopping at blind intersections if another forklift is detected in the immediate vicinity. However, in the developed world it is still far more common to pick-by-paper, which is the term applied to the manual human picking of goods from a shelf. Forklifts, autonomous vehicles, and robots are great for heavy lifting of large pallets, but not much use for picking small intricate articles out of a stock bin. This is where human workers are in their element. Remember all those pedestrians being injured in the warehouse by forklifts? Well those pedestrians are most likely to be the pick-by-paper workforce.

In an industrial scenario, accuracy and resolution are both critical components of the control functions, and as such, the logic, the compute element, is usually situated as close to the sensors as is technically feasible. Examples of control domains may be in a large IIS system, for example, a control room in a nuclear plant or in smaller IISs a microprocessor in an autonomous vehicle, which controls temperature in a smart office. The control domain is made up of a set of common functions, which may well vary in their complexity. An example could be that the IIS will require sensors and therefore the control domain will require a function to be able to read sensor activity.


pages: 323 words: 90,868

The Wealth of Humans: Work, Power, and Status in the Twenty-First Century by Ryan Avent

3D printing, Airbnb, American energy revolution, assortative mating, autonomous vehicles, Bakken shale, barriers to entry, basic income, Bernie Sanders, Big Tech, BRICs, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Clayton Christensen, cloud computing, collective bargaining, computer age, creative destruction, currency risk, dark matter, David Ricardo: comparative advantage, deindustrialization, dematerialisation, Deng Xiaoping, deskilling, disruptive innovation, Dissolution of the Soviet Union, Donald Trump, Downton Abbey, driverless car, Edward Glaeser, Erik Brynjolfsson, eurozone crisis, everywhere but in the productivity statistics, falling living standards, financial engineering, first square of the chessboard, first square of the chessboard / second half of the chessboard, Ford paid five dollars a day, Francis Fukuyama: the end of history, future of work, general purpose technology, gig economy, global supply chain, global value chain, heat death of the universe, hydraulic fracturing, income inequality, independent contractor, indoor plumbing, industrial robot, intangible asset, interchangeable parts, Internet of things, inventory management, invisible hand, James Watt: steam engine, Jeff Bezos, Jeremy Corbyn, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph-Marie Jacquard, knowledge economy, low interest rates, low skilled workers, lump of labour, Lyft, machine translation, manufacturing employment, Marc Andreessen, mass immigration, means of production, new economy, performance metric, pets.com, post-work, price mechanism, quantitative easing, Ray Kurzweil, rent-seeking, reshoring, rising living standards, Robert Gordon, Robert Solow, Ronald Coase, savings glut, Second Machine Age, secular stagnation, self-driving car, sharing economy, Silicon Valley, single-payer health, software is eating the world, supply-chain management, supply-chain management software, tacit knowledge, TaskRabbit, tech billionaire, The Future of Employment, The Nature of the Firm, The Rise and Fall of American Growth, The Spirit Level, The Wealth of Nations by Adam Smith, trade liberalization, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber and Lyft, Uber for X, uber lyft, very high income, warehouse robotics, working-age population

About five million Americans work providing ‘transportation services’, including about half a million cab drivers and nearly one and a half million drivers of freight trucks.13 Autonomous vehicles could eliminate all of that work. But that would only be the beginning. Driverless vehicles might double as nannies, picking up youngsters from school and delivering them to a parent’s office or an after-school activity. They could facilitate the near-complete automation of massive amounts of retail; many grocery shops might vanish as consumers could instead get into the habit of mentioning to their smartphone when a bottle of wine is needed, which could then be ferried from a nearby warehouse by autonomous car. Car ownership might itself become obsolete, since vehicles of any sort could be hailed instantly.

With a few keystrokes they can see whether rearranging the enormous machines will save time or leave robots banging their metal arms together. Today, automobile manufacturing is first and foremost a software business, as opposed to an industrial operation. The value of the code in the machines becomes relatively more important as cars get smarter; Volvo, like many manufacturers, is working to get autonomous vehicles in regular operation on Swedish streets within the next few years. Already the cars are smart enough to do much of the brainwork involved in driving, from plotting routes to keeping a safe distance from the car ahead. Driverless cars are not yet generating discomfort among the men who drive cabs around central Gothenburg, many of whom are immigrants or the children of immigrants.

Karabarbounis, Loukas, and Neiman, Brent, ‘The Global Decline of the Labor Share’, Quarterly Journal of Economics; Elsby, Michael, Hobijn, Bart, and Sahin, Aysegul, ‘The Decline of the US Labor Share’, Brookings Papers on Economic Activity, Fall 2013. 3. In Search of a Better Sponge   1. EIA (http://www.eia.gov/beta/international).   2. BLS, State and Metro Area Employment, hours and earnings.   3. Logan, Bryan, ‘Mercedes-Benz’s Self-driving Big-rig Proves that Autonomous Vehicles are Coming Sooner than We Think’, Tech Insider, 5 October 2015.   4. Crooks, Ed, and Hornby, Lucy, ‘Sunshine Revolution: The Age of Solar Power’, Financial Times, 5 November 2015.   5. BLS, Current Employment Statistics.   6. From the author’s own conversations with Michael Mandel.   7. 


pages: 413 words: 115,274

Paved Paradise: How Parking Explains the World by Henry Grabar

A Pattern Language, Adam Neumann (WeWork), Airbnb, Albert Einstein, autonomous vehicles, availability heuristic, big-box store, bike sharing, Blue Bottle Coffee, car-free, congestion pricing, coronavirus, COVID-19, digital map, Donald Shoup, edge city, Ferguson, Missouri, Ford Model T, Frank Gehry, General Motors Futurama, gentrification, Google Earth, income inequality, indoor plumbing, Jane Jacobs, Lewis Mumford, Lyft, mandatory minimum, market clearing, megastructure, New Urbanism, parking minimums, power law, remote working, rent control, restrictive zoning, ride hailing / ride sharing, Ronald Reagan, Seaside, Florida, side hustle, Sidewalk Labs, Silicon Valley, SimCity, social distancing, Stop de Kindermoord, streetcar suburb, text mining, the built environment, The Death and Life of Great American Cities, TikTok, traffic fines, Uber and Lyft, uber lyft, upwardly mobile, urban planning, urban renewal, urban sprawl, Victor Gruen, walkable city, WeWork, white flight, Yogi Berra, young professional

Daley Oral Histories, University of Illinois–Chicago Digital Collection, collections.carli.illinois.edu/digital/collection/uic_rmdoh/id/18/rec/43. Go to note reference in text When Daley offered him the job: Paul Volpe, interview. Go to note reference in text In 2016, for example, the country’s: Michael Maciag, “How Autonomous Vehicles Could Affect City Budgets,” Governing, July 28, 2017, governing.com/gov-data/gov-how-autonomous-vehicles-could-effect-city-budgets.html. Go to note reference in text off by more than a thousand: Michael Condon, The Chicago Parking Meter Concession of 2008 (Chicago: Windy City Publishing, 2017), 4. Go to note reference in text “strengthen our city finances”: “Chicago Privatization Blitz Draws Critics,” NPR, December 8, 2008, npr.org/templates/story/story.php?

To avoid the prohibitive cost and waste of a car charger in every parking spot, cities will need to come up with more sophisticated ways to dictate how curbs are used to make sure every driver gets the power they need, and to ensure older neighborhoods where people park on the street don’t get left behind. Extension cords hanging out the window are not going to cut it. But that is nothing compared to the shake-up in store if autonomous vehicles one day master the roadway. Already new cars can park themselves; if that technology can be applied at the scale of a parking lot, the amount of space needed to store cars will shrink dramatically as stalls get narrower and access lanes vanish. Imagine your morning commute in a future of autonomy: You live in a big house, far from the city, because what’s an extra few miles driving to work when you don’t have to steer?

See Adaptive Reuse Ordinance Aspen, Colorado, 261–62 The Atlanta Journal-Constitution (newspaper), 109 attendants, parking, 31, 32, 35, 37, 92–93, 106 theft by, 94–96, 97–98 Austin, Texas, 29, 85, 182, 205, 213–14, 282–83 Australia, Melbourne, 21 automobiles, xv, 32, 55, 74, 216, 235–36 dependency on, 158–59 effect on housing, 216, 239 electric vehicle, 278–79 homeless people sleeping in, 229, 243 autonomous vehicles, 26, 279 B backyards, garage effect on, 236–38 Baer, Kate, 269 Bailey, Beth, 239 Banning, Christine, 108 Barter, Paul, 210 Bartholomew, Harland, 55 Bayley, Lindsay, 201–2, 203–4, 210 beaches, 8–9, 13 Beame, Abe, 99–100 Beckerman, Dan, 194 Bela, John, 251–52 Belmore, Bruce, 159–60 Berens, Jeff, 224–25 Berkeley, California, xi, 10–11, 210 Berkley, George, 73 Berkowitz, David, 37 Berra, Yogi, 57 Berry, Wendell, 130 bicycles, 256, 257–58, 264, 271, 275 “Big Yellow Taxi” (song), 160 Bill & Melinda Gates Foundation, 83–84 black market, for parking placards, 45 Blackwell, Clifton, 23 de Blasio, Bill, 270, 271 Bloom, Richard, 233–34 Bloomberg, Michael, 44–45, 253 Boston, Massachusetts, xii, 20, 86, 94 Bothell, Seattle, 208 Bothellites for People-Oriented Places, 208 Boxer, Leonard, 100 Braun, Carol Moseley, 136 Brays Bayou, Houston, 77 Brondolo, Elizabeth, 38–39 Brooklyn, New York, 82 Brown, Bridget, 208 Brown, Denise Scott, 181 Brown, Herb, 15 Bruck, Connie, 103–4 Buffalo, New York, 20–21, 66 buildings, xi–xii, xiii–xiv, 21 as expression of values, xviii mixed-use, 217 Bullocks department store, 68, 171–73 Bureau for Street Traffic Research, Harvard, 53 Burke, Ed, 126, 134 BusinessWeek (magazine), 61 bus stops, in San Francisco, 258–59 C Caesar, Julius, xv Cafaro, Vincent, 102–3 California, 7–8, 85, 148, 212.


pages: 225 words: 70,241

Silicon City: San Francisco in the Long Shadow of the Valley by Cary McClelland

affirmative action, Airbnb, algorithmic bias, Apple II, autonomous vehicles, barriers to entry, Black Lives Matter, Burning Man, clean water, cloud computing, cognitive dissonance, Columbine, computer vision, creative destruction, driverless car, El Camino Real, Elon Musk, Fairchild Semiconductor, full employment, gamification, gentrification, gig economy, Golden Gate Park, Google bus, Google Glasses, high net worth, housing crisis, housing justice, income inequality, John Gilmore, John Perry Barlow, Joseph Schumpeter, Loma Prieta earthquake, Lyft, mass immigration, means of production, Menlo Park, Mitch Kapor, open immigration, PalmPilot, rent control, Salesforce, San Francisco homelessness, self-driving car, sharing economy, Silicon Valley, Skype, Social Justice Warrior, Steve Jobs, Steve Wozniak, TaskRabbit, tech bro, tech worker, transcontinental railway, Travis Kalanick, Uber and Lyft, uber lyft, urban planning, vertical integration, William Shockley: the traitorous eight, young professional

* Apple’s first iconic product—with a housing, monitor, and keyboard—the Apple II was a huge leap forward from the Apple I, which was just a spare circuit board hobbyists could use to build their own computer. † The Defense Advanced Research Projects Agency (DARPA) invests on behalf of the US government in groundbreaking technology for national security. It hosted a series of competitions, challenging students from the nation’s top universities to demonstrate breakthroughs in robotics and autonomous vehicles. ‡ Economist Joseph Schumpeter described “creative destruction” as a kind of mutagenic or Darwinian force at the heart of capitalism—one that “incessantly revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one.” § The Stanford University Founding Grant provides that the gifted land “shall constitute the foundation and endowment for the University herein provided, and upon the trust that the principal thereof shall forever remain intact, and that the rents, issues, and profits thereof shall be devoted to the foundation and maintenance of the University hereby founded and endowed, and to the uses and purposes herein mentioned

I only had an hour a day, but that made it even more interesting to squeeze the most out of it. I arrived here in September of 2004 to get my PhD, and by October, I was in the Mojave Desert. The research group I joined had started a really cool project, building a car that drives itself. There was a race called the DARPA Grand Challenge, 150 miles, autonomous cars racing through the desert.† I built the computer vision: using a combination of lasers and cameras to figure out what the road looks like and to decide when we could drive faster and slower. In the desert, you just had to drive straight ahead, but on real roads, you had to look all around you for other cars, for lane markers and so on.

The internet was built by DARPA. GPS, accelerometers, they were funded by DARPA. Government funding did everything here, all the instruction, the training. The self-driving cars were a DARPA program twenty years ago. It wasn’t invented by Sebastian Thrun, who will be now known as the inventor of the autonomous car. He won the second DARPA Grand Challenge, not the first one. He didn’t even enter into the first one. What enables Uber? The internet. GPS in every car, on every phone. A huge number of programming languages—the first thirteen layers of the software stack, all of that structure was written on government funds.


pages: 561 words: 157,589

WTF?: What's the Future and Why It's Up to Us by Tim O'Reilly

"Friedman doctrine" OR "shareholder theory", 4chan, Affordable Care Act / Obamacare, Airbnb, AlphaGo, Alvin Roth, Amazon Mechanical Turk, Amazon Robotics, Amazon Web Services, AOL-Time Warner, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, basic income, behavioural economics, benefit corporation, Bernie Madoff, Bernie Sanders, Bill Joy: nanobots, bitcoin, Blitzscaling, blockchain, book value, Bretton Woods, Brewster Kahle, British Empire, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, carbon tax, Carl Icahn, Chuck Templeton: OpenTable:, Clayton Christensen, clean water, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, computer vision, congestion pricing, corporate governance, corporate raider, creative destruction, CRISPR, crowdsourcing, Danny Hillis, data acquisition, data science, deep learning, DeepMind, Demis Hassabis, Dennis Ritchie, deskilling, DevOps, Didi Chuxing, digital capitalism, disinformation, do well by doing good, Donald Davies, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, fake news, Filter Bubble, Firefox, Flash crash, Free Software Foundation, fulfillment center, full employment, future of work, George Akerlof, gig economy, glass ceiling, Glass-Steagall Act, Goodhart's law, Google Glasses, Gordon Gekko, gravity well, greed is good, Greyball, Guido van Rossum, High speed trading, hiring and firing, Home mortgage interest deduction, Hyperloop, income inequality, independent contractor, index fund, informal economy, information asymmetry, Internet Archive, Internet of things, invention of movable type, invisible hand, iterative process, Jaron Lanier, Jeff Bezos, jitney, job automation, job satisfaction, John Bogle, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Zimmer (Lyft cofounder), Kaizen: continuous improvement, Ken Thompson, Kevin Kelly, Khan Academy, Kickstarter, Kim Stanley Robinson, knowledge worker, Kodak vs Instagram, Lao Tzu, Larry Ellison, Larry Wall, Lean Startup, Leonard Kleinrock, Lyft, machine readable, machine translation, Marc Andreessen, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, McMansion, microbiome, microservices, minimum viable product, mortgage tax deduction, move fast and break things, Network effects, new economy, Nicholas Carr, Nick Bostrom, obamacare, Oculus Rift, OpenAI, OSI model, Overton Window, packet switching, PageRank, pattern recognition, Paul Buchheit, peer-to-peer, peer-to-peer model, Ponzi scheme, post-truth, race to the bottom, Ralph Nader, randomized controlled trial, RFC: Request For Comment, Richard Feynman, Richard Stallman, ride hailing / ride sharing, Robert Gordon, Robert Metcalfe, Ronald Coase, Rutger Bregman, Salesforce, Sam Altman, school choice, Second Machine Age, secular stagnation, self-driving car, SETI@home, shareholder value, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart contracts, Snapchat, Social Responsibility of Business Is to Increase Its Profits, social web, software as a service, software patent, spectrum auction, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, stock buybacks, strong AI, synthetic biology, TaskRabbit, telepresence, the built environment, the Cathedral and the Bazaar, The future is already here, The Future of Employment, the map is not the territory, The Nature of the Firm, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Davenport, Tony Fadell, Tragedy of the Commons, transaction costs, transcontinental railway, transportation-network company, Travis Kalanick, trickle-down economics, two-pizza team, Uber and Lyft, Uber for X, uber lyft, ubercab, universal basic income, US Airways Flight 1549, VA Linux, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, We are the 99%, web application, Whole Earth Catalog, winner-take-all economy, women in the workforce, Y Combinator, yellow journalism, zero-sum game, Zipcar

In many ways, this would change their business model to one closer to that of Airbnb, in which the participants in the marketplace provide an asset they own rather than their labor. But for this plan to work, Uber or Lyft would not need to develop their own autonomous vehicles, but instead could promote interoperability between different autonomous vehicle vendors. If the plan is something like “Buy your autonomous Tesla, drive it to work, and then let us use it for the rest of the day,” it would imply a mixed fleet of vehicles, requiring investments in interoperable control and dispatch. (Tesla seems to have other plans, though, forbidding their drivers from using their cars for Uber and Lyft, with the intention of rolling out its own competing service.

When people asked me what came after Web 2.0, I was quick to answer “collective intelligence applications driven by data from sensors rather than from people typing on keyboards.” Sure enough, advances in areas like speech recognition and image recognition, real-time traffic and self-driving cars, all depend on massive amounts of data harvested from sensors on connected devices. The current race in autonomous vehicles is a race not just to develop new algorithms, but to collect larger and larger amounts of data from human drivers about road conditions, and ever-more-detailed maps of the world created by millions of unwitting contributors. It’s easy to forget that in 2007, when Stanford won the DARPA Grand Challenge for self-driving vehicles, they did so by completing a seven-mile course in seven hours.

Ensuring interoperability of self-driving cars is as important as was the original interoperability that drove the Internet revolution. Open standards in this area will help ordinary people, not just big companies, to reap the benefits of the next wave of automation. Betsy Masiello, who works in public policy at Uber, responded to my questions on how the peer-to-peer model might mix with autonomous vehicles by saying that right now, people think of Uber as a replacement for taxis; perhaps instead, it will end up closer to peer-to-peer fractional car rental. It is likely that the reality will be a mix of both. Finally, if the augmented worker is indeed central to Uber and Lyft’s business model, perhaps the right way to think about self-driving cars is as a further augmentation, enabling new kinds of services.


pages: 420 words: 135,569

Imaginable: How to See the Future Coming and Feel Ready for Anything―Even Things That Seem Impossible Today by Jane McGonigal

2021 United States Capitol attack, Airbnb, airport security, Alvin Toffler, augmented reality, autism spectrum disorder, autonomous vehicles, availability heuristic, basic income, biodiversity loss, bitcoin, Black Lives Matter, blockchain, circular economy, clean water, climate change refugee, cognitive bias, cognitive dissonance, Community Supported Agriculture, coronavirus, COVID-19, CRISPR, cryptocurrency, data science, decarbonisation, digital divide, disinformation, Donald Trump, drone strike, Elon Musk, fake news, fiat currency, future of work, Future Shock, game design, George Floyd, global pandemic, global supply chain, Greta Thunberg, income inequality, index card, Internet of things, Jane Jacobs, Jeff Bezos, Kickstarter, labor-force participation, lockdown, longitudinal study, Mason jar, mass immigration, meta-analysis, microbiome, Minecraft, moral hazard, open borders, pattern recognition, place-making, plant based meat, post-truth, QAnon, QR code, remote working, RFID, risk tolerance, School Strike for Climate, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, Silicon Valley startup, Snapchat, social distancing, stem cell, TED Talk, telepresence, telepresence robot, The future is already here, TikTok, traumatic brain injury, universal basic income, women in the workforce, work culture , Y Combinator

I’ve learned a lot about futures thinking since that conversation with the innovation team more than ten years ago. What would I do differently if I had known then what I know now? I’d definitely invite them to take a quick mental time trip with me, so they could imagine their very first time riding in a completely autonomous vehicle. “Imagine it as vividly and realistically as you can,” I’d say to them. “What color is the car? Where are you going? How comfortable is the seat? Is anyone with you?” I’d give them a moment to pre-feel this future, and then I’d ask, “In one word, how would you describe the emotion you’re feeling during this first ride?”

I’ve since had thousands of students take on this exact imagination challenge as a mental warm-up for class. (Although in recent years, as self-driving technology has actually entered the marketplace, I’ve had to revise it slightly: “If you’ve already had this experience of riding in a completely autonomous vehicle, what was the main emotion you felt?”) I invite everyone to share their one-word emotional reaction on a giant whiteboard at the front of the classroom or, if we’re learning online, in chat. This is my favorite part, as everyone takes in the inevitably wide range of feelings. People predict they would feel excited, nervous, awed, terrified, curious, nauseated, grateful, thrilled, confused, vigilant, asleep, free.

A little over ten years ago, I was invited to the corporate headquarters of a major automotive manufacturer to give a talk to their innovation team about how video game technology might be incorporated into future cars. During a tour of a research facility, I got into a heated discussion with a few senior executives about whether self-driving, or autonomous, cars would ever take off as a popular alternative to people-driven cars. “Absolutely not,” one of them said. “We’re not even looking at that as a serious possibility,” another said. I asked why they were so confident in their opinion. “Cars are the ultimate expression of individual freedom,” one responded.


pages: 302 words: 95,965

How to Be the Startup Hero: A Guide and Textbook for Entrepreneurs and Aspiring Entrepreneurs by Tim Draper

3D printing, Airbnb, Apple's 1984 Super Bowl advert, augmented reality, autonomous vehicles, basic income, Berlin Wall, bitcoin, blockchain, Buckminster Fuller, business climate, carried interest, connected car, CRISPR, crowdsourcing, cryptocurrency, deal flow, Deng Xiaoping, discounted cash flows, disintermediation, Donald Trump, Elon Musk, Ethereum, ethereum blockchain, fake news, family office, fiat currency, frictionless, frictionless market, growth hacking, high net worth, hiring and firing, initial coin offering, Jeff Bezos, Kickstarter, Larry Ellison, low earth orbit, Lyft, Mahatma Gandhi, Marc Benioff, Mark Zuckerberg, Menlo Park, Metcalfe's law, Metcalfe’s law, Michael Milken, Mikhail Gorbachev, Minecraft, Moneyball by Michael Lewis explains big data, Nelson Mandela, Network effects, peer-to-peer, Peter Thiel, pez dispenser, Ralph Waldo Emerson, risk tolerance, Robert Metcalfe, Ronald Reagan, Rosa Parks, Salesforce, Sand Hill Road, school choice, school vouchers, self-driving car, sharing economy, Sheryl Sandberg, short selling, Silicon Valley, Skype, smart contracts, Snapchat, sovereign wealth fund, stealth mode startup, stem cell, Steve Jobs, Steve Jurvetson, Tesla Model S, Twitter Arab Spring, Uber for X, uber lyft, universal basic income, women in the workforce, Y Combinator, zero-sum game

Every new innovation adds to the wealth of a society, and with all the innovation that comes from a world of people who are educated and up to date on new innovations, our world should grow substantially. The people of the world will have access to global information, global governance, global currency and global markets. They will be mobile and less tied to any single geographic region. And now for my wilder predictions: People will be traveling in autonomous vehicles and communicating through virtual (or augmented) reality. They will be living very long lives, while cures for cancer and the aging gene are discovered. Their health will be monitored by sensors designed into their clothes, which will be optically programmable to the owner’s tastes to match what is required for any occasion.

The jobs that computers can potentially do better are the monotonous jobs, like driving us or our things from one place to another, analyzing data patterns of customers, and administering regulations. These monotonous jobs will give way to jobs that are more abstract (and frankly more interesting) like monitoring autonomous vehicles, enhancing customer experiences, and improving banking, legal, accounting or even government service. While some may have difficulty adjusting to the new world, the jobs in the new world will be more interesting and more fulfilling. After all, before the industrial revolution, most people had to be out working on the farms, but with automation, a lot of those manual jobs were replaced with more interesting abstract jobs, and we adjusted.

A more abstract business takes what is out there today and anticipates or even “guesses” what will happen in the future. Seeing Uber’s success, you might think of a delivery business, but there are many of them already out there, so you can abstract from there and think of a drone delivery business or an autonomous car delivery business. You might think that those abstract businesses would have a lower chance of success. They do! But I want you to think about success as a Startup Hero. A Startup Hero transforms the world, disrupts the status quo and looks for opportunities that others might think are crazy.


pages: 293 words: 90,714

Copenhagenize: The Definitive Guide to Global Bicycle Urbanism by Mikael Colville-Andersen

active transport: walking or cycling, Airbnb, Albert Einstein, autonomous vehicles, bike sharing, business cycle, car-free, congestion charging, corporate social responsibility, Donald Trump, Edward Snowden, Enrique Peñalosa, functional fixedness, gamification, if you build it, they will come, Induced demand, intermodal, Jane Jacobs, Johann Wolfgang von Goethe, Kickstarter, Mahatma Gandhi, megaproject, meta-analysis, neurotypical, out of africa, place-making, Ralph Waldo Emerson, safety bicycle, self-driving car, sharing economy, smart cities, starchitect, transcontinental railway, urban planning, urban sprawl, Yogi Berra

There are, it must be said, examples of car companies greenwashing with bikes in the commercial, filming the car in the city from bikes, and even producing bikes that fit in the trunks of their cars. They’re worried and they don’t know exactly what to do. Two of the main Big Auto players, BMW and Ford, are trying to reinvent themselves as “mobility companies,” but largely the industry is still stuck in its ways. Add to this the concerted effort being made to hype electric vehicles and autonomous vehicles as the next big thing that will change the world. The former only eliminates one aspect of the problem—emissions. The latter brings new problems with it. I recall reading a quote on Twitter that “In Amsterdam, a Google self-driving car would park itself after a few minutes and start crying.”

Desperately trying to cement, in the public consciousness of its citizens, the rather outdated philosophy that cars rule supreme and everyone else is a mere pawn to be swept aside without regret. When look at similarities between all these organizations, one thing is shockingly clear. None of them will ever say that a drastic reduction of cars would save lives. It’s all talk and no serious action. They also have a tendency to support electric vehicles and autonomous vehicles, not at all aware of the irony that such vehicles still take up public space and will still contribute to death and injury. None of them have urban-planning experience, or if they do touch upon it, they never mention it. They spend most of their time vehemently protecting their status as “traffic safety authorities.”

I told him it was 1985. He laughed. “So Doc went 30 years into the future … that’s like … NOW! But there are no flying cars and goofy clothes …” Nope. He nailed it. A century of technological—and fashion—promises that failed to deliver. A saeculum horribilis from which we need to recover. Feel free to lump autonomous cars and the hype surrounding them into the same category. When I speak of the importance of going to back to the future, I mean to a place where we were rational and realistic. Back to a time—or times—where we did things that made sense. Graph by Professor Phil Goodwin showing traffic projections by Britain’s Department for Transport (in color) and the actual car traffic growth (black).


pages: 375 words: 88,306

The Sharing Economy: The End of Employment and the Rise of Crowd-Based Capitalism by Arun Sundararajan

"World Economic Forum" Davos, additive manufacturing, Airbnb, AltaVista, Amazon Mechanical Turk, asset light, autonomous vehicles, barriers to entry, basic income, benefit corporation, bike sharing, bitcoin, blockchain, book value, Burning Man, call centre, Carl Icahn, collaborative consumption, collaborative economy, collective bargaining, commoditize, commons-based peer production, corporate social responsibility, cryptocurrency, data science, David Graeber, distributed ledger, driverless car, Eben Moglen, employer provided health coverage, Erik Brynjolfsson, Ethereum, ethereum blockchain, Frank Levy and Richard Murnane: The New Division of Labor, future of work, general purpose technology, George Akerlof, gig economy, housing crisis, Howard Rheingold, independent contractor, information asymmetry, Internet of things, inventory management, invisible hand, job automation, job-hopping, John Zimmer (Lyft cofounder), Kickstarter, knowledge worker, Kula ring, Lyft, Marc Andreessen, Mary Meeker, megacity, minimum wage unemployment, moral hazard, moral panic, Network effects, new economy, Oculus Rift, off-the-grid, pattern recognition, peer-to-peer, peer-to-peer lending, peer-to-peer model, peer-to-peer rental, profit motive, public intellectual, purchasing power parity, race to the bottom, recommendation engine, regulatory arbitrage, rent control, Richard Florida, ride hailing / ride sharing, Robert Gordon, Ronald Coase, Ross Ulbricht, Second Machine Age, self-driving car, sharing economy, Silicon Valley, smart contracts, Snapchat, social software, supply-chain management, TaskRabbit, TED Talk, the long tail, The Nature of the Firm, total factor productivity, transaction costs, transportation-network company, two-sided market, Uber and Lyft, Uber for X, uber lyft, universal basic income, Vitalik Buterin, WeWork, Yochai Benkler, Zipcar

The results of a wide variety of independent study projects undertaken by my NYU undergraduates and MBA students have helped mold my early-stage research and thinking: the ones that stand out were by Humaira Faiz, Sydnee Grushack, Andrew Ng, and Jara Small (on inclusive growth in the sharing economy); Jonah Blumstein, Valeriya Greene and Eric Jacobson (on Airbnb and city regulations); Andrew Covell, Varun Jain, and June Khin (on the organization of sharing economy platforms); Phil Hayes (on surge pricing); Dmitrios Theocharis and Siri Zhan (on the on-demand workforce); Ann Dang, Louise Lai, and Daniella Tapia (on the global variation in regulation); Lauren Tai (on regulating autonomous vehicles); Karl Gourgue, Manasa Grandhi, and Joyce Fei (on decentralized models of research); Arra Malek, Ansh Patel, and Haley Zhou (on apparel rental models); Laura Kettell and Karina Alkhasyan (on peer-to-peer finance); and Keerthi Moudgal (on peer-to-peer retailing). Although I have been captivated by the sharing economy for many years now, the emergence of this book was catalyzed by a series of email messages that my editor at the MIT Press, Emily Taber, sent me in April 2015.

The accommodation, transportation, and freelance labor sectors have been the earliest to see big changes induced by crowd-based capitalism, but commercial real estate, health care provision and energy production and distribution will soon follow. And the digitization of the physical will, over the coming decade, yield mass-market autonomous vehicles in the United States, Western Europe and parts of Asia, radically reshaping the automobile industry, shifting market power away from today’s leading manufacturers and towards a range of technology platforms—Uber, Lyft, Didi Kuaidi and Ola, as well as Apple, Google, and perhaps even Amazon. In parallel, the additive manufacturing revolution will change how artifacts are made, shifting more and more production into the crowd.

Indeed, taxi drivers (most of whom in larger cities do not own their cars or “medallions”) switch to Uber every day; we have already seen evidence of a drop of about 30% in the price of a New York City yellow cab medallion.30 And in July 2015, Evgeny Freidman, the largest owner of yellow cab medallions in New York, filed a petition to put many of his medallion-owning companies into bankruptcy.31 And the eventual impact of on-demand transportation will likely be on the automobile industry as a whole, accelerated by autonomous cars becoming a mass-market commercial reality over the next decade. A significant fraction of consumer spending on automobiles will shift to a growing variety of on-demand mobility services. Industrial organization economics teaches us that as product variety increases, people will consume more rather than less.


pages: 472 words: 117,093

Machine, Platform, Crowd: Harnessing Our Digital Future by Andrew McAfee, Erik Brynjolfsson

"World Economic Forum" Davos, 3D printing, additive manufacturing, AI winter, Airbnb, airline deregulation, airport security, Albert Einstein, algorithmic bias, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, Andy Rubin, AOL-Time Warner, artificial general intelligence, asset light, augmented reality, autism spectrum disorder, autonomous vehicles, backpropagation, backtesting, barriers to entry, behavioural economics, bitcoin, blockchain, blood diamond, British Empire, business cycle, business process, carbon footprint, Cass Sunstein, centralized clearinghouse, Chris Urmson, cloud computing, cognitive bias, commoditize, complexity theory, computer age, creative destruction, CRISPR, crony capitalism, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, Dean Kamen, deep learning, DeepMind, Demis Hassabis, discovery of DNA, disintermediation, disruptive innovation, distributed ledger, double helix, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, everywhere but in the productivity statistics, Evgeny Morozov, fake news, family office, fiat currency, financial innovation, general purpose technology, Geoffrey Hinton, George Akerlof, global supply chain, Great Leap Forward, Gregor Mendel, Hernando de Soto, hive mind, independent contractor, information asymmetry, Internet of things, inventory management, iterative process, Jean Tirole, Jeff Bezos, Jim Simons, jimmy wales, John Markoff, joint-stock company, Joseph Schumpeter, Kickstarter, Kiva Systems, law of one price, longitudinal study, low interest rates, Lyft, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Marc Andreessen, Marc Benioff, Mark Zuckerberg, meta-analysis, Mitch Kapor, moral hazard, multi-sided market, Mustafa Suleyman, Myron Scholes, natural language processing, Network effects, new economy, Norbert Wiener, Oculus Rift, PageRank, pattern recognition, peer-to-peer lending, performance metric, plutocrats, precision agriculture, prediction markets, pre–internet, price stability, principal–agent problem, Project Xanadu, radical decentralization, Ray Kurzweil, Renaissance Technologies, Richard Stallman, ride hailing / ride sharing, risk tolerance, Robert Solow, Ronald Coase, Salesforce, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, slashdot, smart contracts, Snapchat, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Pinker, supply-chain management, synthetic biology, tacit knowledge, TaskRabbit, Ted Nelson, TED Talk, the Cathedral and the Bazaar, The Market for Lemons, The Nature of the Firm, the strength of weak ties, Thomas Davenport, Thomas L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, Travis Kalanick, Two Sigma, two-sided market, Tyler Cowen, Uber and Lyft, Uber for X, uber lyft, ubercab, Vitalik Buterin, warehouse robotics, Watson beat the top human players on Jeopardy!, winner-take-all economy, yield management, zero day

Environmental control is necessary when pieces of automation have primitive brains and no ability to sense their environments. As all the elements of DANCE improve together, however, pieces of automation can leave the tightly controlled environment of the factory and head out into the wide world. This is exactly what robots, drones, autonomous vehicles, and many other forms of digital machines are doing at present. They’ll do much more of it in the near future. What Humans Do in a World Full of Robots How will our minds and bodies work in tandem with these machines? There are two main ways. First, as the machines are able to do more work in the physical world, we’ll do less and less of it, and instead use our brains in the ways described in earlier chapters, and in the next one.

Phase two, which we believe we’re in now, has a start date that’s harder to pin down. It’s the time when science fiction technologies—the stuff of movies, books, and the controlled environments of elite research labs—started to appear in the real world. In 2010, Google unexpectedly announced that a fleet of completely autonomous cars had been driving on US roads without mishap. In 2011, IBM’s Watson supercomputer beat two human champions at the TV quiz show Jeopardy! By the third quarter of 2012, there were more than a billion users of smartphones, devices that combined the communication and sensor capabilities of countless sci-fi films.

As Urmson recounted at the 2015 TED conference, “Our vehicles were driving through Mountain View, and this is what we encountered. This is a woman in an electric wheelchair chasing a duck in circles on the road. Now it turns out, there is nowhere in the DMV handbook that tells you how to deal with that, but our vehicles were able to encounter that, slow down, and drive safely.” Autonomous cars that can drive safely in all circumstances and conditions are not here yet. But we think they’re coming quickly. The ability of machine language to overcome Polanyi’s Paradox is starting to be put to use in white-collar back-office work that has, to date, proved surprisingly resistant to complete automation.


User Friendly by Cliff Kuang, Robert Fabricant

A Pattern Language, Abraham Maslow, Airbnb, anti-communist, Any sufficiently advanced technology is indistinguishable from magic, Apple II, augmented reality, autonomous vehicles, behavioural economics, Bill Atkinson, Brexit referendum, Buckminster Fuller, Burning Man, business logic, call centre, Cambridge Analytica, Chuck Templeton: OpenTable:, cognitive load, computer age, Daniel Kahneman / Amos Tversky, dark pattern, data science, Donald Trump, Douglas Engelbart, Douglas Engelbart, driverless car, Elaine Herzberg, en.wikipedia.org, fake it until you make it, fake news, Ford Model T, Frederick Winslow Taylor, frictionless, Google Glasses, Internet of things, invisible hand, James Dyson, John Markoff, Jony Ive, knowledge economy, Kodak vs Instagram, Lyft, M-Pesa, Mark Zuckerberg, mobile money, Mother of all demos, move fast and break things, Norbert Wiener, Paradox of Choice, planned obsolescence, QWERTY keyboard, randomized controlled trial, replication crisis, RFID, scientific management, self-driving car, seminal paper, Silicon Valley, skeuomorphism, Skinner box, Skype, smart cities, Snapchat, speech recognition, Steve Jobs, Steve Wozniak, tacit knowledge, Tesla Model S, three-martini lunch, Tony Fadell, Uber and Lyft, Uber for X, uber lyft, Vannevar Bush, women in the workforce

That’s the point behind both the rules governing polite conversation and how a user-friendly machine should work. Months after I took my test drive in the Audi that drove itself, the user-experience researchers at Volkswagen gathered in an empty parking lot to try to figure out how pedestrians would behave around an autonomous vehicle. It seemed a given that it would scare them. “Unless people are standing on the pavement with the vehicle, you can’t appreciate how they’re going to feel,” pointed out Erik Glaser, the young project leader. The experiment demanded a giant tent over the parking lot, to control how the light spilled across the bare-bones street intersection they had created overnight.

He runs the user-experience group at Volkswagen’s little-known Electronics Research Laboratory, and his very bland job description belies how much time he spends living in the future.5 A psychologist by training, California born and raised, Lathrop is burly with close-cropped hair like an army sergeant. He speaks with the painstakingly precise diction of a scientist. But he’s also an inventor, the coauthor of several patents that might prove decisive for autonomous cars. Fifteen years ago, Lathrop found his job on Monster.com, and even the guy who hired him didn’t quite know what he’d be doing at Volkswagen. There were fifteen engineers, and when Lathrop arrived they all assumed that he’d be the sixteenth. His first week, they handed him some circuit boards to solder.

What’s more, your fellow drivers haven’t dedicated their lives to training themselves to safely drive a car. They aren’t paid to keep other people safe. They aren’t paid not to put on their makeup or read their email during rush hour. Lathrop thought to himself, What are the odds that someone with a background in aviation was coming to work on autonomous cars in 2010? He was the only one, as far as he could tell. By the time we first met, in 2016, he had logged more years working on driverless cars than all but a few people in the world. He’d been set down that path by a book by another human-factors scientist, Asaf Degani, suggestively titled Taming Hal, after the killer computer in Stanley Kubrick’s 2001.


pages: 180 words: 55,805

The Price of Tomorrow: Why Deflation Is the Key to an Abundant Future by Jeff Booth

3D printing, Abraham Maslow, activist fund / activist shareholder / activist investor, additive manufacturing, AI winter, Airbnb, Albert Einstein, AlphaGo, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, basic income, bitcoin, blockchain, Bretton Woods, business intelligence, butterfly effect, Charles Babbage, Claude Shannon: information theory, clean water, cloud computing, cognitive bias, collapse of Lehman Brothers, Computing Machinery and Intelligence, corporate raider, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, currency manipulation / currency intervention, dark matter, deep learning, DeepMind, deliberate practice, digital twin, distributed ledger, Donald Trump, Elon Musk, fiat currency, Filter Bubble, financial engineering, full employment, future of work, game design, gamification, general purpose technology, Geoffrey Hinton, Gordon Gekko, Great Leap Forward, Hyman Minsky, hype cycle, income inequality, inflation targeting, information asymmetry, invention of movable type, Isaac Newton, Jeff Bezos, John Maynard Keynes: Economic Possibilities for our Grandchildren, John von Neumann, Joseph Schumpeter, late fees, low interest rates, Lyft, Maslow's hierarchy, Milgram experiment, Minsky moment, Modern Monetary Theory, moral hazard, Nelson Mandela, Network effects, Nick Bostrom, oil shock, OpenAI, pattern recognition, Ponzi scheme, quantitative easing, race to the bottom, ride hailing / ride sharing, self-driving car, software as a service, technoutopianism, TED Talk, the long tail, the scientific method, Thomas Bayes, Turing test, Uber and Lyft, uber lyft, universal basic income, winner-take-all economy, X Prize, zero-sum game

A technology that reduces costs so significantly and produces better outcomes is again deflationary in nature and, because of market incentives, impossible to stop. For now, we have both the overhead of the existing legacy system in drivers (more than 3 percent of the United States workforce are drivers), manufacturing capacity to produce for the 5 percent utilization rate, insurance, and accidents, combined with all of the new investment in autonomous vehicles. That means that today’s job numbers and growth rates of the economy are much higher than they will be in the future as the legacy system is transitioned to the new. The deflationary forces of the transition have not even begun to be experienced. For example, self-driving cars today still require oversight from a human operator because of regulation requiring someone to sit in the seat and be paid even if never called upon.

Here’s why: if I can have a car whenever I need it without requiring a driver, I am likely to either 1) decide not to buy a car because I have access to a car whenever needed or 2) if I do buy a car, allow it to be used by others to help me pay for the asset I own. With either choice, utilization rates on cars should move much higher. That means that current forecasts of continually increasing demand for automobiles are very wrong. Instead, automotive production, and the jobs with it, could fall by 50 percent or more as autonomous cars move into the mainstream. Automotive companies, instead of making money through the sale and service of vehicles, will need to adjust their models to remain viable. Most likely that adjustment will have them selling cars as a service option, similar to software-as-a-service models in technology delivery today.


pages: 347 words: 97,721

Only Humans Need Apply: Winners and Losers in the Age of Smart Machines by Thomas H. Davenport, Julia Kirby

"World Economic Forum" Davos, AI winter, Amazon Robotics, Andy Kessler, Apollo Guidance Computer, artificial general intelligence, asset allocation, Automated Insights, autonomous vehicles, basic income, Baxter: Rethink Robotics, behavioural economics, business intelligence, business process, call centre, carbon-based life, Clayton Christensen, clockwork universe, commoditize, conceptual framework, content marketing, dark matter, data science, David Brooks, deep learning, deliberate practice, deskilling, digital map, disruptive innovation, Douglas Engelbart, driverless car, Edward Lloyd's coffeehouse, Elon Musk, Erik Brynjolfsson, estate planning, financial engineering, fixed income, flying shuttle, follow your passion, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, game design, general-purpose programming language, global pandemic, Google Glasses, Hans Lippershey, haute cuisine, income inequality, independent contractor, index fund, industrial robot, information retrieval, intermodal, Internet of things, inventory management, Isaac Newton, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joi Ito, Khan Academy, Kiva Systems, knowledge worker, labor-force participation, lifelogging, longitudinal study, loss aversion, machine translation, Mark Zuckerberg, Narrative Science, natural language processing, Nick Bostrom, Norbert Wiener, nuclear winter, off-the-grid, pattern recognition, performance metric, Peter Thiel, precariat, quantitative trading / quantitative finance, Ray Kurzweil, Richard Feynman, risk tolerance, Robert Shiller, robo advisor, robotic process automation, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, six sigma, Skype, social intelligence, speech recognition, spinning jenny, statistical model, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, superintelligent machines, supply-chain management, tacit knowledge, tech worker, TED Talk, the long tail, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, Works Progress Administration, Zipcar

We see no reason why this wouldn’t happen in surgery over the next couple of decades. Autonomous vehicles are another area of intelligent technology involving physical tasks—moving and getting things around. These vehicles employ a combination of GPS and digital maps, light radar (“lidar”), video cameras, and ultrasonic, radar, and odometry sensors to generate and analyze a massive amount of data about the vehicle’s position and surroundings. We probably don’t have to tell you too much about this area, because it gets more than its share of media attention. But it’s a good bet that autonomous cars and trucks will be commonplace on our streets within the next decade.

And the third: “A robot must protect its own existence, as long as such protection does not conflict with the first or second law.” Plenty of people have pointed out that the laws are problematic, because social situations are complex. Legendary investor Warren Buffett, for example, raised a common question about autonomous vehicles during a forum hosted by the National Automobile Dealers Association. What if, he asked, a toddler runs into the street in front of a self-driving car, and the robot’s only option not to hit that child is to swerve into the path of an oncoming vehicle with four people in it? After that split-second decision is made and fatal accident results, said Buffett, “I am not sure who gets sued.”


pages: 448 words: 117,325

Click Here to Kill Everybody: Security and Survival in a Hyper-Connected World by Bruce Schneier

23andMe, 3D printing, air gap, algorithmic bias, autonomous vehicles, barriers to entry, Big Tech, bitcoin, blockchain, Brian Krebs, business process, Citizen Lab, cloud computing, cognitive bias, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, cuban missile crisis, Daniel Kahneman / Amos Tversky, David Heinemeier Hansson, disinformation, Donald Trump, driverless car, drone strike, Edward Snowden, Elon Musk, end-to-end encryption, fault tolerance, Firefox, Flash crash, George Akerlof, incognito mode, industrial robot, information asymmetry, information security, Internet of things, invention of radio, job automation, job satisfaction, John Gilmore, John Markoff, Kevin Kelly, license plate recognition, loose coupling, market design, medical malpractice, Minecraft, MITM: man-in-the-middle, move fast and break things, national security letter, Network effects, Nick Bostrom, NSO Group, pattern recognition, precautionary principle, printed gun, profit maximization, Ralph Nader, RAND corporation, ransomware, real-name policy, Rodney Brooks, Ross Ulbricht, security theater, self-driving car, Seymour Hersh, Shoshana Zuboff, Silicon Valley, smart cities, smart transportation, Snapchat, sparse data, Stanislav Petrov, Stephen Hawking, Stuxnet, supply-chain attack, surveillance capitalism, The Market for Lemons, Timothy McVeigh, too big to fail, Uber for X, Unsafe at Any Speed, uranium enrichment, Valery Gerasimov, Wayback Machine, web application, WikiLeaks, Yochai Benkler, zero day

For this reason, machine-learning systems are becoming more pervasive in many areas of society. For the same reasons, we’re allowing algorithms to become more autonomous. Autonomy is the ability of systems to act independently, without human supervision or control. Autonomous systems will soon be everywhere. A 2014 book, Autonomous Technologies, has chapters on autonomous vehicles in farming, autonomous landscaping applications, and autonomous environmental monitors. Cars now have autonomous features such as staying within lane markers, following a fixed distance behind another car, and braking without human intervention to avert a collision. Agents—software programs that do things on your behalf, like buying a stock if the price drops below a certain point—are already common.

And companies will give away services for free to get that access. Just as Google and Facebook give away services in exchange for the ability to spy on their users, companies will do the same thing with the IoT. Companies will offer free IoT stuff in exchange for the data they receive from monitoring the people using it. Companies owning fleets of autonomous cars might offer free rides in exchange for the ability to show ads to the passengers, mine their contacts, or route them past or make an intermediate stop at particular stores and restaurants. Battles for control of customers and users are going to heat up in the coming years. And while the monopolistic positions of companies like Amazon, Google, Facebook, and Comcast allow them to exert significant control over their users, smaller, less obviously tech-based companies—like John Deere—are attempting to do the same.

Digital rights management was the technical solution that failed, and the DMCA was the law that came after. It has only been effective at preventing hobbyists from making copies of digital music and movies. It hasn’t prevented professionals from doing the same thing, and it hasn’t prevented the spread of copyrighted works with the DRM protections removed. The fear of hacked autonomous-car software or printed killer viruses will be much greater than the fear of illegally copied songs. The industries that will be affected are much more powerful than the entertainment industry. Both government and the private sector will look at the entertainment industry’s experience with DRM and correctly conclude that the problem is that computers are, by nature, extensible.


pages: 419 words: 109,241

A World Without Work: Technology, Automation, and How We Should Respond by Daniel Susskind

"World Economic Forum" Davos, 3D printing, agricultural Revolution, AI winter, Airbnb, Albert Einstein, algorithmic trading, AlphaGo, artificial general intelligence, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, Big Tech, blue-collar work, Boston Dynamics, British Empire, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, computer age, computer vision, computerized trading, creative destruction, David Graeber, David Ricardo: comparative advantage, deep learning, DeepMind, Demis Hassabis, demographic transition, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, driverless car, drone strike, Edward Glaeser, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, fake news, financial innovation, flying shuttle, Ford Model T, fulfillment center, future of work, gig economy, Gini coefficient, Google Glasses, Gödel, Escher, Bach, Hans Moravec, income inequality, income per capita, industrial robot, interchangeable parts, invisible hand, Isaac Newton, Jacques de Vaucanson, James Hargreaves, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joi Ito, Joseph Schumpeter, Kenneth Arrow, Kevin Roose, Khan Academy, Kickstarter, Larry Ellison, low skilled workers, lump of labour, machine translation, Marc Andreessen, Mark Zuckerberg, means of production, Metcalfe’s law, natural language processing, Neil Armstrong, Network effects, Nick Bostrom, Occupy movement, offshore financial centre, Paul Samuelson, Peter Thiel, pink-collar, precariat, purchasing power parity, Ray Kurzweil, ride hailing / ride sharing, road to serfdom, Robert Gordon, Sam Altman, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, social intelligence, software is eating the world, sovereign wealth fund, spinning jenny, Stephen Hawking, Steve Jobs, strong AI, tacit knowledge, technological solutionism, TED Talk, telemarketer, The Future of Employment, The Rise and Fall of American Growth, the scientific method, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thorstein Veblen, Travis Kalanick, Turing test, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, upwardly mobile, warehouse robotics, Watson beat the top human players on Jeopardy!, We are the 99%, wealth creators, working poor, working-age population, Y Combinator

Avery Hartmans, “These 18 Incredible Products Didn’t Exist 10 Years Ago,” Business Insider UK, 16 July 2017. 10.  Andre Esteva, Brett Kuprel, Roberto A. Novoa, et al., “Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks,” Nature 542 (2017): 115–18. 11.  See Jeff Reinke, “From Old Steel Mill to Autonomous Vehicle Test Track,” Thomas, 19 October 2017; Michael J. Coren, “Tesla Has 780 Million Miles of Driving Data, and Adds Another Million Every 10 Hours,” Quartz, 28 May 2016; and Alexis C. Madrigal, “Inside Waymo’s Secret World for Training Self-Driving Cars,” Atlantic, 23 August 2017. 12.  David McCandless, “Codebases: Millions of Lines of Code,” 24 September 2015, https://informationisbeautiful.net/visualizations/million-lines-of-code/ (accessed 25 April 2018). 13.  

See artificial narrow intelligence antitrust legislation apathy apotheosis Apple Archilochus Arendt, Hannah Ariely, Dan aristocracy Aristotle Arrow, Kenneth artificial general intelligence (AGI) artificial intelligence (AI) automata and bottom-up vs. top-down economics and fallacy of first wave of general history of priority shift and second wave of artificial narrow intelligence (ANI) artificial neural networks artisan class assembly lines AT&T Atari video games Atkinson, Anthony ATMs (automatic teller machines) automata automation, number of jobs at risk of automation anxiety automation risk autonomous vehicles Autor, David bandwagon effect bank tellers basic income conditional overview of universal Becker, Gary Bell, Daniel Berlin, Isaiah Beveridge, William Beveridge Report bigger-pie effect Big State capital-sharing and conditional basic income and income-sharing and labor-supporting meaning creation and overview of taxation and welfare state vs.

Consider what it takes to train and evaluate a car-driving system, for example. To do this, Uber built an entire mock town on the site of an old steel mill in Pennsylvania, complete with plastic pedestrians that occasionally throw themselves into traffic, and gathers data as their cars drive around it. Tesla, meanwhile, collects data from its non-autonomous cars as they are driven by their owners, with about a million miles’ worth of data reportedly flowing in every hour. Google has adopted yet another approach to the problem, creating entire virtual worlds to gather data from cars driving around in these simulations.11 Then there is the matter of the software.


pages: 398 words: 105,032

Soonish: Ten Emerging Technologies That'll Improve And/or Ruin Everything by Kelly Weinersmith, Zach Weinersmith

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 23andMe, 3D printing, Airbnb, Alvin Roth, Apollo 11, augmented reality, autonomous vehicles, connected car, CRISPR, data science, disinformation, double helix, Elon Musk, en.wikipedia.org, Google Glasses, hydraulic fracturing, industrial robot, information asymmetry, ITER tokamak, Kickstarter, low earth orbit, market design, megaproject, megastructure, microbiome, moral hazard, multiplanetary species, orbital mechanics / astrodynamics, personalized medicine, placebo effect, printed gun, Project Plowshare, QR code, Schrödinger's Cat, self-driving car, Skype, space junk, stem cell, synthetic biology, Tunguska event, Virgin Galactic

., Chakravarti, A., Cornish, V. W., Holt, L., et al. The Genome Project-Write. Science 353, no. 6295 (2016):126–27. Bolonkin, Alexander. Non-Rocket Space Launch and Flight. Amsterdam and Oxford: Elsevier Science, 2006. Bonnefon, J.-F., Shariff, A., and Rahwan, I. “The Social Dilemma of Autonomous Vehicles.” Science 352, no. 6293 (2016): 1573–76. Bornholt, J., Lopez, R., Carmean, D. M., Ceze, L., Seelig, G., and Strauss, K. “A DNA-Based Archival Storage System.” Proceedings of the Twenty-First International Conference on Architectural Support (2016):637–49. Bostrom, Nick, and Cirkovic, Milan M.

Instead of comparing a universe of flawed 2D image files, you get the outline of local buildings and compare to a single 3D file. Sounds great! The problem is that it’s historically been superexpensive. Like, only used by huge government agencies expensive. But over time the cost has come down. In fact, one of the reasons autonomous cars are starting to come to market is that you can get a decent LiDAR system on your van for only a few thousand bucks. The downside is that the lightest ones still weigh around 10 to 20 pounds. Still, the technology for visual AR is coming along nicely. “But,” you interject, “what about my other senses?

., 47, 313n arsenic, 211 art, 183 artemisinin, 198–200 artificial intelligence, 136, 139–40 artificial organs, see bioprinting artspeak, 138 Artsutanov, Yuri, 35 Asian elephants, 223 Asians, 196n asteroid mining, 52–69, 320n benefits of, 68–69 environmental degradation in, 66–67 finances of, 54–56 law and order in, 65–66 problems facing, 58–65 rights to, 63–64 safety and, 67 asteroid-moving technology, 67 asteroids: escape velocity of, 55 landing on, 62–63 net capture of, 63 rubble pile, 62 types of, 53–54 atmosphere, density of, 25, 29 atomic bombs, 79, 96, 98 atomic gardening, 191–92 ATP, 286 augmented reality (AR), 8n accuracy required for, 171 audio in, 174 benefits of, 183–86 concerns about, 180–83 hacking of, 183 hardware for, 168 location detection for, 171–74 markers in, 169–71 motion sickness in, 168 possible uses for, 164–66, 168, 177–79, 183–86 reference images in, 172–73 registration in, 166–68, 172–73 smell in, 174–75 vs. virtual reality, 165 where are we now in, 175–79 Augmented Reality Lab, 173, 177–78 Auschwitz, 183 Australia, 219 automotive industry, 136, 137 autonomous cars, 174 baby teeth, 99 bacteria, 203–5, 206, 210, 218 in bioprinted organs, 273 environmental monitoring by, 211–12 immune system of, 212–14 synthetic, 220–21 Bad Astronomy (blog), 36 Barbados, 47 Bartlett School of Graduate Studies, 141 Baseline Study, 254 bases, 192–93 Bauby, Jean-Dominique, 316 B cell, 242 behavior patterns, 231 Belize, 315, 316 Belleau Wood, Battle of, 178 Berger, Theodore, 308 beryllium, 92 Billinghurst, Mark, 176 biochar, 211, 239 bio-ink, 263–66 components of, 266–67 biomarkers, 230–31, 247 bioprinting, 144, 206, 257–81 benefits of, 274–75 concerns about, 272–74 state of the art in, 268–92 sugar sintering method in, 270 two techniques for, 263–66 Biostatistics Research and Consulting Center, 235 bioterrorism, 217 birds, 225 Blenner, Mark, 160 blind people, 310 bloodletting, 229 blood type, 195–96 blood vessels, 262, 268 bioprinting of, 269–71 Bloomberg View, 154 Boeing, 179 Bolonkin, Alexander, 62n bomb threats, 130 Booth, Serena, 129–30 Botella, Cristina, 179 Bovine Elite, LLC, 197n brain: drugs for modifying, 308 electric signals in, 285–86 invasive reading of, 294–95 metabolic signals in, 286–87 noninvasive electromagnetic reading of, 287–90 noninvasive metabolic reading of, 290–93 optimal conditions for learning in, 304–5 reading of, 285–99 superinvasive reading of, 295–99 upgrading of, 299–305 writing to the, 306–8 brain-computer interfaces, 282–317 benefits of, 311–14 brain reading and, 285–99 concerns about, 308–10 games for, 312 brain-to-brain connection, 312–13 brain tumors, 242–43 Brassica oleracea, 190 breast cancer, 240 breast exams, 238 breeding, 191 brewer’s yeast, 199 Brexit, 22n bricklaying, 139–42, 154 Brown University, 28 Brunner, Daniel, 91 Brussels, 50 Bucket of Stuff, 116–20, 124–25, 126, 128 Bull, Gerald, 45–50 Bull’s Eye: The Life and Times of Supergun Inventor Gerald Bull (Adams), 49 Bureau of Labor Statistics, 153, 155 Burj Khalifa, 25n Business Insider, 175 Butcher, Jonathan, 269 calcium, 99 California, University of: at Berkeley, 199, 212 at Davis, 234n, 328 at Santa Barbara, 176 at Santa Cruz, 222 Canada, 45–47, 48, 58, 321n Canadian Space Society, 53 cancer, 3, 206, 231, 234 continuing mutation of, 241 diagnosis of, 238–41 monitoring of, 243–44 treatment of, 241–43 cane toads, 219 Canterbury, University of, 176 capillaries, 262, 271 Caplan, Bryan, 56, 154 caraway seeds, 334–35 carbon, 52, 94, 211 carbon dioxide, 208–9, 210 carbon fiber, 143 carbon nanotube, 35–36 cardiac hypertrophy, 246–47 cars, 15, 24n cartilage, 271–72 Case for Space Solar Power, The (Mankins), 320 Case Western Reserve University, 151n Cas (protein), 213 cat bricks, 111 CD19 (molecule), 242 Cell and Organ Printing (Ringeisen), 259 cells, 192–93, 208, 260 bioprinting and, 264–66 mutant, 238 cellulose, 210 Center for Smell and Taste, 334 Centers for Disease Control, 217n Ceres, 60 Chagan, Lake, 100 Charpentier, Emmanuelle, 212 ChemBot, 124 chemical loop, 205 chemotherapy, 241, 247 Chicken McNuggets, 193n children, 110–11 children’s birthday parties, 178 China, 146, 219, 258 Chinese sweet wormwood plants, 198–99 chirality, 332–33 Church, George, 203, 214, 220, 223n, 252, 332, 335 CIA (Central Intelligence Agency), 48, 50 cilia, 187–88 Clemson University, 160 climate change, 41, 94 clothing, 154 cloud cover, 41 CNSA (China National Space Administration), 65 coal, 73 “Cobotics,” 141n cochlear implants, 306–7, 310 cognitive abilities, 304–5 cold fusion, 5 Cold War, 38 Collins, Francis, 214 Colorado, University of, 176 Comcast, 262 Comic-Con, 78n communications satellites, 34 Complete Anatomy Lab, 185 computerized manufacturing, 137 computers, 2, 101, 139 brain as, 283–84 prosthetics and, 322–23 quantum, 328–30 see also brain-computer interfaces concrete, 145, 155 Congress, U.S., 18, 64, 250, 274 construction, robotic, see robotic construction construction industry, 153–55 Construction Robotics, 141 construction workers, robots as, 139–44 contact lens, 176 Contour Crafting, 145, 146, 149, 156, 158 copper, 325 copper wire, 4, 5 Cornell University, 150, 162 cosmetic surgery, 185, 303 Cosmos 954, 58 Coulomb barrier, 77 cows, 210 CPS, 171 Craig, Alan, 182–83, 184 CRISPR-Cas9, 207, 212–14, 219, 236–37 Crohn’s disease, 247 ctDNA (circulating tumor DNA), 240, 244 C-type (carbonaceous) asteroids, 53 cyborg ear, 271–72 cystic fibrosis, 236–37, 248 D’Andrea, Raffaello, 152 Danforth, Christopher, 247 Daniels, Karen, 63 DAQRI, 179 DARPA (Defense Advanced Research Projects Agency), 124 Darth Vader (char.), 324 data encryption, 329 Dawn mission (NASA), 60 deaf people, 310 deep brain stimulation, 299–302, 304, 309 Deep Space Industries, 53 de-extinction, 221–25 Defense Department, U.S., 47 Delp, Michael, 59n Demaine, Erik, 102, 107–8, 118, 122, 128 dementia, 307 Dempsey, Gaia, 179 depression, 245, 247, 250, 301, 302 depth perception, via smell, 187 Derleth, Jason, 25–27, 35–36, 40 designer babies, 219 deuterium, 73–74, 77, 83 deuterium gas, 81–82 Deutsch, David, 330 diabetes, 245 diminished reality, 181–82 dinosaurs, 225 disease, 198–203, 217 Disney, Walt, 97 Diving Bell and the Butterfly, The (Bauby), 316 d-limonene, 210 DNA, 191, 192–98, 201–2, 204, 205, 213–14, 217, 221, 222, 234, 236, 239, 332 of mammoths, 222–23 as memory storage, 220 Doctor Who (TV show), 82 dogs, 187 Domburg, Jeroen, 161 Dong, Suyang, 177 Doudna, Jennifer, 212 Dowling, Jonathan, 330n drones, 152–53 drugs, 269 drug trials, 254–55, 268–69 Duff, David, 116 ears, 186 Earth, 16, 25, 31, 32, 33, 34, 37, 38, 39, 41, 42, 43, 49, 52, 53, 55, 56, 57, 59, 60, 67, 68, 69, 159, 169, 319 earwax, 196n East Germany, 135 ECoG (electrocorticography), 294–95, 298, 302 École Polytechnique Fédérale de Lausanne (EPFL), 112 ecology, 219 Edison, Thomas, 134, 146 education, 183–84 Edwards, Bradley, 31 EEG (electroencephalogram), 287–90, 291, 292, 294, 298, 299, 310 efficiency, 125–26 eGenesis, 207 EGFRvIII, 243 Egyptians, ancient, 6 Eiben, Gusz, 120n Eiffel Tower, 150, 171 Eisen, Jonathan, 234n electric shock therapy, 299 electromagnetic railgun, 24–25 electrons, 5 Elvis, Martin, 65–67, 68, 320n embryonic stem cells, 273 “emergency guide robot,” 130–32 Empire State Building, 172 environment: biosynthetic monitoring of, 210–12 fusion power and, 94 programmable matter in, 128 robotic construction and, 155–56 space flight damage to, 39–40 synthetic organisms and, 218–19 environmental movement, 97–98 EPFL Laboratory for Timber Construction, 143–44 epilepsy, 295, 302 escape velocity, 55 Escherichia coli, 198 ethanol, 286 Ethnobotany Study Book, 176 European Space Agency (ESA), 22, 27, 65 European Union, 22n Everett, Daniel, 140n evolution, 196 extinction, 221–25 eyes, 186 Faber, Daniel, 53, 68, 69 Fabricated: The New World of 3D Printing (Lipson and Kurman), 159 Fabric of Reality, The (Deutsch), 330 Facebook, 6n, 111, 180, 254 face-tracking software, 180 Falcon 9 rocket, 8n, 19 Faraday, Michael, 4, 6 fiducial marker, 169–70 fingertips, pruney, 126 Fisher, Caitlin, 173, 177–78 fission, 79n FitBit, 252n flexible electrode arrays, 298 Florida, University of, 300 Center for Smell and Taste at, 334 Florida State University, 59n flu, 247 flu vaccines, 217 flux pinning, 326–27 flying cars, 2 fMRI (functional magenetic resonance imaging), 290–91 fMRS (functional magnetic resonance spectroscopy), 292 fNIRS (functional near-infrared spectroscopy), 291 food, printed, 159–63 Food and Drug Administration (FDA), 254, 315, 316 foods, 190–91 Ford Motor Company, 97 Forgacs, Gabor, 268–69, 272 forked tongue, 187 “4D printing,” 103–5 France, 93n Frankenfood, 221 free fall, 42–43 “freezing of gait,” 301 Frostruder, 162 fuel cells, 208–9 fuels, 20, 208–10, 221 furniture, 127 Fusion: The Energy of the Universe (McCracken), 77 fusion bombs, 79 fusion power, 73–100 benefits of, 93–94 blast approach to, 84–85 breakeven point in, 88 concerns about, 91–93 confining and heating approach to, 85 research funding for, 92–93 where we are now with, 86–90 fusion reactors, 314 Fusor.net, 80 fusors, tabletop, 80–84 Gaia (robot), 129–30 Gatenholm, Dr., 269 gene drive, 201–3 gene expression, 239 General Fusion, 89 genes, 195–96, 197, 204, 215 gene sequencing, 2 Genetic Access Control (app), 251–52 genetic disorders, 3, 219, 235–37 Genetic Information Non-discrimination Act (2008), 250–51 genetic mutations, 40, 231 George Mason University, 56 Georgia Institute of Technology, 130 geostationary orbit, 32, 34, 43 Germany, Nazi, 135 Gilpin, Kyle, 118 GitHub, 251 Global Catastrophic Risks (book of essays), 125n glucose, 286 GMOs (genetically modified organisms), 221 Go-Between, The (Hartley), 331 gold, 52, 92 “Golden Promise” barley, 192 Google, 111, 180, 197n, 232, 254, 290 Google Glass, 175–76, 179, 186 Google Scholar, 247 gophers, 96–97 GPS, 171, 173 Gramazio, Fabio, 152 granite, 144 Grant, Dale, 46 gravity, 15–16, 42, 43, 52, 56, 78 “Gray Goo Scenario,” 125 gray wolves, 224 Graz University of Technology, 177 Great Britain, 22 Great Depression, 45 Greeks, ancient, 6 Greenpeace, 94n Gunduz, Aysegul, 300, 301 guns, 3D printed, 125 hacking, of brain implants, 309 hands, 323–24, 332 haptic pen, 175 Haque Design + Research (Umbrellium), 111 hard hats, 179 Hartley, L.


pages: 625 words: 167,349

The Alignment Problem: Machine Learning and Human Values by Brian Christian

Albert Einstein, algorithmic bias, Alignment Problem, AlphaGo, Amazon Mechanical Turk, artificial general intelligence, augmented reality, autonomous vehicles, backpropagation, butterfly effect, Cambridge Analytica, Cass Sunstein, Claude Shannon: information theory, computer vision, Computing Machinery and Intelligence, data science, deep learning, DeepMind, Donald Knuth, Douglas Hofstadter, effective altruism, Elaine Herzberg, Elon Musk, Frances Oldham Kelsey, game design, gamification, Geoffrey Hinton, Goodhart's law, Google Chrome, Google Glasses, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, hedonic treadmill, ImageNet competition, industrial robot, Internet Archive, John von Neumann, Joi Ito, Kenneth Arrow, language acquisition, longitudinal study, machine translation, mandatory minimum, mass incarceration, multi-armed bandit, natural language processing, Nick Bostrom, Norbert Wiener, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, OpenAI, Panopticon Jeremy Bentham, pattern recognition, Peter Singer: altruism, Peter Thiel, precautionary principle, premature optimization, RAND corporation, recommendation engine, Richard Feynman, Rodney Brooks, Saturday Night Live, selection bias, self-driving car, seminal paper, side project, Silicon Valley, Skinner box, sparse data, speech recognition, Stanislav Petrov, statistical model, Steve Jobs, strong AI, the map is not the territory, theory of mind, Tim Cook: Apple, W. E. B. Du Bois, Wayback Machine, zero-sum game

., “Maximum Entropy Inverse Reinforcement Learning,” and Ziebart, Bagnell, and Dey, “Modeling Interaction via the Principle of Maximum Causal Entropy.” Much recent work in robotics and autonomous cars uses this same model of human behavior, sometimes referred to as “noisily rational” behavior or “Boltzmann (ir)rationality.” See, e.g., Finn, Levine, and Abbeel, “Guided Cost Learning”; Sadigh et al., “Planning for Autonomous Cars That Leverage Effects on Human Actions”; and Kwon et al., “When Humans Aren’t Optimal.” 30. As Stuart Russell put it in his original 1998 paper, “Can we determine the reward function by observation during rather than after learning?”

What they wanted were autonomous land vehicles.35 Thorpe successfully defended his doctoral thesis that September, telling his committee he was planning to take several weeks’ vacation and think about what he might be up to next. Instead, the director of the CMU Robotics Institute, Raj Reddy, in one breath congratulated him and said, “What you’re up to next is a meeting in my office starting in five minutes.” The meeting was about building autonomous vehicles for DARPA. “That,” recalls Thorpe—that five-minute window—“was my break between finishing my thesis and starting my postdoc.” “Vehicles” that were in some sense “self-driving” had by 1984 already been around for years, but to call the technology primitive would be perhaps too generous. Robotics pioneer Hans Moravec had, in his own PhD thesis at Stanford in 1980, enabled a robotic “cart” the size and shape of a desk on bicycle wheels to move itself around and avoid chairs and other obstacles using an onboard TV camera.

., “Uncertainty-Aware Reinforcement Learning for Collision Avoidance.” 31. For related work linking uncertainty with unfamiliar environments, see Kenton et al., “Generalizing from a Few Environments in Safety-Critical Reinforcement Learning.” For related work in the context of imitation learning and autonomous cars, see Tigas et al., “Robust Imitative Planning.” 32. Holt et al., “An Unconscious Patient with a DNR Tattoo.” And see Bever, “A Man Collapsed with ‘Do Not Resuscitate’ Tattooed on His Chest,” and Hersher, “When a Tattoo Means Life or Death,” for press accounts. 33. Holt et al., “An Unconscious Patient with a DNR Tattoo.” 34.


pages: 240 words: 78,436

Open for Business Harnessing the Power of Platform Ecosystems by Lauren Turner Claire, Laure Claire Reillier, Benoit Reillier

Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, barriers to entry, basic income, benefit corporation, Blitzscaling, blockchain, carbon footprint, Chuck Templeton: OpenTable:, cloud computing, collaborative consumption, commoditize, crowdsourcing, data science, deep learning, Diane Coyle, Didi Chuxing, disintermediation, distributed ledger, driverless car, fake news, fulfillment center, future of work, George Akerlof, independent contractor, intangible asset, Internet of things, Jean Tirole, Jeff Bezos, Kickstarter, knowledge worker, Lean Startup, Lyft, Mark Zuckerberg, market design, Metcalfe’s law, minimum viable product, multi-sided market, Network effects, Paradox of Choice, Paul Graham, peer-to-peer lending, performance metric, Peter Thiel, platform as a service, price discrimination, price elasticity of demand, profit motive, ride hailing / ride sharing, Sam Altman, search costs, self-driving car, seminal paper, shareholder value, sharing economy, Silicon Valley, Skype, smart contracts, Snapchat, software as a service, Steve Jobs, Steve Wozniak, TaskRabbit, the long tail, The Market for Lemons, Tim Cook: Apple, transaction costs, two-sided market, Uber and Lyft, uber lyft, universal basic income, Y Combinator

The future of platforms 215 10 See, for example, Bloomberg article dated 2 May 2016 on universal basic income: www.bloombergview.com/articles/2016-05-02/a-basic-income-should-be-the-nextbig-thing. 11 www.theverge.com/2014/9/30/6874353/reddit-50-million-funding-give-users-10percent-stock-equity and www.reddit.com/r/AskReddit/comments/2moyiz/serious_ how_should_reddit_inc_distribute_a/. 12 21 September 2015, Kickstarter blog, www.kickstarter.com/blog/kickstarter-is-now-abenefit-corporation?ref=charter. 13 Kickstarter fulfilment report, www.kickstarter.com/fulfillment. 14 At the time of writing, the Singapore Autonomous Vehicle Initiative (SAVI) is running live trials of autonomous cars. 15 Moore’s law states that computer processing power doubles every two years. 16 Sharetribe promises to have your platform business running in a few minutes without the need for a developer . . . give it a go at www.sharetribe.com/. 17 J. Rifkin, The Empathic Civilization, New York: Jeremy P.


pages: 345 words: 75,660

Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, Avi Goldfarb

Abraham Wald, Ada Lovelace, AI winter, Air France Flight 447, Airbus A320, algorithmic bias, AlphaGo, Amazon Picking Challenge, artificial general intelligence, autonomous vehicles, backpropagation, basic income, Bayesian statistics, Black Swan, blockchain, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, carbon tax, Charles Babbage, classic study, collateralized debt obligation, computer age, creative destruction, Daniel Kahneman / Amos Tversky, data acquisition, data is the new oil, data science, deep learning, DeepMind, deskilling, disruptive innovation, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, financial engineering, fulfillment center, general purpose technology, Geoffrey Hinton, Google Glasses, high net worth, ImageNet competition, income inequality, information retrieval, inventory management, invisible hand, Jeff Hawkins, job automation, John Markoff, Joseph Schumpeter, Kevin Kelly, Lyft, Minecraft, Mitch Kapor, Moneyball by Michael Lewis explains big data, Nate Silver, new economy, Nick Bostrom, On the Economy of Machinery and Manufactures, OpenAI, paperclip maximiser, pattern recognition, performance metric, profit maximization, QWERTY keyboard, race to the bottom, randomized controlled trial, Ray Kurzweil, ride hailing / ride sharing, Robert Solow, Salesforce, Second Machine Age, self-driving car, shareholder value, Silicon Valley, statistical model, Stephen Hawking, Steve Jobs, Steve Jurvetson, Steven Levy, strong AI, The Future of Employment, the long tail, The Signal and the Noise by Nate Silver, Tim Cook: Apple, trolley problem, Turing test, Uber and Lyft, uber lyft, US Airways Flight 1549, Vernor Vinge, vertical integration, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, William Langewiesche, Y Combinator, zero-sum game

., driverless cars); and (3) the returns to reduced waiting time for predictions are high (e.g., space exploration). An important distinction between autonomous vehicles operating on a city street versus those in a mine site is that the former generates significant externalities while the latter does not. Autonomous vehicles operating on a city street may cause an accident that incurs costs borne by individuals external to the decision maker. In contrast, accidents caused by autonomous vehicles operating on a mine site only incur costs affecting assets or people associated with the mine. Governments regulate activities that generate externalities.

For example, we are transforming transportation into a prediction problem. Autonomous vehicles have existed in controlled environments for over two decades. They were limited, however, to places with detailed floor plans such as factories and warehouses. The floor plans meant engineers could design their robots to maneuver with basic “if-then” logical intelligence: if a person walks in front of the vehicle, then stop. If the shelf is empty, then move to the next one. However, no one could use those vehicles on a regular city street. Too many things could happen—too many “ifs” to possibly code. Autonomous vehicles could not function outside a highly predictable, controlled environment—until engineers reframed navigation as a prediction problem.

KEY POINTS * * * Enhanced prediction enables decision makers, whether human or machine, to handle more “ifs” and more “thens.” That leads to better outcomes. For example, in the case of navigation, illustrated in this chapter with the mail robot, prediction machines liberate autonomous vehicles from their previous limitation of operating only in controlled environments. These settings are characterized by their limited number of “ifs” (or states). Prediction machines allow autonomous vehicles to operate in uncontrolled environments, like on a city street, because rather than having to code all the potential “ifs” in advance, the machine can instead learn to predict what a human controller would do in any particular situation.


Demystifying Smart Cities by Anders Lisdorf

3D printing, artificial general intelligence, autonomous vehicles, backpropagation, behavioural economics, Big Tech, bike sharing, bitcoin, business intelligence, business logic, business process, chief data officer, circular economy, clean tech, clean water, cloud computing, computer vision, Computing Machinery and Intelligence, congestion pricing, continuous integration, crowdsourcing, data is the new oil, data science, deep learning, digital rights, digital twin, distributed ledger, don't be evil, Elon Musk, en.wikipedia.org, facts on the ground, Google Glasses, hydroponic farming, income inequality, information security, Infrastructure as a Service, Internet of things, Large Hadron Collider, Masdar, microservices, Minecraft, OSI model, platform as a service, pneumatic tube, ransomware, RFID, ride hailing / ride sharing, risk tolerance, Salesforce, self-driving car, smart cities, smart meter, software as a service, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Stuxnet, Thomas Bayes, Turing test, urban sprawl, zero-sum game

This is not just hard but downright impossible for any current artificial intelligence. Autonomous vehicles and ethics Let us look at this through the lens of an existing AI problem. Today many cities have begun allowing companies to test autonomous vehicles (AV) on their streets. On virtually every parameter, they are performing well and well above their human counterparts if the vendors are to be trusted. There is the occasional accident that spurs quite a lot of media attention. Given the low scale AV testing is currently carried out, this will be amplified significantly when it is rolled out. While the autonomous vehicles are very good at following rules and identifying the proper ones in a given situation, what happens in situations where the rules might be conflicting and they even have to make a tradeoff decision with ethical impact?

While the autonomous vehicles are very good at following rules and identifying the proper ones in a given situation, what happens in situations where the rules might be conflicting and they even have to make a tradeoff decision with ethical impact? Here is a thought experiment to illustrate the issue. An autonomous vehicle is driving on a sunny spring afternoon through the streets of New York. It is a good day, and it is able to keep a good pace. On its right is a sidewalk with a lot of pedestrians; on its left is a traffic lane going the opposite direction. Now suddenly a child runs out into the road in front of the AV, and it is impossible for it to brake in time. The autonomous vehicle needs to make a choice. It has three options:1)It runs over the child and kills it while not hurting the people inside the AV or the pedestrians on the sidewalk. 2)It makes an evasive maneuver to the right hitting pedestrians, thereby killing or injuring one or more people while not hurting the people inside the AV. 3)It makes an evasive maneuver to the left hitting cars going the other direction, thereby killing the people in the AV and the people in the other car but sparing the child and the pedestrians.

Working with devices in the city Managing devices Methods for communicating with devices The challenges of protecting devices Developing device standards:​ An interagency effort Security standards Privacy standards Architecture standards Solution spotlights Cities Coalition for Digital Rights Array of things PlowNYC Exteros Summary Chapter 4:​ Data Source systems Systems of record Sensors Online sources Structure of data Structured data Semi-structured data Unstructured data Data services Object storage Relational databases Document database Key value stores Graph databases Block chain Data access Machine-to-machine Graphical user interface Deployment On premise Cloud Comparison between on premise and cloud Regulatory requirements Health data Criminal justice data Personal data in general Data management Data governance Master data management Data quality Summary Chapter 5:​ Intelligence The history of AI The promise and threat of AI What is Artificial Intelligence really?​ Machine learning Popular AI algorithms Key issues in AI for Smart Cities Artificial and human intelligence Autonomous vehicles and ethics Artificial Intelligence meets the real world The optimization paradox The challenges to AI AI solutions in the city Making cities smarter with AI Solution spotlights Project Alvelor Amsterdam 311 Summary Chapter 6:​ Engagement Technology adoption curve Risk and Reward Types of work Modes of working Engagement models Implementing smart city technologies Solution spotlights 100 Resilient Cities Waze Connected Citizens BetaNYC Summary Part II: Toward smarter cities Chapter 7:​ Architect with imagination:​ Could payphones show the way in an emergency?​


pages: 304 words: 80,143

The Autonomous Revolution: Reclaiming the Future We’ve Sold to Machines by William Davidow, Michael Malone

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, agricultural Revolution, Airbnb, AlphaGo, American Society of Civil Engineers: Report Card, Automated Insights, autonomous vehicles, basic income, benefit corporation, bitcoin, blockchain, blue-collar work, Bob Noyce, business process, call centre, Cambridge Analytica, cashless society, citizen journalism, Clayton Christensen, collaborative consumption, collaborative economy, collective bargaining, creative destruction, crowdsourcing, cryptocurrency, deep learning, DeepMind, disintermediation, disruptive innovation, distributed ledger, en.wikipedia.org, Erik Brynjolfsson, fake news, Filter Bubble, Ford Model T, Francis Fukuyama: the end of history, general purpose technology, Geoffrey West, Santa Fe Institute, gig economy, Gini coefficient, high-speed rail, holacracy, Hyperloop, income inequality, industrial robot, Internet of things, invention of agriculture, invention of movable type, invention of the printing press, invisible hand, Jane Jacobs, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, license plate recognition, low interest rates, Lyft, Mark Zuckerberg, mass immigration, Network effects, new economy, peer-to-peer lending, QWERTY keyboard, ransomware, Richard Florida, Robert Gordon, robo advisor, Ronald Reagan, Second Machine Age, self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Simon Kuznets, Skinner box, Snapchat, speech recognition, streetcar suburb, Stuxnet, surveillance capitalism, synthetic biology, TaskRabbit, The Death and Life of Great American Cities, The Rise and Fall of American Growth, the scientific method, trade route, Turing test, two and twenty, Uber and Lyft, uber lyft, universal basic income, uranium enrichment, urban planning, vertical integration, warehouse automation, zero day, zero-sum game, Zipcar

The bad news is that this would cause about 1.5 million jobs to disappear in automotive related industries—manufacturing, service, insurance, and so forth. The effects of autonomous vehicles will also be felt in wider swaths of the economy. Level 5 Autonomous Trucks—aTrucks—will move goods faster, more efficiently, and more safely than trucks driven by people. There are 3.5 million professional truck drivers in our country, and about 8.7 million people employed in the trucking business.26 Many of them will be displaced. If just one-quarter of them are, that’s more than 2 million jobs.27 No doubt autonomous vehicles will also drive down the costs of delivery services. The business model for groceries, retail stores, and many commodity products will consist of large automated warehouses that deliver products ordered over the Internet to the customer’s home or to a convenient location for pick-up within a few hours.

We expected that virtual corporations would take a decade or more to become ubiquitous. But a hidden force was about to burst on the scene that would propel this revolution faster than we envisioned: the World Wide Web. The genesis of this book was similar. Having been present at the birth of social networking, massive multiplayer games, autonomous vehicles, modern artificial intelligence, and all of the other defining new technologies of the twenty-first century, we have watched with growing dismay, even horror, at how many of these developments have morphed into increasingly malevolent threats to human privacy and liberty. Living in Silicon Valley, we watched firsthand, with growing trepidation, the effects of the modern networked, digital—virtual—world on its most passionate users.

They can even try on an item of clothing virtually, using digital imaging. The computer takes their credit card information (an intelligence equivalence of what a flesh-and-blood retail clerk does), and FedEx or UPS provides delivery. For many commodities, the retail substitutional equivalence of the future will consist of shopping online with rapid delivery by autonomous vehicles from large warehouses located near urban centers. The same functions will be carried out, but will use entirely different tools, rules, and processes. Surprisingly, many institutions that do not appear to be heavily dependent on information content for their success are, in fact, quite vulnerable to disruption by information equivalent infrastructures.


pages: 280 words: 74,559

Fully Automated Luxury Communism by Aaron Bastani

"Peter Beck" AND "Rocket Lab", Alan Greenspan, Anthropocene, autonomous vehicles, banking crisis, basic income, Berlin Wall, Bernie Sanders, Boston Dynamics, Bretton Woods, Brexit referendum, capital controls, capitalist realism, cashless society, central bank independence, collapse of Lehman Brothers, computer age, computer vision, CRISPR, David Ricardo: comparative advantage, decarbonisation, deep learning, dematerialisation, DIY culture, Donald Trump, double helix, driverless car, electricity market, Elon Musk, energy transition, Erik Brynjolfsson, fake news, financial independence, Francis Fukuyama: the end of history, future of work, Future Shock, G4S, general purpose technology, Geoffrey Hinton, Gregor Mendel, housing crisis, income inequality, industrial robot, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Isaac Newton, James Watt: steam engine, Jeff Bezos, Jeremy Corbyn, Jevons paradox, job automation, John Markoff, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Kuiper Belt, land reform, Leo Hollis, liberal capitalism, low earth orbit, low interest rates, low skilled workers, M-Pesa, market fundamentalism, means of production, mobile money, more computing power than Apollo, new economy, off grid, pattern recognition, Peter H. Diamandis: Planetary Resources, post scarcity, post-work, price mechanism, price stability, private spaceflight, Productivity paradox, profit motive, race to the bottom, rewilding, RFID, rising living standards, Robert Solow, scientific management, Second Machine Age, self-driving car, sensor fusion, shareholder value, Silicon Valley, Simon Kuznets, Slavoj Žižek, SoftBank, stem cell, Stewart Brand, synthetic biology, technological determinism, technoutopianism, the built environment, the scientific method, The Wealth of Nations by Adam Smith, Thomas Malthus, transatlantic slave trade, Travis Kalanick, universal basic income, V2 rocket, Watson beat the top human players on Jeopardy!, We are as Gods, Whole Earth Catalog, working-age population

As a result these vehicles can navigate streets and motorways by relying on precise GPS data, huge amounts of information regarding maps, and a continuous stream of real-time updates on other cars, potential obstacles, pedestrians and all the variables human drivers have to consider. All of this is achieved with a myriad of sensors, lasers and cameras processing information as 1s and 0s. Even in isolation the arrival of autonomous vehicles likely spells the disappearance of whole professions. In 2014, driving accounted for around 4 million jobs in the US alone, and according to a report by Goldman Sachs the country could see job losses at a rate of 300,000 a year as autonomous vehicles become an integrated feature of modern society. From the perspective of business that would be entirely understandable: logistics vehicles running twenty-four hours a day, seven days a week, offer massive savings.

More vital is how digitisation has allowed progressively greater amounts of cognition and memory to be performed in 0s and 1s, with the price–performance ratio of anything that does so falling every year for decades. It is this which allows contemporary camera technology to land rockets and, increasingly, drive autonomous vehicles; it is what will provide robots with fine motor coordination and dexterity equivalent to that found in humans; it will permit the built environment to know more about us, in certain respects, than we know about ourselves. It will even allow us to edit DNA – the building blocks of life – to remove hereditary disease and sequence genomes at such low cost, and with such regularity, that we will cure ourselves of cancer before it reaches Stage 1.

But while robots whose movements authentically resemble those of humans aren’t quite here yet, another category of machine – drawing on the same gains in digitisation and the dividend of exponential progress – is on the verge of transforming whole industries. It is the leading edge of a transformation which will mean not only the loss of countless jobs, but entire professions. And just like the acrobatics of Atlas, nobody saw it coming – until it was right in front of them. Autonomous Vehicles In 2002 the American defence agency DARPA announced a ‘Grand Challenge’ for driverless cars scheduled to take place in the Mojave Desert in spring 2004. The proposed route was two hundred and forty kilometres long and the prize, for whichever car finished first, was set at $1 million. While some of the most brilliant minds in America applied themselves to the task, not one of the fifteen teams present at the start line was able to complete the course.


Data Action: Using Data for Public Good by Sarah Williams

affirmative action, Amazon Mechanical Turk, Andrei Shleifer, augmented reality, autonomous vehicles, Brexit referendum, Cambridge Analytica, Charles Babbage, City Beautiful movement, commoditize, coronavirus, COVID-19, crowdsourcing, data acquisition, data is the new oil, data philanthropy, data science, digital divide, digital twin, Donald Trump, driverless car, Edward Glaeser, fake news, four colour theorem, global village, Google Earth, informal economy, Internet of things, Jane Jacobs, John Snow's cholera map, Kibera, Lewis Mumford, Marshall McLuhan, mass immigration, mass incarceration, megacity, military-industrial complex, Minecraft, neoliberal agenda, New Urbanism, Norbert Wiener, nowcasting, oil shale / tar sands, openstreetmap, place-making, precautionary principle, RAND corporation, ride hailing / ride sharing, selection bias, self-driving car, sentiment analysis, Sidewalk Labs, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, Steven Levy, the built environment, The Chicago School, The Death and Life of Great American Cities, transatlantic slave trade, Uber for X, upwardly mobile, urban planning, urban renewal, W. E. B. Du Bois, Works Progress Administration

Data Is a Public Infrastructure Now let's look at the same idea in American and European contexts, where private companies currently hold and are set to hold exponentially more data about cities, their populations, and their communities. Let's use Waymo, Google's autonomous vehicle program, as an example. A single Waymo test vehicle scans the environment with LIDAR sensors producing about 30 terabytes of data per day—that's three thousand times the amount of data that Twitter produces daily.6 Programmers at Google use this data to construct 3D representations of the physical world, often referred to as digital twins, which are used to guide autonomous vehicles on the road. In this virtual environment, autonomous vehicles—or any robot for that matter—can be guided and instructed to turn, to stop and pick up a passenger, or to come to a halt for a pedestrian to cross the street.

When this data is added to the digital world created for the car, it can be used to personalize our driving experience. Having captured your buying habits and movements, this data can be mined to identify, for instance, when and where you might want to buy a quart of milk, and your car can be programmed to remind you to do it. Adding this type of information to autonomous vehicles’ databases allows cars to make decisions based on previous human behaviors.7 This alternative reality—really a new digital reality—will be the infrastructure of the future, itself a new “public good.” We will tap into it to perform all kinds of tasks. Much in the same way networked computers created the World Wide Web, this environment will power many innovations created by those who have the capabilities to tap into it.

Much in the same way networked computers created the World Wide Web, this environment will power many innovations created by those who have the capabilities to tap into it. Right now, that ability lies solely with the companies who generate the data, companies such as Waymo, Tesla, Ford, and others experimenting with autonomous vehicles. Companies are already using their virtual worlds to create diverse products beyond the car. Google has developed an augmented reality navigation tool for your smartphone, fueled by this digital environment (figure 5.1a and 5.1b). Microsoft has started to use some of this data to create a product it calls 3D Soundscapes (figure 5.2), which taps into the virtual environment to help a person with vision impairment or loss to navigate through the city.


pages: 346 words: 97,890

The Road to Conscious Machines by Michael Wooldridge

Ada Lovelace, AI winter, algorithmic bias, AlphaGo, Andrew Wiles, Anthropocene, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, basic income, Bletchley Park, Boeing 747, British Empire, call centre, Charles Babbage, combinatorial explosion, computer vision, Computing Machinery and Intelligence, DARPA: Urban Challenge, deep learning, deepfake, DeepMind, Demis Hassabis, don't be evil, Donald Trump, driverless car, Elaine Herzberg, Elon Musk, Eratosthenes, factory automation, fake news, future of work, gamification, general purpose technology, Geoffrey Hinton, gig economy, Google Glasses, intangible asset, James Watt: steam engine, job automation, John von Neumann, Loebner Prize, Minecraft, Mustafa Suleyman, Nash equilibrium, Nick Bostrom, Norbert Wiener, NP-complete, P = NP, P vs NP, paperclip maximiser, pattern recognition, Philippa Foot, RAND corporation, Ray Kurzweil, Rodney Brooks, self-driving car, Silicon Valley, Stephen Hawking, Steven Pinker, strong AI, technological singularity, telemarketer, Tesla Model S, The Coming Technological Singularity, The Future of Employment, the scientific method, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, trolley problem, Turing machine, Turing test, universal basic income, Von Neumann architecture, warehouse robotics

Most cities already have bus lanes and cycle lanes, so why not driverless car lanes? Such lanes might be augmented by sensors and other technology to assist autonomous vehicles. The presence of such lanes would also send a clear signal to human drivers sharing the roads with autonomous vehicles: beware robot drivers! As to the question of Level 5 autonomy, we are still some distance away, I’m afraid. But it is inevitable. My best guess is that it will be at least 20 years from the time of writing before Level 5 autonomous vehicles are widely available. But I am pretty confident that my grandchildren will regard the idea that their grandfather actually drove a car on his own with a mixture of horror and amusement.

A A* 77 À la recherche du temps perdu (Proust) 205–8 accountability 257 Advanced Research Projects Agency (ARPA) 87–8 adversarial machine learning 190 AF (Artificial Flight) parable 127–9, 243 agent-based AI 136–49 agent-based interfaces 147, 149 ‘Agents That Reduce Work and Information Overload’ (Maes) 147–8 AGI (Artificial General Intelligence) 41 AI – difficulty of 24–8 – ethical 246–62, 284, 285 – future of 7–8 – General 42, 53, 116, 119–20 – Golden Age of 47–88 – history of 5–7 – meaning of 2–4 – narrow 42 – origin of name 51–2 – strong 36–8, 41, 309–14 – symbolic 42–3, 44 – varieties of 36–8 – weak 36–8 AI winter 87–8 AI-complete problems 84 ‘Alchemy and AI’ (Dreyfus) 85 AlexNet 187 algorithmic bias 287–9, 292–3 alienation 274–7 allocative harm 287–8 AlphaFold 214 AlphaGo 196–9 AlphaGo Zero 199 AlphaZero 199–200 Alvey programme 100 Amazon 275–6 Apple Watch 218 Argo AI 232 arithmetic 24–6 Arkin, Ron 284 ARPA (Advanced Research Projects Agency) 87–8 Artificial Flight (AF) parable 127–9, 243 Artificial General Intelligence (AGI) 41 artificial intelligence see AI artificial languages 56 Asilomar principles 254–6 Asimov, Isaac 244–6 Atari 2600 games console 192–6, 327–8 augmented reality 296–7 automated diagnosis 220–1 automated translation 204–8 automation 265, 267–72 autonomous drones 282–4 Autonomous Vehicle Disengagement Reports 231 autonomous vehicles see driverless cars autonomous weapons 281–7 autonomy levels 227–8 Autopilot 228–9 B backprop/backpropagation 182–3 backward chaining 94 Bayes nets 158 Bayes’ Theorem 155–8, 365–7 Bayesian networks 158 behavioural AI 132–7 beliefs 108–10 bias 172 black holes 213–14 Blade Runner 38 Blocks World 57–63, 126–7 blood diseases 94–8 board games 26, 75–6 Boole, George 107 brains 43, 306, 330–1 see also electronic brains branching factors 73 Breakout (video game) 193–5 Brooks, Rodney 125–9, 132, 134, 243 bugs 258 C Campaign to Stop Killer Robots 286 CaptionBot 201–4 Cardiogram 215 cars 27–8, 155, 223–35 certainty factors 97 ceteris paribus preferences 262 chain reactions 242–3 chatbots 36 checkers 75–7 chess 163–4, 199 Chinese room 311–14 choice under uncertainty 152–3 combinatorial explosion 74, 80–1 common values and norms 260 common-sense reasoning 121–3 see also reasoning COMPAS 280 complexity barrier 77–85 comprehension 38–41 computational complexity 77–85 computational effort 129 computers – decision making 23–4 – early developments 20 – as electronic brains 20–4 – intelligence 21–2 – programming 21–2 – reliability 23 – speed of 23 – tasks for 24–8 – unsolved problems 28 ‘Computing Machinery and Intelligence’ (Turing) 32 confirmation bias 295 conscious machines 327–30 consciousness 305–10, 314–17, 331–4 consensus reality 296–8 consequentialist theories 249 contradictions 122–3 conventional warfare 286 credit assignment problem 173, 196 Criado Perez, Caroline 291–2 crime 277–81 Cruise Automation 232 curse of dimensionality 172 cutlery 261 Cybernetics (Wiener) 29 Cyc 114–21, 208 D DARPA (Defense Advanced Research Projects Agency) 87–8, 225–6 Dartmouth summer school 1955 50–2 decidable problems 78–9 decision problems 15–19 deduction 106 deep learning 168, 184–90, 208 DeepBlue 163–4 DeepFakes 297–8 DeepMind 167–8, 190–200, 220–1, 327–8 Defense Advanced Research Projects Agency (DARPA) 87–8, 225–6 dementia 219 DENDRAL 98 Dennett, Daniel 319–25 depth-first search 74–5 design stance 320–1 desktop computers 145 diagnosis 220–1 disengagements 231 diversity 290–3 ‘divide and conquer’ assumption 53–6, 128 Do-Much-More 35–6 dot-com bubble 148–9 Dreyfus, Hubert 85–6, 311 driverless cars 27–8, 155, 223–35 drones 282–4 Dunbar, Robin 317–19 Dunbar’s number 318 E ECAI (European Conference on AI) 209–10 electronic brains 20–4 see also computers ELIZA 32–4, 36, 63 employment 264–77 ENIAC 20 Entscheidungsproblem 15–19 epiphenomenalism 316 error correction procedures 180 ethical AI 246–62, 284, 285 European Conference on AI (ECAI) 209–10 evolutionary development 331–3 evolutionary theory 316 exclusive OR (XOR) 180 expected utility 153 expert systems 89–94, 123 see also Cyc; DENDRAL; MYCIN; R1/XCON eye scans 220–1 F Facebook 237 facial recognition 27 fake AI 298–301 fake news 293–8 fake pictures of people 214 Fantasia 261 feature extraction 171–2 feedback 172–3 Ferranti Mark 1 20 Fifth Generation Computer Systems Project 113–14 first-order logic 107 Ford 232 forward chaining 94 Frey, Carl 268–70 ‘The Future of Employment’ (Frey & Osborne) 268–70 G game theory 161–2 game-playing 26 Gangs Matrix 280 gender stereotypes 292–3 General AI 41, 53, 116, 119–20 General Motors 232 Genghis robot 134–6 gig economy 275 globalization 267 Go 73–4, 196–9 Golden Age of AI 47–88 Google 167, 231, 256–7 Google Glass 296–7 Google Translate 205–8, 292–3 GPUs (Graphics Processing Units) 187–8 gradient descent 183 Grand Challenges 2004/5 225–6 graphical user interfaces (GUI) 144–5 Graphics Processing Units (GPUs) 187–8 GUI (graphical user interfaces) 144–5 H hard problem of consciousness 314–17 hard problems 84, 86–7 Harm Assessment Risk Tool (HART) 277–80 Hawking, Stephen 238 healthcare 215–23 Herschel, John 304–6 Herzberg, Elaine 230 heuristic search 75–7, 164 heuristics 91 higher-order intentional reasoning 323–4, 328 high-level programming languages 144 Hilbert, David 15–16 Hinton, Geoff 185–6, 221 HOMER 141–3, 146 homunculus problem 315 human brain 43, 306, 330–1 human intuition 311 human judgement 222 human rights 277–81 human-level intelligence 28–36, 241–3 ‘humans are special’ argument 310–11 I image classification 186–7 image-captioning 200–4 ImageNet 186–7 Imitation Game 30 In Search of Lost Time (Proust) 205–8 incentives 261 indistinguishability 30–1, 37, 38 Industrial Revolutions 265–7 inference engines 92–4 insurance 219–20 intelligence 21–2, 127–8, 200 – human-level 28–36, 241–3 ‘Intelligence Without Representation’ (Brooks) 129 Intelligent Knowledge-Based Systems 100 intentional reasoning 323–4, 328 intentional stance 321–7 intentional systems 321–2 internal mental phenomena 306–7 Internet chatbots 36 intuition 311 inverse reinforcement learning 262 Invisible Women (Criado Perez) 291–2 J Japan 113–14 judgement 222 K Kasparov, Garry 163 knowledge bases 92–4 knowledge elicitation problem 123 knowledge graph 120–1 Knowledge Navigator 146–7 knowledge representation 91, 104, 129–30, 208 knowledge-based AI 89–123, 208 Kurzweil, Ray 239–40 L Lee Sedol 197–8 leisure 272 Lenat, Doug 114–21 lethal autonomous weapons 281–7 Lighthill Report 87–8 LISP 49, 99 Loebner Prize Competition 34–6 logic 104–7, 121–2 logic programming 111–14 logic-based AI 107–11, 130–2 M Mac computers 144–6 McCarthy, John 49–52, 107–8, 326–7 machine learning (ML) 27, 54–5, 168–74, 209–10, 287–9 machines with mental states 326–7 Macintosh computers 144–6 magnetic resonance imaging (MRI) 306 male-orientation 290–3 Manchester Baby computer 20, 24–6, 143–4 Manhattan Project 51 Marx, Karl 274–6 maximizing expected utility 154 Mercedes 231 Mickey Mouse 261 microprocessors 267–8, 271–2 military drones 282–4 mind modelling 42 mind-body problem 314–17 see also consciousness minimax search 76 mining industry 234 Minsky, Marvin 34, 52, 180 ML (machine learning) 27, 54–5, 168–74, 209–10, 287–9 Montezuma’s Revenge (video game) 195–6 Moore’s law 240 Moorfields Eye Hospital 220–1 moral agency 257–8 Moral Machines 251–3 MRI (magnetic resonance imaging) 306 multi-agent systems 160–2 multi-layer perceptrons 177, 180, 182 Musk, Elon 238 MYCIN 94–8, 217 N Nagel, Thomas 307–10 narrow AI 42 Nash, John Forbes Jr 50–1, 161 Nash equilibrium 161–2 natural languages 56 negative feedback 173 neural nets/neural networks 44, 168, 173–90, 369–72 neurons 174 Newell, Alan 52–3 norms 260 NP-complete problems 81–5, 164–5 nuclear energy 242–3 nuclear fusion 305 O ontological engineering 117 Osborne, Michael 268–70 P P vs NP problem 83 paperclips 261 Papert, Seymour 180 Parallel Distributed Processing (PDP) 182–4 Pepper 299 perception 54 perceptron models 174–81, 183 Perceptrons (Minsky & Papert) 180–1, 210 personal healthcare management 217–20 perverse instantiation 260–1 Phaedrus 315 physical stance 319–20 Plato 315 police 277–80 Pratt, Vaughan 117–19 preference relations 151 preferences 150–2, 154 privacy 219 problem solving and planning 55–6, 66–77, 128 programming 21–2 programming languages 144 PROLOG 112–14, 363–4 PROMETHEUS 224–5 protein folding 214 Proust, Marcel 205–8 Q qualia 306–7 QuickSort 26 R R1/XCON 98–9 radiology 215, 221 railway networks 259 RAND Corporation 51 rational decision making 150–5 reasoning 55–6, 121–3, 128–30, 137, 315–16, 323–4, 328 regulation of AI 243 reinforcement learning 172–3, 193, 195, 262 representation harm 288 responsibility 257–8 rewards 172–3, 196 robots – as autonomous weapons 284–5 – Baye’s theorem 157 – beliefs 108–10 – fake 299–300 – indistinguishability 38 – intentional stance 326–7 – SHAKEY 63–6 – Sophia 299–300 – Three Laws of Robotics 244–6 – trivial tasks 61 – vacuum cleaning 132–6 Rosenblatt, Frank 174–81 rules 91–2, 104, 359–62 Russia 261 Rutherford, Ernest (1st Baron Rutherford of Nelson) 242 S Sally-Anne tests 328–9, 330 Samuel, Arthur 75–7 SAT solvers 164–5 Saudi Arabia 299–300 scripts 100–2 search 26, 68–77, 164, 199 search trees 70–1 Searle, John 311–14 self-awareness 41, 305 see also consciousness semantic nets 102 sensors 54 SHAKEY the robot 63–6 SHRDLU 56–63 Simon, Herb 52–3, 86 the Singularity 239–43 The Singularity is Near (Kurzweil) 239 Siri 149, 298 Smith, Matt 201–4 smoking 173 social brain 317–19 see also brains social media 293–6 social reasoning 323, 324–5 social welfare 249 software agents 143–9 software bugs 258 Sophia 299–300 sorting 26 spoken word translation 27 STANLEY 226 STRIPS 65 strong AI 36–8, 41, 309–14 subsumption architecture 132–6 subsumption hierarchy 134 sun 304 supervised learning 169 syllogisms 105, 106 symbolic AI 42–3, 44, 181 synapses 174 Szilard, Leo 242 T tablet computers 146 team-building problem 78–81, 83 Terminator narrative of AI 237–9 Tesla 228–9 text recognition 169–71 Theory of Mind (ToM) 330 Three Laws of Robotics 244–6 TIMIT 292 ToM (Theory of Mind) 330 ToMnet 330 TouringMachines 139–41 Towers of Hanoi 67–72 training data 169–72, 288–9, 292 translation 204–8 transparency 258 travelling salesman problem 82–3 Trolley Problem 246–53 Trump, Donald 294 Turing, Alan 14–15, 17–19, 20, 24–6, 77–8 Turing Machines 18–19, 21 Turing test 29–38 U Uber 168, 230 uncertainty 97–8, 155–8 undecidable problems 19, 78 understanding 201–4, 312–14 unemployment 264–77 unintended consequences 263 universal basic income 272–3 Universal Turing Machines 18, 19 Upanishads 315 Urban Challenge 2007 226–7 utilitarianism 249 utilities 151–4 utopians 271 V vacuum cleaning robots 132–6 values and norms 260 video games 192–6, 327–8 virtue ethics 250 Von Neumann and Morgenstern model 150–5 Von Neumann architecture 20 W warfare 285–6 WARPLAN 113 Waymo 231, 232–3 weak AI 36–8 weapons 281–7 wearable technology 217–20 web search 148–9 Weizenbaum, Joseph 32–4 Winograd schemas 39–40 working memory 92 X XOR (exclusive OR) 180 Z Z3 computer 19–20 PELICAN BOOKS Economics: The User’s Guide Ha-Joon Chang Human Evolution Robin Dunbar Revolutionary Russia: 1891–1991 Orlando Figes The Domesticated Brain Bruce Hood Greek and Roman Political Ideas Melissa Lane Classical Literature Richard Jenkyns Who Governs Britain?

And yet we are accustomed to the danger of road travel – we seem to accept the risk as an occupational hazard of living in the modern world. But AI holds out the real prospect of dramatically reducing those risks: driverless cars are a real possibility in the medium term, and, ultimately, they have the potential to save lives on a massive scale. There are, of course, many other good reasons for wanting autonomous vehicles. Computers can be programmed to drive cars efficiently, making better use of scarce and expensive fuel or power resources, resulting in environmentally friendlier cars with lower running costs. Computers could also potentially make better use of road networks, for example, allowing far greater through-put at congested road junctions.


pages: 328 words: 84,682

The Business of Platforms: Strategy in the Age of Digital Competition, Innovation, and Power by Michael A. Cusumano, Annabelle Gawer, David B. Yoffie

activist fund / activist shareholder / activist investor, Airbnb, AltaVista, Amazon Web Services, AOL-Time Warner, asset light, augmented reality, autonomous vehicles, barriers to entry, bitcoin, blockchain, business logic, Cambridge Analytica, Chuck Templeton: OpenTable:, cloud computing, collective bargaining, commoditize, CRISPR, crowdsourcing, cryptocurrency, deep learning, Didi Chuxing, distributed ledger, Donald Trump, driverless car, en.wikipedia.org, fake news, Firefox, general purpose technology, gig economy, Google Chrome, GPS: selective availability, Greyball, independent contractor, Internet of things, Jeff Bezos, Jeff Hawkins, John Zimmer (Lyft cofounder), Kevin Roose, Lean Startup, Lyft, machine translation, Mark Zuckerberg, market fundamentalism, Metcalfe’s law, move fast and break things, multi-sided market, Network effects, pattern recognition, platform as a service, Ponzi scheme, recommendation engine, Richard Feynman, ride hailing / ride sharing, Robert Metcalfe, Salesforce, self-driving car, sharing economy, Silicon Valley, Skype, Snapchat, SoftBank, software as a service, sovereign wealth fund, speech recognition, stealth mode startup, Steve Ballmer, Steve Jobs, Steven Levy, subscription business, Susan Wojcicki, TaskRabbit, too big to fail, transaction costs, transport as a service, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, vertical integration, Vision Fund, web application, zero-sum game

Lyft’s platform initiative brings together several automakers, including GM, Land Rover, and Ford, to integrate their autonomous vehicle projects into one ride-hailing network.11 Initially, the Open Platform Initiative is offering partners access to ride data for testing purposes, but ultimately plans to make their self-driving vehicles available on its ride-hailing platform. Lyft’s chief strategy officer noted in late 2017 that “we’re focused on partnering with the auto industry because frankly, we think we can’t do this alone and need each other to be successful.”12 Lyft cofounder John Zimmer even predicted that “autonomous vehicle fleets will quickly become widespread and will account for the majority of Lyft rides within 5 years.”13 Lyft’s strategy may signal the emergence of a different type of transaction platform, where Lyft connects riders to self-driving vehicles from a variety of manufacturers.

And some current products and services or path-breaking technologies may turn into new types of platforms. In the remainder of this chapter, we discuss two relatively new platform battlegrounds and their possible evolution, if artificial intelligence impacts them the way we predict: voice wars and autonomous vehicle ride sharing. Then we will look at two emerging and future battlegrounds: quantum computing and gene editing. CURRENT/ONGOING Perhaps the most important new technology in the battle for platforms over the next decade is artificial intelligence and machine learning. For many industries, AI has disruptive potential.

Despite a long history of selling products, even the most conservative car companies see AI as the route toward becoming a service company. As Lyft CEO Logan Green said in 2018, “We are going to move the entire [car] industry from one based on ownership, to one based on subscription.”5 The emergence of autonomous vehicle technology promises to remove human drivers, which could dramatically drive down the marginal cost of transportation services. Amortizing the R&D and fleet costs of self-driving cars is likely to be very high. But the economics could improve because there are no driver payments and cars will be utilized more intensively, dramatically reducing the cost per mile.6 GM estimated that, when it launches its service in 2019, rides would initially cost $1.50 per mile, 40 percent less than current ride-hailing services.7 Some estimates suggested that the cost per mile of a self-driving vehicle could fall as low as 35 cents per mile, down from an average of $2.86 per mile in 2018.8 Observers see the combination of new technology and better economics forcing Uber (and other ride-sharing platforms) to “either figure out a way to buy or at least manage an enormous fleet (possibly by going public to foot the bill), or face annihilation by others who will.”9 Facing this threat, Uber began investing in autonomous vehicle technology in 2014.


pages: 320 words: 95,629

Decoding the World: A Roadmap for the Questioner by Po Bronson

23andMe, 3D printing, 4chan, Abraham Maslow, Affordable Care Act / Obamacare, altcoin, Apple's 1984 Super Bowl advert, Asilomar, autonomous vehicles, basic income, Big Tech, bitcoin, blockchain, Burning Man, call centre, carbon credits, carbon tax, cognitive bias, cognitive dissonance, coronavirus, COVID-19, CRISPR, cryptocurrency, decarbonisation, deep learning, deepfake, DeepMind, dematerialisation, Donald Trump, driverless car, dumpster diving, edge city, Ethereum, ethereum blockchain, Eyjafjallajökull, factory automation, fake news, financial independence, Google X / Alphabet X, green new deal, income inequality, industrial robot, Isaac Newton, Jeff Bezos, Kevin Kelly, Kickstarter, Mars Rover, mass immigration, McMansion, means of production, microbiome, microplastics / micro fibres, oil shale / tar sands, opioid epidemic / opioid crisis, Paul Graham, paypal mafia, phenotype, Ponzi scheme, power law, quantum entanglement, Ronald Reagan, Sand Hill Road, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, smart contracts, source of truth, stem cell, Steve Jobs, Steve Jurvetson, sustainable-tourism, synthetic biology, Tesla Model S, too big to fail, trade route, universal basic income, Watson beat the top human players on Jeopardy!, women in the workforce

The spikes are electrical signals from the neurons when they fire. They are happening fast, ten to twenty a second and at random. We are looking at a primitive artificial brain with sixteen neurons, same number as a dragonfly, hooked up to a computer. Peter turns to me with raised eyebrows. “Not sure this will work. But a fully autonomous vehicle’s AI systems still cannot handle all the edge cases a driver would experience,” he tells me. “It’s hard to get all the data to tell the computer how to react in any specific case, like a foggy road in the rain or another driver making erratic moves.” I reply with a half question half statement.

Robots like to operate away from humans. “Anywhere we can completely remove the humans, the robots do well,” he says. Factories that run in the dark are an example. So is mining; he says the Rio Tinto corporation in Australia has more miles on its self-driving diggers and transporters than any autonomous car company. The biggest challenge to robot companies, I soon learn, isn’t the robots. It’s the humans. To make a robot that works around humans requires a ton of sensors. The robot has to constantly monitor the human in case it does something unpredictable, which it does a lot. Robots have to be prepared to freeze at any second, or stop work and get out of the way for a moment.


pages: 515 words: 126,820

Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World by Don Tapscott, Alex Tapscott

"World Economic Forum" Davos, Airbnb, altcoin, Alvin Toffler, asset-backed security, autonomous vehicles, barriers to entry, behavioural economics, bitcoin, Bitcoin Ponzi scheme, blockchain, Blythe Masters, Bretton Woods, business logic, business process, buy and hold, Capital in the Twenty-First Century by Thomas Piketty, carbon credits, carbon footprint, clean water, cloud computing, cognitive dissonance, commoditize, commons-based peer production, corporate governance, corporate social responsibility, creative destruction, Credit Default Swap, crowdsourcing, cryptocurrency, currency risk, decentralized internet, digital capitalism, disintermediation, disruptive innovation, distributed ledger, do well by doing good, Donald Trump, double entry bookkeeping, driverless car, Edward Snowden, Elon Musk, Erik Brynjolfsson, Ethereum, ethereum blockchain, failed state, fiat currency, financial innovation, Firefox, first square of the chessboard, first square of the chessboard / second half of the chessboard, future of work, Future Shock, Galaxy Zoo, general purpose technology, George Gilder, glass ceiling, Google bus, GPS: selective availability, Hacker News, Hernando de Soto, Higgs boson, holacracy, income inequality, independent contractor, informal economy, information asymmetry, information security, intangible asset, interest rate swap, Internet of things, Jeff Bezos, jimmy wales, Kickstarter, knowledge worker, Kodak vs Instagram, Lean Startup, litecoin, Lyft, M-Pesa, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, means of production, microcredit, mobile money, money market fund, Neal Stephenson, Network effects, new economy, Oculus Rift, off grid, pattern recognition, peer-to-peer, peer-to-peer lending, peer-to-peer model, performance metric, Peter Thiel, planetary scale, Ponzi scheme, prediction markets, price mechanism, Productivity paradox, QR code, quantitative easing, radical decentralization, ransomware, Ray Kurzweil, renewable energy credits, rent-seeking, ride hailing / ride sharing, Ronald Coase, Ronald Reagan, Salesforce, Satoshi Nakamoto, search costs, Second Machine Age, seigniorage, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, smart grid, Snow Crash, social graph, social intelligence, social software, standardized shipping container, Stephen Hawking, Steve Jobs, Steve Wozniak, Stewart Brand, supply-chain management, systems thinking, TaskRabbit, TED Talk, The Fortune at the Bottom of the Pyramid, The Nature of the Firm, The Soul of a New Machine, The Wisdom of Crowds, transaction costs, Turing complete, Turing test, Tyler Cowen, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, unorthodox policies, vertical integration, Vitalik Buterin, wealth creators, X Prize, Y2K, Yochai Benkler, Zipcar

What’s truly powerful, the systems work together—intelligent vehicles operating on an intelligent infrastructure. While there will still be business for drivers of shared vehicles, autonomous vehicles will be able to operate safely on city streets with their built-in navigation and safety systems, often interacting with the intelligent infrastructure to find and pay for an accelerated lane, or parking, or to search for and find a preferred route. The ready availability, affordability, and reliability of the autonomous vehicles will significantly reduce the number of private vehicles that, like the commercial real estate example above, are often just parked waiting and unused.

Huge institutions now control and own this new means of production and social interaction—its underlying infrastructure; massive and growing treasure troves of data; the algorithms that increasingly govern business and daily life; the world of apps; and extraordinary emerging capabilities, machine learning, and autonomous vehicles. From Silicon Valley and Wall Street to Shanghai and Seoul, this new aristocracy uses its insider advantage to exploit the most extraordinary technology ever devised to empower people as economic actors, to build spectacular fortunes and strengthen its power and influence over economies and societies.

These platforms instill subsidiary rights in all our assets. You need to decide the extent to which you want to assign others usage and access rights—even the right to exclude others from using your assets—and what to charge for those rights. This can work for physical assets too. For example, we’ve heard a lot about autonomous vehicles. We can build an open transportation network on the blockchain where owners each have a private encrypted key (number) that lets them reserve a car. Using the public key infrastructure and existing blockchain technologies like EtherLock and Airlock, they can unlock and use the car for a certain amount of time, as specified by the rules of the smart contract—all the while paying the vehicle (or its owners) in real time for the time and energy that they use—as metered on a blockchain.


pages: 305 words: 79,303

The Four: How Amazon, Apple, Facebook, and Google Divided and Conquered the World by Scott Galloway

"Susan Fowler" uber, activist fund / activist shareholder / activist investor, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Amazon Robotics, Amazon Web Services, Apple II, autonomous vehicles, barriers to entry, Ben Horowitz, Bernie Sanders, Big Tech, big-box store, Bob Noyce, Brewster Kahle, business intelligence, California gold rush, Cambridge Analytica, cloud computing, Comet Ping Pong, commoditize, cuban missile crisis, David Brooks, Didi Chuxing, digital divide, disintermediation, don't be evil, Donald Trump, Elon Musk, fake news, follow your passion, fulfillment center, future of journalism, future of work, global supply chain, Google Earth, Google Glasses, Google X / Alphabet X, Hacker Conference 1984, Internet Archive, invisible hand, Jeff Bezos, Jony Ive, Khan Academy, Kiva Systems, longitudinal study, Lyft, Mark Zuckerberg, meta-analysis, Network effects, new economy, obamacare, Oculus Rift, offshore financial centre, passive income, Peter Thiel, profit motive, race to the bottom, RAND corporation, ride hailing / ride sharing, risk tolerance, Robert Mercer, Robert Shiller, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, shareholder value, Sheryl Sandberg, Silicon Valley, Snapchat, software is eating the world, speech recognition, Stephen Hawking, Steve Ballmer, Steve Bannon, Steve Jobs, Steve Wozniak, Stewart Brand, supercomputer in your pocket, Tesla Model S, the long tail, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, undersea cable, vertical integration, warehouse automation, warehouse robotics, Wayback Machine, Whole Earth Catalog, winner-take-all economy, working poor, you are the product, young professional

The difference is other retailers have just the air in their lungs and are drowning. Amazon will surface and have the ocean of retail largely to itself. Making Type 2 investments also desensitizes Amazon’s shareholders to failure. All of the Four share this—look at Apple and Google with their not-so-secret autonomous vehicle projects, and Facebook with its regular introduction of new features to further monetize its users, which it then pulls back when the experiments don’t pan out. Remember Lighthouse? As Bezos also wrote in that first annual letter: “Failure and invention are inseparable twins. To invent you have to experiment, and if you know in advance that it’s going to work, it’s not an experiment.”53 Red, White, and Blue The Four are all disciplined about getting out in front of their skis, taking big, bold, smart bets, and tolerating failure.

In fact, it wasn’t until 2009 that Google’s CEO at the time, Eric Schmidt, saw the conflict of interest collisions ahead and resigned (or was asked to leave) from Apple’s board of directors. Since then, the four giants have moved inexorably into each other’s turf. At least two or three of them now compete in each other’s markets, whether it’s advertising, music, books, movies, social networks, cell phones—or lately, autonomous vehicles. But Apple stands alone as a luxury brand. That difference presents an immense advantage, providing fatter margins and a competitive edge. Luxury insulates the Apple brand, and hoists it above the price wars raging below. For now, I see modest competition for Apple from the other horsemen.

Google is a long way from that fate—but notice that it too is basically a one-trick (and one trick only) pony. There is search (YouTube is a search engine) and there is . . . well, Android—but that’s an industry smartphone standard, devised by Schmidt to counter the iPhone, and its biggest players are other companies. All of the other stuff—autonomous vehicles, drones—is just chaff, designed to keep customers and, even more so, employees pumped up. To date their contribution is less than Microsoft’s fading Internet Explorer. There are other parallels between Google and Microsoft. Microsoft at its peak was notorious for having the most insufferable asshole employees in American business.


pages: 417 words: 109,367

The End of Doom: Environmental Renewal in the Twenty-First Century by Ronald Bailey

3D printing, additive manufacturing, agricultural Revolution, Albert Einstein, Anthropocene, Asilomar, autonomous vehicles, biodiversity loss, business cycle, carbon tax, Cass Sunstein, Climatic Research Unit, commodity super cycle, conceptual framework, corporate governance, creative destruction, credit crunch, David Attenborough, decarbonisation, dematerialisation, demographic transition, disinformation, disruptive innovation, diversified portfolio, double helix, energy security, failed state, financial independence, Ford Model T, Garrett Hardin, Gary Taubes, Great Leap Forward, hydraulic fracturing, income inequality, Induced demand, Intergovernmental Panel on Climate Change (IPCC), invisible hand, knowledge economy, meta-analysis, Naomi Klein, negative emissions, Neolithic agricultural revolution, ocean acidification, oil shale / tar sands, oil shock, pattern recognition, peak oil, Peter Calthorpe, phenotype, planetary scale, precautionary principle, price stability, profit motive, purchasing power parity, race to the bottom, RAND corporation, Recombinant DNA, rent-seeking, rewilding, Stewart Brand, synthetic biology, systematic bias, Tesla Model S, trade liberalization, Tragedy of the Commons, two and twenty, University of East Anglia, uranium enrichment, women in the workforce, yield curve

Researchers at the University of Texas, devising a realistic simulation of vehicle use in cities that took into account issues like congestion and rush-hour usage, found that each shared autonomous vehicle could replace eleven conventional vehicles. Notionally then, it would take only about 800 million vehicles to supply all the transportation services for 9 billion people. That figure is 200 million vehicles fewer than the current world fleet of 1 billion automobiles. In the Texas simulations, riders waited an average of 18 seconds for a driverless vehicle to show up, and each vehicle served 31 to 41 travelers per day. Less than half of 1 percent of travelers waited more than five minutes for a vehicle. In addition, shared autonomous vehicles would also cut an individual’s average cost of travel by as much as 75 percent in comparison to conventional driver-owned vehicles.

Alliance to Save Energy, January 2013, 4. www.ase.org/sites/ase.org/files/resources/Media%20browser/ee_commission_history_report_2-1-13.pdf. a realistic simulation: Daniel J. Fagnant and Kara M. Kockelman, “The Travel and Environmental Implications of Shared Autonomous Vehicles, Using Agent-Based Model Scenarios.” Transportation Research Part C: Emerging Technologies 40 (March 2014): 1–13. www.sciencedirect.com/science/article/pii/S0968090X13002581. shared autonomous vehicles: Lawrence Burns, William Jordan, and Bonnie Scarborough, “Transforming Personal Mobility.” The Earth Institute, Columbia University, New York, 2013. resource consumption trends: Iddo Wernick and Jesse Ausubel, “Making Nature Useless?

In addition, shared autonomous vehicles would also cut an individual’s average cost of travel by as much as 75 percent in comparison to conventional driver-owned vehicles. This could actually lead to the contraction of the world’s vehicle fleet as more people forgo the costs and hassles of ownership. In addition, a shift to fleets of autonomous vehicles makes the clean electrification of transportation much more feasible, since such automobiles could drive themselves off for recharging and cleaning during periods of low demand. Such vehicles would also be much smaller and packed more tightly on roads, since they can travel safely at higher speeds than human-driven automobiles.


pages: 342 words: 72,927

Transport for Humans: Are We Nearly There Yet? by Pete Dyson, Rory Sutherland

Abraham Maslow, Alan Greenspan, autonomous vehicles, barriers to entry, behavioural economics, bitcoin, Black Swan, Boeing 747, BRICs, butterfly effect, car-free, carbon footprint, Charles Babbage, choice architecture, cognitive bias, cognitive load, coronavirus, COVID-19, Crossrail, Daniel Kahneman / Amos Tversky, decarbonisation, demand response, Diane Coyle, digital map, driverless car, Dunning–Kruger effect, Elon Musk, fake news, functional fixedness, gender pay gap, George Akerlof, gig economy, global supply chain, Goodhart's law, Greta Thunberg, Gödel, Escher, Bach, high-speed rail, hive mind, Hyperloop, Induced demand, informal economy, Isaac Newton, Jane Jacobs, lockdown, longitudinal study, loss aversion, low cost airline, Lyft, megaproject, meta-analysis, Network effects, nudge unit, Ocado, overview effect, Paul Samuelson, performance metric, pneumatic tube, RAND corporation, randomized controlled trial, remote working, ride hailing / ride sharing, risk tolerance, Rory Sutherland, Sapir-Whorf hypothesis, selection bias, Skype, smart transportation, social distancing, South Sea Bubble, systems thinking, TED Talk, the map is not the territory, The Market for Lemons, the scientific method, The Wisdom of Crowds, Thomas Malthus, Uber and Lyft, uber lyft, urban planning, Veblen good, When a measure becomes a target, yield management, zero-sum game

If planners close the hard shoulder, preventing emergency vehicle access, we think about our personal risk if we drive on it and can too easily imagine the horror if the worst happened.23 In future, autonomous vehicles will be calibrated to accept risk across the population, which ignores some people’s varying tolerance for risk on different occasions. Will people let go of the steering wheel? Will people tolerate a vehicle that sticks to every road rule? Will some pedestrians trust that autonomous vehicles will stop while others exploit the fact the car must bend to their will? Planners who want the public to accept they have a case of counter-intuitive transport aren’t fighting a lost cause, but they do need to listen and respond to dissident voices.

Scientists know passengers are not conscious of the exact humidity or air pressure, but by applying knowledge of physiology they optimize air conditions and cabin pressure, which has a large effect on passenger experience. People report greater relaxation, better sleep and less achy legs, even if they don’t know why they feel better.38 Emerging mobility technologies can bring novelty and delight to the masses: electric cars, automated and autonomous vehicles, ride-sharing e-scooters and on-demand shared mobility. Some of this may be hype, but why dismiss novel enjoyment as a deviation from the real quantified model just because we can’t measure it using a speedometer? The e-scooter isn’t just a viable ‘last mile’ low-carbon solution to support core public transport: it’s fun as well.

Now Existing digital technologies, innovation from outside-in E-commerce/videoconferencing/digital connectivity/digital platforms Journey planners/shared mobility/car clubs/demand-responsive transport Near Existing physical technologies accelerated by transport Micromobility (e-scooters, e-bikes and more) Electric cars Far Emerging technologies pioneered by transport Driverless cars/autonomous vehicles/drones Hydrogen-powered freight, trains and shipping Hyperloop trains/electric planes The ‘hype curve’ shows how new technologies start more slowly than insiders expect. Skype started in 2003 and Ocado in 2010, and while the benefits of both were clear from the outset, they saw modest usage until an exogenous shock – the Covid-19 pandemic – acted as a trigger for an explosion in home working and online shopping.


pages: 161 words: 39,526

Applied Artificial Intelligence: A Handbook for Business Leaders by Mariya Yao, Adelyn Zhou, Marlene Jia

Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, artificial general intelligence, autonomous vehicles, backpropagation, business intelligence, business process, call centre, chief data officer, cognitive load, computer vision, conceptual framework, data science, deep learning, DeepMind, en.wikipedia.org, fake news, future of work, Geoffrey Hinton, industrial robot, information security, Internet of things, iterative process, Jeff Bezos, job automation, machine translation, Marc Andreessen, natural language processing, new economy, OpenAI, pattern recognition, performance metric, price discrimination, randomized controlled trial, recommendation engine, robotic process automation, Salesforce, self-driving car, sentiment analysis, Silicon Valley, single source of truth, skunkworks, software is eating the world, source of truth, sparse data, speech recognition, statistical model, strong AI, subscription business, technological singularity, The future is already here

High costs, legal regulations, and social resistance to AI all hinder the progress of technology adoption. With the rise of autonomous vehicles, many believe that the jobs of America’s 1.7 million truck drivers are in imminent danger. The reality is that trucking jobs will likely require many years to replace. Michael Chui, a McKinsey partner, told The New York Times that the replacement and retrofitting of America’s truck fleet with autonomous navigation will require a trillion-dollar investment that few, if any, companies will immediately undertake.(51) Even if financing can be secured, autonomous vehicle technology is not yet approved for industrial or for individual use.

Once the machine has produced a prediction on the quality of a lead, the salesperson then applies human judgment to decide how to follow up. More complex systems, such as self-driving cars and industrial robotics, handle everything from gathering the initial data to executing the action resulting from its analysis. For example, an autonomous vehicle must turn video and sensor feeds into accurate predictions of the surrounding world and adjust its driving accordingly. Systems That Create We humans like to think we’re the only beings capable of creativity, but computers have been used for generative design and art for decades. Recent breakthroughs in neural network models have inspired a resurgence of computational creativity, with computers now capable of producing original writing, imagery, music, industrial designs, and even AI software!

This means that our physical security, digital security, and even political security will be at risk of attack. While we spend much of our productive hours tethered to digital devices and roaming cyberspace, we still inhabit physical bodies and live in a material world. Nefarious AI can infect autonomous vehicles, connected appliances, and other devices to inflict bodily harm and property damage. Digital attacks may come as a coordinated and adversarial disruption of corporate data with the goal of compromising, devaluing, or altogether destroying an organization’s data architecture. Finally, the use of technology—including AI, predictive analytics, automation, and social media bots—can have far-ranging social impact.


pages: 394 words: 117,982

The Perfect Weapon: War, Sabotage, and Fear in the Cyber Age by David E. Sanger

active measures, air gap, autonomous vehicles, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, Bletchley Park, British Empire, call centre, Cambridge Analytica, Cass Sunstein, Chelsea Manning, computer age, cryptocurrency, cuban missile crisis, disinformation, Donald Trump, drone strike, Edward Snowden, fake news, Google Chrome, Google Earth, information security, Jacob Appelbaum, John Markoff, Kevin Roose, Laura Poitras, Mark Zuckerberg, MITM: man-in-the-middle, mutually assured destruction, off-the-grid, RAND corporation, ransomware, Sand Hill Road, Sheryl Sandberg, Silicon Valley, Silicon Valley ideology, Skype, South China Sea, Steve Bannon, Steve Jobs, Steven Levy, Stuxnet, Tim Cook: Apple, too big to fail, Twitter Arab Spring, undersea cable, unit 8200, uranium enrichment, Valery Gerasimov, WikiLeaks, zero day

The numbers that Brown and Singh gathered, all from public sources, told the story. China participated in more than 10 percent of all venture deals in 2015, the report found, focusing on early-stage innovations critical to both commercial and military uses: artificial intelligence, robotics, autonomous vehicles, virtual reality, financial technology, and gene-editing. When they broke down who was investing in US-based venture-backed companies between 2015 and 2017, American investors ranked first, with $59 billion in investment. Europe was second, with $36 billion. And China was right behind, with $24 billion.

With huge investments, the top tier of the financial industry and the electric utilities have done the best job of safeguarding their networks—meaning that a North Korean hacker aiming at those industries would likely have more luck targeting smaller banks and rural power companies. But as we put autonomous cars on the road, connect Alexas to our lights and our thermostats, put ill-protected Internet-connected video cameras on our houses, and conduct our financial lives over our cell phones, our vulnerabilities expand exponentially. During the Cold War, we learned how to live, uneasily, with the knowledge that the Soviet Union and China had nuclear weapons pointed at us.


pages: 397 words: 110,222

Habeas Data: Privacy vs. The Rise of Surveillance Tech by Cyrus Farivar

Apple's 1984 Super Bowl advert, autonomous vehicles, call centre, citizen journalism, cloud computing, computer age, connected car, do-ocracy, Donald Trump, Edward Snowden, en.wikipedia.org, failed state, Ferguson, Missouri, Frank Gehry, Golden Gate Park, information security, John Markoff, Laura Poitras, license plate recognition, lock screen, Lyft, national security letter, Occupy movement, operational security, optical character recognition, Port of Oakland, RAND corporation, Ronald Reagan, sharing economy, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, Steven Levy, tech worker, The Hackers Conference, Tim Cook: Apple, transaction costs, uber lyft, WikiLeaks, you are the product, Zimmermann PGP

Same goes for allowing a “TiVo-in-the-sky” to capture days’ worth of human activity down below. Without a legislative body or a judge to step in, it seems inevitable that these actions will continue to expand through pervasive monitoring, advanced facial recognition, DNA, biochemical analysis, constant location capture via autonomous vehicles, and more. Today, so long as the search remains “reasonable” and doesn’t conflict with an “expectation” that “society is prepared to recognize as reasonable,” then law enforcement behavior is permitted. Or, to put it in e-mail spam terms, Fourth Amendment law is basically a blacklist: police actions are generally permitted unless they run into conditions that tell them to stop, such as conducting a physical search of “persons, houses, papers, and effects,” which requires a specific warrant.

May be a craft beer enthusiast—although possibly with a drinking problem.” As I continued to report on LPRs, I realized the same questions I had about this technology applied to so much more: telephone metadata, cell-site simulators (aka stingrays), body-worn cameras, drones, facial-recognition technology, autonomous cars, artificial intelligence, and more. There was a torrent of technology that was becoming more ubiquitous and cheaper by the day, with little standing in its way. Legislators have generally seemed unable or unwilling to halt the ever-advancing technological mission creep. Courts seemed to always lag behind—by the time a technology was finally raised at an appellate court or at the US Supreme Court, it was far out of date.


pages: 335 words: 97,468

Uncharted: How to Map the Future by Margaret Heffernan

"World Economic Forum" Davos, 23andMe, Affordable Care Act / Obamacare, Airbnb, Alan Greenspan, Anne Wojcicki, anti-communist, Atul Gawande, autonomous vehicles, banking crisis, Berlin Wall, Boris Johnson, Brexit referendum, chief data officer, Chris Urmson, clean water, complexity theory, conceptual framework, cosmic microwave background, creative destruction, CRISPR, crowdsourcing, data science, David Attenborough, discovery of penicillin, driverless car, epigenetics, Fall of the Berlin Wall, fear of failure, George Santayana, gig economy, Google Glasses, Greta Thunberg, Higgs boson, index card, Internet of things, Jaron Lanier, job automation, Kickstarter, Large Hadron Collider, late capitalism, lateral thinking, Law of Accelerating Returns, liberation theology, mass immigration, mass incarceration, megaproject, Murray Gell-Mann, Nate Silver, obamacare, oil shale / tar sands, passive investing, pattern recognition, Peter Thiel, prediction markets, RAND corporation, Ray Kurzweil, Rosa Parks, Sam Altman, scientific management, Shoshana Zuboff, Silicon Valley, smart meter, Stephen Hawking, Steve Ballmer, Steve Jobs, surveillance capitalism, TED Talk, The Signal and the Noise by Nate Silver, Tim Cook: Apple, twin studies, University of East Anglia

It isn’t true; the makers just hope, by getting you to believe it, that they can make it true. Nowhere has this subtle shift from prediction to propaganda been more prominent than in the marketing of autonomous vehicles. The driverless car, we have been told for years now, is inevitable. There’s no point even learning to drive any more, so immediately will the liberating technology be upon us. The piratical image of Google’s Sergey Brin sporting Google Glass as he struggled to explain how autonomous vehicles will both free the blind and reclaim green space from car parks shimmers with visual irony. Just as bizarre, as he signs a Bill facilitating the new technology, is Governor Jerry Brown’s celebratory reference to California as the home of the gold rush – an epic of exploiting hopes and dreams if there ever was one.

Providing a productive environment for creative thinking is not the same as learning to think like an artist oneself. That’s an individual choice: to make the effort to notice where we are, what’s around us, what’s missing, to take the time to reflect on what it could mean. The paradox implicit in autonomous vehicles or GPS pertains to us too: if we don’t use our human capacity for creativity, mind wandering, discovery and invention, we lose it. We could be more adventurous – exploring what we don’t know, investigating what makes us uncomfortable, thinking without bannisters. To be where we are sounds simple and it feels like an easy habit of mind to instil.

They are designed to be adaptable and robust enough for events that will occur – even where there’s wild uncertainty about when, or where or how. Both operate on the principle that just because there is no single source of control over events needn’t exclude the possibility of positive action early. *** Today, the rise of autonomous vehicles and drones makes the advent of killer robots a similar but manmade threat that can’t wait to be addressed. The non-profit organisation Article 36 was established to develop treaties and frameworks to prevent unintended, unnecessary or unacceptable harm caused by weapons developed in the future.


pages: 903 words: 235,753

The Stack: On Software and Sovereignty by Benjamin H. Bratton

1960s counterculture, 3D printing, 4chan, Ada Lovelace, Adam Curtis, additive manufacturing, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, Amazon Mechanical Turk, Amazon Robotics, Amazon Web Services, Andy Rubin, Anthropocene, augmented reality, autonomous vehicles, basic income, Benevolent Dictator For Life (BDFL), Berlin Wall, bioinformatics, Biosphere 2, bitcoin, blockchain, Buckminster Fuller, Burning Man, call centre, capitalist realism, carbon credits, carbon footprint, carbon tax, carbon-based life, Cass Sunstein, Celebration, Florida, Charles Babbage, charter city, clean water, cloud computing, company town, congestion pricing, connected car, Conway's law, corporate governance, crowdsourcing, cryptocurrency, dark matter, David Graeber, deglobalization, dematerialisation, digital capitalism, digital divide, disintermediation, distributed generation, don't be evil, Douglas Engelbart, Douglas Engelbart, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, Eratosthenes, Ethereum, ethereum blockchain, Evgeny Morozov, facts on the ground, Flash crash, Frank Gehry, Frederick Winslow Taylor, fulfillment center, functional programming, future of work, Georg Cantor, gig economy, global supply chain, Google Earth, Google Glasses, Guggenheim Bilbao, High speed trading, high-speed rail, Hyperloop, Ian Bogost, illegal immigration, industrial robot, information retrieval, Intergovernmental Panel on Climate Change (IPCC), intermodal, Internet of things, invisible hand, Jacob Appelbaum, James Bridle, Jaron Lanier, Joan Didion, John Markoff, John Perry Barlow, Joi Ito, Jony Ive, Julian Assange, Khan Academy, Kim Stanley Robinson, Kiva Systems, Laura Poitras, liberal capitalism, lifelogging, linked data, lolcat, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, Masdar, McMansion, means of production, megacity, megaproject, megastructure, Menlo Park, Minecraft, MITM: man-in-the-middle, Monroe Doctrine, Neal Stephenson, Network effects, new economy, Nick Bostrom, ocean acidification, off-the-grid, offshore financial centre, oil shale / tar sands, Oklahoma City bombing, OSI model, packet switching, PageRank, pattern recognition, peak oil, peer-to-peer, performance metric, personalized medicine, Peter Eisenman, Peter Thiel, phenotype, Philip Mirowski, Pierre-Simon Laplace, place-making, planetary scale, pneumatic tube, post-Fordism, precautionary principle, RAND corporation, recommendation engine, reserve currency, rewilding, RFID, Robert Bork, Sand Hill Road, scientific management, self-driving car, semantic web, sharing economy, Silicon Valley, Silicon Valley ideology, skeuomorphism, Slavoj Žižek, smart cities, smart grid, smart meter, Snow Crash, social graph, software studies, South China Sea, sovereign wealth fund, special economic zone, spectrum auction, Startup school, statistical arbitrage, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, Superbowl ad, supply-chain management, supply-chain management software, synthetic biology, TaskRabbit, technological determinism, TED Talk, the built environment, The Chicago School, the long tail, the scientific method, Torches of Freedom, transaction costs, Turing complete, Turing machine, Turing test, undersea cable, universal basic income, urban planning, Vernor Vinge, vertical integration, warehouse automation, warehouse robotics, Washington Consensus, web application, Westphalian system, WikiLeaks, working poor, Y Combinator, yottabyte

At the same time, such a system would bring potential problems of the same order of magnitude as those it alleviates. The software and sovereignty questions don't abide easy answers. First, the legal identity of this composite User is not immediately clear. Several states have already passed legislation indicating that autonomous vehicles are legal to operate on their roads, thereby establishing the baseline that such machines are at least not criminal. But considering the quantity, complexity, and sensitivity of the data generated by such technologies, all working in concert, as well as the expertise and infrastructure necessary to conduct the rhythms of the swarm safely and effectively, it's not likely that any Department of Motor Vehicles is a likely candidate to govern a network of pilotless vehicles.

For those who honestly don't know, the Google driverless car project is a research initiative to develop cars that can autonomously navigate all roads without human steerage (or much of it), using a combination of laser-guided mapping, video cameras, radar, motion sensors, on-board computing, and other tools. Prototypes to date have mostly used a customized Prius, though the company recently announced plans to work with auto manufacturers to build autonomous vehicles to Google's own specifications, and some early products could be commercially available in a few years, if some very wicked problems can be worked out first. On these see Lee Gomes, “Hidden Obstacles for Google's Self-Driving Cars,” MIT Technology Review, August 28, 2014. 58.  Levy again: “Why is OpenFlow so advantageous to a company like Google?

To the consternation of suspicious persons, the “mobile phone” with a CCD (charge-coupled device) absorbing light and a microphone absorbing sound waves is also a sensor, and for it the principle of information by absence of interaction holds true. One sensor makes use of the information haul of another, such as an autonomous vehicle that can navigate terrain based on LiDAR mapping (a portmanteau of “laser” and “radar”), motion detection sensors, and street maps (among other sensors). Ultimately, as a User experience design problem, the sense of a device's relative autonomy and intelligence will be a key criterion in everyday HRI (human-robotics interaction) but is a separate issue from the actual autonomy or dependence of that device.


pages: 215 words: 59,188

Seriously Curious: The Facts and Figures That Turn Our World Upside Down by Tom Standage

"World Economic Forum" Davos, agricultural Revolution, augmented reality, autonomous vehicles, Big Tech, blood diamond, business logic, corporate governance, CRISPR, deep learning, Deng Xiaoping, Donald Trump, Dr. Strangelove, driverless car, Elon Musk, failed state, financial independence, gender pay gap, gig economy, Gini coefficient, high net worth, high-speed rail, income inequality, index fund, industrial robot, Internet of things, invisible hand, it's over 9,000, job-hopping, Julian Assange, life extension, Lyft, M-Pesa, Mahatma Gandhi, manufacturing employment, mega-rich, megacity, Minecraft, mobile money, natural language processing, Nelson Mandela, plutocrats, post-truth, price mechanism, private spaceflight, prosperity theology / prosperity gospel / gospel of success, purchasing power parity, ransomware, reshoring, ride hailing / ride sharing, Ronald Coase, self-driving car, Silicon Valley, Snapchat, South China Sea, speech recognition, stem cell, supply-chain management, transaction costs, Uber and Lyft, uber lyft, undersea cable, US Airways Flight 1549, WikiLeaks, zoonotic diseases

As cars did in the 20th century, AVs will redefine retailing and reshape cities, as well as providing a convenient new form of mobility. As with cars, which lead to road deaths, pollution and congestion, there are likely to be unanticipated (and unpleasant) consequences for society from autonomous vehicles, such as a loss of privacy and the potential to use them as a means of social control. Removing the horse from horse-drawn carriages was an apparently simple change that had far-reaching effects. Similarly, there is much more to autonomous vehicles than simply removing the need for a driver – and much of their impact will be a consequence of the fact that they will mostly be shared, not owned. How ride-hailing apps reduce drink-driving Gun violence in America gets plenty of attention, but cars kill more people.

By one estimate, the M-Pesa mobile-money system alone lifted about 2% of Kenyan households out of poverty between 2008 and 2014. Technology cannot solve all of Africa’s problems, but it can help with some of them. Why self-driving cars will mostly be shared, not owned When will you be able to buy a driverless car that will work anywhere? This commonly asked question contains three assumptions: that autonomous vehicles (AVs) will resemble cars; that people will buy them; and that they will be capable of working on all roads in all conditions. All three of those assumptions may be wrong. Although today’s experimental vehicles are modified versions of ordinary cars, with steering wheels that eerily turn by themselves, future AVs will have no steering wheel or pedals and will come in all sorts of shapes and sizes; pods capable of carrying six or eight people may prove to be the most efficient design.

For more explainers and charts from The Economist, visit economist.com Index A Africa child marriage 84 democracy 40 gay and lesbian rights 73, 74 Guinea 32 mobile phones 175–6 see also individual countries agriculture 121–2 Aguiar, Mark 169 air pollution 143–4 air travel and drones 187–8 flight delays 38–9 Akitu (festival) 233 alcohol beer consumption 105–6 consumption in Britain 48, 101–2 craft breweries 97–8 drink-driving 179–80 wine glasses 101–2 Alexa (voice assistant) 225 Algeria food subsidies 31 gay and lesbian rights 73 All I Want for Christmas Is You (Carey) 243 alphabet 217–18 Alternative for Germany (AfD) 223, 224 Alzheimer’s disease 140 Amazon (company) 225 America see United States and 227–8 Angola 73, 74 animals blood transfusions 139–40 dog meat 91–2 gene drives 153–4 size and velocity 163–4 and water pollution 149–50 wolves 161–2 Arctic 147–8 Argentina gay and lesbian rights 73 lemons 95–6 lithium 17–18 Ariel, Barak 191 Arizona 85 arms trade 19–20 Asia belt and road initiative 117–18 high-net-worth individuals 53 wheat consumption 109–10 see also individual countries Assange, Julian 81–3 asteroids 185–6 augmented reality (AR) 181–2 August 239–40 Australia avocados 89 forests 145 inheritance tax 119 lithium 17, 18 shark attacks 201–2 autonomous vehicles (AVs) 177–8 Autor, David 79 avocados 89–90 B Babylonians 233 Baltimore 99 Bangladesh 156 bank notes 133–4 Bateman, Tim 48 beer consumption 105–6 craft breweries 97–8 Beijing air pollution 143–4 dogs 92 belt and road initiative 117–18 betting 209–10 Bier, Ethan 153 Bils, Mark 169 birds and aircraft 187 guinea fowl 32–3 birth rates Europe 81–3 United States 79–80 black money 133–4 Black Power 34, 35 Blade Runner 208 blood transfusions 139–40 board games 199–200 body cameras 191–2 Boko Haram 5, 15–16 Bolivia 17–18 Bollettieri, Nick 197 bookmakers 209–10 Borra, Cristina 75 Bosnia 221–2 brain computers 167–8 Brazil beer consumption 105, 106 Christmas music 243, 244 end-of-life care 141–2 gay and lesbian rights 73 murder rate 45, 46 shark attacks 202 breweries 97–8 Brexit, and car colours 49–50 brides bride price 5 diamonds 13–14 Britain alcohol consumption 101–2 car colours 49–50 Christmas music 244 cigarette sales 23–4 craft breweries 98 crime 47–8 Easter 238 gay population 70–72 housing material 8 inheritance tax 119 Irish immigration 235 life expectancy 125 manufacturing jobs 131 national identity 223–4 new-year resolutions 234 police body cameras 191 sexual harassment 67, 68, 69 sperm donation 61 see also Scotland Brookings Institution 21 Browning, Martin 75 bubonic plague 157–8 Bush, George W. 119 C cables, undersea 193–4 California and Argentine lemons 95, 96 avocados 90 cameras 191–2 Canada diamonds 13 drones 188 lithium 17 national identity 223–4 capitalism, and birth rates 81–2 Carey, Mariah 243 Carnegie Endowment for International Peace 21 cars colours 49–50 self-driving 177–8 Caruana, Fabiano 206 Charles, Kerwin 169 cheetahs 163, 164 chess 205–6 Chetty, Raj 113 Chicago 100 children birth rates 79–80, 81–3 child marriage 84–5 in China 56–7 crime 47–8 and gender pay gap 115–16, 135–6 obesity 93–4 Chile gay and lesbian rights 73 lithium 17–18 China air pollution 143–5 arms sales 19–20 avocados 89 beer consumption 105 belt and road initiative 117–18 childhood obesity 93 construction 7 dog meat 91–2 dragon children 56–7 flight delays 38–9 foreign waste 159–60 lithium 17 rice consumption 109–10 Choi, Roy 99 Christian, Cornelius 26 Christianity Easter 237–8 new year 233–4 Christmas 246–7 music 243–5 cigarettes affordability 151–2 black market 23–4 cities, murder rates 44–6 Citizen Kane 207 citrus wars 95–6 civil wars 5 Clarke, Arthur C. 183 Coase, Ronald 127, 128 cocaine 44 cochlear implants 167 Cohen, Jake 203 Colen, Liesbeth 106 colleges, US 113–14 Colombia 45 colours, cars 49–50 commodities 123–4 companies 127–8 computers augmented reality 181–2 brain computers 167–8 emojis 215–16 and languages 225–6 spam e-mail 189–90 Connecticut 85 Connors, Jimmy 197 contracts 127–8 Costa Rica 89 couples career and family perception gap 77–8 housework 75–6 see also marriage cows 149–50 craft breweries 97–8 crime and avocados 89–90 and dog meat 91–2 murder rates 44–6 young Britons 47–8 CRISPR-Cas9 153 Croatia 222 Croato-Serbian 221–2 D Daily-Diamond, Christopher 9–10 Davis, Mark 216 De Beers 13–14 death 141–2 death taxes 119–20 democracy 40–41 Deng Xiaoping 117 Denmark career and family perception gap 78 gender pay gap 135–6 sex reassignment 65 Denver 99 Devon 72 diamonds 13–14, 124 digitally remastering 207–8 Discovery Channel 163–4 diseases 157–8 dog meat 91–2 Dorn, David 79 Dr Strangelove 207 dragon children 56–7 drink see alcohol drink-driving 179–80 driverless cars 177–8 drones and aircraft 187–8 and sharks 201 drugs cocaine trafficking 44 young Britons 48 D’Souza, Kiran 187 E e-mail 189–90 earnings, gender pay gap 115–16, 135–6 Easter 237–8 economy and birth rates 79–80, 81–2 and car colours 49–50 and witch-hunting 25–6 education and American rich 113–14 dragon children 56–7 Egal, Muhammad Haji Ibrahim 40–41 Egypt gay and lesbian rights 73 marriage 5 new-year resolutions 233 El Paso 100 El Salvador 44, 45 emojis 215–16 employment gender pay gap 115–16, 135–6 and gender perception gap 77–8 job tenure 129–30 in manufacturing 131–2 video games and unemployment 169–70 English language letter names 217–18 Papua New Guinea 219 environment air pollution 143–4 Arctic sea ice 147–8 and food packaging 103–4 waste 159–60 water pollution 149–50 Equatorial Guinea 32 Eritrea 40 Ethiopia 40 Europe craft breweries 97–8 summer holidays 239–40 see also individual countries Everson, Michael 216 exorcism 36–7 F Facebook augmented reality 182 undersea cables 193 FANUC 171, 172 Federer, Roger 197 feminism, and birth rates 81–2 fertility rates see birth rates festivals Christmas 246–7 Christmas music 243–5 new-year 233–4 Feuillet, Catherine 108 films 207–8 firms 127–8 5G 173–4 flight delays 38–9 Florida and Argentine lemons 95 child marriage 85 Foley, William 220 food avocados and crime 89–90 dog meat 91–2 lemons 95–6 wheat consumption 109–10 wheat genome 107–8 food packaging 103–4 food trucks 99–100 football clubs 211–12 football transfers 203–4 forests 145–6, 162 Fountains of Paradise, The (Clarke) 183 fracking 79–80 France career and family perception gap 78 Christmas music 244 exorcism 36–7 gender-inclusive language 229–30 job tenure 130 sex reassignment 66 sexual harassment 68–9 witch-hunting 26, 27 wolves 161–2 G gambling 209–10 games, and unemployment 169–70 Gandhi, Mahatma 155 gang members 34–5 Gantz, Valentino 153 gas 124 gay population 70–72 gay rights, attitudes to 73–4 gender sex reassignment 65–6 see also men; women gender equality and birth rates 81–2 in language 229–30 gender pay gap 115–16, 135–6 gene drives 153–4 Genghis Khan 42 genome, wheat 107–8 ger districts 42–3 Germany beer consumption 105 job tenure 130 national identity 223–4 sexual harassment 68, 69 vocational training 132 witch-hunting 26, 27 Ghana 73 gig economy 128, 130 glasses, wine glasses 101–2 Goddard, Ceri 72 Google 193 Graduate, The 207 Greece forests 145 national identity 223–4 sex reassignment 65 smoking ban 152 Gregg, Christine 9–10 grunting 197–8 Guatemala 45 Guinea 32 guinea fowl 32–3 guinea pig 32 Guinea-Bissau 32 Guo Peng 91–2 Guyana 32 H Haiti 5 Hale, Sarah Josepha 242 Hanson, Gordon 79 Hawaii ’Oumuamua 185 porn consumption 63–4 health child obesity 93–4 life expectancy 125–6 plague 157–8 and sanitation 155 high-net-worth individuals (HNWIs) 53 Hiri Motu 219 holidays Easter 237–8 St Patrick’s Day 235–6 summer holidays 239–40 Thanksgiving 241–2 HoloLens 181–2 homicide 44–6 homosexuality attitudes to 73–4 UK 70–72 Honduras 44, 45 Hong Kong 56 housework 75–6, 77–8 Hudson, Valerie 5 Hungary 223–4 Hurst, Erik 169 I ice 147–8 Ikolo, Prince Anthony 199 India bank notes 133–4 inheritance tax 119 languages 219 rice consumption 109 sand mafia 7 sanitation problems 155–6 Indonesia polygamy and civil war 5 rice consumption 109–10 inheritance taxes 119–20 interest rates 51–2 interpunct 229–30 Ireland aitch 218 forests 145 St Patrick’s Day 235–6 same-sex marriage 73 sex reassignment 65 Italy birth rate 82 end of life care 141–2 forests 145 job tenure 130 life expectancy 126 J Jacob, Nitya 156 Jamaica 45 Japan 141–2 Jighere, Wellington 199 job tenure 129–30 jobs see employment Johnson, Bryan 168 junk mail 189 K Kazakhstan 6 Kearney, Melissa 79–80 Kennedy, John F. 12 Kenya democracy 40 mobile-money systems 176 Kiribati 7 Kleven, Henrik 135–6 knots 9–10 Kohler, Timothy 121 Kyrgyzstan 6 L laces 9–10 Lagos 199 Landais, Camille 135–6 languages and computers 225–6 gender-inclusive 229–30 letter names 217–18 and national identity 223–4 Papua New Guinea 219–20 Serbo-Croatian 221–2 Unicode 215 World Bank writing style 227–8 Latimer, Hugh 246 Leeson, Peter 26 leisure board games in Nigeria 199–200 chess 205–6 gambling 209–10 video games and unemployment 169–70 see also festivals; holidays lemons 95–6 letter names 217–18 Libya 31 life expectancy 125–6 Lincoln, Abraham 242 lithium 17–18 London 71, 72 longevity 125–6 Lozère 161–2 Lucas, George 208 M McEnroe, John 197 McGregor, Andrew 204 machine learning 225–6 Macri, Mauricio 95, 96 Macron, Emmanuel 143 Madagascar 158 Madison, James 242 MagicLeap 182 Maine 216 Malaysia 56 Maldives 7 Mali 31 Malta 65 Manchester United 211–12 manufacturing jobs 131–2 robots 171–2 summer holidays 239 Maori 34–5 marriage child marriage 84–5 polygamy 5–6 same-sex relationships 73–4 see also couples Marteau, Theresa 101–2 Marx, Karl 123 Maryland 85 Massachusetts child marriage 85 Christmas 246 Matfess, Hilary 5, 15 meat dog meat 91–2 packaging 103–4 mega-rich 53 men career and family 77–8 housework 75–6 job tenure 129–30 life expectancy 125 polygamy 5–6 sexual harassment by 67–9 video games and unemployment 169 Mexico avocados 89, 90 gay and lesbian rights 73 murder rate 44, 45 microbreweries 97–8 Microsoft HoloLens 181–2 undersea cables 193 migration, and birth rates 81–3 mining diamonds 13–14 sand 7–8 mobile phones Africa 175–6 5G 173–4 Mocan, Naci 56–7 Mongolia 42–3 Mongrel Mob 34 Monopoly (board game) 199, 200 Monty Python and the Holy Grail 25 Moore, Clement Clarke 247 Moretti, Franco 228 Morocco 7 Moscato, Philippe 36 movies 207–8 Mozambique 73 murder rates 44–6 music, Christmas 243–5 Musk, Elon 168 Myanmar 118 N Nadal, Rafael 197 national identity 223–4 natural gas 124 Netherlands gender 66 national identity 223–4 neurostimulators 167 New Jersey 85 New Mexico 157–8 New York (state), child marriage 85 New York City drink-driving 179–80 food trucks 99–100 New Zealand avocados 89 gang members 34–5 gene drives 154 water pollution 149–50 new-year resolutions 233–4 Neymar 203, 204 Nigeria board games 199–200 Boko Haram 5, 15–16 population 54–5 Nissenbaum, Stephen 247 Northern Ireland 218 Norway Christmas music 243 inheritance tax 119 life expectancy 125, 126 sex reassignment 65 Nucci, Alessandra 36 O obesity 93–4 oceans see seas Odimegwu, Festus 54 O’Reilly, Oliver 9–10 Ortiz de Retez, Yñigo 32 Oster, Emily 25–6 ostriches 163, 164 ’Oumuamua 185–6 P packaging 103–4 Pakistan 5 Palombi, Francis 161 Papua New Guinea languages 219–20 name 32 Paris Saint-Germain (PSG) 203 Passover 237 pasta 31 pay, gender pay gap 115–16, 135–6 Peck, Jessica Lynn 179–80 Pennsylvania 85 Peru 90 Pestre, Dominique 228 Pew Research Centre 22 Phelps, Michael 163–4 Philippe, Édouard 230 phishing 189 Phoenix, Arizona 177 Pilgrims 241 plague 157–8 Plastic China 159 police, body cameras 191–2 pollution air pollution 143–4 water pollution 149–50 polygamy 5–6 pornography and Britain’s gay population 70–72 and Hawaii missile alert 63–4 Portugal 145 Puerto Rico 45 punctuation marks 229–30 Q Qatar 19 R ransomware 190 Ravenscroft, George 101 Real Madrid 211 religious observance and birth rates 81–2 and Christmas music 244 remastering 207–8 Reynolds, Andrew 70 Rhodes, Cecil 13 rice 109–10 rich high-net-worth individuals 53 US 113–14 ride-hailing apps and drink-driving 179–80 see also Uber RIWI 73–4 robotaxis 177–8 robots 171–2 Rogers, Dan 240 Romania birth rate 81 life expectancy 125 Romans 233 Romer, Paul 227–8 Ross, Hana 23 Royal United Services Institute 21 Russ, Jacob 26 Russia arms sales 20 beer consumption 105, 106 fertility rate 81 Rwanda 40 S Sahara 31 St Louis 205–6 St Patrick’s Day 235–6 salt, in seas 11–12 same-sex relationships 73–4 San Antonio 100 sand 7–8 sanitation 155–6 Saudi Arabia 19 Scotland, witch-hunting 25–6, 27 Scott, Keith Lamont 191 Scrabble (board game) 199 seas Arctic sea ice 147–8 salty 11–12 undersea cables 193–4 secularism, and birth rates 81–2 Seles, Monica 197 self-driving cars 177–8 Serbia 222 Serbo-Croatian 221–2 Sevilla, Almudena 75 sex reassignment 65–6 sexual harassment 67–9, 230 Sharapova, Maria 197 sharks deterring attacks 201–2 racing humans 163–4 shipping 148 shoelaces 9–10 Silk Road 117–18 Singapore dragon children 56 land reclamation 7, 8 rice consumption 110 single people, housework 75–6 Sinquefeld, Rex 205 smart glasses 181–2 Smith, Adam 127 smoking black market for cigarettes 23–4 efforts to curb 151–2 smuggling 31 Sogaard, Jakob 135–6 Somalia 40 Somaliland 40–41 South Africa childhood obesity 93 diamonds 13 gay and lesbian rights 73 murder rate 45, 46 South Korea arms sales 20 rice consumption 110 South Sudan failed state 40 polygamy 5 space elevators 183–4 spaghetti 31 Spain forests 145 gay and lesbian rights 73 job tenure 130 spam e-mail 189–90 sperm banks 61–2 sport football clubs 211–12 football transfers 203–4 grunting in tennis 197–8 Sri Lanka 118 Star Wars 208 sterilisation 65–6 Strasbourg 26 submarine cables 193–4 Sudan 40 suicide-bombers 15–16 summer holidays 239–40 Sutton Trust 22 Sweden Christmas music 243, 244 gay and lesbian rights 73 homophobia 70 inheritance tax 119 overpayment of taxes 51–2 sex reassignment 65 sexual harassment 67–8 Swinnen, Johan 106 Switzerland sex reassignment 65 witch-hunting 26, 27 T Taiwan dog meat 91 dragon children 56 Tamil Tigers 15 Tanzania 40 taxes death taxes 119–20 Sweden 51–2 taxis robotaxis 177–8 see also ride-hailing apps tennis players, grunting 197–8 terrorism 15–16 Texas 85 Thailand 110 Thanksgiving 241–2 think-tanks 21–2 Tianjin 143–4 toilets 155–6 Tok Pisin 219, 220 transgender people 65–6 Trump, Donald 223 Argentine lemons 95, 96 estate tax 119 and gender pay gap 115 and manufacturing jobs 131, 132 Tsiolkovsky, Konstantin 183 Turkey 151 turkeys 33 Turkmenistan 6 U Uber 128 and drink-driving 179–80 Uganda 40 Ulaanbaatar 42–3 Uljarevic, Daliborka 221 undersea cables 193–4 unemployment 169–70 Unicode 215–16 United Arab Emirates and Somaliland 41 weapons purchases 19 United Kingdom see Britain United States and Argentine lemons 95–6 arms sales 19 beer consumption 105 chess 205–6 child marriage 84–5 Christmas 246–7 Christmas music 243, 244 drink-driving 179–80 drones 187–8 end of life care 141–2 estate tax 119 fertility rates 79–80 food trucks 99–100 forests 145 gay and lesbian rights 73 getting rich 113–14 Hawaiian porn consumption 63–4 job tenure 129–30 letter names 218 lithium 17 manufacturing jobs 131–2 murder rate 45, 46 national identity 223–4 new-year resolutions 234 plague 157–8 police body cameras 191–2 polygamy 6 robotaxis 177 robots 171–2 St Patrick’s Day 235–6 sexual harassment 67, 68 sperm banks 61–2 Thanksgiving 241–2 video games and unemployment 169–70 wealth inequality 121 unmanned aerial vehicles (UAVs) see drones V video games 169–70 Vietnam weapons purchases 19 wheat consumption 110 Virginia 85 virtual reality (VR) 181, 182 Visit from St Nicholas, A (Moore) 247 W Wang Yi 117 Warner, Jason 15 wars 5 Washington, George 242 Washington DC, food trucks 99 waste 159–60 water pollution 149–50 wealth getting rich in America 113–14 high-net-worth individuals 53 inequality 120, 121–2 weather, and Christmas music 243–5 Weinstein, Harvey 67, 69 Weryk, Rob 185 wheat consumption 109–10 genome 107–8 Wilson, Riley 79–80 wine glasses 101–2 Winslow, Edward 241 wireless technology 173–4 witch-hunting 25–7 wolves 161–2 women birth rates 79–80, 81–3 bride price 5 career and family 77–8 child marriage 84–5 housework 75–6 job tenure 129–30 life expectancy 125 pay gap 115–16 sexual harassment of 67–9 suicide-bombers 15–16 World Bank 227–8 World Health Organisation (WHO) and smoking 151–2 transsexualism 65 X Xi Jinping 117–18 Y young people crime 47–8 job tenure 129–30 video games and unemployment 169–70 Yu, Han 56–7 Yulin 91 yurts 42–3 Z Zubelli, Rita 239


pages: 666 words: 181,495

In the Plex: How Google Thinks, Works, and Shapes Our Lives by Steven Levy

"World Economic Forum" Davos, 23andMe, AltaVista, Andy Rubin, Anne Wojcicki, Apple's 1984 Super Bowl advert, autonomous vehicles, Bill Atkinson, book scanning, Brewster Kahle, Burning Man, business process, clean water, cloud computing, crowdsourcing, Dean Kamen, discounted cash flows, don't be evil, Donald Knuth, Douglas Engelbart, Douglas Engelbart, Dutch auction, El Camino Real, Evgeny Morozov, fault tolerance, Firefox, General Magic , Gerard Salton, Gerard Salton, Google bus, Google Chrome, Google Earth, Googley, high-speed rail, HyperCard, hypertext link, IBM and the Holocaust, informal economy, information retrieval, Internet Archive, Jeff Bezos, John Markoff, Ken Thompson, Kevin Kelly, Kickstarter, large language model, machine translation, Mark Zuckerberg, Menlo Park, one-China policy, optical character recognition, PageRank, PalmPilot, Paul Buchheit, Potemkin village, prediction markets, Project Xanadu, recommendation engine, risk tolerance, Rubik’s Cube, Sand Hill Road, Saturday Night Live, search inside the book, second-price auction, selection bias, Sheryl Sandberg, Silicon Valley, SimCity, skunkworks, Skype, slashdot, social graph, social software, social web, spectrum auction, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Levy, subscription business, Susan Wojcicki, Ted Nelson, telemarketer, The future is already here, the long tail, trade route, traveling salesman, turn-by-turn navigation, undersea cable, Vannevar Bush, web application, WikiLeaks, Y Combinator

Since its earliest days, Brin and Page have been consistent in framing Google as an artificial intelligence company—one that gathers massive amounts of data and processes that information with learning algorithms to create a machinelike intelligence that augments the collective brain of humanity. Google’s autonomous cars are information-collectors, scanning their environment with lasers and sensors, and augmenting their knowledge with Street View data. (Unlike human drivers, they always know what’s around the corner.) “This is all information,” says Thrun. “And it will make our physical world more accessible.” What will Google’s explorations in artificial intelligence eventually yield? Will we routinely cruise in autonomous cars powered by Google—undoubtedly capable of pointing out sightseeing highlights and culinary opportunities as they whisk us to destinations?

Google, after all, was founded on the premise that the best path to success is doing what the conventional wisdom says you cannot do. In an era of unprecedented technology leaps, that has turned out to be an excellent premise. “It’s quite amazing how the horizon of impossibility is drifting these days,” says Thrun. The revelation of the autonomous vehicle program at the end of 2010 had all the earmarks of a Larry Page project—scary ambition, groundbreaking AI, massive processing of information in real time, and rigidly enforced stealth. (Only when a reporter learned of the project did Google agree to talk about it.) The glimpse it provided of Page’s priorities turned out to be more significant than expected when an apparently predestined change in Google’s leaders occurred sooner than observers had expected.


pages: 414 words: 109,622

Genius Makers: The Mavericks Who Brought A. I. To Google, Facebook, and the World by Cade Metz

AI winter, air gap, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, AlphaGo, Amazon Robotics, artificial general intelligence, Asilomar, autonomous vehicles, backpropagation, Big Tech, British Empire, Cambridge Analytica, carbon-based life, cloud computing, company town, computer age, computer vision, deep learning, deepfake, DeepMind, Demis Hassabis, digital map, Donald Trump, driverless car, drone strike, Elon Musk, fake news, Fellow of the Royal Society, Frank Gehry, game design, Geoffrey Hinton, Google Earth, Google X / Alphabet X, Googley, Internet Archive, Isaac Newton, Jeff Hawkins, Jeffrey Epstein, job automation, John Markoff, life extension, machine translation, Mark Zuckerberg, means of production, Menlo Park, move 37, move fast and break things, Mustafa Suleyman, new economy, Nick Bostrom, nuclear winter, OpenAI, PageRank, PalmPilot, pattern recognition, Paul Graham, paypal mafia, Peter Thiel, profit motive, Richard Feynman, ride hailing / ride sharing, Ronald Reagan, Rubik’s Cube, Sam Altman, Sand Hill Road, self-driving car, side project, Silicon Valley, Silicon Valley billionaire, Silicon Valley startup, Skype, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Ballmer, Steven Levy, Steven Pinker, tech worker, telemarketer, The Future of Employment, Turing test, warehouse automation, warehouse robotics, Y Combinator

But she knew the lab’s ongoing research in computer vision—an extension of Krizhevsky’s efforts at the University of Toronto—would remake the way the company built its autonomous vehicles. The Google self-driving car project, known inside the company as Chauffeur, was nearly five years old. That meant Google had spent nearly five years building autonomous vehicles without help from deep learning. At Carnegie Mellon in the late 1980s, Dean Pomerleau had designed a self-driving car with help from a neural network, but when Google went to work on autonomous vehicles nearly two decades later, the heart of the research community, including the many Carnegie Mellon researchers hired for the Google project, had long since discarded the idea.

He said he was building a self-driving car at Tesla, and he asked LeCun who he should hire to run the project. That week, he contacted several other Facebook researchers, asking each the same question—a gambit that eventually raised the ire of Mark Zuckerberg. LeCun told Musk he should contact Urs Muller, an old colleague from Bell Labs who’d built a start-up for exploring autonomous vehicles through deep learning. Before Musk could hire this Swiss researcher, however, someone else did. Days after LeCun got the call from Musk, he fielded an identical request from Jensen Huang, the founder and CEO of Nvidia, and he gave the same answer, which Nvidia acted on without delay. The company’s ambition was to build a lab that would push the boundaries of self-driving and, in the process, help the company sell more GPU chips.

It didn’t actually build the stuff that needed the Next Big Thing in artificial intelligence. As he recovered from the first surgery on his hip, Lu urged the Microsoft brain trust to embrace the idea of a driverless car. Myriad tech companies and carmakers had a long head start with their autonomous vehicles, and Lu wasn’t exactly sure how Microsoft would enter this increasingly crowded market. But that wasn’t the issue. His argument wasn’t that Microsoft should sell a driverless car. It was that Microsoft should build one. This would give the company the skills and the technologies and the insight it needed to succeed in so many other areas.


pages: 300 words: 81,293

Supertall: How the World's Tallest Buildings Are Reshaping Our Cities and Our Lives by Stefan Al

3D printing, autonomous vehicles, biodiversity loss, British Empire, Buckminster Fuller, carbon footprint, Cesare Marchetti: Marchetti’s constant, colonial rule, computer vision, coronavirus, COVID-19, Deng Xiaoping, digital twin, Disneyland with the Death Penalty, Donald Trump, Easter island, Elisha Otis, energy transition, food miles, Ford Model T, gentrification, high net worth, Hyperloop, invention of air conditioning, Kickstarter, Lewis Mumford, Marchetti’s constant, megaproject, megastructure, Mercator projection, New Urbanism, plutocrats, plyscraper, pneumatic tube, ride hailing / ride sharing, Salesforce, self-driving car, Sidewalk Labs, SimCity, smart cities, smart grid, smart meter, social distancing, Steve Jobs, streetcar suburb, synthetic biology, Tacoma Narrows Bridge, the built environment, the High Line, transit-oriented development, Triangle Shirtwaist Factory, tulip mania, urban planning, urban sprawl, value engineering, Victor Gruen, VTOL, white flight, zoonotic diseases

A place derives value from its access to schools, restaurants, grocery stores. Access improves as neighborhoods blend more and more uses or as they densify, for instance with tall, mixed-use buildings. Physical proximity has long been of the essence for access. However, better mobility systems can also improve access. With autonomous vehicles, hyperloops, and aerial ridesharing in the near future, we may be up for a wild ride! Plus, along with it, land that was previously less desirable will become the development hot spots of tomorrow. New mobility systems will change our cities and buildings once again. In 1994, Italian physicist Cesare Marchetti described a principle, now known as “Marchetti’s constant.”

Capsules carrying people can reach a top speed of 760 miles per hour, around the sonic barrier. This may lead to the explosive growth of metropolitan areas. You could commute from your office in downtown Los Angeles to Las Vegas, where prices are lower, and live in a bigger home. The race will be on for the first hyperloop-integrated skyscraper. Autonomous vehicles (AVs) could desensitize people to distance, possibly lengthening Marchetti’s “constant” of commuting time. Without having to attend to the wheel, you can now sleep in your car, or conduct online meetings there. Perhaps you can live a little farther away and buy a cheaper home? In other words, cities will increasingly sprawl out.

This may allow a more peaceful coexistence between people and vehicles in streets. In some ways, the future of the automated street could be a throwback to the era before the car. In those days, the street buzzed with social activity, where people would shop and meet. Perhaps a street for safer, autonomous vehicles has the potential to return to its more sociable days. With safer conditions for pedestrians, streets can remove their curbs. The autonomous streets could look a lot like the “shared streets” that already exist in Europe. For decades, entire Dutch neighborhoods have removed curbs to increase the perception of risk for automobilists, leading to slower traffic.


pages: 196 words: 54,339

Team Human by Douglas Rushkoff

1960s counterculture, Abraham Maslow, Adam Curtis, autonomous vehicles, basic income, Berlin Wall, big-box store, bitcoin, blockchain, Burning Man, carbon footprint, circular economy, clean water, clockwork universe, cloud computing, collective bargaining, Computing Machinery and Intelligence, corporate personhood, digital capitalism, disintermediation, Donald Trump, drone strike, European colonialism, fake news, Filter Bubble, full employment, future of work, game design, gamification, gig economy, Google bus, Gödel, Escher, Bach, hockey-stick growth, Internet of things, invention of the printing press, invention of writing, invisible hand, iterative process, John Perry Barlow, Kevin Kelly, Kevin Roose, knowledge economy, Larry Ellison, Lewis Mumford, life extension, lifelogging, Mark Zuckerberg, Marshall McLuhan, means of production, mirror neurons, multilevel marketing, new economy, patient HM, pattern recognition, peer-to-peer, Peter Thiel, planned obsolescence, power law, prosperity theology / prosperity gospel / gospel of success, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Ronald Reagan, Ronald Reagan: Tear down this wall, shareholder value, sharing economy, Silicon Valley, Silicon Valley billionaire, social intelligence, sovereign wealth fund, Steve Jobs, Steven Pinker, Stewart Brand, tech billionaire, technoutopianism, TED Talk, theory of mind, trade route, Travis Kalanick, Turing test, universal basic income, Vannevar Bush, We are as Gods, winner-take-all economy, zero-sum game

It’s hard for human beings to oppose the dominance of digital technology when we are becoming so highly digital ourselves. Whether by fetish or mere habit, we begin acting in ways that accommodate or imitate our machines, remaking our world and, eventually, ourselves in their image. For instance, the manufacturers of autonomous vehicles are encouraging cities to make their streets and signals more compatible with the navigation and sensor systems of the robotic cars, changing our environment to accommodate the needs of the robots with which we will be sharing the streets, sidewalks, and, presumably, air space. This isn’t so bad in itself, but if history is any guide, remaking the physical world to accommodate a new technology—such as the automobile—favors the companies selling the technologies more than the people living alongside them.

Such businesses end up destroying the marketplaces on which they initially depend. When the big box store does this, it simply closes one location and starts the process again in another. When a digital business does this, it pivots or expands from its original market to the next—say, from books to toys to all of retail, or from ride-sharing to restaurant delivery to autonomous vehicles—increasing the value of its real product, the stock shares, along the way. The problem with this model, from a shareholder perspective, is that it eventually stops working. Even goosed by digital platforms, corporate returns on assets have been steadily declining for over seventy-five years.

So computer scientists feed the algorithms reams and reams of data, and let them recognize patterns and draw conclusions themselves. They get this data by monitoring human workers doing their jobs. The ride-hailing app on cab drivers’ phones also serves as a recording device, detailing the way they handle various road situations. The algorithms then parse data culled from thousands of drivers to write their own autonomous vehicle programs. Online task systems pay people pennies per task to do things that computers can’t yet do, such as translate certain phrases, label the storefronts in photos, or identify abusive social media posts. The companies paying for the millions of human microtasks may not actually need any of the answers themselves.


pages: 320 words: 90,526

Squeezed: Why Our Families Can't Afford America by Alissa Quart

Affordable Care Act / Obamacare, Airbnb, Alvin Toffler, antiwork, Automated Insights, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, business intelligence, do what you love, Donald Trump, Downton Abbey, East Village, Elon Musk, emotional labour, full employment, future of work, gentrification, gig economy, glass ceiling, haute couture, income inequality, independent contractor, information security, Jaron Lanier, Jeremy Corbyn, job automation, late capitalism, Lyft, minimum wage unemployment, moral panic, new economy, nuclear winter, obamacare, peak TV, Ponzi scheme, post-work, precariat, price mechanism, rent control, rent stabilization, ride hailing / ride sharing, school choice, sharing economy, Sheryl Sandberg, Silicon Valley, Skype, Snapchat, stop buying avocado toast, surplus humans, TaskRabbit, tech worker, TED Talk, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, union organizing, universal basic income, upwardly mobile, wages for housework, WeWork, women in the workforce, work culture , working poor

The not-for-profit organization New York Communities for Change (NYCC), for instance, has been agitating against automation in trucking and driving and launched a campaign targeting the U.S. Department of Transportation, which has billions of dollars set aside to subsidize the development and spread of autonomous vehicles. “Many truckers are very fearful,” said Zachary Lerner, the group’s senior director of labor organizing, who has been organizing drivers against the autonomous vehicles. “Trucking is not the best job, but it pays the most in lots of rural communities. They worry: Are they going to support their families? And what will happen to all of the small towns built off the trucking economy?”

(Ironies compound: as the writer Douglas Rushkoff has noted, today’s drivers are themselves now part of the research and development for what will most likely be the driverless future, building up a company with their labor in preparation for a time when the company will do away with them.) “Our demand is to freeze all the subsidies for the research on autonomous vehicles until there is a plan for workers who are going to lose their jobs,” Lerner said. As part of this effort, NYCC regularly puts together conference calls between dozens of taxi, Uber, and Lyft drivers. They discuss how they’ve all gotten massive loans to buy cars for Uber and how they are still going to be paying off these loans when the robots come for their jobs—the robot vehicles Uber has promised within the decade.

Or what if we simply decelerated our robot interlopers and established a “slow tech” movement to match our “slow food” and “slow fashion” trends? Or at the very least, what if we started to rethink who owns, say, autonomous trucks? The effect of robotization would be profoundly different if truckers themselves owned their own autonomous vehicles rather than a corporation controlling them all. Robots are always spoken of as emblems of the future, but they seem to me part of the past as well, akin to the Luddites’ weaving machines. What if the Luddites had been members of co-ops, with a stake in the automated looms that replaced them?


pages: 384 words: 93,754

Green Swans: The Coming Boom in Regenerative Capitalism by John Elkington

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, agricultural Revolution, Anthropocene, anti-fragile, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, Berlin Wall, bitcoin, Black Swan, blockchain, Boeing 737 MAX, Boeing 747, Buckminster Fuller, business cycle, Cambridge Analytica, carbon footprint, carbon tax, circular economy, Clayton Christensen, clean water, cloud computing, corporate governance, corporate social responsibility, correlation does not imply causation, creative destruction, CRISPR, crowdsourcing, David Attenborough, deglobalization, degrowth, discounted cash flows, distributed ledger, do well by doing good, Donald Trump, double entry bookkeeping, drone strike, Elon Musk, en.wikipedia.org, energy transition, Extinction Rebellion, Future Shock, Gail Bradbrook, Geoffrey West, Santa Fe Institute, George Akerlof, global supply chain, Google X / Alphabet X, green new deal, green transition, Greta Thunberg, Hans Rosling, hype cycle, impact investing, intangible asset, Internet of things, invention of the wheel, invisible hand, Iridium satellite, Jeff Bezos, John Elkington, Jony Ive, Joseph Schumpeter, junk bonds, Kevin Kelly, Kickstarter, M-Pesa, Marc Benioff, Mark Zuckerberg, Martin Wolf, microplastics / micro fibres, more computing power than Apollo, move fast and break things, Naomi Klein, Nelson Mandela, new economy, Nikolai Kondratiev, ocean acidification, oil shale / tar sands, oil shock, opioid epidemic / opioid crisis, placebo effect, Planet Labs, planetary scale, plant based meat, plutocrats, Ponzi scheme, radical decentralization, Ralph Nader, reality distortion field, Recombinant DNA, Rubik’s Cube, Salesforce, self-driving car, shareholder value, sharing economy, Sheryl Sandberg, Silicon Valley, smart cities, smart grid, sovereign wealth fund, space junk, Steven Pinker, Stewart Brand, supply-chain management, synthetic biology, systems thinking, The future is already here, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, Tim Cook: Apple, urban planning, Whole Earth Catalog

In the economic domain, recent Green Swan breakthroughs have included the rapid spread of cell phone technology and the internet, linking us in new ways and massively boosting the prospects for self-education, and the staggering cost reductions for solar and wind power systems. The accelerating shift to electric vehicles is a linked example, especially when coupled with the rapid evolution of battery technology and the impact of digitalization on autonomous vehicles, the internet of everything, and the sharing economy. In the social domain, Green Swan trajectories have been followed by universal schooling in many countries, the evolution of vaccine technology (despite recent anti-vaccine rumors and propaganda), and the growth of social movements focusing on environmentalism, social enterprise, and impact investment.

Too many change agents, meanwhile, remain distracted by the task of driving incremental improvements in existing and incumbent technologies, ranging from gasoline-powered automobiles to energy- and chemical-intensive air-conditioning systems. All-important work, but we must now move well beyond that. We must engage and shape the thinking of those who will transform our future with such things as artificial intelligence, the internet of everything, autonomous vehicles, synthetic biology, and, some insist, geoengineering. Do not misunderstand me: This rapid evolution of technologies is exciting and has the potential to tackle many of the central challenges we face in this century, if properly used. In terms of potential upsides, I know from personal experience that a radically better future is already here, or taking shape fast.

The ability to control corruption is very welcome, but human ingenuity is such that it presumably cannot be long before such technologies are also used to make corrupt practices even stealthier. •Autonomous and Urban Mobility: As urbanization intensifies, WEF argues that we urgently need new mobility solutions, “while also minimizing increasingly complex social, economic, and environmental challenges. Autonomous vehicles have the potential to improve road safety, decrease pollution levels, reduce congestion and increase traffic efficiency. However, this transition involves a disruptive industry shift bound to reshape public and private transportation.” Collaboration is now needed across industries, sectors, and geographies to identify the best strategies for promoting the wider adoption of autonomous mobility solutions in a “safe, clean, and inclusive manner.”


pages: 311 words: 90,172

Nothing But Net by Mark Mahaney

Airbnb, AltaVista, Amazon Web Services, AOL-Time Warner, augmented reality, autonomous vehicles, Big Tech, Black Swan, Burning Man, buy and hold, Cambridge Analytica, Chuck Templeton: OpenTable:, cloud computing, COVID-19, cryptocurrency, discounted cash flows, disintermediation, diversification, don't be evil, Donald Trump, Elon Musk, financial engineering, gamification, gig economy, global pandemic, Google Glasses, Jeff Bezos, John Zimmer (Lyft cofounder), knowledge economy, lockdown, low interest rates, Lyft, Marc Andreessen, Mark Zuckerberg, Mary Meeker, medical malpractice, meme stock, Network effects, PageRank, pets.com, ride hailing / ride sharing, Salesforce, Saturday Night Live, shareholder value, short squeeze, Silicon Valley, Skype, Snapchat, social graph, Steve Jobs, stocks for the long run, subscription business, super pumped, the rule of 72, TikTok, Travis Kalanick, Uber and Lyft, uber lyft

These words were actually lifted from the founders’ letter included in the S1 filed 11 years earlier. And those “smaller bets” included Life Sciences (which was developing glucose-sensing contact lenses), Calico (focused on longevity . . . as in human longevity . . . as in curing death), Wing (a drone delivery service), and Waymo (Google’s autonomous vehicle unit). It all sounded wonderful, until the market realized that those “smaller bets” were generating almost $4 billion in operating losses in 2015. Loss levels that were anything but “small” and were going to continue at that level for at least five years with de minimis revenue to show for it.

Table 9.10 is a quick summary of Uber’s financials for 2018 (in the S1), 2019, and 2020. The key takes were: TABLE 9.10 UBER: Baby, You Can Drive My Car . . . and Cut Costs 1. Uber’s Rides segment was actually reasonably profitable—17 percent EBITDA margin in 2018. 2. Uber’s Other Segments, which contained its Freight business and its Autonomous Vehicle group, were losing a lot of money—$689 million. 3. Uber was spending a shockingly large amount on G&A and what it called Platform R&D—$1.9 billion in 2018. Through the selling off of underperforming assets like Uber Eats in South Korea and through forced austerity measures in the wake of the Covid-19 pandemic, Uber was able to materially improve its operations over the next two years without materially sacrificing growth.

There was not a history of consistent 20%+ growth—that 2018 growth of 42% marked a sharp deceleration from prior years. Hence, the partial check. Continuous and successful product innovation? Partial check. This is a hard one to judge convincingly, but Uber in 2019 was rolling out a reasonably new loyalty program for its Uber Rides and Eats customers, and it was investing aggressively in autonomous vehicle technology. The company was also investing aggressively in ways to reduce Rides wait times for both riders and drivers and in batching solutions for its Eats offering. Large TAM? Check. Per the “TAM, SAM, and DAM” section in the TAM lesson, Uber faced a multitrillion-dollar TAM, given its core ridesharing business and its online food order delivery business, both of which were already operating globally in 2019.


pages: 502 words: 132,062

Ways of Being: Beyond Human Intelligence by James Bridle

Ada Lovelace, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Anthropocene, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, behavioural economics, Benoit Mandelbrot, Berlin Wall, Big Tech, Black Lives Matter, blockchain, Californian Ideology, Cambridge Analytica, carbon tax, Charles Babbage, cloud computing, coastline paradox / Richardson effect, Computing Machinery and Intelligence, corporate personhood, COVID-19, cryptocurrency, DeepMind, Donald Trump, Douglas Hofstadter, Elon Musk, experimental subject, factory automation, fake news, friendly AI, gig economy, global pandemic, Gödel, Escher, Bach, impulse control, James Bridle, James Webb Space Telescope, John von Neumann, Kickstarter, Kim Stanley Robinson, language acquisition, life extension, mandelbrot fractal, Marshall McLuhan, microbiome, music of the spheres, negative emissions, Nick Bostrom, Norbert Wiener, paperclip maximiser, pattern recognition, peer-to-peer, planetary scale, RAND corporation, random walk, recommendation engine, self-driving car, SETI@home, shareholder value, Silicon Valley, Silicon Valley ideology, speech recognition, statistical model, surveillance capitalism, techno-determinism, technological determinism, technoutopianism, the long tail, the scientific method, The Soul of a New Machine, theory of mind, traveling salesman, trolley problem, Turing complete, Turing machine, Turing test, UNCLOS, undersea cable, urban planning, Von Neumann architecture, wikimedia commons, zero-sum game

Questions about who gets to do that rewriting of reality, which decisions are made along the way, and who gains from it, are all too often missed and forgotten in the excitement. That is why I believe that it’s crucially important for as many of us as possible to be engaged in thinking through the implications of new technologies; and that this process has to include learning about and tinkering with the things ourselves. My attempt at building an autonomous vehicle consisted of a rented SEAT hatchback, a few cheap webcams, a smartphone taped to the steering wheel, and some software copied and pasted from the internet.1 This wasn’t a case of programming a dumb machine with everything it needed to know in advance, however. Like the commercial systems developed by Google, Tesla and others, my car would learn to drive by watching me drive: by comparing the view from the cameras with my speed, acceleration, steering wheel position and so forth, the system matched my behaviour with the road shape and condition, and after a couple of weeks it had learned how to keep a vehicle on the road – in a simulator at least.

And so, using several kilo bags of salt, I poured out onto the ground a solid circle a few metres in diameter, and then around it I drew a dashed circle. Together, these circles formed a closed space in which the (European) road marking for ‘No Entry’ is projected inwards. As a result, any well-trained, law-abiding autonomous vehicle, on entering the circle, would find itself unable to leave it. I called it the Autonomous Trap. Autonomous Trap 001, Mount Parnassus, 2017. This crude attack on the machine’s sense of the world was intended to make a few points. The first is political: by working with these technologies, we can learn something of their world, and this knowledge can be used to turn them to more interesting and equitable ends – or to stop them in their tracks.

The Trolley problem asks what an automated vehicle should do if there are two unavoidable paths for it to take: one towards a group of people and one towards an individual, for example. Whose life is worth more? The Trolley problem has even been turned into an online game, the Moral Machine, by researchers at MIT seeking to formulate rules for autonomous vehicles.21 The problem with the Trolley problem is that it was originally formulated for a human operator at the controls of a runaway tram car: the power of the problem resides in the unavoidable nature of its two outcomes. But its generalization to automated systems is deeply flawed. By focusing on only the final fork in the path, we are led to ignore all the other decisions made along the path that led to this critical moment.


pages: 419 words: 102,488

Chaos Engineering: System Resiliency in Practice by Casey Rosenthal, Nora Jones

Amazon Web Services, Asilomar, autonomous vehicles, barriers to entry, blockchain, business continuity plan, business intelligence, business logic, business process, cloud computing, cognitive load, complexity theory, continuous integration, cyber-physical system, database schema, DevOps, fail fast, fault tolerance, hindsight bias, human-factors engineering, information security, Kanban, Kubernetes, leftpad, linear programming, loose coupling, microservices, MITM: man-in-the-middle, no silver bullet, node package manager, operational security, OSI model, pull request, ransomware, risk tolerance, scientific management, Silicon Valley, six sigma, Skype, software as a service, statistical model, systems thinking, the scientific method, value engineering, WebSocket

Calling these out and explicitly naming it Chaos Engineering allows us to strategize about its purpose and application, and take lessons learned from other fields and apply them to our own. In that spirit, we can explore Chaos Engineering in industries and companies that look very different from the prototypical microservice-at-scale examples commonly associated with the practice. FinTech, Autonomous Vehicles (AV), and Adversarial Machine Learning can teach us about the potential and the limitations of Chaos Engineering. Mechanical Engineering and Aviation expand our understanding even further, taking us outside the realm of software into hardware and physical prototyping. Chaos Engineering has even expanded beyond availability into security, which is the other side of the coin from a system safety perspective.

The Rise of Cyber-Physical Systems There are many ecosystems unfamiliar to most software engineers where software is still the centerpiece driving innovation. Cyber-physical systems (CPSs) comprise one such ecosystem. A CPS is an interconnected hardware-software system that is deployed into, and interacts with, the physical world around it. Examples of such systems range from avionics systems or autonomous vehicles to traditional operational technology deployments like industrial control systems in a chemical refinery. Typically they’re composed of a menagerie of sensors, embedded devices, communication transports and protocols, and half-a-dozen different obscure operating systems, and often they have critical dependencies on numerous proprietary black box components.

Embedded engineers and safety-critical system engineers are slowly coming to the realization that they are no longer component engineers building a self-contained piece of local functionality in a box. We are all distributed systems engineers now. Chaos Engineering’s origins in distributed software systems can help CPSs because distributed systems engineers are predisposed to worrying about things that are just now entering the radar of embedded engineers. Working with engineers in the autonomous vehicle development space, I often suggest going after timing constraints as a first target for Chaos Engineering and experiments. Not once has it left anybody wanting for interesting and in some cases existential insights about how their system behaves when their clocks play tricks on them. For anybody reading this who works on critical software-intensive systems of systems looking for a place to start applying Chaos Engineering practices: start with timing.


pages: 484 words: 104,873

Rise of the Robots: Technology and the Threat of a Jobless Future by Martin Ford

3D printing, additive manufacturing, Affordable Care Act / Obamacare, AI winter, algorithmic management, algorithmic trading, Amazon Mechanical Turk, artificial general intelligence, assortative mating, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Bernie Madoff, Bill Joy: nanobots, bond market vigilante , business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon tax, Charles Babbage, Chris Urmson, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, commoditize, computer age, creative destruction, data science, debt deflation, deep learning, deskilling, digital divide, disruptive innovation, diversified portfolio, driverless car, Erik Brynjolfsson, factory automation, financial innovation, Flash crash, Ford Model T, Fractional reserve banking, Freestyle chess, full employment, general purpose technology, Geoffrey Hinton, Goldman Sachs: Vampire Squid, Gunnar Myrdal, High speed trading, income inequality, indoor plumbing, industrial robot, informal economy, iterative process, Jaron Lanier, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kenneth Arrow, Khan Academy, Kiva Systems, knowledge worker, labor-force participation, large language model, liquidity trap, low interest rates, low skilled workers, low-wage service sector, Lyft, machine readable, machine translation, manufacturing employment, Marc Andreessen, McJob, moral hazard, Narrative Science, Network effects, new economy, Nicholas Carr, Norbert Wiener, obamacare, optical character recognition, passive income, Paul Samuelson, performance metric, Peter Thiel, plutocrats, post scarcity, precision agriculture, price mechanism, public intellectual, Ray Kurzweil, rent control, rent-seeking, reshoring, RFID, Richard Feynman, Robert Solow, Rodney Brooks, Salesforce, Sam Peltzman, secular stagnation, self-driving car, Silicon Valley, Silicon Valley billionaire, Silicon Valley startup, single-payer health, software is eating the world, sovereign wealth fund, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Steven Pinker, strong AI, Stuxnet, technological singularity, telepresence, telepresence robot, The Bell Curve by Richard Herrnstein and Charles Murray, The Coming Technological Singularity, The Future of Employment, the long tail, Thomas L Friedman, too big to fail, Tragedy of the Commons, Tyler Cowen, Tyler Cowen: Great Stagnation, uber lyft, union organizing, Vernor Vinge, very high income, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, women in the workforce

., 150n risk, Peltzman effect and, 267–268 RoboBusiness conference/tradeshow, 7 Robot & Frank (film), 155 robotics, 6–8 cloud, 20–23 See also automation; robots robotic walkers, 157 robots in agriculture, 23–26 box-moving, 1–2, 5–6 consumer, 197n educational, 7 elder-care, 155–158 hospital and pharmacy, 153–155 industrial, 1–5, 10–11 personal, 7 telepresence, 119–120, 157 Rolling Stone (magazine), 56 Romney, Mitt, 272 Roosevelt, Franklin, 279 Rosenthal, Elisabeth, 160, 163 Rosenwald, Michael, 107 ROS (Robot Operating System), 6, 7 Russell, Stuart, 229 Rutter, Brad, 101 Sachs, Jeffrey, 60 Saez, Emmanuel, 46 safety, autonomous cars and, 184–185, 187 Salesforce.com, 134 Samsung Electronics, 70n Samuelson, Paul, x Sand, Benjamin M., 127 San Jose State University, 134 Sankai, Yoshiyuki, 156–157 Santelli, Rick, 170 savings, China’s high rate of, 224–225 SBTC. See skill biased technological change (SBTC) Schlosser, Eric, 210 Schmidt, Michael, 108, 109 Schwarzenegger, Arnold, 22 S-curves, 66–67, 68, 69, 70–71, 250 secular stagnation, 274n self-driving cars, See autonomous cars Selingo, Jeffrey J., 140, 141 Semiconductor Industry Association, 80 service sector, 12–20 The Shallows (Carr), 254 Shang-Jin Wei, 225 Silvercar, 20 Simonyi, Charles, 71 single-payer health care system, 165–167, 169 The Singularity, 233–238, 248 The Singularity Is Near (Kurzweil), 234 Singularity University, 234 Siu, Henry E., 49, 50 skill biased technological change (SBTC), 48 skills, acquisition of by computers, xv–xvi Skipper, John, 201 “Skynet,” 22 Slate (magazine), 153 Smalley, Richard, 244–245 Smith, Adam, 73 Smith, Noah, 219–220, 273 Smith, Will, 111 social media response program, 93–94 social safety net, 278.

YouTube, Instagram, and WhatsApp are, of course, all examples drawn directly from the information technology sector, where we’ve come to expect tiny workforces and huge valuations and revenues. To illustrate how a similar phenomenon is likely to unfold on a much broader front, let’s look in a bit more depth at two specific technologies that have the potential to loom large in the future: 3D printing and autonomous cars. Both are poised to have a significant impact within the next decade or so, and could eventually unleash a dramatic transformation in both the job market and the overall economy. 3D Printing Three-dimensional printing, also known as additive manufacturing, employs a computer-controlled print head that fabricates solid objects by repeatedly depositing thin layers of material.

In the United States alone nearly 6 million people are employed in the construction sector, while the International Labour Organisation estimates that global construction employment is nearly 110 million.7 Three-dimensional construction printers might someday result in better and cheaper homes, as well as radically new architectural possibilities—but the technology could also eliminate untold millions of jobs. Autonomous Cars The self-driving car entered the final stretch on the road that would take it from science fiction to everyday reality on March 13, 2004. That date marked the first DARPA Grand Challenge—a race that the Defense Advanced Research Projects Agency hoped would help jump-start progress in the development of autonomous military vehicles.


pages: 286 words: 79,305

99%: Mass Impoverishment and How We Can End It by Mark Thomas

"there is no alternative" (TINA), "World Economic Forum" Davos, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, additive manufacturing, Alan Greenspan, Albert Einstein, anti-communist, autonomous vehicles, bank run, banks create money, behavioural economics, bitcoin, business cycle, call centre, Cambridge Analytica, central bank independence, circular economy, complexity theory, conceptual framework, creative destruction, credit crunch, CRISPR, declining real wages, distributed ledger, Donald Trump, driverless car, Erik Brynjolfsson, eurozone crisis, fake news, fiat currency, Filter Bubble, full employment, future of work, Gini coefficient, gravity well, income inequality, inflation targeting, Internet of things, invisible hand, ITER tokamak, Jeff Bezos, jimmy wales, job automation, Kickstarter, labour market flexibility, laissez-faire capitalism, Larry Ellison, light touch regulation, Mark Zuckerberg, market clearing, market fundamentalism, Martin Wolf, Modern Monetary Theory, Money creation, money: store of value / unit of account / medium of exchange, Nelson Mandela, Nick Bostrom, North Sea oil, Occupy movement, offshore financial centre, Own Your Own Home, Peter Thiel, Piper Alpha, plutocrats, post-truth, profit maximization, quantitative easing, rent-seeking, Robert Solow, Ronald Reagan, Second Machine Age, self-driving car, Silicon Valley, smart cities, Steve Jobs, The Great Moderation, The Wealth of Nations by Adam Smith, Tyler Cowen, warehouse automation, wealth creators, working-age population

Other problems which may benefit from this kind of high-power computing include mapping of entire proteins in the way that genes can be mapped today – or even entire genomes – and, of course, the development of full AI. Narrow AI for applications such as autonomous vehicles Proof of concept studies on self-driving cars have been underway for almost a decade and large-scale trials are now in progress. Google, for example, has more than twenty autonomous vehicles in the US, and NuTonomy has trials of taxis underway in Singapore. Many commentators believe that the first commercially available self-driving cars will hit the market before 2020.9 The heads of automakers General Motors and Nissan have both confirmed that they expect driverless cars on the roads by 2020.

In combination, these new technologies give us the power to create an improvement in human lives over the next thirty-five years at least comparable to that during the Golden Age of Capitalism. A huge range of high-value products and services, and indeed new business models, will become feasible. The total cost to the world – including environmental cost – will fall dramatically. And, economically, the size of the pie could expand enormously. Autonomous vehicles alone will have a huge impact on society: people who are not mobile because they cannot drive or cannot afford a car will become mobile; fewer people will feel the need to own a car since one could arrive at their doorstep when they need it; roads lined with cars that spend most of their lives immobile will be a thing of the past; the total number of cars needed will be far fewer and congestion may even reduce; and people whose jobs today involve driving buses, taxis, lorries and trucks will no longer be employed in those areas.


pages: 491 words: 77,650

Humans as a Service: The Promise and Perils of Work in the Gig Economy by Jeremias Prassl

3D printing, Affordable Care Act / Obamacare, Airbnb, algorithmic management, Amazon Mechanical Turk, Andrei Shleifer, asset light, autonomous vehicles, barriers to entry, call centre, cashless society, Clayton Christensen, collaborative consumption, collaborative economy, collective bargaining, creative destruction, crowdsourcing, death from overwork, Didi Chuxing, disruptive innovation, Donald Trump, driverless car, Erik Brynjolfsson, full employment, future of work, George Akerlof, gig economy, global supply chain, Greyball, hiring and firing, income inequality, independent contractor, information asymmetry, invisible hand, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Roose, Kickstarter, low skilled workers, Lyft, machine readable, Mahatma Gandhi, Mark Zuckerberg, market friction, means of production, moral hazard, Network effects, new economy, obamacare, pattern recognition, platform as a service, Productivity paradox, race to the bottom, regulatory arbitrage, remote working, ride hailing / ride sharing, Robert Gordon, Ronald Coase, Rosa Parks, scientific management, Second Machine Age, secular stagnation, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Simon Singh, software as a service, Steve Jobs, TaskRabbit, TechCrunch disrupt, The Future of Employment, The Market for Lemons, The Nature of the Firm, The Rise and Fall of American Growth, transaction costs, transportation-network company, Travis Kalanick, two tier labour market, two-sided market, Uber and Lyft, Uber for X, uber lyft, union organizing, warehouse automation, work culture , working-age population

First, what about Uber’s well-publicized efforts to develop self-driving cars? And, in any event, might a delay in innovative automation not be to the benefit of workers whose jobs would otherwise be threatened by a rise of the robots? As regards the first of these arguments, Uber’s emphasis on autonomous vehicles has increasingly been questioned by experts from both technological and eco- nomic perspectives: the company’s efforts seem to lag significantly behind competitors’ technological advances.75 In any event, why would Uber replace its current asset-light model, under which drivers bear the full cost of pro- viding cars, petrol, and their time, with a massive investment in an expensive fleet of self-driving cars?

This book is dedicated to Abi with my deepest love and admiration. J.F.B.B.P. Magdalen College, Oxford Hilary Term MMXVIII * * * * * * Index Aasmäe, Mailin 183 automata 1 Adams, Abi 111, 178 automation 89, 135, 136 ‘additional income’ 81–2 limits of 137–9 airbnb 143 robots 136–7 Airtasker 114 autonomous vehicles 89, 137 Akerlof, George 158 autonomy 53–5 (see also Albin, Einat 175, 176 self-determination) algorithms 2, 5, 7, 8, 12, 13, 84 algorithmic control and 55–8 control mechanisms 55–8 sanctions and 61–3 limitations 138, 139 wages and 58–61 rating algorithms 54, 55, 87–8 Autor, David 138–9, 185, 186 discrimination 113 Avent, Ryan 89, 171 Amazon ‘artificial artificial intelligence’ 6, 139 Badger, Emily 182 CEO 1–2, 3, 6 Balaram, Brhmie 38, 149, 150, 153, ‘humans as a service’ 3 155, 158, 180 MTurk 2, 3, 4, 11, 12, 24–5, 76, 139, Balkin, Jack 170 161–2, 163 bargaining power 9, 48, 65, 66, 82, 107, algorithmic control mechanisms 56 110, 111, 113, 116 (see also business model 100, 101, 103, 104 collective bargaining) commission deductions 63 Barry, Erin 166 digital work intermediation 14, 15 Benjamin, Robert 73 matching 19 Bertram, Jo 115 payment in gift vouchers 105 Berwick, Barbara Ann 99 quality control 120 Bevin, Ernest 86 TurkOpticon 114, 162, 163, 179 Bezos, Jeff 1–2, 3, 6, 72 wage rates 59, 60, 61 Bhuiyan, Johana 162 termination of agreements 63 Biewald, Lukas 4 ‘web services’ 1–2 bilateral relationships 100 Andersen, Hans Christian 71, 166 BlaBlaCar 43 Apple 35 BlancRide 43 apps 5 Blasio, Bill de 36 arbitration clauses 67, 165 Bonaparte, Napoleon 1 Arlidge, John 163 Booth, Robert 182 ‘artificial artificial intelligence’ 6, 139 Boswell, Josh 182 Ashley, Mike 40 Bradshaw, Tim 151 associated costs 60 Brazil, Noli 133, 184 asymmetric information 32, 54, 87, 131 Bruckner, Caroline 126–7, 183 Australia 109, 110–11, 114, 121, 176, 177 Brynjolfsson, Erik 137, 138, 185 * * * 192 Index Burger King 60 consumer satisfaction 25 business models 12–13, 44, 100, 101, 102 contracts of employment 94 structural imbalances 130–2 bilateral relationships 100 Busque, Leah 46, 51 contractual agreements 8 Butler, Sarah 155 contractual prohibitions 66–7 Bythell, Duncan 89, 166, 167, 168, control mechanisms 54, 55–7 169, 172 ‘cost of switching’ 165 Craigslist 20 Cala, Ryan 123, 131, 182, 184 Croft, Jane 173, 182, 186 Callaway, Andrew 58, 161 ‘crowd-based capitalism’ 40, 73 capitalism 2, 3, 40, 73 CrowdFlower 4, 58 Carr, Paul Bradley 39, 154 wage rates 59 Carson, Biz 173 crowdsourcing 7, 11 Case, Steve 73, 166 classification and differentiation 13 cash burn 22–3 crowdwork 2, 54 cashless payment 5 classification and differentiation 13 ‘casual earners’ 29 Crump, W.

L. 176 Chen, Keith 122 Davies, Paul 174 Cherry 38 Davies, Rob 151 Cherry, Miriam 97, 99, 132, 173, 174, 184 Day, Iris 177 chess robots 1, 6 Deakin, Simon 36, 112, 130, 131, 152, 172, China 12, 38, 153 174, 177, 178, 184, 185 Chowdhry, Amit 181 deductions from pay 15, 19, 60, 63, 67 Christenson, Clayton M. 39 Deep Blue 1 ‘churn’/worker turnover 68 Deliveroo 2, 11, 12, 13, 115 Clark, Shelby 46 collective action by drivers 113 classificatory schemes 13, 28–9, 147 contractual prohibitions 66–7 misclassification 95, 96–100 employment litigation 99 Clement, Barrie 162 internal guidelines 43–4 Clover, Charles 153 safety and liability 122–3 Coase, Ronald 19, 94, 101, 172 wage rates 65 Coase’s theory 19, 20 delivery apps 2 Codagnone, Cristiano 150 demand fluctuations 78 Cohen, Molly 36, 37, 152, 157 Denmark 36 ‘collaborative consumption’ 42 deregulation 37, 40 (see also regulation) collective action 113–15 Dholakia, Utpal 150 collective bargaining rights 48, 65, 82 Didi 2, 12, 38 commission deductions 15, 19, 60, 63, 67 differential wage rates 109–11 commodification of work 76, 77, 110 digital disruption 49, 50 competition 88 ‘digital feudalism’ 83 consumer demand 17–18 digital innovation see innovation consumer protection 10, 112, 121, 128–9 digital market manipulation 123 safety and liability 122–3, 128–9 digital payment systems 5 * * * Index 193 digital work intermediation 5, 11, 13–16 borderline cases 100 disability discrimination 62, 121 identifying the employer 100 discriminatory practices 62, 94, 113, easy cases 102–3 121, 180 functional concept of the disputes 66 employer 101–2, 104 disruptive innovation 39–40, 49, 50, 95 genuine entrepreneurs 103 dockyards 78, 79–80 harder cases 103–4 ‘doublespeak’ 31–50, 71, 95, 97–8, 133 multiple employers 103 Doug H 160, 163 platforms as employers 102–3 down-time 60, 65, 76, 77 ‘independent worker’ 48 Downs, Julie 180 misclassification 95, 96–100 Drake, Barbara 168 ‘personal scope question’ 93 drink driving 133, 184–5 employment taxes 125–7 Dzieza, Josh 163 Engels, Friedrich 81, 168 ‘entrepreneur-coordinator’ 101 economic crises 145 entrepreneurship 6, 8, 21, 32, 42, 43, economic drivers 7, 18–24 45–6, 50, 52 (see also micro- Edwards, Jim 146 entrepreneurs) efficiency 7 autonomy 53–5 Elejalde-Ruiz, Alexia 175 algorithmic control and 55–8 ‘elite worker’ status 61, 67 sanctions and 61–3 ‘emperor’s new clothes’ 71 wages and 58–61 empirical studies 28–9 freedom 8, 14, 27, 29, 47, 49, 51, 52, employer responsibility 104 53, 55, 65–8, 69, 85, 96, 108, 110, employment contracts 94 112, 113 bilateral relationships 100 on-demand trap and 68–70 employment law 4, 9, 10, 38, 84 risk and 86 (see also regulation) genuine entrepreneurs 102, 103 continuing importance 139–40 misclassification 96–7, 98, 101 control/protection trade-off 93–4, 95 ‘personal scope question’ 93 European Union 107, 111, 112, 178 self-determination 63–5 flexibility and environmental impacts 21, 26 innovation and 90 Estlund, Cynthia 137, 185 measuring working time 105–7 Estonia 127 mutuality of obligation 174 Estrada, David 41 new proposals 46–9 euphemisms 44–5 rebalancing the scales 107–8 European Union law 107, 111, 112, 178 collective action 113–15 exploitation 26–7 portable ratings 111–13 Ezrachi, Ariel 150 surge pricing 108–11 ‘risk function’ 131, 132 Facebook 35, 57 workers’ rights 105 FairCrowdWork 114, 179 rights vs flexibility 115–17 Farrell, Sean 164 employment litigation FedEx 97 FedEx 97, 173 feedback 5, 15–16 France 99 Feeney, Matthew 35, 151 Uber 45, 48, 54–5, 98, 99, 106, 115 Field, Frank 26 UK 45, 48, 98–9, 106, 115 financial losses 22–3 US 54–5, 97, 98, 99 ‘financially strapped’ 29 employment status 21, 45, 47 Finkin, Matthew 74, 84, 166, 169 * * * 194 Index Fiverr 12, 13, 24, 78 historical precedents and CEO 17 problems 72, 73–85 Fleischer, Victor 20, 147 rebranding work 4–6, 32 flexibility 8, 10, 12, 107, 108 labour as a technology 5–6 vs rights 115–17 market entrants 88 food-delivery apps 12 matching 13, 14, 18–20 Foodora 2, 12 monopoly power 23–4, 28 Foucault, Michel 55, 159 network effects 23–4 founding myths 34–5 overview 2–3 Fox, Justin 182 perils 6, 26–8, 31 fragmented labour markets 83, 84, 86, platform paradox 5 90, 113 platforms as a service 7–8 France 78 consumer protection 10 employment litigation 99 potential 6, 7, 12, 24–6, 31 Labour Code 114, 176, 179 regulation 9–10 (see also regulation) regulatory battles 36 real cost of on-demand services 119, tax liability 126 121–2 (see also structural ‘free agents’ 28–9 imbalances) Freedland, Mark 174, 175 regulation see regulation Freedman, Judith 111, 178 regulatory arbitrage 20–2 freedom 8, 14, 27, 29, 47, 49, 51, 52, 53, size of the phenomenon 16–17, 145–6 55, 65–8, 69, 85, 96, 108, 110, work on demand 11–29 112, 113 gigwork 13 on-demand trap and 68–70 Giliker, Paula 183 risk and 86 global economic crises 145 Frey, Carl 136, 185 Goodley, Simon 173 Fried, Ina 183 GPS 5, 57 Greenhouse, Steven 66, 164 Gardner-Selby, W. 185 Griswold, Alison 164, 181 gender parity 144 (see also Grossman, Nick 46 discriminatory practices) Gumtree 20 Germany Gurley, Bill 161 regulatory battles 36 Guyoncourt, Sally 178 workers’ rights 114 gift vouchers 105 Hacker, Jacob 86, 170 gig economy Hall, Jonathan 60, 162, 165 business models 12–13, 44, 100 Hammond, Philip 126, 182 cash burn 22–3 Hancock, Matthew 46, 166 clash of narratives 8 Handy 18 classification 13, 28–9 Hardy, Tess 176 critics 2, 3, 8 Harman, Greg 163 digital work intermediation 5, 11, Harris, Seth 48, 49, 105, 157, 175 13–16 Hatton, Erin 82, 169 economic drivers 7, 18–24 Heap, Lisa 177 empirical studies 28–9 Helpling 2 employment law and see employment Hemel, Daniel 147, 170 law Hesketh, Scott 181 enthusiasts 3, 4, 8 hiring practices: historical gigwork vs crowdwork 13 perspective 78, 79 growth 17–18 historical perspective 72, 73–85 ‘humans as a service’ 3–6 Hitch 38 * * * Index 195 Hitlin, Paul 162 Internet Holtgrewe, Ursula 169 collective action 113 HomeJoy 132 Third Wave 73 Hook, Leslie 153 Irani, Lilly 6, 114, 142, 162, 179 Horan, Hubert 22, 148 Isaac, Mike 170, 171 Horowith, Sara 144 Issa, Darrell 41 hostile takeovers 111–12 Howe, Jeff 7, 11, 142 jargon 42–5 Huet, Ellen 153 Jensen, Vernon 167, 168, 170 Human Intelligence Tasks (HITs) 60, 93 Jobs, Steve 35 ‘humans as a service’ 3–6 joint and several liability 104 historical precedents and problems 72, Justia Trademarks 143 73–85 rebranding work 4–6, 32, 40–50 Kalanick, Travis 43, 86 Hunter, Rachel 106, 176 Kalman, Frank 16, 144 Huws, Ursula 27, 141, 150 Kaminska, Izabella 22–3, 44, 90, 148, 156, 169, 171, 172 ‘idle’ time 60, 65, 76, 77 Kaplow, Louis 184 illegal practices 57 Kasparov, Garry 1 immigrant workers 77 Katz, Lawrence 16 incentive structures 67–8 Katz, Vanessa 116, 179 independent contractors 21 Kaufman, Micha 17, 145, 149 Independent Workers Union of Great Kempelen, Wolfgang von 1 Britain (IWGB) 113, 179 Kennedy, John F. 135, 185 industrialization 75 Kenya 36 industry narratives 32–3, 49–50 Kessler, Sarah 151 information asymmetries 32, 54, 87, 131 Keynes, John Maynard 135, 185 innovation 3, 6, 8, 9, 10, 31, 32, 42, 45–6, King, Tom, Lord King of Bridgwater 71 110 cheap labour and 89 Kirk, David 133, 184 disruptive innovation 39–40, 49, 95 Kitchell, Susan 166 historical precedents and problems 72, Klemperer, Paul 165 73–85 Krueger, Alan 16, 48, 49, 60, 105, 106, incentives 86–90 157, 162, 165, 175 myths 72, 83 Krugman, Paul 170 obstacles to 88–90 Kucera, David 186 paradox 72, 87 problematic aspects 85–90 labour law see employment law productivity and 87 Lagarde, Christine 86, 170 shifting risk 85–6 Leimeister, Jan Marco 13 workers’ interests and 89–90 Leonard, Andrew 33, 151 innovation law perspective 36 Lewis, Mervyn 168 ‘Innovation Paradox’ 9 Liepman, Lindsay 184 insecure work 9, 10, 12, 27, 42, 107 Lloyd-Jones, Roger 168 historical perspective 80, 81 loan facilities 68 insurance 123 lobbying groups 32, 47, 48 intermediaries 83 (see also digital work Lobel, Orly 11, 37–8 intermediation) low-paid work 9, 26–7, 40–2, historical perspective 79–80 59, 61 International Labour Organization low-skilled work 76, 77, 82 (ILO) 4, 83, 97, 169, 173 automation and 138 * * * 196 Index Lukes, Steven 159 Murgia, Madhumita 182 Lyft 2, 12, 13, 38, 41, 42, 76 mutuality of obligation 174 algorithmic control mechanisms 56 network effects 23–4 regulatory battles 35 Newcomer, Eric 148, 165 Uber’s competitive strategies 88 Newton, Casey 164 Nowag, Julian 183 McAfee, Andrew 137, 138, 185 Machiavelli, Niccolo 93, 172 O’Connor, Sarah 43, 155 machine learning 136, 137 ODesk 60 McCurry, Justin 186 O’Donovan, Caroline 144, 164, 181 Malone, Tom 73 Oei, Shu-Yi 124, 125, 132, 147, 182, 184 Mamertino, Mariano 161, 163 Ola 2, 12 market entrants 88 on-demand trap 68–70 market manipulation 123 on-demand work 11– 29 Markowitz, Harry 184 real cost of on-demand services 119, Marsh, Grace 182 121–2 (see also structural Marshall, Aarian 186 imbalances) Martens, Bertin 150 Orwell, George 31, 151 Marvit, Moshe 142 Osborne, Hilary 164 Marx, Patricia 119–20, 180 Osborne, Michael 136, 185 matching 13, 14, 18–20 outsourcing Maugham, Jolyon 182 agencies 40 Mayhew, Henry 77, 78, 79, 167 ‘web services’ 2 Mechanical Turk 1, 2, 6 outwork industry 74–5, 76–7, 79, 80, 89 mental harm 57–8 Owen, Jonathan 178 Meyer, Jared 149 ‘micro-entrepreneurs’ 8, 21, 46, 49, Padget, Marty 186 52–3, 63 Pannick, David, Lord Pannick 110 ‘micro-wages’ 27 Pasquale, Frank 8, 40, 154 middlemen 80 Peck, Jessica Lynn 26 minimum wage levels 3, 9, 21, 26, 27, 59, peer-to-peer collaboration 42, 43 94, 104, 105 Peers.org 32–3 minimum working hour guarantees 108 performance standard probations 61 misidentification 95, 96–100 personal data 112, 178 mobile payment mechanisms 5 ‘personal scope question’ 93 monopoly power 23–4, 28 Pissarides, Christopher 19, 147 Morris, David Z. 171 platform paradox 5 Morris, Gillian 174 platform responsibility 122–3, 128 MTurk 2, 3, 4, 11, 12, 24–5, 76, 139, platforms as a service 7–8 161–2, 163 consumer protection 10 algorithmic control mechanisms 56 regulation 9–10 (see also regulation) business model 100, 101, 103, 104 Plouffe, David 154 commission deductions 63 Poe, Edgar Allen 1 digital work intermediation 14, 15 Polanyi’s paradox 138–9 matching 19 political activism 114 payment in gift vouchers 105 portable ratings 111–13 quality control 120 Porter, Eduardo 171 TurkOpticon 114 ‘postindustrial corporations’ 20 wage rates 59, 60, 61 Postmates 57, 63, 121 * * * Index 197 Poyntz, Juliet Stuart 168 structural imbalances 130, 131 Prassl, Jeremias 174, 175, 176, 177, robots 136–7 178, 183 Mechanical Turk 1, 6 precarious work 9, 10, 12, 27, 42, 107 Rodgers, Joan 177 historical perspective 80, 81 Rodriguez, Joe Fitzgerald 181 price quotes 121–2 Rönnmar, Mia 175 surge pricing 58, 108–11, 122 Roosevelt, Franklin D. 133, 185 Primack, Dan 148 Rosenblat, Alex 54, 56, 65, 123, 131, 159, productivity 87 160, 163, 164, 182, 184 public discourse 69 Rosenblat, Joel 165 public health implications 27 Rubery, Jill 84, 169 punishment 57 (see also sanctions) Ryall, Jenny 181 quality control 5, 80, 120 safe harbours 47, 49 safety and liability 122–3, 128–9 rating mechanisms 5, 15–16, 53–4 sanctions 61–3 (see also punishment) algorithms 54, 55, 87–8 Sandbu, Martin 87, 170 discrimination 62, 113 Scheiber, Noam 164 portable ratings 111–13 Schmiechen, James 167, 168, 169 sanctions and 61–3 Schumpeter, Joseph 133 rebranding work 4–6, 32, 40–50 self-dealing 123 regulation 9–10 (see also employment law) self-determination 36–7, 47, 63–5 industry narratives 32–3, 49–50 (see also autonomy) new proposals 31, 46–9, 50 self-driving cars 89, 137 opponents 31, 33–4 sexual assaults 121, 180–1 Disruptive Davids 34–7 sexual discrimination 62, 144, 180 disruptive innovation theory ‘sham self-employment’ 97 39–40, 49 sharing economy 7, 20, 51 New Goliaths 37–40 critics 32–3 regulatory battles 35–7, 47–9 disruptive innovation 39, 49 safe harbours 47, 49 enthusiasts 61 self-regulation 36–7, 47 Sharing Economy UK 33, 37 shaping 32–3, 45–9 sharing platforms 116 regulatory arbitrage 20 –2, 147 Shavell, Steven 184 regulatory experimentation 36 Shleifer, Andrei 111, 178 Reich, Robert 108, 176 Shontell, Alyson 161 Relay Rides 46 Silberman, Six 61, 114, 162, 163, 179 ‘reluctants’ 29 Silver, James 156, 158 reputation algorithms 54 Singer, Natasha 43, 155, 156 ride-sharing/ridesharing 2, 21, 38, 41 Slee, Tom 32, 53, 142, 151, 155, 158, 159 (see also taxi apps) Smith, Adam 73 algorithmic control mechanisms 55–6 Smith, Jennifer 170 business model 102–3 Smith, Yves 148 discriminatory practices 62, 121 social media 114 maltreatment of passengers 121 social partners 10, 94 ride-sharing laws 47 social security contributions 21, 125–7 Ries, Brian 181 social security provision 3, 48, 131 Ring, Diane 124, 125, 132, 147, 182, 184 sociological critique 27–8 Risak, Martin 102, 175 specialization 75 risk shift 85–6 Spera 51, 158 * * * 198 Index Sports Direct 40–1 taxi regulation 21, 36, 37, 38, 114 Standage, Tom 141 vetting procedures 121 standardized tasks 76 tech:NYC 33 Stark, Luke 54, 56, 65, 159, 160, 163, 164 technological exceptionalism 6, 128 start-up loans 68 technological innovation see innovation Stefano, Valerio De 84, 169 technology 5–6, 27 Stigler, George 32, 151 unemployment and 135, 137, 140 Stone, Katherine 67, 165 terminology 42–5 structural imbalances time pressure 57 business model 130–2 Titova, Jurate 183 digital market manipulation 123 TNC, see transportation network levelling the playing field 127–32 company platform responsibility 122–3, 128 Tolentino, Jia 166 real cost of on-demand services 119, Tomassetti, Julia 20, 147, 156, 171 121–2 Tomlinson, Daniel 163 safety and liability 128–9 trade unions 65, 113, 114, 178, 179 sustainability 132–3 transaction cost 19 tax obligations 123–4, 129, 131, 132 transport network company (TNC) employment taxes and social regulation 47–8 security contributions 125–7 Truck arrangements 105 VAT 124–5, 129 Tsotsis, Alex 151 Stucke, Maurice 150 TurkOpticon 114, 162, 163, 179 Sullivan, Mike 180 Summers, Lawrence 111, 131, 178, 184 Uber 2, 11, 12, 43 Sundararajan, Arun 36, 37, 41, 73, 74, 75, algorithmic control mechanisms 56, 151, 152, 157, 166, 167 57, 58 Supiot, Alain 130–1, 177, 184 arbitration 165 surge pricing 58, 108–11, 122 autonomous vehicles and 89 survey responses 120 ‘churn’/worker turnover 68 Swalwell, Eric 41, 154 commission deductions 63 competitive strategies 88 takeovers 111–12 consumer demand 18 ‘task economies’ 76, 77, 79 control mechanisms 54 Task Rabbit 2, 12, 13, 46, 143–4, 163 creation of new job business model 100, 101, 160 opportunities 77–8 company law 56 digital work intermediation 14, 15 contractual prohibitions 66 disruptive innovation 39 digital work intermediation 14, 15–16 driver income projections 51 financial losses 22 Driver-Partner Stories 25, 149 founding myth 34–5 driver-rating system 158, 160 regulatory arbitrage 20 employment litigation terms of service 44, 53, 122, 158, 181 France 99 wage rates 64 UK 45, 48, 98, 106, 115 working conditions 57 US 54–5, 99 Taylor, Frederick 52–3, 72, 158 financial losses 22, 23 tax laws 84 ‘Greyball’ 88, 170 tax obligations 123–4, 129, 131, 132 ‘Hell’ 88, 170 employment taxes and social security loss-making tactics and market share 64 contributions 125–7 monopoly power 23 VAT 124–5, 129 positive externality claims 132–3 taxi apps 12, 20 regulatory arbitrage 20 * * * Index 199 regulatory battles 35, 36 Vaidhyanathan, Siva 40, 154 resistance to unionization 65, 178 value creation 18–19, 20 risk shift 86 van de Casteele, Mounia 182 safety and liability 122–3, 180–1 VAT 124–5, 129 sale of Chinese operation 38 Verhage, Julie 147 surge pricing 58, 122 vicarious liability 128 tax liability 125, 126, 127 unexpected benefits 26 wage rates 58–61, 64, 65 wage rates 58, 59, 60–1, 64, 65, 127 Wakabayashi, Daisuke 171 working conditions 113, 178 Warne, Dan 115 UberLUX 14 Warner, Mark 16 UberX 14, 51, 60 Warren, Elizabeth 127, 183 UK Webb, Beatrice and Sidney 80, 168 collective action 113 Weil, David 83, 169 employment litigation 45, 48, 98–9, 106 welfare state 130, 131 tax liability 124–5, 126 Wilkinson, Frank 84, 130, 131, 169, unemployment 135, 137, 140, 145 172, 184, 185 Union Square Ventures 46 Wong, Julia Carrie 170 unionization 10, 65, 113, 114, 178, 179 work on demand 11–29 ‘unpooling’ 147 worker classification 28–9, 147 Unterschutz, Joanna 178 misclassification 95, 96–100 Upwork 12, 76, 144 workers’ rights 105 algorithmic control mechanisms 56 vs flexibility 115–17 business model 100, 160 working conditions 57, 68–9 commission deductions 63, 67 historical perspective 77, 81 US Uber 113, 178 discriminatory practices 121 working time 105–7 employment litigation 54–5, 97, 98, 99 Wosskow, Debbie 157 regulatory battles 36, 47 Wujczyk, Marcin 178 tax liabilities 126–7 taxi regulation 36, 114 Yates, Joanne 73 transport network company (TNC) YouTube 58 regulation 47–8 user ratings 5, 15–16, 53–4, 55 Zaleski, Olivia 165 portable ratings 111–13 zero-hours contracts 40, 41, 107 sanctions and 61–3 Zuckerberg, Mark 35 * * * Document Outline Cover Humans as a Service: The Promise and Perils of Work in the Gig Economy Copyright Dedication Contents Introduction Welcome to the Gig Economy Humans as a Service Rebranding Work The Platform Paradox Labour as a Technology Making the Gig Economy Work Platforms as a Service Exploring the Gig Economy Charting Solutions A Broader Perspective 1.


pages: 305 words: 75,697

Cogs and Monsters: What Economics Is, and What It Should Be by Diane Coyle

3D printing, additive manufacturing, Airbnb, Al Roth, Alan Greenspan, algorithmic management, Amazon Web Services, autonomous vehicles, banking crisis, barriers to entry, behavioural economics, Big bang: deregulation of the City of London, biodiversity loss, bitcoin, Black Lives Matter, Boston Dynamics, Bretton Woods, Brexit referendum, business cycle, call centre, Carmen Reinhart, central bank independence, choice architecture, Chuck Templeton: OpenTable:, cloud computing, complexity theory, computer age, conceptual framework, congestion charging, constrained optimization, coronavirus, COVID-19, creative destruction, credit crunch, data science, DeepMind, deglobalization, deindustrialization, Diane Coyle, discounted cash flows, disintermediation, Donald Trump, Edward Glaeser, en.wikipedia.org, endogenous growth, endowment effect, Erik Brynjolfsson, eurozone crisis, everywhere but in the productivity statistics, Evgeny Morozov, experimental subject, financial deregulation, financial innovation, financial intermediation, Flash crash, framing effect, general purpose technology, George Akerlof, global supply chain, Goodhart's law, Google bus, haute cuisine, High speed trading, hockey-stick growth, Ida Tarbell, information asymmetry, intangible asset, Internet of things, invisible hand, Jaron Lanier, Jean Tirole, job automation, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, knowledge economy, knowledge worker, Les Trente Glorieuses, libertarian paternalism, linear programming, lockdown, Long Term Capital Management, loss aversion, low earth orbit, lump of labour, machine readable, market bubble, market design, Menlo Park, millennium bug, Modern Monetary Theory, Mont Pelerin Society, multi-sided market, Myron Scholes, Nash equilibrium, Nate Silver, Network effects, Occupy movement, Pareto efficiency, payday loans, payment for order flow, Phillips curve, post-industrial society, price mechanism, Productivity paradox, quantitative easing, randomized controlled trial, rent control, rent-seeking, ride hailing / ride sharing, road to serfdom, Robert Gordon, Robert Shiller, Robert Solow, Robinhood: mobile stock trading app, Ronald Coase, Ronald Reagan, San Francisco homelessness, savings glut, school vouchers, sharing economy, Silicon Valley, software is eating the world, spectrum auction, statistical model, Steven Pinker, tacit knowledge, The Chicago School, The Future of Employment, The Great Moderation, the map is not the territory, The Rise and Fall of American Growth, the scientific method, The Signal and the Noise by Nate Silver, the strength of weak ties, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Uber for X, urban planning, winner-take-all economy, Winter of Discontent, women in the workforce, Y2K

The alternative would have been balkanised or competing standards. Eventually, one would probably have prevailed, but at a significant opportunity cost in terms of the cost of building out networks and all the progress that unleashed as mobiles spread in developing economies. We need standards to be set now for areas of innovation ranging from autonomous vehicles to data to ‘smart’ urban networks. Most economists will be nodding agreement, but in general there is surprising sluggishness in thinking about policy in any other way than an analytical, almost mechanical, way. A dangerous dog savages a baby—so ban dangerous dogs. How to define them? Draw up a list of the relevant breeds.

Policy-makers naturally have a problem accepting black box solutions, and for some good reasons. These include questions about the robustness of AI to change in the data-generating process, and about bias due to the data sets. At least as important is the question of legal responsibility or political accountability: are we really going to accept that autonomous vehicles can motor round the streets with occasionally lethal consequences if they are owned by limited liability corporate entities? Will a minister of justice ever be accountable for sentencing or parole decisions if most are made by machine learning systems? How are the trade-offs between outcomes to be encoded in the machine’s objective function?

New types of digital service have emerged. Although it is still impossible to get a haircut without going to a hairdresser, a surprising number of services can be delivered in bit form. This is one lesson of the 2020–2021 lockdowns. Data usage has soared, even before the famed Internet of Things and autonomous vehicles had come into existence, with their data and communication demands. Zvi Griliches (1994) long ago distinguished between easy-to-measure and hard-to-measure sectors of the economy. Among the former he put agriculture, mining, manufacturing, transportation, communications, and public utilities.


There Is No Planet B: A Handbook for the Make or Break Years by Mike Berners-Lee

air freight, Anthropocene, autonomous vehicles, Big Tech, biodiversity loss, call centre, carbon footprint, carbon tax, cloud computing, dematerialisation, disinformation, driverless car, Easter island, Elon Musk, energy security, energy transition, fake news, food miles, Gini coefficient, global supply chain, global village, Hans Rosling, high-speed rail, income inequality, Intergovernmental Panel on Climate Change (IPCC), Jevons paradox, land reform, microplastics / micro fibres, negative emissions, neoliberal agenda, off grid, performance metric, post-truth, profit motive, shareholder value, Silicon Valley, smart cities, Stephen Hawking, systems thinking, TED Talk, The Spirit Level, The Wealth of Nations by Adam Smith, trickle-down economics, urban planning

Whatever type you chose, it’s good if you get a small one, drive it less and share it more. Could autonomous cars be a disaster? Or brilliant? It all depends on how much we use them. Driverless cars undoubtedly stand to be more efficient because they can slip stream each other and optimise every 110 4 TRAVEL AND TRANSPORT manoeuvre. The first difficulty, as we’ve seen, is that efficiency leads to yet more trouble unless the total global carbon use is capped. The issue is particularly extreme with autonomous cars because they also stand to be far less stressful and safer. We could sleep on the way to work or sleep all night while it drives us hundreds of miles to a meeting.

Is the experience of being alive in a driverless car going to be better than the experience of being behind the wheel, or not in a car at all? My instinct is that once the novelty has worn off, it will be almost as inherently dull as frequent flying. The question of autonomous cars takes us back to two questions. Can we cap the carbon? And, more widely, can enough be enough? If the answers are yes, then autonomous cars can help us towards sustainable living in the Anthropocene. If not, they will only make things worse. How can we fly in the low carbon world? An A380 carrying 550 passengers from New York to Hong Kong burns through 192 tonnes of fuel.

4 TRAVEL AND TRANSPORT How much do we travel today? How much travel will we want in the future? How many travel miles can we get from a square meter of land? How can we sort out urban transport? Will shared transport make life better or worse? Should I buy an electric car? How urgently should I ditch my diesel? Could autonomous cars be a disaster? Or brilliant? How can we fly in the low carbon world? Should I fly? Do virtual meetings save energy and carbon? How bad are boats? And can they be electrified? E-bikes or pedals? When might we emigrate to another planet? ix 73 74 75 75 77 78 79 81 82 84 85 87 89 91 93 94 95 97 99 99 100 101 104 105 106 107 109 110 112 113 114 116 117 x CONTENTS 5 GROWTH, MONEY AND METRICS Which kinds of growth can be healthy in the Anthropocene?


pages: 286 words: 87,401

Blitzscaling: The Lightning-Fast Path to Building Massively Valuable Companies by Reid Hoffman, Chris Yeh

"Susan Fowler" uber, activist fund / activist shareholder / activist investor, adjacent possible, Airbnb, Amazon Web Services, Andy Rubin, autonomous vehicles, Benchmark Capital, bitcoin, Blitzscaling, blockchain, Bob Noyce, business intelligence, Cambridge Analytica, Chuck Templeton: OpenTable:, cloud computing, CRISPR, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, database schema, DeepMind, Didi Chuxing, discounted cash flows, Elon Musk, fake news, Firefox, Ford Model T, forensic accounting, fulfillment center, Future Shock, George Gilder, global pandemic, Google Hangouts, Google X / Alphabet X, Greyball, growth hacking, high-speed rail, hockey-stick growth, hydraulic fracturing, Hyperloop, initial coin offering, inventory management, Isaac Newton, Jeff Bezos, Joi Ito, Khan Academy, late fees, Lean Startup, Lyft, M-Pesa, Marc Andreessen, Marc Benioff, margin call, Mark Zuckerberg, Max Levchin, minimum viable product, move fast and break things, Network effects, Oculus Rift, oil shale / tar sands, PalmPilot, Paul Buchheit, Paul Graham, Peter Thiel, pre–internet, Quicken Loans, recommendation engine, ride hailing / ride sharing, Salesforce, Sam Altman, Sand Hill Road, Saturday Night Live, self-driving car, shareholder value, sharing economy, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Skype, smart grid, social graph, SoftBank, software as a service, software is eating the world, speech recognition, stem cell, Steve Jobs, subscription business, synthetic biology, Tesla Model S, thinkpad, three-martini lunch, transaction costs, transport as a service, Travis Kalanick, Uber for X, uber lyft, web application, winner-take-all economy, work culture , Y Combinator, yellow journalism

After all, if one technology innovation can create a new market, another technology innovation can render it obsolete, seemingly overnight. While Uber has achieved massive scale, the greatest threat to its future doesn’t come in the form of direct competitors like Didi Chuxing, though these are formidable threats. The greatest threat to Uber’s business is the technology innovation of autonomous vehicles, which could make obsolete one of Uber’s biggest competitive advantages—its carefully cultivated network of drivers—essentially overnight. The key is to combine new technologies with effective distribution to potential customers, a scalable and high-margin revenue model, and an approach that allows you to serve those customers given your probable resource constraints.

In Uber’s case, it has been able to raise nearly $9 billion between its founding and the writing of this book. At some point, Uber will have to demonstrate the ability to significantly improve its unit economics, or its investors will get very grumpy. This concern helps explain Uber’s significant investments in autonomous vehicle technology, which could eliminate its biggest expense—driver payments—in one fell swoop. The willingness to take on the risks of blitzscaling is one of the major reasons why Silicon Valley has produced such a disproportionate share of blockbuster companies in comparison to other geographies.

While Brandeis is right that society needs to prevent monopolies that block technology or business innovation in the way that the old AT&T monopoly suppressed the progress of telecommunications, today’s largest companies have actually enabled innovation and the creation of even more value by providing a platform for everything from business productivity software (Slack) to entertainment (Netflix). Even the concentration of capital that scale has produced isn’t all bad; it has allowed blitzscalers to tackle “moonshots” like space travel (SpaceX) and autonomous vehicles (Google’s Waymo) that may dramatically improve our lives. As opposed to reflexively calling for the breakup of big companies, the better approach to tempering the potential abuses of scale is to leverage the principles for a healthy republic that James Madison laid out in “Federalist No. 10.”


pages: 301 words: 89,076

The Globotics Upheaval: Globalisation, Robotics and the Future of Work by Richard Baldwin

agricultural Revolution, Airbnb, AlphaGo, AltaVista, Amazon Web Services, Apollo 11, augmented reality, autonomous vehicles, basic income, Big Tech, bread and circuses, business process, business process outsourcing, call centre, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, commoditize, computer vision, Corn Laws, correlation does not imply causation, Credit Default Swap, data science, David Ricardo: comparative advantage, declining real wages, deep learning, DeepMind, deindustrialization, deskilling, Donald Trump, Douglas Hofstadter, Downton Abbey, Elon Musk, Erik Brynjolfsson, facts on the ground, Fairchild Semiconductor, future of journalism, future of work, George Gilder, Google Glasses, Google Hangouts, Hans Moravec, hiring and firing, hype cycle, impulse control, income inequality, industrial robot, intangible asset, Internet of things, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, Kevin Roose, knowledge worker, laissez-faire capitalism, Les Trente Glorieuses, low skilled workers, machine translation, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, manufacturing employment, Mark Zuckerberg, mass immigration, mass incarceration, Metcalfe’s law, mirror neurons, new economy, optical character recognition, pattern recognition, Ponzi scheme, post-industrial society, post-work, profit motive, remote working, reshoring, ride hailing / ride sharing, Robert Gordon, Robert Metcalfe, robotic process automation, Ronald Reagan, Salesforce, San Francisco homelessness, Second Machine Age, self-driving car, side project, Silicon Valley, Skype, Snapchat, social intelligence, sovereign wealth fund, standardized shipping container, statistical model, Stephen Hawking, Steve Jobs, supply-chain management, systems thinking, TaskRabbit, telepresence, telepresence robot, telerobotics, Thomas Malthus, trade liberalization, universal basic income, warehouse automation

Indeed, these are probably the service-sector jobs where the threat of service-sector automation is most widely discussed. Self-driving trucks and cars are a reality, but it is not yet clear how fast the technology will take off. As David Rotman of MIT Technology Review magazine observes, “any so-called autonomous vehicle will require a driver, albeit one who is often passive. But the potential loss of millions of jobs is Exhibit A” in the threat AI poses to service-sector jobs that were previously considered safe from automation.23 A report by President Obama’s White House economists and science advisors, Artificial Intelligence, Automation, and the Economy, estimates that automated vehicles could threaten 2 to 3 million US jobs.

If a guy makes $100,000 for driving a truck where is he going to get a job like that?” But he doesn’t want to be viewed as a modern Luddite, as he adds: “Obviously we can’t stop progress.”14 The sentiment is finding a voice. Two New York lobby groups, Upstate Transportation Association and Independent Drivers Guild, pressed for bans on autonomous vehicles to avoid losing thousands of transportation jobs. Labor came out OK in this battle of big business and big labor. The US House of Representatives passed a bill on self-driving vehicles that was generally pro-automation with one notable exception—trucks. The legislation, which still hadn’t made it into law when this book went to print, grants nationwide permission for up to 100,000 vehicles to be tested without safety approval, but explicitly excludes commercial trucks.15 Since the Joshua Brown accident involved a robot-driven car and a human-driven truck, it is easy to believe that a law which allows robots into cars but not trucks is not only about safety.

Keith Laing, “Senators Drop Trucks from Self-Driving Bill,” Detroit News, September 28, 2017. The House version of the bill had passed by the time this book went to press; the Senate version was pending; Chris Teale, “US Senate Considers ‘Different Possibilities’ to Pass Av START Act,” SmartCitiesDive.com, June 14, 2018. 16. “Stick Shift: Autonomous vehicles, Driving Jobs, and the Future of Work”, Center for Global Policy Solution, March 2017. 17. Quoted in “Anxiety about Automation and Jobs: Will We See Anti-Tech Laws?” James Pethokoukis, www.AEI.org (blog). 18. Quotes from Luke Muelhauswer, “What Should We Learn from Past AI Forecasts?,” Open Philanthropy Project, September 2016. 19.


pages: 292 words: 85,151

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It) by Salim Ismail, Yuri van Geest

23andMe, 3D printing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, anti-fragile, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, behavioural economics, Ben Horowitz, bike sharing, bioinformatics, bitcoin, Black Swan, blockchain, Blue Ocean Strategy, book value, Burning Man, business intelligence, business process, call centre, chief data officer, Chris Wanstrath, circular economy, Clayton Christensen, clean water, cloud computing, cognitive bias, collaborative consumption, collaborative economy, commoditize, corporate social responsibility, cross-subsidies, crowdsourcing, cryptocurrency, dark matter, data science, Dean Kamen, deep learning, DeepMind, dematerialisation, discounted cash flows, disruptive innovation, distributed ledger, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, fail fast, game design, gamification, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, hiring and firing, holacracy, Hyperloop, industrial robot, Innovator's Dilemma, intangible asset, Internet of things, Iridium satellite, Isaac Newton, Jeff Bezos, Joi Ito, Kevin Kelly, Kickstarter, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, lifelogging, loose coupling, loss aversion, low earth orbit, Lyft, Marc Andreessen, Mark Zuckerberg, market design, Max Levchin, means of production, Michael Milken, minimum viable product, natural language processing, Netflix Prize, NetJets, Network effects, new economy, Oculus Rift, offshore financial centre, PageRank, pattern recognition, Paul Graham, paypal mafia, peer-to-peer, peer-to-peer model, Peter H. Diamandis: Planetary Resources, Peter Thiel, Planet Labs, prediction markets, profit motive, publish or perish, radical decentralization, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Ronald Coase, Rutger Bregman, Salesforce, Second Machine Age, self-driving car, sharing economy, Silicon Valley, skunkworks, Skype, smart contracts, Snapchat, social software, software is eating the world, SpaceShipOne, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, Steve Jurvetson, subscription business, supply-chain management, synthetic biology, TaskRabbit, TED Talk, telepresence, telepresence robot, the long tail, Tony Hsieh, transaction costs, Travis Kalanick, Tyler Cowen, Tyler Cowen: Great Stagnation, uber lyft, urban planning, Virgin Galactic, WikiLeaks, winner-take-all economy, X Prize, Y Combinator, zero-sum game

We now know what he meant. In launching the Google[X] lab, Google has taken the classic skunkworks approach to new product development further than anyone ever imagined. Google[X] offers two fascinating new extensions to the traditional approach. First, it aims for moonshot-quality ideas (e.g., life extension, autonomous vehicles, Google Glass, smart contact lenses, Project Loon, etc.). Second, unlike traditional corporate labs that focus on existing markets, Google[X] combines breakthrough technologies with Google’s core information competencies to create entirely new markets. We strongly recommend that every big company attempt something similar by creating a lab that is a playground for breakthrough technologies.

Payment systems and money transfer mechanisms haven’t changed for decades, but with Square, PayPal and now Clinkle and Bitcoin, this domain is ready for a major transformation. One form will come via mobile/social wallets and seamless transactions. A second will come via micropayments (probably via the block chain). The ability to move infinitesimal transaction amounts will underpin entirely new business models. Autonomous vehicles Implications: In September 2014, California will issue the first license plates for driverless cars. Starting with delivery vehicles and then taxis, predictions call for existing road capacity to increase 8-10 times once a critical mass of AVs is reached. Ridesharing is an intermediate step toward fully automated transportation, which may have a bigger visible impact on society than anything else, including sustainability, urban planning (almost no parking lots) and fewer traffic fatalities.

Sustainable production and logistics Greener and more self-sufficient production driven by robo-transport, sensors, AI, flexible solar panels and perovskite solar cells. Nanomaterials (graphene) that can be added to buildings, vehicles, machines and equipment. Transformation in Logistics (road, water and air transport). Autonomous transport and delivery Leveraging autonomous vehicles (e.g., Google’s self-driving car) and drones (e.g., Matternet) for the transport and delivery of supplies and products, especially in remote areas. Full supply chain tracking/monitoring Internet of Things sensors used to monitor the entire supply chain. Location, status, preservation and safety of most substances can be monitored (chemical substance traces, pollution, quality of life).


pages: 291 words: 80,068

Framers: Human Advantage in an Age of Technology and Turmoil by Kenneth Cukier, Viktor Mayer-Schönberger, Francis de Véricourt

Albert Einstein, Andrew Wiles, Apollo 11, autonomous vehicles, Ben Bernanke: helicopter money, Berlin Wall, bitcoin, Black Lives Matter, blockchain, Blue Ocean Strategy, circular economy, Claude Shannon: information theory, cognitive dissonance, cognitive load, contact tracing, coronavirus, correlation does not imply causation, COVID-19, credit crunch, CRISPR, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, deep learning, DeepMind, defund the police, Demis Hassabis, discovery of DNA, Donald Trump, double helix, Douglas Hofstadter, Elon Musk, en.wikipedia.org, fake news, fiat currency, framing effect, Francis Fukuyama: the end of history, Frank Gehry, game design, George Floyd, George Gilder, global pandemic, global village, Gödel, Escher, Bach, Higgs boson, Ignaz Semmelweis: hand washing, informal economy, Isaac Newton, Jaron Lanier, Jeff Bezos, job-hopping, knowledge economy, Large Hadron Collider, lockdown, Louis Pasteur, Mark Zuckerberg, Mercator projection, meta-analysis, microaggression, Mustafa Suleyman, Neil Armstrong, nudge unit, OpenAI, packet switching, pattern recognition, Peter Thiel, public intellectual, quantitative easing, Ray Kurzweil, Richard Florida, Schrödinger's Cat, scientific management, self-driving car, Silicon Valley, Steve Jobs, Steven Pinker, TED Talk, The Structural Transformation of the Public Sphere, Thomas Kuhn: the structure of scientific revolutions, TikTok, Tim Cook: Apple, too big to fail, transaction costs, Tyler Cowen

The car braked hard and decelerated quickly—but not before it slammed into the boundary and was poised to go off the road. In the end, it came to a rest . . . on a thin, purple line of pixels on the screen. The minor accident was a digital simulation. It happened only in the computer servers of Waymo, Google’s self-driving-car company. The simulation is designed to overcome a serious shortcoming of all autonomous vehicles: a lack of data on rare events because they are, well, rare. For well over a decade, the industry has been collecting real-world road data to train the AI models that power the self-driving systems. Fleets of cars with sophisticated sensors and video cameras have cruised the streets collecting zillions of data points every second.

Then there is the approach of many high-performance athletes and executives who practice “visualizations”: developing a realistic mental image of a situation (whether a ski jump or a board meeting) and simulating within that world the diverse actions and responses that might work, much like Carcraft does for autonomous vehicles. Imagining alternative realities makes causal frames actionable. But it requires more than imagination. The trick, as toddlers quickly learn, is not to conceive of just any alternative reality but to construct one carefully that can help us achieve our goal. Counterfactuals are functional; their effectiveness depends on how well they are shaped given the goals we have and the context in which we intend to use them.

Data on the amount of simulation driving: “The Virtual World Helps Waymo Learn Advanced Real-World Driving Skills,” Let’s Talk Self-Driving, accessed November 2, 2020, https://letstalkselfdriving.com/safety/simulation.html. On self-driving technology: Bansal, Krizhevsky, and Ogale, “ChauffeurNet.” Waymo’s performance relative to rivals’: California Department of Motor Vehicles, “2020 Disengagement Reports,” https://www.dmv.ca.gov/portal/vehicle-industry-services/autonomous-vehicles/disengagement-reports. On counterfactuals and AI: In the AI community, there has been an increase in interest in counterfactual logic to improve explainability of AI systems; see, e.g., Sandra Wachter et al., “Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR,” Harvard Journal of Law & Technology 31, no. 2 (2018), https://jolt.law.harvard.edu/assets/articlePDFs/v31/Counterfactual-Explanations-without-Opening-the-Black-Box-Sandra-Wachter-et-al.pdf. 5. constraints On the Entebbe raid: Interview with Noam Tamir by Kenneth Cukier in March 2020.


pages: 479 words: 144,453

Homo Deus: A Brief History of Tomorrow by Yuval Noah Harari

23andMe, Aaron Swartz, agricultural Revolution, algorithmic trading, Anne Wojcicki, Anthropocene, anti-communist, Anton Chekhov, autonomous vehicles, behavioural economics, Berlin Wall, call centre, Chekhov's gun, Chris Urmson, cognitive dissonance, Columbian Exchange, computer age, DeepMind, Demis Hassabis, Deng Xiaoping, don't be evil, driverless car, drone strike, European colonialism, experimental subject, falling living standards, Flash crash, Frank Levy and Richard Murnane: The New Division of Labor, glass ceiling, global village, Great Leap Forward, Intergovernmental Panel on Climate Change (IPCC), invention of writing, invisible hand, Isaac Newton, job automation, John Markoff, Kevin Kelly, lifelogging, low interest rates, means of production, Mikhail Gorbachev, Minecraft, Moneyball by Michael Lewis explains big data, Monkeys Reject Unequal Pay, mutually assured destruction, new economy, Nick Bostrom, pattern recognition, peak-end rule, Peter Thiel, placebo effect, Ray Kurzweil, self-driving car, Silicon Valley, Silicon Valley ideology, stem cell, Steven Pinker, telemarketer, The future is already here, The Future of Employment, too big to fail, trade route, Turing machine, Turing test, ultimatum game, Watson beat the top human players on Jeopardy!, zero-sum game

As the brain tries to create a model of its own decisions, it gets trapped in an infinite digression, and abracadabra! Out of this loop, consciousness pops out. Fifty years ago this might have sounded plausible, but not in 2016. Several corporations, such as Google and Tesla, are engineering autonomous cars that already cruise our roads. The algorithms controlling the autonomous car make millions of calculations each second concerning other cars, pedestrians, traffic lights and potholes. The autonomous car successfully stops at red lights, bypasses obstacles and keeps a safe distance from other vehicles – without feeling any fear. The car also needs to take itself into account and to communicate its plans and desires to the surrounding vehicles, because if it decides to swerve to the right, doing so will impact on their behaviour.

We can create a smart car-pool system, run by computer algorithms. The computer would know that I need to leave home at 8:04, and would route the nearest autonomous car to pick me up at that precise moment. After dropping me off at campus, it would be available for other uses instead of waiting in the car park. At 18:11 sharp, as I leave the university gate, another communal car would stop right in front of me, and take me home. In such a way, 50 million communal autonomous cars may replace 1 billion private cars, and we would also need far fewer roads, bridges, tunnels and parking spaces. Provided, of course, I renounce my privacy and allow the algorithms to always know where I am and where I want to go.

The car also needs to take itself into account and to communicate its plans and desires to the surrounding vehicles, because if it decides to swerve to the right, doing so will impact on their behaviour. The car does all that without any problem – but without any consciousness either. The autonomous car isn’t special. Many other computer programs make allowances for their own actions, yet none of them has developed consciousness, and none feels or desires anything.6 If we cannot explain the mind, and if we don’t know what function it fulfils, why not just discard it? The history of science is replete with abandoned concepts and theories. For instance, early modern scientists who tried to account for the movement of light postulated the existence of a substance called ether, which supposedly fills the entire universe.


pages: 301 words: 85,126

AIQ: How People and Machines Are Smarter Together by Nick Polson, James Scott

Abraham Wald, Air France Flight 447, Albert Einstein, algorithmic bias, Amazon Web Services, Atul Gawande, autonomous vehicles, availability heuristic, basic income, Bayesian statistics, Big Tech, Black Lives Matter, Bletchley Park, business cycle, Cepheid variable, Checklist Manifesto, cloud computing, combinatorial explosion, computer age, computer vision, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, Donald Trump, Douglas Hofstadter, Edward Charles Pickering, Elon Musk, epigenetics, fake news, Flash crash, Grace Hopper, Gödel, Escher, Bach, Hans Moravec, Harvard Computers: women astronomers, Higgs boson, index fund, information security, Isaac Newton, John von Neumann, late fees, low earth orbit, Lyft, machine translation, Magellanic Cloud, mass incarceration, Moneyball by Michael Lewis explains big data, Moravec's paradox, more computing power than Apollo, natural language processing, Netflix Prize, North Sea oil, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, pattern recognition, Pierre-Simon Laplace, ransomware, recommendation engine, Ronald Reagan, Salesforce, self-driving car, sentiment analysis, side project, Silicon Valley, Skype, smart cities, speech recognition, statistical model, survivorship bias, systems thinking, the scientific method, Thomas Bayes, Uber for X, uber lyft, universal basic income, Watson beat the top human players on Jeopardy!, young professional

But that will be their choice and their problem, because no option on the table today even remotely foreordains such a possibility. Now, and for the foreseeable future, “smart” machines are smart only in their specific domains: • Alexa can read you a recipe for spaghetti Bolognese, but she can’t chop the onions, and she certainly can’t turn on you with a kitchen knife. • An autonomous car can drive you to the soccer field, but it can’t even referee the match, much less decide on its own to tie you to the goalposts and kick the ball at your sensitive bits. Moreover, consider the opportunity cost of worrying that we’ll soon be conquered by self-aware robots. To focus on this possibility now is analogous to the de Havilland Aircraft Company, having flown the first commercial jetliner in 1952, worrying about the implications of warp-speed travel to distant galaxies.

Isn’t it astonishing that we’re talking about mobs of kangaroos as one of the big technological problems here—as opposed to, say, getting out of the driveway, or not crashing into your neighbors’ living room? Ask yourself a simple question. If you had to put a loved one in a taxi, would you rather have them driven by a randomly sampled 16-year-old with a driver’s license, or by one of Waymo’s cars? (Waymo is the autonomous-car company spun out of Google.) If you have to think about this question, we encourage you to consider a few facts.1 56% of American teenagers talk on the phone while driving. In 2015, 2,715 American teenagers died, and 221,313 went to the emergency room, because of car-crash injuries. Half of all crashes involving teenage drivers are single-vehicle crashes.

.* The current revolution in autonomous robots has become possible only because all the research put into SLAM systems has finally paid off. Robots have gone from dodging chairs to dodging other drivers; from five hours traversing a room to five gigabytes of sensor data per second; from an autonomous mouse that can navigate a 25-square grid to an autonomous car that can navigate millions of miles of public road. SLAM is one of AI’s most smashing success stories. So in this chapter, we’d like to address two SLAM-related questions—one obvious, and one a bit more unexpected. 1. How does a robot car know where it is? 2. How can you become a smarter person by thinking a bit more like a robot car?


pages: 49 words: 12,968

Industrial Internet by Jon Bruner

air gap, autonomous vehicles, barriers to entry, Boeing 747, commoditize, computer vision, data acquisition, demand response, electricity market, en.wikipedia.org, factory automation, Google X / Alphabet X, industrial robot, Internet of things, job automation, loose coupling, natural language processing, performance metric, Silicon Valley, slashdot, smart grid, smart meter, statistical model, the Cathedral and the Bazaar, web application

The 150-foot-long blades on a wind turbine, for instance, chop at the air as they move through it, sending turbulence to the next row of turbines and reducing efficiency. By analyzing performance metrics from existing wind installations, planners can recommend new layouts that take into account common wind patterns and minimize interference. Automotive Google captured the public imagination when, in 2010, it announced that its autonomous cars had already driven 140,000 miles of winding California roads without incident. The idea of a car that drives itself was finally realized in a practical way by software that has strong links to the physical world around it: inbound, through computer vision software that takes in images and rangefinder data and builds an accurate model of the environment around the car; and outbound, through a full linkage to the car’s controls.

The idea of a car that drives itself was finally realized in a practical way by software that has strong links to the physical world around it: inbound, through computer vision software that takes in images and rangefinder data and builds an accurate model of the environment around the car; and outbound, through a full linkage to the car’s controls. The entire system is encompassed in a machine-learning algorithm that observes the results of its actions to become a better driver, and that draws software updates and useful data from the Internet. The autonomous car is a full expression of the industrial internet: software connects a machine to a network, links its components together, ingests context, and uses learned intelligence to control a complicated machine in real-time. Google hasn’t announced any plans to make its cars available to the public, but elements of the industrial internet are widely visible in new cars today.

Cheap, easy-to-program microcontrollers; powerful open-source software; and the support of hardware collectives and innovation labs[41] make it possible for enthusiasts and minimally-funded entrepreneurs to create sophisticated projects of the sort that would have been available only to well-funded electrical engineers just a few years ago — anything from autonomous cars to small-scale industrial robots. In the same way that expertise in software isn’t necessary to create a successful Web app, expertise barriers will fall in software-machine interfaces, opening innovation to a big, broad, smart community. Neil Gershenfeld, director of the Center for Bits and Atoms at MIT, compares the development of the amateur hardware movement to the development of the computer from mainframe to minicomputer to hobbyist computer and then to the ubiquitous personal computer.


pages: 170 words: 49,193

The People vs Tech: How the Internet Is Killing Democracy (And How We Save It) by Jamie Bartlett

Ada Lovelace, Airbnb, AlphaGo, Amazon Mechanical Turk, Andrew Keen, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, blockchain, Boris Johnson, Californian Ideology, Cambridge Analytica, central bank independence, Chelsea Manning, cloud computing, computer vision, creative destruction, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, disinformation, Dominic Cummings, Donald Trump, driverless car, Edward Snowden, Elon Musk, Evgeny Morozov, fake news, Filter Bubble, future of work, general purpose technology, gig economy, global village, Google bus, Hans Moravec, hive mind, Howard Rheingold, information retrieval, initial coin offering, Internet of things, Jeff Bezos, Jeremy Corbyn, job automation, John Gilmore, John Maynard Keynes: technological unemployment, John Perry Barlow, Julian Assange, manufacturing employment, Mark Zuckerberg, Marshall McLuhan, Menlo Park, meta-analysis, mittelstand, move fast and break things, Network effects, Nicholas Carr, Nick Bostrom, off grid, Panopticon Jeremy Bentham, payday loans, Peter Thiel, post-truth, prediction markets, QR code, ransomware, Ray Kurzweil, recommendation engine, Renaissance Technologies, ride hailing / ride sharing, Robert Mercer, Ross Ulbricht, Sam Altman, Satoshi Nakamoto, Second Machine Age, sharing economy, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Silicon Valley startup, smart cities, smart contracts, smart meter, Snapchat, Stanford prison experiment, Steve Bannon, Steve Jobs, Steven Levy, strong AI, surveillance capitalism, TaskRabbit, tech worker, technological singularity, technoutopianism, Ted Kaczynski, TED Talk, the long tail, the medium is the message, the scientific method, The Spirit Level, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, too big to fail, ultimatum game, universal basic income, WikiLeaks, World Values Survey, Y Combinator, you are the product

This felt appropriate; some engineers worry about the risk of ‘vigilance decrement’, as humans lose practice and become less able to deal with emergencies. My tedium in turn gave way to the dawning realisation that what I’d thought was sci-fi was in fact fast becoming sci-fact.* In 2004, respected AI-researchers from the Massachusetts Institute of Technology (MIT) concluded that autonomous vehicles were a pipedream, driving being a skill that required too much human intuition and motor skills.1 But we should never underestimate the speed at which digital technology can advance. Millions of dollars of investment are now pouring in from Uber, Google, Tesla, Mercedes, Volvo, Starsky and others.

Research from Georg Graetz and Guy Michaels has found that, while manufacturing employment has fallen in most developed countries between 1996 and 2012, it has fallen less sharply where investment in robotics has been greatest. 4 ‘Automation and anxiety’, The Economist, 25 June 2016. 5 According to Martin Ford, futurist and author of the award winning book Rise of the Robots it won’t happen immediately but within a decade or so. 6 Stick Shift: Autonomous Vehicles, Driving Jobs, and the Future of Work, March 2017, Centre for Global Policy Solutions. 7 Mark Fahey, ‘Driverless cars will kill the most jobs in select US states’, www.cnbc.com, 2 September 2016. 8 ‘Real wages have been falling for longest period for at least 50 years, ONS says’, Guardian, 31 January 2014.


pages: 677 words: 206,548

Future Crimes: Everything Is Connected, Everyone Is Vulnerable and What We Can Do About It by Marc Goodman

23andMe, 3D printing, active measures, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, algorithmic trading, Alvin Toffler, Apollo 11, Apollo 13, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, Bill Joy: nanobots, bitcoin, Black Swan, blockchain, borderless world, Boston Dynamics, Brian Krebs, business process, butterfly effect, call centre, Charles Lindbergh, Chelsea Manning, Citizen Lab, cloud computing, Cody Wilson, cognitive dissonance, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, data acquisition, data is the new oil, data science, Dean Kamen, deep learning, DeepMind, digital rights, disinformation, disintermediation, Dogecoin, don't be evil, double helix, Downton Abbey, driverless car, drone strike, Edward Snowden, Elon Musk, Erik Brynjolfsson, Evgeny Morozov, Filter Bubble, Firefox, Flash crash, Free Software Foundation, future of work, game design, gamification, global pandemic, Google Chrome, Google Earth, Google Glasses, Gordon Gekko, Hacker News, high net worth, High speed trading, hive mind, Howard Rheingold, hypertext link, illegal immigration, impulse control, industrial robot, information security, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jaron Lanier, Jeff Bezos, job automation, John Harrison: Longitude, John Markoff, Joi Ito, Jony Ive, Julian Assange, Kevin Kelly, Khan Academy, Kickstarter, Kiva Systems, knowledge worker, Kuwabatake Sanjuro: assassination market, Large Hadron Collider, Larry Ellison, Laura Poitras, Law of Accelerating Returns, Lean Startup, license plate recognition, lifelogging, litecoin, low earth orbit, M-Pesa, machine translation, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Metcalfe’s law, MITM: man-in-the-middle, mobile money, more computing power than Apollo, move fast and break things, Nate Silver, national security letter, natural language processing, Nick Bostrom, obamacare, Occupy movement, Oculus Rift, off grid, off-the-grid, offshore financial centre, operational security, optical character recognition, Parag Khanna, pattern recognition, peer-to-peer, personalized medicine, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, printed gun, RAND corporation, ransomware, Ray Kurzweil, Recombinant DNA, refrigerator car, RFID, ride hailing / ride sharing, Rodney Brooks, Ross Ulbricht, Russell Brand, Salesforce, Satoshi Nakamoto, Second Machine Age, security theater, self-driving car, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, SimCity, Skype, smart cities, smart grid, smart meter, Snapchat, social graph, SoftBank, software as a service, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, subscription business, supply-chain management, synthetic biology, tech worker, technological singularity, TED Talk, telepresence, telepresence robot, Tesla Model S, The future is already here, The Future of Employment, the long tail, The Wisdom of Crowds, Tim Cook: Apple, trade route, uranium enrichment, Virgin Galactic, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, Wave and Pay, We are Anonymous. We are Legion, web application, Westphalian system, WikiLeaks, Y Combinator, you are the product, zero day

A fully automated, well-functioning autonomous vehicle network could avoid thousands of needless deaths and save billions in associated economic costs. As the price of these technologies plunges, you can expect UPS drivers and taxis to be replaced by autonomous and cheaper non-union alternatives. But modern cars, whether driven by people, artificial intelligence, big data, or sensor networks, are still just computers on wheels, powered by insecure data systems, communicating via entirely hackable transmission protocols. As such, things might not turn out quite as rosy as proponents of autonomous vehicles suggest. When the majority of vehicles join the IoT, it won’t be long before some rogue attacker seizes control of a car and turns it into a multi-ton weapon of metal, glass, and explosive fuel.

In the same way both Crime, Inc. and crazed exes are targeting computers and mobile phones, it’s only logical that they will go after cars in the future too, bringing scenes like those in Stephen King’s 1983 horror thriller about a possessed car named Christine many steps closer to reality. Law enforcement officials clearly see the threat, and in July 2014 the FBI warned in an internal report that driverless cars could be used as “lethal weapons, with terrorists potentially packing explosives into a self-driving car aimed at a specific destination.” Autonomous vehicles could also potentially be turned off en masse, bringing traffic to a complete standstill in a city or country. To be certain, some of these vehicular attacks require a high degree of computer savvy to pull off, but as we have seen with other exploits, soon there will be point-and-click crimeware options for car hacking as well.

A 2013 study by Oxford University on the future of work conducted a detailed analysis of over seven hundred occupations and concluded that 47 percent of U.S. employees are at high risk of losing their jobs to robotic automation as soon as 2023. Those working in the transportation field (taxi drivers, bus drivers, long-haul truck drivers, FedEx drivers, pizza delivery drivers) face particular risk, with up to a 90 percent certainty that their jobs will be replaced by autonomous vehicles. But it’s not just low-level positions that are at risk. News outlets such as the Associated Press and the Los Angeles Times are using bots and algorithms to automatically write thousands of articles on topics as diverse as homicides, earthquakes, and the latest business earnings. Biopsies can be “analyzed more efficiently by image-processing software than lab techs,” and QuickBooks can handle the majority of tasks performed by an accountant.


pages: 416 words: 106,532

Cryptoassets: The Innovative Investor's Guide to Bitcoin and Beyond: The Innovative Investor's Guide to Bitcoin and Beyond by Chris Burniske, Jack Tatar

Airbnb, Alan Greenspan, altcoin, Alvin Toffler, asset allocation, asset-backed security, autonomous vehicles, Bear Stearns, bitcoin, Bitcoin Ponzi scheme, blockchain, Blythe Masters, book value, business cycle, business process, buy and hold, capital controls, carbon tax, Carmen Reinhart, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, commoditize, correlation coefficient, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, disintermediation, distributed ledger, diversification, diversified portfolio, Dogecoin, Donald Trump, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, fiat currency, financial engineering, financial innovation, fixed income, Future Shock, general purpose technology, George Gilder, Google Hangouts, high net worth, hype cycle, information security, initial coin offering, it's over 9,000, Jeff Bezos, Kenneth Rogoff, Kickstarter, Leonard Kleinrock, litecoin, low interest rates, Marc Andreessen, Mark Zuckerberg, market bubble, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, Network effects, packet switching, passive investing, peer-to-peer, peer-to-peer lending, Peter Thiel, pets.com, Ponzi scheme, prediction markets, quantitative easing, quantum cryptography, RAND corporation, random walk, Renaissance Technologies, risk free rate, risk tolerance, risk-adjusted returns, Robert Shiller, Ross Ulbricht, Salesforce, Satoshi Nakamoto, seminal paper, Sharpe ratio, Silicon Valley, Simon Singh, Skype, smart contracts, social web, South Sea Bubble, Steve Jobs, transaction costs, tulip mania, Turing complete, two and twenty, Uber for X, Vanguard fund, Vitalik Buterin, WikiLeaks, Y2K

Holders of The DAO would be able to vote on what projects they wanted to support, and if developers raised enough funding from The DAO holders, they would receive the funds necessary to build their projects. Over time, investors in these projects would be rewarded through dividends or appreciation of the service provided. The vision of a decentralized autonomous organization like The DAO is somewhat like autonomous vehicles—whereas humans used to have to drive cars, the cars increasingly can drive themselves. Similarly, whereas humans used to be needed for all aspects of business processes, often in manual paper pushing, approval, orchestration, and so on, a decentralized autonomous organization can codify much of those processes so that the company better drives itself.

Both combine bitcoin exposure with a portfolio of growth stocks, and have been some of the highest performing ETFs in the market. Using Grayscale’s BIT, ARK Invest became the first public fund manager to invest in bitcoin in September of 2015, and as of this writing still has the only ETFs on the market with bitcoin exposure. Given ARK’s focus on fast-moving technologies like machine learning, autonomous vehicles, and genomics, investing in bitcoin was a natural fit for the firm. THE ETN OPTION Outside of the United States, more options for capital market-based bitcoin products exist, such as two exchange traded notes (ETN) offered by XBT Provider on Nasdaq Nordic in Stockholm, Sweden. Nasdaq Nordic is a regulated exchange system that is a subsidiary of the well-known Nasdaq in the United States.

In the twenty-first century, the innovation lab concept has been embraced most famously by Google, which encourages creativity and innovation beyond an employee’s current position. The company has created the Google Garage26 as a (somewhat) formal structure in which employees can pursue innovations with others in the company. This has resulted in projects, such as its autonomous vehicles effort, that Google has grown organically in the hopes of providing additional future revenue. A key feature that needs to be reinforced from Christensen’s quote is the need to “set up an autonomous organization.” Just setting up an innovation lab within a company is not a guarantee of success.


The Deep Learning Revolution (The MIT Press) by Terrence J. Sejnowski

AI winter, Albert Einstein, algorithmic bias, algorithmic trading, AlphaGo, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, autonomous vehicles, backpropagation, Baxter: Rethink Robotics, behavioural economics, bioinformatics, cellular automata, Claude Shannon: information theory, cloud computing, complexity theory, computer vision, conceptual framework, constrained optimization, Conway's Game of Life, correlation does not imply causation, crowdsourcing, Danny Hillis, data science, deep learning, DeepMind, delayed gratification, Demis Hassabis, Dennis Ritchie, discovery of DNA, Donald Trump, Douglas Engelbart, driverless car, Drosophila, Elon Musk, en.wikipedia.org, epigenetics, Flynn Effect, Frank Gehry, future of work, Geoffrey Hinton, Google Glasses, Google X / Alphabet X, Guggenheim Bilbao, Gödel, Escher, Bach, haute couture, Henri Poincaré, I think there is a world market for maybe five computers, industrial robot, informal economy, Internet of things, Isaac Newton, Jim Simons, John Conway, John Markoff, John von Neumann, language acquisition, Large Hadron Collider, machine readable, Mark Zuckerberg, Minecraft, natural language processing, Neil Armstrong, Netflix Prize, Norbert Wiener, OpenAI, orbital mechanics / astrodynamics, PageRank, pattern recognition, pneumatic tube, prediction markets, randomized controlled trial, Recombinant DNA, recommendation engine, Renaissance Technologies, Rodney Brooks, self-driving car, Silicon Valley, Silicon Valley startup, Socratic dialogue, speech recognition, statistical model, Stephen Hawking, Stuart Kauffman, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, Turing machine, Von Neumann architecture, Watson beat the top human players on Jeopardy!, world market for maybe five computers, X Prize, Yogi Berra

And they encountered further difficulties in collecting and entering the patients’ symptoms and medical histories into the system’s computer, a process that could take a half hour or longer per patient, more time than a busy physician could afford. Not surprisingly, MYCIN was never used clinically. Although many expert systems were written for other applications such as toxic spill management, mission planning for autonomous vehicles, and speech recognition, few are in use today. Researchers tried many different approaches in the early decades of AI, but their approaches were more clever than they were practical. Not only did they underestimate the complexity of real-world problems, but the solutions they proposed scaled badly.

Skinner” in the New York Review of Books, an essay that steered a generation of cognitive scientists away from learning. 1982—Claude Shannon publishes the seminal book A Mathematical Theory of Communication, which laid the foundation for modern digital communication. 1989—Carver Mead publishes Analog VLSI and Neural Systems, founding the field of neuromorphic engineering, which builds computer chips inspired by biology. 2002—Stephen Wolfram publishes A New Kind of Science, which explored the computational capabilities of cellular automata, algorithms that are even simpler than neural networks but still capable of powerful computing. 2005—Sebastian Thrun’s team wins the DARPA Grand Challenge for an autonomous vehicle. 2008—Tobias Delbrück develops a highly successful spiking retina chip called the “Dynamic Vision Sensor” (DVS) that uses asynchronous spikes rather than synchronous frames used in current digital cameras. 2013—U.S. BRAIN Initiative, to develop innovative neurotechnologies that accelerate our understanding of brain function, is announced in the White House. 12 The Future of Machine Learning Chapter The Future of Machine 12 Learning © Massachusetts Institute of TechnologyAll Rights Reserved The age of cognitive computing is dawning.

See also MIT Artificial Intelligence Laboratory and the AI business, 191–194 as an existential threat, 23–25 Dartmouth Artificial Intelligence Conference of 2006 (AI@50), 256–258 Dartmouth Artificial Intelligence Summer Research Project, 1, 258 dynamic external memory and, 259 early clues for how computers might achieve intelligent behavior, 37–39 Frank Rosenblatt and, 39, 47 historical perspective on, 193–194 human intelligence and, 25 learning how to be more intelligent, 20–21 Marvin Minsky and, 27, 47, 255f, 256–259 neural networks and, 259 322 Artificial intelligence (AI) (cont.) recent progress in, ix, 174 regulation and bans of, 125 terminology, 32 “Artificial life,” 196 Artificial neural networks (ANNies), 57f, 159, 285n1. See also Neural networks Associative learning, 247 ATMs (automated teller machines), 22 Attractor states, 93, 94, 94f, 95b Auditory perception and language acquisition, 184 Automated teller machines (ATMs), 22 Autonomous vehicles. See Self-driving cars Avoid being hit, 148 Backgammon, 34, 144f, 148. See also TD-Gammon backgammon board, 144f learning how to play, 143–146, 148–149 Backpropagation (backprop) learning algorithm, 114f, 217, 299n2 Backpropagation of errors (backprop), 111b, 112, 118, 148 Bag-of-words model, 251 Ballard, Dana H., 96, 297nn11–12, 314n8 Baltimore, David A., 307n5 Bar-Joseph, Ziv, 319n13 Barlow, Horace, 84, 296n8 Barry, Susan R., 294n5 Bartlett, Marian “Marni” Stewart, 181–182, 181f, 184, 308nn19–20 Barto, Andrew, 144, 146f Bartol, Thomas M., Jr., 296n14, 300n18 Basal ganglia, motivation and, 151, 153–154 Bates, Elizabeth A., 107, 298n24 Bats, 263–264 Index Bavelier, Daphne, 189–190, 309n33 Baxter (robot), 177f, 178 Bayes, Thomas, 128 Bayes networks, 128 Bear off pieces, 148 Beck, Andrew H., 287n17 Beer Bottle Pass, 4, 5f Bees, learning in, 151 Behaviorism and behaviorists, 149, 247–248 cognitive science and, 248, 249f, 250, 253 Behrens, M.


pages: 205 words: 61,903

Survival of the Richest: Escape Fantasies of the Tech Billionaires by Douglas Rushkoff

"World Economic Forum" Davos, 4chan, A Declaration of the Independence of Cyberspace, agricultural Revolution, Airbnb, Alan Greenspan, Amazon Mechanical Turk, Amazon Web Services, Andrew Keen, AOL-Time Warner, artificial general intelligence, augmented reality, autonomous vehicles, basic income, behavioural economics, Big Tech, biodiversity loss, Biosphere 2, bitcoin, blockchain, Boston Dynamics, Burning Man, buy low sell high, Californian Ideology, carbon credits, carbon footprint, circular economy, clean water, cognitive dissonance, Colonization of Mars, coronavirus, COVID-19, creative destruction, Credit Default Swap, CRISPR, data science, David Graeber, DeepMind, degrowth, Demis Hassabis, deplatforming, digital capitalism, digital map, disinformation, Donald Trump, Elon Musk, en.wikipedia.org, energy transition, Ethereum, ethereum blockchain, European colonialism, Evgeny Morozov, Extinction Rebellion, Fairphone, fake news, Filter Bubble, game design, gamification, gig economy, Gini coefficient, global pandemic, Google bus, green new deal, Greta Thunberg, Haight Ashbury, hockey-stick growth, Howard Rheingold, if you build it, they will come, impact investing, income inequality, independent contractor, Jane Jacobs, Jeff Bezos, Jeffrey Epstein, job automation, John Nash: game theory, John Perry Barlow, Joseph Schumpeter, Just-in-time delivery, liberal capitalism, Mark Zuckerberg, Marshall McLuhan, mass immigration, megaproject, meme stock, mental accounting, Michael Milken, microplastics / micro fibres, military-industrial complex, Minecraft, mirror neurons, move fast and break things, Naomi Klein, New Urbanism, Norbert Wiener, Oculus Rift, One Laptop per Child (OLPC), operational security, Patri Friedman, pattern recognition, Peter Thiel, planetary scale, Plato's cave, Ponzi scheme, profit motive, QAnon, RAND corporation, Ray Kurzweil, rent-seeking, Richard Thaler, ride hailing / ride sharing, Robinhood: mobile stock trading app, Sam Altman, Shoshana Zuboff, Silicon Valley, Silicon Valley billionaire, SimCity, Singularitarianism, Skinner box, Snapchat, sovereign wealth fund, Stephen Hawking, Steve Bannon, Steve Jobs, Steven Levy, Steven Pinker, Stewart Brand, surveillance capitalism, tech billionaire, tech bro, technological solutionism, technoutopianism, Ted Nelson, TED Talk, the medium is the message, theory of mind, TikTok, Torches of Freedom, Tragedy of the Commons, universal basic income, urban renewal, warehouse robotics, We are as Gods, WeWork, Whole Earth Catalog, work culture , working poor

Market sensibilities overpowered much of the media and intellectual space that would have normally been filled by a consideration of the practical ethics of impoverishing the many in the name of the few. Too much mainstream debate centered instead on abstract hypotheticals about our predestined high-tech future: Is it fair for a stock trader to use smart drugs? Should children get implants for foreign languages? Do we want autonomous vehicles to prioritize the lives of pedestrians over those of its passengers? Should the first Mars colonies be run as democracies? Does changing my DNA undermine my identity? Should robots have rights? Asking these sorts of questions, which we still do today, may be philosophically entertaining. But it is a poor substitute for wrestling with the real moral quandaries associated with unbridled technological development in the name of corporate capitalism.

Whether AI will develop human and superhuman abilities in the next decade, century, millennium, if ever, may matter less right now than AI’s grip over the tech elite, and what this obsession tells us about The Mindset. Holders of The Mindset appear less immediately afraid of AI technology itself than the people this technology is bound to replace. They know that Uber’s autonomous vehicles, Amazon’s robot T-shirt tailors, and future generations of AI lawyers, mortgage actuaries, and TV writers will put a whole lot of people out of work. Billionaire tech entrepreneur Mark Cuban says AI “scares the shit out of me”—but only because of how many workers will be displaced. “Things are getting faster, processing is getting faster, machines are starting to think,” he explained on CNBC, adding ambiguously, “and either you make them think for you or they will take your place and do the thinking for you.”


pages: 409 words: 112,055

The Fifth Domain: Defending Our Country, Our Companies, and Ourselves in the Age of Cyber Threats by Richard A. Clarke, Robert K. Knake

"World Economic Forum" Davos, A Declaration of the Independence of Cyberspace, Affordable Care Act / Obamacare, air gap, Airbnb, Albert Einstein, Amazon Web Services, autonomous vehicles, barriers to entry, bitcoin, Black Lives Matter, Black Swan, blockchain, Boeing 737 MAX, borderless world, Boston Dynamics, business cycle, business intelligence, call centre, Cass Sunstein, cloud computing, cognitive bias, commoditize, computer vision, corporate governance, cryptocurrency, data acquisition, data science, deep learning, DevOps, disinformation, don't be evil, Donald Trump, Dr. Strangelove, driverless car, Edward Snowden, Exxon Valdez, false flag, geopolitical risk, global village, immigration reform, information security, Infrastructure as a Service, Internet of things, Jeff Bezos, John Perry Barlow, Julian Assange, Kubernetes, machine readable, Marc Benioff, Mark Zuckerberg, Metcalfe’s law, MITM: man-in-the-middle, Morris worm, move fast and break things, Network effects, open borders, platform as a service, Ponzi scheme, quantum cryptography, ransomware, Richard Thaler, Salesforce, Sand Hill Road, Schrödinger's Cat, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Skype, smart cities, Snapchat, software as a service, Steven Levy, Stuxnet, technoutopianism, The future is already here, Tim Cook: Apple, undersea cable, unit 8200, WikiLeaks, Y2K, zero day

One thing telecommunications experts agree on is that 5G will make it possible to connect many more devices, either directly or indirectly, to the internet and give all of those devices the ability to work much more rapidly. There will be no more latency, no more buffering, which will make possible more types of devices, including those that require reliable and instantaneous connectivity to the Internet of Things, such as autonomous vehicles. For autonomous vehicles, otherwise known as driverless cars, to achieve their full potential, multiple sensors and devices on each vehicle will have to communicate instantly with nearby vehicles, sensors in the road, street signs, and traffic lights. To do all of that, the vehicles will need 5G communications, fast, unbuffered, and able to handle many devices in a small space.

Indeed, they have argued that it would stifle innovation and do any number of horrific things if anyone ever regulated anything related to the internet. So, here come 5G and the Internet of Things without any security regulations. Get ready. Strap in. IoT: Down on the Farm Even before autonomous vehicles begin running around on your street, they are driving about on some farms. Our favorite story about the IoT, one that demonstrates how it is creeping into every walk of life and simultaneously opening up vulnerabilities to global hackers, comes from down on the farm. If you have opened up your internal combustion engine car’s hood lately to attempt do-it-yourself maintenance on the engine, you have been met with a sealed box that is relatively impervious to any owner’s attempts to manipulate it.


pages: 296 words: 66,815

The AI-First Company by Ash Fontana

23andMe, Amazon Mechanical Turk, Amazon Web Services, autonomous vehicles, barriers to entry, blockchain, business intelligence, business process, business process outsourcing, call centre, Charles Babbage, chief data officer, Clayton Christensen, cloud computing, combinatorial explosion, computer vision, crowdsourcing, data acquisition, data science, deep learning, DevOps, en.wikipedia.org, Geoffrey Hinton, independent contractor, industrial robot, inventory management, John Conway, knowledge economy, Kubernetes, Lean Startup, machine readable, minimum viable product, natural language processing, Network effects, optical character recognition, Pareto efficiency, performance metric, price discrimination, recommendation engine, Ronald Coase, Salesforce, single source of truth, software as a service, source of truth, speech recognition, the scientific method, transaction costs, vertical integration, yield management

This is different from a standard software product where the value proposition is based on a set of features that are easy to demonstrate and doesn’t differ much between vendors. Running a successful POC requires setting clear expectations around accuracy, timeline, and cost. Briefly, here are the elements of a good POC. Accuracy: Set a benchmark for predictions based on honest assessments of what’s feasible technically. For example, fully autonomous vehicles are not feasible at the time of this writing, but sensors that detect potholes and alert maintenance crews work quite well. The extrinsic way to set a benchmark is based on what accuracy a customer already achieved through their own efforts. Business goal: This is the metric that gets closest to what customers need to hit to make money.

First movers build technological leadership over competitors by getting a head start on technology research and development; AI-First companies build intelligent systems that are better than competitors’ systems both by collecting data and by developing ways to design intelligent systems that yield accurate predictions. Importantly, the research that precedes novel AI development requires data to run experiments. Google started driving cars to collect data and investing in research on autonomous driving before its competitors, and arguably has the most promising autonomous vehicles today. First movers create switching costs by integrating with incumbent systems and benefiting from the uncertainty that surrounds new entrants; AI-First companies create switching costs by centralizing customers’ data into their own databases and proving superior model accuracy. Google centralizes and secures data across consumers’ email, location, and documents to generate the best recommendations for things to buy, places to go, and even phrases to write.


pages: 364 words: 99,897

The Industries of the Future by Alec Ross

"World Economic Forum" Davos, 23andMe, 3D printing, Airbnb, Alan Greenspan, algorithmic bias, algorithmic trading, AltaVista, Anne Wojcicki, autonomous vehicles, banking crisis, barriers to entry, Bernie Madoff, bioinformatics, bitcoin, Black Lives Matter, blockchain, Boston Dynamics, Brian Krebs, British Empire, business intelligence, call centre, carbon footprint, clean tech, cloud computing, collaborative consumption, connected car, corporate governance, Credit Default Swap, cryptocurrency, data science, David Brooks, DeepMind, Demis Hassabis, disintermediation, Dissolution of the Soviet Union, distributed ledger, driverless car, Edward Glaeser, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, fiat currency, future of work, General Motors Futurama, global supply chain, Google X / Alphabet X, Gregor Mendel, industrial robot, information security, Internet of things, invention of the printing press, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Joi Ito, Kevin Roose, Kickstarter, knowledge economy, knowledge worker, lifelogging, litecoin, low interest rates, M-Pesa, machine translation, Marc Andreessen, Mark Zuckerberg, Max Levchin, Mikhail Gorbachev, military-industrial complex, mobile money, money: store of value / unit of account / medium of exchange, Nelson Mandela, new economy, off-the-grid, offshore financial centre, open economy, Parag Khanna, paypal mafia, peer-to-peer, peer-to-peer lending, personalized medicine, Peter Thiel, precision agriculture, pre–internet, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Rubik’s Cube, Satoshi Nakamoto, selective serotonin reuptake inhibitor (SSRI), self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, social graph, software as a service, special economic zone, supply-chain management, supply-chain management software, technoutopianism, TED Talk, The Future of Employment, Travis Kalanick, underbanked, unit 8200, Vernor Vinge, Watson beat the top human players on Jeopardy!, women in the workforce, work culture , Y Combinator, young professional

The feasibility of the Google car depends on a range of technological, legal, safety, and commercial considerations. Will the technology work? Will it actually make the roads safer? Will people trust and purchase it? Will it even be legal? These are not academic questions. While only California, Florida, and Nevada have passed laws as of 2013 permitting autonomous cars on the roads, these already represent huge driving cultures and markets. The driverless car has the potential to fundamentally disrupt the modern automotive industry and all of its various branches. As with every other development in robotics, many people will gain—some, like Google’s executives and shareholders, may gain immensely—but it’s inevitable that others will be displaced.

See also mental illnesses Apple, 36, 79, 87 application programming interfaces (APIs), 168 Apps4Africa, 236 Aramco, 122–23, 138, 224 Argentina, 104, 223 ASIMO (Advanced Step in Innovative Mobility robot), 16–17. See also robotics Ask Jeeves, 119 Atari, 25 Atlantis (Internet), 110. See also cryptocurrency ATMs, 40, 77, 87 autism, 35, 67 Autism Speaks, 67 autonomous cars, 28–31 Avis, 93 Bank of America, 167 Barclays, 167 BaseHealth, 60–61 bearer instrument, 100 Belarus, 205, 208, 212, 214, 222 belief space, 23 Berman, Dror, 191–92 Berners-Lee, Tim, 115 Bezos, Jeff, 93. See also Amazon BGI, 67 Bitcoin: Andreessen on, 103–4, 116–17 benefits of, 102–5, 116–17 blockchain and, 101–6 CoinDesk, 167 criticism of, 111–12 establishment and, 111–15 explained, 98–100 financial system and, 99 future of, 115–17 governments and, 111–15 hacking and, 106–11 micropayments and, 105–6 mining and, 102–3 competitors and, 117–19 Songhurst on, 104 widespread use of, 98 see also blockchain blockchain: Bitcoin and, 101–6 efficiency and, 104 establishment and, 111–15 explained, 101 future of, 120 hacking and, 106, 108–9 law enforcement and, 111 as next protocol, 115–17 regulation of, 103 transaction history and, 114 see also Bitcoin Bloomberg, Michael, 167–68 Booker, Cory, 167–68 Booker T.

See also antidepressants Putin, Vladimir, 139, 202–4, 213 Qatari Petroleum, 122 RasGas, 122 RBS, 170 Reilly, David, 167 remittance systems, 87–88, 119–20 Revive & Restore project, 64. See also extinct animals RIKEN, 17 Ripple Labs, 118–19 Robot for Interactive Body Assistance (RIBA), 17 Robotdalen award, 25 robotics: agriculture and, 192 autonomous cars, 29–32 Belarus and, 208 car industry and, 16–18, 28–32 caregiving and, 18–19 China and, 218 data and, 73 economy and, 12–13 Estonia and, 211 fear of, 21–22, 180 future of, 6, 42–43, 189, 240, 244, 247–48 growth of industry, 19–21, 187 hacking and, 134 humanizing, 22–28 innovation in, 6, 12 Japan and, 15–18 jobs and, 35–42, 208 medicine and, 32–35 South Korea and, 3 Romney, Mitt, 155 Roubini, Nuriel, 111–12, 220 Rwanda, 83, 237–38, 243 Safaricom, 87 Saidenberg, Douglas, 106–7 schizophrenia, 55, 113, 203, 223.


pages: 327 words: 84,627

The Green New Deal: Why the Fossil Fuel Civilization Will Collapse by 2028, and the Bold Economic Plan to Save Life on Earth by Jeremy Rifkin

"World Economic Forum" Davos, 1919 Motor Transport Corps convoy, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, American Society of Civil Engineers: Report Card, autonomous vehicles, Bernie Sanders, Big Tech, bike sharing, blockchain, book value, borderless world, business cycle, business process, carbon footprint, carbon tax, circular economy, collective bargaining, corporate governance, corporate social responsibility, creative destruction, decarbonisation, digital rights, do well by doing good, electricity market, en.wikipedia.org, energy transition, failed state, general purpose technology, ghettoisation, green new deal, Greta Thunberg, high-speed rail, hydrogen economy, impact investing, information asymmetry, intangible asset, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invisible hand, it's over 9,000, Joseph Schumpeter, means of production, megacity, megaproject, military-industrial complex, Network effects, new economy, off grid, off-the-grid, oil shale / tar sands, peak oil, planetary scale, prudent man rule, remunicipalization, renewable energy credits, rewilding, Ronald Reagan, shareholder value, sharing economy, Sidewalk Labs, Silicon Valley, Skype, smart cities, smart grid, sovereign wealth fund, Steven Levy, subprime mortgage crisis, the built environment, The Wealth of Nations by Adam Smith, Tim Cook: Apple, trade route, union organizing, urban planning, vertical integration, warehouse automation, women in the workforce, zero-sum game

It should be noted that 96 million barrels of oil are consumed around the world each day, and transport accounts for approximately 62.5 percent of all the oil used.19 The numbers speak for themselves. While the shift to green-powered electric vehicles is a transformational event that, by itself, will rock the global economy in the biggest disruption since the advent of the gasoline-powered automobile, the accompanying shift to driverless autonomous vehicles in car-sharing services will have a comparable impact on changing the way we organize mobility and logistics in society. The speed of the transformation has caught the industry and society off guard. A 2017 study by RethinkX, a leading transportation research forecaster, reports that today’s car-sharing services will quickly transition into shared passenger services and electric vehicle fleets in the 2020s.20 The increased efficiencies in vehicle utilization will be considerable.

The bottom line, according to the findings of the study, is that mobility as a service will make available a much lower cost of transportation than existing alternatives and be “four to ten times cheaper per mile than buying a new car and two to four times cheaper than operating an existing vehicle by 2021.”21 A more surprising finding is that provider/user transportation in autonomous vehicles, operating with near-zero marginal cost human labor and powered by near-zero marginal cost solar and wind electricity, plunges the cost of providing mobility while simultaneously allowing the provider to commodify the time passengers spend in the vehicle by offering various types of entertainment and commercial purchases via the internet, similar to the offerings from airlines in long-distance air travel.


pages: 389 words: 87,758

No Ordinary Disruption: The Four Global Forces Breaking All the Trends by Richard Dobbs, James Manyika

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, access to a mobile phone, additive manufacturing, Airbnb, Amazon Mechanical Turk, American Society of Civil Engineers: Report Card, asset light, autonomous vehicles, Bakken shale, barriers to entry, business cycle, business intelligence, carbon tax, Carmen Reinhart, central bank independence, circular economy, cloud computing, corporate governance, creative destruction, crowdsourcing, data science, demographic dividend, deskilling, digital capitalism, disintermediation, disruptive innovation, distributed generation, driverless car, Erik Brynjolfsson, financial innovation, first square of the chessboard, first square of the chessboard / second half of the chessboard, Gini coefficient, global supply chain, global village, high-speed rail, hydraulic fracturing, illegal immigration, income inequality, index fund, industrial robot, intangible asset, Intergovernmental Panel on Climate Change (IPCC), Internet of things, inventory management, job automation, Just-in-time delivery, Kenneth Rogoff, Kickstarter, knowledge worker, labor-force participation, low interest rates, low skilled workers, Lyft, M-Pesa, machine readable, mass immigration, megacity, megaproject, mobile money, Mohammed Bouazizi, Network effects, new economy, New Urbanism, ocean acidification, oil shale / tar sands, oil shock, old age dependency ratio, openstreetmap, peer-to-peer lending, pension reform, pension time bomb, private sector deleveraging, purchasing power parity, quantitative easing, recommendation engine, Report Card for America’s Infrastructure, RFID, ride hailing / ride sharing, Salesforce, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Snapchat, sovereign wealth fund, spinning jenny, stem cell, Steve Jobs, subscription business, supply-chain management, synthetic biology, TaskRabbit, The Great Moderation, trade route, transaction costs, Travis Kalanick, uber lyft, urban sprawl, Watson beat the top human players on Jeopardy!, working-age population, Zipcar

Baxter even has a “head,” which it nods to indicate that it has understood instructions, and a “face” with a pair of eyes, which take on different expressions. As their capabilities grow, robots are performing tasks once considered too expensive or delicate to automate. Their application extends beyond industry to services, robotic surgery, and even human augmentation. Autonomous vehicles are another disruptive technology that has made dramatic advances in a single decade. In 2004, DARPA (Defense Advanced Research Projects Agency) funded the Grand Challenge, a competition that offered $1 million to the first driverless car that could drive 150 miles across the Mojave Desert.

., strength, weight, conductivity) or functionality RETHINKING ENERGY COMES OF AGE 3.Energy storage Devices or systems that store energy for later use, including batteries 4.Advanced oil and gas exploration and recovery Exploration and recovery techniques that make extraction of unconventional oil and gas economical 5.Renewable energy Generation of electricity from renewable sources with reduced harmful climate impact MACHINES WORKING FOR US 6.Advanced robotics Increasingly capable robots with enhanced senses, dexterity, and intelligence used to automate tasks or augment humans 7.Autonomous and near-autonomous vehicles Vehicles that can navigate and operate with reduced or no human intervention 8.3-D printing Additive manufacturing techniques to create objects by printing layers of material based on digital models IT AND HOW WE USE IT 9.Mobile Internet Increasingly inexpensive and capable mobile computing devices and Internet connectivity 10.Internet of things Networks of low-cost sensors and actuators for data collection, monitoring, decision making, and process optimization 11.Cloud technology Use of computer hardware and software resources delivered over a network or the Internet, often as a service 12.Automation of knowledge work Intelligent software systems that can perform knowledge work tasks involving unstructured commands and subtle judgments The data avalanche is set to become more powerful only because of a movement toward “open data,” in which data are freely shared beyond their originating organizations—including governments and businesses—in a machine-readable format at low cost.


pages: 292 words: 94,660

The Loop: How Technology Is Creating a World Without Choices and How to Fight Back by Jacob Ward

2021 United States Capitol attack, 4chan, Abraham Wald, AI winter, Albert Einstein, Albert Michelson, Amazon Mechanical Turk, assortative mating, autonomous vehicles, availability heuristic, barriers to entry, Bayesian statistics, Benoit Mandelbrot, Big Tech, bitcoin, Black Lives Matter, Black Swan, blockchain, Broken windows theory, call centre, Cass Sunstein, cloud computing, contact tracing, coronavirus, COVID-19, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, dark matter, data science, deep learning, Donald Trump, drone strike, endowment effect, George Akerlof, George Floyd, hindsight bias, invisible hand, Isaac Newton, Jeffrey Epstein, license plate recognition, lockdown, longitudinal study, Lyft, mandelbrot fractal, Mark Zuckerberg, meta-analysis, natural language processing, non-fungible token, nudge unit, OpenAI, opioid epidemic / opioid crisis, pattern recognition, QAnon, RAND corporation, Richard Thaler, Robert Shiller, selection bias, self-driving car, seminal paper, shareholder value, smart cities, social contagion, social distancing, Steven Levy, survivorship bias, TikTok, Turing test

And at the end of it all, the wheels of justice—the police, the courts, the juries—arrive at some sort of conclusion that determines fault and restitution and the policies that might prevent this sort of tragedy from taking place again. With autonomous systems, that kind of evaluation has to take place ahead of time. An autonomous vehicle will only do what its rules tell it to do. It must be trained in advance to properly evaluate whether to go ahead and plow through a chicken in the road, but veer away from a child. We must teach it to choose the cliff or oncoming traffic. Can enough stories teach a robotic system to make a perfect decision every time?

He said he thought car makers would be happy to take legal liability because the pool of liability would still be smaller than it is today. “If you reduce accidents by 50 percent, who cares if you hold the bag on the remaining lawsuits? You’ve still saved yourself a lot of money.” If we get the technology right, and the number of accidents would be so much fewer, perhaps it’s worth thinking of autonomous vehicles as an inoculation against human error, a literal vaccine against bad driving. We have a vaccine court for flu shots. Perhaps we’ll have to build a vaccine court for robot cars and other forms of AI-based technology. There are instances, however, when human beings have decided that in spite of the math, we have to build new systems for society simply because living without them is just too horrible, too emotionally unacceptable, to consider.


pages: 237 words: 74,109

Uncanny Valley: A Memoir by Anna Wiener

autonomous vehicles, back-to-the-land, basic income, behavioural economics, Blitzscaling, blockchain, blood diamond, Burning Man, call centre, charter city, cloud computing, cognitive bias, cognitive dissonance, commoditize, crowdsourcing, cryptocurrency, dark triade / dark tetrad, data science, digital divide, digital nomad, digital rights, end-to-end encryption, Extropian, functional programming, future of work, gentrification, Golden Gate Park, growth hacking, guns versus butter model, housing crisis, Jane Jacobs, job automation, knowledge worker, Lean Startup, means of production, medical residency, microaggression, microapartment, microdosing, new economy, New Urbanism, Overton Window, passive income, Plato's cave, pull request, rent control, ride hailing / ride sharing, San Francisco homelessness, Sand Hill Road, self-driving car, sharing economy, Shenzhen special economic zone , side project, Silicon Valley, Silicon Valley startup, Social Justice Warrior, social web, South of Market, San Francisco, special economic zone, subprime mortgage crisis, systems thinking, tech bro, tech worker, technoutopianism, telepresence, telepresence robot, union organizing, universal basic income, unpaid internship, urban planning, urban renewal, warehouse robotics, women in the workforce, work culture , Y2K, young professional

Talk turned to self-driving cars. One of the engineers mentioned a recent Take Your Child to Work Day, where the autonomous-car unit had asked visiting children to jump and dance and roll around in front of the sensors. The technology was world-class, but it still needed to train on nonadults. It was an incredibly exciting moment for transportation, he said: the hurdles they faced weren’t technical, but cultural. The biggest obstacle was public opinion. How plausible were autonomous cars, really, I asked loudly. I had finished my beer and I was bored. I wanted attention, some acknowledgment. I wanted to make sure everyone knew I wasn’t just some engineer’s girlfriend who stood around at parties waiting for him to finish geeking out—though of course that’s exactly what I was doing.

“It feels like we’re going to have a seat at the table for something really big.” How big? I wanted to know. There were public rumors about what the robotics suite was working on, but Ian was forbidden from speaking about their projects. He refused to confirm my guesses. Was he working on the autonomous cars? I had so many questions. Was it the search-and-rescue robots? The delivery drones? Was there a space shuttle? How soon would we see humanoids? How scared should the rest of us be? “Everyone always asks me that,” he said, frowning. “Not scared. Really.” More, I said—say more. In a city where bars and coffee shops and parties were trade-secret word clouds, this was a regionally specific litmus test.


pages: 441 words: 96,534

Streetfight: Handbook for an Urban Revolution by Janette Sadik-Khan

autonomous vehicles, bike sharing, Boris Johnson, business cycle, call centre, car-free, carbon footprint, clean water, congestion charging, congestion pricing, Cornelius Vanderbilt, crowdsourcing, digital map, Donald Shoup, edge city, Edward Glaeser, en.wikipedia.org, Enrique Peñalosa, fixed-gear, gentrification, high-speed rail, Hyperloop, Induced demand, Jane Jacobs, Lewis Mumford, Loma Prieta earthquake, Lyft, megaproject, New Urbanism, off-the-grid, place-making, self-driving car, sharing economy, the built environment, The Death and Life of Great American Cities, the High Line, transportation-network company, Uber and Lyft, uber lyft, urban decay, urban planning, urban renewal, urban sprawl, walkable city, white flight, Works Progress Administration, Zipcar

Ideally, these technologies will make it possible to get around without owning a car at all. Big data, digital networks, and artificial intelligence are an increasing part of how we plan, operate, and build infrastructure. We see it in road pricing, new traffic system management technology, and new bus planning apps to optimize transit routes and buses. A future with autonomous vehicles, delivery drones, and unified payment systems is on the near-term horizon. This wave of change has landed on our streets, and these changes will advance how we get around cities and use our streets. A smartphone can eliminate the anxiety of getting around, whether you’re in Boston, Bangalore, or Buenos Aires.

But the road to a driver-free road network is likely to take many years, and it’s not yet clear how driverless and conventional cars will interact with one another, particularly in extreme weather or poor road conditions. The regulatory framework is not yet in place to ensure that these new services or autonomous vehicles under development don’t exacerbate or mask the very problems we’re just beginning to solve. If shared rides and driverless vehicles give suburban residents all the benefits of private vehicle ownership with fewer of the costs or disadvantages, it could encourage further sprawl. Meanwhile, within cities, shared rides run the risk of shifting millions of commuters not only from private to shared cars, but also from public transportation into the backseat of one of the growing menu of TNC options.


pages: 332 words: 100,601

Rebooting India: Realizing a Billion Aspirations by Nandan Nilekani

Airbnb, Atul Gawande, autonomous vehicles, barriers to entry, bitcoin, call centre, carbon credits, cashless society, clean water, cloud computing, collaborative consumption, congestion charging, DARPA: Urban Challenge, data science, dematerialisation, demographic dividend, digital rights, driverless car, Edward Snowden, en.wikipedia.org, energy security, fail fast, financial exclusion, gamification, Google Hangouts, illegal immigration, informal economy, information security, Khan Academy, Kickstarter, knowledge economy, land reform, law of one price, M-Pesa, machine readable, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, mobile money, Mohammed Bouazizi, more computing power than Apollo, Negawatt, Network effects, new economy, off-the-grid, offshore financial centre, price mechanism, price stability, rent-seeking, RFID, Ronald Coase, school choice, school vouchers, self-driving car, sharing economy, Silicon Valley, single source of truth, Skype, smart grid, smart meter, software is eating the world, source of truth, Steve Jobs, systems thinking, The future is already here, The Nature of the Firm, transaction costs, vertical integration, WikiLeaks, work culture

Private weather forecasters like Skymet and the Bangalore-based Citizen Weather Network are entering a domain that was until recently the exclusive preserve of the Indian Meteorological Department (IMD).13 The holy grail of weather forecasting in India is predicting the onset of the annual monsoon season, and Skymet has already clashed with the IMD by releasing monsoon predictions and analyses that differ from the IMD’s interpretation.14 Much like GPS, a more recent instance of a military technology being opened to the public is that of autonomous vehicles. Through DARPA—coincidentally the agency that also birthed the internet—the US government has been funding such endeavours for over a decade.15 The underlying technology has now entered the commercial space; Google is testing self-driving cars using its Google Chauffeur platform, Uber has just announced an academic collaboration with Carnegie Mellon University to ‘develop driverless car and mapping technology’, and Apple is reportedly investigating technologies for building electric and self-driving cars.16 While we may not see a fleet of self-driving cars taking over our streets in the near future, it’s worthwhile to consider that various US state governments are already starting to pass laws that permit driverless cars to operate on state roads.17 Once again, government regulations need to anticipate innovation by keeping a close eye on emerging trends and assessing their potential impact and chances of widespread adoption.

http://blogs.wsj.com/digits/2015/02/02/uber-chases-google-in-self-driving-cars/ Taylor, Edward, and Oreskovic, Alexei. 14 February 2015. ‘Apple studies self-driving car, auto industry source says’. Reuters. http://www.reuters.com/article/2015/02/14/us-apple-autos-idUSKBN0LI0IJ20150214. 17. 18 September 2014. ‘Coming to a street near you’. Economist. http://www.economist.com/news/business-and-finance/21618531-making-autonomous-vehicles-reality-coming-street-near-you 18. Pauker, Benjamin. 29 April 2013. ‘Epiphanies from Chris Anderson’. Foreign Policy. http://foreignpolicy.com/2013/04/29/epiphanies-from-chris-anderson/ 19. Amazon prime air. http://www.amazon.com/b?node=8037720011 20. SpaceX. http://www.spacex.com 21.


Interplanetary Robots by Rod Pyle

Apollo 11, autonomous vehicles, Elon Musk, independent contractor, James Webb Space Telescope, Jeff Bezos, Kickstarter, low earth orbit, Mars Rover, orbital mechanics / astrodynamics, Pierre-Simon Laplace, Pluto: dwarf planet, Search for Extraterrestrial Intelligence, SpaceShipOne, Stephen Hawking, Strategic Defense Initiative, X Prize

When it reached Titan, the X-37B would glide into the atmosphere, just as it does in Earth, and eject the submersible above the lake. The X-37B would then ditch and sink, leaving the submersible to continue on its mission of exploration. The X-37B is currently flying regularly, and UAVs—Underwater Autonomous Vehicles—are in use all over the planet. The key to success on Titan is to blend these technologies, develop alternate methods to deal with the exotic environment, and engineer into it a high level of autonomy. If NASA has its way, we could be exploring the seas of Titan within twenty years. In the next few years, the Mars rover Curiosity will have a twin on the planet, much as the Mars Exploration Rover Opportunity did when its sibling Spirit was still roving half a planet away.

See Project A119 study submarines, exploring Kraken Mare with, 299, 301–302 sulfur, 195, 255, 258 sun sensors, 74, 186 SuperCam, 304 superconducting magnets, 235 supernova, 211 Surveyor program, 83, 84–88 mockup, 85 news media and, 86 Surveyor 1, 85, 86 Surveyor 2, 86 Surveyor 3, 86–87 Surveyor 4, 87 Surveyor 5, 87 Surveyor 6, 87 Surveyor 7, 87–88 Synergy Moon, 94 taikonauts, 323 TDRSS (Tracking and Data Relay Satellite System), 244 Team Hakuto (Japan), 94, 95 TeamIndus (India), 94 telescopes Galileo observing Saturn's rings through, 284 Hubble Space Telescope, 206–207, 255, 263, 320 James Webb Space Telescope (JWST), 220, 318, 319–20 Lunokhods, 91 Mount Palomar, of Mars, 66, 66, 126 observations of Mars, 70 for Venus/Mars flyby, 160–61 Teller, Edward, 36 temperature(s) AREE (Venus rover) and extreme, 165–67 on Jupiter, 196 of Kraken Mare, 299 on Mars, 137 on the moon, 86 on Saturn, 199–200 on Titan, 203, 301–302 on Triton, 208 on Uranus, 206 on Venus, 137, 151, 153, 154 “Ten” (Io), 194 Terrain Relative Navigation (TRN), 311 TESS (Transiting Exoplanet Survey Satellite), 220 thermal mapping, 62 thermocouples, 16, 255 thermonuclear hydrogen bomb, 35 Titan (moon of Saturn), 111, 180, 278, 294 atmosphere of, 202–203, 292, 302 Cassini mission, 279, 280, 282, 290, 294 exploring seas of, 299–302 Huygens probe on, 9, 279, 292 imaged by Pioneer 11, 180 Kraken Mare, 299, 300 lakes, exploring, 299–302 life on, 293 size of, 202, 291 smog on, 291 Voyager 1 passing, 202, 204 water/liquids on, 291, 293 Titan ICBM, 277 Titan IV rocket, 277 Titan rocket, 111, 117, 240, 281 Triton (moon of Neptune), 204, 206, 208 TRN (Terrain Relative Navigation), 311 Trump administration, 61, 93, 276 TRW (contractor), 178 TV cameras in the early 1970s, 177 Lunokhods, 90 Mariners, 71–72, 73, 74, 148, 152 Mars 3, 227 Near Surface Floater (NSF), 162 Ranger program, 52, 54 Surveyor 3, 87 Tycho (crater), 87 UAVs (Underwater Autonomous Vehicles), 302 UFA-Palast am Zoo cinema, Berlin, Germany, 39 Ultima (video game), 92 Ulysses (robotic solar probe), 241 United States first space station (1973), 159 intelligence gathering on Soviet Luna probes, 48–49 Project A119 study, 34–35 space race and, 31–34 Wernher von Braun / V-2 missile and, 41–42 See also Jet Propulsion Laboratory (JPL); NASA University of California at Los Angeles (UCLA), 62 Uranus 90-degree tilt of, 206 Grand Tour of outer planets, 171, 172, 173, 174, 176 moons of, 204–205, 205 rings of, 206 temperatures of, 206 Voyager 2 to, 202, 204–206 Voyager Uranus-Interstellar Mission, 202 Uruk Sulcus (Ganymede), 259 US Air Force, 31, 35, 36, 240, 242, 302 US Army, 31, 106 US Army Redstone, 41 US Naval Academy, 221 US Navy, 39 USRA (Universities Space Research Association; nonprofit), 221 US Ranger program.


pages: 340 words: 97,723

The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity by Amy Webb

"Friedman doctrine" OR "shareholder theory", Ada Lovelace, AI winter, air gap, Airbnb, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic bias, AlphaGo, Andy Rubin, artificial general intelligence, Asilomar, autonomous vehicles, backpropagation, Bayesian statistics, behavioural economics, Bernie Sanders, Big Tech, bioinformatics, Black Lives Matter, blockchain, Bretton Woods, business intelligence, Cambridge Analytica, Cass Sunstein, Charles Babbage, Claude Shannon: information theory, cloud computing, cognitive bias, complexity theory, computer vision, Computing Machinery and Intelligence, CRISPR, cross-border payments, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, Demis Hassabis, Deng Xiaoping, disinformation, distributed ledger, don't be evil, Donald Trump, Elon Musk, fail fast, fake news, Filter Bubble, Flynn Effect, Geoffrey Hinton, gig economy, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Herman Kahn, high-speed rail, Inbox Zero, Internet of things, Jacques de Vaucanson, Jeff Bezos, Joan Didion, job automation, John von Neumann, knowledge worker, Lyft, machine translation, Mark Zuckerberg, Menlo Park, move fast and break things, Mustafa Suleyman, natural language processing, New Urbanism, Nick Bostrom, one-China policy, optical character recognition, packet switching, paperclip maximiser, pattern recognition, personalized medicine, RAND corporation, Ray Kurzweil, Recombinant DNA, ride hailing / ride sharing, Rodney Brooks, Rubik’s Cube, Salesforce, Sand Hill Road, Second Machine Age, self-driving car, seminal paper, SETI@home, side project, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart cities, South China Sea, sovereign wealth fund, speech recognition, Stephen Hawking, strong AI, superintelligent machines, surveillance capitalism, technological singularity, The Coming Technological Singularity, the long tail, theory of mind, Tim Cook: Apple, trade route, Turing machine, Turing test, uber lyft, Von Neumann architecture, Watson beat the top human players on Jeopardy!, zero day

Our Personal Values Drive Decisions In the absence of codified humanistic values within the Big Nine, personal experiences and ideals are driving decision-making. This is particularly dangerous when it comes to AI, because students, professors, researchers, employees, and managers are making millions of decisions every day, from seemingly insignificant (what database to use) to profound (who gets killed if an autonomous vehicle needs to crash). Artificial intelligence might be inspired by our human brains, but humans and AI make decisions and choices differently. Princeton professor Daniel Kahneman and Hebrew University of Jerusalem professor Amos Tversky spent years studying the human mind and how we make decisions, ultimately discovering that we have two systems of thinking: one that uses logic to analyze problems, and one that is automatic, fast, and nearly imperceptible to us.

We’ve been willing—if unwitting—participants in a future that’s being created hastily and without first answering all those questions. As AI systems advance and more of everyday life gets automated, the less control we actually have over the decisions being made about and for us. This, in turn, has a compounding effect on the future of many other technologies adjacent to or directly intersecting with AI: autonomous vehicles, CRISPR and genomic editing, precision medicine, home robotics, automated medical diagnoses, green- and geoengineering technologies, space travel, cryptocurrencies and blockchain, smart farms and agricultural technologies, the Internet of Things, autonomous factories, stock-trading algorithms, search engines, facial and voice recognition, banking technologies, fraud and risk detection, policing and judicial technologies… I could make a list that spans dozens of pages.


pages: 387 words: 106,753

Why Startups Fail: A New Roadmap for Entrepreneurial Success by Tom Eisenmann

Airbnb, Atul Gawande, autonomous vehicles, Ben Horowitz, Big Tech, bitcoin, Blitzscaling, blockchain, call centre, carbon footprint, Checklist Manifesto, clean tech, conceptual framework, coronavirus, corporate governance, correlation does not imply causation, COVID-19, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, Dean Kamen, drop ship, Elon Musk, fail fast, fundamental attribution error, gig economy, growth hacking, Hyperloop, income inequality, initial coin offering, inventory management, Iridium satellite, Jeff Bezos, Jeff Hawkins, Larry Ellison, Lean Startup, Lyft, Marc Andreessen, margin call, Mark Zuckerberg, minimum viable product, Network effects, nuclear winter, Oculus Rift, PalmPilot, Paul Graham, performance metric, Peter Pan Syndrome, Peter Thiel, reality distortion field, Richard Thaler, ride hailing / ride sharing, risk/return, Salesforce, Sam Altman, Sand Hill Road, side project, Silicon Valley, Silicon Valley startup, Skype, social graph, software as a service, Solyndra, speech recognition, stealth mode startup, Steve Jobs, TED Talk, two-sided market, Uber and Lyft, Uber for X, uber lyft, vertical integration, We wanted flying cars, instead we got 140 characters, WeWork, Y Combinator, young professional, Zenefits

Then, the flow of capital suddenly stopped, and startups, starved for capital, struggled to survive. Boom-bust investment cycles do not always impact entire industry sectors. Sometimes they are limited to certain segments, like meal delivery services, virtual reality, pet care, bitcoin/blockchain, direct-to-consumer brands, robo-investing, autonomous vehicles, and so forth. Investment bubbles typically start when entrepreneurs and investors recognize a big, new opportunity, often triggered by technology breakthroughs, like machine learning, gene editing, or voice recognition software (e.g., Jibo). Or, entrepreneurs might see many different ways to leverage novel business models, as with flash sales (e.g., Fab), “gig economy” labor (e.g., Baroo), or “direct-to-consumer” retailing (e.g., Quincy).

Federal Express did; when Fred Smith founded the company in the early 1970s, it was the biggest venture capital bet in history. More recently, we have Elon Musk’s Tesla and SpaceX, both of which have soaring valuations as I write this. So, expect more moonshots; indeed, we need them to address grand societal challenges like climate change. Visionary entrepreneurs around the world are working on hyperloops, autonomous vehicles, gene editing, and quantum computing. One day, you’ll surely be able to chat with Jibo’s grandchildren. We may even get flying cars. CHAPTER 10 Running on Empty Failure is not the worst thing; the worst thing is working on something for years with no end in sight. —Andrew Lee, Esper co-founder When they founded Quincy Apparel, Nelson and Wallace promised not to let conflict over how to run the venture threaten their close friendship.


pages: 133 words: 36,528

Peak Car: The Future of Travel by David Metz

autonomous vehicles, behavioural economics, bike sharing, Clayton Christensen, congestion charging, Crossrail, crowdsourcing, David Attenborough, decarbonisation, disruptive innovation, driverless car, edge city, Edward Glaeser, Ford Model T, gentrification, high-speed rail, Just-in-time delivery, low cost airline, megaproject, Network effects, Ocado, Richard Florida, Robert Gordon, seminal paper, Silicon Valley, Skype, Suez canal 1869, The future is already here, urban sprawl, yield management, young professional

A segregated highway would allow platoons of driverless cars to operate in synchrony, thus increasing the capacity of the road. There have been successful experimental trials of platooning, but both wide practical implementation and economic attractiveness seem problematic. This is an chicken-and-egg situation: investment in a segregated highway could only be justified if there were a sufficient number of autonomous vehicles wiling to pay tolls for use, but investment in these vehicles could only be justified if the segregated highway existed. My expectation is that driverless cars, to the extent they penetrate the market, will amount to an incremental improvement, not a disruptive innovation—best regarded as robot chauffeurs.


pages: 405 words: 117,219

In Our Own Image: Savior or Destroyer? The History and Future of Artificial Intelligence by George Zarkadakis

3D printing, Ada Lovelace, agricultural Revolution, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, animal electricity, anthropic principle, Asperger Syndrome, autonomous vehicles, barriers to entry, battle of ideas, Berlin Wall, bioinformatics, Bletchley Park, British Empire, business process, carbon-based life, cellular automata, Charles Babbage, Claude Shannon: information theory, combinatorial explosion, complexity theory, Computing Machinery and Intelligence, continuous integration, Conway's Game of Life, cosmological principle, dark matter, data science, deep learning, DeepMind, dematerialisation, double helix, Douglas Hofstadter, driverless car, Edward Snowden, epigenetics, Flash crash, Google Glasses, Gödel, Escher, Bach, Hans Moravec, income inequality, index card, industrial robot, intentional community, Internet of things, invention of agriculture, invention of the steam engine, invisible hand, Isaac Newton, Jacquard loom, Jacques de Vaucanson, James Watt: steam engine, job automation, John von Neumann, Joseph-Marie Jacquard, Kickstarter, liberal capitalism, lifelogging, machine translation, millennium bug, mirror neurons, Moravec's paradox, natural language processing, Nick Bostrom, Norbert Wiener, off grid, On the Economy of Machinery and Manufactures, packet switching, pattern recognition, Paul Erdős, Plato's cave, post-industrial society, power law, precautionary principle, prediction markets, Ray Kurzweil, Recombinant DNA, Rodney Brooks, Second Machine Age, self-driving car, seminal paper, Silicon Valley, social intelligence, speech recognition, stem cell, Stephen Hawking, Steven Pinker, Strategic Defense Initiative, strong AI, Stuart Kauffman, synthetic biology, systems thinking, technological singularity, The Coming Technological Singularity, The Future of Employment, the scientific method, theory of mind, Turing complete, Turing machine, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, Vernor Vinge, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Y2K

None of the robot vehicles that took part finished the route that year. The furthest any of the contestants managed was 7.3 miles. Next year, DARPA repeated the challenge. This time five vehicles completed the course, the team from Stanford University gaining first place. Since then DARPA has repeated the robotics challenge, to include autonomous vehicles capable of finding their way in an urban environment, as well as humanoid robots. The contestants have consistently produced better products over the years. This rapid evolution in performance is very telling of how quickly engineers can integrate new systems nowadays, and innovate. Google and others are currently developing prototype commercial driverless cars, which we should expect to become part of our everyday lives by the next decade.

Dick. 1989: Tim Berners-Lee invents the World Wide Web. 1990: Seiji Ogawa presents the first fMRI machine. 1993: Rodney Brooks and others start the MIT Cog Project, an attempt to build a humanoid robot child in five years. 1997: Deep Blue defeats Garry Kasparov at chess. 2000: Cynthia Breazeal at MIT describes Kismet, a robot with a face that simulates expressions. 2004: DARPA launches the Grand Challenge for autonomous vehicles. 2009: Google builds the self-driving car. 2011: IBM’s Watson wins the TV game show Jeopardy!. 2014: Google buys UK company Deep Mind for $650 million. 2014: Eugene Goostman, a computer program that simulates a thirteen-year-old boy, passes the Turing Test. 2014: Estimated number of robots in the world reaches 8.6 million.1 2015: Estimated number of PCs in the world reaches two billion.2 NOTES Introduction 1PCs (‘Personal computers’) started becoming widely available in the early 1980s: IBM 5150 in 1981, Commodore PET in 1983.


Super Thinking: The Big Book of Mental Models by Gabriel Weinberg, Lauren McCann

Abraham Maslow, Abraham Wald, affirmative action, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, anti-pattern, Anton Chekhov, Apollo 13, Apple Newton, autonomous vehicles, bank run, barriers to entry, Bayesian statistics, Bernie Madoff, Bernie Sanders, Black Swan, Broken windows theory, business process, butterfly effect, Cal Newport, Clayton Christensen, cognitive dissonance, commoditize, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, dark pattern, David Attenborough, delayed gratification, deliberate practice, discounted cash flows, disruptive innovation, Donald Trump, Douglas Hofstadter, Dunning–Kruger effect, Edward Lorenz: Chaos theory, Edward Snowden, effective altruism, Elon Musk, en.wikipedia.org, experimental subject, fake news, fear of failure, feminist movement, Filter Bubble, framing effect, friendly fire, fundamental attribution error, Goodhart's law, Gödel, Escher, Bach, heat death of the universe, hindsight bias, housing crisis, if you see hoof prints, think horses—not zebras, Ignaz Semmelweis: hand washing, illegal immigration, imposter syndrome, incognito mode, income inequality, information asymmetry, Isaac Newton, Jeff Bezos, John Nash: game theory, karōshi / gwarosa / guolaosi, lateral thinking, loss aversion, Louis Pasteur, LuLaRoe, Lyft, mail merge, Mark Zuckerberg, meta-analysis, Metcalfe’s law, Milgram experiment, minimum viable product, moral hazard, mutually assured destruction, Nash equilibrium, Network effects, nocebo, nuclear winter, offshore financial centre, p-value, Paradox of Choice, Parkinson's law, Paul Graham, peak oil, Peter Thiel, phenotype, Pierre-Simon Laplace, placebo effect, Potemkin village, power law, precautionary principle, prediction markets, premature optimization, price anchoring, principal–agent problem, publication bias, recommendation engine, remote working, replication crisis, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, ride hailing / ride sharing, Robert Metcalfe, Ronald Coase, Ronald Reagan, Salesforce, school choice, Schrödinger's Cat, selection bias, Shai Danziger, side project, Silicon Valley, Silicon Valley startup, speech recognition, statistical model, Steve Jobs, Steve Wozniak, Steven Pinker, Streisand effect, sunk-cost fallacy, survivorship bias, systems thinking, The future is already here, The last Blockbuster video rental store is in Bend, Oregon, The Present Situation in Quantum Mechanics, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Tragedy of the Commons, transaction costs, uber lyft, ultimatum game, uranium enrichment, urban planning, vertical integration, Vilfredo Pareto, warehouse robotics, WarGames: Global Thermonuclear War, When a measure becomes a target, wikimedia commons

Now, though, a better argument for peak oil is starting to form as the oil market’s underlying structure is proving to be unhealthy. The effects of climate change are looming. Solar energy is quickly becoming cost-competitive with oil on a global scale. Increasing cost-competitiveness of electric cars and the advent of autonomous vehicles and ride-sharing services are threatening to collapse the car and truck markets as we know them. All of these have the potential to create lasting effects on the oil market. Whether you are a market observer or a market participant, these structural changes are worth considering when you’re thinking about a possible new reality for the oil market.

A parallel in business is when companies amass large patent portfolios, but generally don’t use them on one another for fear of escalating lawsuits that could potentially destabilize all the companies involved. Occasionally you see these suits and countersuits, such as the ones between Apple and Qualcomm (over chip patents), Oracle and Google (over Java patents), and Uber and Google (over autonomous vehicle patents), but these companies often have so many patents (sometimes tens of thousands each) that there could be literally hundreds of suits like these if not for MAD. There are countless possible destructive outcomes to a conflict besides this arguably most extreme outcome of MAD. Engaging in any direct conflict is dangerous, though, because conflicts are unpredictable and often cause collateral damage (see Chapter 2).


pages: 444 words: 117,770

The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma by Mustafa Suleyman

"World Economic Forum" Davos, 23andMe, 3D printing, active measures, Ada Lovelace, additive manufacturing, agricultural Revolution, AI winter, air gap, Airbnb, Alan Greenspan, algorithmic bias, Alignment Problem, AlphaGo, Alvin Toffler, Amazon Web Services, Anthropocene, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, ASML, autonomous vehicles, backpropagation, barriers to entry, basic income, benefit corporation, Big Tech, biodiversity loss, bioinformatics, Bletchley Park, Blitzscaling, Boston Dynamics, business process, business process outsourcing, call centre, Capital in the Twenty-First Century by Thomas Piketty, ChatGPT, choice architecture, circular economy, classic study, clean tech, cloud computing, commoditize, computer vision, coronavirus, corporate governance, correlation does not imply causation, COVID-19, creative destruction, CRISPR, critical race theory, crowdsourcing, cryptocurrency, cuban missile crisis, data science, decarbonisation, deep learning, deepfake, DeepMind, deindustrialization, dematerialisation, Demis Hassabis, disinformation, drone strike, drop ship, dual-use technology, Easter island, Edward Snowden, effective altruism, energy transition, epigenetics, Erik Brynjolfsson, Ernest Rutherford, Extinction Rebellion, facts on the ground, failed state, Fairchild Semiconductor, fear of failure, flying shuttle, Ford Model T, future of work, general purpose technology, Geoffrey Hinton, global pandemic, GPT-3, GPT-4, hallucination problem, hive mind, hype cycle, Intergovernmental Panel on Climate Change (IPCC), Internet Archive, Internet of things, invention of the wheel, job automation, John Maynard Keynes: technological unemployment, John von Neumann, Joi Ito, Joseph Schumpeter, Kickstarter, lab leak, large language model, Law of Accelerating Returns, Lewis Mumford, license plate recognition, lockdown, machine readable, Marc Andreessen, meta-analysis, microcredit, move 37, Mustafa Suleyman, mutually assured destruction, new economy, Nick Bostrom, Nikolai Kondratiev, off grid, OpenAI, paperclip maximiser, personalized medicine, Peter Thiel, planetary scale, plutocrats, precautionary principle, profit motive, prompt engineering, QAnon, quantum entanglement, ransomware, Ray Kurzweil, Recombinant DNA, Richard Feynman, Robert Gordon, Ronald Reagan, Sam Altman, Sand Hill Road, satellite internet, Silicon Valley, smart cities, South China Sea, space junk, SpaceX Starlink, stealth mode startup, stem cell, Stephen Fry, Steven Levy, strong AI, synthetic biology, tacit knowledge, tail risk, techlash, techno-determinism, technoutopianism, Ted Kaczynski, the long tail, The Rise and Fall of American Growth, Thomas Malthus, TikTok, TSMC, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, uranium enrichment, warehouse robotics, William MacAskill, working-age population, world market for maybe five computers, zero day

Everything from road markings to seatbelts to traffic police helped. Although the motorcar was one of history’s fastest-proliferating and most globalized technologies, accidents were inherently local, discrete events whose ultimate damage was contained. But now a fleet of vehicles might be networked together. Or a single system could control autonomous vehicles throughout a territory. However many safeguards and security protocols are in place, the scale of impact is far wider than we’ve seen before. AI creates asymmetric risks beyond those of a bad batch of food, a plane accident, or a faulty product. Its risks extend to entire societies, making it not so much a blunt tool as a lever with global consequences.

Technology has always been about allowing us to do more, but crucially with humans still doing the doing. It has leveraged our existing abilities and automated precisely codified tasks. Until now, constant oversight and management have been the default. Technology remained to greater or lesser degrees under meaningful human control. Full autonomy is qualitatively different. Take autonomous vehicles. In certain conditions today, they can drive on roads with minimal or no direct input from the driver. Researchers in the field categorize autonomy from level 0, no autonomy whatsoever, to level 5, where a vehicle can drive itself under all conditions and the driver simply inputs a destination and then can fall happily asleep.


Four Battlegrounds by Paul Scharre

2021 United States Capitol attack, 3D printing, active measures, activist lawyer, AI winter, AlphaGo, amateurs talk tactics, professionals talk logistics, artificial general intelligence, ASML, augmented reality, Automated Insights, autonomous vehicles, barriers to entry, Berlin Wall, Big Tech, bitcoin, Black Lives Matter, Boeing 737 MAX, Boris Johnson, Brexit referendum, business continuity plan, business process, carbon footprint, chief data officer, Citizen Lab, clean water, cloud computing, commoditize, computer vision, coronavirus, COVID-19, crisis actor, crowdsourcing, DALL-E, data is not the new oil, data is the new oil, data science, deep learning, deepfake, DeepMind, Demis Hassabis, Deng Xiaoping, digital map, digital rights, disinformation, Donald Trump, drone strike, dual-use technology, Elon Musk, en.wikipedia.org, endowment effect, fake news, Francis Fukuyama: the end of history, future of journalism, future of work, game design, general purpose technology, Geoffrey Hinton, geopolitical risk, George Floyd, global supply chain, GPT-3, Great Leap Forward, hive mind, hustle culture, ImageNet competition, immigration reform, income per capita, interchangeable parts, Internet Archive, Internet of things, iterative process, Jeff Bezos, job automation, Kevin Kelly, Kevin Roose, large language model, lockdown, Mark Zuckerberg, military-industrial complex, move fast and break things, Nate Silver, natural language processing, new economy, Nick Bostrom, one-China policy, Open Library, OpenAI, PalmPilot, Parler "social media", pattern recognition, phenotype, post-truth, purchasing power parity, QAnon, QR code, race to the bottom, RAND corporation, recommendation engine, reshoring, ride hailing / ride sharing, robotic process automation, Rodney Brooks, Rubik’s Cube, self-driving car, Shoshana Zuboff, side project, Silicon Valley, slashdot, smart cities, smart meter, Snapchat, social software, sorting algorithm, South China Sea, sparse data, speech recognition, Steve Bannon, Steven Levy, Stuxnet, supply-chain attack, surveillance capitalism, systems thinking, tech worker, techlash, telemarketer, The Brussels Effect, The Signal and the Noise by Nate Silver, TikTok, trade route, TSMC

Strategy games are a special case since they can be perfectly simulated, while the complexity of the real world oftentimes cannot, but synthetic data can help augment datasets when real-world data may be limited. The autonomous car company Waymo stated in 2020 that they had driven over 20 million miles on public roads, building up a large dataset of real-world driving interactions. To augment this real-world data, Waymo has been simulating 10 million driving miles every single day in computer simulations, racking up a total of 10 billion simulated miles as of 2020. These simulations are another form of synthetic data, which can then be used to improve autonomous car algorithms. Simulations allow Waymo to create thousands of variations of situations, ensuring its algorithms are robust to a range of driving conditions.

The compute requirements for trained AI models doing inference are much less, sometimes orders of magnitude less than what was needed for training. While AI models are often trained at large data centers, the lower compute requirements mean that inference can increasingly be done on edge devices, such as smartphones, IoT devices, intelligent video cameras, or autonomous cars. Both training and inference are done on computer chips, and advances in computing hardware has been fundamental to the deep learning revolution. Graphics processing units (GPUs) have emerged as a key enabler for deep learning because of their ability to do parallel computation (which is valuable for neural networks) better than traditional central processing units (CPUs).

Adversarial attacks have been embedded into physical objects, such as a 3D-printed turtle that was subtly altered to fool an image classifier into misidentifying it as a rifle. Even more alarming than stickers on stop signs, in a real-world driving experiment researchers used black box methods to create a physical adversarial object that was able to evade detection by the laser-based detection systems used by self-driving cars. To the autonomous car’s sensors, the object simply wasn’t there. In theory, adversarial attacks could be used to subvert deployed AI systems in a variety of real-world settings. An individual wearing manipulated clothing—a hat, shirt, or glasses—could fool a facial recognition system into believing they are someone else.


pages: 138 words: 40,525

This Is Not a Drill: An Extinction Rebellion Handbook by Extinction Rebellion

3D printing, autonomous vehicles, banks create money, biodiversity loss, bitcoin, blockchain, Buckminster Fuller, car-free, carbon footprint, carbon tax, circular economy, clean water, Colonization of Mars, CRISPR, crowdsourcing, David Attenborough, David Graeber, decarbonisation, deindustrialization, digital capitalism, Donald Trump, driverless car, drug harm reduction, Elon Musk, Ethereum, ethereum blockchain, Extinction Rebellion, Fairphone, feminist movement, full employment, Gail Bradbrook, gig economy, global pandemic, green new deal, Greta Thunberg, ice-free Arctic, Intergovernmental Panel on Climate Change (IPCC), Jeremy Corbyn, job automation, mass immigration, negative emissions, Peter Thiel, place-making, quantitative easing, Ray Kurzweil, retail therapy, rewilding, Sam Altman, smart grid, supply-chain management, tech billionaire, the scientific method, union organizing, urban sprawl, wealth creators

So instead of considering the practical ethics of impoverishing and exploiting the many in the name of the few, most academics, journalists and science-fiction writers instead considered much more abstract and fanciful conundrums: Is it fair for a stock trader to use smart drugs? Should children get implants for foreign languages? Do we want autonomous vehicles to prioritize the lives of pedestrians over those of its passengers? Should the first Mars colonies be run as democracies? Does changing my DNA undermine my identity? Should robots have rights? Asking these sorts of questions, while philosophically entertaining, is a poor substitute for wrestling with the real moral quandaries associated with unbridled technological development in the name of corporate capitalism.


pages: 504 words: 126,835

The Innovation Illusion: How So Little Is Created by So Many Working So Hard by Fredrik Erixon, Bjorn Weigel

Airbnb, Alan Greenspan, Albert Einstein, American ideology, asset allocation, autonomous vehicles, barriers to entry, Basel III, Bernie Madoff, bitcoin, Black Swan, blockchain, Blue Ocean Strategy, BRICs, Burning Man, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, classic study, Clayton Christensen, Colonization of Mars, commoditize, commodity super cycle, corporate governance, corporate social responsibility, creative destruction, crony capitalism, dark matter, David Graeber, David Ricardo: comparative advantage, discounted cash flows, distributed ledger, Donald Trump, Dr. Strangelove, driverless car, Elon Musk, Erik Brynjolfsson, Fairchild Semiconductor, fear of failure, financial engineering, first square of the chessboard / second half of the chessboard, Francis Fukuyama: the end of history, general purpose technology, George Gilder, global supply chain, global value chain, Google Glasses, Google X / Alphabet X, Gordon Gekko, Greenspan put, Herman Kahn, high net worth, hiring and firing, hockey-stick growth, Hyman Minsky, income inequality, income per capita, index fund, industrial robot, Internet of things, Jeff Bezos, job automation, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, joint-stock company, Joseph Schumpeter, Just-in-time delivery, Kevin Kelly, knowledge economy, laissez-faire capitalism, low interest rates, Lyft, manufacturing employment, Mark Zuckerberg, market design, Martin Wolf, mass affluent, means of production, middle-income trap, Mont Pelerin Society, Network effects, new economy, offshore financial centre, pensions crisis, Peter Thiel, Potemkin village, precautionary principle, price mechanism, principal–agent problem, Productivity paradox, QWERTY keyboard, RAND corporation, Ray Kurzweil, rent-seeking, risk tolerance, risk/return, Robert Gordon, Robert Solow, Ronald Coase, Ronald Reagan, savings glut, Second Machine Age, secular stagnation, Silicon Valley, Silicon Valley startup, Skype, sovereign wealth fund, Steve Ballmer, Steve Jobs, Steve Wozniak, subprime mortgage crisis, technological determinism, technological singularity, TED Talk, telemarketer, The Chicago School, The Future of Employment, The Nature of the Firm, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, too big to fail, total factor productivity, transaction costs, transportation-network company, tulip mania, Tyler Cowen, Tyler Cowen: Great Stagnation, uber lyft, University of East Anglia, unpaid internship, Vanguard fund, vertical integration, Yogi Berra

., “Assistance to the Commission.” 4.CSES, “Interim Evaluation.” 5.Dekkers, “Why Europe Lags on Innovation.” 6.Sunstein, “Beyond the Precautionary Principle.” 7.Bailey, “Precautionary Tale.” 8.European Commission, “Nanomaterials.” 9.Rabesandratana, “EU Court Annuls GM Potato Approval.” 10.Dunmore, “Monsantoo Withdraw EU Approval Requests.” 11.Moynihan, “What Are Quantum Dots.” 12.Ahmed, “Quantum dots.” 13.NASA, “High Efficiency Quantum Dot III-V Thermophotovoltaic Cell.” 14.Stern, “Innovation under Regulatory Uncertainty.” 15.Mandel and Carew, “Regulatory Improvement Commission.” 16.Carnegy and Foy, “French Ban on Mercedes Cars.” 17.McLaughlin and Williams, “The Consequences of Regulatory Accumulation.” 18.Al-Ubaydli and McLaughlin, “RegData: A Numerical Database on Industry-Specific Regulations.” 19.Anderson et al., Autonomous Vehicle Technology. 20.Thierer and Hagemann, “Removing Roadblocks to Intelligent Vehicles.” 21.Winston and Mannering, “Implementing Technology to Improve Public Highway Performance.” 22.Rodrik, “Policy Uncertainty and Private Investment”; Hassett and Metcalf, “Investment with Uncertain Tax Policy.” 23.Gulen and Ion, “Policy Uncertainty and Corporate Investment.” 24.Baker, Bloom, and Davis, “Has Economic Policy Uncertainty Hampered the Recovery?”

Anders, George, “WhatsApp’s Growth Exceeds Christianity’s First 19 Centuries.” Forbes, Jan. 8, 2015. At http://www.forbes.com/sites/georgeanders/2015/01/08/whatsapps-growth-exceeds-christianitys-first-19-centuries/. Anderson, James M., Nidhi Kalra, Karlyn D. Stanley, Paul Sorensen, Constantine Samaras, and Oluwatobi A. Oluwatola, Autonomous Vehicle Technology: A Guide for Policymakers. Rand Corporation, 2014. Andrews, Dan, Chiara Criscuolo, and Peter N. Gal, “Frontier Firms, Technology Diffusion and Public Policy: Micro Evidence from OECD Countries.” OECD Productivity Working Paper. Organisation for Economic Co-operation and Development, Nov. 2015.


pages: 158 words: 46,353

Future War: Preparing for the New Global Battlefield by Robert H. Latiff

Asilomar, Asilomar Conference on Recombinant DNA, autonomous vehicles, Berlin Wall, Boeing 747, CRISPR, cyber-physical system, Danny Hillis, defense in depth, drone strike, dual-use technology, Elon Musk, failed state, friendly fire, Howard Zinn, Internet of things, low earth orbit, military-industrial complex, Nicholas Carr, orbital mechanics / astrodynamics, post-truth, precautionary principle, Recombinant DNA, self-driving car, Seymour Hersh, South China Sea, Stephen Hawking, Stewart Brand, Strategic Defense Initiative, Stuxnet, synthetic biology, VTOL, Wall-E

The soldier will become the slowest element in an engagement, or simply irrelevant. A soldier who feels himself to have no autonomy, authority, or responsibility will no longer want to apply independent judgment. Adherence to the rules of war will become less relevant as well. With ongoing work in computers, artificial intelligence, robotics, and autonomous vehicles, we are attempting to make machines—inanimate objects—approximate the behavior of humans. At the same time, with work in soldier enhancements, be they physical, neural, pharmaceutical, or performance-based, we are attempting to make humans behave more like machines. In both cases, we are blurring the concept of what it means to be human.


pages: 496 words: 131,938

The Future Is Asian by Parag Khanna

3D printing, Admiral Zheng, affirmative action, Airbnb, Amazon Web Services, anti-communist, Asian financial crisis, asset-backed security, augmented reality, autonomous vehicles, Ayatollah Khomeini, barriers to entry, Basel III, bike sharing, birth tourism , blockchain, Boycotts of Israel, Branko Milanovic, British Empire, call centre, capital controls, carbon footprint, cashless society, clean tech, clean water, cloud computing, colonial rule, commodity super cycle, computer vision, connected car, corporate governance, CRISPR, crony capitalism, cross-border payments, currency peg, death from overwork, deindustrialization, Deng Xiaoping, Didi Chuxing, Dissolution of the Soviet Union, Donald Trump, driverless car, dual-use technology, energy security, European colonialism, factory automation, failed state, fake news, falling living standards, family office, financial engineering, fixed income, flex fuel, gig economy, global reserve currency, global supply chain, Great Leap Forward, green transition, haute couture, haute cuisine, illegal immigration, impact investing, income inequality, industrial robot, informal economy, initial coin offering, Internet of things, karōshi / gwarosa / guolaosi, Kevin Kelly, Kickstarter, knowledge worker, light touch regulation, low cost airline, low skilled workers, Lyft, machine translation, Malacca Straits, Marc Benioff, Mark Zuckerberg, Masayoshi Son, megacity, megaproject, middle-income trap, Mikhail Gorbachev, money market fund, Monroe Doctrine, mortgage debt, natural language processing, Netflix Prize, new economy, off grid, oil shale / tar sands, open economy, Parag Khanna, payday loans, Pearl River Delta, prediction markets, purchasing power parity, race to the bottom, RAND corporation, rent-seeking, reserve currency, ride hailing / ride sharing, Ronald Reagan, Salesforce, Scramble for Africa, self-driving car, Shenzhen special economic zone , Silicon Valley, smart cities, SoftBank, South China Sea, sovereign wealth fund, special economic zone, stem cell, Steve Jobs, Steven Pinker, supply-chain management, sustainable-tourism, synthetic biology, systems thinking, tech billionaire, tech worker, trade liberalization, trade route, transaction costs, Travis Kalanick, uber lyft, upwardly mobile, urban planning, Vision Fund, warehouse robotics, Washington Consensus, working-age population, Yom Kippur War

Where cities have become too large and congested, such as Changsha, the government has attempted to motivate people to shift to second-tier cities to distribute the population better.50 Now, instead of just four cities representing nearly half the country’s middle class—as was the case with Beijing, Shanghai, Guangzhou, and Shenzhen in 2002—by 2020, inland China is expected to be home to 40 percent of the country’s middle class. Given the density of Asian cities, last-mile bike sharing and autonomous vehicles are other areas in which Asians are making strategic investments to navigate around the traditional path of universal vehicle ownership and crippling traffic congestion. Bike stations and dockless biking have been pioneered by companies such as Mobike and Ofo, which have spread from China across Asia and into Europe.

These examples demonstrate that the winners of a perceived competition between US and Chinese tech companies to innovate and compete for Asian markets are first and foremost Asians themselves. Even in capital-intensive arenas such as particle physics and quantum computing, the combination of China’s appropriation of US technology, luring Chinese American talent from Silicon Valley, and thriving enterprise culture have collectively driven Chinese innovation. Autonomous vehicles, energy-efficient power grids, and urban surveillance systems all rest on breakthroughs in AI such as neural networks, which Asians have developed at least a year ahead of their Western counterparts. Andrew Ng, a cofounder of Google Brain and Coursera who then became chief scientist at Baidu, argues that the complexities of Chinese characters and tones pushed Baidu toward advances in natural language processing (NLP) and voice recognition faster than its Western peers.


pages: 430 words: 135,418

Power Play: Tesla, Elon Musk, and the Bet of the Century by Tim Higgins

air freight, asset light, autonomous vehicles, big-box store, call centre, Colonization of Mars, coronavirus, corporate governance, COVID-19, Donald Trump, electricity market, Elon Musk, family office, Ford Model T, gigafactory, global pandemic, Henry Ford's grandson gave labor union leader Walter Reuther a tour of the company’s new, automated factory…, Jeff Bezos, Jeffrey Epstein, junk bonds, Larry Ellison, low earth orbit, Lyft, margin call, Mark Zuckerberg, Masayoshi Son, Menlo Park, Michael Milken, paypal mafia, ride hailing / ride sharing, Sand Hill Road, self-driving car, Sheryl Sandberg, short selling, side project, Silicon Valley, Silicon Valley startup, skunkworks, SoftBank, Solyndra, sovereign wealth fund, stealth mode startup, Steve Jobs, Steve Jurvetson, Tesla Model S, Tim Cook: Apple, Travis Kalanick, Uber for X, uber lyft, vertical integration

Peter Rawlinson had warned Musk in late 2011 about them, but those words were long forgotten. By spring of 2015, the team was grappling with how to get them to open upward like a bird’s wings in flight. The hydraulics weren’t standing up to testing; they were leaking on passengers. Sterling Anderson, an MIT researcher who had attracted attention for his work in autonomous cars, was hired in late 2014 as the program manager of the Model X and was quietly engineering a new design for the doors, one that would use a less complicated electromechanical system. Musk liked it and ordered the last-minute change. Changing the door design so late in the game was risky. It would require adjustments to the body of the vehicle, requiring new dies to be created.

He was painting the kind of vision for the future of cars that Silicon Valley had fantasized about for generations. But somehow, when he said it, it seemed plausible. (Trying to catch up, General Motors in March announced it was acquiring a little-known San Francisco startup called Cruise to jump-start its own autonomous car program. What attracted everyone’s attention was the headline number for the price of the deal: more than $1 billion.) Musk didn’t lay out how he would pay for his future or belabor the timelines. He didn’t need to. His pronouncement had one convincing bit of history in its favor: Ten years earlier, his talk of bringing out an all-electric luxury sedan and compact car had seemed far-fetched.

In dense language, the government said it had reviewed Tesla data from the system and found its vehicles’ crash rate dropped by almost 40 percent after “autosteer” was installed. Musk was ecstatic—he quickly highlighted the 40 percent findings on Twitter. Some members of the team were stunned. Where had the 40 percent figure come from? All of the attention on Autopilot, and Musk’s dream for fully autonomous cars, was stirring new excitement on Wall Street. The same day as the NHTSA’s findings, Adam Jonas at Morgan Stanley published a research report predicting Tesla stock could rise 25 percent to $305 a share, a startlingly high level that, if achieved, would mark an unbelievable milestone: Tesla would be valued higher than Ford or General Motors.


pages: 188 words: 54,942

Drone Warfare: Killing by Remote Control by Medea Benjamin

air gap, airport security, autonomous vehicles, Chelsea Manning, clean water, Clive Stafford Smith, crowdsourcing, drone strike, friendly fire, illegal immigration, Jeff Hawkins, Khyber Pass, megacity, military-industrial complex, no-fly zone, nuremberg principles, performance metric, private military company, Ralph Nader, WikiLeaks

“Of that you can be certain.”293 Williams thinks the best chance the international community has to curb the use of drones is to stop autonomous robotic weapons—weapons that operate independently according to pre-programmed missions—because they are not yet fully developed and because they bring up the most difficult ethical and legal questions. “If we think it’s bad now, imagine a fully autonomous vehicle going out and wiping out several villages,” said Williams. “Who’s accountable? The company who made them? The military who used them? The software developer? Perhaps they all should be taken to court but that probably isn’t going to happen. So we need to stop them before they’re used. And this is something I think an international coalition could accomplish.”


pages: 524 words: 154,652

Blood in the Machine: The Origins of the Rebellion Against Big Tech by Brian Merchant

"World Economic Forum" Davos, Ada Lovelace, algorithmic management, Amazon Mechanical Turk, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, basic income, Bernie Sanders, Big Tech, big-box store, Black Lives Matter, Cambridge Analytica, Charles Babbage, ChatGPT, collective bargaining, colonial rule, commoditize, company town, computer age, computer vision, coronavirus, cotton gin, COVID-19, cryptocurrency, DALL-E, decarbonisation, deskilling, digital rights, Donald Trump, Edward Jenner, Elon Musk, Erik Brynjolfsson, factory automation, flying shuttle, Frederick Winslow Taylor, fulfillment center, full employment, future of work, George Floyd, gig economy, gigafactory, hiring and firing, hockey-stick growth, independent contractor, industrial robot, information asymmetry, Internet Archive, invisible hand, Isaac Newton, James Hargreaves, James Watt: steam engine, Jeff Bezos, Jessica Bruder, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Roose, Kickstarter, Lyft, Mark Zuckerberg, Marshall McLuhan, means of production, military-industrial complex, move fast and break things, Naomi Klein, New Journalism, On the Economy of Machinery and Manufactures, OpenAI, precariat, profit motive, ride hailing / ride sharing, Sam Bankman-Fried, scientific management, Second Machine Age, self-driving car, sharing economy, Silicon Valley, sovereign wealth fund, spinning jenny, Steve Jobs, Steve Wozniak, super pumped, TaskRabbit, tech billionaire, tech bro, tech worker, techlash, technological determinism, Ted Kaczynski, The Future of Employment, The Wealth of Nations by Adam Smith, Thomas Malthus, Travis Kalanick, Uber and Lyft, uber lyft, union organizing, universal basic income, W. E. B. Du Bois, warehouse automation, warehouse robotics, working poor, workplace surveillance

De Blasio also proposed creating an authority, the Federal Automation and Worker Protection Agency, that would gather data on automation and employment and help protect workers. He argued that the bureau should cover work displacement, not just from automation, but from app-based companies as well. “We’ve had some initial fights over autonomous vehicles already,” de Blasio said; companies that want to come onto the streets of New York that we would not allow for safety reasons alone, that might have a huge negative impact, employment-wise. But on just the for-hire vehicle sector, you know, Uber and Lyft—it’s not automation in some ways in the fullest sense, obviously, but simply a new technology that automated some functions—and had a massive dislocating impact on our transportation sector.

It may be only a matter of time before the rebel workers of this new machine age see the injustices of the algorithmic platforms as too much to bear, the surveillance apparatus of Big Tech too intrusive, the robotic pace of work too ruthlessly body-breaking. And if they feel the rage of Frankenstein’s monster, rebooted in a new era of boundless entrepreneurial adventuring, and they catch sight of those autonomous vehicles assembling like ghosts on the horizon, they might just reach, once again, for their hammers. ACKNOWLEDGMENTS The first round of thanks must go out to the machine breakers: the protestors, the organizers, the artists and artisans, the poets and prognosticators, the workers and critics who, Luddites all, pushed back—even when called idiots or deluded or worse—rather than be trampled by someone else’s machinery.


pages: 259 words: 84,261

Scary Smart: The Future of Artificial Intelligence and How You Can Save Our World by Mo Gawdat

3D printing, accounting loophole / creative accounting, AI winter, AlphaGo, anthropic principle, artificial general intelligence, autonomous vehicles, basic income, Big Tech, Black Lives Matter, Black Monday: stock market crash in 1987, butterfly effect, call centre, carbon footprint, cloud computing, computer vision, coronavirus, COVID-19, CRISPR, cryptocurrency, deep learning, deepfake, DeepMind, Demis Hassabis, digital divide, digital map, Donald Trump, Elon Musk, fake news, fulfillment center, game design, George Floyd, global pandemic, Google Glasses, Google X / Alphabet X, Law of Accelerating Returns, lockdown, microplastics / micro fibres, Nick Bostrom, off-the-grid, OpenAI, optical character recognition, out of africa, pattern recognition, Ponzi scheme, Ray Kurzweil, recommendation engine, self-driving car, Silicon Valley, smart contracts, Stanislav Petrov, Stephen Hawking, subprime mortgage crisis, superintelligent machines, TED Talk, TikTok, Turing machine, Turing test, universal basic income, Watson beat the top human players on Jeopardy!, Y2K

Well, the cars of the past didn’t. That is no longer the case. Autonomous, self-driving vehicles and every other type of similarly AI-powered tech will have a will of its own. Think about that because this is where the intelligence will start to manifest itself. If it is too hot in the parking lot, autonomous cars may choose, without your blessing, to move to a shady spot. They may choose to navigate their path to the airport through a different route and could even choose to commit suicide and throw themselves off a mountaintop, if their intelligence told them this could save the life of a child or perhaps even another car.

They may choose to navigate their path to the airport through a different route and could even choose to commit suicide and throw themselves off a mountaintop, if their intelligence told them this could save the life of a child or perhaps even another car. Yes. This is the true meaning of the word autonomous – the ability to make their own decisions and, in the near future, develop their own decision-making methods. Rather disconcerting when you consider it. I mean, think about those autonomous war machines that are a bit like an autonomous car but with a machine gun attached to them. And what about other autonomous machines that make decisions we can’t even observe with our own eyes? While a car in motion is still sort of manageable, in terms of predicting what it might do, AIs that perform billions of transactions a minute, such as the ones that decide which ad or content to show you on the internet, are already way faster than anything we can control.


pages: 207 words: 59,298

The Gig Economy: A Critical Introduction by Jamie Woodcock, Mark Graham

Airbnb, algorithmic management, Amazon Mechanical Turk, autonomous vehicles, barriers to entry, British Empire, business process, business process outsourcing, Californian Ideology, call centre, collective bargaining, commoditize, corporate social responsibility, crowdsourcing, data science, David Graeber, deindustrialization, Didi Chuxing, digital divide, disintermediation, emotional labour, en.wikipedia.org, full employment, future of work, gamification, gender pay gap, gig economy, global value chain, Greyball, independent contractor, informal economy, information asymmetry, inventory management, Jaron Lanier, Jeff Bezos, job automation, knowledge economy, low interest rates, Lyft, mass immigration, means of production, Network effects, new economy, Panopticon Jeremy Bentham, planetary scale, precariat, rent-seeking, RFID, ride hailing / ride sharing, Ronald Reagan, scientific management, self-driving car, sentiment analysis, sharing economy, Silicon Valley, Silicon Valley ideology, TaskRabbit, The Future of Employment, transaction costs, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, union organizing, women in the workforce, working poor, young professional

Think of babysitters or security guards as jobs in which people tend to use personal recommendations, etc., that are hard to codify into platform ratings or databases. On the other hand, too much legibility and there is the risk that jobs become automated away. The Amazon dream of autonomous drones that can deliver parcels or the Uber dream of autonomous vehicles that can transport passengers are only possible in a world in which multiple overlapping spaces, activities and processes are highly digitally legible. Having a standardized addressing system, high-quality geospatial data, and the technology to produce and read those data has allowed large platforms to more effectively operate in some countries rather than others.


pages: 343 words: 91,080

Uberland: How Algorithms Are Rewriting the Rules of Work by Alex Rosenblat

"Susan Fowler" uber, Affordable Care Act / Obamacare, Airbnb, algorithmic management, Amazon Mechanical Turk, autonomous vehicles, barriers to entry, basic income, big-box store, bike sharing, Black Lives Matter, business logic, call centre, cashless society, Cass Sunstein, choice architecture, cognitive load, collaborative economy, collective bargaining, creative destruction, crowdsourcing, data science, death from overwork, digital divide, disinformation, disruptive innovation, don't be evil, Donald Trump, driverless car, emotional labour, en.wikipedia.org, fake news, future of work, gender pay gap, gig economy, Google Chrome, Greyball, income inequality, independent contractor, information asymmetry, information security, Jaron Lanier, Jessica Bruder, job automation, job satisfaction, Lyft, marginal employment, Mark Zuckerberg, move fast and break things, Network effects, new economy, obamacare, performance metric, Peter Thiel, price discrimination, proprietary trading, Ralph Waldo Emerson, regulatory arbitrage, ride hailing / ride sharing, Salesforce, self-driving car, sharing economy, side hustle, Silicon Valley, Silicon Valley ideology, Skype, social software, SoftBank, stealth mode startup, Steve Jobs, strikebreaker, TaskRabbit, technological determinism, Tim Cook: Apple, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, union organizing, universal basic income, urban planning, Wolfgang Streeck, work culture , workplace surveillance , Yochai Benkler, Zipcar

In concrete terms, it looks like this: let’s say Uber and others are working to get approval from municipalities to test autonomous cars. Behind these particular negotiations is a larger, cultural conversation in the United States which says that advances in automation will result in mass joblessness. Against this cultural backdrop, Uber simultaneously approaches state regulators to pass laws that legislate the independent-contractor status of drivers,41 even stripping them of worker rights. But the logic of automation debates frames this effort as a small concession: ceding workers’ rights seems relatively innocuous if their jobs give way to a future with autonomous cars, a future that renders driving redundant.


pages: 196 words: 61,981

Blockchain Chicken Farm: And Other Stories of Tech in China's Countryside by Xiaowei Wang

4chan, AI winter, Amazon Web Services, artificial general intelligence, autonomous vehicles, back-to-the-land, basic income, Big Tech, bitcoin, blockchain, business cycle, cloud computing, Community Supported Agriculture, computer vision, COVID-19, cryptocurrency, data science, deep learning, Deng Xiaoping, Didi Chuxing, disruptive innovation, Donald Trump, drop ship, emotional labour, Ethereum, ethereum blockchain, Francis Fukuyama: the end of history, Garrett Hardin, gig economy, global pandemic, Great Leap Forward, high-speed rail, Huaqiangbei: the electronics market of Shenzhen, China, hype cycle, income inequality, informal economy, information asymmetry, Internet Archive, Internet of things, job automation, Kaizen: continuous improvement, Kickstarter, knowledge worker, land reform, Marc Andreessen, Mark Zuckerberg, Menlo Park, multilevel marketing, One Laptop per Child (OLPC), Pearl River Delta, peer-to-peer lending, precision agriculture, QR code, ride hailing / ride sharing, risk tolerance, Salesforce, Satoshi Nakamoto, scientific management, self-driving car, Silicon Valley, Snapchat, SoftBank, software is eating the world, surveillance capitalism, TaskRabbit, tech worker, technological solutionism, the long tail, TikTok, Tragedy of the Commons, universal basic income, vertical integration, Vision Fund, WeWork, Y Combinator, zoonotic diseases

Gourmands also insist that the moon’s terroir, the special moon-maize cultivar, coupled with the unique gravitational field of the moon, give a complex, hearty aroma to moon-farmed foods. Moon maize is not just a specialty crop, however. Currently, private farming companies are doing research in using small, cost-effective UAVs (unmanned autonomous vehicles) for sowing and harvesting, so that everyone on earth can access moon-farmed food. For this recipe, we highly suggest using the “Lora” strain, as it is organically grown and combines the best of heritage maize with the sweetness of modern corn. While these moon-maize mooncakes fetch a hefty price at Hema, you can make them at home for a fraction of the cost.


pages: 592 words: 161,798

The Future of War by Lawrence Freedman

Albert Einstein, autonomous vehicles, Berlin Wall, Black Swan, Boeing 747, British Empire, colonial rule, conceptual framework, crowdsourcing, cuban missile crisis, currency manipulation / currency intervention, disinformation, Donald Trump, Dr. Strangelove, driverless car, drone strike, en.wikipedia.org, energy security, Ernest Rutherford, failed state, Fall of the Berlin Wall, Francis Fukuyama: the end of history, global village, Google Glasses, Herman Kahn, Intergovernmental Panel on Climate Change (IPCC), John Markoff, long peace, megacity, Mikhail Gorbachev, military-industrial complex, moral hazard, mutually assured destruction, New Journalism, Norbert Wiener, nuclear taboo, open economy, pattern recognition, Peace of Westphalia, RAND corporation, Ronald Reagan, South China Sea, speech recognition, Steven Pinker, Strategic Defense Initiative, Stuxnet, Suez canal 1869, Suez crisis 1956, systematic bias, the scientific method, uranium enrichment, urban sprawl, Valery Gerasimov, Wargames Reagan, WarGames: Global Thermonuclear War, WikiLeaks, zero day

Military organisations had been known to resist anything which threatened human redundancy, for example in the 1950s Strategic Air Command resisted ICBMs as alternatives to manned bombers. In addition the record of turning exciting new technologies into actual systems was less impressive than often supposed, with funding, bureaucratic, and engineering issues often causing severe delays.22 Another factor affecting the introduction of autonomous vehicles was that the lead with the new technologies was taken by the private sector. The most developed example was a driverless car, a much more challenging machine than a drone and one expected to have much more autonomy. As it moved forward on the ground it had to be aware of numerous potential obstacles and other vehicles with their own dynamics.

Driverless cars were first developed as a Pentagon programme in 2004 but resources were only poured into it as a commercial venture, which not only meant that the advances were out of state control but also that the state took second place in competition for the skilled engineers and software developers needed to take the work forward. Competition for a mass market and vast R&D expenditures moved driverless cars to viable products while military programmes for autonomous vehicles lagged behind. A key feature of many of the vital systems introduced for the digital age, including Internet providers, search engines, hardware manufacturers, and software developers, was that they were owned and operated by private companies with global interests. Smartphones carried capabilities such as satellite imaging, navigations, data stores, and instant, encrypted communications of a quality once available to only the most advanced military organisations.


pages: 512 words: 165,704

Traffic: Why We Drive the Way We Do (And What It Says About Us) by Tom Vanderbilt

Albert Einstein, autonomous vehicles, availability heuristic, Berlin Wall, Boeing 747, call centre, cellular automata, Cesare Marchetti: Marchetti’s constant, cognitive dissonance, computer vision, congestion charging, congestion pricing, Daniel Kahneman / Amos Tversky, DARPA: Urban Challenge, Donald Shoup, endowment effect, extreme commuting, fundamental attribution error, Garrett Hardin, Google Earth, hedonic treadmill, Herman Kahn, hindsight bias, hive mind, human-factors engineering, if you build it, they will come, impulse control, income inequality, Induced demand, invisible hand, Isaac Newton, Jane Jacobs, John Nash: game theory, Kenneth Arrow, lake wobegon effect, loss aversion, megacity, Milgram experiment, Nash equilibrium, PalmPilot, power law, Sam Peltzman, Silicon Valley, SimCity, statistical model, the built environment, The Death and Life of Great American Cities, Timothy McVeigh, traffic fines, Tragedy of the Commons, traumatic brain injury, ultimatum game, urban planning, urban sprawl, women in the workforce, working poor

This is a problem that Sebastian Thrun, director of the Artificial Intelligence Laboratory at Stanford University, and his team have dedicated themselves to for the last few years. In 2005, Thrun and his colleagues won the Defense Advanced Research Projects Agency’s Grand Challenge, a 132-mile race through a tortuous course in the Mojave Desert. Their “autonomous vehicle,” a Volkswagen Touareg named Stanley, using only GPS coordinates, cameras, and a variety of sensors, completed the course in just under seven hours, averaging a rather robust 19.1 miles per hour. Stanley won because Thrun and his team, after a series of failures, changed their method of driving instruction.

The randomness of traffic overwhelms these tiny instances. At the same time, some of these little optimizations, like being a jerk at a stop sign, cause problems for everyone. They slow everyone down.” It took a group of some of the world’s leading robotics researchers years of work to come up with an autonomous vehicle that, while clever and adept at certain driving tasks, would quickly go haywire in real traffic. That should be both a testament to the remarkable human ability that driving is as well as a cautionary reminder not to take this activity for granted. The advantage robots have in the long run is that the hardware and software keep getting better.


Alpha Trader by Brent Donnelly

Abraham Wald, algorithmic trading, Asian financial crisis, Atul Gawande, autonomous vehicles, backtesting, barriers to entry, beat the dealer, behavioural economics, bitcoin, Boeing 747, buy low sell high, Checklist Manifesto, commodity trading advisor, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, cryptocurrency, currency manipulation / currency intervention, currency risk, deep learning, diversification, Edward Thorp, Elliott wave, Elon Musk, endowment effect, eurozone crisis, fail fast, financial engineering, fixed income, Flash crash, full employment, global macro, global pandemic, Gordon Gekko, hedonic treadmill, helicopter parent, high net worth, hindsight bias, implied volatility, impulse control, Inbox Zero, index fund, inflation targeting, information asymmetry, invisible hand, iterative process, junk bonds, Kaizen: continuous improvement, law of one price, loss aversion, low interest rates, margin call, market bubble, market microstructure, Market Wizards by Jack D. Schwager, McMansion, Monty Hall problem, Network effects, nowcasting, PalmPilot, paper trading, pattern recognition, Peter Thiel, prediction markets, price anchoring, price discovery process, price stability, quantitative easing, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, reserve currency, risk tolerance, Robert Shiller, secular stagnation, Sharpe ratio, short selling, side project, Stanford marshmallow experiment, Stanford prison experiment, survivorship bias, tail risk, TED Talk, the scientific method, The Wisdom of Crowds, theory of mind, time dilation, too big to fail, transaction costs, value at risk, very high income, yield curve, you are the product, zero-sum game

Let’s look at a very simple example of how understanding the narrative can help you when news comes out. You have been trading the stock of a hydrogen-powered truck manufacturer for a while and it has been in a steady up trend. The market has attached a huge premium to electric and autonomous vehicle stocks and the stock you trade benefits from the halo effect of TSLA. The main narrative is that autonomous vehicles have massive upside and almost any market cap can be justified. With this stock, there is a bearish counternarrative humming in the background. That story is that the company has not yet delivered a single working truck to any customer and behind the scenes there is strife inside the company as the founder is viewed by some as a classic visionary entrepreneur and by others as a marketing genius with no technical skill.


pages: 665 words: 159,350

Shape: The Hidden Geometry of Information, Biology, Strategy, Democracy, and Everything Else by Jordan Ellenberg

Albert Einstein, AlphaGo, Andrew Wiles, autonomous vehicles, British Empire, Brownian motion, Charles Babbage, Claude Shannon: information theory, computer age, coronavirus, COVID-19, deep learning, DeepMind, Donald Knuth, Donald Trump, double entry bookkeeping, East Village, Edmond Halley, Edward Jenner, Elliott wave, Erdős number, facts on the ground, Fellow of the Royal Society, Geoffrey Hinton, germ theory of disease, global pandemic, government statistician, GPT-3, greed is good, Henri Poincaré, index card, index fund, Isaac Newton, Johannes Kepler, John Conway, John Nash: game theory, John Snow's cholera map, Louis Bachelier, machine translation, Mercator projection, Mercator projection distort size, especially Greenland and Africa, Milgram experiment, multi-armed bandit, Nate Silver, OpenAI, Paul Erdős, pets.com, pez dispenser, probability theory / Blaise Pascal / Pierre de Fermat, Ralph Nelson Elliott, random walk, Rubik’s Cube, self-driving car, side hustle, Snapchat, social distancing, social graph, transcontinental railway, urban renewal

AI pioneer Oliver Selfridge, in a television interview from the early 1960s, said, “I am convinced that machines can and will think in our lifetime,” though with the proviso “I don’t think my daughter will ever marry a computer.” (There is no technical advance so abstract that people can’t feel sexual anxiety about it.) The multidimensional geometry of difficulty should remind us that it’s very hard to know which competencies machines are on the verge of acquiring. An autonomous vehicle may be able to make the right choice 95% of the time, but that doesn’t mean it’s 95% of the way to making the right choice all the time; that last 5%, those outlier cases, might well be a problem our sloppy brains are better equipped to solve than any current or near-future machine. And of course there’s the question, naturally of interest to me, of whether machine learning can replace mathematicians.

See also geometric progressions Arkansas Voters First, 409 Arnell Group, 278 Aronofsky, Darren, 276–77 Articles of Confederation, 353n artificial intelligence (AI) car key search analogy, 186–87 and chess-playing computers, 145 and deep learning, 177–86 and Go-playing programs, 141 and gradient descent, 166–68, 169–73, 174–76 and image analysis, 168–73 and parity problem, 204 sexual anxiety touched off by, 204 and strategy assessment, 173–77 See also machine learning; neural networks Ash, Robert, 290 Ashbery, John, 217 associative learning, 222 astronomy, 250 asymmetry, 89, 135, 257, 306, 342–43, 386, 398 athoni, 190–91 atoms (numerical sequences), 265 Aubrey, John, 11–12 audioactive decay, 265 Australasian Journal of Philosophy, 33–34 authalic projection, 308 autocompletion, 263 automata, 252–53, 253n autonomous vehicles, 177–78, 204–5 Awesome Theorem, 308–10 axioms and appeal of geometry, 3 and commutativity, 297 and geometry pedagogy, 12–13, 15, 18, 22, 24–26 and gradient descent, 172 and metaphorical value of geometry, 411 and transitivity of equality, 20 ayahuasca, 2, 3–4 Babbage, Charles, 133, 155, 252–53 babies and geometry, 2 Babson, Roger, 280–81 Babson College, 280 baby examples, 158–59 Bachelier, Louis, 80–82, 88, 90, 279, 324 Bacon, Kevin, 314–15, 334.


pages: 918 words: 257,605

The Age of Surveillance Capitalism by Shoshana Zuboff

"World Economic Forum" Davos, algorithmic bias, Amazon Web Services, Andrew Keen, augmented reality, autonomous vehicles, barriers to entry, Bartolomé de las Casas, behavioural economics, Berlin Wall, Big Tech, bitcoin, blockchain, blue-collar work, book scanning, Broken windows theory, California gold rush, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, choice architecture, citizen journalism, Citizen Lab, classic study, cloud computing, collective bargaining, Computer Numeric Control, computer vision, connected car, context collapse, corporate governance, corporate personhood, creative destruction, cryptocurrency, data science, deep learning, digital capitalism, disinformation, dogs of the Dow, don't be evil, Donald Trump, Dr. Strangelove, driverless car, Easter island, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, facts on the ground, fake news, Ford Model T, Ford paid five dollars a day, future of work, game design, gamification, Google Earth, Google Glasses, Google X / Alphabet X, Herman Kahn, hive mind, Ian Bogost, impulse control, income inequality, information security, Internet of things, invention of the printing press, invisible hand, Jean Tirole, job automation, Johann Wolfgang von Goethe, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Kevin Roose, knowledge economy, Lewis Mumford, linked data, longitudinal study, low skilled workers, Mark Zuckerberg, market bubble, means of production, multi-sided market, Naomi Klein, natural language processing, Network effects, new economy, Occupy movement, off grid, off-the-grid, PageRank, Panopticon Jeremy Bentham, pattern recognition, Paul Buchheit, performance metric, Philip Mirowski, precision agriculture, price mechanism, profit maximization, profit motive, public intellectual, recommendation engine, refrigerator car, RFID, Richard Thaler, ride hailing / ride sharing, Robert Bork, Robert Mercer, Salesforce, Second Machine Age, self-driving car, sentiment analysis, shareholder value, Sheryl Sandberg, Shoshana Zuboff, Sidewalk Labs, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, slashdot, smart cities, Snapchat, social contagion, social distancing, social graph, social web, software as a service, speech recognition, statistical model, Steve Bannon, Steve Jobs, Steven Levy, structural adjustment programs, surveillance capitalism, technological determinism, TED Talk, The Future of Employment, The Wealth of Nations by Adam Smith, Tim Cook: Apple, two-sided market, union organizing, vertical integration, Watson beat the top human players on Jeopardy!, winner-take-all economy, Wolfgang Streeck, work culture , Yochai Benkler, you are the product

The machine hive—the confluent mind created by machine learning—is the material means to the final elimination of the chaotic elements that interfere with guaranteed outcomes. Eric Schmidt and Sebastian Thrun, the machine intelligence guru who once directed Google’s X Lab and helped lead the development of Street View and Google’s self-driving car, make this point in championing Alphabet’s autonomous vehicles. “Let’s stop freaking out about artificial intelligence,” they write. Schmidt and Thrun emphasize the “crucial insight that differentiates AI from the way people learn.”32 Instead of the typical assurances that machines can be designed to be more like human beings and therefore less threatening, Schmidt and Thrun argue just the opposite: it is necessary for people to become more machine-like.

Nadella did not fail to remind his audience of the crushing velocity that drives the instrumentarian project in an explosion of shock and awe, especially in the years since surveillance capitalism came to dominate digital services: internet traffic increased by a factor of 17.5 million over 1992’s 100 gigabytes per day; 90 percent of the data in 2017 was generated in the prior two years; a single autonomous car will generate 100 gigabytes per second; there will be an estimated 25 billion intelligent devices by 2020. “It’s stunning to see the progress across the depth and breadth of our society and economy and how digital technology is so pervasive.… It’s about what you can do with that technology to have broad impact.”


pages: 391 words: 71,600

Hit Refresh: The Quest to Rediscover Microsoft's Soul and Imagine a Better Future for Everyone by Satya Nadella, Greg Shaw, Jill Tracie Nichols

3D printing, AlphaGo, Amazon Web Services, anti-globalists, artificial general intelligence, augmented reality, autonomous vehicles, basic income, Bretton Woods, business process, cashless society, charter city, cloud computing, complexity theory, computer age, computer vision, corporate social responsibility, crowdsourcing, data science, DeepMind, Deng Xiaoping, Donald Trump, Douglas Engelbart, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, equal pay for equal work, everywhere but in the productivity statistics, fault tolerance, fulfillment center, Gini coefficient, global supply chain, Google Glasses, Grace Hopper, growth hacking, hype cycle, industrial robot, Internet of things, Jeff Bezos, job automation, John Markoff, John von Neumann, knowledge worker, late capitalism, Mars Rover, Minecraft, Mother of all demos, Neal Stephenson, NP-complete, Oculus Rift, pattern recognition, place-making, Richard Feynman, Robert Gordon, Robert Solow, Ronald Reagan, Salesforce, Second Machine Age, self-driving car, side project, Silicon Valley, Skype, Snapchat, Snow Crash, special economic zone, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, subscription business, TED Talk, telepresence, telerobotics, The Rise and Fall of American Growth, The Soul of a New Machine, Tim Cook: Apple, trade liberalization, two-sided market, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, young professional, zero-sum game

While there is no clear road map for what lies ahead, in previous industrial revolutions we’ve seen society transition, not always smoothly, through a series of phases. First, we invent and design the technologies of transformation, which is where we are today. Second, we retrofit for the future. We’ll be entering this phase shortly. For example, drone pilots will need training; conversion of traditional cars into autonomous vehicles will require redesign and rebuilding. Third, we navigate distortion, dissonance, and dislocation. This phase will raise challenging new questions. What is a radiologist’s job when the machines can read the X-ray better? What is the function of a lawyer when computers can detect legal patterns in millions of documents that no human can spot?


pages: 97 words: 31,550

Money: Vintage Minis by Yuval Noah Harari

23andMe, agricultural Revolution, algorithmic trading, AlphaGo, Anne Wojcicki, autonomous vehicles, British Empire, call centre, credit crunch, DeepMind, European colonialism, Flash crash, Ford Model T, greed is good, job automation, joint-stock company, joint-stock limited liability company, lifelogging, low interest rates, Nick Bostrom, pattern recognition, peak-end rule, Ponzi scheme, self-driving car, Suez canal 1869, telemarketer, The future is already here, The Future of Employment, The Wealth of Nations by Adam Smith, trade route, transatlantic slave trade, Watson beat the top human players on Jeopardy!, zero-sum game

His mind may expand in awe as he looks up at the stars and contemplates the mysteries of the universe. His eyes may fill with tears of joy when he sees his baby girl taking her very first step. But the system doesn’t need all that from a taxi driver. All it really wants is to bring passengers from point A to point B as quickly, safely and cheaply as possible. And the autonomous car will soon be able to do that far better than a human driver, even though it cannot enjoy music or be awestruck by the magic of existence. We should remind ourselves of the fate of horses during the Industrial Revolution. An ordinary farm horse can smell, love, recognise faces, jump over fences and do a thousand other things far better than a Model T Ford or a million-dollar Lamborghini.


pages: 391 words: 105,382

Utopia Is Creepy: And Other Provocations by Nicholas Carr

Abraham Maslow, Air France Flight 447, Airbnb, Airbus A320, AltaVista, Amazon Mechanical Turk, augmented reality, autonomous vehicles, Bernie Sanders, book scanning, Brewster Kahle, Buckminster Fuller, Burning Man, Captain Sullenberger Hudson, centralized clearinghouse, Charles Lindbergh, cloud computing, cognitive bias, collaborative consumption, computer age, corporate governance, CRISPR, crowdsourcing, Danny Hillis, data science, deskilling, digital capitalism, digital map, disruptive innovation, Donald Trump, driverless car, Electric Kool-Aid Acid Test, Elon Musk, Evgeny Morozov, factory automation, failed state, feminist movement, Frederick Winslow Taylor, friendly fire, game design, global village, Google bus, Google Glasses, Google X / Alphabet X, Googley, hive mind, impulse control, indoor plumbing, interchangeable parts, Internet Archive, invention of movable type, invention of the steam engine, invisible hand, Isaac Newton, Jeff Bezos, jimmy wales, Joan Didion, job automation, John Perry Barlow, Kevin Kelly, Larry Ellison, Lewis Mumford, lifelogging, lolcat, low skilled workers, machine readable, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Max Levchin, means of production, Menlo Park, mental accounting, natural language processing, Neal Stephenson, Network effects, new economy, Nicholas Carr, Nick Bostrom, Norman Mailer, off grid, oil shale / tar sands, Peter Thiel, plutocrats, profit motive, Ralph Waldo Emerson, Ray Kurzweil, recommendation engine, Republic of Letters, robot derives from the Czech word robota Czech, meaning slave, Ronald Reagan, scientific management, self-driving car, SETI@home, side project, Silicon Valley, Silicon Valley ideology, Singularitarianism, Snapchat, social graph, social web, speech recognition, Startup school, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, technoutopianism, TED Talk, the long tail, the medium is the message, theory of mind, Turing test, Tyler Cowen, Whole Earth Catalog, Y Combinator, Yochai Benkler

And they’re blinding themselves to the social and cultural challenges they’re going to face as they try to convince people to be passengers rather than drivers. Even if all the technical hurdles to achieving perfect vehicular automation are overcome—and despite rosy predictions, that remains a sizable if—the developers of autonomous cars are going to discover that the psychology of driving is far more complicated than they assume and far different from the psychology of being a passenger. Back in the 1970s, the public rebelled, en masse, when the federal government, for seemingly solid safety and fuel-economy reasons, imposed a national fifty-five-mile-per-hour speed limit.

Pilots, physicians, and other professionals routinely navigate unexpected dangers with great aplomb but little credit. Even in our daily routines, we perform feats of perception and skill that lie far beyond the capacity of the sharpest computers. Google is quick to tell us about how few accidents its autonomous cars are involved in, but it doesn’t trumpet the many times the cars’ backup drivers have had to take the wheel to steer the machines out of danger. Computers are wonderful at following instructions, but they’re lousy at improvisation. They resemble, in the words of computer scientist Hector Levesque, “idiot savants” who are “hopeless outside their area of expertise.”


pages: 611 words: 188,732

Valley of Genius: The Uncensored History of Silicon Valley (As Told by the Hackers, Founders, and Freaks Who Made It Boom) by Adam Fisher

adjacent possible, Airbnb, Albert Einstein, AltaVista, An Inconvenient Truth, Andy Rubin, AOL-Time Warner, Apple II, Apple Newton, Apple's 1984 Super Bowl advert, augmented reality, autonomous vehicles, Bill Atkinson, Bob Noyce, Brownian motion, Buckminster Fuller, Burning Man, Byte Shop, circular economy, cognitive dissonance, Colossal Cave Adventure, Computer Lib, disintermediation, Do you want to sell sugared water for the rest of your life?, don't be evil, Donald Trump, Douglas Engelbart, driverless car, dual-use technology, Dynabook, Elon Musk, Fairchild Semiconductor, fake it until you make it, fake news, frictionless, General Magic , glass ceiling, Hacker Conference 1984, Hacker Ethic, Henry Singleton, Howard Rheingold, HyperCard, hypertext link, index card, informal economy, information retrieval, Ivan Sutherland, Jaron Lanier, Jeff Bezos, Jeff Rulifson, John Markoff, John Perry Barlow, Jony Ive, Kevin Kelly, Kickstarter, knowledge worker, Larry Ellison, life extension, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Marshall McLuhan, Maui Hawaii, Menlo Park, Metcalfe’s law, Mondo 2000, Mother of all demos, move fast and break things, Neal Stephenson, Network effects, new economy, nuclear winter, off-the-grid, PageRank, Paul Buchheit, paypal mafia, peer-to-peer, Peter Thiel, pets.com, pez dispenser, popular electronics, quantum entanglement, random walk, reality distortion field, risk tolerance, Robert Metcalfe, rolodex, Salesforce, self-driving car, side project, Silicon Valley, Silicon Valley startup, skeuomorphism, skunkworks, Skype, Snow Crash, social graph, social web, South of Market, San Francisco, Startup school, Steve Jobs, Steve Jurvetson, Steve Wozniak, Steven Levy, Stewart Brand, Susan Wojcicki, synthetic biology, Ted Nelson, telerobotics, The future is already here, The Hackers Conference, the long tail, the new new thing, Tim Cook: Apple, Tony Fadell, tulip mania, V2 rocket, We are as Gods, Whole Earth Catalog, Whole Earth Review, Y Combinator

Scott Hassan: I like the concept of self-driving cars, but I worry that our legal system can’t really handle them. The problem with autonomous cars is that it’s the manufacturer who is driving that car. Kevin Kelly: Humans should not be allowed to drive! We’re just terrible drivers. In the last twelve months humans killed one million other humans driving. Scott Hassan: If we can bring that down to, like, three a day, that will be amazing, right? It will be a thousand times less, right? But the problem is those three deaths are going to be caused by an autonomous car killing them. And that could sink any company making autonomous cars, because the payout—three per day—that’s a lot of lawsuits.


pages: 602 words: 177,874

Thank You for Being Late: An Optimist's Guide to Thriving in the Age of Accelerations by Thomas L. Friedman

3D printing, additive manufacturing, affirmative action, Airbnb, AltaVista, Amazon Web Services, Anthropocene, Apple Newton, autonomous vehicles, Ayatollah Khomeini, barriers to entry, Berlin Wall, Bernie Sanders, Big Tech, biodiversity loss, bitcoin, blockchain, Bob Noyce, business cycle, business process, call centre, carbon tax, centre right, Chris Wanstrath, Clayton Christensen, clean tech, clean water, cloud computing, cognitive load, corporate social responsibility, creative destruction, CRISPR, crowdsourcing, data science, David Brooks, deep learning, demand response, demographic dividend, demographic transition, Deng Xiaoping, digital divide, disinformation, Donald Trump, dual-use technology, end-to-end encryption, Erik Brynjolfsson, fail fast, failed state, Fairchild Semiconductor, Fall of the Berlin Wall, Ferguson, Missouri, first square of the chessboard / second half of the chessboard, Flash crash, fulfillment center, game design, gig economy, global pandemic, global supply chain, Great Leap Forward, illegal immigration, immigration reform, income inequality, indoor plumbing, intangible asset, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the steam engine, inventory management, Irwin Jacobs: Qualcomm, Jeff Bezos, job automation, John Markoff, John von Neumann, Khan Academy, Kickstarter, knowledge economy, knowledge worker, land tenure, linear programming, Live Aid, low interest rates, low skilled workers, Lyft, Marc Andreessen, Mark Zuckerberg, mass immigration, Maui Hawaii, Menlo Park, Mikhail Gorbachev, mutually assured destruction, Neil Armstrong, Nelson Mandela, ocean acidification, PalmPilot, pattern recognition, planetary scale, power law, pull request, Ralph Waldo Emerson, ransomware, Ray Kurzweil, Richard Florida, ride hailing / ride sharing, Robert Gordon, Ronald Reagan, Salesforce, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, smart cities, Solyndra, South China Sea, Steve Jobs, subscription business, supercomputer in your pocket, synthetic biology, systems thinking, TaskRabbit, tech worker, TED Talk, The Rise and Fall of American Growth, Thomas L Friedman, Tony Fadell, transaction costs, Transnistria, uber lyft, undersea cable, urban decay, urban planning, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y2K, Yogi Berra, zero-sum game

So when I confessed to the Google engineer in the front seat of the autonomous vehicle driving me around how relaxed I felt, she calmly turned away from her laptop—which was tracking every move the car made—and gave me a quote I had never heard as a reporter. “Mr. Friedman,” she said, “the car has no blind spots. Almost all the accidents are drivers rear-ending us because they were not paying attention.” This car has no blind spots! I wrote that down in my reporter’s notebook. Google’s cofounder Sergey Brin picked up the tour when we returned back to X’s headquarters. There, he showed me Google’s prototype of a two-person autonomous vehicle. It does not yet have a name, but it looks like a big egg on wheels or something you would ride up a mountain in on a ski lift.


pages: 230 words: 71,834

Building the Cycling City: The Dutch Blueprint for Urban Vitality by Melissa Bruntlett, Chris Bruntlett

"World Economic Forum" Davos, active transport: walking or cycling, ASML, autonomous vehicles, bike sharing, car-free, crowdsourcing, en.wikipedia.org, fixed-gear, Frank Gehry, Guggenheim Bilbao, intermodal, Jones Act, Loma Prieta earthquake, megacity, new economy, oil shale / tar sands, safety bicycle, side project, Silicon Valley, Skype, smart cities, starchitect, Stop de Kindermoord, the built environment, the High Line, transit-oriented development, urban planning, urban renewal, wikimedia commons

The approach so far has been to react to a disruptive problem after it becomes one—as has been the case for diesel-powered mopeds and speed pedelecs (electric-assisted bicycles that travel upwards of 45 km/h [28 mph]) using the cycle tracks—instead of looking forward and creating policies and visions to protect what it is the people want their city to be. Te Brömmelstroet also notes that Amsterdam is experiencing similar pressures to those challenging other global cities: as economic growth increases, it creates a push for greater investments in public transportation and new innovations in the field—such as autonomous vehicles—many of which could have an adverse effect on cycling. As engineers focus on improving travel times and efficiency, there is the potential to lose something much more valuable—the cohesiveness created by social interaction within a city. “In the seventies, Amsterdam citizens demanded a social city designed for the people who live there and not the ones who travel through it,” he recalls.


pages: 238 words: 73,824

Makers by Chris Anderson

3D printing, Airbnb, Any sufficiently advanced technology is indistinguishable from magic, Apple II, autonomous vehicles, barriers to entry, Buckminster Fuller, Build a better mousetrap, business process, carbon tax, commoditize, company town, Computer Numeric Control, crowdsourcing, dark matter, David Ricardo: comparative advantage, deal flow, death of newspapers, dematerialisation, digital capitalism, DIY culture, drop ship, Elon Musk, factory automation, Firefox, Ford Model T, future of work, global supply chain, global village, hockey-stick growth, hype cycle, IKEA effect, industrial robot, interchangeable parts, Internet of things, inventory management, James Hargreaves, James Watt: steam engine, Jeff Bezos, job automation, Joseph Schumpeter, Kickstarter, Lean Startup, manufacturing employment, Mark Zuckerberg, means of production, Menlo Park, Neal Stephenson, Network effects, planned obsolescence, private spaceflight, profit maximization, QR code, race to the bottom, Richard Feynman, Ronald Coase, Rubik’s Cube, Scaled Composites, self-driving car, Sheryl Sandberg, side project, Silicon Valley, Silicon Valley startup, Skype, slashdot, South of Market, San Francisco, SpaceShipOne, spinning jenny, Startup school, stem cell, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, supply-chain management, the long tail, The Nature of the Firm, The Wealth of Nations by Adam Smith, TikTok, Tragedy of the Commons, transaction costs, trickle-down economics, vertical integration, Virgin Galactic, Whole Earth Catalog, X Prize, Y Combinator

Many of them were actually designed by customers and simply reviewed and improved by Sparkfun engineers to make them easier to manufacture. It’s a classic community-centric company. The front of its website features not products but its blog, with chatty tutorials and videos from its employees. Its forums are full of customers helping one another. Every year Sparkfun throws an autonomous vehicle competition, featuring a live band playing robot-themed songs of its own composition, and lots of kids chasing self-driving cars (I’ve been competing in the aerial category every year since it started—no wins yet). At Maker festivals around the country, Sparkfun engineers teach people how to solder, which is actually a lot more fun than it may sound.


pages: 256 words: 73,068

12 Bytes: How We Got Here. Where We Might Go Next by Jeanette Winterson

"Margaret Hamilton" Apollo, "World Economic Forum" Davos, 3D printing, Ada Lovelace, Airbnb, Albert Einstein, Alignment Problem, Amazon Mechanical Turk, Anthropocene, Apollo 11, Apple's 1984 Super Bowl advert, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, basic income, Big Tech, bitcoin, Bletchley Park, blockchain, Boston Dynamics, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Charles Babbage, computer age, Computing Machinery and Intelligence, coronavirus, COVID-19, CRISPR, cryptocurrency, dark matter, Dava Sobel, David Graeber, deep learning, deskilling, digital rights, discovery of DNA, Dominic Cummings, Donald Trump, double helix, driverless car, Elon Musk, fake news, flying shuttle, friendly AI, gender pay gap, global village, Grace Hopper, Gregor Mendel, hive mind, housing crisis, Internet of things, Isaac Newton, Jacquard loom, James Hargreaves, Jeff Bezos, Johannes Kepler, John von Neumann, Joseph-Marie Jacquard, Kickstarter, Large Hadron Collider, life extension, lockdown, lone genius, Mark Zuckerberg, means of production, microdosing, more computing power than Apollo, move fast and break things, natural language processing, Nick Bostrom, Norbert Wiener, off grid, OpenAI, operation paperclip, packet switching, Peter Thiel, pink-collar, Plato's cave, public intellectual, QAnon, QWERTY keyboard, Ray Kurzweil, rewilding, ride hailing / ride sharing, Rutger Bregman, Sam Altman, self-driving car, sharing economy, Sheryl Sandberg, Shoshana Zuboff, Silicon Valley, Skype, Snapchat, SoftBank, SpaceX Starlink, speech recognition, spinning jenny, stem cell, Stephen Hawking, Steve Bannon, Steve Jobs, Steven Levy, Steven Pinker, superintelligent machines, surveillance capitalism, synthetic biology, systems thinking, tech billionaire, tech worker, TED Talk, telepresence, telepresence robot, TikTok, trade route, Turing test, universal basic income, Virgin Galactic, Watson beat the top human players on Jeopardy!, women in the workforce, Y Combinator

And do we favour pedestrians over passengers? The least damage to me in the pod or to you on the pavement? What if a dog runs into the road, and the car needs to brake, at the same time as I need to accelerate to stop someone shunting me from the rear? In such situations a human makes a split-second decision. Autonomous vehicles will have to be programmed ethically – if that turns out to be the right word – ahead of time, and they will run according to programme … until the spooky day when they rewrite their own programme. And drive us all off the cliff we deserve. * * * Cars you can still pilot yourself will be factory-fitted with smart sensors.


pages: 421 words: 110,406

Platform Revolution: How Networked Markets Are Transforming the Economy--And How to Make Them Work for You by Sangeet Paul Choudary, Marshall W. van Alstyne, Geoffrey G. Parker

3D printing, Affordable Care Act / Obamacare, Airbnb, Alvin Roth, Amazon Mechanical Turk, Amazon Web Services, Andrei Shleifer, Apple's 1984 Super Bowl advert, autonomous vehicles, barriers to entry, Benchmark Capital, big data - Walmart - Pop Tarts, bitcoin, blockchain, business cycle, business logic, business process, buy low sell high, chief data officer, Chuck Templeton: OpenTable:, clean water, cloud computing, connected car, corporate governance, crowdsourcing, data acquisition, data is the new oil, data science, digital map, discounted cash flows, disintermediation, driverless car, Edward Glaeser, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, financial innovation, Free Software Foundation, gigafactory, growth hacking, Haber-Bosch Process, High speed trading, independent contractor, information asymmetry, Internet of things, inventory management, invisible hand, Jean Tirole, Jeff Bezos, jimmy wales, John Markoff, Kevin Roose, Khan Academy, Kickstarter, Lean Startup, Lyft, Marc Andreessen, market design, Max Levchin, Metcalfe’s law, multi-sided market, Network effects, new economy, PalmPilot, payday loans, peer-to-peer lending, Peter Thiel, pets.com, pre–internet, price mechanism, recommendation engine, RFID, Richard Stallman, ride hailing / ride sharing, Robert Metcalfe, Ronald Coase, Salesforce, Satoshi Nakamoto, search costs, self-driving car, shareholder value, sharing economy, side project, Silicon Valley, Skype, smart contracts, smart grid, Snapchat, social bookmarking, social contagion, software is eating the world, Steve Jobs, TaskRabbit, The Chicago School, the long tail, the payments system, Tim Cook: Apple, transaction costs, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, vertical integration, winner-take-all economy, zero-sum game, Zipcar

Kara Swisher, “Man and Uber Man,” Vanity Fair, December 2014; Jessica Kwong, “Head of SF Taxis to Retire,” San Francisco Examiner, May 30, 2014; Alison Griswold, “The Million-Dollar New York City Taxi Medallion May Be a Thing of the Past,” Slate, December 1, 2014, http://www.slate.com/blogs/moneybox/2014/12/01/new_york_taxi_medallions_did_tlc_transaction_data_inflate_the_price_of_driving.html. 3. Swisher, “Man and Uber Man.” 4. Zack Kanter, “How Uber’s Autonomous Cars Will Destroy 10 Million Jobs and Reshape the Economy by 2025,” CBS SF Bay Area, sanfrancisco.cbslocal.com/2015/01/27/how-ubers-autonomous-cars-will-destroy-10-million-jobs-and-reshape-the-economy-by-2025-lyft-google-zack-kanter/. 5. Swisher, “Man and Uber Man.” 6. Marc Andreessen, “Why Software Is Eating the World,” Wall Street Journal, August 20, 2011, http://www.wsj.com/articles/SB10001424053111903480904576512250915629460. 7.


pages: 138 words: 40,787

The Silent Intelligence: The Internet of Things by Daniel Kellmereit, Daniel Obodovski

Airbnb, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, business intelligence, call centre, Clayton Christensen, cloud computing, commoditize, connected car, crowdsourcing, data acquisition, driverless car, en.wikipedia.org, Erik Brynjolfsson, first square of the chessboard, first square of the chessboard / second half of the chessboard, Freestyle chess, Google X / Alphabet X, Internet of things, lifelogging, Metcalfe’s law, Network effects, Paul Graham, Ray Kurzweil, RFID, Robert Metcalfe, Salesforce, self-driving car, Silicon Valley, smart cities, smart grid, software as a service, Steve Jobs, The future is already here, the long tail, Tony Fadell, vertical integration, web application, Y Combinator, yield management

That is absolutely going to happen in the next decade. I believe that very strongly. Whether Google does it or not, reasonable people could disagree, but whether that generally is going to happen, that I feel very strongly about. In reality, the Google-X driverless car project has demonstrated that autonomous cars can already navigate traffic and follow the speed limit. And that’s just the tip of the proverbial iceberg. Air traffic control might be another area to see more automation soon, as planes learn to communicate better with the ground and with each other, avoiding humans in the process. Astro says, “I bet you there are a lot of places where that’s going to be one of the big pieces of news — the radically changing rhythms, styles, interaction modalities between people and computers for solving hard, big, distributed problems.”


pages: 309 words: 84,038

Bike Boom: The Unexpected Resurgence of Cycling by Carlton Reid

1960s counterculture, autonomous vehicles, Beeching cuts, bike sharing, California gold rush, car-free, cognitive dissonance, driverless car, Ford Model T, Haight Ashbury, Jane Jacobs, Kickstarter, military-industrial complex, peak oil, Ponzi scheme, Silicon Valley, Skype, Steve Jobs, Steven Pinker, Stop de Kindermoord, the built environment, The Death and Life of Great American Cities, Traffic in Towns by Colin Buchanan, urban planning, urban renewal, Whole Earth Catalog, Yom Kippur War

Carmageddon awaits for those cities that aren’t planning for a vehicle-free future. Driverless cars will work well on grade-separated, go-faster motor roads, but they have a far less certain future in city centers where cyclists and pedestrians could easily block their progress, thanks to the knowledge that autonomous vehicles are programmed not to bully humans out of the way, resulting in what risk specialist John Adams calls “deferential paralysis.” Even if pedestrians didn’t play “chicken” in front of them, driverless cars would still be wrong for cities: if even a quarter of the 1.3 million daily commuters into central London switched to driverless cars, the roads would seize solid.


pages: 301 words: 85,263

New Dark Age: Technology and the End of the Future by James Bridle

AI winter, Airbnb, Alfred Russel Wallace, AlphaGo, Anthropocene, Automated Insights, autonomous vehicles, back-to-the-land, Benoit Mandelbrot, Bernie Sanders, bitcoin, Boeing 747, British Empire, Brownian motion, Buckminster Fuller, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, coastline paradox / Richardson effect, cognitive bias, cognitive dissonance, combinatorial explosion, computer vision, congestion charging, cryptocurrency, data is the new oil, disinformation, Donald Trump, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dr. Strangelove, drone strike, Edward Snowden, Eyjafjallajökull, Fairchild Semiconductor, fake news, fear of failure, Flash crash, fulfillment center, Google Earth, Greyball, Haber-Bosch Process, Higgs boson, hive mind, income inequality, informal economy, Internet of things, Isaac Newton, ITER tokamak, James Bridle, John von Neumann, Julian Assange, Kickstarter, Kim Stanley Robinson, Large Hadron Collider, late capitalism, Laura Poitras, Leo Hollis, lone genius, machine translation, mandelbrot fractal, meta-analysis, Minecraft, mutually assured destruction, natural language processing, Network effects, oil shock, p-value, pattern recognition, peak oil, recommendation engine, road to serfdom, Robert Mercer, Ronald Reagan, security theater, self-driving car, Seymour Hersh, Silicon Valley, Silicon Valley ideology, Skype, social graph, sorting algorithm, South China Sea, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stem cell, Stuxnet, technoutopianism, the built environment, the scientific method, Uber for X, undersea cable, University of East Anglia, uranium enrichment, Vannevar Bush, warehouse robotics, WikiLeaks

Thanks to the pervasive availability of network access and the self-replicating nature of corporate and centralising code, more and more daily activities become dependent on their accompanying software. Daily, even private, travel is reliant on satellite routing, traffic information, and increasingly ‘autonomous’ vehicles – which, of course, are not autonomous at all, requiring constant updates and input to proceed. Labour is increasingly coded, whether by end-to-end logistics systems or email servers, which in turn require constant attention and monitoring by workers who are dependent upon them. Our social lives are mediated through connectivity and algorithmic revision.


pages: 252 words: 78,780

Lab Rats: How Silicon Valley Made Work Miserable for the Rest of Us by Dan Lyons

"Friedman doctrine" OR "shareholder theory", "Susan Fowler" uber, "World Economic Forum" Davos, Airbnb, Amazon Robotics, Amazon Web Services, antiwork, Apple II, augmented reality, autonomous vehicles, basic income, Big Tech, bitcoin, blockchain, Blue Ocean Strategy, business process, call centre, Cambridge Analytica, Clayton Christensen, clean water, collective bargaining, corporate governance, corporate social responsibility, creative destruction, cryptocurrency, data science, David Heinemeier Hansson, digital rights, Donald Trump, Elon Musk, Ethereum, ethereum blockchain, fake news, full employment, future of work, gig economy, Gordon Gekko, greed is good, Hacker News, hiring and firing, holacracy, housing crisis, impact investing, income inequality, informal economy, initial coin offering, Jeff Bezos, job automation, job satisfaction, job-hopping, John Gruber, John Perry Barlow, Joseph Schumpeter, junk bonds, Kanban, Kevin Kelly, knowledge worker, Larry Ellison, Lean Startup, loose coupling, Lyft, Marc Andreessen, Mark Zuckerberg, McMansion, Menlo Park, Milgram experiment, minimum viable product, Mitch Kapor, move fast and break things, new economy, Panopticon Jeremy Bentham, Parker Conrad, Paul Graham, paypal mafia, Peter Thiel, plutocrats, precariat, prosperity theology / prosperity gospel / gospel of success, public intellectual, RAND corporation, remote working, RFID, ride hailing / ride sharing, Ronald Reagan, Rubik’s Cube, Ruby on Rails, Sam Altman, San Francisco homelessness, Sand Hill Road, scientific management, self-driving car, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, six sigma, Skinner box, Skype, Social Responsibility of Business Is to Increase Its Profits, SoftBank, software is eating the world, Stanford prison experiment, stem cell, Steve Jobs, Steve Wozniak, Stewart Brand, stock buybacks, super pumped, TaskRabbit, tech bro, tech worker, TechCrunch disrupt, TED Talk, telemarketer, Tesla Model S, Thomas Davenport, Tony Hsieh, Toyota Production System, traveling salesman, Travis Kalanick, tulip mania, Uber and Lyft, Uber for X, uber lyft, universal basic income, web application, WeWork, Whole Earth Catalog, work culture , workplace surveillance , Y Combinator, young professional, Zenefits

Though Ford generates thirteen times as much revenue as Tesla and sells sixty-six times as many vehicles, Tesla’s stock market valuation is about the same as Ford’s. Tesla’s soaring stock is held up mostly by hype. CEO Elon Musk is a masterful marketer, a genius at generating buzz. But Tesla also has raced ahead of Detroit in developing the two biggest new car technologies: electric motors and autonomous vehicles. Tesla is not the only Silicon Valley company that threatens Ford. Google and Uber are working on self-driving cars. Apple is rumored to be operating a secret automotive laboratory. The Silicon Valley guys realize that transportation is becoming a technology business. Self-driving cars depend on artificial intelligence, which means sensors and lots of software, stuff they know how to do.


pages: 302 words: 85,877

Cult of the Dead Cow: How the Original Hacking Supergroup Might Just Save the World by Joseph Menn

"World Economic Forum" Davos, 4chan, A Declaration of the Independence of Cyberspace, Andy Rubin, Apple II, autonomous vehicles, Berlin Wall, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, Cambridge Analytica, Chelsea Manning, Citizen Lab, commoditize, corporate governance, digital rights, disinformation, Donald Trump, dumpster diving, Edward Snowden, end-to-end encryption, fake news, Firefox, Gabriella Coleman, Google Chrome, Haight Ashbury, independent contractor, information security, Internet of things, Jacob Appelbaum, Jason Scott: textfiles.com, John Gilmore, John Markoff, John Perry Barlow, Julian Assange, Laura Poitras, machine readable, Mark Zuckerberg, military-industrial complex, Mitch Kapor, Mondo 2000, Naomi Klein, NSO Group, Peter Thiel, pirate software, pre–internet, Ralph Nader, ransomware, Richard Stallman, Robert Mercer, Russian election interference, self-driving car, Sheryl Sandberg, side project, Silicon Valley, Skype, slashdot, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Stuxnet, tech worker, Whole Earth Catalog, WikiLeaks, zero day

Cofounder Brandon Brewer, once known as Sid Vicious, is as straight as it gets: senior vice president of real estate–services firm Republic Title, based in Fort Worth. Sam Anthony went to work as a programmer in a Harvard University lab, then started graduate school there, working on biological models for computation. He earned a PhD in 2018. Along the way, he cofounded a self-driving car technology company, Perceptive Automata. Autonomous vehicles “are super good at knowing where the road is, how fast the car is going, whether something’s a tree or a person,” Sam explained. “They’re miserably bad at solving the psychology problem of guessing what’s in a human’s head. The techniques we developed while I was doing my PhD are perfect for situations where you want machine learning to do something where humans are incredible.”


pages: 309 words: 81,975

Brave New Work: Are You Ready to Reinvent Your Organization? by Aaron Dignan

"Friedman doctrine" OR "shareholder theory", Abraham Maslow, activist fund / activist shareholder / activist investor, adjacent possible, Airbnb, Albert Einstein, autonomous vehicles, basic income, benefit corporation, Bertrand Russell: In Praise of Idleness, bitcoin, Black Lives Matter, Black Swan, blockchain, Buckminster Fuller, Burning Man, butterfly effect, cashless society, Clayton Christensen, clean water, cognitive bias, cognitive dissonance, content marketing, corporate governance, corporate social responsibility, correlation does not imply causation, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, David Heinemeier Hansson, deliberate practice, DevOps, disruptive innovation, don't be evil, Elon Musk, endowment effect, Ethereum, ethereum blockchain, financial engineering, Frederick Winslow Taylor, fulfillment center, future of work, gender pay gap, Geoffrey West, Santa Fe Institute, gig economy, Goodhart's law, Google X / Alphabet X, hiring and firing, hive mind, holacracy, impact investing, income inequality, information asymmetry, Internet of things, Jeff Bezos, job satisfaction, Kanban, Kevin Kelly, Kickstarter, Lean Startup, loose coupling, loss aversion, Lyft, Marc Andreessen, Mark Zuckerberg, minimum viable product, mirror neurons, new economy, Paul Graham, Quicken Loans, race to the bottom, reality distortion field, remote working, Richard Thaler, Rochdale Principles, Salesforce, scientific management, shareholder value, side hustle, Silicon Valley, single source of truth, six sigma, smart contracts, Social Responsibility of Business Is to Increase Its Profits, software is eating the world, source of truth, Stanford marshmallow experiment, Steve Jobs, subprime mortgage crisis, systems thinking, TaskRabbit, TED Talk, The future is already here, the High Line, too big to fail, Toyota Production System, Tragedy of the Commons, uber lyft, universal basic income, WeWork, Y Combinator, zero-sum game

If I showed you a house, a car, a dress, or a phone from 1910 and asked you whether it was modern or antique, you’d have a pretty good idea. Because almost everything has changed. But not management. Not really. Information flows up. Decisions flow down. A place for everyone, and everyone in their place. Somehow, amid a period of relentless innovation, including the internet, mobile computing, autonomous vehicles, artificial intelligence, and rockets to space that can land themselves, the way we come together as human beings to solve problems and invent our future has stayed remarkably constant. Which means one of two things is true: either we’ve perfected the way we organize, and we should all submit to the power of the pyramid, or we’re stuck in a Gordian knot of our own design, unable to break free and realize a better way.


pages: 322 words: 84,580

The Economics of Belonging: A Radical Plan to Win Back the Left Behind and Achieve Prosperity for All by Martin Sandbu

air traffic controllers' union, Airbnb, Alan Greenspan, autonomous vehicles, balance sheet recession, bank run, banking crisis, basic income, Berlin Wall, Bernie Sanders, Big Tech, Boris Johnson, Branko Milanovic, Bretton Woods, business cycle, call centre, capital controls, carbon footprint, carbon tax, Carmen Reinhart, centre right, collective bargaining, company town, debt deflation, deindustrialization, deskilling, Diane Coyle, Donald Trump, Edward Glaeser, eurozone crisis, Fall of the Berlin Wall, financial engineering, financial intermediation, full employment, future of work, gig economy, Gini coefficient, green new deal, hiring and firing, income inequality, income per capita, industrial robot, intangible asset, job automation, John Maynard Keynes: technological unemployment, Kenneth Rogoff, knowledge economy, knowledge worker, labour market flexibility, liquidity trap, longitudinal study, low interest rates, low skilled workers, manufacturing employment, Martin Wolf, meta-analysis, mini-job, Money creation, mortgage debt, new economy, offshore financial centre, oil shock, open economy, pattern recognition, pink-collar, precariat, public intellectual, quantitative easing, race to the bottom, Richard Florida, Robert Shiller, Robert Solow, Ronald Reagan, secular stagnation, social intelligence, TaskRabbit, total factor productivity, universal basic income, very high income, winner-take-all economy, working poor

Machine-driven productivity growth put paid to most physical production jobs that existed in 1970 (and as the next chapter shows, would have done so even in the absence of globalisation). Automation and digitisation are now doing the same to a swath of service jobs—including some done largely by the sort of people who would formerly have taken manual factory jobs. Autonomous vehicles are an important example. Truck driving is, by some measures, the most common single profession in the United States, and one of the few providing decently paid jobs to men with little formal education. When the self-driving truck becomes technically and commercially viable, it will make most of them obsolete.


pages: 297 words: 84,447

The Star Builders: Nuclear Fusion and the Race to Power the Planet by Arthur Turrell

Albert Einstein, Arthur Eddington, autonomous vehicles, Boeing 747, Boris Johnson, carbon tax, coronavirus, COVID-19, data science, decarbonisation, deep learning, Donald Trump, Eddington experiment, energy security, energy transition, Ernest Rutherford, Extinction Rebellion, green new deal, Greta Thunberg, Higgs boson, Intergovernmental Panel on Climate Change (IPCC), ITER tokamak, Jeff Bezos, Kickstarter, Large Hadron Collider, lockdown, New Journalism, nuclear winter, Peter Thiel, planetary scale, precautionary principle, Project Plowshare, Silicon Valley, social distancing, sovereign wealth fund, statistical model, Stephen Hawking, Steve Bannon, TED Talk, The Rise and Fall of American Growth, Tunguska event

The Centre for Fusion Energy, where the magnetic confinement fusion experiment I’m visiting is located, occupies a large portion of the site. Some of the buildings show their age. Many have lackluster squares of glass alternating with dark green panels. In among the aggressive architecture, there are hints of the future. As I walk toward the building that houses Culham’s star machine, several autonomous vehicles pass me, scanning the quiet roads of the facility with mounted radar. I see signs for firms with names like Reaction Engines (reusable space launch vehicles), GeneFirst (molecular diagnostics), and Neuro-Bio (Alzheimer’s treatments). When I get to the lobby of the Culham Centre for Fusion Energy, I find that it’s also a contradiction in time—it resembles a careworn smoking room last redecorated in the 1970s, and yet vivid printed posters showing the “future of fusion” take up one wall.


pages: 326 words: 84,180

Dark Matters: On the Surveillance of Blackness by Simone Browne

4chan, affirmative action, Affordable Care Act / Obamacare, airport security, autonomous vehicles, bitcoin, British Empire, cloud computing, colonial rule, computer vision, crowdsourcing, dark matter, disinformation, Edward Snowden, European colonialism, ghettoisation, Google Glasses, Internet Archive, job satisfaction, lifelogging, machine readable, mass incarceration, obamacare, Panopticon Jeremy Bentham, pattern recognition, r/findbostonbombers, Scientific racism, security theater, sexual politics, transatlantic slave trade, urban renewal, US Airways Flight 1549, W. E. B. Du Bois, Wayback Machine, Works Progress Administration

It is through this archive and that of black life after the Middle Passage that I want to further complicate understandings of surveillance by questioning how a realization of the conditions of blackness—the historical, the present, and the historical present—can help social theorists understand our contemporary conditions of surveillance. Put another way, rather than seeing surveillance as something inaugurated by new technologies, such as automated facial recognition or unmanned autonomous vehicles (or drones), to see it as ongoing is to insist that we factor in how racism and antiblackness undergird and sustain the intersecting surveillances of our present order. Patricia Hill Collins uses the term “intersectional paradigms” to signal that “oppression cannot be reduced to one fundamental type, and that oppressions work together in producing injustice.”17 Indebted to black feminist scholarship, by “intersecting surveillances” I am referring to the interdependent and interlocking ways that practices, performances, and policies regarding surveillance operate.


pages: 433 words: 127,171

The Grid: The Fraying Wires Between Americans and Our Energy Future by Gretchen Bakke

addicted to oil, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, back-to-the-land, big-box store, Buckminster Fuller, demand response, dematerialisation, distributed generation, electricity market, energy security, energy transition, full employment, Gabriella Coleman, illegal immigration, indoor plumbing, Internet of things, Kickstarter, laissez-faire capitalism, Menlo Park, Neal Stephenson, Negawatt, new economy, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, off grid, off-the-grid, post-oil, profit motive, rolling blackouts, Ronald Reagan, self-driving car, Silicon Valley, smart grid, smart meter, the built environment, too big to fail, Twitter Arab Spring, vertical integration, washing machines reduced drudgery, Whole Earth Catalog

has always been their charm: Though somewhat scalable and somewhat portable, fuel cells suffer from being expensive and from the fact that they do need constant exposure to their fuel (they need to be plugged into natural gas pipelines, for example). it seems, will be electric: Zack Kanter, “Autonomous Cars Will Destroy Millions of Jobs and Reshape U.S. Economy by 2025,” Quartz, May 14, 2015, http://www.nextgov.com/emerging-tech/2015/05/autonomous-cars-will-destroy-millions-jobs-and-reshape-us-economy-2025/112762/. It sounds a little like Marxism: Karl Marx, Critique of the Gotha Program (Rockville, MD: Wildside Press, 2008 [1875]). “when you are getting it fixed”: quoted in Ryan Koronowski, “Why the U.S.


pages: 193 words: 51,445

On the Future: Prospects for Humanity by Martin J. Rees

23andMe, 3D printing, air freight, Alfred Russel Wallace, AlphaGo, Anthropocene, Asilomar, autonomous vehicles, Benoit Mandelbrot, biodiversity loss, blockchain, Boston Dynamics, carbon tax, circular economy, CRISPR, cryptocurrency, cuban missile crisis, dark matter, decarbonisation, DeepMind, Demis Hassabis, demographic transition, Dennis Tito, distributed ledger, double helix, driverless car, effective altruism, Elon Musk, en.wikipedia.org, Geoffrey Hinton, global village, Great Leap Forward, Higgs boson, Hyperloop, Intergovernmental Panel on Climate Change (IPCC), Internet of things, James Webb Space Telescope, Jeff Bezos, job automation, Johannes Kepler, John Conway, Large Hadron Collider, life extension, mandelbrot fractal, mass immigration, megacity, Neil Armstrong, Nick Bostrom, nuclear winter, ocean acidification, off-the-grid, pattern recognition, precautionary principle, quantitative hedge fund, Ray Kurzweil, Recombinant DNA, Rodney Brooks, Search for Extraterrestrial Intelligence, sharing economy, Silicon Valley, smart grid, speech recognition, Stanford marshmallow experiment, Stanislav Petrov, stem cell, Stephen Hawking, Steven Pinker, Stuxnet, supervolcano, technological singularity, the scientific method, Tunguska event, uranium enrichment, Walter Mischel, William MacAskill, Yogi Berra

But what is not so clear is whether automated vehicles will ever be able to operate safely when confronted with all the complexities of routine driving—navigating small, winding roads and sharing city streets with human-driven vehicles and cycles and pedestrians. I think there will be public resistance to this. Would a fully autonomous car be safer than a car with a human driver? If an object obstructs the road ahead, could it distinguish between a paper bag, a dog, or a child? The claim is that it cannot infallibly do so but will do better than the average human driver. Is that true? Some would say yes. If the cars are wirelessly connected to one another, they would learn faster by sharing experiences.


pages: 197 words: 49,296

The Future We Choose: Surviving the Climate Crisis by Christiana Figueres, Tom Rivett-Carnac

3D printing, Airbnb, AlphaGo, Anthropocene, autonomous vehicles, Berlin Wall, biodiversity loss, carbon footprint, circular economy, clean water, David Attenborough, decarbonisation, DeepMind, dematerialisation, Demis Hassabis, disinformation, Donald Trump, driverless car, en.wikipedia.org, Extinction Rebellion, F. W. de Klerk, Fall of the Berlin Wall, Gail Bradbrook, General Motors Futurama, green new deal, Greta Thunberg, high-speed rail, income inequality, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jeff Bezos, job automation, Lyft, Mahatma Gandhi, Marc Benioff, Martin Wolf, mass immigration, Mustafa Suleyman, Nelson Mandela, new economy, ocean acidification, plant based meat, post-truth, rewilding, ride hailing / ride sharing, self-driving car, smart grid, sovereign wealth fund, the scientific method, trade route, uber lyft, urban planning, urban sprawl, Yogi Berra

Office of the Historian, Department of State, “The Collapse of the Soviet Union,” https://history.state.gov/​milestones/​1989-1992/​collapse-soviet-union. 16. “Futurama: ‘Magic City of Progress’ ” in World’s Fair: Enter the World of Tomorrow, Biblion, http://exhibitions.nypl.org/​biblion/​worldsfair/​enter-world-tomorrow-futurama-and-beyond/​story/​story-gmfuturama. 17. Abby Norman, “Aliens, Autonomous Cars, and AI: This Is the World of 2118,” Futurism.com, January 11, 2018, https://futurism.com/​2118-century-predictions; Matthew Claudel and Carlo Ratti, “Full Speed Ahead: How the Driverless Car Could Transform Cities,” McKinsey & Company, August 2015, https://www.mckinsey.com/​business-functions/​sustainability/​our-insights/​full-speed-ahead-how-the-driverless-car-could-transform-cities. 18.


pages: 295 words: 89,430

Small Data: The Tiny Clues That Uncover Huge Trends by Martin Lindstrom

autonomous vehicles, Berlin Wall, big-box store, correlation does not imply causation, driverless car, Edward Snowden, Fall of the Berlin Wall, land reform, Mikhail Gorbachev, Murano, Venice glass, Richard Florida, rolodex, self-driving car, Skype, Snapchat, Steve Jobs, Steven Pinker, too big to fail, urban sprawl

Later, another self-driving Google car found that it wasn’t able to advance through a four-way stop, as its sensors were calibrated to wait for other drivers to make a complete stop, as opposed to inching continuously forward, which most did. Noted the Times, “Researchers in the fledgling field of autonomous vehicles say that one of the biggest challenges facing automated cars is blending them into a world in which humans don’t behave by the book.”15 As accurate, then, as big data can be while connecting millions of data points to generate correlations, big data is often compromised whenever humans act like, well, humans.


pages: 292 words: 87,720

Volt Rush: The Winners and Losers in the Race to Go Green by Henry Sanderson

"World Economic Forum" Davos, activist fund / activist shareholder / activist investor, animal electricity, autonomous vehicles, Boris Johnson, carbon footprint, Carl Icahn, circular economy, commodity super cycle, corporate governance, corporate social responsibility, COVID-19, David Attenborough, decarbonisation, Deng Xiaoping, Dissolution of the Soviet Union, Donald Trump, Elon Musk, energy transition, Extinction Rebellion, Exxon Valdez, Fairphone, Ford Model T, gigafactory, global supply chain, Global Witness, income per capita, Internet of things, invention of the steam engine, Kickstarter, lockdown, megacity, Menlo Park, oil shale / tar sands, planned obsolescence, popular capitalism, purchasing power parity, QR code, reality distortion field, Ronald Reagan, Scramble for Africa, short squeeze, Silicon Valley, Silicon Valley startup, smart grid, sovereign wealth fund, Steve Jobs, supply-chain management, tech billionaire, Tesla Model S, The Chicago School, the new new thing, three-masted sailing ship, Tony Fadell, UNCLOS, WikiLeaks, work culture

The sediment meanwhile is expelled out of giant vents at the back of the vehicle. Van Nijen took me to the back of the vehicle where the electronics were encased in thick titanium and bound together with large bolts, the only part that had to be kept dry at the immense pressures of the deep sea. Patania reminded me of a larger version of the autonomous vehicles used by the US military in Iraq to disarm bombs. The absence of any human on board was jarring, prompting me to think about the miners on land who will be replaced. Not that this robot would care. Van Nijen told me the pressure is so great in the deep sea that there is no way to replicate it onshore in a tank.


pages: 211 words: 55,075

Soft City: Building Density for Everyday Life by David Sim

A Pattern Language, active transport: walking or cycling, anti-fragile, autonomous vehicles, car-free, carbon footprint, Jane Jacobs, megaproject, megastructure, New Urbanism, place-making, smart cities, the built environment, The Death and Life of Great American Cities, the market place, transit-oriented development, urban planning, urban renewal, walkable city

Instead, we need to build relationships. As we face climate change, segregation, congestion, and rapid urbanization, we need to build better relationships with the planet, with people, and with place. Building stand-alone, air-conditioned buildings up in the sky or in gated communities, or building more roads and having autonomous cars won’t connect us to the global challenges or to each other, so that we can ultimately deal with them together. The town or city is a system of relationships, a place where multiple, overlapping systems of different relationships are co-located—public and private, common and individual, formal and informal.


pages: 328 words: 96,141

Rocket Billionaires: Elon Musk, Jeff Bezos, and the New Space Race by Tim Fernholz

Amazon Web Services, Apollo 13, autonomous vehicles, business climate, Charles Lindbergh, Clayton Christensen, cloud computing, Colonization of Mars, corporate governance, corporate social responsibility, deep learning, disruptive innovation, Donald Trump, Elon Musk, fail fast, fulfillment center, Gene Kranz, high net worth, high-speed rail, Iridium satellite, Jeff Bezos, Kickstarter, Kim Stanley Robinson, Kwajalein Atoll, low earth orbit, Marc Andreessen, Mark Zuckerberg, Mars Society, Masayoshi Son, megaproject, military-industrial complex, minimum viable product, multiplanetary species, mutually assured destruction, Neal Stephenson, Neil Armstrong, new economy, no-fly zone, nuclear paranoia, paypal mafia, Peter H. Diamandis: Planetary Resources, Peter Thiel, pets.com, planetary scale, private spaceflight, profit maximization, RAND corporation, Richard Feynman, Richard Feynman: Challenger O-ring, Ronald Reagan, satellite internet, Scaled Composites, shareholder value, Silicon Valley, skunkworks, SoftBank, sovereign wealth fund, space junk, SpaceShipOne, Stephen Hawking, Steve Jobs, Strategic Defense Initiative, trade route, undersea cable, vertical integration, Virgin Galactic, VTOL, We wanted flying cars, instead we got 140 characters, X Prize, Y2K

“In the culture of NASA, we were going to do a big testing program. Elon Musk just tried it. And if it works, it works.” In 2011, after the company had flown its Falcon 9, SpaceX hired an engineer named Lars Blackmore from the Jet Propulsion Lab. A product of MIT, Blackmore was an expert in designing software for autonomous vehicles to navigate extreme environments; one academic project had guided a deep-sea submersible robot, and at JPL he wrote a critical algorithm to guide landers arriving on Mars. His graduate adviser, a NASA veteran himself, said Blackmore would have made a tremendous professor, but he went to SpaceX because it offered “an opportunity for the current generation of engineers to make their vision real.”


pages: 371 words: 98,534

Red Flags: Why Xi's China Is in Jeopardy by George Magnus

"World Economic Forum" Davos, 3D printing, 9 dash line, Admiral Zheng, AlphaGo, Asian financial crisis, autonomous vehicles, balance sheet recession, banking crisis, Bear Stearns, Bretton Woods, Brexit referendum, BRICs, British Empire, business process, capital controls, carbon footprint, Carmen Reinhart, cloud computing, colonial exploitation, corporate governance, crony capitalism, currency manipulation / currency intervention, currency peg, demographic dividend, demographic transition, Deng Xiaoping, Doha Development Round, Donald Trump, financial deregulation, financial innovation, financial repression, fixed income, floating exchange rates, full employment, general purpose technology, Gini coefficient, global reserve currency, Great Leap Forward, high net worth, high-speed rail, hiring and firing, Hyman Minsky, income inequality, industrial robot, information security, Internet of things, invention of movable type, Joseph Schumpeter, Kenneth Rogoff, Kickstarter, labour market flexibility, labour mobility, land reform, Malacca Straits, means of production, megacity, megaproject, middle-income trap, Minsky moment, money market fund, moral hazard, non-tariff barriers, Northern Rock, offshore financial centre, old age dependency ratio, open economy, peer-to-peer lending, pension reform, price mechanism, purchasing power parity, regulatory arbitrage, rent-seeking, reserve currency, rising living standards, risk tolerance, Shenzhen special economic zone , smart cities, South China Sea, sovereign wealth fund, special drawing rights, special economic zone, speech recognition, The Wealth of Nations by Adam Smith, total factor productivity, trade route, urban planning, vertical integration, Washington Consensus, women in the workforce, working-age population, zero-sum game

Xiaomi, which designs and sells smartphones, mobile apps and laptops is a global top five company in smartphones. Alibaba, the e-commerce giant, has a cloud subsidiary that is working on smart cities. Tencent and Baidu, which are internet services companies, are exploring medical imaging and facial recognition, and autonomous vehicles, respectively. Lenovo computers, Air China, and Moutai, the beverage company, also exemplify Chinese companies that got big in China and turned their attention to foreign markets. A strong domestic focus and large home-market size have certainly helped these and other companies, and there’s no question that they have excelled in more efficient production and in adapting imported technologies and products to local consumer tastes.


pages: 346 words: 97,330

Ghost Work: How to Stop Silicon Valley From Building a New Global Underclass by Mary L. Gray, Siddharth Suri

"World Economic Forum" Davos, Affordable Care Act / Obamacare, AlphaGo, Amazon Mechanical Turk, Apollo 13, augmented reality, autonomous vehicles, barriers to entry, basic income, benefit corporation, Big Tech, big-box store, bitcoin, blue-collar work, business process, business process outsourcing, call centre, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, cognitive load, collaborative consumption, collective bargaining, computer vision, corporate social responsibility, cotton gin, crowdsourcing, data is the new oil, data science, deep learning, DeepMind, deindustrialization, deskilling, digital divide, do well by doing good, do what you love, don't be evil, Donald Trump, Elon Musk, employer provided health coverage, en.wikipedia.org, equal pay for equal work, Erik Brynjolfsson, fake news, financial independence, Frank Levy and Richard Murnane: The New Division of Labor, fulfillment center, future of work, gig economy, glass ceiling, global supply chain, hiring and firing, ImageNet competition, independent contractor, industrial robot, informal economy, information asymmetry, Jeff Bezos, job automation, knowledge economy, low skilled workers, low-wage service sector, machine translation, market friction, Mars Rover, natural language processing, new economy, operational security, passive income, pattern recognition, post-materialism, post-work, power law, race to the bottom, Rana Plaza, recommendation engine, ride hailing / ride sharing, Ronald Coase, scientific management, search costs, Second Machine Age, sentiment analysis, sharing economy, Shoshana Zuboff, side project, Silicon Valley, Silicon Valley startup, Skype, software as a service, speech recognition, spinning jenny, Stephen Hawking, TED Talk, The Future of Employment, The Nature of the Firm, Tragedy of the Commons, transaction costs, two-sided market, union organizing, universal basic income, Vilfredo Pareto, Wayback Machine, women in the workforce, work culture , Works Progress Administration, Y Combinator, Yochai Benkler

The company’s Smart Glasses, for example, include a computer chip, cameras, and sensors that can project a 3-D map or repair manuals and can scan for signs of dangerous heat and pressure buildup in piping. It can help an on-site worker patch a complicated hydraulic system with the expertise of an on-demand pipe fitter pitching in. As autonomous vehicles advance, Daqri’s Smart Glasses could be the headpiece of an industrial robot augmented by an on-demand worker controlling robotic arms to complete a dangerous repair. Could BP’s Deepwater Horizon environmental disaster of 2010 have been prevented with a 24/7 on-demand crew tasked with taking turns to monitor all industrial systems and repair them as sensors brought attention to any fitting starting to wear or needing to be replaced?


pages: 372 words: 100,947

An Ugly Truth: Inside Facebook's Battle for Domination by Sheera Frenkel, Cecilia Kang

"World Economic Forum" Davos, 2021 United States Capitol attack, affirmative action, augmented reality, autonomous vehicles, Ben Horowitz, Bernie Sanders, Big Tech, Black Lives Matter, blockchain, Cambridge Analytica, clean water, coronavirus, COVID-19, data science, disinformation, don't be evil, Donald Trump, Edward Snowden, end-to-end encryption, fake news, George Floyd, global pandemic, green new deal, hockey-stick growth, Ian Bogost, illegal immigration, immigration reform, independent contractor, information security, Jeff Bezos, Kevin Roose, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Menlo Park, natural language processing, offshore financial centre, Parler "social media", Peter Thiel, QAnon, RAND corporation, ride hailing / ride sharing, Robert Mercer, Russian election interference, Salesforce, Sam Altman, Saturday Night Live, Sheryl Sandberg, Shoshana Zuboff, Silicon Valley, Snapchat, social web, Steve Bannon, Steve Jobs, Steven Levy, subscription business, surveillance capitalism, TechCrunch disrupt, TikTok, Travis Kalanick, WikiLeaks

The company’s most popular utility may not be status updates and shares, but something like blockchain payments for retail goods or the production and distribution of blockbuster entertainment. With $55 billion in cash reserves, the company has endless options to buy or innovate its way into new lines of business, as Google has with autonomous vehicles and Apple with health devices. Even during his annus horribilis of 2020, Zuckerberg was looking toward the future. In a quest to break into the lucrative field of corporate communications software, in late November, Facebook bought Kustomer for $1 billion. The popularity of the Zoom tool during the pandemic had rankled, and Zuckerberg challenged employees to come up with a videoconferencing rival.


The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do by Erik J. Larson

AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Alignment Problem, AlphaGo, Amazon Mechanical Turk, artificial general intelligence, autonomous vehicles, Big Tech, Black Swan, Bletchley Park, Boeing 737 MAX, business intelligence, Charles Babbage, Claude Shannon: information theory, Computing Machinery and Intelligence, conceptual framework, correlation does not imply causation, data science, deep learning, DeepMind, driverless car, Elon Musk, Ernest Rutherford, Filter Bubble, Geoffrey Hinton, Georg Cantor, Higgs boson, hive mind, ImageNet competition, information retrieval, invention of the printing press, invention of the wheel, Isaac Newton, Jaron Lanier, Jeff Hawkins, John von Neumann, Kevin Kelly, Large Hadron Collider, Law of Accelerating Returns, Lewis Mumford, Loebner Prize, machine readable, machine translation, Nate Silver, natural language processing, Nick Bostrom, Norbert Wiener, PageRank, PalmPilot, paperclip maximiser, pattern recognition, Peter Thiel, public intellectual, Ray Kurzweil, retrograde motion, self-driving car, semantic web, Silicon Valley, social intelligence, speech recognition, statistical model, Stephen Hawking, superintelligent machines, tacit knowledge, technological singularity, TED Talk, The Coming Technological Singularity, the long tail, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, theory of mind, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, Yochai Benkler

And ­there are similar real-­world examples, including autonomous naviga- P rob­lems with D eduction and I nduction 127 tion systems on self-­driving cars that have misclassified a school bus as a snowplow, and a turning truck as an overpass. Machine learning is inductive b­ ecause it acquires knowledge from observation of data. The technique known as deep learning is a type of machine learning—­a neural network—­that has shown much promise in recognizing objects in photos, boosting per­for­mance on autonomous vehicles, and playing seemingly difficult games. For example, Google’s DeepMind system learned to play a number of classic Atari video games to much fanfare. It was heralded as general intelligence, ­because the same system was able to master dif­fer­ent games using the so-­c alled deep reinforcement learning approach that powered AlphaGo and AlphaZero.


Artificial Whiteness by Yarden Katz

affirmative action, AI winter, algorithmic bias, AlphaGo, Amazon Mechanical Turk, autonomous vehicles, benefit corporation, Black Lives Matter, blue-collar work, Californian Ideology, Cambridge Analytica, cellular automata, Charles Babbage, cloud computing, colonial rule, computer vision, conceptual framework, Danny Hillis, data science, David Graeber, deep learning, DeepMind, desegregation, Donald Trump, Dr. Strangelove, driverless car, Edward Snowden, Elon Musk, Erik Brynjolfsson, European colonialism, fake news, Ferguson, Missouri, general purpose technology, gentrification, Hans Moravec, housing crisis, income inequality, information retrieval, invisible hand, Jeff Bezos, Kevin Kelly, knowledge worker, machine readable, Mark Zuckerberg, mass incarceration, Menlo Park, military-industrial complex, Nate Silver, natural language processing, Nick Bostrom, Norbert Wiener, pattern recognition, phenotype, Philip Mirowski, RAND corporation, recommendation engine, rent control, Rodney Brooks, Ronald Reagan, Salesforce, Seymour Hersh, Shoshana Zuboff, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Skype, speech recognition, statistical model, Stephen Hawking, Stewart Brand, Strategic Defense Initiative, surveillance capitalism, talking drums, telemarketer, The Signal and the Noise by Nate Silver, W. E. B. Du Bois, Whole Earth Catalog, WikiLeaks

,” Weizenbaum explained that the military presents its aims in terms vague enough to cover every branch of AI, and practitioners then formulate all their projects to fit within the militaristic frame. As he pointed out, the three wings of DARPA’s Strategic Computing program—the “battle management system,” the “autonomous vehicle,” and the “pilot’s assistant”—were meant to capture “almost every branch of work in AI.”69 Contrary to many of his colleagues, Weizenbaum rejected the idea that the military’s vague rubric gives practitioners intellectual freedom to pursue whatever they like. (This was in fact one of the notions that SFTP worked hard to refute.)


pages: 328 words: 96,678

MegaThreats: Ten Dangerous Trends That Imperil Our Future, and How to Survive Them by Nouriel Roubini

"World Economic Forum" Davos, 2021 United States Capitol attack, 3D printing, 9 dash line, AI winter, AlphaGo, artificial general intelligence, asset allocation, assortative mating, autonomous vehicles, bank run, banking crisis, basic income, Bear Stearns, Big Tech, bitcoin, Bletchley Park, blockchain, Boston Dynamics, Bretton Woods, British Empire, business cycle, business process, call centre, carbon tax, Carmen Reinhart, cashless society, central bank independence, collateralized debt obligation, Computing Machinery and Intelligence, coronavirus, COVID-19, creative destruction, credit crunch, crony capitalism, cryptocurrency, currency manipulation / currency intervention, currency peg, data is the new oil, David Ricardo: comparative advantage, debt deflation, decarbonisation, deep learning, DeepMind, deglobalization, Demis Hassabis, democratizing finance, Deng Xiaoping, disintermediation, Dogecoin, Donald Trump, Elon Musk, en.wikipedia.org, energy security, energy transition, Erik Brynjolfsson, Ethereum, ethereum blockchain, eurozone crisis, failed state, fake news, family office, fiat currency, financial deregulation, financial innovation, financial repression, fixed income, floating exchange rates, forward guidance, Fractional reserve banking, Francis Fukuyama: the end of history, full employment, future of work, game design, geopolitical risk, George Santayana, Gini coefficient, global pandemic, global reserve currency, global supply chain, GPS: selective availability, green transition, Greensill Capital, Greenspan put, Herbert Marcuse, high-speed rail, Hyman Minsky, income inequality, inflation targeting, initial coin offering, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of movable type, Isaac Newton, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, junk bonds, Kenneth Rogoff, knowledge worker, Long Term Capital Management, low interest rates, low skilled workers, low-wage service sector, M-Pesa, margin call, market bubble, Martin Wolf, mass immigration, means of production, meme stock, Michael Milken, middle-income trap, Mikhail Gorbachev, Minsky moment, Modern Monetary Theory, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, Mustafa Suleyman, Nash equilibrium, natural language processing, negative equity, Nick Bostrom, non-fungible token, non-tariff barriers, ocean acidification, oil shale / tar sands, oil shock, paradox of thrift, pets.com, Phillips curve, planetary scale, Ponzi scheme, precariat, price mechanism, price stability, public intellectual, purchasing power parity, quantitative easing, race to the bottom, Ralph Waldo Emerson, ransomware, Ray Kurzweil, regulatory arbitrage, reserve currency, reshoring, Robert Shiller, Ronald Reagan, Salesforce, Satoshi Nakamoto, Savings and loan crisis, Second Machine Age, short selling, Silicon Valley, smart contracts, South China Sea, sovereign wealth fund, Stephen Hawking, TED Talk, The Great Moderation, the payments system, Thomas L Friedman, TikTok, too big to fail, Turing test, universal basic income, War on Poverty, warehouse robotics, Washington Consensus, Watson beat the top human players on Jeopardy!, working-age population, Yogi Berra, Yom Kippur War, zero-sum game, zoonotic diseases

On the economic side the alarm bells rang in the United States in 2015 when China presented its new industrial policy plan named Made in China 2025; this plan aims to use vast subsidies and financial incentives to move China away from low valued-added and labor-intensive manufacturing to leadership in the key industries of the future: information technologies (including AI, the Internet of Things, smart appliances, semiconductors), robotics (including automation and machine learning), green energy and green vehicles (including EVs and autonomous vehicles), aerospace equipment, ocean engineering and high-tech ships, railway equipment, power equipment, new materials, biotech, medicine and medical devices, and agriculture machinery. On top of that, in 2017 China presented its “New Generation Artificial Intelligence Plan” that aims to make China the world leader in AI by 2030.


pages: 219 words: 63,495

50 Future Ideas You Really Need to Know by Richard Watson

23andMe, 3D printing, access to a mobile phone, Albert Einstein, Alvin Toffler, artificial general intelligence, augmented reality, autonomous vehicles, BRICs, Buckminster Fuller, call centre, carbon credits, Charles Babbage, clean water, cloud computing, collaborative consumption, computer age, computer vision, crowdsourcing, dark matter, dematerialisation, Dennis Tito, digital Maoism, digital map, digital nomad, driverless car, Elon Musk, energy security, Eyjafjallajökull, failed state, Ford Model T, future of work, Future Shock, gamification, Geoffrey West, Santa Fe Institute, germ theory of disease, global pandemic, happiness index / gross national happiness, Higgs boson, high-speed rail, hive mind, hydrogen economy, Internet of things, Jaron Lanier, life extension, Mark Shuttleworth, Marshall McLuhan, megacity, natural language processing, Neil Armstrong, Network effects, new economy, ocean acidification, oil shale / tar sands, pattern recognition, peak oil, personalized medicine, phenotype, precision agriculture, private spaceflight, profit maximization, RAND corporation, Ray Kurzweil, RFID, Richard Florida, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Skype, smart cities, smart meter, smart transportation, space junk, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, supervolcano, synthetic biology, tech billionaire, telepresence, The Wisdom of Crowds, Thomas Malthus, Turing test, urban decay, Vernor Vinge, Virgin Galactic, Watson beat the top human players on Jeopardy!, web application, women in the workforce, working-age population, young professional

Think of automated disease diagnosis and surgery, military planning and battle command, customer-service avatars, artificial creativity and autonomous robots that predict then respond to crime (a “Department of Future Crime”—see also Chapter 32 and Biocriminology). Self-driving cars Gone are the days when Google was just a search engine and cars needed a driver. Google’s autonomous car project, started by Sebastian Thrun of Stanford Artificial Intelligence Laboratory, uses a Toyota Prius equipped with sensors to follow a GPS route all by itself. A robotics scientist sits in the car, but doesn’t actually drive it. Already, seven cars have traveled 1,600km (1,000 miles) with no driver and 225,000km (140,000 miles) with occasional human intervention.


pages: 201 words: 60,431

Long Game: How Long-Term Thinker Shorthb by Dorie Clark

3D printing, autonomous vehicles, Big Tech, Blue Ocean Strategy, buy low sell high, cognitive load, corporate social responsibility, COVID-19, crowdsourcing, delayed gratification, digital nomad, driverless car, Elon Musk, fail fast, Google X / Alphabet X, hedonic treadmill, Jeff Bezos, knowledge worker, lake wobegon effect, Lean Startup, lockdown, minimum viable product, passive income, pre–internet, rolodex, self-driving car, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, solopreneur, Stanford marshmallow experiment, Steven Levy, the strength of weak ties, Walter Mischel, zero-sum game

But if you push when you’re able and you do the hard work of carving out 20% time, you’re often in rare company—and your experience has the potential to be transformative. That’s what happened for Adam Ruxton, the head of marketing for a robotics project at X, formerly known as Google X, the company’s “moon shot factory,” which has launched initiatives around everything from delivery drones to autonomous cars. A native of Ireland, Adam started at Google’s Dublin office in 2011. By the end of his first year, he was already volunteering part of his 20% time to help the London office think through how to introduce Google apps in different European countries. He saw it as a form of professional development.


pages: 406 words: 105,602

The Startup Way: Making Entrepreneurship a Fundamental Discipline of Every Enterprise by Eric Ries

activist fund / activist shareholder / activist investor, Affordable Care Act / Obamacare, Airbnb, AOL-Time Warner, autonomous vehicles, barriers to entry, basic income, Ben Horowitz, billion-dollar mistake, Black-Scholes formula, Blitzscaling, call centre, centralized clearinghouse, Clayton Christensen, cognitive dissonance, connected car, corporate governance, DevOps, Elon Musk, en.wikipedia.org, fault tolerance, financial engineering, Frederick Winslow Taylor, global supply chain, Great Leap Forward, hockey-stick growth, index card, Jeff Bezos, Kickstarter, Lean Startup, loss aversion, machine readable, Marc Andreessen, Mark Zuckerberg, means of production, minimum viable product, moral hazard, move fast and break things, obamacare, PalmPilot, peer-to-peer, place-making, rent-seeking, Richard Florida, Sam Altman, Sand Hill Road, scientific management, secular stagnation, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, six sigma, skunkworks, Steve Jobs, TechCrunch disrupt, the scientific method, time value of money, Toyota Production System, two-pizza team, Uber for X, universal basic income, web of trust, Y Combinator

Toyota has become world-leading in its ability to mass-produce high-quality products on time, on budget, and with industry-leading cost. The company has had some very successful innovations, like the Prius hybrid drive technology, but at the time of my meeting, they had not had the same level of success incorporating digital platform–style innovations into their products. As consumer preferences and autonomous vehicle technology both evolve, this threatens to become a company-defining vulnerability. In getting the original project approved (you’ll learn more about it in Chapter 6), I met with leaders up and down the corporate hierarchy, culminating in a sit-down with one of Toyota’s most senior leaders, Shigeki Tomoyama (who at the time was the chief officer of the IT and ITS groups).


pages: 565 words: 122,605

The Human City: Urbanism for the Rest of Us by Joel Kotkin

"World Economic Forum" Davos, Alvin Toffler, autonomous vehicles, birth tourism , blue-collar work, British Empire, carbon footprint, Celebration, Florida, citizen journalism, colonial rule, crony capitalism, deindustrialization, demographic winter, Deng Xiaoping, Downton Abbey, edge city, Edward Glaeser, financial engineering, financial independence, Frank Gehry, gentrification, Gini coefficient, Google bus, housing crisis, illegal immigration, income inequality, informal economy, intentional community, Jane Jacobs, labor-force participation, land reform, Lewis Mumford, life extension, market bubble, mass immigration, McMansion, megacity, megaproject, microapartment, new economy, New Urbanism, Own Your Own Home, peak oil, pensions crisis, Peter Calthorpe, post-industrial society, RAND corporation, Richard Florida, rising living standards, Ronald Reagan, Salesforce, Seaside, Florida, self-driving car, Shenzhen was a fishing village, Silicon Valley, starchitect, Stewart Brand, streetcar suburb, Ted Nelson, the built environment, trade route, transit-oriented development, upwardly mobile, urban planning, urban renewal, urban sprawl, Victor Gruen, Whole Earth Catalog, women in the workforce, young professional

DAVIES, Alan. (2012, March 20). “Is suburban living a neurotic condition?,” Crikey, http://blogs.crikey.com.au/theurbanist/2012/03/20/is-suburban-living-a-neurotic-condition/. DAVIES, Alex. (2015, March 9). “Self-Driving Cars Will Make Us Want Fewer Cars,” Wired, http://www.wired.com/2015/03/the-economic-impact-of-autonomous-vehicles/. DAVIS, Bob and PAGE, Jeremy. (2011, March 7). “China’s Focus Turns to its Poor,” Wall Street Journal, http://www.wsj.com/articles/SB10001424052748703362804576184364247082474. de BARY, William Theodore, CHAN, Wing-Tsit and WATSON, Burton. (1960). The Sources of Chinese Tradition, New York: Columbia University Press.


pages: 370 words: 107,983

Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All by Robert Elliott Smith

"World Economic Forum" Davos, Ada Lovelace, adjacent possible, affirmative action, AI winter, Alfred Russel Wallace, algorithmic bias, algorithmic management, AlphaGo, Amazon Mechanical Turk, animal electricity, autonomous vehicles, behavioural economics, Black Swan, Brexit referendum, British Empire, Cambridge Analytica, cellular automata, Charles Babbage, citizen journalism, Claude Shannon: information theory, combinatorial explosion, Computing Machinery and Intelligence, corporate personhood, correlation coefficient, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, desegregation, discovery of DNA, disinformation, Douglas Hofstadter, Elon Musk, fake news, Fellow of the Royal Society, feminist movement, Filter Bubble, Flash crash, Geoffrey Hinton, Gerolamo Cardano, gig economy, Gödel, Escher, Bach, invention of the wheel, invisible hand, Jacquard loom, Jacques de Vaucanson, John Harrison: Longitude, John von Neumann, Kenneth Arrow, Linda problem, low skilled workers, Mark Zuckerberg, mass immigration, meta-analysis, mutually assured destruction, natural language processing, new economy, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, On the Economy of Machinery and Manufactures, p-value, pattern recognition, Paul Samuelson, performance metric, Pierre-Simon Laplace, post-truth, precariat, profit maximization, profit motive, Silicon Valley, social intelligence, statistical model, Stephen Hawking, stochastic process, Stuart Kauffman, telemarketer, The Bell Curve by Richard Herrnstein and Charles Murray, The Future of Employment, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, Thomas Malthus, traveling salesman, Turing machine, Turing test, twin studies, Vilfredo Pareto, Von Neumann architecture, warehouse robotics, women in the workforce, Yochai Benkler

Throughout the new ‘gig’ economy, there are people working with limited protections, responding to algorithmic management. The drivers of services like Deliveroo and Uber have their work assigned by algorithms and do the part of their task that the algorithms simply cannot do: speedy delivery to the door, in navigation situations (on bike and on foot) that are unlikely to be effectively addressed by autonomous vehicles anytime soon (although that, too, is wildly promised). Likewise, Amazon warehouse workers select products from shelves that would be difficult for robots to identify or handle, but feed an otherwise mechanized system of sales, order and delivery (excepting those final delivery steps, which are also increasingly done by algorithmically controlled humans).


pages: 409 words: 105,551

Team of Teams: New Rules of Engagement for a Complex World by General Stanley McChrystal, Tantum Collins, David Silverman, Chris Fussell

Airbus A320, Albert Einstein, Apollo 11, Atul Gawande, autonomous vehicles, bank run, barriers to entry, Black Swan, Boeing 747, butterfly effect, call centre, Captain Sullenberger Hudson, Chelsea Manning, clockwork universe, crew resource management, crowdsourcing, driverless car, Edward Snowden, Flash crash, Frederick Winslow Taylor, global supply chain, Henri Poincaré, high batting average, Ida Tarbell, information security, interchangeable parts, invisible hand, Isaac Newton, Jane Jacobs, job automation, job satisfaction, John Nash: game theory, knowledge economy, Mark Zuckerberg, Mohammed Bouazizi, Nate Silver, Neil Armstrong, Pierre-Simon Laplace, pneumatic tube, radical decentralization, RAND corporation, scientific management, self-driving car, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, supply-chain management, systems thinking, The Wealth of Nations by Adam Smith, urban sprawl, US Airways Flight 1549, vertical integration, WikiLeaks, zero-sum game

The Autonomous Intersection Management project is conducted by Professor Peter Stone at the Artificial Intelligence Laboratory in the University of Texas at Austin’s Department of Computer Science. Instead, cars going in all four . . . “Computer Scientist Developing Intersections of the Future with Fully Autonomous Vehicles,” University of Texas News, February 20, 2012, http://www.utexas.edu/news/2012/02/20/autonomous_intersec tion/. 25 percent of accidents . . . “No Lights, No Signs, No Accidents: Future Intersections for Driverless Cars,” Reuters Video, March 22, 2012, http://www.reuters.com/video/2012/03/22/no-lights-no-signs-no-accidents-future-i?


pages: 392 words: 108,745

Talk to Me: How Voice Computing Will Transform the Way We Live, Work, and Think by James Vlahos

Albert Einstein, AltaVista, Amazon Mechanical Turk, Amazon Web Services, augmented reality, Automated Insights, autonomous vehicles, backpropagation, Big Tech, Cambridge Analytica, Chuck Templeton: OpenTable:, cloud computing, Colossal Cave Adventure, computer age, deep learning, DeepMind, Donald Trump, Elon Musk, fake news, Geoffrey Hinton, information retrieval, Internet of things, Jacques de Vaucanson, Jeff Bezos, lateral thinking, Loebner Prize, machine readable, machine translation, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Mark Zuckerberg, Menlo Park, natural language processing, Neal Stephenson, Neil Armstrong, OpenAI, PageRank, pattern recognition, Ponzi scheme, randomized controlled trial, Ray Kurzweil, Ronald Reagan, Rubik’s Cube, self-driving car, sentiment analysis, Silicon Valley, Skype, Snapchat, speech recognition, statistical model, Steve Jobs, Steve Wozniak, Steven Levy, TechCrunch disrupt, Turing test, Watson beat the top human players on Jeopardy!

For the first edition of the annually recurring contest, more than one hundred university teams applied to compete, and Amazon selected the fifteen squads whose proposals seemed the most promising. If any team actually succeeded, its members would snare academic glory and the promise of brilliant future careers. (Consider that alums of the DARPA Grand Challenges, an early set of autonomous-vehicle competitions, went on to run the self-driving-car divisions of Google, Ford, Uber, and General Motors.) The victors would also walk away with the Alexa Prize itself—a $1 million purse. The Alexa Prize is not the only contest that tries to squeeze more humanlike rapport out of the world’s chatbots; recall the Loebner Prize, the one that Mauldin entered, from chapter 4.


pages: 363 words: 109,077

The Raging 2020s: Companies, Countries, People - and the Fight for Our Future by Alec Ross

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, Affordable Care Act / Obamacare, air gap, air traffic controllers' union, Airbnb, Albert Einstein, An Inconvenient Truth, autonomous vehicles, barriers to entry, benefit corporation, Bernie Sanders, Big Tech, big-box store, British Empire, call centre, capital controls, clean water, collective bargaining, computer vision, coronavirus, corporate governance, corporate raider, COVID-19, deep learning, Deng Xiaoping, Didi Chuxing, disinformation, Dissolution of the Soviet Union, Donald Trump, Double Irish / Dutch Sandwich, drone strike, dumpster diving, employer provided health coverage, Francis Fukuyama: the end of history, future of work, general purpose technology, gig economy, Gini coefficient, global supply chain, Goldman Sachs: Vampire Squid, Gordon Gekko, greed is good, high-speed rail, hiring and firing, income inequality, independent contractor, information security, intangible asset, invisible hand, Jeff Bezos, knowledge worker, late capitalism, low skilled workers, Lyft, Marc Andreessen, Marc Benioff, mass immigration, megacity, military-industrial complex, minimum wage unemployment, mittelstand, mortgage tax deduction, natural language processing, Oculus Rift, off-the-grid, offshore financial centre, open economy, OpenAI, Parag Khanna, Paris climate accords, profit motive, race to the bottom, RAND corporation, ride hailing / ride sharing, Robert Bork, rolodex, Ronald Reagan, Salesforce, self-driving car, shareholder value, side hustle, side project, Silicon Valley, smart cities, Social Responsibility of Business Is to Increase Its Profits, sovereign wealth fund, sparse data, special economic zone, Steven Levy, stock buybacks, strikebreaker, TaskRabbit, tech bro, tech worker, transcontinental railway, transfer pricing, Travis Kalanick, trickle-down economics, Uber and Lyft, uber lyft, union organizing, Upton Sinclair, vertical integration, working poor

As soon as the plan was released, the government and private sector kicked into gear. Local governments started pouring funds into AI start-ups, and industry partnerships began to form. The following month, the government drafted a “national AI team,” selecting four domestic companies to take the lead in strategic AI fields including autonomous vehicles (Baidu), medical imaging (Tencent), natural language processing (iFLYTEK), and smart city technology (Alibaba). By August 2019, the team had expanded to fifteen members, each with its own area of expertise. These national champions are granted special access to government funds and databases.


Big Data at Work: Dispelling the Myths, Uncovering the Opportunities by Thomas H. Davenport

Automated Insights, autonomous vehicles, bioinformatics, business intelligence, business process, call centre, chief data officer, cloud computing, commoditize, data acquisition, data science, disruptive innovation, Edward Snowden, Erik Brynjolfsson, intermodal, Internet of things, Jeff Bezos, knowledge worker, lifelogging, Mark Zuckerberg, move fast and break things, Narrative Science, natural language processing, Netflix Prize, New Journalism, recommendation engine, RFID, self-driving car, sentiment analysis, Silicon Valley, smart grid, smart meter, social graph, sorting algorithm, statistical model, Tesla Model S, text mining, Thomas Davenport, three-martini lunch

Kerem Tomak, in “Two Expert Perspectives on High-Performance Analytics,” Intelligence Quarterly (a SAS publication), 2nd quarter (2012): 6. 5. The interview of this manager, who wished to remain anonymous, was ­conducted by the author by telephone on March 19, 2013. 6. Tom Vanderbilt, “Let the Robot Drive: The Autonomous Car of the Future Is Here,” Wired, January 20, 2012, http://www.wired.com/magazine/2012/01/ ff_­autonomouscars/. 7. Joe Jimenez interview with Geoffrey Colvin, “Joe Jimenez Lays Out His Path to Business Longevity,” Fortune, March 21, 2013, http://money.cnn.com/2013/03/21/ news/companies/novartis-joe-jimenez.pr.fortune/index.html. 8.


pages: 281 words: 69,107

Belt and Road: A Chinese World Order by Bruno Maçães

"World Economic Forum" Davos, active measures, Admiral Zheng, autonomous vehicles, Branko Milanovic, BRICs, cloud computing, deindustrialization, demographic dividend, Deng Xiaoping, different worldview, Donald Trump, energy security, European colonialism, eurozone crisis, export processing zone, Francis Fukuyama: the end of history, global supply chain, global value chain, high-speed rail, industrial cluster, industrial robot, Internet of things, Kenneth Rogoff, land reform, liberal world order, Malacca Straits, middle-income trap, one-China policy, Pearl River Delta, public intellectual, smart cities, South China Sea, sovereign wealth fund, special economic zone, subprime mortgage crisis, trade liberalization, trade route, zero-sum game

“We both agree that Chinese companies should be united and must not be provoked by outsiders,” he added, before speaking about his company’s efforts over the past thirty years and expressing zero tolerance for any questioning of the loyalty of the “national brand.” The dispute highlights how much national champions are expected to benefit from the definition of which technologies will be used to power the coming revolution in autonomous cars and the internet of things. * * * Because Germany’s top firms have become so dependent on the Chinese market, the government in Berlin has avoided confronting China head-on.16 The United States took longer to react, but when it finally did the response was considerably more aggressive. The ongoing dispute was initially centered around the country’s trade deficit with China but quickly turned to Made in China 2025.


pages: 225 words: 70,590

Curbing Traffic: The Human Case for Fewer Cars in Our Lives by Chris Bruntlett, Melissa Bruntlett

15-minute city, An Inconvenient Truth, autonomous vehicles, bike sharing, BIPOC, car-free, coronavirus, COVID-19, emotional labour, en.wikipedia.org, global pandemic, green new deal, Jane Jacobs, lockdown, Lyft, microplastics / micro fibres, New Urbanism, post-work, RAND corporation, ride hailing / ride sharing, self-driving car, social distancing, streetcar suburb, the built environment, Uber and Lyft, uber lyft, urban planning, white flight, working-age population, World Values Survey

This leads to one more reason why social trust can start dropping as driving becomes more prevalent on your streets. “If you are in a car, with this traffic light issue, you’re never in a prisoner’s dilemma, because this situation is solved by an external algorithm,” reveals Te Brömmelstroet. And that state of affairs will only be worsened by the (seemingly inevitable) introduction of autonomous cars: “Imagine all of us being in self-driving vehicles. The algorithm of the self-driving car solves all of these conflicts by itself. Imagine doing that for a year, and what that would do to your sense of trust of others.” On a smaller scale, this is already happening with ride-hailing services, such as Uber and Lyft, gradually diminishing our capacity to trust, and willingness to go out of our way to help one another.


pages: 829 words: 187,394

The Price of Time: The Real Story of Interest by Edward Chancellor

"World Economic Forum" Davos, 3D printing, activist fund / activist shareholder / activist investor, Airbnb, Alan Greenspan, asset allocation, asset-backed security, assortative mating, autonomous vehicles, balance sheet recession, bank run, banking crisis, barriers to entry, Basel III, Bear Stearns, Ben Bernanke: helicopter money, Bernie Sanders, Big Tech, bitcoin, blockchain, bond market vigilante , bonus culture, book value, Bretton Woods, BRICs, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, carried interest, cashless society, cloud computing, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, commodity super cycle, computer age, coronavirus, corporate governance, COVID-19, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, cryptocurrency, currency peg, currency risk, David Graeber, debt deflation, deglobalization, delayed gratification, Deng Xiaoping, Detroit bankruptcy, distributed ledger, diversified portfolio, Dogecoin, Donald Trump, double entry bookkeeping, Elon Musk, equity risk premium, Ethereum, ethereum blockchain, eurozone crisis, everywhere but in the productivity statistics, Extinction Rebellion, fiat currency, financial engineering, financial innovation, financial intermediation, financial repression, fixed income, Flash crash, forward guidance, full employment, gig economy, Gini coefficient, Glass-Steagall Act, global reserve currency, global supply chain, Goodhart's law, Great Leap Forward, green new deal, Greenspan put, high net worth, high-speed rail, housing crisis, Hyman Minsky, implied volatility, income inequality, income per capita, inflation targeting, initial coin offering, intangible asset, Internet of things, inventory management, invisible hand, Japanese asset price bubble, Jean Tirole, Jeff Bezos, joint-stock company, Joseph Schumpeter, junk bonds, Kenneth Rogoff, land bank, large denomination, Les Trente Glorieuses, liquidity trap, lockdown, Long Term Capital Management, low interest rates, Lyft, manufacturing employment, margin call, Mark Spitznagel, market bubble, market clearing, market fundamentalism, Martin Wolf, mega-rich, megaproject, meme stock, Michael Milken, Minsky moment, Modern Monetary Theory, Mohammed Bouazizi, Money creation, money market fund, moral hazard, mortgage debt, negative equity, new economy, Northern Rock, offshore financial centre, operational security, Panopticon Jeremy Bentham, Paul Samuelson, payday loans, peer-to-peer lending, pensions crisis, Peter Thiel, Philip Mirowski, plutocrats, Ponzi scheme, price mechanism, price stability, quantitative easing, railway mania, reality distortion field, regulatory arbitrage, rent-seeking, reserve currency, ride hailing / ride sharing, risk free rate, risk tolerance, risk/return, road to serfdom, Robert Gordon, Robinhood: mobile stock trading app, Satoshi Nakamoto, Satyajit Das, Savings and loan crisis, savings glut, Second Machine Age, secular stagnation, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, South Sea Bubble, Stanford marshmallow experiment, Steve Jobs, stock buybacks, subprime mortgage crisis, Suez canal 1869, tech billionaire, The Great Moderation, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thorstein Veblen, Tim Haywood, time value of money, too big to fail, total factor productivity, trickle-down economics, tulip mania, Tyler Cowen, Uber and Lyft, Uber for X, uber lyft, Walter Mischel, WeWork, When a measure becomes a target, yield curve

Gordon’s Northwestern colleague Joel Mokyr suggested that a ‘shortfall of imagination [is] largely responsible for much of today’s pessimism’. Mokyr listed a number of revolutionary new technologies then under development, including 3D printing, graphene and genetic engineering, to which might be added autonomous cars and clean energy.19 Finance writer William Bernstein accused secular stagnationists of conflating what they couldn’t conceive with that which was not possible.20 Hansen made the same mistake. The most reliable prediction, Bernstein concluded, is to assume that past economic trends continue. Hansen argued that a slowdown in economic growth would produce an excess of savings over investment.

Low interest rates have messed with the temporal properties of the market and created a wormhole in time and in Tesla’s stock … It will take years, maybe even a decade, for Tesla to produce enough cars to justify its valuation. Today’s market valuation assumes it has already happened – that the capital has been raised and spent and that it cost nothing.20 In the era of ultra-low interest rates, time had no cost. Investors’ appetite for concept stocks at nosebleed valuations extended well beyond autonomous cars (Tesla) to animal-free protein (Beyond Meat), biotech (gene therapy stocks), Chinese internet (Alibaba, Tencent) and cloud computing. Investors were flying high. During an outbreak of ‘marijuana madness’ in September 2018, a Canadian cannabis producer was briefly valued at more than American Airlines.21 CRYPTO BUBBLES As the world’s financial system imploded in the summer of 2008, an anonymous software engineer circulated a paper containing a cure for all monetary ills.


pages: 373 words: 112,822

The Upstarts: How Uber, Airbnb, and the Killer Companies of the New Silicon Valley Are Changing the World by Brad Stone

Affordable Care Act / Obamacare, Airbnb, Amazon Web Services, Andy Kessler, autonomous vehicles, Ben Horowitz, Benchmark Capital, Boris Johnson, Burning Man, call centre, Chuck Templeton: OpenTable:, collaborative consumption, data science, Didi Chuxing, Dr. Strangelove, driverless car, East Village, fake it until you make it, fixed income, gentrification, Google X / Alphabet X, growth hacking, Hacker News, hockey-stick growth, housing crisis, inflight wifi, Jeff Bezos, John Zimmer (Lyft cofounder), Justin.tv, Kickstarter, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Menlo Park, Mitch Kapor, Necker cube, obamacare, PalmPilot, Paul Graham, peer-to-peer, Peter Thiel, power law, race to the bottom, rent control, ride hailing / ride sharing, Ruby on Rails, San Francisco homelessness, Sand Hill Road, self-driving car, semantic web, sharing economy, side project, Silicon Valley, Silicon Valley startup, Skype, SoftBank, South of Market, San Francisco, Startup school, Steve Jobs, TaskRabbit, tech bro, TechCrunch disrupt, Tony Hsieh, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, ubercab, Y Combinator, Y2K, Zipcar

I think that might happen. We’re just at the beginning. But when you feel that, that’s going to be a big deal.” “This will be because of carpooling services like UberPool and Lyft Line? Or driverless cars?” I asked. A few weeks before, the company had begun testing fourteen Ford Fusions tricked out with autonomous vehicle technology on the streets of Pittsburgh. It had also recently announced a partnership with Volvo to develop driverless-car technology and had acquired Otto, a San Francisco–based startup run by former Google engineers that was working on driverless trucks.1 “All these things that are going to happen, whether it’s human-driven transportation, carpooling, commuting with carpooling, driverless cars,” Kalanick said, “cars are coming off the road.


pages: 549 words: 116,200

With a Little Help by Cory Efram Doctorow, Jonathan Coulton, Russell Galen

autonomous vehicles, big-box store, Burning Man, call centre, carbon credits, carbon footprint, carbon tax, death of newspapers, don't be evil, game design, Google Earth, high net worth, lifelogging, lolcat, margin call, Mark Shuttleworth, offshore financial centre, packet switching, Ponzi scheme, reality distortion field, rolodex, Sand Hill Road, sensible shoes, skunkworks, Skype, traffic fines, traveling salesman, Turing test, urban planning, Y2K

For me and for Cory this means allowing people to share our work freely, and to re-use it to create new things. The first time the concept was explained to me I felt as though someone had set my brain on fire - it was the most exciting idea I had ever heard. 32 In my head, songs became little autonomous vehicles that I could release into the wild, letting them bounce around and find their way to the people who would enjoy them. It was a way to let this new "Internet" thing do all the heavy lifting, an organic and efficient method of targeting an audience of fans who did not yet know they were fans.


pages: 425 words: 112,220

The Messy Middle: Finding Your Way Through the Hardest and Most Crucial Part of Any Bold Venture by Scott Belsky

23andMe, 3D printing, Airbnb, Albert Einstein, Anne Wojcicki, augmented reality, autonomous vehicles, behavioural economics, Ben Horowitz, bitcoin, blockchain, Chuck Templeton: OpenTable:, commoditize, correlation does not imply causation, cryptocurrency, data science, delayed gratification, DevOps, Donald Trump, Elon Musk, endowment effect, fake it until you make it, hiring and firing, Inbox Zero, iterative process, Jeff Bezos, knowledge worker, Lean Startup, Lyft, Mark Zuckerberg, Marshall McLuhan, minimum viable product, move fast and break things, NetJets, Network effects, new economy, old-boy network, Paradox of Choice, pattern recognition, Paul Graham, private spaceflight, reality distortion field, ride hailing / ride sharing, Salesforce, Sheryl Sandberg, Silicon Valley, skeuomorphism, slashdot, Snapchat, Steve Jobs, subscription business, sugar pill, systems thinking, TaskRabbit, TED Talk, the medium is the message, Tony Fadell, Travis Kalanick, Uber for X, uber lyft, WeWork, Y Combinator, young professional

The historic decision in 2015 to rename the entire company “Alphabet” and treat Google itself as just one of many companies in the holding company was an effort to structurally protect new projects with longtime horizons as independent companies outside of Google’s operating business. As a result, you get companies like Waymo pursuing autonomous-vehicle technology. These portfolio companies are liberated from the need to add near-term value and rationalize their existence on a quarterly basis. On a smaller scale, some companies will separate certain teams in terms of who they report to, where they are physically located, and how they are measured to provide protection against the quarterly drive for profits and shorter-term impact.


pages: 389 words: 112,319

Think Like a Rocket Scientist: Simple Strategies You Can Use to Make Giant Leaps in Work and Life by Ozan Varol

Abraham Maslow, Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, Amazon Web Services, Andrew Wiles, Apollo 11, Apollo 13, Apple's 1984 Super Bowl advert, Arthur Eddington, autonomous vehicles, Ben Horowitz, Boeing 747, Cal Newport, Clayton Christensen, cloud computing, Colonization of Mars, dark matter, delayed gratification, different worldview, discovery of DNA, double helix, Elon Musk, fail fast, fake news, fear of failure, functional fixedness, Gary Taubes, Gene Kranz, George Santayana, Google Glasses, Google X / Alphabet X, Inbox Zero, index fund, Isaac Newton, it is difficult to get a man to understand something, when his salary depends on his not understanding it, James Dyson, Jeff Bezos, job satisfaction, Johannes Kepler, Kickstarter, knowledge worker, Large Hadron Collider, late fees, lateral thinking, lone genius, longitudinal study, Louis Pasteur, low earth orbit, Marc Andreessen, Mars Rover, meta-analysis, move fast and break things, multiplanetary species, Neal Stephenson, Neil Armstrong, Nick Bostrom, obamacare, Occam's razor, out of africa, Peter Pan Syndrome, Peter Thiel, Pluto: dwarf planet, private spaceflight, Ralph Waldo Emerson, reality distortion field, Richard Feynman, Richard Feynman: Challenger O-ring, Ronald Reagan, Sam Altman, Schrödinger's Cat, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, Simon Singh, Skinner box, SpaceShipOne, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, subprime mortgage crisis, sunk-cost fallacy, TED Talk, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, Tyler Cowen, Upton Sinclair, Vilfredo Pareto, We wanted flying cars, instead we got 140 characters, Whole Earth Catalog, women in the workforce, Yogi Berra

Here’s an example.11 If your goal is to improve car safety, you can make gradual improvements to the design of a car to better protect human life in an accident. But if your goal is a moonshot of eliminating all accidents, you must start with a blank slate and question all assumptions—including the human operator behind the wheel. This first-principles approach paves the way for the possibility of autonomous vehicles. Consider also the planned moonshots of SpaceX. If the company’s aim were to simply put satellites into Earth orbit, there would have been no reason to do things differently. The company would have relied on the same technology that NASA had been using since the 1960s. There’s little reason to reduce the cost of rocket launches by a factor of ten, as SpaceX is on its way to doing, unless you’re aiming for a moonshot.


pages: 521 words: 110,286

Them and Us: How Immigrants and Locals Can Thrive Together by Philippe Legrain

affirmative action, Albert Einstein, AlphaGo, autonomous vehicles, Berlin Wall, Black Lives Matter, Boris Johnson, Brexit referendum, British Empire, call centre, centre right, Chelsea Manning, clean tech, coronavirus, corporate social responsibility, COVID-19, creative destruction, crowdsourcing, data science, David Attenborough, DeepMind, Demis Hassabis, demographic dividend, digital divide, discovery of DNA, Donald Trump, double helix, Edward Glaeser, en.wikipedia.org, eurozone crisis, failed state, Fall of the Berlin Wall, future of work, illegal immigration, immigration reform, informal economy, Jane Jacobs, job automation, Jony Ive, labour market flexibility, lockdown, low cost airline, low interest rates, low skilled workers, lump of labour, Mahatma Gandhi, Mark Zuckerberg, Martin Wolf, Mary Meeker, mass immigration, moral hazard, Mustafa Suleyman, Network effects, new economy, offshore financial centre, open borders, open immigration, postnationalism / post nation state, purchasing power parity, remote working, Richard Florida, ride hailing / ride sharing, Rishi Sunak, Ronald Reagan, Silicon Valley, Skype, SoftBank, Steve Jobs, tech worker, The Death and Life of Great American Cities, The future is already here, The Future of Employment, Tim Cook: Apple, Tyler Cowen, urban sprawl, WeWork, Winter of Discontent, women in the workforce, working-age population

The OECD estimates that only 9 percent of jobs in twenty-one rich OECD countries are fully automatable.7 Whatever the correct figure, economies have previously adapted to huge technological changes that automated many tasks, such as the deployment of electricity and the popularisation of personal computers (PCs), without incurring job losses overall; new and better jobs were also created. Jobs tend to consist of a number of tasks, some of which may be readily automatable, others not. Fully autonomous vehicles, for instance, may do away with the need for many human drivers – although aeroplanes that are capable of flying on autopilot still have human pilots. But while in some cases AI will fully substitute for human labour, they will often complement each other. For instance, AI may make it easier and faster to collect and process data about a business’s logistic operations, making managers more productive, not replacing them.


pages: 447 words: 111,991

Exponential: How Accelerating Technology Is Leaving Us Behind and What to Do About It by Azeem Azhar

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 23andMe, 3D printing, A Declaration of the Independence of Cyberspace, Ada Lovelace, additive manufacturing, air traffic controllers' union, Airbnb, algorithmic management, algorithmic trading, Amazon Mechanical Turk, autonomous vehicles, basic income, Berlin Wall, Bernie Sanders, Big Tech, Bletchley Park, Blitzscaling, Boeing 737 MAX, book value, Boris Johnson, Bretton Woods, carbon footprint, Chris Urmson, Citizen Lab, Clayton Christensen, cloud computing, collective bargaining, computer age, computer vision, contact tracing, contact tracing app, coronavirus, COVID-19, creative destruction, crowdsourcing, cryptocurrency, cuban missile crisis, Daniel Kahneman / Amos Tversky, data science, David Graeber, David Ricardo: comparative advantage, decarbonisation, deep learning, deglobalization, deindustrialization, dematerialisation, Demis Hassabis, Diane Coyle, digital map, digital rights, disinformation, Dissolution of the Soviet Union, Donald Trump, Double Irish / Dutch Sandwich, drone strike, Elon Musk, emotional labour, energy security, Fairchild Semiconductor, fake news, Fall of the Berlin Wall, Firefox, Frederick Winslow Taylor, fulfillment center, future of work, Garrett Hardin, gender pay gap, general purpose technology, Geoffrey Hinton, gig economy, global macro, global pandemic, global supply chain, global value chain, global village, GPT-3, Hans Moravec, happiness index / gross national happiness, hiring and firing, hockey-stick growth, ImageNet competition, income inequality, independent contractor, industrial robot, intangible asset, Jane Jacobs, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, Just-in-time delivery, Kickstarter, Kiva Systems, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, lockdown, low skilled workers, lump of labour, Lyft, manufacturing employment, Marc Benioff, Mark Zuckerberg, megacity, Mitch Kapor, Mustafa Suleyman, Network effects, new economy, NSO Group, Ocado, offshore financial centre, OpenAI, PalmPilot, Panopticon Jeremy Bentham, Peter Thiel, Planet Labs, price anchoring, RAND corporation, ransomware, Ray Kurzweil, remote working, RFC: Request For Comment, Richard Florida, ride hailing / ride sharing, Robert Bork, Ronald Coase, Ronald Reagan, Salesforce, Sam Altman, scientific management, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, software as a service, Steve Ballmer, Steve Jobs, Stuxnet, subscription business, synthetic biology, tacit knowledge, TaskRabbit, tech worker, The Death and Life of Great American Cities, The Future of Employment, The Nature of the Firm, Thomas Malthus, TikTok, Tragedy of the Commons, Turing machine, Uber and Lyft, Uber for X, uber lyft, universal basic income, uranium enrichment, vertical integration, warehouse automation, winner-take-all economy, workplace surveillance , Yom Kippur War

It was late 2019, and US president Donald Trump had spent much of the previous two years tweeting increasingly bellicose denunciations of the Chinese government. At the same time, the White House had been progressively ratcheting up tariffs on Chinese imports to the US. This looked like bad news for Yu’s start-up, Pix Moving. Based in Guiyang, a city 1,000 kilometres to the north-west of Shenzhen, the firm makes the chassis for a new class of autonomous vehicle. The tariffs made everything more expensive. A lesser entrepreneur may have had to raise prices for his first customers. Not so Yu. He had a solution: Pix Moving was using only the most modern manufacturing methods – ‘dematerialised’ techniques. Rather than exporting cars, Yu explained, they ‘export the technique that is needed to produce the cars’.1 Vehicles are not loaded onto container ships and sent to their destination.


Visual Thinking: The Hidden Gifts of People Who Think in Pictures, Patterns, and Abstractions by Temple Grandin, Ph.D.

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, a long time ago in a galaxy far, far away, air gap, Albert Einstein, American Society of Civil Engineers: Report Card, Apollo 11, Apple II, ASML, Asperger Syndrome, autism spectrum disorder, autonomous vehicles, Black Lives Matter, Boeing 737 MAX, Captain Sullenberger Hudson, clean water, cloud computing, computer vision, Computing Machinery and Intelligence, coronavirus, cotton gin, COVID-19, defense in depth, Drosophila, Elon Musk, en.wikipedia.org, GPT-3, Gregor Mendel, Greta Thunberg, hallucination problem, helicopter parent, income inequality, industrial robot, invention of movable type, Isaac Newton, James Webb Space Telescope, John Nash: game theory, John von Neumann, Jony Ive, language acquisition, longitudinal study, Mark Zuckerberg, Mars Rover, meta-analysis, Neil Armstrong, neurotypical, pattern recognition, Peter Thiel, phenotype, ransomware, replication crisis, Report Card for America’s Infrastructure, Robert X Cringely, Saturday Night Live, self-driving car, seminal paper, Silicon Valley, Skinner box, space junk, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Tacoma Narrows Bridge, TaskRabbit, theory of mind, TikTok, twin studies, unpaid internship, upwardly mobile, US Airways Flight 1549, warehouse automation, warehouse robotics, web application, William Langewiesche, Y Combinator

Rahu, M. “Health Effects of the Chernobyl Accident: Fears, Rumors and Truth.” European Journal of Cancer 39 (2003): 295–99. Rausand, M. Risk Assessment: Theory, Methods, and Applications. Hoboken, NJ: Wiley, 2011. Razdan, R. “Temple Grandin, Elon Musk, and the Interesting Parallels between Autonomous Vehicles and Autism.” Forbes, June 7, 2020. Rice, J. “Massachusetts Utility Pleads Guilty to 2018 Gas Explosion.” ENR, Engineering News- Record, March 9, 2020. Robison, P. Flying Blind: The MAX Tragedy and the Fall of Boeing. New York: Doubleday, 2021. Ropeik, D. “How Risky Is Flying?” Nova, PBS. https://www.pbs.org/wgbh/nova/planecrash/risky.html/.


pages: 294 words: 77,356

Automating Inequality by Virginia Eubanks

autonomous vehicles, basic income, Black Lives Matter, business process, call centre, cognitive dissonance, collective bargaining, correlation does not imply causation, data science, deindustrialization, digital divide, disruptive innovation, Donald Trump, driverless car, Elon Musk, ending welfare as we know it, experimental subject, fake news, gentrification, housing crisis, Housing First, IBM and the Holocaust, income inequality, job automation, mandatory minimum, Mark Zuckerberg, mass incarceration, minimum wage unemployment, mortgage tax deduction, new economy, New Urbanism, payday loans, performance metric, Ronald Reagan, San Francisco homelessness, self-driving car, sparse data, statistical model, strikebreaker, underbanked, universal basic income, urban renewal, W. E. B. Du Bois, War on Poverty, warehouse automation, working poor, Works Progress Administration, young professional, zero-sum game

I spent much of my November 2016 trip to Pittsburgh trying to spy one of Uber’s famous driverless cars. I didn’t have any luck because the cars are found mostly downtown and in the Strip District, neighborhoods that are gentrifying quickly. I spent my time in Duquesne, Wilkinsburg, the Hill District, and Homestead. I didn’t see a single one. The autonomous cars use a vast store of geospatial data collected from Uber’s human drivers and a two-person team of onboard engineers to learn how to get around the city and interact with other vehicles, bikes, and pedestrians. Asked by Julia Carrie Wong of The Guardian how he felt about his role in Uber’s future, Rob Judge, who had been driving for the company for three months, said, “It feels like we’re just rentals.


pages: 252 words: 73,131

The Inner Lives of Markets: How People Shape Them—And They Shape Us by Tim Sullivan

Abraham Wald, Airbnb, airport security, Al Roth, Alvin Roth, Andrei Shleifer, attribution theory, autonomous vehicles, barriers to entry, behavioural economics, Brownian motion, business cycle, buy and hold, centralized clearinghouse, Chuck Templeton: OpenTable:, classic study, clean water, conceptual framework, congestion pricing, constrained optimization, continuous double auction, creative destruction, data science, deferred acceptance, Donald Trump, Dutch auction, Edward Glaeser, experimental subject, first-price auction, framing effect, frictionless, fundamental attribution error, George Akerlof, Goldman Sachs: Vampire Squid, Gunnar Myrdal, helicopter parent, information asymmetry, Internet of things, invisible hand, Isaac Newton, iterative process, Jean Tirole, Jeff Bezos, Johann Wolfgang von Goethe, John Nash: game theory, John von Neumann, Joseph Schumpeter, Kenneth Arrow, late fees, linear programming, Lyft, market clearing, market design, market friction, medical residency, multi-sided market, mutually assured destruction, Nash equilibrium, Occupy movement, opioid epidemic / opioid crisis, Pareto efficiency, Paul Samuelson, Peter Thiel, pets.com, pez dispenser, power law, pre–internet, price mechanism, price stability, prisoner's dilemma, profit motive, proxy bid, RAND corporation, ride hailing / ride sharing, Robert Shiller, Robert Solow, Ronald Coase, school choice, school vouchers, scientific management, sealed-bid auction, second-price auction, second-price sealed-bid, sharing economy, Silicon Valley, spectrum auction, Steve Jobs, Tacoma Narrows Bridge, techno-determinism, technoutopianism, telemarketer, The Market for Lemons, The Wisdom of Crowds, Thomas Malthus, Thorstein Veblen, trade route, transaction costs, two-sided market, uber lyft, uranium enrichment, Vickrey auction, Vilfredo Pareto, WarGames: Global Thermonuclear War, winner-take-all economy

More recently, the much-vaunted internet of things is bringing us yet another generation of platform business models, some amazing, some terrifying, and some, like internet-enabled cars, a bit of both. As cars move from being internal combustion engines with wheels to software platforms that are connected to the internet and to one another, we can imagine all sorts of potential for them, some of which will make our lives better (fewer accidents with autonomous cars and more apps that plug into them) and some of which will make us even more vulnerable (long-distance software hacks). But regardless, they’ll be governed by the same rules that make other platforms tick. Currently, entrepreneurs and venture capitalists—spurred by the success of Facebook, LinkedIn, Uber, and many others—are pouring money into new platform businesses.


pages: 293 words: 78,439

Dual Transformation: How to Reposition Today's Business While Creating the Future by Scott D. Anthony, Mark W. Johnson

activist fund / activist shareholder / activist investor, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Amazon Web Services, Andy Rubin, Apollo 13, asset light, autonomous vehicles, barriers to entry, behavioural economics, Ben Horowitz, Big Tech, blockchain, business process, business process outsourcing, call centre, Carl Icahn, Clayton Christensen, cloud computing, commoditize, corporate governance, creative destruction, crowdsourcing, death of newspapers, disintermediation, disruptive innovation, distributed ledger, diversified portfolio, driverless car, Internet of things, invention of hypertext, inventory management, Jeff Bezos, job automation, job satisfaction, Joseph Schumpeter, Kickstarter, late fees, Lean Startup, long term incentive plan, Lyft, M-Pesa, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Minecraft, obamacare, Parag Khanna, Paul Graham, peer-to-peer lending, pez dispenser, recommendation engine, Salesforce, self-driving car, shareholder value, side project, Silicon Valley, SimCity, Skype, software as a service, software is eating the world, Steve Jobs, subscription business, the long tail, the market place, the scientific method, Thomas Kuhn: the structure of scientific revolutions, transfer pricing, uber lyft, Watson beat the top human players on Jeopardy!, Y Combinator, Zipcar

Instead of owning a car, they can participate in a fractional ownership program like Zipcar (purchased by Avis Budget Group for $500 million in 2013). Then in 2005, Sebastian Thrun, coinventor of Google Street View, led a team whose robotic car won a $2 million prize from the US Department of Defense. Over the next decade Google invested to further develop the technology behind self-driving cars and to change local regulations to welcome autonomous cars. In 2014 it introduced a new car with no wheels and no pedals. In August 2016, Singapore’s first autonomous taxi debuted on the roads of a cluster of buildings with far-out names like Fusionopolis. Scenes in movies with legions of driverless cars—such as I, Robot and Minority Report—increasingly seem less like science fiction and more like a preview of the next decade.


The Smartphone Society by Nicole Aschoff

"Susan Fowler" uber, 4chan, A Declaration of the Independence of Cyberspace, Airbnb, algorithmic bias, algorithmic management, Amazon Web Services, artificial general intelligence, autonomous vehicles, barriers to entry, Bay Area Rapid Transit, Bernie Sanders, Big Tech, Black Lives Matter, blockchain, carbon footprint, Carl Icahn, Cass Sunstein, citizen journalism, cloud computing, correlation does not imply causation, crony capitalism, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, degrowth, Demis Hassabis, deplatforming, deskilling, digital capitalism, digital divide, do what you love, don't be evil, Donald Trump, Downton Abbey, Edward Snowden, Elon Musk, Evgeny Morozov, fake news, feminist movement, Ferguson, Missouri, Filter Bubble, financial independence, future of work, gamification, gig economy, global value chain, Google Chrome, Google Earth, Googley, green new deal, housing crisis, income inequality, independent contractor, Jaron Lanier, Jeff Bezos, Jessica Bruder, job automation, John Perry Barlow, knowledge economy, late capitalism, low interest rates, Lyft, M-Pesa, Mark Zuckerberg, minimum wage unemployment, mobile money, moral panic, move fast and break things, Naomi Klein, Network effects, new economy, Nicholas Carr, Nomadland, occupational segregation, Occupy movement, off-the-grid, offshore financial centre, opioid epidemic / opioid crisis, PageRank, Patri Friedman, peer-to-peer, Peter Thiel, pets.com, planned obsolescence, quantitative easing, Ralph Waldo Emerson, RAND corporation, Ray Kurzweil, RFID, Richard Stallman, ride hailing / ride sharing, Rodney Brooks, Ronald Reagan, Salesforce, Second Machine Age, self-driving car, shareholder value, sharing economy, Sheryl Sandberg, Shoshana Zuboff, Sidewalk Labs, Silicon Valley, single-payer health, Skype, Snapchat, SoftBank, statistical model, Steve Bannon, Steve Jobs, surveillance capitalism, TaskRabbit, tech worker, technological determinism, TED Talk, the scientific method, The Structural Transformation of the Public Sphere, TikTok, transcontinental railway, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, upwardly mobile, Vision Fund, W. E. B. Du Bois, wages for housework, warehouse robotics, WikiLeaks, women in the workforce, yottabyte

Instead of reveling in the conveniences and connections brought by our smartphones, society is plagued with doubts about the dystopian future we’re creating. How could we not worry? Every day, ordinary people are presented with fresh evidence of their near-term irrelevance. Stories of self-teaching algorithms, autonomous cars, expert reports predicting the disappearance of at least half the world’s jobs in the next couple of decades due to automation and robots abound. It’s a wonder we don’t stay in bed, scrolling through our feeds, awaiting the Singularity in dignified repose. Indeed, a chorus of warnings from tech naysayers and handwringers suggests we should be terrified of what a Silicon Valley future holds.


pages: 482 words: 121,173

Tools and Weapons: The Promise and the Peril of the Digital Age by Brad Smith, Carol Ann Browne

"World Economic Forum" Davos, Affordable Care Act / Obamacare, AI winter, air gap, airport security, Alan Greenspan, Albert Einstein, algorithmic bias, augmented reality, autonomous vehicles, barriers to entry, Berlin Wall, Big Tech, Bletchley Park, Blitzscaling, Boeing 737 MAX, business process, call centre, Cambridge Analytica, Celtic Tiger, Charlie Hebdo massacre, chief data officer, cloud computing, computer vision, corporate social responsibility, data science, deep learning, digital divide, disinformation, Donald Trump, Eben Moglen, Edward Snowden, en.wikipedia.org, Hacker News, immigration reform, income inequality, Internet of things, invention of movable type, invention of the telephone, Jeff Bezos, Kevin Roose, Laura Poitras, machine readable, Mark Zuckerberg, minimum viable product, national security letter, natural language processing, Network effects, new economy, Nick Bostrom, off-the-grid, operational security, opioid epidemic / opioid crisis, pattern recognition, precision agriculture, race to the bottom, ransomware, Ronald Reagan, Rubik’s Cube, Salesforce, school vouchers, self-driving car, Sheryl Sandberg, Shoshana Zuboff, Silicon Valley, Skype, speech recognition, Steve Ballmer, Steve Jobs, surveillance capitalism, tech worker, The Rise and Fall of American Growth, Tim Cook: Apple, Wargames Reagan, WikiLeaks, women in the workforce

If a city loses its electricity, telephones, gas lines, water system, and internet, it can be thrown back into something that can feel like the Stone Age. If it’s winter, people may freeze. If it’s summer, people may overheat. Those who rely on medical devices to survive could lose their lives. And in a future with autonomous vehicles, imagine a cyberattack that penetrates automobile control systems as cars barrel down the highway. All these are sobering reminders of the new world we live in. Following NotPetya, Maersk took the unusual step of reassuring the public that its ships remained under the control of their captains.


pages: 540 words: 119,731

Samsung Rising: The Inside Story of the South Korean Giant That Set Out to Beat Apple and Conquer Tech by Geoffrey Cain

Andy Rubin, Apple's 1984 Super Bowl advert, Asian financial crisis, autonomous vehicles, Berlin Wall, business intelligence, cloud computing, corporate governance, creative destruction, don't be evil, Donald Trump, double helix, Dynabook, Elon Musk, Fairchild Semiconductor, fake news, fear of failure, Hacker News, independent contractor, Internet of things, John Markoff, Jony Ive, Kickstarter, Mahatma Gandhi, Mark Zuckerberg, megacity, Mikhail Gorbachev, Nelson Mandela, patent troll, Pepsi Challenge, rolodex, Russell Brand, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, Superbowl ad, Tim Cook: Apple, Tony Fadell, too big to fail, WikiLeaks, wikimedia commons

The memory semiconductor market—the bedrock of Samsung’s empire—was volatile and crowded. Samsung announced a new expansion called “Semiconductor Vision 2030.” It pledged an investment of $115 billion in another promising field of semiconductors—non-memory chips—that power rising technologies like autonomous vehicles, medical robots, and devices that depend on artificial intelligence. “The government will actively support this mission,” South Korean President Moon announced at a Samsung semiconductor plant. “As you asked, Samsung will become the first in the non-memory sector as well as in the memory sector,” Jay told President Moon.


pages: 521 words: 118,183

The Wires of War: Technology and the Global Struggle for Power by Jacob Helberg

"World Economic Forum" Davos, 2021 United States Capitol attack, A Declaration of the Independence of Cyberspace, active measures, Affordable Care Act / Obamacare, air gap, Airbnb, algorithmic management, augmented reality, autonomous vehicles, Berlin Wall, Bernie Sanders, Big Tech, bike sharing, Black Lives Matter, blockchain, Boris Johnson, Brexit referendum, cable laying ship, call centre, Cambridge Analytica, Cass Sunstein, cloud computing, coronavirus, COVID-19, creative destruction, crisis actor, data is the new oil, data science, decentralized internet, deep learning, deepfake, deglobalization, deindustrialization, Deng Xiaoping, deplatforming, digital nomad, disinformation, don't be evil, Donald Trump, dual-use technology, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, fail fast, fake news, Filter Bubble, Francis Fukuyama: the end of history, geopolitical risk, glass ceiling, global pandemic, global supply chain, Google bus, Google Chrome, GPT-3, green new deal, information security, Internet of things, Jeff Bezos, Jeffrey Epstein, John Markoff, John Perry Barlow, knowledge economy, Larry Ellison, lockdown, Loma Prieta earthquake, low earth orbit, low skilled workers, Lyft, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Mary Meeker, Mikhail Gorbachev, military-industrial complex, Mohammed Bouazizi, move fast and break things, Nate Silver, natural language processing, Network effects, new economy, one-China policy, open economy, OpenAI, Parler "social media", Peter Thiel, QAnon, QR code, race to the bottom, Ralph Nader, RAND corporation, reshoring, ride hailing / ride sharing, Ronald Reagan, Russian election interference, Salesforce, Sam Altman, satellite internet, self-driving car, Sheryl Sandberg, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, smart grid, SoftBank, Solyndra, South China Sea, SpaceX Starlink, Steve Jobs, Steven Levy, Stuxnet, supply-chain attack, Susan Wojcicki, tech worker, techlash, technoutopianism, TikTok, Tim Cook: Apple, trade route, TSMC, Twitter Arab Spring, uber lyft, undersea cable, Unsafe at Any Speed, Valery Gerasimov, vertical integration, Wargames Reagan, Westphalian system, white picket fence, WikiLeaks, Y Combinator, zero-sum game

All told, 5G could inject $12 trillion into the global economy by 2035 and add 22 million jobs just in the United States.120 With good reason, 5G has been hailed as “the central nervous system of the 21st-century economy.”121 Whoever presides over that central nervous system could have unprecedented control over virtually everything in our lives—who reads our emails and texts, how our homes operate, where our autonomous vehicles are going. Not to mention that properly transmitting 5G signals will require millions of antennas and cell relays—at least one per city block, by some estimates—exponentially increasing opportunities for surveillance. That’s where things really get scary. Because the undisputed leader in 5G technology isn’t America’s Qualcomm, Sweden’s Ericsson, or Finland’s Nokia.


pages: 294 words: 80,084

Tomorrowland: Our Journey From Science Fiction to Science Fact by Steven Kotler

adjacent possible, Albert Einstein, Alexander Shulgin, autonomous vehicles, barriers to entry, Biosphere 2, Burning Man, carbon footprint, carbon tax, Colonization of Mars, crowdsourcing, Dean Kamen, Dennis Tito, epigenetics, gravity well, Great Leap Forward, haute couture, Helicobacter pylori, interchangeable parts, Kevin Kelly, life extension, Louis Pasteur, low earth orbit, North Sea oil, Oculus Rift, off-the-grid, oil shale / tar sands, peak oil, personalized medicine, Peter H. Diamandis: Planetary Resources, private spaceflight, RAND corporation, Ray Kurzweil, Richard Feynman, Ronald Reagan, self-driving car, SpaceShipOne, stem cell, Stephen Hawking, Stewart Brand, synthetic biology, theory of mind, Virgin Galactic, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, WikiLeaks

If so, consider that since the turn of the twenty-first century, rapidly accelerating technology has shown a distinct tendency to turn the impossible into the everyday in no time at all. A few years back, IBM’s Watson, an artificial intelligence, whipped the human champion, Ken Jennings, on Jeopardy. As we write this, soldiers with bionic limbs are fighting our enemies and autonomous cars are driving down our streets. Yet most of these advances are small in comparison to the great leap forward currently underway in the biosciences — a leap with consequences we’ve only begun to imagine. More to the point, consider that the Secret Service is already taking extraordinary steps to protect presidential DNA.


pages: 292 words: 85,381

The Story of Crossrail by Christian Wolmar

Ada Lovelace, autonomous vehicles, Beeching cuts, Big bang: deregulation of the City of London, Boris Johnson, Crossrail, data acquisition, driverless car, Kickstarter, megacity, megaproject

His previous books include The Subterranean Railway, a history of the London underground, Fire and Steam: How the Railways Transformed Britain, To the Edge of the World: The Story of the Trans-Siberian Railway and Railways and the Raj: How the Age of Steam Transformed India. His other work includes an analysis of transport policy, Are Trams Socialist?, and a debunking of the myths around autonomous cars, Driverless Cars: On a Road to Nowhere. Find me on Twitter Visit my website An Invitation from the Publisher Apollo is an imprint of Head of Zeus. We hope you enjoyed this book. We are an independent publisher dedicated to discovering brilliant books, new authors and great storytelling.


pages: 308 words: 85,880

How to Fix the Future: Staying Human in the Digital Age by Andrew Keen

"World Economic Forum" Davos, 23andMe, Ada Lovelace, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, AlphaGo, Andrew Keen, Apple's 1984 Super Bowl advert, augmented reality, autonomous vehicles, basic income, Bernie Sanders, Big Tech, bitcoin, Black Swan, blockchain, Brewster Kahle, British Empire, carbon tax, Charles Babbage, computer age, Cornelius Vanderbilt, creative destruction, crowdsourcing, data is the new oil, death from overwork, DeepMind, Demis Hassabis, Didi Chuxing, digital capitalism, digital map, digital rights, disinformation, don't be evil, Donald Trump, driverless car, Edward Snowden, Elon Musk, Erik Brynjolfsson, European colonialism, fake news, Filter Bubble, Firefox, fulfillment center, full employment, future of work, gig economy, global village, income inequality, independent contractor, informal economy, Internet Archive, Internet of things, invisible hand, Isaac Newton, James Watt: steam engine, Jane Jacobs, Jaron Lanier, Jeff Bezos, jimmy wales, job automation, Joi Ito, Kevin Kelly, knowledge economy, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Mitch Kapor, move fast and break things, Network effects, new economy, Nicholas Carr, Norbert Wiener, OpenAI, Parag Khanna, peer-to-peer, Peter Thiel, plutocrats, post-truth, postindustrial economy, precariat, Ralph Nader, Ray Kurzweil, Recombinant DNA, rent-seeking, ride hailing / ride sharing, Rutger Bregman, Salesforce, Sam Altman, Sand Hill Road, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Silicon Valley startup, Skype, smart cities, Snapchat, social graph, software is eating the world, Stephen Hawking, Steve Jobs, Steve Wozniak, subscription business, surveillance capitalism, Susan Wojcicki, tech baron, tech billionaire, tech worker, technological determinism, technoutopianism, The Future of Employment, the High Line, the new new thing, Thomas L Friedman, Tim Cook: Apple, Travis Kalanick, Triangle Shirtwaist Factory, Uber and Lyft, Uber for X, uber lyft, universal basic income, Unsafe at Any Speed, Upton Sinclair, urban planning, WikiLeaks, winner-take-all economy, Y Combinator, Yogi Berra, Zipcar

And as a tech company, every company is now, like it or not, in competition with every other tech company. In the automotive industry, for example, one of the most pressing challenges for German car manufacturers is to determine where they will fit in the so-called software stack that will empower self-driving vehicles. In a new ecosystem driven, so to speak, by autonomous cars, their challenge is to avoid becoming the dumb commodified hardware at the bottom of the stack in an economy where, as you’ll remember Marc Andreessen saying, software is eating the world. So for Mercedes and BMW, the most dangerous long-term competition now comes from Silicon Valley. Their existential threat is that they could be eaten by Google’s, Tesla’s, or Apple’s algorithms, not by Toyota or Ford cars.


Know Thyself by Stephen M Fleming

Abraham Wald, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, AlphaGo, autism spectrum disorder, autonomous vehicles, availability heuristic, backpropagation, citation needed, computer vision, confounding variable, data science, deep learning, DeepMind, Demis Hassabis, Douglas Hofstadter, Dunning–Kruger effect, Elon Musk, Estimating the Reproducibility of Psychological Science, fake news, global pandemic, higher-order functions, index card, Jeff Bezos, l'esprit de l'escalier, Lao Tzu, lifelogging, longitudinal study, meta-analysis, mutually assured destruction, Network effects, patient HM, Pierre-Simon Laplace, power law, prediction markets, QWERTY keyboard, recommendation engine, replication crisis, self-driving car, side project, Skype, Stanislav Petrov, statistical model, theory of mind, Thomas Bayes, traumatic brain injury

These signals could be used by their human operators to take control in situations of high uncertainty and increase the humans’ trust that the car did know what it was doing at all other times. Even more intriguing is the idea that these machines could share metacognitive information with each other, just as self-awareness comes into its own when humans begin to collaborate and interact. Imagine two autonomous cars approaching an intersection, each signaling to turn in different directions. If both have a healthy blue glow, then they can proceed, safe in the knowledge that the other car has a good idea of what is happening. But if one or both of them begins to glow yellow, it would be wise to slow down and proceed with caution, just as we would do if a driver on the other side of the intersection didn’t seem to know what our intentions were.


pages: 431 words: 129,071

Selfie: How We Became So Self-Obsessed and What It's Doing to Us by Will Storr

Abraham Maslow, Adam Curtis, Alan Greenspan, Albert Einstein, autonomous vehicles, banking crisis, bitcoin, classic study, computer age, correlation does not imply causation, Donald Trump, Douglas Engelbart, Douglas Engelbart, Elon Musk, en.wikipedia.org, gamification, gig economy, greed is good, intentional community, invisible hand, job automation, John Markoff, Kevin Roose, Kickstarter, Lewis Mumford, longitudinal study, low interest rates, Lyft, Menlo Park, meta-analysis, military-industrial complex, Mont Pelerin Society, mortgage debt, Mother of all demos, Nixon shock, Peter Thiel, prosperity theology / prosperity gospel / gospel of success, QWERTY keyboard, Rainbow Mansion, rising living standards, road to serfdom, Robert Gordon, Ronald Reagan, selective serotonin reuptake inhibitor (SSRI), Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Steve Bannon, Steve Jobs, Steven Levy, Stewart Brand, synthetic biology, tech bro, tech worker, The Future of Employment, The Rise and Fall of American Growth, Tim Cook: Apple, Travis Kalanick, twin studies, Uber and Lyft, uber lyft, War on Poverty, We are as Gods, Whole Earth Catalog

It did, facilitating and accelerating the neoliberal project of globalization immensely from the 1990s onwards. It has plenty in store for the future, too, with automation and artificial intelligence predicted to further decimate middle- and working-class jobs. There are 1.7 million truck drivers in the US alone whose livelihoods are at risk from the introduction of autonomous vehicles. Researchers at the University of Oxford have predicted that, by 2033, nearly half of all US jobs could be automated. The technologists promised us a ‘Long Boom’. They didn’t tell us that boom would be directed mostly at the top. It was another Silicon Valley product, social media, that enabled Donald Trump to connect directly with his supporters, bypassing traditional journalists and undermining their reporting by calling them liars.


pages: 742 words: 137,937

The Future of the Professions: How Technology Will Transform the Work of Human Experts by Richard Susskind, Daniel Susskind

23andMe, 3D printing, Abraham Maslow, additive manufacturing, AI winter, Albert Einstein, Amazon Mechanical Turk, Amazon Robotics, Amazon Web Services, Andrew Keen, Atul Gawande, Automated Insights, autonomous vehicles, Big bang: deregulation of the City of London, big data - Walmart - Pop Tarts, Bill Joy: nanobots, Blue Ocean Strategy, business process, business process outsourcing, Cass Sunstein, Checklist Manifesto, Clapham omnibus, Clayton Christensen, clean water, cloud computing, commoditize, computer age, Computer Numeric Control, computer vision, Computing Machinery and Intelligence, conceptual framework, corporate governance, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, death of newspapers, disintermediation, Douglas Hofstadter, driverless car, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, Filter Bubble, full employment, future of work, Garrett Hardin, Google Glasses, Google X / Alphabet X, Hacker Ethic, industrial robot, informal economy, information retrieval, interchangeable parts, Internet of things, Isaac Newton, James Hargreaves, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Khan Academy, knowledge economy, Large Hadron Collider, lifelogging, lump of labour, machine translation, Marshall McLuhan, Metcalfe’s law, Narrative Science, natural language processing, Network effects, Nick Bostrom, optical character recognition, Paul Samuelson, personalized medicine, planned obsolescence, pre–internet, Ray Kurzweil, Richard Feynman, Second Machine Age, self-driving car, semantic web, Shoshana Zuboff, Skype, social web, speech recognition, spinning jenny, strong AI, supply-chain management, Susan Wojcicki, tacit knowledge, TED Talk, telepresence, The Future of Employment, the market place, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Tragedy of the Commons, transaction costs, Turing test, Two Sigma, warehouse robotics, Watson beat the top human players on Jeopardy!, WikiLeaks, world market for maybe five computers, Yochai Benkler, young professional

In ten years robots have moved ‘from making cars to driving them’.55 By 2014, Google’s vehicles had travelled almost 700,000 miles, with only one incident (said to be caused by a car driven by a human being). In the United States legislation has been passed in four states and in Washington, DC, allowing driverless cars.56 By 2020 most major car manufacturers also expect to be selling autonomous vehicles. Our guess is that, in due course, people will look back with incredulity and say, ‘it’s amazing people actually used to drive cars’. Other illustrations of advanced robotics abound. Every year, in manufacturing, an additional 200,000 industrial robots are installed (adding to an expected total of 1.5 million robots in 2015).57 In 2014, for example, Amazon had more than 15,000 robots in ten of its warehouses.


pages: 469 words: 132,438

Taming the Sun: Innovations to Harness Solar Energy and Power the Planet by Varun Sivaram

"World Economic Forum" Davos, accelerated depreciation, addicted to oil, Albert Einstein, An Inconvenient Truth, asset light, asset-backed security, autonomous vehicles, bitcoin, blockchain, carbon footprint, carbon tax, clean tech, collateralized debt obligation, Colonization of Mars, currency risk, decarbonisation, deep learning, demand response, disruptive innovation, distributed generation, diversified portfolio, Donald Trump, electricity market, Elon Musk, energy security, energy transition, financial engineering, financial innovation, fixed income, gigafactory, global supply chain, global village, Google Earth, hive mind, hydrogen economy, index fund, Indoor air pollution, Intergovernmental Panel on Climate Change (IPCC), Internet of things, low interest rates, M-Pesa, market clearing, market design, Masayoshi Son, mass immigration, megacity, Michael Shellenberger, mobile money, Negawatt, ocean acidification, off grid, off-the-grid, oil shock, peer-to-peer lending, performance metric, renewable energy transition, Richard Feynman, ride hailing / ride sharing, rolling blackouts, Ronald Reagan, Silicon Valley, Silicon Valley startup, smart grid, smart meter, SoftBank, Solyndra, sovereign wealth fund, Ted Nordhaus, Tesla Model S, time value of money, undersea cable, vertical integration, wikimedia commons

Those needs are met almost exclusively by fossil fuels, and they have only grown as emerging economies have industrialized, with staggering consequences. Industrial facilities, like cement and steel plants, belch out soot from burning coal. Skyrocketing demand for transportation has also defiled the air and caused crippling congestion. Although fewer people own cars today than in decades past thanks to fleets of autonomous vehicles and convenient ridesharing, these advances have made it easier and cheaper than ever to get around; the resulting surge in travelers has packed more cars on the road at any given time.3 Many had hoped that electric vehicles might reduce local air pollution, and indeed they have risen to lead the pack in new vehicle sales.


pages: 611 words: 130,419

Narrative Economics: How Stories Go Viral and Drive Major Economic Events by Robert J. Shiller

agricultural Revolution, Alan Greenspan, Albert Einstein, algorithmic trading, Andrei Shleifer, autism spectrum disorder, autonomous vehicles, bank run, banking crisis, basic income, behavioural economics, bitcoin, blockchain, business cycle, butterfly effect, buy and hold, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, central bank independence, collective bargaining, computerized trading, corporate raider, correlation does not imply causation, cryptocurrency, Daniel Kahneman / Amos Tversky, debt deflation, digital divide, disintermediation, Donald Trump, driverless car, Edmond Halley, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, fake news, financial engineering, Ford Model T, full employment, George Akerlof, germ theory of disease, German hyperinflation, Great Leap Forward, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, implied volatility, income inequality, inflation targeting, initial coin offering, invention of radio, invention of the telegraph, Jean Tirole, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, litecoin, low interest rates, machine translation, market bubble, Modern Monetary Theory, money market fund, moral hazard, Northern Rock, nudge unit, Own Your Own Home, Paul Samuelson, Philip Mirowski, plutocrats, Ponzi scheme, public intellectual, publish or perish, random walk, Richard Thaler, Robert Shiller, Ronald Reagan, Rubik’s Cube, Satoshi Nakamoto, secular stagnation, shareholder value, Silicon Valley, speech recognition, Steve Jobs, Steven Pinker, stochastic process, stocks for the long run, superstar cities, The Rise and Fall of American Growth, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, theory of mind, Thorstein Veblen, traveling salesman, trickle-down economics, tulip mania, universal basic income, Watson beat the top human players on Jeopardy!, We are the 99%, yellow journalism, yield curve, Yom Kippur War

Around the same time, other inventions also attracted great public attention, notably driverless cars, which, despite some worries about safety, are predicted to replace many jobs. Though very few of us had actually seen a driverless car, we all knew that prototypes were already on our highways. These autonomous vehicles can already do things that we assumed were not programmable, like slowing down when the car senses children running around near the street. Human common sense can be reduced to a list of signals to a driverless car, which means that human common sense can be replaced. Recent talk has stressed machine learning, in which computers are designed to learn for themselves rather than be programmed using human intelligence.


pages: 299 words: 88,375

Gray Day: My Undercover Mission to Expose America's First Cyber Spy by Eric O'Neill

active measures, autonomous vehicles, Berlin Wall, bitcoin, computer age, cryptocurrency, deep learning, disinformation, Dissolution of the Soviet Union, Edward Snowden, Fall of the Berlin Wall, false flag, fear of failure, full text search, index card, information security, Internet of things, Kickstarter, messenger bag, Mikhail Gorbachev, operational security, PalmPilot, ransomware, rent control, Robert Hanssen: Double agent, Ronald Reagan, Skype, thinkpad, Timothy McVeigh, web application, white picket fence, WikiLeaks, young professional

Ever wondered if someone is watching you through your laptop camera or the new home-security system you just bought off eBay, or maybe through your new smart-home device? Did you open an email attachment or click on a link that made you think twice when your screen flickered? Do you have concerns about the future of autonomous cars and networked robotics and whether these innovations will open us up to future tragedy? I do. After leaving the FBI, I realized that it wasn’t enough to ride through towns shouting that the cyber spies were coming. My success in Room 9930 had come when I’d broken Hanssen’s OODA loop of his action and my reaction.


Gods and Robots: Myths, Machines, and Ancient Dreams of Technology by Adrienne Mayor

AlphaGo, Any sufficiently advanced technology is indistinguishable from magic, Asilomar, autonomous vehicles, caloric restriction, caloric restriction, classic study, deep learning, driverless car, Elon Musk, industrial robot, Islamic Golden Age, Jacquard loom, life extension, Menlo Park, Nick Bostrom, Panopticon Jeremy Bentham, popular electronics, self-driving car, Silicon Valley, Stephen Hawking, Thales and the olive presses, Thales of Miletus, theory of mind, TikTok, Turing test

In Global Issues and Ethical Considerations in Human Enhancement Technologies, ed. S. J. Thompson, 119–60. IGI Global. Lin, Patrick, Keith Abney, and George Bekey, eds. 2014. Robot Ethics: The Ethical and Social Implications of Robotics. Cambridge, MA: MIT Press. Lin, Patrick, Ryan Jenkins, and Keith Abney, eds. 2017. Robot Ethics 2.0: From Autonomous Cars to Artificial Intelligence. Oxford: Oxford University Press. Liu, Lydia. 2011. The Freudian Robot. Chicago: University of Chicago Press. “Longevity: Adding Ages.” 2016. Economist, August 13, 14–16. Lowe, Dunstan. 2016. “Suspending Disbelief: Magnetic and Miraculous Levitation from Antiquity to the Middle Ages.”


Rockonomics: A Backstage Tour of What the Music Industry Can Teach Us About Economics and Life by Alan B. Krueger

"Friedman doctrine" OR "shareholder theory", accounting loophole / creative accounting, Affordable Care Act / Obamacare, Airbnb, Alan Greenspan, autonomous vehicles, bank run, behavioural economics, Berlin Wall, bitcoin, Bob Geldof, butterfly effect, buy and hold, congestion pricing, creative destruction, crowdsourcing, digital rights, disintermediation, diversified portfolio, Donald Trump, endogenous growth, Gary Kildall, George Akerlof, gig economy, income inequality, independent contractor, index fund, invisible hand, Jeff Bezos, John Maynard Keynes: Economic Possibilities for our Grandchildren, Kenneth Arrow, Kickstarter, Larry Ellison, Live Aid, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, moral hazard, Multics, Network effects, obamacare, offshore financial centre, opioid epidemic / opioid crisis, Paul Samuelson, personalized medicine, power law, pre–internet, price discrimination, profit maximization, random walk, recommendation engine, rent-seeking, Richard Thaler, ride hailing / ride sharing, Saturday Night Live, Skype, Steve Jobs, the long tail, The Wealth of Nations by Adam Smith, TikTok, too big to fail, transaction costs, traumatic brain injury, Tyler Cowen, ultimatum game, winner-take-all economy, women in the workforce, Y Combinator, zero-sum game

If the Echo Dot and Alexa prove to be popular portals that draw millions more customers to buy sneakers and other goods from the Amazon retail site, Amazon would be willing to sustain losses from Amazon Music. A similar dynamic is taking place elsewhere in the economy. Google, together with its subsidiary autonomous car development company, Waymo, for example, is challenging traditional automobile companies, such as Ford and General Motors. Google’s combination of technological prowess, deep pockets, and complementary activities poses a formidable threat to standalone car companies. Some banking functions could be similarly challenged by Apple Wallet.


pages: 416 words: 100,130

New Power: How Power Works in Our Hyperconnected World--And How to Make It Work for You by Jeremy Heimans, Henry Timms

"Susan Fowler" uber, "World Economic Forum" Davos, 3D printing, 4chan, Affordable Care Act / Obamacare, Airbnb, algorithmic management, augmented reality, autonomous vehicles, battle of ideas, benefit corporation, Benjamin Mako Hill, Big Tech, bitcoin, Black Lives Matter, blockchain, British Empire, Chris Wanstrath, Columbine, Corn Laws, crowdsourcing, data science, David Attenborough, death from overwork, Donald Trump, driverless car, Elon Musk, fake news, Ferguson, Missouri, future of work, game design, gig economy, hiring and firing, holacracy, hustle culture, IKEA effect, impact investing, income inequality, informal economy, job satisfaction, John Zimmer (Lyft cofounder), Jony Ive, Kevin Roose, Kibera, Kickstarter, Lean Startup, Lyft, Mark Zuckerberg, Minecraft, Network effects, new economy, Nicholas Carr, obamacare, Occupy movement, post-truth, profit motive, race to the bottom, radical decentralization, ride hailing / ride sharing, rolling blackouts, rolodex, Salesforce, Saturday Night Live, sharing economy, side hustle, Silicon Valley, six sigma, Snapchat, social web, subscription business, TaskRabbit, tech billionaire, TED Talk, the scientific method, transaction costs, Travis Kalanick, Uber and Lyft, uber lyft, upwardly mobile, web application, WikiLeaks, Yochai Benkler

These losses are driven by the ride subsidies it must offer all over the world as it pushes for market dominance. Uber will have to increasingly squeeze its drivers, surge its prices for riders, and crush its competitors if it is going to find a path to profitability. We see particular tension ahead with the rise of autonomous cars, a cost-saving move for Uber that could threaten the livelihood of hundreds of thousands of drivers. The next “buy local”–style movement could very well be “ride human.” For all Uber might do to reform, there are others working on ridesharing platforms that aim to replace the worst aspects of its model, creating a harmonious triangle and a more perfect circle, promising a better deal for everyone involved: riders, drivers, and the wider world.


pages: 393 words: 91,257

The Coming of Neo-Feudalism: A Warning to the Global Middle Class by Joel Kotkin

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", "World Economic Forum" Davos, Admiral Zheng, Alvin Toffler, Andy Kessler, autonomous vehicles, basic income, Bernie Sanders, Big Tech, bread and circuses, Brexit referendum, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon credits, carbon footprint, Cass Sunstein, clean water, company town, content marketing, Cornelius Vanderbilt, creative destruction, data science, deindustrialization, demographic transition, deplatforming, don't be evil, Donald Trump, driverless car, edge city, Elon Musk, European colonialism, Evgeny Morozov, financial independence, Francis Fukuyama: the end of history, Future Shock, gentrification, gig economy, Gini coefficient, Google bus, Great Leap Forward, green new deal, guest worker program, Hans Rosling, Herbert Marcuse, housing crisis, income inequality, informal economy, Jane Jacobs, Jaron Lanier, Jeff Bezos, Jeremy Corbyn, job automation, job polarisation, job satisfaction, Joseph Schumpeter, land reform, liberal capitalism, life extension, low skilled workers, Lyft, Marc Benioff, Mark Zuckerberg, market fundamentalism, Martin Wolf, mass immigration, megacity, Michael Shellenberger, Nate Silver, new economy, New Urbanism, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, Occupy movement, Parag Khanna, Peter Thiel, plutocrats, post-industrial society, post-work, postindustrial economy, postnationalism / post nation state, precariat, profit motive, public intellectual, RAND corporation, Ray Kurzweil, rent control, Richard Florida, road to serfdom, Robert Gordon, Salesforce, Sam Altman, San Francisco homelessness, Satyajit Das, sharing economy, Sidewalk Labs, Silicon Valley, smart cities, Social Justice Warrior, Steve Jobs, Stewart Brand, superstar cities, technological determinism, Ted Nordhaus, The Death and Life of Great American Cities, The future is already here, The Future of Employment, The Rise and Fall of American Growth, Thomas L Friedman, too big to fail, trade route, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, unpaid internship, upwardly mobile, Virgin Galactic, We are the 99%, Wolfgang Streeck, women in the workforce, work culture , working-age population, Y Combinator

But the top firms tend to exist as properties of a small number of financiers and technologists who operate within a narrow, self-referential universe.22 This concentration of technological power portends a far less democratic future.23 With their huge cash reserves, the tech oligarchs have plans to dominate older industries like entertainment, finance, education, and retail, as well as industries of the future: autonomous cars, drones, space exploration, and most critically artificial intelligence. Firms like Google, Amazon, and Apple have invested billions to gain post position in both traditional and emerging industries.24 Izabella Kaminska, a technology analyst, compares the giant tech firms to the Soviet planners who operated Gosplan, the economic planning agency that allocated state resources across the USSR.25 Some may consider it preferable to cede such power to private capital rather than party hacks, but it still amounts to a great deal of power in a few hands, with little accountability.26 The China Syndrome China, with its lack of legal restraints, may prove to be the cutting edge of a new technocratic despotism.


pages: 372 words: 98,659

The Miracle Pill by Peter Walker

active transport: walking or cycling, agricultural Revolution, autonomous vehicles, Boris Johnson, Brexit referendum, call centre, car-free, Coronary heart disease and physical activity of work, coronavirus, COVID-19, driverless car, experimental subject, James Watt: steam engine, Kickstarter, lockdown, longitudinal study, meta-analysis, randomized controlled trial, Sidewalk Labs, social distancing, Stop de Kindermoord, the built environment, Traffic in Towns by Colin Buchanan, twin studies, Wall-E, washing machines reduced drudgery

A few years ago I had a long chat with a senior executive from Sidewalk Labs, a Google spinoff company which seeks to use tech to reimagine how cities could work in the future. We discussed how driverless cars – which at the time, as now, were confidently billed as being just around the corner from ubiquity – could affect active travel. While stressing that predictions were essentially guesswork, he conjured up one scenario in which autonomous cars sped people from distant suburbs to city centres, but with the last half mile or so, where people lived and worked, reserved for modes like cycling and walking. Again, I wasn’t so sure. There is definitely an urban transport revolution coming, perhaps more quickly than most people realise, but it could go in several different ways.


pages: 497 words: 144,283

Connectography: Mapping the Future of Global Civilization by Parag Khanna

"World Economic Forum" Davos, 1919 Motor Transport Corps convoy, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 9 dash line, additive manufacturing, Admiral Zheng, affirmative action, agricultural Revolution, Airbnb, Albert Einstein, amateurs talk tactics, professionals talk logistics, Amazon Mechanical Turk, Anthropocene, Asian financial crisis, asset allocation, autonomous vehicles, banking crisis, Basel III, Berlin Wall, bitcoin, Black Swan, blockchain, borderless world, Boycotts of Israel, Branko Milanovic, BRICs, British Empire, business intelligence, call centre, capital controls, Carl Icahn, charter city, circular economy, clean water, cloud computing, collateralized debt obligation, commoditize, complexity theory, continuation of politics by other means, corporate governance, corporate social responsibility, credit crunch, crony capitalism, crowdsourcing, cryptocurrency, cuban missile crisis, data is the new oil, David Ricardo: comparative advantage, deglobalization, deindustrialization, dematerialisation, Deng Xiaoping, Detroit bankruptcy, digital capitalism, digital divide, digital map, disruptive innovation, diversification, Doha Development Round, driverless car, Easter island, edge city, Edward Snowden, Elon Musk, energy security, Ethereum, ethereum blockchain, European colonialism, eurozone crisis, export processing zone, failed state, Fairphone, Fall of the Berlin Wall, family office, Ferguson, Missouri, financial innovation, financial repression, fixed income, forward guidance, gentrification, geopolitical risk, global supply chain, global value chain, global village, Google Earth, Great Leap Forward, Hernando de Soto, high net worth, high-speed rail, Hyperloop, ice-free Arctic, if you build it, they will come, illegal immigration, income inequality, income per capita, industrial cluster, industrial robot, informal economy, Infrastructure as a Service, interest rate swap, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Isaac Newton, Jane Jacobs, Jaron Lanier, John von Neumann, Julian Assange, Just-in-time delivery, Kevin Kelly, Khyber Pass, Kibera, Kickstarter, LNG terminal, low cost airline, low earth orbit, low interest rates, manufacturing employment, mass affluent, mass immigration, megacity, Mercator projection, Metcalfe’s law, microcredit, middle-income trap, mittelstand, Monroe Doctrine, Multics, mutually assured destruction, Neal Stephenson, New Economic Geography, new economy, New Urbanism, off grid, offshore financial centre, oil rush, oil shale / tar sands, oil shock, openstreetmap, out of africa, Panamax, Parag Khanna, Peace of Westphalia, peak oil, Pearl River Delta, Peter Thiel, Philip Mirowski, Planet Labs, plutocrats, post-oil, post-Panamax, precautionary principle, private military company, purchasing power parity, quantum entanglement, Quicken Loans, QWERTY keyboard, race to the bottom, Rana Plaza, rent-seeking, reserve currency, Robert Gordon, Robert Shiller, Robert Solow, rolling blackouts, Ronald Coase, Scramble for Africa, Second Machine Age, sharing economy, Shenzhen special economic zone , Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, six sigma, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, South China Sea, South Sea Bubble, sovereign wealth fund, special economic zone, spice trade, Stuxnet, supply-chain management, sustainable-tourism, systems thinking, TaskRabbit, tech worker, TED Talk, telepresence, the built environment, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, Tim Cook: Apple, trade route, Tragedy of the Commons, transaction costs, Tyler Cowen, UNCLOS, uranium enrichment, urban planning, urban sprawl, vertical integration, WikiLeaks, Yochai Benkler, young professional, zero day

Shanghai is now connected via the thirty-two-kilometer Donghai Bridge to the Yangshan Island mega-port, which features state-of-the-art traffic control towers, management nerve centers tracking hundreds of ships, tens of thousands of containers, and hundreds of (soon driverless) trucks at the same time. From Yangshan to Melbourne to Long Beach, terminal operators are using electronic data interchange software to optimize berthing schedules, deploying autonomous vehicles and virtual reality to accelerate their loading and unloading speeds, and partnering with logistics companies such as Shipwire to coordinate warehouse inventories with freight rail to efficiently distribute goods like blood vessels through the planetary circulatory system. Throughout history, competition among port cities has revealed who is winning the supply chain tug-of-war.


pages: 565 words: 151,129

The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism by Jeremy Rifkin

3D printing, active measures, additive manufacturing, Airbnb, autonomous vehicles, back-to-the-land, benefit corporation, big-box store, bike sharing, bioinformatics, bitcoin, business logic, business process, Chris Urmson, circular economy, clean tech, clean water, cloud computing, collaborative consumption, collaborative economy, commons-based peer production, Community Supported Agriculture, Computer Numeric Control, computer vision, crowdsourcing, demographic transition, distributed generation, DIY culture, driverless car, Eben Moglen, electricity market, en.wikipedia.org, Frederick Winslow Taylor, Free Software Foundation, Garrett Hardin, general purpose technology, global supply chain, global village, Hacker Conference 1984, Hacker Ethic, industrial robot, informal economy, information security, Intergovernmental Panel on Climate Change (IPCC), intermodal, Internet of things, invisible hand, Isaac Newton, James Watt: steam engine, job automation, John Elkington, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Julian Assange, Kickstarter, knowledge worker, longitudinal study, low interest rates, machine translation, Mahatma Gandhi, manufacturing employment, Mark Zuckerberg, market design, mass immigration, means of production, meta-analysis, Michael Milken, mirror neurons, natural language processing, new economy, New Urbanism, nuclear winter, Occupy movement, off grid, off-the-grid, oil shale / tar sands, pattern recognition, peer-to-peer, peer-to-peer lending, personalized medicine, phenotype, planetary scale, price discrimination, profit motive, QR code, RAND corporation, randomized controlled trial, Ray Kurzweil, rewilding, RFID, Richard Stallman, risk/return, Robert Solow, Rochdale Principles, Ronald Coase, scientific management, search inside the book, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, smart cities, smart grid, smart meter, social web, software as a service, spectrum auction, Steve Jobs, Stewart Brand, the built environment, the Cathedral and the Bazaar, the long tail, The Nature of the Firm, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, too big to fail, Tragedy of the Commons, transaction costs, urban planning, vertical integration, warehouse automation, Watson beat the top human players on Jeopardy!, web application, Whole Earth Catalog, Whole Earth Review, WikiLeaks, working poor, Yochai Benkler, zero-sum game, Zipcar

Jack Ewing, “A Benz with a Virtual Chauffeur,” New York Times, May 16, 2013, http://www.ny times.com/2013/05/19/automobiles/a-benz-with-a-virtual-chauffeur.html?pagewanted=all& _r=0 (accessed May 28, 2013). 26. Emi Kolawole, “A Win For Google’s Driverless Car: Calif. Governor Signs a Bill Regulating Autonomous Vehicles,” Washington Post, September 25, 2012, http://www.washingtonpost.com (accessed June 2, 2013). 27. Jeremy Rifkin, The Age of Access: The New Culture of Hypercapitalism Where All of Life Is a Paid-For Experience (New York: Tracher/Penguin, 2000), 6, 14. 28. Matthew Ruben, “Forgive Us Our Trespasses?


pages: 524 words: 155,947

More: The 10,000-Year Rise of the World Economy by Philip Coggan

accounting loophole / creative accounting, Ada Lovelace, agricultural Revolution, Airbnb, airline deregulation, Alan Greenspan, Andrei Shleifer, anti-communist, Apollo 11, assortative mating, autonomous vehicles, bank run, banking crisis, banks create money, basic income, Bear Stearns, Berlin Wall, Black Monday: stock market crash in 1987, Bletchley Park, Bob Noyce, Boeing 747, bond market vigilante , Branko Milanovic, Bretton Woods, Brexit referendum, British Empire, business cycle, call centre, capital controls, carbon footprint, carbon tax, Carl Icahn, Carmen Reinhart, Celtic Tiger, central bank independence, Charles Babbage, Charles Lindbergh, clean water, collective bargaining, Columbian Exchange, Columbine, Corn Laws, cotton gin, credit crunch, Credit Default Swap, crony capitalism, cross-border payments, currency peg, currency risk, debt deflation, DeepMind, Deng Xiaoping, discovery of the americas, Donald Trump, driverless car, Easter island, Erik Brynjolfsson, European colonialism, eurozone crisis, Fairchild Semiconductor, falling living standards, financial engineering, financial innovation, financial intermediation, floating exchange rates, flying shuttle, Ford Model T, Fractional reserve banking, Frederick Winslow Taylor, full employment, general purpose technology, germ theory of disease, German hyperinflation, gig economy, Gini coefficient, Glass-Steagall Act, global supply chain, global value chain, Gordon Gekko, Great Leap Forward, greed is good, Greenspan put, guns versus butter model, Haber-Bosch Process, Hans Rosling, Hernando de Soto, hydraulic fracturing, hydroponic farming, Ignaz Semmelweis: hand washing, income inequality, income per capita, independent contractor, indoor plumbing, industrial robot, inflation targeting, Isaac Newton, James Watt: steam engine, job automation, John Snow's cholera map, joint-stock company, joint-stock limited liability company, Jon Ronson, Kenneth Arrow, Kula ring, labour market flexibility, land reform, land tenure, Lao Tzu, large denomination, Les Trente Glorieuses, liquidity trap, Long Term Capital Management, Louis Blériot, low cost airline, low interest rates, low skilled workers, lump of labour, M-Pesa, Malcom McLean invented shipping containers, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Martin Wolf, McJob, means of production, Mikhail Gorbachev, mittelstand, Modern Monetary Theory, moral hazard, Murano, Venice glass, Myron Scholes, Nelson Mandela, Network effects, Northern Rock, oil shale / tar sands, oil shock, Paul Samuelson, Paul Volcker talking about ATMs, Phillips curve, popular capitalism, popular electronics, price stability, principal–agent problem, profit maximization, purchasing power parity, quantitative easing, railway mania, Ralph Nader, regulatory arbitrage, road to serfdom, Robert Gordon, Robert Shiller, Robert Solow, Ronald Coase, Ronald Reagan, savings glut, scientific management, Scramble for Africa, Second Machine Age, secular stagnation, Silicon Valley, Simon Kuznets, South China Sea, South Sea Bubble, special drawing rights, spice trade, spinning jenny, Steven Pinker, Suez canal 1869, TaskRabbit, techlash, Thales and the olive presses, Thales of Miletus, The Great Moderation, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, The Rise and Fall of American Growth, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Malthus, Thorstein Veblen, trade route, Tragedy of the Commons, transaction costs, transatlantic slave trade, transcontinental railway, Triangle Shirtwaist Factory, universal basic income, Unsafe at Any Speed, Upton Sinclair, V2 rocket, Veblen good, War on Poverty, Washington Consensus, Watson beat the top human players on Jeopardy!, women in the workforce, world market for maybe five computers, Yom Kippur War, you are the product, zero-sum game

Global car sales in 2017 were 79m, more than double the 1990s average.44 But the trend may head in the opposite direction in the rich world. Young people struggle to afford car ownership and can rely on taxi services like Uber, short-term car hire, or shared ownership, for the journeys they need. In the future, autonomous vehicles may provide another option. Of course, cars are not the only motorised vehicle on the roads. Trucks or lorries play a vital part in taking goods direct to retailers and to our doorstep. According to the American Trucking Association, the US alone has 3.6m heavy-duty trucks, which, between them, transport 10.5bn tons of freight a year, 71% of all the goods moved within the country. 45 Other countries are equally dependent on these giants of the road.


pages: 486 words: 150,849

Evil Geniuses: The Unmaking of America: A Recent History by Kurt Andersen

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, affirmative action, Affordable Care Act / Obamacare, air traffic controllers' union, airline deregulation, airport security, Alan Greenspan, always be closing, American ideology, American Legislative Exchange Council, An Inconvenient Truth, anti-communist, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, basic income, Bear Stearns, Bernie Sanders, blue-collar work, Bonfire of the Vanities, bonus culture, Burning Man, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon tax, Cass Sunstein, centre right, computer age, contact tracing, coronavirus, corporate governance, corporate raider, cotton gin, COVID-19, creative destruction, Credit Default Swap, cryptocurrency, deep learning, DeepMind, deindustrialization, Donald Trump, Dr. Strangelove, Elon Musk, ending welfare as we know it, Erik Brynjolfsson, feminist movement, financial deregulation, financial innovation, Francis Fukuyama: the end of history, future of work, Future Shock, game design, General Motors Futurama, George Floyd, George Gilder, Gordon Gekko, greed is good, Herbert Marcuse, Herman Kahn, High speed trading, hive mind, income inequality, industrial robot, interchangeable parts, invisible hand, Isaac Newton, It's morning again in America, James Watt: steam engine, Jane Jacobs, Jaron Lanier, Jeff Bezos, jitney, Joan Didion, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, junk bonds, Kevin Roose, knowledge worker, lockdown, low skilled workers, Lyft, Mark Zuckerberg, market bubble, mass immigration, mass incarceration, Menlo Park, Naomi Klein, new economy, Norbert Wiener, Norman Mailer, obamacare, Overton Window, Peter Thiel, Picturephone, plutocrats, post-industrial society, Powell Memorandum, pre–internet, public intellectual, Ralph Nader, Right to Buy, road to serfdom, Robert Bork, Robert Gordon, Robert Mercer, Ronald Reagan, Saturday Night Live, Seaside, Florida, Second Machine Age, shareholder value, Silicon Valley, social distancing, Social Responsibility of Business Is to Increase Its Profits, Steve Jobs, Stewart Brand, stock buybacks, strikebreaker, tech billionaire, The Death and Life of Great American Cities, The Future of Employment, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Tim Cook: Apple, too big to fail, trickle-down economics, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, union organizing, universal basic income, Unsafe at Any Speed, urban planning, urban renewal, very high income, wage slave, Wall-E, War on Poverty, We are all Keynesians now, Whole Earth Catalog, winner-take-all economy, women in the workforce, working poor, young professional, éminence grise

Starting now, retail chains will have a public health argument for replacing workers behind the counters with machines. The most common American job, however, has been driver—the 4 or 5 million FedEx and UPS and tractor-trailer and bus drivers, and the maybe 2 million taxi and Uber and Lyft drivers. During this decade, autonomous vehicles will begin making the 6 or 7 million (potentially infectious) people doing those jobs redundant as well. That debate over whether to blame automation or cheap labor for eliminating U.S. jobs and suppressing wages is continuing to become moot, because robots are replacing foreign workers as well, both here and abroad.


pages: 807 words: 154,435

Radical Uncertainty: Decision-Making for an Unknowable Future by Mervyn King, John Kay

Airbus A320, Alan Greenspan, Albert Einstein, Albert Michelson, algorithmic trading, anti-fragile, Antoine Gombaud: Chevalier de Méré, Arthur Eddington, autonomous vehicles, availability heuristic, banking crisis, Barry Marshall: ulcers, battle of ideas, Bear Stearns, behavioural economics, Benoit Mandelbrot, bitcoin, Black Swan, Boeing 737 MAX, Bonfire of the Vanities, Brexit referendum, Brownian motion, business cycle, business process, capital asset pricing model, central bank independence, collapse of Lehman Brothers, correlation does not imply causation, credit crunch, cryptocurrency, cuban missile crisis, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, DeepMind, demographic transition, discounted cash flows, disruptive innovation, diversification, diversified portfolio, Donald Trump, Dutch auction, easy for humans, difficult for computers, eat what you kill, Eddington experiment, Edmond Halley, Edward Lloyd's coffeehouse, Edward Thorp, Elon Musk, Ethereum, Eugene Fama: efficient market hypothesis, experimental economics, experimental subject, fear of failure, feminist movement, financial deregulation, George Akerlof, germ theory of disease, Goodhart's law, Hans Rosling, Helicobacter pylori, high-speed rail, Ignaz Semmelweis: hand washing, income per capita, incomplete markets, inflation targeting, information asymmetry, invention of the wheel, invisible hand, Jeff Bezos, Jim Simons, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Snow's cholera map, John von Neumann, Kenneth Arrow, Kōnosuke Matsushita, Linda problem, Long Term Capital Management, loss aversion, Louis Pasteur, mandelbrot fractal, market bubble, market fundamentalism, military-industrial complex, Money creation, Moneyball by Michael Lewis explains big data, Monty Hall problem, Nash equilibrium, Nate Silver, new economy, Nick Leeson, Northern Rock, nudge theory, oil shock, PalmPilot, Paul Samuelson, peak oil, Peter Thiel, Philip Mirowski, Phillips curve, Pierre-Simon Laplace, popular electronics, power law, price mechanism, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, railway mania, RAND corporation, reality distortion field, rent-seeking, Richard Feynman, Richard Thaler, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Solow, Ronald Coase, sealed-bid auction, shareholder value, Silicon Valley, Simon Kuznets, Socratic dialogue, South Sea Bubble, spectrum auction, Steve Ballmer, Steve Jobs, Steve Wozniak, Suez crisis 1956, Tacoma Narrows Bridge, Thales and the olive presses, Thales of Miletus, The Chicago School, the map is not the territory, The Market for Lemons, The Nature of the Firm, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Bayes, Thomas Davenport, Thomas Malthus, Toyota Production System, transaction costs, ultimatum game, urban planning, value at risk, world market for maybe five computers, World Values Survey, Yom Kippur War, zero-sum game

What Google programmers are not trying to do is to construct a general theory of traffic from which all motorists could anticipate the trajectories of all other motorists for the next several years and make their own decisions accordingly, iterating towards an equilibrium in which every journey plan is optimal given the optimal journey plans of all other motorists. And – significantly – they are not trying to do this even when they plan for a future of autonomous vehicles from which much, though not all, of the human element in decision-making has been removed. The construction of an optimal traffic plan for all vehicles for the foreseeable future is beyond the capacity even of Google’s programmers and the most powerful computer available. Even if Waze had built such a model, it would have to be nearly perfect before it could be even slightly useful in providing information relevant to our particular journey.


pages: 661 words: 156,009

Your Computer Is on Fire by Thomas S. Mullaney, Benjamin Peters, Mar Hicks, Kavita Philip

"Susan Fowler" uber, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, A Declaration of the Independence of Cyberspace, affirmative action, Airbnb, algorithmic bias, AlphaGo, AltaVista, Amazon Mechanical Turk, Amazon Web Services, American Society of Civil Engineers: Report Card, An Inconvenient Truth, Asilomar, autonomous vehicles, Big Tech, bitcoin, Bletchley Park, blockchain, Boeing 737 MAX, book value, British Empire, business cycle, business process, Californian Ideology, call centre, Cambridge Analytica, carbon footprint, Charles Babbage, cloud computing, collective bargaining, computer age, computer vision, connected car, corporate governance, corporate social responsibility, COVID-19, creative destruction, cryptocurrency, dark matter, data science, Dennis Ritchie, deskilling, digital divide, digital map, don't be evil, Donald Davies, Donald Trump, Edward Snowden, en.wikipedia.org, European colonialism, fake news, financial innovation, Ford Model T, fulfillment center, game design, gentrification, George Floyd, glass ceiling, global pandemic, global supply chain, Grace Hopper, hiring and firing, IBM and the Holocaust, industrial robot, informal economy, Internet Archive, Internet of things, Jeff Bezos, job automation, John Perry Barlow, Julian Assange, Ken Thompson, Kevin Kelly, Kickstarter, knowledge economy, Landlord’s Game, Lewis Mumford, low-wage service sector, M-Pesa, Mark Zuckerberg, mass incarceration, Menlo Park, meta-analysis, mobile money, moral panic, move fast and break things, Multics, mutually assured destruction, natural language processing, Neal Stephenson, new economy, Norbert Wiener, off-the-grid, old-boy network, On the Economy of Machinery and Manufactures, One Laptop per Child (OLPC), packet switching, pattern recognition, Paul Graham, pink-collar, pneumatic tube, postindustrial economy, profit motive, public intellectual, QWERTY keyboard, Ray Kurzweil, Reflections on Trusting Trust, Report Card for America’s Infrastructure, Salesforce, sentiment analysis, Sheryl Sandberg, Silicon Valley, Silicon Valley ideology, smart cities, Snapchat, speech recognition, SQL injection, statistical model, Steve Jobs, Stewart Brand, tacit knowledge, tech worker, techlash, technoutopianism, telepresence, the built environment, the map is not the territory, Thomas L Friedman, TikTok, Triangle Shirtwaist Factory, undersea cable, union organizing, vertical integration, warehouse robotics, WikiLeaks, wikimedia commons, women in the workforce, Y2K

In addition to thinking about the work that happens in and around the Cloud facility itself, we might also consider the changes to work that the Cloud enables in other industries. For example, automated vehicles are made possible in no small part by the computational activities that happen in the Cloud. In this sense, the Cloud is an element of the larger technological environment in which autonomous vehicles operate. Are they all part of the same factory? And if so, what does it mean for the trucking industry—and for the truck drivers whose jobs will soon be automated out of existence by this new technology? In thirty of the fifty United States, the single most common occupation for men is truck driver.43 What are the social and economic ramifications of the industrialization and computerization of such an industry?


pages: 569 words: 156,139

Amazon Unbound: Jeff Bezos and the Invention of a Global Empire by Brad Stone

activist fund / activist shareholder / activist investor, air freight, Airbnb, Amazon Picking Challenge, Amazon Robotics, Amazon Web Services, autonomous vehicles, Bernie Sanders, big data - Walmart - Pop Tarts, Big Tech, Black Lives Matter, business climate, call centre, carbon footprint, Clayton Christensen, cloud computing, Colonization of Mars, commoditize, company town, computer vision, contact tracing, coronavirus, corporate governance, COVID-19, crowdsourcing, data science, deep learning, disinformation, disintermediation, Donald Trump, Downton Abbey, Elon Musk, fake news, fulfillment center, future of work, gentrification, George Floyd, gigafactory, global pandemic, Greta Thunberg, income inequality, independent contractor, invisible hand, Jeff Bezos, John Markoff, Kiva Systems, Larry Ellison, lockdown, Mahatma Gandhi, Mark Zuckerberg, Masayoshi Son, mass immigration, minimum viable product, move fast and break things, Neal Stephenson, NSO Group, Paris climate accords, Peter Thiel, Ponzi scheme, Potemkin village, private spaceflight, quantitative hedge fund, remote working, rent stabilization, RFID, Robert Bork, Ronald Reagan, search inside the book, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Snapchat, social distancing, SoftBank, SpaceX Starlink, speech recognition, Steve Ballmer, Steve Jobs, Steven Levy, tech billionaire, tech bro, techlash, TED Talk, Tim Cook: Apple, Tony Hsieh, too big to fail, Tragedy of the Commons, two-pizza team, Uber for X, union organizing, warehouse robotics, WeWork

Blue remained secretive, struggling with the dysfunction encoded into its genetic makeup by Bezos, who had otherwise succeeded in nearly everything else he had created. Still, the entertaining exchange of barbs between the tycoons continued—about their plans for the moon, for Mars, whether Amazon was copying SpaceX with its plans to launch a constellation of low Earth satellites in space, and over Amazon’s purchase of Zoox, an autonomous vehicle company that might one day compete with Tesla. Musk and Bezos were a lot alike—relentless, competitive, and absorbed with their self-images. But Musk eagerly sought the spotlight and cultivated a kind of cultlike adoration at his companies and among his fans, preening on stage at Tesla events and extemporaneously (and often recklessly) riffing on Twitter.


pages: 334 words: 104,382

Brotopia: Breaking Up the Boys' Club of Silicon Valley by Emily Chang

"Margaret Hamilton" Apollo, "Susan Fowler" uber, "World Economic Forum" Davos, 23andMe, 4chan, Ada Lovelace, affirmative action, Airbnb, Alan Greenspan, Andy Rubin, Apollo 11, Apple II, augmented reality, autism spectrum disorder, autonomous vehicles, barriers to entry, Benchmark Capital, Bernie Sanders, Big Tech, Burning Man, California gold rush, Chuck Templeton: OpenTable:, clean tech, company town, data science, David Brooks, deal flow, Donald Trump, Dr. Strangelove, driverless car, Elon Musk, emotional labour, equal pay for equal work, fail fast, Fairchild Semiconductor, fake news, Ferguson, Missouri, game design, gender pay gap, Google Glasses, Google X / Alphabet X, Grace Hopper, Hacker News, high net worth, Hyperloop, imposter syndrome, Jeff Bezos, job satisfaction, Khan Academy, Lyft, Marc Andreessen, Mark Zuckerberg, Mary Meeker, Maui Hawaii, Max Levchin, Menlo Park, meritocracy, meta-analysis, microservices, Parker Conrad, paypal mafia, Peter Thiel, post-work, pull request, reality distortion field, Richard Hendricks, ride hailing / ride sharing, rolodex, Salesforce, Saturday Night Live, shareholder value, Sheryl Sandberg, side project, Silicon Valley, Silicon Valley startup, Skype, Snapchat, Steve Jobs, Steve Jurvetson, Steve Wozniak, Steven Levy, subscription business, Susan Wojcicki, tech billionaire, tech bro, tech worker, TED Talk, Tim Cook: Apple, Travis Kalanick, uber lyft, women in the workforce, Zenefits

The machines and devices and the programs that run on them have become a ubiquitous part of our daily lives. All that world-bending technology has been created largely by men. This technology is disrupting businesses from agriculture to manufacturing, finance, and real estate. And it’s not slowing down. We face a near-term future of autonomous cars, augmented reality, and artificial intelligence, and yet we are at risk of embedding gender bias into all of these new algorithms. “It’s bad for shareholder value,” Megan Smith, who has worked as a Google VP and chief technology officer of the United States, told me. “We want the genetic flourishing of all humanity . . . in on making these products, especially as we move to AI and data sciences.”


The Smart Wife: Why Siri, Alexa, and Other Smart Home Devices Need a Feminist Reboot by Yolande Strengers, Jenny Kennedy

active measures, Amazon Robotics, Anthropocene, autonomous vehicles, Big Tech, Boston Dynamics, cloud computing, cognitive load, computer vision, Computing Machinery and Intelligence, crowdsourcing, cyber-physical system, data science, deepfake, Donald Trump, emotional labour, en.wikipedia.org, Evgeny Morozov, fake news, feminist movement, game design, gender pay gap, Grace Hopper, hive mind, Ian Bogost, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jeff Bezos, John Markoff, Kitchen Debate, knowledge economy, Masayoshi Son, Milgram experiment, Minecraft, natural language processing, Network effects, new economy, pattern recognition, planned obsolescence, precautionary principle, robot derives from the Czech word robota Czech, meaning slave, self-driving car, Shoshana Zuboff, side hustle, side project, Silicon Valley, smart grid, smart meter, social intelligence, SoftBank, Steve Jobs, surveillance capitalism, systems thinking, technological solutionism, technoutopianism, TED Talk, Turing test, Wall-E, Wayback Machine, women in the workforce

Such “add-ons” to socially conventional human behavior more closely resemble sci-fi and fantasy situations—unrealistic scenarios in which, some have argued, we have learned how to treat today’s real smart wives.59 Of course, regular, run-of-the-mill abuse can and is directed at all machines and robots, regardless of their assumed gender or andromorphic associations. This includes autonomous cars being scratched, security robots having their sensors covered in barbeque sauce, and delivery drones being tipped over.60 In one reported case in Colorado, a man shot at his computer and called it a “bitch.” The police who were called to his home entered thinking that they were responding to a domestic violence situation.61 No machine, then, is safe from human-inflicted harm or bullying.


pages: 533

Future Politics: Living Together in a World Transformed by Tech by Jamie Susskind

3D printing, additive manufacturing, affirmative action, agricultural Revolution, Airbnb, airport security, algorithmic bias, AlphaGo, Amazon Robotics, Andrew Keen, Apollo Guidance Computer, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, Big Tech, bitcoin, Bletchley Park, blockchain, Boeing 747, brain emulation, Brexit referendum, British Empire, business process, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Cass Sunstein, cellular automata, Citizen Lab, cloud computing, commons-based peer production, computer age, computer vision, continuation of politics by other means, correlation does not imply causation, CRISPR, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, digital divide, digital map, disinformation, distributed ledger, Donald Trump, driverless car, easy for humans, difficult for computers, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, Erik Brynjolfsson, Ethereum, ethereum blockchain, Evgeny Morozov, fake news, Filter Bubble, future of work, Future Shock, Gabriella Coleman, Google bus, Google X / Alphabet X, Googley, industrial robot, informal economy, intangible asset, Internet of things, invention of the printing press, invention of writing, Isaac Newton, Jaron Lanier, John Markoff, Joseph Schumpeter, Kevin Kelly, knowledge economy, Large Hadron Collider, Lewis Mumford, lifelogging, machine translation, Metcalfe’s law, mittelstand, more computing power than Apollo, move fast and break things, natural language processing, Neil Armstrong, Network effects, new economy, Nick Bostrom, night-watchman state, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, payday loans, Philippa Foot, post-truth, power law, price discrimination, price mechanism, RAND corporation, ransomware, Ray Kurzweil, Richard Stallman, ride hailing / ride sharing, road to serfdom, Robert Mercer, Satoshi Nakamoto, Second Machine Age, selection bias, self-driving car, sexual politics, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart contracts, Snapchat, speech recognition, Steve Bannon, Steve Jobs, Steve Wozniak, Steven Levy, tech bro, technological determinism, technological singularity, technological solutionism, the built environment, the Cathedral and the Bazaar, The Structural Transformation of the Public Sphere, The Wisdom of Crowds, Thomas L Friedman, Tragedy of the Commons, trolley problem, universal basic income, urban planning, Watson beat the top human players on Jeopardy!, work culture , working-age population, Yochai Benkler

Using advanced robotics, a team of surgeons in the United States was able to remove the gall bladder of a woman in France, nearly 4,000 miles across the Atlantic.88 Perhaps the most commonplace robots in the future will be self-driving cars, able to navigate the physical world safely ‘without getting tired or distracted’.89 Google’s fleet of autonomous vehicles has driven more than 2 million miles with only a handful of incidents, only one of which is said to have been the fault of the vehicle itself.90 Since human error is the ‘certain’ cause of at least 80 per cent of all crashes, increased safety will be one of the principal advantages.91 We are likely to see, in the next decade, driverless OUP CORRECTED PROOF – FINAL, 28/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS Increasingly Integrated Technology 55 trucks and boats, as well as airborne drones of varying autonomy: the Federal Aviation Administration (FAA) estimates that 10,000 civilian drones could be flying in the United States by 2020.92 Nature has been the inspiration for many recent developments in robotic locomotion.


pages: 574 words: 164,509

Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

agricultural Revolution, AI winter, Albert Einstein, algorithmic trading, anthropic principle, Anthropocene, anti-communist, artificial general intelligence, autism spectrum disorder, autonomous vehicles, backpropagation, barriers to entry, Bayesian statistics, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, Computing Machinery and Intelligence, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, Demis Hassabis, demographic transition, different worldview, Donald Knuth, Douglas Hofstadter, driverless car, Drosophila, Elon Musk, en.wikipedia.org, endogenous growth, epigenetics, fear of failure, Flash crash, Flynn Effect, friendly AI, general purpose technology, Geoffrey Hinton, Gödel, Escher, Bach, hallucination problem, Hans Moravec, income inequality, industrial robot, informal economy, information retrieval, interchangeable parts, iterative process, job automation, John Markoff, John von Neumann, knowledge worker, Large Hadron Collider, longitudinal study, machine translation, megaproject, Menlo Park, meta-analysis, mutually assured destruction, Nash equilibrium, Netflix Prize, new economy, Nick Bostrom, Norbert Wiener, NP-complete, nuclear winter, operational security, optical character recognition, paperclip maximiser, pattern recognition, performance metric, phenotype, prediction markets, price stability, principal–agent problem, race to the bottom, random walk, Ray Kurzweil, recommendation engine, reversible computing, search costs, social graph, speech recognition, Stanislav Petrov, statistical model, stem cell, Stephen Hawking, Strategic Defense Initiative, strong AI, superintelligent machines, supervolcano, synthetic biology, technological singularity, technoutopianism, The Coming Technological Singularity, The Nature of the Firm, Thomas Kuhn: the structure of scientific revolutions, time dilation, Tragedy of the Commons, transaction costs, trolley problem, Turing machine, Vernor Vinge, WarGames: Global Thermonuclear War, Watson beat the top human players on Jeopardy!, World Values Survey, zero-sum game

Brinton, Crane. 1965. The Anatomy of Revolution. Revised ed. New York: Vintage Books. Bryson, Arthur E., Jr., and Ho, Yu-Chi. 1969. Applied Optimal Control: Optimization, Estimation, and Control. Waltham, MA: Blaisdell. Buehler, Martin, Iagnemma, Karl, and Singh, Sanjiv, eds. 2009. The DARPA Urban Challenge: Autonomous Vehicles in City Traffic. Springer Tracts in Advanced Robotics 56. Berlin: Springer. Burch-Brown, J. 2014. “Clues for Consequentialists.” Utilitas 26 (1): 105–19. Burke, Colin. 2001. “Agnes Meyer Driscoll vs. the Enigma and the Bombe.” Unpublished manuscript. Retrieved February 22, 2013. Available at http://userpages.umbc.edu/~burke/driscoll1-2011.pdf.


pages: 769 words: 169,096

Order Without Design: How Markets Shape Cities by Alain Bertaud

autonomous vehicles, call centre, colonial rule, congestion charging, congestion pricing, creative destruction, cross-subsidies, Deng Xiaoping, discounted cash flows, Donald Trump, Edward Glaeser, en.wikipedia.org, extreme commuting, garden city movement, gentrification, Google Earth, Great Leap Forward, Jane Jacobs, job satisfaction, Joseph Schumpeter, land tenure, manufacturing employment, market design, market fragmentation, megacity, microapartment, new economy, New Urbanism, openstreetmap, Pearl River Delta, price mechanism, rent control, Right to Buy, Ronald Coase, self-driving car, Shenzhen special economic zone , Silicon Valley, special economic zone, the built environment, trade route, transaction costs, transit-oriented development, trickle-down economics, urban planning, urban sprawl, zero-sum game

The invention of the BRT system in Curitiba, Brazil, in 1974 is only the application to buses of a technology applied to tramways at the end of the nineteenth century. However, it is quite possible that during the next 20 years, we will see the emergence of completely new modes of transport. The possibilities presented by the combination of vehicle sharing and autonomous vehicles could completely revolutionize urban transport as we know it today. While no new mode of urban transport has emerged during the past 100 years, the dominant mode is often changing rapidly in emerging economies. The changes in mode reflect changes in income, city size, and the geographic coverage of public transport systems.


pages: 735 words: 165,375

The Survival of the City: Human Flourishing in an Age of Isolation by Edward Glaeser, David Cutler

Affordable Care Act / Obamacare, agricultural Revolution, Alvin Toffler, Andrei Shleifer, autonomous vehicles, basic income, Big bang: deregulation of the City of London, Big Tech, Black Lives Matter, British Empire, business cycle, buttonwood tree, call centre, carbon footprint, Cass Sunstein, classic study, clean water, collective bargaining, Columbian Exchange, contact tracing, Corn Laws, Cornelius Vanderbilt, coronavirus, COVID-19, crack epidemic, defund the police, deindustrialization, Deng Xiaoping, desegregation, discovery of penicillin, Donald Trump, Edward Glaeser, Elisha Otis, Fellow of the Royal Society, flying shuttle, future of work, Future Shock, gentrification, George Floyd, germ theory of disease, global pandemic, global village, hiring and firing, Home mortgage interest deduction, Honoré de Balzac, income inequality, industrial cluster, James Hargreaves, Jane Jacobs, Jevons paradox, job automation, jobless men, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Snow's cholera map, knowledge worker, lockdown, Louis Pasteur, Mahatma Gandhi, manufacturing employment, mass incarceration, Maui Hawaii, means of production, megacity, meta-analysis, new economy, New Urbanism, Occupy movement, opioid epidemic / opioid crisis, out of africa, place-making, precautionary principle, RAND corporation, randomized controlled trial, remote working, Richard Florida, Salesforce, Saturday Night Live, Silicon Valley, Skype, smart cities, social distancing, Socratic dialogue, spinning jenny, superstar cities, Tax Reform Act of 1986, tech baron, TED Talk, The Death and Life of Great American Cities, The Wealth of Nations by Adam Smith, TikTok, trade route, union organizing, universal basic income, Upton Sinclair, urban planning, working poor, Works Progress Administration, zero-sum game, zoonotic diseases

A year later, the sector had 7.42 million workers, a drop of only 4.3 percent. Similarly, the number of workers in warehousing and storage has been rock steady, moving down only from 1.181 million to 1.178 million. How many of these jobs will remain when robots get better and better? The rise of autonomous vehicles puts America’s 1.5 million trucking-related jobs at risk, but we’d bet that plumbers and electricians will survive. A lot of buildings—even high-rises—can be built in a capital-intensive factory and then plopped in place quickly with a minimum amount of human sweat, so demand for construction labor may fall—though not dry up entirely.


pages: 437 words: 113,173

Age of Discovery: Navigating the Risks and Rewards of Our New Renaissance by Ian Goldin, Chris Kutarna

"World Economic Forum" Davos, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, Airbnb, Albert Einstein, AltaVista, Asian financial crisis, asset-backed security, autonomous vehicles, banking crisis, barriers to entry, battle of ideas, Bear Stearns, Berlin Wall, bioinformatics, bitcoin, Boeing 747, Bonfire of the Vanities, bread and circuses, carbon tax, clean water, collective bargaining, Colonization of Mars, Credit Default Swap, CRISPR, crowdsourcing, cryptocurrency, Dava Sobel, demographic dividend, Deng Xiaoping, digital divide, Doha Development Round, double helix, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, epigenetics, experimental economics, Eyjafjallajökull, failed state, Fall of the Berlin Wall, financial innovation, full employment, Galaxy Zoo, general purpose technology, Glass-Steagall Act, global pandemic, global supply chain, Higgs boson, Hyperloop, immigration reform, income inequality, indoor plumbing, industrial cluster, industrial robot, information retrieval, information security, Intergovernmental Panel on Climate Change (IPCC), intermodal, Internet of things, invention of the printing press, Isaac Newton, Islamic Golden Age, Johannes Kepler, Khan Academy, Kickstarter, Large Hadron Collider, low cost airline, low skilled workers, Lyft, Mahbub ul Haq, Malacca Straits, mass immigration, Max Levchin, megacity, Mikhail Gorbachev, moral hazard, Nelson Mandela, Network effects, New Urbanism, non-tariff barriers, Occupy movement, On the Revolutions of the Heavenly Spheres, open economy, Panamax, Paris climate accords, Pearl River Delta, personalized medicine, Peter Thiel, post-Panamax, profit motive, public intellectual, quantum cryptography, rent-seeking, reshoring, Robert Gordon, Robert Metcalfe, Search for Extraterrestrial Intelligence, Second Machine Age, self-driving car, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Skype, smart grid, Snapchat, special economic zone, spice trade, statistical model, Stephen Hawking, Steve Jobs, Stuxnet, synthetic biology, TED Talk, The Future of Employment, too big to fail, trade liberalization, trade route, transaction costs, transatlantic slave trade, uber lyft, undersea cable, uranium enrichment, We are the 99%, We wanted flying cars, instead we got 140 characters, working poor, working-age population, zero day

Customers want freshness, variety, creativity, spontaneity and friendliness, and those are difficult values to deliver through automation. We can automate the assembly of an engine; so far, it’s proven harder to automate the assembly of a good haircut, or a good book. Thanks to recent advances in artificial intelligence and robotics, that’s changing. In 2004, autonomous cars seemed unlikely: “Executing a left turn against oncoming traffic involves so many factors that it is hard to imagine discovering the set of rules that can replicate a driver’s behavior,” stated a pair of prominent economists.66 Six years later, Google announced that it had done so. Other cognitive tasks that were once deemed too complicated to automate, but which machines can now do, range from showing empathy to mental health patients, to writing routine news stories, performing surgery, making financial trades, teaching themselves how to play Space Invaders and winning Jeopardy (IBM’s Watson system, which did so in 2011, now has a job diagnosing cancer patients and suggesting treatment plans).


pages: 492 words: 118,882

The Blockchain Alternative: Rethinking Macroeconomic Policy and Economic Theory by Kariappa Bheemaiah

"World Economic Forum" Davos, accounting loophole / creative accounting, Ada Lovelace, Adam Curtis, Airbnb, Alan Greenspan, algorithmic trading, asset allocation, autonomous vehicles, balance sheet recession, bank run, banks create money, Basel III, basic income, behavioural economics, Ben Bernanke: helicopter money, bitcoin, Bletchley Park, blockchain, Bretton Woods, Brexit referendum, business cycle, business process, call centre, capital controls, Capital in the Twenty-First Century by Thomas Piketty, cashless society, cellular automata, central bank independence, Charles Babbage, Claude Shannon: information theory, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, complexity theory, constrained optimization, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, cross-border payments, crowdsourcing, cryptocurrency, data science, David Graeber, deep learning, deskilling, Diane Coyle, discrete time, disruptive innovation, distributed ledger, diversification, double entry bookkeeping, Ethereum, ethereum blockchain, fiat currency, financial engineering, financial innovation, financial intermediation, Flash crash, floating exchange rates, Fractional reserve banking, full employment, George Akerlof, Glass-Steagall Act, Higgs boson, illegal immigration, income inequality, income per capita, inflation targeting, information asymmetry, interest rate derivative, inventory management, invisible hand, John Maynard Keynes: technological unemployment, John von Neumann, joint-stock company, Joseph Schumpeter, junk bonds, Kenneth Arrow, Kenneth Rogoff, Kevin Kelly, knowledge economy, large denomination, Large Hadron Collider, Lewis Mumford, liquidity trap, London Whale, low interest rates, low skilled workers, M-Pesa, machine readable, Marc Andreessen, market bubble, market fundamentalism, Mexican peso crisis / tequila crisis, Michael Milken, MITM: man-in-the-middle, Money creation, money market fund, money: store of value / unit of account / medium of exchange, mortgage debt, natural language processing, Network effects, new economy, Nikolai Kondratiev, offshore financial centre, packet switching, Pareto efficiency, pattern recognition, peer-to-peer lending, Ponzi scheme, power law, precariat, pre–internet, price mechanism, price stability, private sector deleveraging, profit maximization, QR code, quantitative easing, quantitative trading / quantitative finance, Ray Kurzweil, Real Time Gross Settlement, rent control, rent-seeking, robo advisor, Satoshi Nakamoto, Satyajit Das, Savings and loan crisis, savings glut, seigniorage, seminal paper, Silicon Valley, Skype, smart contracts, software as a service, software is eating the world, speech recognition, statistical model, Stephen Hawking, Stuart Kauffman, supply-chain management, technology bubble, The Chicago School, The Future of Employment, The Great Moderation, the market place, The Nature of the Firm, the payments system, the scientific method, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, too big to fail, trade liberalization, transaction costs, Turing machine, Turing test, universal basic income, Vitalik Buterin, Von Neumann architecture, Washington Consensus

Insurance Stance: Customer-facing Main technologies: Biometrics, Big Data, IoT, Sensors, Machine Learning One of the recent sectors to be engulfed by the FinTech wave is the gargantuan insurance sector (Global life insurance premiums: $2.7 trillion, Global non-life insurance premiums: $1.4 trillion, Source: CB Insights, 2016), which has led to the coining of the term “InsurTech.” As autonomous cars become a reality, the data that can now be acquired from IoT sensors and telematics is changing the way insurance plans are made. Previously insureds had few options when it came to an insurance plan. But with the rise of granular levels of personal data provided from a whole range of sensors in houses, cars, FitBits, and other everyday objects, new entrants are proposing insurance plans that are customised to the lifestyle and risks of the client.


pages: 481 words: 125,946

What to Think About Machines That Think: Today's Leading Thinkers on the Age of Machine Intelligence by John Brockman

Adam Curtis, agricultural Revolution, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, Anthropocene, artificial general intelligence, augmented reality, autism spectrum disorder, autonomous vehicles, backpropagation, basic income, behavioural economics, bitcoin, blockchain, bread and circuses, Charles Babbage, clean water, cognitive dissonance, Colonization of Mars, complexity theory, computer age, computer vision, constrained optimization, corporate personhood, cosmological principle, cryptocurrency, cuban missile crisis, Danny Hillis, dark matter, data science, deep learning, DeepMind, Demis Hassabis, digital capitalism, digital divide, digital rights, discrete time, Douglas Engelbart, driverless car, Elon Musk, Emanuel Derman, endowment effect, epigenetics, Ernest Rutherford, experimental economics, financial engineering, Flash crash, friendly AI, functional fixedness, global pandemic, Google Glasses, Great Leap Forward, Hans Moravec, hive mind, Ian Bogost, income inequality, information trail, Internet of things, invention of writing, iterative process, James Webb Space Telescope, Jaron Lanier, job automation, Johannes Kepler, John Markoff, John von Neumann, Kevin Kelly, knowledge worker, Large Hadron Collider, lolcat, loose coupling, machine translation, microbiome, mirror neurons, Moneyball by Michael Lewis explains big data, Mustafa Suleyman, natural language processing, Network effects, Nick Bostrom, Norbert Wiener, paperclip maximiser, pattern recognition, Peter Singer: altruism, phenotype, planetary scale, Ray Kurzweil, Recombinant DNA, recommendation engine, Republic of Letters, RFID, Richard Thaler, Rory Sutherland, Satyajit Das, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, social intelligence, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, superintelligent machines, supervolcano, synthetic biology, systems thinking, tacit knowledge, TED Talk, the scientific method, The Wisdom of Crowds, theory of mind, Thorstein Veblen, too big to fail, Turing machine, Turing test, Von Neumann architecture, Watson beat the top human players on Jeopardy!, We are as Gods, Y2K

If collateral damage can be blamed on the decisions of machines, then military mistakes are less likely to dampen someone’s election chances. Moreover, if minded machines can be overhauled or removed—machine “punishment”—people will feel less need to punish those in charge, whether for fatalities of war, botched (robotic) surgeries, or (autonomous) car accidents. Thinking machines are complex, but the human urge to blame is relatively simple. Death and destruction compel us to find a single mind to hold responsible. Sufficiently smart machines—if placed between destruction and ourselves—should absorb the weight of wrongdoing, shielding our own minds from others’ condemnation.


pages: 635 words: 186,208

House of Suns by Alastair Reynolds

autonomous vehicles, cosmic microwave background, data acquisition, disinformation, gravity well, megastructure, planetary scale, space junk, sparse data, time dilation

I could only presume that the weapon had exhausted itself and was now either recharging or standing down while another was readied to continue the assault. An order formed in my head, but Dalliance had already anticipated it. While her bubble was still raised, hatches in her hull opened to release several dozen lampreys: small, autonomous vehicles equipped with weapons and limited-range skein-drives. The lampreys grouped into squadrons and raced to the limit of the bubble. The bubble’s hardness was tuned down sufficiently for the lampreys to slip through into open space, and then restored to full effectiveness. Debris slipped inside the bubble during that interval of permeability, drumming against the hull like the claws of a thousand witches.


pages: 615 words: 187,426

Chinese Spies: From Chairman Mao to Xi Jinping by Roger Faligot

active measures, Albert Einstein, anti-communist, autonomous vehicles, Ayatollah Khomeini, Berlin Wall, British Empire, business intelligence, Deng Xiaoping, disinformation, Donald Trump, Edward Snowden, fake news, Fall of the Berlin Wall, Great Leap Forward, housing crisis, illegal immigration, index card, information security, megacity, Mikhail Gorbachev, military-industrial complex, new economy, offshore financial centre, Pearl River Delta, Port of Oakland, RAND corporation, Ronald Reagan, Shenzhen special economic zone , Silicon Valley, South China Sea, special economic zone, stem cell, union organizing, young professional, éminence grise

In France, Huawei was not allowed to erect 5G antennae close to the Paris headquarters of the Ministry of Defence, while the prime minister’s SGDSN (General Secretariat for Defence and National Security) suspected Huawei of being a Trojan horse that provided Beijing with the ability to freeze 5G networks in case of conflict, which would thereby debilitate connected devices and internet-controlled autonomous vehicles.19 French intelligence (DGSE) uncovered an attempt by Huawei agents to build up a private biographical data system on leaders of the French competitor company Orange, which ironically favoured a strategic alliance with the Chinese firm.20 Together with the Australians, German and other European security services agree that, despite disclaimers by its leaders, Huawei was intimately linked to the PLA interception and cyberwar effort (see chapter 11).


pages: 816 words: 191,889

The Long Game: China's Grand Strategy to Displace American Order by Rush Doshi

"World Economic Forum" Davos, American ideology, anti-communist, Asian financial crisis, autonomous vehicles, Black Lives Matter, Bretton Woods, capital controls, coronavirus, COVID-19, crony capitalism, cross-border payments, cryptocurrency, defense in depth, deindustrialization, Deng Xiaoping, deplatforming, disinformation, Dissolution of the Soviet Union, Donald Trump, drone strike, energy security, European colonialism, eurozone crisis, financial innovation, George Floyd, global pandemic, global reserve currency, global supply chain, global value chain, Great Leap Forward, high-speed rail, Internet Archive, Internet of things, Kickstarter, kremlinology, Malacca Straits, middle-income trap, Mikhail Gorbachev, MITM: man-in-the-middle, Monroe Doctrine, Network effects, Nixon triggered the end of the Bretton Woods system, offshore financial centre, positional goods, post-truth, purchasing power parity, RAND corporation, reserve currency, rolodex, Ronald Reagan, South China Sea, special drawing rights, special economic zone, TikTok, trade liberalization, transaction costs, UNCLOS, UNCLOS, undersea cable, zero-sum game

In addition, the possibility that Beijing will export not only its engineering standards on traditional infrastructure like rail lines but also new high-tech infrastructure supporting the Internet or 5G creates path dependence in connectivity—that is, it could make it far easier for Beijing to lock in its ties with Asian states and far harder for those states to diversify toward Western countries. One could imagine, for example, that future American-made autonomous vehicles could be unable to connect to Chinese wireless networks in BRI countries.47 Domestic Leverage Finally, at the domestic-political level, the BRI creates clear opportunities to bribe powerful constituencies in recipient countries, altering their politics. Indeed, China has used its state-owned enterprises (SOEs) that are involved in these projects expressly for that purpose.


pages: 486 words: 132,784

Inventors at Work: The Minds and Motivation Behind Modern Inventions by Brett Stern

Apple II, augmented reality, autonomous vehicles, bioinformatics, Build a better mousetrap, business process, cloud computing, computer vision, cyber-physical system, distributed generation, driverless car, game design, Grace Hopper, human-factors engineering, Richard Feynman, Silicon Valley, skunkworks, Skype, smart transportation, speech recognition, statistical model, stealth mode startup, Steve Jobs, Steve Wozniak, the market place, value engineering, Yogi Berra

Stern: Portland. Swider-Lyons: Not too bad. Stern: We ride bikes all day. Swider-Lyons: Yeah, here it’s different. I drive on Interstate 395 to get to work. And you don’t have the weather we have here either. Either snowing or 100 degrees is not good biking weather. But if you have people in autonomous cars? If you have a satellite or maybe a series of UAVs for a secure uplink, then you could literally just hand over your car and then it would drive. There are sensors on the car so you don’t get in accidents and you don’t get in traffic jams. You don’t have sixteen-year-olds dying while texting, and it helps older people, too, because they could actually have a lot more freedom.


pages: 460 words: 130,621

The Last Astronaut by David Wellington

augmented reality, autonomous vehicles, clean water, crewed spaceflight, gravity well, low earth orbit, megastructure, operational security, orbital mechanics / astrodynamics, overview effect, telepresence

Talk about understatement. “If there was anyone else…” “Julia Obrador. Or Ali Dinwari,” she suggested. “They were on Orion 6. They have the skills you need, and the training.” “Neither of them ever served as MC on a mission. Besides which—Ali died about four years ago. He was run down by an autonomous car in San Francisco. As for Julia, she’s living in Mexico, making high-end pottery. She has three kids and a husband.” Jansen understood what he meant by that. Julia had something to lose. This was going to be a dangerous mission, maybe the most dangerous NASA mission since the moon landings eighty years earlier.


The Code: Silicon Valley and the Remaking of America by Margaret O'Mara

A Declaration of the Independence of Cyberspace, accounting loophole / creative accounting, affirmative action, Airbnb, Alan Greenspan, AltaVista, Alvin Toffler, Amazon Web Services, An Inconvenient Truth, AOL-Time Warner, Apple II, Apple's 1984 Super Bowl advert, autonomous vehicles, back-to-the-land, barriers to entry, Ben Horowitz, Berlin Wall, Big Tech, Black Lives Matter, Bob Noyce, Buckminster Fuller, Burning Man, business climate, Byte Shop, California gold rush, Californian Ideology, carried interest, clean tech, clean water, cloud computing, cognitive dissonance, commoditize, company town, Compatible Time-Sharing System, computer age, Computer Lib, continuous integration, cuban missile crisis, Danny Hillis, DARPA: Urban Challenge, deindustrialization, different worldview, digital divide, Do you want to sell sugared water for the rest of your life?, don't be evil, Donald Trump, Doomsday Clock, Douglas Engelbart, driverless car, Dynabook, Edward Snowden, El Camino Real, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Fairchild Semiconductor, Frank Gehry, Future Shock, Gary Kildall, General Magic , George Gilder, gig economy, Googley, Hacker Ethic, Hacker News, high net worth, hockey-stick growth, Hush-A-Phone, immigration reform, income inequality, industrial research laboratory, informal economy, information retrieval, invention of movable type, invisible hand, Isaac Newton, It's morning again in America, Jeff Bezos, Joan Didion, job automation, job-hopping, John Gilmore, John Markoff, John Perry Barlow, Julian Assange, Kitchen Debate, knowledge economy, knowledge worker, Larry Ellison, Laura Poitras, Lyft, Marc Andreessen, Mark Zuckerberg, market bubble, Mary Meeker, mass immigration, means of production, mega-rich, Menlo Park, Mikhail Gorbachev, military-industrial complex, millennium bug, Mitch Kapor, Mother of all demos, move fast and break things, mutually assured destruction, Neil Armstrong, new economy, Norbert Wiener, old-boy network, Palm Treo, pattern recognition, Paul Graham, Paul Terrell, paypal mafia, Peter Thiel, pets.com, pirate software, popular electronics, pre–internet, prudent man rule, Ralph Nader, RAND corporation, Richard Florida, ride hailing / ride sharing, risk tolerance, Robert Metcalfe, ROLM, Ronald Reagan, Salesforce, Sand Hill Road, Second Machine Age, self-driving car, shareholder value, Sheryl Sandberg, side hustle, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, skunkworks, Snapchat, social graph, software is eating the world, Solyndra, speech recognition, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Strategic Defense Initiative, supercomputer in your pocket, Susan Wojcicki, tacit knowledge, tech billionaire, tech worker, technoutopianism, Ted Nelson, TED Talk, the Cathedral and the Bazaar, the market place, the new new thing, The Soul of a New Machine, There's no reason for any individual to have a computer in his home - Ken Olsen, Thomas L Friedman, Tim Cook: Apple, Timothy McVeigh, transcontinental railway, Twitter Arab Spring, Uber and Lyft, uber lyft, Unsafe at Any Speed, upwardly mobile, Vannevar Bush, War on Poverty, Wargames Reagan, WarGames: Global Thermonuclear War, We wanted flying cars, instead we got 140 characters, Whole Earth Catalog, WikiLeaks, William Shockley: the traitorous eight, work culture , Y Combinator, Y2K

Japan was working on its “Fifth Generation” computing program, an ambitious push for supercomputing, AI, and machine learning. DARPA’s new push aimed to go one better, and to harness the power of cutting-edge computing to more ordinary defense challenges along the way. SCI’s opening agenda included projects to build autonomous vehicles, a computerized copilot for fighter jets, and AI software to aid in battlefield decision-making. The program’s first months were rocky, to say the least—Conway and other Class A technical talent hightailed it out of there quickly, and scholars had decidedly mixed feelings about the program’s military aims—but the computing push ultimately had a tremendous impact.


pages: 516 words: 157,437

Principles: Life and Work by Ray Dalio

Alan Greenspan, Albert Einstein, asset allocation, autonomous vehicles, backtesting, Bear Stearns, Black Monday: stock market crash in 1987, cognitive bias, currency risk, Deng Xiaoping, diversification, Dunning–Kruger effect, Elon Musk, financial engineering, follow your passion, global macro, Greenspan put, hiring and firing, iterative process, Jeff Bezos, Long Term Capital Management, margin call, Market Wizards by Jack D. Schwager, microcredit, oil shock, performance metric, planetary scale, quantitative easing, risk tolerance, Ronald Reagan, Silicon Valley, Steve Jobs, transaction costs, yield curve

For example, think about how you choose and maintain a safe distance behind the car in front of you when you are driving. Now describe the process in enough detail that someone who has never driven a car before can do it as well as you can, or so that it can be programmed into the computer that controls an autonomous car. I bet you can’t. Now think about the challenge of making all of your decisions well, in a systematic, repeatable way, and then being able to describe the processes so clearly and precisely that anyone else can make the same quality decisions under the same circumstances. That is what I aspire to do and have found to be invaluable, even when highly imperfect.


pages: 523 words: 143,139

Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian, Tom Griffiths

4chan, Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, algorithmic bias, algorithmic trading, anthropic principle, asset allocation, autonomous vehicles, Bayesian statistics, behavioural economics, Berlin Wall, Big Tech, Bill Duvall, bitcoin, Boeing 747, Charles Babbage, cognitive load, Community Supported Agriculture, complexity theory, constrained optimization, cosmological principle, cryptocurrency, Danny Hillis, data science, David Heinemeier Hansson, David Sedaris, delayed gratification, dematerialisation, diversification, Donald Knuth, Donald Shoup, double helix, Dutch auction, Elon Musk, exponential backoff, fault tolerance, Fellow of the Royal Society, Firefox, first-price auction, Flash crash, Frederick Winslow Taylor, fulfillment center, Garrett Hardin, Geoffrey Hinton, George Akerlof, global supply chain, Google Chrome, heat death of the universe, Henri Poincaré, information retrieval, Internet Archive, Jeff Bezos, Johannes Kepler, John Nash: game theory, John von Neumann, Kickstarter, knapsack problem, Lao Tzu, Leonard Kleinrock, level 1 cache, linear programming, martingale, multi-armed bandit, Nash equilibrium, natural language processing, NP-complete, P = NP, packet switching, Pierre-Simon Laplace, power law, prediction markets, race to the bottom, RAND corporation, RFC: Request For Comment, Robert X Cringely, Sam Altman, scientific management, sealed-bid auction, second-price auction, self-driving car, Silicon Valley, Skype, sorting algorithm, spectrum auction, Stanford marshmallow experiment, Steve Jobs, stochastic process, Thomas Bayes, Thomas Malthus, Tragedy of the Commons, traveling salesman, Turing machine, urban planning, Vickrey auction, Vilfredo Pareto, Walter Mischel, Y Combinator, zero-sum game

Even if such coordination were possible, it wouldn’t add very much. When it comes to traffic of the human kind, the low price of anarchy cuts both ways. The good news is that the lack of centralized coordination is making your commute at most only 33% worse. On the other hand, if you’re hoping that networked, self-driving autonomous cars will bring us a future of traffic utopia, it may be disheartening to learn that today’s selfish, uncoordinated drivers are already pretty close to optimal. It’s true that self-driving cars should reduce the number of road accidents and may be able to drive more closely together, both of which would speed up traffic.


Mastering Blockchain, Second Edition by Imran Bashir

3D printing, altcoin, augmented reality, autonomous vehicles, bitcoin, blockchain, business logic, business process, carbon footprint, centralized clearinghouse, cloud computing, connected car, cryptocurrency, data acquisition, Debian, disintermediation, disruptive innovation, distributed ledger, Dogecoin, domain-specific language, en.wikipedia.org, Ethereum, ethereum blockchain, fault tolerance, fiat currency, Firefox, full stack developer, general-purpose programming language, gravity well, information security, initial coin offering, interest rate swap, Internet of things, litecoin, loose coupling, machine readable, MITM: man-in-the-middle, MVC pattern, Network effects, new economy, node package manager, Oculus Rift, peer-to-peer, platform as a service, prediction markets, QR code, RAND corporation, Real Time Gross Settlement, reversible computing, RFC: Request For Comment, RFID, ride hailing / ride sharing, Satoshi Nakamoto, seminal paper, single page application, smart cities, smart contracts, smart grid, smart meter, supply-chain management, transaction costs, Turing complete, Turing machine, Vitalik Buterin, web application, x509 certificate

There are few careful predictions being made in the section that is based on the current advancement and speed of progress in the concerned field. All of these predictions are likely to come true between the years 2020 and 2050: The IoT will run on multiple blockchains and will give rise to an M2M economy. This can include energy devices, autonomous cars, and house hold accessories. Medical records will be shared securely while preserving the privacy of patients between various private blockchains run by consortia of health providers. It may well be a single private blockchain shared among all service providers including pharmacies, hospitals, and clinics.


pages: 558 words: 175,965

When the Heavens Went on Sale: The Misfits and Geniuses Racing to Put Space Within Reach by Ashlee Vance

"Peter Beck" AND "Rocket Lab", 3D printing, Airbnb, autonomous vehicles, barriers to entry, Biosphere 2, bitcoin, Burning Man, Charles Lindbergh, cloud computing, Colonization of Mars, COVID-19, cryptocurrency, deepfake, disinformation, Elon Musk, Ernest Rutherford, fake it until you make it, Google Earth, hacker house, Hyperloop, intentional community, Iridium satellite, James Webb Space Telescope, Jeff Bezos, Kwajalein Atoll, lockdown, low earth orbit, Maui Hawaii, McMansion, Menlo Park, Mikhail Gorbachev, new economy, off-the-grid, overview effect, Peter Thiel, Planet Labs, private spaceflight, Rainbow Mansion, risk tolerance, Ronald Reagan, self-driving car, side project, Silicon Valley, Silicon Valley startup, skunkworks, SoftBank, South China Sea, South of Market, San Francisco, SpaceX Starlink, Stephen Hawking, Steve Jobs, Steve Jurvetson, Steve Wozniak, Strategic Defense Initiative, synthetic biology, tech billionaire, TikTok, Virgin Galactic

We are in the early days of the next great infrastructure build-out. A communication system is being constructed that will surround the earth with a digital heartbeat. Our computers and cell phones will never be outside the reach of an internet connection. What’s more interesting, though, is that neither will our self-flying planes, autonomous cars, or drones. Almost every sci-fi contraption that you’ve been promised over the past twenty to fifty years will come to depend on this ever-present information network. What’s more, so will a host of new computing devices that we’re only now seeing glimpses of. Farmers will put moisture sensors all over their land and have the devices report back on what they detect to the computer in the sky.


pages: 761 words: 231,902

The Singularity Is Near: When Humans Transcend Biology by Ray Kurzweil

additive manufacturing, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, anthropic principle, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, Benoit Mandelbrot, Bill Joy: nanobots, bioinformatics, brain emulation, Brewster Kahle, Brownian motion, business cycle, business intelligence, c2.com, call centre, carbon-based life, cellular automata, Charles Babbage, Claude Shannon: information theory, complexity theory, conceptual framework, Conway's Game of Life, coronavirus, cosmological constant, cosmological principle, cuban missile crisis, data acquisition, Dava Sobel, David Brooks, Dean Kamen, digital divide, disintermediation, double helix, Douglas Hofstadter, en.wikipedia.org, epigenetics, factory automation, friendly AI, functional programming, George Gilder, Gödel, Escher, Bach, Hans Moravec, hype cycle, informal economy, information retrieval, information security, invention of the telephone, invention of the telescope, invention of writing, iterative process, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, lifelogging, linked data, Loebner Prize, Louis Pasteur, mandelbrot fractal, Marshall McLuhan, Mikhail Gorbachev, Mitch Kapor, mouse model, Murray Gell-Mann, mutually assured destruction, natural language processing, Network effects, new economy, Nick Bostrom, Norbert Wiener, oil shale / tar sands, optical character recognition, PalmPilot, pattern recognition, phenotype, power law, precautionary principle, premature optimization, punch-card reader, quantum cryptography, quantum entanglement, radical life extension, randomized controlled trial, Ray Kurzweil, remote working, reversible computing, Richard Feynman, Robert Metcalfe, Rodney Brooks, scientific worldview, Search for Extraterrestrial Intelligence, selection bias, semantic web, seminal paper, Silicon Valley, Singularitarianism, speech recognition, statistical model, stem cell, Stephen Hawking, Stewart Brand, strong AI, Stuart Kauffman, superintelligent machines, technological singularity, Ted Kaczynski, telepresence, The Coming Technological Singularity, Thomas Bayes, transaction costs, Turing machine, Turing test, two and twenty, Vernor Vinge, Y2K, Yogi Berra

The robot can then use the map and its own reasoning ability to determine an optimal and obstacle-free path to carry out its assigned mission. This technology enables autonomous carts to transfer materials throughout a manufacturing process without the high degree of preparation required with conventional preprogrammed robotic systems. In military situations autonomous vehicles could carry out precise missions while adjusting to rapidly changing environments and battlefield conditions. Machine vision is also improving the ability of robots to interact with humans. Using small, inexpensive cameras, head- and eye-tracking software can sense where a human user is, allowing robots, as well as virtual personalities on a screen, to maintain eye contact, a key element for natural interactions.


Growth: From Microorganisms to Megacities by Vaclav Smil

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, agricultural Revolution, air freight, Alan Greenspan, American Society of Civil Engineers: Report Card, Anthropocene, Apollo 11, Apollo Guidance Computer, autonomous vehicles, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Boeing 747, Bretton Woods, British Empire, business cycle, caloric restriction, caloric restriction, carbon tax, circular economy, colonial rule, complexity theory, coronavirus, decarbonisation, degrowth, deindustrialization, dematerialisation, demographic dividend, demographic transition, Deng Xiaoping, disruptive innovation, Dissolution of the Soviet Union, Easter island, endogenous growth, energy transition, epigenetics, Fairchild Semiconductor, Ford Model T, general purpose technology, Gregor Mendel, happiness index / gross national happiness, Helicobacter pylori, high-speed rail, hydraulic fracturing, hydrogen economy, Hyperloop, illegal immigration, income inequality, income per capita, industrial robot, Intergovernmental Panel on Climate Change (IPCC), invention of movable type, Isaac Newton, James Watt: steam engine, knowledge economy, Kondratiev cycle, labor-force participation, Law of Accelerating Returns, longitudinal study, low interest rates, mandelbrot fractal, market bubble, mass immigration, McMansion, megacity, megaproject, megastructure, meta-analysis, microbiome, microplastics / micro fibres, moral hazard, Network effects, new economy, New Urbanism, old age dependency ratio, optical character recognition, out of africa, peak oil, Pearl River Delta, phenotype, Pierre-Simon Laplace, planetary scale, Ponzi scheme, power law, Productivity paradox, profit motive, purchasing power parity, random walk, Ray Kurzweil, Report Card for America’s Infrastructure, Republic of Letters, rolodex, Silicon Valley, Simon Kuznets, social distancing, South China Sea, synthetic biology, techno-determinism, technoutopianism, the market place, The Rise and Fall of American Growth, three-masted sailing ship, total factor productivity, trade liberalization, trade route, urban sprawl, Vilfredo Pareto, yield curve

Car ownership continues to grow rapidly in all low- and medium-income Asian countries, but the total number of US passenger cars shows clear signs of saturation. If the trajectory were to follow the projected Gaussian fit, the country would have no more than about 65 million vehicles by the year 2100 compared to nearly 190 million in 2015 (figure 6.11). This decline could be further accelerated by convenient on-demand availability of future autonomous vehicles but I suspect that this innovation will make a substantial difference much later than is now widely assumed. But it is certain that car ownership declines will be much steeper in countries experiencing relatively fast population decline, above all in Japan. Figure 6.11 The historical growth of the US passenger car fleet can be fitted quite well into a normal curve peaking around 2030.


pages: 1,261 words: 294,715

Behave: The Biology of Humans at Our Best and Worst by Robert M. Sapolsky

autism spectrum disorder, autonomous vehicles, behavioural economics, Bernie Madoff, biofilm, blood diamond, British Empire, Broken windows theory, Brownian motion, car-free, classic study, clean water, cognitive dissonance, cognitive load, corporate personhood, corporate social responsibility, Daniel Kahneman / Amos Tversky, delayed gratification, desegregation, different worldview, domesticated silver fox, double helix, Drosophila, Edward Snowden, en.wikipedia.org, epigenetics, Flynn Effect, framing effect, fudge factor, George Santayana, global pandemic, Golden arches theory, Great Leap Forward, hiring and firing, illegal immigration, impulse control, income inequality, intentional community, John von Neumann, Loma Prieta earthquake, long peace, longitudinal study, loss aversion, Mahatma Gandhi, meta-analysis, microaggression, mirror neurons, Mohammed Bouazizi, Monkeys Reject Unequal Pay, mouse model, mutually assured destruction, Nelson Mandela, Network effects, nocebo, out of africa, Peter Singer: altruism, phenotype, Philippa Foot, placebo effect, publication bias, RAND corporation, risk tolerance, Rosa Parks, selective serotonin reuptake inhibitor (SSRI), self-driving car, Silicon Valley, Skinner box, social contagion, social distancing, social intelligence, Stanford marshmallow experiment, Stanford prison experiment, stem cell, Steven Pinker, strikebreaker, theory of mind, Tragedy of the Commons, transatlantic slave trade, traveling salesman, trickle-down economics, trolley problem, twin studies, ultimatum game, Walter Mischel, wikimedia commons, zero-sum game, zoonotic diseases

., “Limbic Justice: Amygdala Involvement in Immediate Rejections in the Ultimatum Game,” PLoS ONE 9 (2011): e1001054; Buckholtz, “Neural Correlates of Third-Party Punishment.”. 33. D. de Quervain et al., “The Neural Basis of Altruistic Punishment,” Sci 305 (2004): 1254; B. Knutson, “Sweet Revenge?” Sci 305 (2004): 1246. 34. Footnote: J. Bonnefon et al., “The Social Dilemma of Autonomous Vehicles,” Sci 352 (2016): 1573; J. Greene, “Our Driverless Dilemma,” Sci 352 (2016): 1514. Chapter 17: War and Peace 1. M. Fisher, “The Country Where Slavery Is Still Normal,” Atlantic, June 28, 2011; C. Welzel, Freedom Rising: Human Empowerment and the Quest for Emancipation (Cambridge: Cambridge University Press, 2013). 2.


pages: 993 words: 318,161

Fall; Or, Dodge in Hell by Neal Stephenson

Ada Lovelace, augmented reality, autonomous vehicles, back-to-the-land, bitcoin, blockchain, cloud computing, coherent worldview, computer vision, crisis actor, crossover SUV, cryptocurrency, defense in depth, demographic transition, distributed ledger, drone strike, easy for humans, difficult for computers, fake news, false flag, game design, gamification, index fund, Jaron Lanier, life extension, messenger bag, microaggression, microbiome, Neal Stephenson, Network effects, no-fly zone, off grid, off-the-grid, offshore financial centre, pattern recognition, planetary scale, ride hailing / ride sharing, sensible shoes, short selling, Silicon Valley, Snow Crash, tech bro, telepresence, telepresence robot, telerobotics, The Hackers Conference, Turing test, Works Progress Administration

The blog was witty but a little too unsparing and scary-smart to attract a wide audience—she had maybe ten thousand followers at peak, but they had a tendency to age out as their kids got into their teens. Anyway, the era of the awesomely huge gleaming luxury crossover SUV was coming to an end. Like Tolkien’s elves fading away and going into the west, they were dissolving into the used market as many families were downsizing their fleets in favor of ride-sharing services, and then fully autonomous vehicles that were owned by no one and everyone. So by the time Maeve’s and Corvallis’s kids were in the nine to twelve range, she had stopped writing the blog and, intellectually/artistically, gone dark for a decade. And then suddenly the kids were out of the house, and Maeve, like a butterfly unfolding itself from a chrysalis, had begun to unfurl some of what she had quietly been making of herself in that darkness.