driverless car

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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

And while the machine learning abilities that underpin driverless car decision making will continue to improve, waiting for a completely demonstrably fool-proof system would likely delay the introduction of driverless cars by many years. To help deliver the benefits that driverless cars can bring in the majority of everyday driving scenarios, we need a solution to help the driverless cars make decisions in unfamiliar conditions. Just as we insist that learner drivers are accompanied by an experienced driver, companies are working to create similar backup advisors for driverless cars. In the event that a driverless car encounters a situation it cannot safely navigate, it will be able to call on the services of a remote contact centre for support.

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While humans are quite adept at identifying traffic lights and their state (if not always excellent at acting legally in accordance with their state), computers can struggle to pick out traffic lights and discern their colour, especially in difficult conditions, e.g. strong sunshine behind the light. Driverless car makers have put a lot of effort into solving this seemingly simple problem. There is also the risk of a computer being confused by signs like this, which they could mistake for an actual traffic signal: Could a driverless car mistake these signs for a traffic signal? However, driverless cars are being taught through deep learning to identify this and similar signs so as not to confuse them with traffic lights. The best solution to date is for driverless car manufacturers to create a prior map of traffic signals, enabling the driverless cars and its perception systems to anticipate the locations of traffic lights and improve detection of the light state.[270] Thus the vehicle can predict when it should expect traffic lights and concentrate its search.

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Volvo[280] have already announced that the first 100 vehicles they will make available for trialists will not be visibly marked as driverless cars. This is to avoid situations where human motorists might slam on their brakes or drive erratically to force the driverless cars into submission. As driving regulations are updated, it is likely they will be amended to cater for these challenges and make the intentional interference with the progress of a driverless car an offense - one which will of course be captured in HD by the driverless car’s cameras. AV Phone Home Given the unpredictable nature of scenarios that a driverless car could ultimately find itself in, it is likely to be virtually impossible to pre-programme a suitable course of action for every instance.

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

Insurance companies already quantify the potential costs of many aspects of our lives. Driverless cars need to have a nearly perfect driving record to be safe enough. The benefits of driverless cars will begin as soon as they have a safety record that exceeds the average human driver. Waiting until driverless cars have a flawless safety record will unnecessarily maintain our society’s high death toll from car accidents. Driverless cars should be made legal not when they’re perfect, but when they’re twice as safe as an average human driver. The adoption of driverless cars will be abrupt. Some people like to ask for the “year” when driverless cars will take over the roads.

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We predict that Google’s first sales of driverless cars will be not to consumers for everyday driving, but more likely to corporations and some municipalities as a niche transportation solution. At some point there will be a quiet crossing over, and the autonomous vehicles will venture outside their enclosed territories and onto city highways. Another aspect of the transition to driverless cars is location, that is, where the first driverless cars will come into everyday use. Some countries will adopt driverless cars sooner than others. Within countries, some states or provinces will agree to legalize driverless cars before others.

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Regulators must resist the temptation to wait until driverless cars are perfect drivers, since that day may never come. Instead, federal regulators need to set a practical new goal line for safety: as we discussed earlier, driverless-car technology should be considered viable when a driverless car drives twice as safely as a human. Another way this hypothetical new agency, the AVA, could help would be to generate public and corporate excitement about driverless cars. To ensure that more minds than just those employed by Google, Apple, and car companies are thinking about driverless cars, the AVA should earmark funding for regular competitions where teams from industry and universities compete to create better solutions to the hard technical problems.

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

There was a tone of inevitability in all this coverage that soon began to permeate through to the politicians. Phrases such as ‘the upcoming driverless car revolution’ and ‘the disappearance of the privately owned car’ started to pepper their speeches. Some even assumed that the advent of driverless cars was imminent and that transport policies therefore needed to be adapted urgently. The driverless car revolution was upon us, and those who ignored it were simply doing the three monkeys trick. They were Luddites, losers. Had not the PC, the smartphone and the internet already changed our lives? ‘Smart cities’ were all the rage and driverless cars were one of their obvious key building blocks.

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The implication of his statement is that driverless cars would, by contrast, have no accidents: When Google started working on self-driving vehicles over seven years ago, our goal was to transform mobility by making it safer, easier, and more enjoyable to get around. What drives our team is the potential that this technology has to make our roads safer.9 Figure 1. Google’s driverless car. (Photo by Grendelkhan. Note: full photo credits for all figures appear on pages 119 and 120.) The other carrot is to increase access to vehicles for people with disabilities, like the blind girl in the Shanghai 17 Driverless Cars: On a Road to Nowhere film.

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That junction could be dealt with now, with the right transport planning and regulatory policies, potentially saving lives. Despite all the hype, despite the millions of words written about driverless cars, despite the billions of dollars being invested by the auto manufacturers and tech companies, and despite all the tests and trials across the world, nothing is really known about how this will pan out. Driverless cars could remain as purely theoretical 109 Driverless Cars: On a Road to Nowhere as teleportation or personal jetpacks. The wise Lords on the Science and Technology Committee summed it up admirably: There is little hard evidence to substantiate the potential benefits and disadvantages of CAV because most of them are at a prototype or testing stage.

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

Although there have been a range of experiments in this field since the 1940s, it is only since the emergence of microprocessor technologies in the 1970s that they really began to be feasible. But the challenge of driverless cars is immense: and the fundamental problem is perception. If you could find a way for a car to know precisely where it was and what was around it, then you would have solved the problem of driverless cars. The solution to this problem was to be modern machine learning techniques: without them, driverless cars would not be possible. The PROMETHEUS project, funded by the pan-governmental EUREKA research funding organization in Europe, is widely seen as a forerunner of today’s driverless car technology. PROMETHEUS, which ran from 1987 to 1995, led to a demonstration in 1995, in which a car drove itself from Munich in Germany to Odense in Denmark and back.

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Well, the first conclusion we can draw, from the relatively poor performance of automobile giants like BMW, Mercedes and Volkswagen, is that a historical track record in the automotive industry is not the key requirement for success in driverless car technology. On reflection, this should come as no surprise: the key to driverless cars is not the internal combustion engine, but software – AI software. No surprise, then, that the US automobile giant General Motors acquired driverless car company Cruise Automation for an undisclosed (but obviously very large) sum in 2016, while Ford invested one billion dollars in self-driving start-up company Argo AI. Both companies made public, very ambitious claims about the roll-out of commercial driverless cars: Ford predicted they would have a ‘fully autonomous vehicle in commercial operation’ by 2021.10 Of course, we don’t know the precise criteria that companies use to decide when a disengagement occurs.

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Some of these guidelines are in clear contrast to what some of the participants in the Moral Machines experiments indicated that the software should do: for example, the rule forbidding any discrimination based on personal characteristics is in sharp contrast to an internationally held preference revealed by the Moral Machine for saving young people. Imagine the outrage, then, if a driverless car followed the German guidelines and chose not to make a distinction, and as a result a child was killed rather than an elderly terminal cancer patient. My example is tasteless – apologies for that – but you get the point. Intriguing and ingenious though it undoubtedly is, the Moral Machine experiment and the Trolley Problem upon which it is based do not, I think, have much of value to tell us about AI software for driverless cars. I don’t believe that the driverless cars we are likely to encounter in the coming decades will do ethical reasoning of this type.

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

In late 2017, Philip Hammond, the British Chancellor of the Exchequer, announced the government’s intention to have fully driverless cars – without a safety attendant on board – on British roads by 2021. Daimler has promised driverless cars by 2020,15 Ford by 2021,16 and other manufacturers have made their own, similar forecasts. Talk in the press has moved on from questioning whether driverless cars will happen to addressing the challenges we’ll face when they do. ‘Should your driverless car hit a pedestrian to save your life?’ asked the New York Times in June 2016;17 and, in November 2017: ‘What happens to roadkill or traffic tickets when our vehicles are in control?’

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‘ambiguous images 211n13’ 23andMe 108–9 profit 109 promises of anonymity 109 sale of data 109 volume of customers 110 52Metro 177 abnormalities 84, 87, 95 acute kidney injuries 104 Acxiom 31 Adele 193 advertising 33 online adverts 33–5 exploitative potential 35 inferences 35 personality traits and 40–1 political 39–43 targeted 41 AF447 (flight) 131–3, 137 Afigbo, Chukwuemeka 2 AI (artificial intelligence) 16–19 algorithms 58, 86 omnipotence 13 threat of 12 see also DeepMind AI Music 192 Air France 131–3 Airbnb, random forests 59 Airbus A330 132–3 algebra 8 algorithmic art 194 algorithmic regulating body 70 algorithms aversion 23 Alhambra 156 Alton Towers 20–1 ALVINN (Autonomous Land Vehicle In a Neural Network) 118–19 Alzheimer’s disease 90–1, 92 Amazon 178 recommendation engine 9 ambiguous images 211n13 American Civil Liberties Union (ACLU) 17 Ancestry.com 110 anchoring effect 73 Anthropometric Laboratory 107–8 antibiotics 111 AOL accounts 2 Apple 47 Face ID system 165–6 arithmetic 8 art 175–95 algorithms 184, 188–9 similarity 187 books 178 films 180–4 popularity 183–4 judging the aesthetic value of 184 machines and 194 meaning of 194 measuring beauty 184–5 music 176–80 piano experiment 188–90 popularity 177, 178, 179 quality 179, 180 quantifying 184–8 social proof 177–8, 179 artifacts, power of 1-2 artificial intelligence (AI) see AI (artificial intelligence) association algorithms 9 asthma 101–2 identifying warning signs 102 preventable deaths 102 Audi slow-moving traffic 136 traffic jam pilot 136 authority of algorithms 16, 198, 199, 201 misuse of 200 automation aircraft 131–3 hidden dangers 133–4 ironies of 133–7 reduction in human ability 134, 137 see also driverless cars Autonomous Emergency Braking system 139 autonomy 129, 130 full 127, 130, 134, 138 autopilot systems A330 132 driverless cars 134 pilot training 134 sloppy 137 Tesla 134, 135, 138 bail comparing algorithms to human judges 59–61 contrasting predictions 60 success of algorithms 60–1 high-risk scores 70 Bainbridge, Lisanne 133–4, 135, 138 balance 112 Banksy 147, 185 Baril, David 171–2 Barstow 113 Bartlett, Jamie 44 Barwell, Clive 145–7 Bayes’ theorem 121–4, 225n30 driverless cars 124 red ball experiment 123–4 simultaneous hypotheses 122–3 Bayes, Thomas 123–4 Bayesian inference 99 beauty 184–5 Beck, Andy 82, 95 Bell, Joshua 185–6 Berk, Richard 61–2, 64 bias of judges 70–1, 75 in machines 65–71 societal and cultural 71 biometric measurements 108 blind faith 14–16, 18 Bonin, Pierre-Cédric ‘company baby‘ 131–3 books 178 boost effect 151, 152 Bratton, Bill 148–50, 152 breast cancer aggressive screening 94 detecting abnormalities 84, 87, 95 diagnoses 82–4 mammogram screenings 94, 96 over-diagnosis and over-treatment 94–5 research on corpses 92–3 ‘in situ’ cancer cells 93 screening algorithms for 87 tumours, unwittingly ­carrying 93 bridges (route to Jones Beach) racist 1 unusual features 1 Brixton fighting 49 looting and violence 49–50 Brooks, Christopher Drew 64, 77 Brown, Joshua 135 browser history see internet browsing ­history buffer zone 144 Burgess, Ernest W. 55–6 burglary 150–1 the boost 151, 152 connections with earthquakes 152 the flag 150–1, 152 Caixin Media 45 calculations 8 calculus 8 Caldicott, Dame Fiona 223n48 Cambridge Analytica 39 advertising 42 fake news 42 personality profiles 41–2 techniques 41–2 whistleblowers 42 CAMELYON16 competition 88, 89 cameras 119–20 cancer benign 94 detection 88–9 and the immune system 93 malignant 94 ‘in situ’ 93, 94 uncertainty of tumours 93–4 see also breast cancer cancer diagnoses study 79–80 Car and Driver magazine 130–1 Carnegie 117 Carnegie Mellon University 115 cars 113–40 driverless see driverless cars see also DARPA (US Defence Advanced Research Projects Agency) categories of algorithms association 9 classification 9 filtering 9–10 prioritization 8 Centaur Chess 202 Charts of the Future 148–50 chauffeur mode 139 chess 5-7 Chicago Police Department 158 China 168 citizen scoring system 45–6 breaking trust 46 punishments 46 Sesame Credit 45–6, 168 smallpox inoculation 81 citizen scoring system 45–6 Citroen DS19 116, 116–17 Citymapper 23 classification algorithms 9 Clinical vs Statistical Prediction (Meehl) 21–2 Clinton Foundation 42 Clubcard (Tesco) 26 Cohen’s Kappa 215n12 cold cases 172 Cold War 18 Colgan, Steyve 155 Commodore 64 ix COMPAS algorithm 63, 64 ProPublica analysis accuracy of scores 65 false positives 66 mistakes 65–8 racial groups 65–6 secrecy of 69 CompStat 149 computational statistics 12 computer code 8 computer intelligence 13 see also AI (artificial intelligence) computer science 8 computing power 5 considered thought 72 cookies 34 Cope, David 189, 190–1, 193 cops on the dots 155–6 Corelogic 31 counter-intuition 122 creativity, human 192–3 Creemers, Rogier 46 creepy line 28, 30, 39 crime 141–73 algorithmic regulation 173 boost effect 151, 152 burglary 150–1 cops on the dots 155–6 geographical patterns 142–3 gun 158 hotspots 148, 149, 150–1, 155 HunchLab algorithm 157–8 New York City subway 147–50 predictability of 144 PredPol algorithm 152–7, 158 proximity of offenders’ homes 144 recognizable patterns 143–4 retail 170 Strategic Subject List 158 target hardening 154–5 see also facial recognition crime data 143–4 Crimewatch programme 142 criminals buffer zone 144 distance decay 144 knowledge of local geographic area 144 serial offenders 144, 145 customers data profiles 32 inferred data 32–4 insurance data 30–1 shopping habits 28, 29, 31 supermarket data 26–8 superstore data 28–31 cyclists 129 Daimler 115, 130 DARPA (US Defence Advanced Research Projects Agency) driverless cars 113–16 investment in 113 Grand Challenge (2004) 113–14, 117 course 114 diversity of vehicles 114 GPS coordinates 114 problems 114–15 top-scoring vehicle 115 vehicles’ failure to finish 115 Grand Challenge (2005) 115 targeting of military vehicles 113–14 data 25–47 exchange of 25, 26, 44–5 dangers of 45 healthcare 105 insurance 30–1 internet browsing history 36–7, 36–8 internet giants 36 manipulation and 39–44 medical records 102–7 benefits of algorithms 106 DeepMind 104–5 disconnected 102–3 misuse of data 106 privacy 105–7 patterns in 79–81, 108 personal 108 regulation of America 46–7 Europe 46–7 global trend 47 sale of 36–7 Sesame Credit 45–6, 168 shopping habits 28, 29, 31 supermarkets and 26–8 superstores and 28–31 data brokers 31–9 benefits provided by 32 Cambridge Analytica 39–42 data profiles 32 inferred data 32–4, 35 murky practices of 47 online adverts 33–5 rich and detailed datasets 103 Sesame Credit 45–6 unregulated 36 in America 36 dating algorithms 9 Davies, Toby 156, 157 decision trees 56–8 Deep Blue 5-7, 8 deep learning 86 DeepMind access to full medical ­histories 104–5 consent ignored 105 outrage 104 contract with Royal Free NHS Trust 104 dementia 90–2 Dewes, Andreas 36–7 Dhami, Mandeep 75, 76 diabetic retinopathy 96 Diaconis, Pesri 124 diagnostic machines 98–101, 110–11 differential diagnosis 99 discrimination 71 disease Alzheimer’s disease 90–1, 92 diabetic retinopathy 96 diagnosing 59, 99, 100 disease (continued) hereditary causes 108 Hippocrates’s understanding of 80 Huntington’s disease 110 motor neurone disease 100 pre-modern medicine 80 see also breast cancer distance decay 144 DNA (deoxyribonucleic acid) 106, 109 testing 164–5 doctors 81 unique skills of 81–2 Dodds, Peter 176–7 doppelgängers 161–3, 164, 169 Douglas, Neil 162–3 driver-assistance technology 131 driverless cars 113–40 advantages 137 algorithms and 117 Bayes’ red ball analogy 123–4 ALVINN (Autonomous Land Vehicle In a Neural Network) 118–19 autonomy 129, 130 full 127, 130, 134, 138 Bayes’ theorem 121–4 breaking the rules of the road 128 bullying by people 129 cameras and 117–18 conditions for 129 cyclists and 129 dealing with people 128–9 difficulties of building 117–18, 127–8 early technology 116–17 framing of technology 138 inevitability of errors 140 measurement 119, 120 neural networks 117–18 potential issues 116 pre-decided go-zones 130 sci-fi era 116 simulations 136–7 speed and direction 117 support for drivers 139 trolley problem 125–6 Uber 135 Waymo 129–30 driverless technology 131 Dubois, Captain 133, 137 Duggan, Mark 49 Dunn, Edwina 26 early warning systems 18 earthquakes 151–2 eBureau 31 Eckert, Svea 36–7 empathy 81–2 ensembles 58 Eppink, Richard 17, 18 Epstein, Robert 14–15 equations 8 Equivant (formerly Northpointe) 69, 217n38 errors in algorithms 18–19, 61–2, 76, 159–60, 197–9, 200–201 false negatives 62, 87, 88 false positives 62, 66, 87, 88 Eureka Prometheus Project 117 expectant mothers 28–9 expectations 7 Experiments in Musical Intelligence (EMI) 189–91, 193 Face ID (Apple) 165–6 Facebook 2, 9, 36, 40 filtering 10 Likes 39–40 news feeds experiment 42–3 personality scores 39 privacy issues 25 severing ties with data brokers 47 FaceFirst 170, 171 FaceNet (Google) 167, 169 facial recognition accuracy 171 falling 168 increasing 169 algorithms 160–3, 165, 201–2 2D images 166–7 3D model of face 165–6 Face ID (Apple) 165–6 FaceFirst 170 FaceNet (Google) 167, 169 measurements 163 MegaFace 168–9 statistical approach 166–7 Tencent YouTu Lab 169 in China 168 cold cases 172 David Baril incident 171–2 differences from DNA testing 164–5 doppelgängers 161–3, 164, 169 gambling addicts 169–70 identical looks 162–3, 164, 165 misidentification 168 neural networks 166–7 NYPD statistics 172 passport officers 161, 164 police databases of facial images 168 resemblance 164, 165 shoplifters 170 pros and cons of techno­logy 170–1 software 160 trade-off 171–3 Youssef Zaghba incident 172 fairness 66–8, 201 tweaking 70 fake news 42 false negatives 62, 87, 88 false positives 62, 66, 87, 88 FBI (Federal Bureau of Investigation) 168 Federal Communications Commission (FCC) 36 Federal Trade Commission 47 feedback loops 156–7 films 180–4 algorithms for 183 edits 182–3 IMDb website 181–2 investment in 180 John Carter (film) 180 novelty and 182 popularity 183–4 predicting success 180–1 Rotten Tomatoes website 181 study 181–2 keywords 181–2 filtering algorithms 9–10 Financial Times 116 fingerprinting 145, 171 Firebird II 116 Firefox 47 Foothill 156 Ford 115, 130 forecasts, decision trees 57–8 free technology 44 Fuchs, Thomas 101 Galton, Francis 107–8 gambling addicts 169–70 GDPR (General Data Protection Regulation) 46 General Motors 116 genetic algorithms 191–2 genetic testing 108, 110 genome, human 108, 110 geographical patterns 142–3 geoprofiling 147 algorithm 144 Germany facial recognition ­algorithms 161 linking of healthcare ­records 103 Goldman, William 181, 184 Google 14–15, 36 creepy line 28, 30, 39 data security record 105 FaceNet algorithm 167, 169 high-paying executive jobs 35 see also DeepMind Google Brain 96 Google Chrome plugins 36–7 Google Images 69 Google Maps 120 Google Search 8 Google Translate 38 GPS 3, 13–14, 114 potential errors 120 guardian mode 139 Guerry, André-Michel 143–4 gun crime 158 Hamm, John 99 Hammond, Philip 115 Harkness, Timandra 105–6 Harvard researchers experiment (2013) 88–9 healthcare common goal 111–12 exhibition (1884) 107 linking of medical records 102–3 sparse and disconnected dataset 103 healthcare data 105 Hinton, Geoffrey 86 Hippocrates 80 Hofstadter, Douglas 189–90, 194 home cooks 30–1 homosexuality 22 hotspots, crime 148, 149, 150–1, 155 Hugo, Christoph von 124–5 human characteristics, study of 107 human genome 108, 110 human intuition 71–4, 77, 122 humans and algorithms opposite skills to 139 prediction 22, 59–61, 62–5 struggle between 20–4 understanding the ­human mind 6 domination by machines 5-6 vs machines 59–61, 62–4 power of veto 19 PredPol (PREDictive ­POLicing) 153–4 strengths of 139 weaknesses of 139 Humby, Clive 26, 27, 28 Hume, David 184–5 HunchLab 157–8 Huntington’s disease 110 IBM 97–8 see also Deep Blue Ibrahim, Rahinah 197–8 Idaho Department of Health and Welfare budget tool 16 arbitrary numbers 16–17 bugs and errors 17 Excel spreadsheet 17 legally unconstitutional 17 naive trust 17–18 random results 17 cuts to Medicaid assistance 16–17 Medicaid team 17 secrecy of software 17 Illinois prisons 55, 56 image recognition 11, 84–7, 211n13 inferred data 32–4, 35 personality traits 40 Innocence Project 164 Instagram 36 insurance 30–1 genetic tests for Huntington’s disease 110 life insurance stipulations 109 unavailability for obese patients 106 intelligence tracking prevention 47 internet browsing history 36–8 anonymous 36, 37 de-anonymizing 37–8 personal identifiers 37–8 sale of 36–7 Internet Movie Database (IMDb) 181–2 intuition see human intuition jay-walking 129 Jemaah Islam 198 Jemaah Islamiyah 198 Jennings, Ken 97–8 Jeopardy!

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They would openly invite any interested people across the country to design their own driverless cars and race them against each other on a long-distance track, with a prize of $1 million for the winner.6 It would be the first event of its kind in the world, and a quick and cheap way to give DARPA a head start in pursuing their goal. The course was laid out over 142 miles, and DARPA hadn’t made it easy. There were steep climbs, boulders, dips, gullies, rough terrain and the odd cactus to contend with. The driverless cars would have to navigate dirt tracks that were sometimes only a few feet wide. Two hours before the start, the organizers gave each team a CD of GPS coordinates.7 These represented two thousand waypoints that were sprinkled along the route like breadcrumbs – just enough to give the cars a rough sense of where to go, but not enough to help them navigate the obstacles that lay ahead.

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

President Barack Obama himself wrote an op-ed on behalf of driverless cars in the Pittsburgh Post-Gazette the same day. President Donald Trump affirmed these priorities early in his administration.1 Driverless cars will finally solve the problem of moving people around with maximum efficiency, by ceding human control to impersonal algorithms. They promise to bring a messy, dangerous domain of life under control at last. Traffic jams will likely become a thing of the past, and accidents will be greatly reduced. So we are told, at any rate. In this we can detect a familiar pattern. Driverless cars are one instance of a wider shift in our relationship to the physical world, in which the demands of competence give way to a promise of safety and convenience.

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Notes INTRODUCTION: DRIVING AS A HUMANISM 1.Uber’s then chief executive Travis Kalanick was named to the president’s Strategic and Policy Forum, and Trump selected Elaine Chao for transportation secretary. The driverless car industry is “salivating over Elaine Chao’s light touch when it comes to regulations and her vocal support for the ride-sharing economy,” according to the Hill. Paul Brubaker, a spokesperson for the industry, said, “She has a keen understanding that technology presents a great opportunity to . . . create new mobility paradigms.” Melanie Zanona, “Driverless Car Industry Embraces Trump’s Transportation Pick,” Hill, December 4, 2016, http://thehill.com/policy/transportation/308590-driverless-car-industry-embraces-trumps-transportation-pick. 2.See Neal E.

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For self-driving cars to realize their full potential to reduce traffic and accidents, we can’t have rogue dissidents bypassing the system of coordination that they make possible.6 Their inherent logic presses toward their becoming mandatory—if not by fiat of the state, then by the prohibitive calculations of insurance companies, who will have to distribute risk among fewer human drivers. Or by the portioning out of scarce road surface, with preference given to driverless cars. At bottom, the all-colonizing character of driverless cars derives from the fact that robots are a very awkward fit with human drivers, and are likely to remain so. The prospects for these two kinds of intelligence to share the road gracefully are quite dim, as we will learn from the literature on “human factors” in automation.

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

Furthermore, self-driving cars are designed to improve the flow of traffic and reduce the number and duration of traffic jams. In the eyes of commuters around the world, this is a particularly critical argument for driverless cars, especially in megacities [64]. It is also linked to the hope of reducing fuel consumption and emissions of carbon dioxide and particulate matter. Amidst all of the euphoria about driverless cars, study respondents repeatedly asked about an issue touched upon in Chapter 7: What will autonomous cars do to the pleasure of driving? All over the world, there are plenty of people who actively enjoy driving regardless of the increasing Customers’ Expectations 205 Table 19.1.

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Mobility, freedom and independence, as well as social status are all advantages that many drivers associate with their cars [83]. Although the automobile gives people control and power over a machine and the feeling of freedom, movement, pride and pleasure the desire for autonomous vehicles is not something new [116]. Driverless cars have been described in some detail for several decades now: at first as science fiction and later in scientific publications. In some fictional cases, the authors’ fantasies stretch to self-driving cars living lives of their own and taking on human traits by independently setting a route or even expressing emotions.

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A similar argument is presented by Jen-Hsun Huang, CEO of Nvidia, who demands accuracy of 99.999999 per cent in the development of autonomous cars, whereby the last percentage point can only be achieved at very great expense. Toyota and Rand Corporation have published calculations of the number of miles self-driving cars have to be tested before they can be assessed as roadworthy because the algorithms required for driverless cars undergo self-learning in multiple road traffic situations. The more traffic situations these algorithms are exposed to, the better prepared they are to master a new situation. Designing this training process so that the accuracy demanded by Jen-Hsun Huang is obtained will be the crucial challenge in the development of autonomous vehicles.

Are Trams Socialist?: Why Britain Has No Transport Policy by Christian Wolmar

active transport: walking or cycling, Beeching cuts, Berlin Wall, Boris Johnson, BRICs, congestion charging, Crossrail, Diane Coyle, driverless car, financial independence, full employment, joint-stock company, Kickstarter, low cost airline, Network effects, railway mania, trade route, Traffic in Towns by Colin Buchanan, Tragedy of the Commons, urban sprawl, wikimedia commons, Zipcar

The twin revolution of driverless cars and common ownership may well prove to be a myth, or at least not deliverable in the sort of time frame that would allow today’s policymakers to consider it. The key issue is whether driverless cars will result in an increase or a reduction in mileage. If driverless cars live up to their promise, it appears logical that they would attract people out of public transport given their flexibility and the fact that they provide private space – all the current attractions of driving without the need to waste time controlling the damn thing. Driverless cars may well, then, replicate the failed transport policies of the past and exacerbate, rather than solve, the fundamental problem of the commons, i.e. the overuse of communal road space that is provided free.

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In particular, they do not address the fundamental issue of the shortage of road space, nor of the environmental degradation caused by existing vehicles – not only pollution but the land use taken up by roads and parking. Even if cars used no energy and created no polluting ­gases, they would still be a blight on urban areas. The driverless car: a game changer? Source: photo by Grendelkhan (see page 115 for full details). In any case, despite the billions being invested in them, robot cars so far remain a highly experimental concept that is nowhere near coming to fruition in any meaningful way. While aspects of driverless cars such as cruise control and automatic parking have been introduced to today’s car fleet, the technology of allowing autonomous control of a car while the driver sits and reads her copy of the Financial Times or plays with his tablet is nowhere near ready.

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Moreover, they have had a disproportionately high number of crashes, albeit minor ones. The cars in California had accumulated eleven such accidents by the middle of 2015, and in February 2016 a prang with a bus was actually caused by the ‘driverless car’. While the earlier incidents were not the fault of the automation, it may be that human drivers find the behaviour of driverless cars less predictable. Most of these accidents were rear-end shunts and the excessive caution of the robot cars is thought to have been a contributory cause. They slowed down or stopped in a situation that the driver behind did not perceive as being dangerous.

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

THE DRIVER IN THE DRIVERLESS CAR VIVEK WADHWA WITH ALEX SALKEVER THE DRIVER IN THE DRIVERLESS CAR HOW OUR TECHNOLOGY CHOICES WILL CREATE THE FUTURE The Driver in the Driverless Car Copyright © 2017 by Vivek Wadhwa and Alex Salkever All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the publisher, except in the case of brief quotations embodied in critical reviews and certain other noncommercial uses permitted by copyright law.

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The automated vehicle takes the initiative and smoothly accelerates through the intersection. The passenger, I notice, appears preternaturally calm. I am both amazed and unsettled. I have heard from friends and colleagues that my reaction is not uncommon. A driverless car can challenge many assumptions about human superiority to machines. Though I live in Silicon Valley, the reality of a driverless car is one of the most startling manifestations of the future unknowns we all face in this age of rapid technology development. Learning to drive is a rite of passage for people in materially rich nations (and becoming so in the rest of the world): a symbol of freedom, of power, and of the agency of adulthood, a parable of how brains can overcome physical limitations to expand the boundaries of what is physically possible.

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Pedestrians will stop worrying about getting hit by cars in intersections. Let me paint a picture of what streets will look like in an age of driverless cars. We will no longer need traffic lights: robot cars will synchronize wirelessly to time mass movements across city intersections and entries onto freeways or balletic dances around four-way stop signs. Having no human eyes behind the wheel will obviate much of the need for signaling and signage. When all the driverless cars are talking to each other, there will be no need for them to ever come to a complete halt and waste all their kinetic energy. So we will be able to forget traffic lights—and stop signs, yield signs, lighting on freeways, and dozens of other transportation-infrastructure elements catering to human drivers.

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

In some ways, the driverless car is a natural next step following all the technological and demographic changes that contributed to the original Millennial-led driving revolution that is the subject of this book, especially the information oversupply that made smartphones into a tool for transportation planning. Driverless cars are also, in their way, a new army on the battlefield over which the original war for right-of-way was fought nearly a century ago, in which roads were transformed from multipurpose commercial and social real estate into single-use arteries for automobile travel. Driverless cars are unarguably transportation anywhere, and anytime.

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Driverless cars are unarguably transportation anywhere, and anytime. And, to the degree that driverless cars will be available to all levels of society, they’re transportation for everybody, too. They can even coexist, peacefully and profitably, with streetcars, commuter rail, bike paths, and subways. Still, it isn’t clear whether that new army of driverless cars is a Street Smart ally or opponent. There are any number of potential benefits to be found in a world dominated by driverless cars, including the possibility of turning nine out of every ten vehicles currently on the road into planters. As hardly needs underlining, the environmental and social benefits of taking 90 percent of existing cars off the streets are almost incalculably high.

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The rosiest scenarios for autonomous driving, the ones that forecast a precipitous drop in the number of cars on the road, assume that most of those cars won’t be personal vehicles but part of fleets of what we might call A-taxis. This means that virtually all of them will be in motion, virtually all of the time. In cities, particularly the densest parts of cities, driverless cars may be a recipe for constant gridlock. However, even if driverless cars can, theoretically, reduce traffic delays because more of them can travel safely on a given stretch of road, this doesn’t mean that they will. One reason for the anticipated appeal of driverless cars is that they are expected to mimic travel by train: smooth enough for reading, or working, or Internet gaming. But in order to do this, they would have to also mimic the (slower) acceleration profile of trains—and when they do, according to a recent simulation by a group of researchers at University College London, they don’t improve travel times.

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

What have we learned from extreme environments that might shed light on possible futures for autonomous cars? We know that driverless cars will be susceptible to all of the problems that surround people’s use of automation in the environments we have examined—system failures, variability of skills among users, problems of attention management, the degradation of manual skills, and rising automation bias as people come to rely on automated systems. The most challenging problem for a driverless car will be the transfer of control between automation and the driver—what we might call “the Air France 447 problem.”

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Google discovered that “people are lazy”: Tom Simonite, “Lazy Humans Shaped Google’s New Autonomous Car,” MIT Technology Review (May 30, 2014), http://www.technologyreview.com/news/527756/lazy-humans-shaped-googles-new-autonomous-car/. Will Knight, “Driverless Cars Are Further Away Than You Think,” MIT Technology Review (October 22, 2013), http://www.technologyreview.com/featuredstory/520431/driverless-cars-are-further-away-than-you-think/. “kick back, relax, and enjoy the ride”: Chris Urmson, “Just Press Go: Designing a Self-Driving Vehicle,” Google official blog, May 27, 2014, http://googleblog.blogspot.com/2014/05/just-press-go-designing-self-driving.html, accessed July 9, 2014.

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Abbott, Kathy, 75–76 ABE (autonomous benthic explorer), 54, 191–96 acoustic communications and, 195–96 geological mapping by, 192–93, 194 loss of, 191–92 nature of autonomy of, 194–97 original mission of, 192 acoustic communications, and AUVs, 195–96 acoustic transponder networks, for navigation, 29 Afghanistan, 139 Airbus, 86–87 A-310, 82 Flight QF32, 71–72, 77 aircraft/aviation, 69–111, 226–29 adding unmanned automation technology to, 215–18 Automated Labor In-cockpit System (ALIAS), 217–18 drones (See drones) FAA survey of technology and pilot skill, 2013, 75–76 future of, 110–11 heads-up display (HUD) and, 88–108, 225 history of, 77–84 landings and, 84–88 optionally piloted aircraft (OPAs), 213–15 pilots role in flying modern aircraft, 69–72, 75–77 synthetic vision and, 108–9, 225 technological change and increasing automation, effect of, 72–75 unmanned helicopters, 210–13 Air France Flight 447, 1–2, 1–4, 69–70, 72, 73, 81, 162, 196 Akers, Thomas, 170, 171–72 Alaska Airlines, 92 Alvin (deep-sea submersible), 26–30, 33–34, 35, 45–51, 57, 59–66, 176, 194, 197, 225 ABE’s geological mapping and, 192–93, 194 acoustic navigation system of, 29 arguments and justifications for new, 63–65 hydrogen bomb recovery effort using, 27 Jason, differences between and rivalry with, 59–62 plate tectonics evidence gathered by, 28–29 Titanic wreck and, 45–51 Amber, 126 amphoras, 23–24 AMUVS (Advanced Maneuverable Underwater Vehicle System), 43–44 ANGUS (Acoustically Navigated Geologic Underwater Survey System), 30–34 Apollo missions, 225 Apollo 11, 159–61 Apollo 13, 72 Apollo 15, 178 Apollo 17, 177, 179 field geology and, 176–79 Argo (tethered sled), 35–36, 41–43 Armstrong, Neil, 77, 78, 159–61, 221 Asiana Airlines, 106–7 Flight 214, 72, 106 Association for Unmanned Vehicle Systems International (AUVSI), 219 Atlantis II (research vessel), 45 Aurora Flight Sciences, 211, 214, 217 Automated Labor In-cockpit System (ALIAS), 217–18 automatic landing systems (autoland) Apollo landings and, 159–61 in commercial aviation, 86–88, 94, 97 space shuttles and, 161–63 automation, 4–6, 10, 11 automation bias, 74 automation dependency, 74 automation surprise, 74 automobiles, driverless. See driverless cars AutoNav, 186 autonomous underwater vehicles (AUVs), 2–3, 12, 66 ABE (See ABE [autonomous benthic explorer]) acoustic communications and, 195–96 nature of autonomy of, 194–97 autonomy/autonomous systems, 4–6 adding unmanned automation technology to airplanes and, 215–18 Automated Labor In-cockpit System (ALIAS), 217–18 automation, distinguished, 11 Crawford’s critique of, 222–23 defined, 11–12, 220–21 driverless cars and, 198–210 full autonomy myth and, 9–10 human approvals and intervention and, 4–5, 8, 221–22, 224–26 human intentions and assumptions built into, 10 increasing, 12 Mars rovers and, 186 optionally piloted aircraft (OPAs) and, 213–15 problem of, in human settings, 223–24 terminology of, 219–20 transition from lab to field, tempering effect of, 221–22 underwater vehicles (See autonomous underwater vehicles (AUVs)) unmanned helicopters and, 210–13 Aviation Week & Space Technology, 213 Ballard, Robert, 18, 20, 26–30, 33–34, 59, 176 Alvin and, 27–30 Argo, development and first uses of, 41–43 Deep Submergence Laboratory founded and staffed by, 37–40 early conception of Argo/Jason and commitment to project from ONR, 35–36 Jason Jr., development of, 44–45 naval career of, 26 as naval liaison to Woods Hole, 26–27 telepresence vision of, 26, 43, 57 Titanic, discovery and exploration of, 42–43, 45–51 bathyscaphe, 35 bathysphere, 35 Beebe, William, 35 Benthos, 39, 40 Big Safari, 129, 130, 136, 138–39 Bin Laden, Osama, 137, 138 Blair, Dave, 155 Boeing, 86–87 707, 82 747, 87 757, 82 767, 82 Bonestell, Chesley, 164, 165 Bonin, Pierre Cedric, 1–2 Bowen, Martin, 39–41, 46, 47, 48–49, 55–56, 57 Bowersox, Ken, 169–70, 171 Bradley, Al, 192 British Airways, 86 Bryant, Brandon, 152–53 Buck, Robert, 79, 84 Bush, George W., 139, 172 Cameron, James, 51 Camilli, Rich, 195 Cape Cod Times, 45 Carolyn Chouest (ship), 17–18, 22 cars, driverless.

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

People could drink without hesitation when they go out at night—good news for restaurants, bars, and the alcohol industry. Since a driverless car can flow through traffic more efficiently, road congestion and pollution would likely fall. And if driverless cars could be summoned to pick us up or drop us off, we’d no longer need to park at our destination, freeing up millions of acres of prime real estate. In many U.S. cities, 30 to 40 percent of the downtown surface area is devoted to parking. Well, that all sounds pretty perfect, doesn’t it? But of course no new technology is perfect, especially something as vast as a driverless-car revolution. So if you want your argument to be taken seriously, you’d do well to admit the potential downsides.

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Nearly every major automaker in the world, as well as Google, has successfully tested cars that use an onboard computer, GPS, cameras, radar, laser scanners, and actuators to do everything a human driver can do—but better. And since roughly 90 percent of the world’s 1.2 million traffic deaths each year—yes, 1.2 million deaths, every year!—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.

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Furthermore, an opponent who feels his argument is ignored isn’t likely to engage with you at all. He may shout at you and you may shout back at him, but it is hard to persuade someone with whom you can’t even hold a conversation. Think back to the driverless car that just mowed down a flock of schoolchildren. Is there any value in pretending that such accidents won’t happen? None that we can think of. The death of these children would horrify everyone who heard about it; for the victims’ parents, the very idea of a driverless car would become repugnant. But let’s consider a different set of parents: the ones whose children are currently dying in traffic accidents. Around the world, some 180,000 kids are killed each year, or roughly 500 a day.

Virtual Competition by Ariel Ezrachi, Maurice E. Stucke

"World Economic Forum" Davos, Airbnb, Alan Greenspan, Albert Einstein, algorithmic management, algorithmic trading, Arthur D. Levinson, barriers to entry, behavioural economics, cloud computing, collaborative economy, commoditize, confounding variable, corporate governance, crony capitalism, crowdsourcing, Daniel Kahneman / Amos Tversky, David Graeber, deep learning, demand response, Didi Chuxing, digital capitalism, disintermediation, disruptive innovation, double helix, Downton Abbey, driverless car, electricity market, Erik Brynjolfsson, Evgeny Morozov, experimental economics, Firefox, framing effect, Google Chrome, independent contractor, index arbitrage, information asymmetry, interest rate derivative, Internet of things, invisible hand, Jean Tirole, John Markoff, Joseph Schumpeter, Kenneth Arrow, light touch regulation, linked data, loss aversion, Lyft, Mark Zuckerberg, market clearing, market friction, Milgram experiment, multi-sided market, natural language processing, Network effects, new economy, nowcasting, offshore financial centre, pattern recognition, power law, prediction markets, price discrimination, price elasticity of demand, price stability, profit maximization, profit motive, race to the bottom, rent-seeking, Richard Thaler, ride hailing / ride sharing, road to serfdom, Robert Bork, Ronald Reagan, search costs, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, smart meter, Snapchat, social graph, Steve Jobs, sunk-cost fallacy, supply-chain management, telemarketer, The Chicago School, The Myth of the Rational Market, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, Travis Kalanick, turn-by-turn navigation, two-sided market, Uber and Lyft, Uber for X, uber lyft, vertical integration, Watson beat the top human players on Jeopardy!, women in the workforce, yield management

That means that the more cars Google puts on the road compared to its competitors, the greater its learning advantage.”30 Unlike Uber, the super-platforms do incur a significant upfront cost for developing the driverless car. But Google and Apple can spread that cost over many passengers. And, unlike Uber, the super-platforms would not have to pay for the expensive recruitment of drivers; nor would they need to pay 80 percent of each fare to drivers. Nor would Google and Apple have to impose heft y surcharges to entice drivers onto the roads when demand exceeds supply: their algorithms could simply summon available driverless cars from local parking lots. Thus, as the cost of driverless car technology decreases, and the technology improves through learning by doing, the super-platforms could enjoy a significant cost advantage over Uber and become a powerful enemy.

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Google, by virtue of its calendar and e-mail apps, could anticipate when you need a car and where you need to go. As your Nest Labs smart thermostat lowers the heat when you leave your home, a Google driverless car pulls up to your front door. Even for those who prefer to drive their own cars, Google could nonetheless leverage its driverless car technology. Given the scale benefits of learning by doing, car manufacturers might prefer (or be forced) to license Google’s technology (including the daily or hourly updates from the millions of Google cars on the road). (If Apple did not license its driverless car technology, then it would lack the scale, and its quality would lag.) The car manufacturers also see a potential relationship in which they manufacture the car’s “dumb” commoditized parts (like Foxconn Technology Group for Apple), while the super-platforms earn the lion’s share of profits from technology and design.

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The possible leveraging of market power to push out an “as efficient” operator downstream, and clear the way for the super-platform’s own operation downstream, is worrying.29 The Dynamic Interplay among Frenemies 153 Beyond the super-platforms’ ability to leverage their power to support car-sharing ser vices, the true game changer is likely to be their driverless car technology. Google and Apple could in the future enjoy a significant cost advantage over Uber. The super-platforms’ algorithms for their driverless cars will also benefit from learning by doing, which provides important scale. As Forbes has noted, “unlike human drivers who must rely on their own experience for learning, Google’s cars will learn from every Google car’s experience.

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

I will start by discussing the positive case and reviewing the potential impact, before moving on to the criticisms and problems. The potential and the promise Driverless cars are not a fantasy: they are already working – admittedly only, so far, in restricted areas, such as parts of the city of Phoenix, Arizona. The state of California has recently approved new rules allowing driverless cars to operate without a human driver sitting behind the wheel. 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.

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A joint study by the World Economic Forum and the Boston Consulting Group sees substantial scope for the sharing of rides in driverless cars, thereby undermining the market for public transport.6 Elon Musk has said that “owning a human-driven vehicle will be similar to owning a horse – rare and optional.7 The results would include fewer cars needing to be built (as well as sold, repaired, insured, etc.). Additionally, there would be less demand for space to park cars that remain idle most of the time. While they are waiting for users, driverless cars can be parked end to end and stacked. This could potentially transform urban landscapes and free up much scarce space for other uses.

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Despite the claims of the manufacturers and developers of driverless vehicles that they are ultrasafe, a 2015 study from the University of Michigan discovered that the crash rate is higher for driverless vehicles.9 The study suggested that, when they occur, crashes are almost always not the fault of the driverless cars. The problem seems to be that human drivers find it difficult to interact with other vehicles when the latter are driverless. 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.

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

Pisarski, ‘Commuting in the 21st Century’, Final Data, 29 June 2011. 314 ‘planners who dream of a future’, see Brad Edmondson, ‘Personal Travel’. 314 ‘fuel based transportation’, Financial Times, 7 November 2013: http://www.ft.com/cms/s/0/1edaf8f6-479a-11e3-9398-00144feabdc0.html#axzz2k4AdHGQz. 315 ‘Driverless, it will start its own motor,’ Milwaukee Sentinel, 8 December 1926. 319 ‘Google’s first car on YouTube: http://www.youtube.com/watch?v=CqSDWoAhvLU. 319 /326 For Google and Mercedes driverless cars, see Dan Neil, ‘Driverless Cars for the Road Ahead’, Wall Street Journal, 27 September 2013. 320 ‘We take machines and stuff ’em with information until they’re smarter than we are’, International Movie Database online: http://m.imdb.com/title/tt0064 603/quotes?qt=qt0375405. 320 For Chunka Mui and driverless cars, see: http://www.forbes.com/sites/chunkamui/. 321 ‘typical commuter arteries’, Chris Knapman, ‘Large-scale trial of driverless cars to begin on public roads’, Daily Telegraph, 2 December 2013: http://www.telegraph.co.uk/motoring/news/10484839/Large-scale-trial-of-driverless-cars-to-begin-on-public- roads.html. 321 For KPMG and driverless cars, see ‘Self-driving cars: the next revolution’: https://www.kpmg.com/US/en/IssuesAndInsights/ArticlesPublications/Documents/self-driving-cars-next-revolution.pdf. 326 ‘Something suddenly falls off a truck ahead of the car.

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qt=qt0375405. 320 For Chunka Mui and driverless cars, see: http://www.forbes.com/sites/chunkamui/. 321 ‘typical commuter arteries’, Chris Knapman, ‘Large-scale trial of driverless cars to begin on public roads’, Daily Telegraph, 2 December 2013: http://www.telegraph.co.uk/motoring/news/10484839/Large-scale-trial-of-driverless-cars-to-begin-on-public- roads.html. 321 For KPMG and driverless cars, see ‘Self-driving cars: the next revolution’: https://www.kpmg.com/US/en/IssuesAndInsights/ArticlesPublications/Documents/self-driving-cars-next-revolution.pdf. 326 ‘Something suddenly falls off a truck ahead of the car. Can the system react faster’, Dan Neil, ‘Driverless Cars’. 328 A ‘bold commitment to modernisation’, Jim Pickard, ‘Mandelson fears HS2 will prove an “expensive mistake”’, Financial Times, 2 July 2013. 328 ‘live in a rail bubble’, Matt Ridley, ‘Hadrian’s wall was a marvellous mistake; so is HS2’, 26 July 2013: http://www.rationaloptimist.com/blog/hadrian%27s-wall-was-a-marvellous-mistake-so- is-hs2.aspx. 329 For ‘reached the end of its life’, see: http://www.businessweek.com/articles/2013-07-12/french-wreck-reveals-hidden-danger-in-its-vaunted-train-system. 331 For ‘barf ride’, see: http://pedestrianobservations.wordpress.com/2013/08/13/loopy-ideas-are-fine-if- youre-an-entrepreneur/. 333 For population estimates and the world’s carrying capacity, see World Population Monitoring 2001, United Nations, Department of Economic and Social Affairs, Population Division, New York, 2001, pp. 31ff. 333 ‘occasional vertical and random horizontal low speed vehicular or moving-belt travel’, John Heaver Fremlin, ‘How Many People Can the World Support?’

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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 grand challenges advanced autonomous technology by leaps and bounds, and also created a pool of engineering expertise, eager to take the concept further. The potential that the DARPA challengers had demonstrated revived commercial interest in driverless cars. Companies including Nissan, GM, Lexus, Google, Mercedes Benz, Ford, Skoda, Audi and Volvo are now researching or building vehicles with varying degrees of autonomy.

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

Kerry and Jeff Karsten, “Report: Gauging Investment in Self-Driving Cars,” Brookings Institution, October 16, 2017, https://www.brookings.edu/research/gauging-investment-in-self-driving-cars/. 8first truly self-driving taxi service, in Chandler, Arizona: John Krafcik, “Waymo One: The Next Step on Our Self-Driving Journey,” Waymo (blog), Medium, December 5, 2018, https://medium.com/waymo/waymo-one-the-next-step-on-our-self-driving-journey-6d0c075b0e9b. 8set aside more than $10 billion: “Waymo CEO on Future of Autonomous Vehicles,” Bloomberg, video, September 13, 2017, https://www.bloomberg.com/news/videos/2017-09-14/waymo-ceo-on-future-of-autonomous-vehicles-video. 8“There is hardly a task that horse-drawn vehicles”: “Practical Automobiles: This Type to Form Large Portion of Coming Exhibition,” New York Times, January 12, 1903, https://timesmachine.nytimes.com/timesmach ine/1903/01/12/101965824.pdf. 9“I was ready to make a leap into the future”: David Leonhardt, “Driverless Cars Made Me Nervous. 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?”

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Cheape, Moving the Masses: Urban Public Transit in New York, Boston, and Philadelphia 1880–1912 (Cambridge, MA: Harvard University Press, 1980), 1. 175companies merely joined forces: Cheape, Moving the Masses, 172. 175the most powerful, reviled traction monopoly: Walt Crowley, “City Light’s Birth and Seattle’s Early Power Struggles, 1886–1950,” History Link, April 26, 2000, https://www.historylink.org/File/2318. 175enjoyed decades of unrivaled power: Owain James, “We Miss Streetcars’ Frequent and Reliable Service, Not Streetcars Themselves,” Mobility Lab, April 17, 2019, https://mobilitylab.org/2019/04/17/we-miss-streetcars-frequent-and-reliable-service-not-streetcars-themselves/; combination of technological change and federal intervention: “Jersey Trolley Merger,” Wall Street Journal, May 13, 1905, 2. 176$100 billion Vision Fund: Katrina Brooker, “The Most Powerful Person in Silicon Valley,” Fast Company, January 14, 2019, https://www.fastcompany.com/90285552/the-most-powerful-person-in-silicon-valley. 176its total commitment to some $9 billion: Pavel Alpeyev, Jie Ma, and Won Jae Ko, “Taxi-Hailing Apps Take Root in Japan as SoftBank, Didi Join Fray,” Bloomberg, July 19, 2018, https://www.bloomberg.com/news/articles/2018-07-19/softbank-didi-to-roll-out-taxi-hailing-business-in-japan. 177$2 billion into Singapore-based Grab: Yoolim Lee, “Grab Vanquishes Uber with Local Strategy, Billions from SoftBank,” Bloomberg, March 26, 2018, https://www.bloomberg.com/news/articles/2018-03-26/grab-vanquishes-uber-with-local-strategy-billions-from-softbank. 177Ola downloaded $2 billion: Saritha Rai, “India’s Ola Raises $2 Billion from SoftBank, Tencent,” Bloomberg, October 2, 2017, https://www.bloomberg.com/news/articles/2017-10-02/india-s-ola-is-said-to-raise-2-billion-from-softbank-tencent. 17715 percent stake in Uber: Alison Griswold, “SoftBank—not Uber—Is the Real King of Ride-Hailing,” Quartz, January 23, 2018, https://qz.com/1187144/softbank-not-uber-is-the-real-king-of-ride-hailing/. 177Uber picked off Dubai-based Careem: Adam Satariano, “This Estonian Start-Up Has Become a Thorn in Uber’s Side,” New York Times, April 23, 2019, https://www.nytimes.com/2019/04/23/technology/bolt-taxify-uber-lyft.html. 177The damage to consumers: Justina Lee, “Singapore Fine Is ‘Minor Bump’ in Grab’s Ride-Hailing Dominance,” Nikkei Asian Review, September 25, 2018, https://asia.nikkei.com/Spotlight/Sharing-Economy/Singapore-fine-is-minor-bump-in-Grab-s-ride-hailing-dominance. 177Grab cornered more than 80 percent: Ardhana Aravindan, “Singapore Fines Grab and Uber, Imposes Measures to Open Up Market,” Reuters, September 23, 2018, https://www.reuters.com/article/us-uber-grab-singapore/singapore-fines-grab-and-uber-imposes-measures-to-open-up-market-idUSKCN1M406J. 177all launched antitrust investigations: Mai Nguyen, “Vietnam Says Eyeing Formal Antitrust Probe into Uber-Grab Deal,” Reuters, May 16, 2018, https://www.reuters.com/article/us-uber-grab-vietnam-idUSKCN1IH0XNiAikaRey, “Antitrust Watchdog Fines Grab P16 Million over Uber Deal,” Rappler, October 17, 2018, https://www.rappler.com/business/214502-philippine-competition-commission-fines-grab-philippines-over-uber-deal; Yoolim Lee, “Singapore Watchdog Fines Uber, Grab $9.5 Million over Merger,” Bloomberg, September 24, 2018, https://www.bloomberg.com/news/articles/2018-09-24/singapore-fines-uber-grab-s-13-million-for-merger-infringement. 177another fare-slashing battle with Ola: “Steering Group: A Bold Scheme to Dominate Ride-Hailing,” The Economist, May 10, 2018, https://www.economist.com/briefing/2018/05/10/a-bold-scheme-to-dominate-ride-hailing. 177“SoftBank is playing the ride-hailing”: Alison Griswold, “Softbank Has Spread Its Ride-Hailing Bets and Didi Looks Like an Early Win,” Quartz, April 24, 2018, https://qz.com/1261177/softbanks-winner-in-ride-hailing-is-chinas-didi-chuxing-not-uber/. 177“driver incentives, passenger discounts”: Tim O’Reilly, “The Fundamental Problem with Silicon Valley’s Favorite Growth Strategy,” Quartz, February 5, 2019, https://qz.com/1540608/the-problem-with-silicon-valleys-obsession-with-blitzscaling-growth/. 178“locked in a capital-fueled deathmatch”: O’Reilly, “The Fundamental Problem.” 178The Vision Fund’s biggest investor: Brooker, “The Most Powerful Person.” 178the proceeds of an earlier liquidation: Catherine Shu, “Saudi Arabia’s Sovereign Fund Will Also Invest $45B in SoftBank’s Second Vision Fund,” Tech-Crunch, October 2018, https://techcrunch.com/2018/10/07/saudi-arabias-sovereign-fund-will-also-invest-45b-in-softbanks-second-vision-fund/. 178Uber’s multi-billion-dollar quarterly losses: “Aramco Value to Top $2 Trillion, Less Than 5 Percent to Be Sold, Says Prince,” Reuters, April 25, 2016, https://www.reuters.com/article/us-saudi-plan-aramco-idUSKCN0XM16M. 178the House of Saud: Brooker, “The Most Powerful Person.” 178thwart municipal officials’ attempts at enforcement: Mike Isaac, “How Uber Deceives the Authorities Worldwide,” New York Times, March 3, 2017, https://www.nytimes.com/2017/03/03/technology/uber-greyball-program-evade-authorities.html. 179“Even if that means paying money”: Dara Khosrowshahi, “The Campaign for Sustainable Mobility,” Uber, September 26, 2018, https://www.uber.com/newsroom/campaign-sustainable-mobility/. 180Five-cent nickel fares: Cheape, Moving the Masses, 174–75. 180cities . . . grant a ride-hail monopoly: “Free Exchange: The Market for Driverless Cars Will Head towards Monopoly,” The Economist, June 7, 2018, https://www.economist.com/finance-and-economics/2018/06/07/the-market-for-driverless-cars-will-head-towards-monopoly. 180“corrupt and contented”: Cheape, Moving the Masses, 177. 180Jay Gould’s Manhattan Railway Company: Terry Golway, Machine Made: Tammany Hall and the Creation of Modern American Politics (New York: Live-right, 2014), 135. 180took over Puget Sound’s streetcar: Crowley, “City Light’s Birth.” 181Public transit was the competition: United States Securities and Exchange Commission, Registration Statement under the Securities Act of 1933: Uber Technologies, April 11, 2019, 25, https://www.sec.gov/Archives/edgar/data/1543151/000119312519103850/d647752ds1.htm#toc. 181deploy predatory pricing: United States Securities and Exchange Commission, Registration Statement. 182“have been created based on cash flows”: “Asset-Backed Security,” Investo-pedia, accessed December 7, 2018, https://www.investopedia.com/terms/a/asset-backedsecurity.asp. 183Amazon’s body-tracking technology: “The Learning Machine: Amazon’s Empire Rests on its Low-Key Approach to AI,” The Economist, April 11, 2019, https://www.economist.com/business/2019/04/13/amazons-empire-rests-on-its-low-key-approach-to-ai. 8.

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Just One Thing Might Stand in Their Way,” MIT Technology Review, December 19, 2018, https://www.technologyreview.com/s/612598/chinas-tech-giants-want-to-go-global-just-one-thing-might-stand-in-their-way/. 199“sheds with beds”: Citi GPS and Oxford Martin Programme on Technology and Employment, Technology at Work v. 3.0: Automating E-commerce from Click to Pick to Door, Global Perspectives and Solutions (Citigroup, 2017), 83. 202synergy of software trains and compact neighborhoods: John Markoff, “Urban Planning Guru Says Driverless Cars Won’t Fix Congestion,” New York Times, October 27, 2018, https://www.nytimes.com/2018/10/27/technology/driverless-cars-congestion.html. 202Switzerland’s Les Vergers Ecoquartier: “Venir aux Verges,” Les Vergers Ecoquartier, accessed May 23, 2019, https://www.lesvergers-meyrin. ch/ecoquartier/venir-aux-vergers. 202Micromobility, as this emerging sector is called: Joe Cortright, “What Drives Ride-Hailing: Parking, Drinking, Flying, Peaking, Pricing,” City Commentary, February 19, 2018, http://cityobservatory.org/what-drives-ride-hailing-parking-drinking-flying-peaking-pricing/; under-five-mile journeys: “Disrupting the Car: How Shared Cars, Bikes, Scooters, Are Reshaping Transportation and Cannibalizing Car Ownership,” CB Insights Research Briefs, September 5, 2018, https://www.cbinsights.com/research/disrupting-cars-car-sharing-scooters-ebikes/. 202more rides via its Jump bike-share: Tony Bizjack, “What’s More Popular Than Uber?

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

I spent hours at a recent show, ducking in and out of new models and talking with auto executives and sales representatives. I sat in an SUV as sleek as a shark, the buttons and gears and dials on its dashboard replaced with a virtual cockpit straight out of science fiction. A race car so aerodynamic and low that I had to crouch to get in it. And driverless car after driverless car after driverless car. The displays ranged in degrees of technological spectacle from the cool to the oh-my-word. One massive Ford truck, for instance, offered a souped-up cruise control that would brake for pedestrians and take over stop-and-go driving in heavy traffic. “No need to keep ramming the pedals yourself,” a representative said as I gripped the oversize steering wheel.

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If so, mass unemployment might be a result—and a UBI might be a necessary salve. But the argument emanating from Silicon Valley feels speculative and distant at the moment. Those driverless cars are miraculous, and stepping into one does feel like stepping into the future. Those AI systems are amazing, and watching them work does feel like slipping into a sci-fi novel. Yet people remain firmly behind the wheel of those driverless cars. And those AI systems remain far removed from most people’s jobs and lives. Opening a discussion about a UBI as a solution to a world with far less demand for human labor feels wise, but insisting the discussion needs to happen now and on those terms seems foolish and myopic.

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It came up in books, at conferences, in meetings with politicians, in discussions with progressives and libertarians, around the dinner table. I covered it as it happened. I wrote about that failed Swiss referendum, and about a Canadian basic-income experiment that has provided evidence for the contemporary debate. I talked with Silicon Valley investors terrified by the prospect of a jobless future and rode in a driverless car, wondering how long it would be before artificial intelligence started to threaten my job. I chatted with members of Congress on both sides of the aisle about the failing middle class and whether the country needed a new, big redistributive policy to strengthen it. I had beers with European intellectuals enthralled with the idea.

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

Networks Physical Networks Digital Networks Nouns Things [almost all focus has been here] Travel, Goods Delivery 3D printing Data Mail, Books, Newspapers, CDs, DVDs Streaming music or movies 7. Autonomous Autos Set in the Mojave Desert straddling the Nevada/California border — in the middle of nowhere — DARPA153 hosted the first Grand Challenge on vehicle automation in March 2004. With the potential of $1 million going to the winner, the objective was for driverless cars to complete a 150 mile (240 km) route. Carnegie Mellon University's robot vehicle made it the furthest, completing almost 5 percent of the route, but was ultimately not awarded the prize. A second run of the event saw five vehicles completing the course in October 2005 with Stanford University's team winning with a time of just under 7 hours.154 Within 18 months, vehicle automation technology rapidly improved.

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The share of ownership versus MaaS is thus an, if not the most, important predictor of travel demand in the coming years. But two seemingly contradictory, but complementary, outcomes are likely as vehicular automation and Mobility-as-a-Service play out. We label these scenarios "Up" and "Out." Up: Less vehicle ownership with increased use of MaaS in cities, raising the value of cities. Driverless cars which can be summoned on demand allow people to avoid vehicle ownership altogether. This will reduce vehicle travel, as people will pay more to rent by the minute than exploit the sunk costs of vehicular ownership. By saving total expenditures on transport, more funds are available to pay for rent in cities, and more trips are by walk, bike, and transit.

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People who seek the set of urban amenities (entertainment, restaurants, a larger dating pool) will find these amenities increasing in response to the population. The greater value in cities with the new more convenient technology leads to more and taller development. (Hence the use of the word 'Up.') Out: More vehicle travel with increased exurbanization. Fast, driverless cars that allow their passenger to do other things than steer and brake and find parking impose fewer requirements on the traveler than actively driving the same distance. Decreases in the cost of traveling (i.e., availability of multitasking) makes travel easier. Easier travel means increases in accessibility and subsequently increases in the spread of development and a greater separation between home and work, (pejoratively, sprawl), just as commuter trains today enable exurban living or living in a different city.250 This reinforces the disconnected, dendritic suburban street grid and makes transit service that much more difficult (as if low density suburbs weren't hard enough).

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

Innovation in robotics will produce advances in degree—robots doing things faster, safer, or less expensively than humans—and also in kind: they’ll be doing things that would be impossible for humans to do, like allowing a sick, homebound 12-year-old to go to school, or giving those who are deaf and mute the power of speech. STEP ON IT, ROBOTIC JEEVES People have been thinking about building driverless cars for almost as long as cars have been around. General Motors introduced the modern concept of the driverless car at the 1939 World’s Fair in New York, conceiving of a radio-guided car that could be developed alongside a modern highway system. Then in 1958, GM developed the first driverless test car, the Firebird, which would connect to a track wired with electrical cable.

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Any time not spent behind the wheel is time you can spend using a Google product. But will it work? There is ample reason to think that robodrivers will be safer than we are now. Accidents are caused by the four Ds: distraction, drowsiness, drunkenness, and driver error. The driverless car promises to reduce all of these significantly. Chris Gerdes, a Stanford professor of engineering, cautions that driverless cars won’t fully wipe out human error, but rather will shift it from driver to programmer; that’s in all likelihood a significant step forward, especially if a human driver and the programmer can work together. A similar process has unfolded over the years with airplanes, which are now largely driven by autopilot with the pilot still stepping in during crucial times.

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I met the CEO of a company that develops high-tech access control systems (like the new parking garage system at the airport that tells you how many open spaces are available on each floor) and asked him what worries him about the future. He cited a disruption that I’d never considered before: what driverless cars might mean for parking garages. Would the cars just drive themselves back home and come back when needed? Why have your car sit in a garage and have to pay for it? The degree to which delivery drones fill the sky or driverless cars fill the streets will eventually be determined not by whether it is feasible technologically and economically—at some point it will be—but by whether humans accept the changes they bring about.

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

Agreeing a common language can be a smart behavioural solution to addressing optimism. Driverless cars would warrant this treatment. This amorphous technology divides opinion. Christian Wolmar’s Driverless Cars: On a Road to Nowhere? (from the Perspectives series in which this book appears) makes the case that technological change at the level of the vehicle may not be practical, it might not offer benefits that are meaningful to us, and it may fail to make society better.28 Wolmar argues that empty rhetoric about technology benefits is being used to sell us new cars. We are just as interested in the behavioural effects. How would driverless cars coexist with human drivers and pedestrians?

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A Manifesto for an Intelligent Transport Policy — David Metz Britain’s Cities, Britain’s Future — Mike Emmerich Before Babylon, Beyond Bitcoin: From Money That We Understand To Money That Understands Us — David Birch The Weaponization of Trade: The Great Unbalancing of Politics and Economics — Rebecca Harding and Jack Harding Driverless Cars: On a Road to Nowhere? — Christian Wolmar Digital Transformation at Scale: Why the Strategy Is Delivery — Andrew Greenway, Ben Terrett, Mike Bracken and Tom Loosemore Gaming Trade: Win–Win Strategies for the Digital Era — Rebecca Harding and Jack Harding The Currency Cold War: Cash and Cryptography, Hash Rates and Hegemony — David Birch Catastrophe and Systemic Change: Learning from the Grenfell Tower Fire and Other Disasters — Gill Kernick Transport for Humans: Are We Nearly There Yet?

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International hub airports have historically been most reliant on retail concessions from high-end shopping. In a world of more discretionary travel (and more online shopping), they will be the first to diversify their offering to satisfy a wider range of passenger needs. A travelling bathroom The case for driverless cars could be strengthened if it was possible to put in-vehicle time to new uses. Being able to sleep, shower and dress en route would transform commuting behaviours and long-distance trips as it would free up time elsewhere in the day. By pioneering autonomous RVs or caravans, large countries like the United States could transform the many long journeys of over eight hours, on smooth and uncomplicated highways, that their vast expanses afford.

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

Apart from serving as a mouthpiece for the city to tell residents about food hazards, the collaboration is potentially a way to shame repeat-offender restaurants into complying with health standards.” Source: http://www.citylab.com/cityfixer/2015/04/3-cities-using-opendata-in-creative-ways-to-solve-problems/391035/ Shift 12: Driverless Cars The tipping point: Driverless cars equalling 10% of all cars on US roads By 2025: 79% of respondents expected this tipping point to have occurred Trials of driverless cars from large companies such as Audi and Google are already taking place, with a number of other enterprises ramping up efforts to develop new solutions. These vehicles can potentially be more efficient and safer than cars with people behind the steering wheel.

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Source: http://www.wired.com/2015/07/hackers-remotely-kill-jeep-highway/ The first state in the United States (Nevada) to pass a law allowing driverless (autonomous) cares did so in 2012. Source: Alex Knapp, 22 June 2011, “Nevada Passes Law Authorizing Driverless Cars”, Forbes: http://www.forbes.com/sites/alexknapp/2011/06/22/nevada-passes-law-authorizing-driverless-cars/ Shift 13: Artificial Intelligence and Decision-Making The tipping point: The first Artificial Intelligence (AI) machine on a corporate board of directors By 2025: 45% of respondents expected this tipping point to have occurred Beyond driving cars, AI can learn from previous situations to provide input and automate complex future decision processes, making it easier and faster to arrive at concrete conclusions based on data and past experiences.

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Implantable Technologies 2. Our Digital Presence 3. Vision as the New Interface 4. Wearable Internet 5. Ubiquitous Computing 6. A Supercomputer in Your Pocket 7. Storage for All 8. The Internet of and for Things 9. The Connected Home 10. Smart Cities 11. Big Data for Decisions 12. Driverless Cars 13. Artificial Intelligence and Decision-Making 14. AI and White-Collar Jobs 15. Robotics and Services 16. Bitcoin and the Blockchain 17. The Sharing Economy 18. Governments and the Blockchain 19. 3D Printing and Manufacturing 20. 3D Printing and Human Health 21. 3D Printing and Consumer Products 22.

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

But “he has to a high degree a sense of the ultimate use to which commodities will be put, a feeling for the comfort of the man who is going to use the fountain pen for writing more than as a decorative adjunct to his desk.” (Emphasis mine.) This is the spine of the user-friendly world, unchanged whether you’re talking about smartphones or toothbrushes or driverless cars: a deference to the complexity of understanding people as they live. Dreyfuss was gleeful when describing what miracles might come from a life made easier at the edges: a peanut butter jar with sloped shoulders so that every last bit could be scooped out with a spoon; a shaving brush with a handle of the right proportions so as not to mess your hand with lather; and a stove with cleverly shielded handles so that the users never burned their hands.

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All the nuances of designing new products can be reduced to one of two basic strategies: either finding what causes us pain and trying to eliminate it, or reinforcing what we already do with a new object that makes it so easy it becomes second nature. The truest material for making new things isn’t aluminum or carbon fiber. It’s behavior.30 Tesla Model S steering wheel (2012) 4 Trust It was January of the year 2016, which would prove to be a breakthrough year in mainstream media coverage of driverless cars. We were rolling eastward across the San Mateo Bridge in an Audi A7, picking our way through traffic. One of the car’s engineers was driving, while I was riding in the passenger seat. There was another engineer in the back seat, monitoring the car from a laptop. Traffic was getting thick as the workday drew to a close on all the area’s tech campuses.

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(By 2019, European Audi A8s came with an optional “traffic jam pilot” that allowed limited hands-free operation; that option couldn’t be offered in America, because of the inconsistency in federal and state laws.)1 The supervising engineer piped up, “The first three minutes you’re thinking, This is crazy, this is the future! Then you get bored.” We all laughed. But the very fact of the drive’s boringness was a feat. Boringness implied ease rather than fear, a comfort with what was happening even if it was totally novel. Amid all the headlines about driverless cars, it’s easy to miss just how far they have come, and how fast. We can already buy cars that park themselves or swerve to avoid accidents or brake to avoid surprise obstacles. Look a little closer and you can see how awkward the adjustment has been at times. There’s a slapstick viral video on YouTube from 2015, with more than 7 million views.

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

., “US Navy Torpedoes,” Submarine Review, April 1996, http://www.webcitation.org/5kmz4U6ZZ. 4 Kirby, Geoff, “Navies in Transition,” Journal of the Royal Navy Scientific Service, Vol 27, No 1, https://archive.is/DIiw. 5 Felton, Ryan, “The Man Who Tested the First Driverless Car in 1925 Had a Bizarre Feud with Harry Houdini,” Jalopnik, https://jalopnik.com/the-man-who-tested-the-first-driverless-car-in -1925­-had-1792312207. 6 New York Times, “Radio-Driven Auto Runs Down Escort,” July 28, 1925, https://timesmachine.nytimes.com/timesmachine/1925/07/28/99984359.html?pageNumber=28. 7 Google Patents, Sperry, E. A., “Gyroscopic Apparatus,” Patented June 13, 1916, https://patents.google.com/patent/US1186856. 8 Exo Cruiser, “‘Mechanical Mike’ (The Evolution of the Modern Airplane Autopilot),” https://dodlithr.blogspot.com/2013/07/mechanical-mike-1930s-forefather-of.html. 9 Lehman, Staci, “The Pigeon-Guided Missiles and Bat Bombs of World War II,” Gizmodo, https://gizmodo.com/the-pigeon-guided-missiles­­-and-bat-bombs-of-world-war-i-1477007090. 10 Moore, James and Nero, Paul, “Pigeon-Guided Missiles,” Military History Matters, November 10, 2010, https://www.military-history.org/articles/pigeon-guided-missiles.htm. 11 Donnelly, Jim, “Ralph R.

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Teetor,” Hemmings Classic Car, July 2009, https://www.hemmings.com/magazine/hcc/2009/07/Ralph-R--Teetor/1846418.html. 12 Sears, David, “The Sightless Visionary Who Invented Cruise Control,” Smithsonian.com, March 8, 2018, https://www.smithsonianmag.com/innovation/sightless-visionary-who-invented-cruise­­­-control­-180968418/. 13 Old Car Brochures,“GM Corporate and Concepts, 1956 GM Firebird II,” http://www.oldcarbrochures.com/static/NA/GM%20Corporate%20and%20Concepts/1956_GM_Firebird_II/1956%20Firebird%20II-12-13.html. 14 Hicks, Nancy, “Nebraska tested driverless car technology 60 years ago,” Lincoln Journal Star, September 12, 2017, http://journalstar.com/news/local/govt-and-politics/nebraska-tested-driverless-car -technology-years-ago/article_a702fab9-cac3-5a6e-a95c -9b597fdab078.html. 15 Reynolds, John, “Cruising into the future,” Telegraph, May 26, 2001, https://www.telegraph.co.uk/motoring/4750544/Cruising-into-the ­-future.html. 16 General Motors, Key to the Future, https://www.youtube.com/watch?

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* * * 35 “Full Self-Driving Hardware on All Cars,” Tesla.com, https://www.tesla.com/autopilot. 36 Thatcham Research Press Release, “Automated Driving Hype is Dangerously Confusing Drivers,” October 18, 2018, https://news.thatcham.org/pressreleases/autonomous-driving-hype-is-dangerously -confusing-drivers-study-reveals-2767283. 37 Felton, Ryan, “Tesla Says Autopilot Was On Before Fatal Model X Crash, But That Driver Didn't Abide Warnings,” Jalopnik, March 30, 2018, https://jalopnik.com/tesla-admits-autopilot-was-on-before-fatal -model-x-cras-1824224176. 38 Noyes, Dan, “Victim Who Dies in Tesla Crash Had Complained About Autopilot,” ABC7 News, March 28, 2018, http://abc7news.com/automotive/i-team-exclusive-victim-who-died-in-tesla-crash-had -complained-about-auto-pilot/3275600/. 39 Westbrook, Justin, “Tesla Blames Driver in Fatal Model X Autopilot Crash As Family Considers Legal Action,” Jalopnik, April 11, 2018, https://jalopnik.com/tesla-blames-driver-in-fatal-model-x-autopilot -crash-as-1825193432. 40 “Discover Cadillac,” Super Cruise, https://www.cadillac.com/world -of-cadillac/innovation/super-cruise. 41 King, Alanis, “Stop Doing This Shit with Autonomous Cars,” Jalopnik, January 15, 2018, https://jalopnik.com/stop-doing-this-shit -with-semi-autonomous-cars­­-1822090627. 42 Davies, Alex, “Ford’s Working on a Remote Control for Your Car,” Wired, January 26, 2015, https://www.wired.com/2015/01/fords-working-remote-control-car/. 43 Coxworth, Ben, “Full Size Remote-Control Cars - Coming Soon to a Road Near You?” New Atlas, July 30, 2013, http://www.gizmag.com/remote-control-cars/28521/. 44 Hawkins, Andrew J., “Elon Musk Still Doesn’t Think Lidar is Necessary for Fully Driverless Cars,” The Verge, February 7, 2018, https://www.theverge.com/­­2018/2/7/16988628/elon-musk-lidar-self -driving-car-tesla. 45 Orlove, Raphael, “Angry Owners Sue Tesla for Using Them as Beta Testers of ‘Dangerously Defective’ Autopilot,” Jalopnik, April 20, 2017, https://jalopnik.com/angry-owners-sue-tesla-for-using-them -as-beta-testers-o-1794503348.

Age of Context: Mobile, Sensors, Data and the Future of Privacy by Robert Scoble, Shel Israel

Albert Einstein, Apple II, augmented reality, call centre, Chelsea Manning, cloud computing, connected car, driverless car, Edward Snowden, Edward Thorp, Elon Musk, factory automation, Filter Bubble, G4S, gamification, Google Earth, Google Glasses, Internet of things, job automation, John Markoff, Kickstarter, lifelogging, Marc Andreessen, Marc Benioff, Mars Rover, Menlo Park, Metcalfe’s law, New Urbanism, PageRank, pattern recognition, RFID, ride hailing / ride sharing, Robert Metcalfe, Salesforce, Saturday Night Live, self-driving car, sensor fusion, Silicon Valley, Skype, smart grid, social graph, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Tesla Model S, Tim Cook: Apple, TSMC, ubercab, urban planning, Zipcar

Lots of cars have sensors that start wipers when they sense water. Some now automatically send and receive messages to other cars. At Toyota, they told us of cars talking with traffic lights to avoid bottlenecks and collisions at intersections. Some experts are more optimistic about when driverless cars will arrive in a showroom near you. Bob Lutz, GM’s retired vice chairman, predicts that driverless cars would be ubiquitous in about 20 years. We think it will be more like ten years before the vehicles are technically ready and affordable. In the time it took us to write this chapter, our friend John Markoff, a New York Times technology reporter, may have witnessed a technological breakthrough when he was a passenger in a driverless, lidar-less Audi A7 that zipped down an Israeli highway at 65 miles an hour.

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Software will be easier to keep current. A broken device will be easier to replace than a damaged windshield. The issues of regulation and adoption bring us to what may be the most significant and controversial changes in car making since horsepower replaced horses. We refer, of course, to the driverless car. Chapter 5 Driving Over the Freaky Line People are so bad at driving cars that computers don’t have to be that good to be much better. Marc Andreessen, investor Picture this: It is not very far into the future and you have just arrived in a big city. You summon an Uber car via a mobile app and a nice-looking upscale sedan soon pulls up to the curb in front of you.

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Then, no one at all maneuvers city traffic and delivers you safely to your destination, where you leave without paying as your transaction takes place automatically online. Even Robert Scoble, a legendary lover of new technologies, says that this scenario freaks him out; yet some time in the next 5 to 50 years, this will be commonplace. Once people get past their discomfort, they will discover that driverless cars are safer than those driven by people. They are also more energy efficient and don’t wander over lanes like people do. The pilots don’t daydream, or get competitive with other vehicles or demonstrate road rage because the last passenger didn’t tip. Instead of driving, people will be able to do some work, catch up on their social networks, grab a few winks or watch a video.

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

Get up nice and early, based on the auto-set alarm that knows your calendar and average getting ready time (factoring for typical traffic). A data-driven breakfast would be proposed after a quick analysis of the health stats of you and thousands of others like you, to ensure the perfect balance of nutrients you might need today. (Plus: a small reduction in your health insurance premiums, if you take its advice.) Hop in your driverless car, which is just returning from a night shift earning money for you as an autonomous taxi. And, as you relax into the journey, your personal AI assistant bot will advise you on what to say in today’s key sales meeting, based on previous performance and who else will be present. Before being whisked back home . . .

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.* 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. Several countries (including the UK) are encouraging ‘real world’ testing, and the British Chancellor expects that driverless cars will be on the road by 2021. The way it’s going, regulations and insurance issues are more likely to slow this down than the technology. A world without work? Driverless vehicles are just one application of an artificial intelligence revolution that is sweeping through the global economy.

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Apple splashed out $200 million for Turi, a machine learning start-up, in 2016, and Intel has invested over $1 billion in AI companies over the past couple of years.7 Market leaders in AI like Google, with the data, the geniuses, the experience and the computing power, won’t be limited to just search and information retrieval. They will also be able to leap ahead in almost anything where AI is important: logistics, driverless cars, medical research, television, factory production, city planning, agriculture, energy use, storage, clerical work, education and who knows what else. Amazon is already a retailer, marketing platform, delivery and logistics network, payment system, credit lender, auction house, book publisher, TV production company, fashion designer and cloud computing provider.8 What next?

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

Detection of the emotional state or sleepiness level of the driver via voice and facial cues could also be used to promote safety.56 Still, with more than 40,000 fatal car accidents in the United States in 2017, nearly all due to human error, the biggest payoff of AI in cars might be in driving them for us. Clearly, we’re in need of some help.57 DRIVERLESS CARS You’d have to be living in a cave in recent years to avoid being barraged with grandiose claims about driverless cars. Especially if you watch YouTube videos of Tesla “drivers” playing games, writing, jumping into the back seat, and reading, it would be easy to have the sense that they are just around the corner.58 While it’s the pinnacle achievement to date for AI, it’s not exactly how it seems.

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There are two fundamental levels of the ethics of AI: machine ethics, which refers to the AI systems per se, and the wider domain, not specific to the algorithms. The prototypical example of machine ethics involves how driverless cars handle the dilemma of choosing between evils in the case of an impending accident, when no matter how it responds, people are going to die. It’s the modern-day version of the trolley problem introduced more than fifty years ago. Jean-Francois Bonnefon and colleagues examined the driverless car dilemma in depth using simulations and input from more than 1,900 people.59 In each of the three scenarios (Figure 5.1), there is no good choice; it’s just a matter of who and how many people are killed, whether the car’s passenger, a pedestrian, or several of them.

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At the top layer, the neurons have now fully differentiated the features and are ready for output—predicting what the chest X-ray shows, based on its training.23 DNNs, with this structural backbone, can functionally be regarded as a general-utility, or a general-purpose, technology, much like the steam engine or electricity.24 And, much like those technologies, these neural networks can be applied to all sorts of problems. Before medicine, these networks were applied principally to four major areas: games, images, voice and speech, and driverless cars. Each has lessons for us as we explore what deep learning can do for medicine. GAMES Even before the historic 1997 Deep Blue–Kasparov chess match, AI had prevailed over human experts in other games including Othello, checkers (which has 500 billion-billion possible positions), and Scrabble.25 But for all of these, AI used rules-based algorithms, sometimes known as GOFAI, for “good old-fashioned AI.”

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

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.

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They had built what was called a neural network, a mathematical system modeled on the web of neurons in the brain, and it could identify common objects—like flowers, dogs, and cars—with an accuracy that had previously seemed impossible. As Hinton and his students showed, a neural network could learn this very human skill by analyzing vast amounts of data. He called this “deep learning,” and its potential was enormous. It promised to transform not just computer vision but everything from talking digital assistants to driverless cars to drug discovery. The idea of a neural network dated back to the 1950s, but the early pioneers had never gotten it working as well as they had hoped. By the new millennium, most researchers had given up on the idea, convinced it was a technological dead end and bewildered by the fifty-year-old conceit that these mathematical systems somehow mimicked the human brain.

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Alongside a small group of other scientists—spread across those same four companies, one more American Internet giant, and, eventually, a new upstart—Hinton and his students soon pushed this single idea into the heart of the tech industry. In doing so, they suddenly and dramatically accelerated the progress of artificial intelligence, including talking digital assistants, driverless cars, robotics, automated healthcare, and—though this was never their intention—automated warfare and surveillance. “It changed the way that I looked at technology,” Alan Eustace says. “It changed the way many others looked at it, too.” Some researchers, most notably Demis Hassabis, the young neuroscientist behind DeepMind, even believed they were on their way to building a machine that could do anything the human brain could do, only better, a possibility that has captured the imagination since the earliest days of the computer age.

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

In our current economy, how robots—both machines and intelligent systems—are designed and how they are used is overwhelmingly defined by cost and benefit, and costs are falling at an increasingly rapid rate. In our society, economics dictate that if a task can be done more cheaply by machine—software or hardware—in most cases it will be. It’s just a matter of when. The decision to come down on either side of the debates is doubly difficult because there are no obvious right answers. Although driverless cars will displace millions of jobs, they will also save many lives. Today, decisions about implementing technologies are made largely on the basis of profitability and efficiency, but there is an obvious need for a new moral calculus. The devil, however, is in more than the details. As with nuclear weapons and nuclear power, artificial intelligence, genetic engineering, and robotics will have society-wide consequences, both intended and unintended, in the next decade. 2|A CRASH IN THE DESERT On a desert road near Florence, Arizona, one morning in the fall of 2005, a Volkswagen Touareg was kicking up a dust cloud, bouncing along at a steady twenty to twenty-five miles per hour, carrying four passengers.

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They had even been the stuff of TV series like Knight Rider, a 1980s show featuring a crime fighter assisted by an artificially intelligent car. There was also a dark-side vision of automated driving, perhaps best expressed in Daniel Suarez’s 2009 sci-fi thriller Daemon, in which AI-controlled cars not only drove themselves, but ran people down as well. Still, the general perception was a deep well of skepticism about whether driverless cars would ever become a reality. However, Sebastian Thrun had made his point abundantly clear that humans are terrible drivers, largely the consequence of human fallibility and inattention. By the time his project was discovered, Google cars had driven more than a hundred thousand miles without an accident, and over the next several years that number would rise above a half-million miles.

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Like it or not, we are no longer in a biblical world, and the future is not about geographical territory, but rather about a rapidly approaching technological wonder world. Machines that will begin as golems are becoming brilliant and capable of performing many human tasks, from physical labor to rocket science. Google had a problem. More than three years into the company’s driverless car program, the small research team at the Mountain View–based Internet search company had safely driven more than a half-million miles autonomously. They had made startling progress in areas that had been generally believed impossible within the traditional automotive industry. Google cars could drive during the day and at night, could change lanes, and could even navigate the twisty Lombard Street in San Francisco.

The New Digital Age: Transforming Nations, Businesses, and Our Lives by Eric Schmidt, Jared Cohen

access to a mobile phone, additive manufacturing, airport security, Amazon Mechanical Turk, Amazon Web Services, Andy Carvin, Andy Rubin, anti-communist, augmented reality, Ayatollah Khomeini, barriers to entry, bitcoin, borderless world, call centre, Chelsea Manning, citizen journalism, clean water, cloud computing, crowdsourcing, data acquisition, Dean Kamen, disinformation, driverless car, drone strike, Elon Musk, Evgeny Morozov, failed state, false flag, fear of failure, Filter Bubble, Google Earth, Google Glasses, Hacker Conference 1984, hive mind, income inequality, information security, information trail, invention of the printing press, job automation, John Markoff, Julian Assange, Khan Academy, Kickstarter, knowledge economy, Law of Accelerating Returns, market fundamentalism, Mary Meeker, means of production, military-industrial complex, MITM: man-in-the-middle, mobile money, mutually assured destruction, Naomi Klein, Nelson Mandela, no-fly zone, off-the-grid, offshore financial centre, Parag Khanna, peer-to-peer, peer-to-peer lending, personalized medicine, Peter Singer: altruism, power law, Ray Kurzweil, RFID, Robert Bork, self-driving car, sentiment analysis, Silicon Valley, Skype, Snapchat, social graph, speech recognition, Steve Jobs, Steven Pinker, Stewart Brand, Stuxnet, Susan Wojcicki, The Wisdom of Crowds, upwardly mobile, Whole Earth Catalog, WikiLeaks, young professional, zero day

in 2012, Nevada became the first state to issue licenses to driverless cars: Chris Gaylord, “Ready for a Self-Driving Car? Check Your Driveway,” Christian Science Monitor, June 25, 2012, http://www.csmonitor.com/Innovation/Tech/2012/0625/Ready-for-a-self-driving-car-Check-your-driveway. California also affirmed their legality: James Temple, “California Affirms Legality of Driverless Cars,” The Tech Chronicles (blog), San Francisco Chronicle, September 25, 2012, http://blog.sfgate.com/techchron/2012/09/25/california-legalizes-driverless-cars/; Florida has passed a similar law. See Joann Muller, “With Driverless Cars, Once Again It Is California Leading the Way,” Forbes, September 26, 2012, http://www.forbes.com/sites/joannmuller/2012/09/26/with-driverless-cars-once-again-it-is-california-leading-the-way/.

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While some of the very exciting new possibilities in transportation, like supersonic tube commutes and suborbital space travel, are still far in the distance, ubiquitous self-driving cars are imminent. Google’s fleet of driverless cars, built by a team of Google and Stanford University engineers, has logged hundreds of thousands of miles without incident, and other models will soon join it on the road. Rather than replacing drivers altogether, the liminal step will be a “driver-assist” approach, where the self-driving option can be turned on, just as an airline captain turns on the autopilot. Government authorities are already well versed on self-driving cars and their potential—in 2012, Nevada became the first state to issue licenses to driverless cars, and later that same year California also affirmed their legality.

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Imagine having the holodeck from the world of Star Trek, which was a fully immersive virtual-reality environment for those aboard a ship, but this one is able to both project a beach landscape and re-create a famous Elvis Presley performance in front of your eyes. Indeed, the next moments in our technological evolution promise to turn a host of popular science-fiction concepts into science facts: driverless cars, thought-controlled robotic motion, artificial intelligence (AI) and fully integrated augmented reality, which promises a visual overlay of digital information onto our physical environment. Such developments will join with and enhance elements of our natural world. This is our future, and these remarkable things are already beginning to take shape.

The Patient Will See You Now: The Future of Medicine Is in Your Hands by Eric Topol

23andMe, 3D printing, Affordable Care Act / Obamacare, Anne Wojcicki, Atul Gawande, augmented reality, Big Tech, bioinformatics, call centre, Clayton Christensen, clean water, cloud computing, commoditize, computer vision, conceptual framework, connected car, correlation does not imply causation, creative destruction, crowdsourcing, dark matter, data acquisition, data science, deep learning, digital divide, disintermediation, disruptive innovation, don't be evil, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Firefox, gamification, global village, Google Glasses, Google X / Alphabet X, Ignaz Semmelweis: hand washing, information asymmetry, interchangeable parts, Internet of things, Isaac Newton, it's over 9,000, job automation, Julian Assange, Kevin Kelly, license plate recognition, lifelogging, Lyft, Mark Zuckerberg, Marshall McLuhan, meta-analysis, microbiome, Nate Silver, natural language processing, Network effects, Nicholas Carr, obamacare, pattern recognition, personalized medicine, phenotype, placebo effect, quantum cryptography, RAND corporation, randomized controlled trial, Salesforce, Second Machine Age, self-driving car, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, Snapchat, social graph, speech recognition, stealth mode startup, Steve Jobs, synthetic biology, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, traumatic brain injury, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, WikiLeaks, X Prize

A. Salkever, “What Google’s Driverless Car Future Might Really Look Like,” Read Write, May 28, 2014, http://readwrite.com/2014/05/28/googles-driverless-car-future?awesm=readwr.it_p20r-awesm=~oFWmYrlzbbCpi0. 27. C. C. Miller, “When Driverless Cars Break the Law,” New York Times, May 14, 2014, http://www.nytimes.com/2014/05/14/upshot/when-driverless-cars-break-the-law.html. 28. G. Sullivan, “Google’s New Driverless Car Has No Brakes or Steering Wheel,” Washington Post, May 28, 2014, http://www.washingtonpost.com/news/morning-mix/wp/2014/05/28/googles-new-driverless-car-has-no-brakes-or-steering-wheel//?

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As in every other sector in our lives, when data becomes eminently portable and granular, when there’s so much more of it and it’s free flowing, fully transparent, and there’s seemingly unlimited computing power to process it, historic change takes place. Just think of the impact of Google or Amazon. But searching and purchasing anywhere, anytime is old stuff. The ability to share data and contextually compute it led the nonmedical world to progress many could not have even imagined, leading to advances like driverless cars. The shake-up of medicine will be just as strong. Driverless Cars and Doctorless Patients? When I’m driving in back of a big truck and can’t see anything in front of me except the truck, I feel vulnerable because I’m lacking critical information about the traffic and road conditions. The parallels to health care are irresistible.

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Lawler, “Google X Built a Fully Self-Driving Car from Scratch, Sans Steering Wheel and Pedals,” TechCrunch, May 27, 2014, http://techcrunch.com/2014/05/27/google-x-introduces-a-fully-self-driving-car-sans-steering-wheel-and-pedals/. 30. L. Gannes, “Google’s New Self-Driving Car Ditches the Steering Wheel,” Recode, May 27, 2014, http://recode.net/2014/05/27/googles-new-self-driving-car-ditches-the-steering-wheel/. 31. R. W. Lucky, “The Drive for Driverless Cars,” IEEE Spectrum, June 26, 2014, http://spectrum.ieee.org/computing/embedded-systems/the-drive-for-driverless-cars. 32. C. Smith, “‘I No Longer Have to Go to See the Doctor’: How the Patient Portal is Changing Medical Practice,” Journal of Participatory Medicine 6 (2014): e6. 33. L. Collar, “Are Physicians Still Relevant?,” Sapphire Equinox, April 5, 2014, http://sapphireequinox.com/blog/are-physicians-still-relevant/. 34.

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

But full autonomy on public roads, with no safety driver, still seems a distant dream. Perhaps the industry will steadily inch its way toward fully driverless vehicles. But without some kind of breakthrough, self-driving cars are still not quite safe enough to be let loose on public roads without supervision. THE DREAM OF THE DRIVERLESS CAR Proponents of driverless cars have not given up on the dream. They argue that if AVs could be deployed in large numbers, they would offer enormous benefits. Mary Barra, the chief executive of General Motors, has declared that her company’s goal “is a future with zero crashes, zero emissions, and zero congestion” made possible by “autonomous, electric shared and connected vehicles.”

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“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.

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Fifteen vehicles made it through to the race itself, which was held on March 13, 2004. Just as in Paris in July 1894, the lineup of vehicles at the starting line was a motley assortment of very different-looking machines. A 1957 advertisement for America’s Electric Light and Power Companies depicts a driverless car. “One day your car may speed along an electric super-highway, its speed and steering automatically controlled by electronic devices embedded in the road. Travel will be more enjoyable. Highways will be made safe—by electricity! No traffic jams … no collisions … no driver fatigue.” Sandstorm, built by Red Team, from Carnegie Mellon University, was a bright red Humvee, its upper half sheared off and replaced by a metal frame supporting a collection of cameras and sensors.

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

Technologies that forgive us our errors are always helpful, especially in later life, although there will be the need to settle tricky questions of responsibility for crashes. However, it is hard to see any radical change for road users generally, given continued unconstrained access to the road system. You might think it would be useful to send your driverless car travelling around the block while you go into a shop. But then the streets would be jammed with ‘parked’ cars on the move, which evidently could not be permitted. 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.

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At present we have examples of digital technologies improving journeys with Satnav and smartcard ticketing, and of allowing travel substitution, as when we work at home with access to the full range of information and contacts that we would have in our workplace. But these are not radical innovations. Are there disruptive innovations on the horizon for the transport sector? Consider the driverless cars that are being developed by some manufacturers as well as by Google. The technologies required have been worked on for some years and those already introduced include adaptive cruise control, lane detection, pedestrian recognition, emergency braking and automated parking assistance. Hands‑free driving is the logical extension.

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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. One possible application of this technology would be to taxis, if lower fares increased their attraction to passengers (in which case the innovation would be disruptive to taxi drivers).

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

“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.

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So one evening in August 2013, Kalanick checked into a suite at the Four Seasons Hotel in East Palo Alto, paid for by Google, and woke up the next morning for a ten o’clock meeting with the most powerful man in Silicon Valley. Krane had orchestrated an experience that would blow Kalanick’s mind. When Uber’s CEO came down to the lobby, a prototype driverless car from the Google X lab idled in front of the hotel, waiting to ferry him to Mountain View. Sitting in the front seat was a Google engineer who could answer all his questions. It was Kalanick’s first ride in a self-driving car on real roads. At the Google campus, Kalanick met with Page, Google senior lawyer David Drummond, and Krane’s boss at GV at the time, Bill Maris.

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After the round closed, Kalanick climbed aboard TPG’s Gulfstream jet with Bonderman, TPG co-founder James Coulter, and Trujillo, as well as investor Shervin Pishevar and his partner Scott Stanford, to visit countries in Asia and gauge the company’s expansion opportunities there. The world seemed wide open. Yet nearly every assumption Kalanick and his investors were making about the future in the fall of 2013 turned out, in the end, to be at least partially incorrect. Google was reluctant to cede the results of its driverless car research to another company and would soon look more like Uber’s mortal enemy, not its ally. Within a year, David Bonderman would leave the board of General Motors, which in 2016 would make a sizable investment in archrival Lyft. And remarkably, according to multiple people familiar with the transaction, when the time came for TPG to purchase its second $88 million allotment of Uber shares at the same valuation, the private equity firm wavered and waited until the last possible moment before attempting to exercise the option.

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

Unfortunately, things went a little awry. As the New York Times reported in a story headlined “Radio-Driven Auto Runs Down Escort,” the driverless car “careened from left to right down Broadway, around Columbus Circle, and south on Fifth Avenue.” It barely missed two trucks and a milk wagon, collided with a car filled with photographers, and then almost hit a fire engine at 43rd Street. Decades later, in the 1950s, Nebraska’s Department of Roads experimented with driverless cars that would be guided by electric circuits embedded in the road. In the 1980s and 1990s, research groups in the United States and Europe investigated driverless concepts to improve road safety.

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While operators were in the cars just in case, the key interaction was through the cars’ informational tether back to Google’s enormously powerful data centers.8 The word was out. Google—with its scale and capabilities—had put a stamp on the viability of the driverless car. Google was hardly alone. By then, a host of other tech companies and entrepreneurs were jumping in to tackle the driverless car. Technological advances and falling costs were making it all possible. “There was no way, before 2000, to make something interesting,” Thrun said. “The sensors weren’t there, the computers weren’t there, and the mapping wasn’t there.

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The sensor data needs to be constantly processed with instant decision following, and that requires a very big brain, exponentially more powerful computing power than found in today’s cars. Software is key; and here the rapid advances in AI—artificial intelligence—and machine learning for image recognition have propelled the driverless car forward more rapidly than might have been expected a few years ago. At lightning speed, the computer has to barrel through vast amounts of data coming from the sensing devices to identify a stop sign or a dog and instantaneously come to a stop. This big brain computer also has to manage the electronic systems units that control braking and steering and ensure that the car swerves around pedestrians who obliviously step into the street without looking up from their smartphones.

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

Sebastian Thrun, “Google's Driverless Car,” TED, March 2011, http://www.ted.com/talks/sebastian_thrun_google_s_driverless_car. The relative “autonomy” of the car from the driver is a gradient. The design problem is not one of full autonomy of the car replacing full autonomy of the driver, but of varying degrees of cyborgization, drawing on those with which car culture is already comfortable. 56.  Steven Levy, In the Plex: How Google Thinks, Works, and Shapes Our Lives (New York: Simon & Schuster, 2011). 57.  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.

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From this, other applications are easy to imagine, from the mundane (videoconferencing) to the whimsical (site-specific virtual flora and fauna superimposed over the passing outlines of the landscape) to the ugly (advertising-supported toll routes). See also Ryan Lawler, “Dispatch from the Future: Uber to Purchase 2,500 Driverless Cars from Google,” TechCrunch, April 25, 2013, http://techcrunch.com/2013/08/25/uberauto/. Chunka Mui, “Dispatch from 2023: Google Considers Buying 250,000 Driverless Cars from Tesla, but Buys Tesla Instead,” Forbes, August 29, 2013, http://www.forbes.com/sites/chunkamui/2013/08/29/dispatch-from-2023-google-considers-buying-250000-driverless-cars-from-tesla-but-buys-tesla-instead/. 61.  Gary Marcus, “Moral Machines,” New Yorker, November 24, 2012, http://www.newyorker.com/news/news-desk/moral-machines. 62. 

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As Marcus puts it, “These driverless cars will be safer, but when accidents do happen, they may be on the scale of airline disasters” though he may be off by an order of magnitude or two, depending on how deep and pervasive the Cloud vehicle platform does or does not become. 67.  From User-Centered Design to the Design of the User These three models for the design of posthuman User—the animal User, as exemplified by the design of reciprocal interfaces between species; the AI User, by the design of idiosyncratic companion personalities; and the machine User, by the infrastructural driverless car platform—in no way exhaust the range of problems and opportunities, but they are instructive in several important ways.

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

This was necessary and valuable, but the real learning, and the real fun, started when we went out into the world. We spoke with inventors, investors, entrepreneurs, engineers, scientists, and many others who make technology and put it to work. Thanks to their openness and generosity, we had some futuristic experiences in today’s incredible environment of digital innovation. We’ve ridden in a driverless car, watched a computer beat teams of Harvard and MIT students in a game of Jeopardy!, trained an industrial robot by grabbing its wrist and guiding it through a series of steps, handled a beautiful metal bowl that was made in a 3D printer, and had countless other mind-melting encounters with technology.

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But articulating this knowledge and embedding it in software for all but highly structured situations are at present enormously difficult tasks. . . . Computers cannot easily substitute for humans in [jobs like driving]. So Much for That Distinction We were convinced by Levy and Murnane’s arguments when we read The New Division of Labor in 2004. We were further convinced that year by the initial results of the DARPA Grand Challenge for driverless cars. 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.

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These videos had been viewed more than 250 million times, and the Academy’s students had tackled more than one billion automatically generated problems.15 Khan Academy was originally aimed at primary-school children, but similar tools and techniques have been also applied to higher education, where they’re known as massive online open courses, or MOOCs. One of the most interesting experiments in this area came in 2011 when Sebastian Thrun, a top artificial intelligence researcher (and one of the main people behind Google’s driverless car), announced with a single email that he would be teaching his graduate-level AI course not only to students at Stanford but also as a MOOC available for free over the Internet. Over 160,000 students signed up for the course. Tens of thousands of them completed all exercises, exams, and other requirements, and some of them did quite well.

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

MARTIN FORD: I usually tell people a 10-15-year time frame when they ask me about self-driving cars. Your estimate of five years seems quite optimistic. STUART J. RUSSELL: Yes, five years is optimistic. As I said, I think we’ll be lucky if we see driverless cars in five years, and it could well be longer. One thing that is clear, though, is that many of the early ideas of fairly simple architectures for driverless cars are now being abandoned, as we gain more experience. In the early versions of Google’s car, they had chip-based vision systems that were pretty good at detecting other vehicles, lane markers, obstacles, and pedestrians.

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I’d recently become engaged, and I had also received an offer from McKinsey to join them in Silicon Valley; and I thought it would be a brief, interesting detour, to go to McKinsey. At the time, like many of my friends and colleagues, such as Bobby Rao, who had also been in the Robotics Research Lab with me, I was interested in building systems that could compete in the DARPA driverless car challenge. This was because a lot of our algorithms were applicable to autonomous vehicles and driverless cars and back then, the DARPA challenge was one of the places where you could apply those algorithms. All of my friends were moving to Silicon Valley then. Bobby was at that time a post-doc at Berkeley working with Stuart Russell and others, and so I thought I should take this McKinsey offer in San Francisco.

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GARY MARCUS: That’s right, and for the principal reason that if you’re talking about driving in a very heavy metropolitan location like Manhattan or Mumbai, then the AI will face a lot of unpredictability. It’s one thing to have a driverless car in Phoenix, where the weather is good and the population is a lot less densely packed. The problem in Manhattan is that anything goes at any moment, nobody is particularly well-behaved and everybody is aggressive, the chance of having unpredictable things occur is much higher. Even simple road elements like barricades to protect people can cause issues for an AI. These are complex situations that humans deal with by using reasoning. Right now, driverless cars navigate by having highly detailed maps and things like LIDAR, but no real understanding of the motives and behavior of other drivers.

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

The emphasis is Latour’s. 33.Bernhard Seefeld, “Meet the New Google Maps: A Map for Every Person and Place,” Google Lat Long (blog), May 15, 2013, google-latlong.blogspot.com/2013/05/meet-new-google-maps-map-for-every.html. 34.Evgeny Morozov, “My Map or Yours?,” Slate, May 28, 2013, slate.com/articles/technology/future_tense/2013/05/google_maps_personalization_will_hurt_public_space_and_engagement.html. 35.Kirkpatrick, Facebook Effect, 199. 36.Sebastian Thrun, “Google’s Driverless Car,” speech at TED2011, March 2011, ted.com/talks/sebastian_thrun_google_s_driverless_car.html. 37.National Safety Council, “Annual Estimate of Cell Phone Crashes 2012,” white paper, 2014. 38.See Sigfried Giedion, Mechanization Takes Command (New York: Oxford University Press, 1948), 628–712. 39.Langdon Winner, Autonomous Technology: Technics-out-of-Control as a Theme in Political Thought (Cambridge, Mass.: MIT Press, 1977), 285.

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Many technical challenges remain to be met, such as navigating snowy or leaf-covered roads, dealing with unexpected detours, and interpreting the hand signals of traffic cops and road workers. Even the most powerful computers still have a hard time distinguishing a bit of harmless road debris (a flattened cardboard box, say) from a dangerous obstacle (a nail-studded chunk of plywood). Most daunting of all are the many legal, cultural, and ethical hurdles a driverless car faces. Where, for instance, will culpability and liability reside should a computer-driven automobile cause an accident that kills or injures someone? With the car’s owner? With the manufacturer that installed the self-driving system? With the programmers who wrote the software? Until such thorny questions get sorted out, fully automated cars are unlikely to grace dealer showrooms.

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They don’t, they haven’t, and they won’t. Artificial intelligence is not human intelligence. People are mindful; computers are mindless. But when it comes to performing demanding tasks, whether with the brain or the body, computers are able to replicate our ends without replicating our means. When a driverless car makes a left turn in traffic, it’s not tapping into a well of intuition and skill; it’s following a program. But while the strategies are different, the outcomes, for practical purposes, are the same. The superhuman speed with which computers can follow instructions, calculate probabilities, and receive and send data means that they can use explicit knowledge to perform many of the complicated tasks that we do with tacit knowledge.

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

Thanks to the growing number of people who check the “organ donation” box on their driver’s licenses, fatal automobile accidents have become a significant source of donor organs used in transplants. Should they work as advertised, then, driverless cars might indirectly disrupt the brilliant system through which a tragic accident winds up saving a life. How many of us would be able to make the link between the technology behind driverless cars and the fourth-order impact on the need for artificial hearts, and then plan for the scenario? It may seem far-fetched, but this is how our world works. Like a stone tossed into a placid pond, a breakthrough or innovation in one sector will create ripples that spread outward.

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Rajat Gupta, Shirish Sankhe, Richard Dobbs, Jonathan Woetzel, Anu Madgavkar, and Ashwin Hasyagar, From poverty to empowerment: India’s imperative for jobs, growth, and effective basic services, McKinsey Global Institute, February 2014. 23. Justin Pritchard, “California pushes to finish driverless car rules,” Associated Press, March 12, 2014, bigstory.ap.org/article/california-pushes-finish-driverless-car-rules. 24. “Striking back: Germany considers counterespionage against US,” Spiegel Online International, February 18, 2014, www.spiegel.de/international/germany/germany-considers-counterespionage-measures-against-united-states-a-953985.html. 25.

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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. Nobody won the prize money; the best-performing car (from Carnegie Mellon) managed a little over 7 miles. Ten years later, Google’s fleet of self-driving cars has already logged 700,000 miles in city streets—with the only accident occurring when a human was operating one of the Toyota Prius cars.

Physics of the Future: How Science Will Shape Human Destiny and Our Daily Lives by the Year 2100 by Michio Kaku

agricultural Revolution, AI winter, Albert Einstein, Alvin Toffler, Apollo 11, Asilomar, augmented reality, Bill Joy: nanobots, bioinformatics, blue-collar work, British Empire, Brownian motion, caloric restriction, caloric restriction, cloud computing, Colonization of Mars, DARPA: Urban Challenge, data science, delayed gratification, digital divide, double helix, Douglas Hofstadter, driverless car, en.wikipedia.org, Ford Model T, friendly AI, Gödel, Escher, Bach, Hans Moravec, hydrogen economy, I think there is a world market for maybe five computers, industrial robot, Intergovernmental Panel on Climate Change (IPCC), invention of movable type, invention of the telescope, Isaac Newton, John Markoff, John von Neumann, Large Hadron Collider, life extension, Louis Pasteur, Mahatma Gandhi, Mars Rover, Mars Society, mass immigration, megacity, Mitch Kapor, Murray Gell-Mann, Neil Armstrong, new economy, Nick Bostrom, oil shale / tar sands, optical character recognition, pattern recognition, planetary scale, postindustrial economy, Ray Kurzweil, refrigerator car, Richard Feynman, Rodney Brooks, Ronald Reagan, Search for Extraterrestrial Intelligence, Silicon Valley, Simon Singh, social intelligence, SpaceShipOne, speech recognition, stem cell, Stephen Hawking, Steve Jobs, synthetic biology, telepresence, The future is already here, The Wealth of Nations by Adam Smith, Thomas L Friedman, Thomas Malthus, trade route, Turing machine, uranium enrichment, Vernor Vinge, Virgin Galactic, Wall-E, Walter Mischel, Whole Earth Review, world market for maybe five computers, X Prize

Like transistors, you could conceivably pack millions of lasers onto a chip the size of your fingernail. DRIVERLESS CAR In the near future, you will also be able to safely surf the Web via your contact lens while driving a car. Commuting to work won’t be such an agonizing chore because cars will drive themselves. Already, driverless cars, using GPS to locate their position within a few feet, can drive over hundreds of miles. The Pentagon’s Defense Advanced Research Projects Agency (DARPA) sponsored a contest, called the DARPA Grand Challenge, in which laboratories were invited to submit driverless cars for a race across the Mojave Desert to claim a $1 million prize.

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(Some examples of Pentagon projects include the Internet, which was originally designed to connect scientists and officials during and after a nuclear war, and the GPS system, which was originally designed to guide ICBM missiles. But both the Internet and GPS were declassified and given to the public after the end of the Cold War.) In 2004, the contest had an embarrassing beginning, when not a single driverless car was able to travel the 150 miles of rugged terrain and cross the finish line. The robotic cars either broke down or got lost. But the next year, five cars completed an even more demanding course. They had to drive on roads that included 100 sharp turns, three narrow tunnels, and paths with sheer drop-offs on either side.

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At first, it was a bit eerie noticing that the steering wheel and accelerator pedal were moving by themselves. It felt like there was an invisible, ghostlike driver who had taken control, but after a while I got used to it. In fact, later it became a joy to be able to relax in a car that drove itself with superhuman accuracy and skill. I could sit back and enjoy the ride. The heart of the driverless car was the GPS system, which allowed the computer to locate its position to within a few feet. (Sometimes, the engineers told me, the GPS system could determine the car’s position to within inches.) The GPS system itself is a marvel of modern technology. Each of the thirty-two GPS satellites orbiting the earth emits a specific radio wave, which is then picked up by the GPS receivers in my car.

The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age by Robert Wachter

activist fund / activist shareholder / activist investor, Affordable Care Act / Obamacare, AI winter, Airbnb, Atul Gawande, Captain Sullenberger Hudson, Checklist Manifesto, Chuck Templeton: OpenTable:, Clayton Christensen, cognitive load, collapse of Lehman Brothers, computer age, creative destruction, crowdsourcing, deep learning, deskilling, disruptive innovation, driverless car, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, Firefox, Frank Levy and Richard Murnane: The New Division of Labor, general purpose technology, Google Glasses, human-factors engineering, hype cycle, Ignaz Semmelweis: hand washing, Internet of things, job satisfaction, Joseph Schumpeter, Kickstarter, knowledge worker, lifelogging, Marc Benioff, medical malpractice, medical residency, Menlo Park, minimum viable product, natural language processing, Network effects, Nicholas Carr, obamacare, pattern recognition, peer-to-peer, personalized medicine, pets.com, pneumatic tube, Productivity paradox, Ralph Nader, RAND corporation, Richard Hendricks, Robert Solow, Salesforce, Second Machine Age, self-driving car, seminal paper, Silicon Valley, Silicon Valley startup, six sigma, Skype, Snapchat, software as a service, Steve Jobs, Steven Levy, TED Talk, The future is already here, the payments system, The Wisdom of Crowds, Thomas Bayes, Toyota Production System, Uber for X, US Airways Flight 1549, Watson beat the top human players on Jeopardy!, Yogi Berra

More important, as recently as a decade ago, some very smart and savvy computer engineers and economists believed that another seemingly intractable problem, building a driverless car, was beyond the reach of modern technology. Just consider the challenges: a kid running into the street after a soccer ball; a texting teenager swerving in the middle lane; a traffic light on the fritz. And the left turn. “Executing a left turn across oncoming traffic,” two highly respected economists, MIT’s Frank Levy and Harvard’s Richard Murnane, wrote in 2004, “involves so many factors that it is hard to imagine the set of rules that can replicate a driver’s behavior.” Yet six years later, Google’s unveiling of its driverless car demonstrated that this “insoluble problem” had been solved.

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As of April 2014, the Google car had clocked nearly 700,000 miles and been involved in two accidents. One reportedly occurred after a human took over the wheel, and the other when the Google car was rear-ended by, one presumes, another human driver. The driverless car might well be safer than one controlled by a fallible Homo sapiens. If the driverless car weren’t enough of a challenge to human superiority, who could have watched IBM’s Watson supercomputer defeat the Jeopardy Hall of Famers in 2011 and not fretted about the future of physicians, or any highly skilled workers, for that matter? “Just as factory jobs were eliminated in the twentieth century by new assemblyline robots,” wrote all-time (human) Jeopardy champion Ken Jennings soon after the lopsided match ended, “Brad [Rutter, the other defeated champ] and I were the first knowledge-industry workers put out of work by the new generation of ‘thinking’ machines.

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Interestingly, this bite-sized approach may ultimately prove to be the best path to the more ambitious goal of the fully digital doctor. While the Google car might technically be ready for a road near you, there’s no shortage of hurdles to surmount, including existing laws that insist on human drivers and the absence of driverless car insurance. But, observed technology journalist Alexis Madrigal in 2014, while it may be many years before our highways are packed with driverless cars, most of us will encounter autonomous driving “in little autopilot moments when we cede temporary control to a computer” as we move from the familiar wonder of cruise control to new features that nudge us back into our lane when we wander or help us parallel park in a tight space.

Average Is Over: Powering America Beyond the Age of the Great Stagnation by Tyler Cowen

Amazon Mechanical Turk, behavioural economics, Black Swan, brain emulation, Brownian motion, business cycle, Cass Sunstein, Charles Babbage, choice architecture, complexity theory, computer age, computer vision, computerized trading, cosmological constant, crowdsourcing, dark matter, David Brooks, David Ricardo: comparative advantage, deliberate practice, driverless car, Drosophila, en.wikipedia.org, endowment effect, epigenetics, Erik Brynjolfsson, eurozone crisis, experimental economics, Flynn Effect, Freestyle chess, full employment, future of work, game design, Higgs boson, income inequality, industrial robot, informal economy, Isaac Newton, Johannes Kepler, John Markoff, Ken Thompson, Khan Academy, labor-force participation, Loebner Prize, low interest rates, low skilled workers, machine readable, manufacturing employment, Mark Zuckerberg, meta-analysis, microcredit, Myron Scholes, Narrative Science, Netflix Prize, Nicholas Carr, off-the-grid, P = NP, P vs NP, pattern recognition, Peter Thiel, randomized controlled trial, Ray Kurzweil, reshoring, Richard Florida, Richard Thaler, Ronald Reagan, Silicon Valley, Skype, statistical model, stem cell, Steve Jobs, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, upwardly mobile, Yogi Berra

Robot-guided mechanical arms are common in the operating room, and computers spend more time flying our planes than do the pilots. South Korea is experimenting with robotic prison wardens that patrol when the inmates do something wrong and report the misdeeds. Driverless cars are already operating on the streets of Berlin and Nevada, and Florida and California have passed bills to legalize computer-commanded “driverless cars” on their roads. Google’s team has test-driven hundreds of thousands of miles with these cars, so far without an accident or major incident; the one reported five-car pileup happened after a human took over from the computer.

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Health care is an ethical minefield and arguably we should be especially cautious when evaluating new medical and institutional breakthroughs. In any case, we can expect slower progress. When it comes to mechanized intelligent analysis, patent law can be a problem but for the most part the paths forward are relatively free of regulatory obstacles. Some applications, such as driverless cars, do face potential lawsuits; for instance, imagine the first time an out-of-control driverless car runs down a child. The parents would likely sue the deep-pocketed company that made the car, in addition to suing the owner of the car and maybe also the person who programmed the software of the car. Given this likelihood, there may be delays in turning the cars into marketable products.

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Mistakes, including moral mistakes, will be ignored and covered up, or possibly never rooted out in the first place. We are again back to that scary world without much accountability for some very important decisions. In other contexts we will not be able to avoid making some very direct moral judgments. Let’s say for instance that you own a driverless car. How should such a car be programmed in case an accident is imminent? Should the car swerve away from hitting a baby carriage, but at the risk of running into two elderly people? Should the car be programmed to crash you into a telephone pole, rather than run a p = 0.6 chance of knocking over a pedestrian?

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

Self-Driving Cars Did Not Deliver 1 James Niccolai, “Self-driving Cars a Reality for ‘Ordinary People’ within 5 Years, Says Google’s Sergey Brin,” Computer World, September 25, 2012, Computerworld.com. 2 John Paczkowski, “Google’s Self-Driving Cars Now Legal in California,” All Things D, September 25, 2012, Allthingsd.com. 3 Nicholas Carlson, “Uber Is Planning for a World Without Drivers—Just a Self-Driving Fleet,” Business Insider, May 28, 2014, Businessinsider.com. 4 Kara Swisher, “Self-Driving into the Future: Full Code Conference Video of Google’s Sergey Brin,” Recode, June 11, 2014, Vox.com. 5 Ibid. 6 Adrienne LaFrance, “Your Grandmother’s Driverless Car,” Atlantic, June 29, 2016, Theatlantic.com. 7 Doc Quigg, “Reporter Rides Driverless Car,” Press-Courier, June 7, 1960, News.google.com. 8 “A Brief History of Autonomous Vehicle Technology,” Wired, n.d., Wired.com. 9 Robert D. Leighty, “DARPA ALV (Autonomous Land Vehicle) Summary,” U.S. Army Engineer Topographic Laboratories, March 1986, Apps.dtic.mil. 10 Robert A.

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But this was hardly the first time that we had been told that autonomous vehicles were going to revolutionize automotive mobility. As vehicles were swamping urban streets and racking up a high death toll through the 1920s and 1930s, demonstrations of “phantom autos” were stunning audiences across the United States. A newspaper in Ohio remarked in 1932 that, “it is true that the driverless car will travel about the city through the heaviest traffic—starting, stopping, sounding its horn, turning right or left, making U-turns and circles, and proceeding thus as though there were an invisible driver at the wheel.”6 They were “one of the most amazing products of modern science,” piloted not by a computer system, but by remote control.

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The Radio Corporation of America, better known as RCA, started testing a system of wires buried in the pavement to guide vehicles along highways in the 1950s. The “electronic chauffeurs” were designed to eliminate accidents and make the roads safer. When the system was demonstrated for journalists in 1960 using GM vehicles with coils in their front ends to pick up the electromagnetic fields, the Press-Courier declared that RCA had “put the driverless car on the road” and that riding in one “tends to be scary as the ghost, until you get used to it. But someday we may have to live with it.”7 Sixty years later, we still do not have to live with any such thing, but development on the self-driving car continued in the United States and abroad. In 1977, Tsukuba Mechanical showed off its own autonomous vehicle in Japan that improved upon earlier experiments by using two cameras that could detect street markings with a maximum speed of twenty miles per hour.

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

Peter Campbell, ‘Ford Plans Mass-market Self-driving Car by 2021’, Financial Times, 16 August 2016 <https://www.ft.com/content/ d2cfc64e-63c0-11e6-a08a-c7ac04ef00aa#axzz4HOGiWvHT> (accessed 28 November 2017); David Millward, ‘How Ford Will Create a New Generation of Driverless Cars’, Telegraph, 27 February 2017 <http://www.telegraph.co.uk/business/2017/02/27/ford-seeks-­ pioneer-new-generation-driverless-cars/> (accessed 28 November 2017). 5. Wei Xiong et al.,‘Achieving Human Parity in Conversational Speech Recognition’, arXiv, 17 February 2017 <https://arxiv.org/abs/ 1610.05256> (accessed 28 November 2017). 6. Yannis M. Assael et al.

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Alison Sander and Meldon Wolfgang, ‘The Rise of Robotics’, BCG Perspectives, 27 August 2014 <https://www.bcgperspectives.com/ content/articles/business_unit_strategy_innovation_rise_of_robotics/> (accessed 30 November 2017). 87. Winfield, Robotics, vii. 88. Susskind and Susskind, Future of the Professions, 50. 89. Waymo, Google <https://www.google.com/selfdrivingcar/> (accessed 30 November 2017). 90. Danielle Muoio, ‘Here’s Everything We Know About Google’s Driverless Cars’, Business Insider, 25 July 2016 <http://uk.businessinsider.com/google-driverless-car-facts-2016-7?r=US&IR=T/#thecars-have-been-in-a-few-minor-accidents-only-one-of-whichcould-be-argued-to-have-been-the-google-cars-fault-11> (accessed 30 November 2017). 91. Wallach, Dangerous Master, 220; Bryant Walker Smith, ‘Human Error as a Cause of Vehicle Crashes’, Stanford Center for Internet and Society, 18 December 2013 <http://cyberlaw.stanford.edu/blog/ 2013/12/human-error-cause-vehicle-crashes> (accessed 30 November 2017). 92.

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Future of Work Hub, 4 Apr. 2017 <http://www.futureofworkhub.info/comment/2017/4/4/numbers-data-and-algorithmswhy-hr-professionals-and-employment-lawyers-should-take-data-­ science-seriously> (accessed 1 Dec. 2017). Millward, David. ‘How Ford Will create a new generation of driverless cars’. Telegraph, 27 Feb. 2017 <http://www.telegraph.co.uk/business/ 2017/02/27/ford-seeks-pioneer-new-generation-driverless-cars/> (accessed 28 Nov. 2017). Milton, John. Paradise Lost. London: Penguin, 2003. Misra,Tanvi.‘3 Cities Using Open Data in Creative Ways to Solve Problems’. CityLab, 22 Apr. 2015 <http://www.citylab.com/cityfixer/2015/04/3cities-using-open-data-in-creative-ways-to-solve-­problems/391035/> (accessed 29 Nov. 2017).

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

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?”

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Gaikwad, Edmond Awad, Sohan Dsouza, Iyad Rahwan, Pradeep Ravikumar, and Ariel D. Procaccia, “A Voting-Based System for Ethical Decision Making”, arXiv:1709.06692v1 [cs.AI], accessed 1 June 2018. 43Oliver Smith, “A Huge Global Study On Driverless Car Ethics Found the Elderly Are Expendable”, Forbes, 21 March 2018, https://​www.​forbes.​com/​sites/​oliversmith/​2018/​03/​21/​the-results-of-the-biggest-global-study-on-driverless-car-ethics-are-in/​#7fbb629f4a9f, accessed 1 June 2018. 44Joel D’Silva and Geert van Calster, “For Me to Know and You to Find Out? Participatory Mechanisms, the Aarhus Convention and New Technologies”, Studies in Ethics, Law, and Technology, Vol. 4, No. 2 (2010). 45Strictly speaking, DeepMind is UK based, though it is a subsidiary of Alphabet, the US-based parent of Google. 46“Homepage”, Website of the Partnership on AI , https://​www.​partnershiponai.​org/​, accessed 1 June 2018.

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However, these will be autonomous in the relevant sense so long as the software within the vehicles, which may come from a single central hub and be sent to the individual vehicles via the Internet, contains features which would qualify as AI within this book’s definition. See, for example, Joel Achenbach, “Driverless Cars Are Colliding with the Creepy Trolley Problem”, Washington Post, 29 December 2015, https://​www.​washingtonpost.​com/​news/​innovations/​wp/​2015/​12/​29/​will-self-driving-cars-ever-solve-the-famous-and-creepy-trolley-problem/​?​utm_​term=​.​30f91abdad96, accessed 1 June 2018; Jean-François Bonnefon, Azim Shariff, and Iyad Rahwan, “The Social Dilemma of Autonomous Vehicles”, Cornell University Library Working Paper, 4 July 2016, https://​arxiv.​org/​abs/​1510.​03346, accessed 1 June 2018. 107The scenario involving a criminal pedestrian was posed by researchers at MIT, in their “Moral Machine” game, which is described by its designers as “A platform for gathering a human perspective on moral decisions made by machine intelligence, such as self-driving cars.

How Cycling Can Save the World by Peter Walker

active transport: walking or cycling, bike sharing, Boris Johnson, car-free, correlation does not imply causation, Crossrail, Donald Shoup, driverless car, Enrique Peñalosa, fixed-gear, gentrification, Intergovernmental Panel on Climate Change (IPCC), Ken Thompson, Kickstarter, meta-analysis, New Journalism, New Urbanism, post-work, publication bias, safety bicycle, Sidewalk Labs, Stop de Kindermoord, TED Talk, the built environment, traffic fines, Traffic in Towns by Colin Buchanan, transit-oriented development, urban planning

Rechargeable lights now fit into a pocket, and the main issue is trying not to dazzle or hypnotize drivers with the multiple flashing LEDs. So all praise to innovation. But sometimes the benefits come in less obvious ways—for example, the advent of bar-code keys for cycle-share systems, or improved batteries in e-bikes. Elsewhere cycling can be the almost indirect beneficiary of other tech upheavals. The imminent arrival of driverless cars and automated traffic systems is likely to reshape our cities in ways we can barely imagine, but around which the bike seems likely to play a crucial role. The revolution has arrived. It’s just not always the one you expected. But How Do You Move a Fridge? Technology also plays a big role in answering one of the most common arguments you hear against making over more of a city’s road space to bikes.

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“Just now the walking distances don’t allow the metro systems to penetrate very far into residential districts. A bicycle-sharing system extends that by about three times.”11 Anand Babu, meanwhile, is imagining bikes having a key role to connect with a more high-tech transport system—high-speed driverless cars. Babu, formerly head of special projects for Google, is now chief operating officer of Sidewalk Labs, a new and vastly ambitious Google spin-off that has as its modest intention to reshape cities through the conjunction of clever planning and high-tech solutions. Sidewalk Labs has so far devised LinkNYC, a project to turn New York City’s defunct pay-phone booths into digital hubs, where people can either access Wi-Fi or, if they don’t have a smartphone or similar device, find information directly.

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He does, however, predict that in transport terms there are likely to be big changes in what he calls the “20–100 mph range,” taking in cars and what might replace them, and the “10–20 mph range,” covering cycling and other short-range travel modes. Not surprisingly for someone whose parent company is at the forefront of developing driverless car technology, Babu expects such creations to be in use soon. They could have a huge and immediate impact, he believes, especially on spread-out US cities, where some suburbs have become neglected, in part because of the time it takes to travel to and from them. Since existing cars would take time to be replaced, Babu says, the near future could see lanes of freeways reserved for driverless vehicles, transporting people between distant points at high speed.

Explaining Humans: What Science Can Teach Us About Life, Love and Relationships by Camilla Pang

autism spectrum disorder, backpropagation, bioinformatics, Brownian motion, correlation does not imply causation, data science, deep learning, driverless car, frictionless, job automation, John Nash: game theory, John von Neumann, Kickstarter, Nash equilibrium, neurotypical, phenotype, random walk, self-driving car, stem cell, Stephen Hawking

Compared to traditional machine learning, a neural network is more independent and requires less input from the programmer to define what it should be searching for, since, through internal layers of logic, it is able to create its own connections. All of the more radical examples of artificial intelligence you may have read about – from fully driverless cars to mass automation of people’s jobs – ultimately rely on deep learning, the closest we have so far got to developing a computer program that can think (within considerable limitations) like a human. Deep learning is also responsible for applications, including criminal checks, drug design, and the computer programs that rival the most competent chess players, all of which depend on an ability to simulate the connective capability of the human mind.

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Deep learning is also responsible for applications, including criminal checks, drug design, and the computer programs that rival the most competent chess players, all of which depend on an ability to simulate the connective capability of the human mind. Modelled on the brain, a neural network is made up of neurons – in this case, the various data inputs. These come in three layers: input, output and in the middle what is called ‘hidden’, the place where the algorithm does its thinking. For example, if we take a driverless car, inputs will include the angle of the road, the speed of the car, proximity to other vehicles, the weight of passengers, and any obstacles on the road – all factors that determine the nature of the outputs, which are the decisions the algorithm makes about how to drive safely. It is the connections between these neurons, and how they fire, that really matter.

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The crucial aspect to a neural network is that the connections have a virtual ‘weight’ assigned to them, affecting their influence on the network and output. It’s by comparing and calculating the weights of these inputs that the program reaches its decisions, learning which inputs to trust as most indicative of a particular result. With our driverless car example, it’s likely to be speed and proximity of obstacles (which could be pedestrians or other vehicles) that have the heaviest weighting and the greatest influence on decisions. The ultimate aim of a neural network is, over time and extensive trial and error, to assign the most accurate values to these weighted connections, so it can consider new inputs with the priority (high or low) that they deserve.

Alchemy: The Dark Art and Curious Science of Creating Magic in Brands, Business, and Life by Rory Sutherland

"World Economic Forum" Davos, 3D printing, Alfred Russel Wallace, barriers to entry, basic income, behavioural economics, Black Swan, Brexit referendum, butterfly effect, California gold rush, call centre, Captain Sullenberger Hudson, Cass Sunstein, cognitive dissonance, confounding variable, Daniel Kahneman / Amos Tversky, Dava Sobel, delayed gratification, Donald Trump, double helix, Downton Abbey, driverless car, Easter island, Edward Jenner, Elon Musk, Firefox, Ford Model T, General Magic , George Akerlof, gig economy, Google Chrome, Google X / Alphabet X, Grace Hopper, Hyperloop, Ignaz Semmelweis: hand washing, IKEA effect, information asymmetry, it is difficult to get a man to understand something, when his salary depends on his not understanding it, James Dyson, John Harrison: Longitude, loss aversion, low cost airline, Mason jar, Murray Gell-Mann, nudge theory, Peter Thiel, placebo effect, race to the bottom, Richard Feynman, Richard Thaler, Rory Sutherland, shareholder value, Silicon Valley, social intelligence, Steve Jobs, supply-chain management, systems thinking, TED Talk, the map is not the territory, The Market for Lemons, The Wealth of Nations by Adam Smith, ultimatum game, universal basic income, Upton Sinclair, US Airways Flight 1549, Veblen good, work culture

Hillary thinks like an economist, while Donald is a game theorist, and is able to achieve with one tweet what would take Clinton four years of congressional infighting. That’s alchemy; you may hate it, but it works. Some scientists believe that driverless cars will not work unless they learn to be irrational. If such cars stop reliably whenever a pedestrian appears in front of them, pedestrian crossings will be unnecessary and jaywalkers will be able to march into the road, forcing the driverless car to stop suddenly, at great discomfort to its occupants. To prevent this, driverless cars may have to learn to be ‘angry’, and to occasionally maliciously fail to stop in time and strike the pedestrian on the shins. If you are wholly predictable, people learn to hack you.

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Among heavy drinkers, the brain doesn’t wait for the booze to kick in – it shortcuts straight to the expected level of drunkenness. *Respectively a kind of supercharged jet engine and a form of high-speed land travel through tunnels from which all air, and hence air-resistance, has been removed. *Before we get too excited by the economic prospects of the driverless car, it is worth considering what this technology might offer to terrorists, for instance. A driverless car is effectively a cruise missile on wheels. *Goldfinger. In the film, Bond tracks Auric Goldfinger’s Rolls-Royce to his Alpine lair using an animated map installed in his Aston Martin. *I’m largely with Thomas – I don’t do much DIY, but I would acknowledge that my principal interest in cooking is not primarily the preparation of food: it’s an excuse to buy kitchen gadgetry.

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If that is the case, the good news for the vaping industry is that one common objection to it can be rejected, though the bad news is that e-cigarette sales may shrink as they run out of former smokers to convert. 1.3: Psychological Moonshots Alphabet, the parent company of Google, runs a division that is now simply called ‘X’. It was founded as Google X, with the aim of developing what the company calls ‘moonshots’. A moonshot is an incredibly ambitious innovation; instead of pursuing change by increments, it aims to change something by a factor of ten. For instance, X funds research into driverless cars, with the explicit aim of reducing road-accident fatalities by at least 90 per cent. The argument for X is that the major advances in human civilisation have come from things that, rather than resulting in modest improvement, were game-changers – steam power versus horse power, train versus canal, electricity versus gaslight.

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

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. Of course, in assessing the question of safety, we need to ask how safe is safe enough for driverless cars to become the norm. It’s important to remember that a driverless car need not be perfect, it just needs to be better than a human driver. And as the statistics indicate, humans aren’t such great drivers—we are error prone, sometimes piloting vehicles while overly tired, distracted by text messaging, or drunk. Numerous other arguments have been made for the benefits of driverless vehicles, including making commuting time more productive (estimated to save hundreds of billions of dollars annually in the United States alone), reducing fuel consumption by having cars “platoon” or flock together by coordinating their movements, increasing the throughput of existing roads (since self-driving cars won’t need as much space separating them while they drive), and even reducing the need for parking spaces.

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Numerous other arguments have been made for the benefits of driverless vehicles, including making commuting time more productive (estimated to save hundreds of billions of dollars annually in the United States alone), reducing fuel consumption by having cars “platoon” or flock together by coordinating their movements, increasing the throughput of existing roads (since self-driving cars won’t need as much space separating them while they drive), and even reducing the need for parking spaces. So what could possibly be the downside of deploying driverless cars? Well, first we need to figure out whether they work, and that turns out to be a tough question to answer. Beyond demonstrating that driverless cars can make low-level decisions, such as following the rules of the road and avoiding accidents, we need to understand how they make more complex high-level decisions. For example, when confronted with a choice about whether to swerve into a bicycle lane to protect the car’s driver or to harm parents bicycling with their children, what should the autonomous system that pilots the car be programmed to do?

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Hal Varian: Aaron Smith and Janna Anderson, “AI, Robotics, and the Future of Jobs,” Pew Research Center, August 6, 2014, https://www.pewresearch.org/internet/2014/08/06/future-of-jobs/. dentists and clergy remain relatively safe: Ibid. More than 40 percent: Mark Fahey, “Driverless Cars Will Kill the Most Jobs in Select US States,” CNBC, September 2, 2016, https://www.cnbc.com/2016/09/02/driverless-cars-will-kill-the-most-jobs-in-select-us-states.html. automation may have beneficial effects: Daron Acemoglu and Pascual Restrepo, “Artificial Intelligence, Automation and Work” (NBER Working Paper Series Working Paper 24196, January 2018), 43.

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

We are already seeing the hackability of the ever more sophisticated software and security systems found in automobiles. Can we confidently protect brakes and steering against hacking? An oft-quoted benefit of driverless cars is that they will be hired and shared rather than owned. This could reduce the amount of parking space needed in cities—blurring the line between public and private transport. But what is not clear is how far this will go—whether the wish to possess one’s own car will indeed disappear. If driverless cars catch on, they will boost road travel at the expense of traditional rail travel. Many people in Europe prefer taking the train for a 200-mile journey; it is less stressful than driving and opens up time to work or read.

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But for interactions with humans, or even with the complex and fast-changing environment encountered by a driverless car on an ordinary road, processing power is not enough; computers would need sensors that enable them to see and hear as well as humans do, and the software to process and interpret what the sensors relay. But even that would not be sufficient. Computers learn from a ‘training set’ of similar activities, where success is immediately ‘rewarded’ and reinforced. Game-playing computers play millions of games; photo-interpreting computers gain expertise by studying millions of images; for driverless cars to achieve this expertise, they would need to communicate with one another, to share and update their knowledge.

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

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. Widespread adoption is harder to predict but the CEO of Uber has said that he expects his entire fleet to be driverless by 2030.10 Initially, like many other innovations in the automotive sector, driverless cars may be more expensive but the costs are likely to fall over time until – like automatic gearboxes, electric windows and in-car entertainment systems – they become commonplace. So commonplace that sometime in the 2020s, you will find yourself riding in a driverless car. Full AI As Professor Nick Bostrom of Oxford University has written: … expert opinions about the future of AI vary wildly.

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Currently, for example, around 13 per cent of the US population are employed in the Retail and Transportation industries.25 There are signs that many of these jobs will not exist in a few years’ time – we have self-service checkouts in supermarkets; some fast food restaurants have introduced screen-based ordering; driverless trains are a reality today; driverless cars have already reached a high level of technical proficiency, and cars with some degree of autonomy (e.g. motorway driving) are already on the market; warehouses increasingly use automated picking and packing technologies. In these areas alone, we could see tens of millions of jobs disappear. But this is just the beginning.

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., 2017 18 Greenfield & Marsh, 2018 19 Fleming, 2018 20 Wikipedia, 2018 Wikipedia, 2018 21 Mayer, 2016 22 Freedland, 2017 Chapter 5: The Fork in the Road 1 Carney, Mark, ‘Keeping the patient alive: Monetary policy in a time of great disruption’, World Economic Forum, 6 December 2016 2 Miller, 2014 3 US Census Bureau, 2015 4 Rigby, 2016 5 Royal Academy of Engineering, 2013 6 University of Manchester, 2016 7 Walsh, 2016 8 Kirkpatrick & Light, 2015 9 Driverless car market watch, 2016 10 Yadron, 2016 11 Bostrom, Superintelligence: paths, dangers, strategies, 2014 12 United Nations, 2016 13 ITER, 2016 14 Culham Centre for Fusion Energy, 2016 15 Noakes, 2016 16 Hudson, 2013 17 Murgia, 2016 18 Rajesh, 2015 19 Smart Cities Council, 2016 20 The Ellen MacArthur Foundation, 2016 21 Andersen, 2006 22 Allen R.

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

He should know because he has analyzed 158,000 early companies, and almost two-thirds of them experienced one or more consecutive years of decline in the third to fifth year of existence.19 Behind those numbers hides an army of entrepreneurs battling with market complexity every single day. Take driverless cars. Listening to the current hype may make you think you will be able to own and be driven by one soon. But the technological development of driverless cars has been going on for decades and, from a market perspective, it has not come particularly far. Not more than 20 percent of the cars sold in 2015 had embedded connectivity solutions. Industry analysts believe it will take many years for the necessary infrastructure to be in place before they become commonplace.

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The world, as The Economist put it, will be a “planet of phones.”7 Advances in technology are not just confined to the mobile sector. Many discoveries and innovations coming out of science labs are astonishing. In 1964 Isaac Asimov predicted a world of robots, and cars with “robot brains”; this no longer seems like science fiction.8 Driverless cars can be seen on American roads, and the flying car may not be far behind. Commercial and affordable space flights could be a reality in the next decade. Our generation, born in the 1970s, may live to see the first human mission to Mars. Medical devices are also experiencing a revolution. Advanced surgery can now be performed by robots, observed and managed remotely by a surgeon.

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The coordination required to make the various regulations bearing on intelligent vehicles interoperable can easily fail, squandering a substantial part of their promise. In several countries, there have to be regulatory and legal innovations to circumvent the potential effects of such a basic legal provision as the assignment of liability in the event of accidents. Or, to put it differently: if a driverless car crashes or causes an accident, who should be blamed – the owner of the car or the producer of the car, or perhaps the company producing its software? Some companies are experimenting with different solutions, but it is a highly contentious issue. As two leading experts in transportation in the United States soberly note, “the major obstacle to motorists and firms from adopting them as soon as possible is whether the government will take prudent and expeditious approaches to help resolve important questions about assigning liability in the event of an accident, the availability of insurance, and safety regulations.”21 Prudent and expeditious approaches, however, are not the main characteristics of Western regulators and their political masters.

Platform Capitalism by Nick Srnicek

"World Economic Forum" Davos, 3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Big Tech, Californian Ideology, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, collaborative economy, collective bargaining, data science, deindustrialization, deskilling, Didi Chuxing, digital capitalism, digital divide, disintermediation, driverless car, Ford Model T, future of work, gig economy, independent contractor, Infrastructure as a Service, Internet of things, Jean Tirole, Jeff Bezos, knowledge economy, knowledge worker, liquidity trap, low interest rates, low skilled workers, Lyft, Mark Zuckerberg, means of production, mittelstand, multi-sided market, natural language processing, Network effects, new economy, Oculus Rift, offshore financial centre, pattern recognition, platform as a service, quantitative easing, RFID, ride hailing / ride sharing, Robert Gordon, Salesforce, self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, software as a service, surveillance capitalism, TaskRabbit, the built environment, total factor productivity, two-sided market, Uber and Lyft, Uber for X, uber lyft, unconventional monetary instruments, unorthodox policies, vertical integration, warehouse robotics, Zipcar

By 2015, global mergers and acquisitions had shot up more than 40 per cent above pre-crisis levels,30 and the leading platforms have all made major moves in acquiring the resources needed to compete with their rivals. Ultimately, we see convergence – and therefore competition – across the field: smartphones, e-book readers, consumer IoT, cloud platforms, videochat services, payment services, driverless cars, drones, virtual reality, social networking, interfaces, network provision, search, and probably much more in the future. A third dominant tendency is the funnelling of data extraction into siloed platforms. When extensive means are not sufficient for competitive advantage, this approach tries to tie users and data to the platform by locking them in through various measures: dependency on a service, inability to use alternatives, or lack of data portability, for instance.

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Combined with stagnant wages and rising inequality, this future depicts a world with a massively increased digital divide. Finally, in the event of a major cutback on advertising, these platforms could be forced to cut back on all extravagant spending on long-shot ventures (drones, virtual reality, driverless cars, etc.), and to return to their core businesses. The cross-subsidisation of these ventures would come to an end, as would their ability to compete with other major platforms. In any case, the capitalist imperative to generate a profit means that these platforms will be forced either to develop novel means of extracting a surplus from the general economic pie or to fold their expansive cross-subsidising monopolies into much more traditional business forms.

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Platforms continue to expand across the economy, and competition drives them to enclose themselves increasingly. Platforms dependent on advertising revenues are compelled to transition more into direct payment businesses. Meanwhile lean platforms dependent on outsourcing costs and on venture capital largesse either go bankrupt or shift into product platforms (as Uber is attempting to do with driverless cars). In the end, it appears that platform capitalism has inbuilt tendencies to move towards extracting rents by providing services (in the form of cloud platforms, infrastructural platforms, or product platforms). In terms of profitability, Amazon is more the future than Google, Facebook, or Uber.

The New Class Conflict by Joel Kotkin

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, affirmative action, Affordable Care Act / Obamacare, Alvin Toffler, American Society of Civil Engineers: Report Card, back-to-the-city movement, Bob Noyce, Boston Dynamics, California gold rush, Californian Ideology, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, classic study, Cornelius Vanderbilt, creative destruction, crony capitalism, David Graeber, degrowth, deindustrialization, do what you love, don't be evil, Downton Abbey, driverless car, Edward Glaeser, Elon Musk, energy security, falling living standards, future of work, Future Shock, Gini coefficient, Google bus, Herman Kahn, housing crisis, income inequality, independent contractor, informal economy, Internet of things, Jane Jacobs, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John von Neumann, Joseph Schumpeter, Kevin Kelly, Kevin Roose, labor-force participation, Larry Ellison, Lewis Mumford, low interest rates, low-wage service sector, Marc Andreessen, Mark Zuckerberg, Mary Meeker, mass affluent, McJob, McMansion, medical bankruptcy, microapartment, Nate Silver, National Debt Clock, New Economic Geography, new economy, New Urbanism, obamacare, offshore financial centre, Paul Buchheit, payday loans, Peter Calthorpe, plutocrats, post-industrial society, public intellectual, RAND corporation, Ray Kurzweil, rent control, rent-seeking, Report Card for America’s Infrastructure, Richard Florida, Sheryl Sandberg, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Solyndra, Steve Jobs, stock buybacks, tech worker, techlash, technoutopianism, The Death and Life of Great American Cities, Thomas L Friedman, Tony Fadell, too big to fail, transcontinental railway, trickle-down economics, Tyler Cowen, Tyler Cowen: Great Stagnation, upwardly mobile, urban planning, urban sprawl, Virgin Galactic, War on Poverty, women in the workforce, working poor, young professional

Jeff Foust, Twitter post, October 16, 2013, 8:24am, https://twitter.com/jeff_foust/status/390498497428209664; Jaclyn Trop and Diane Cardwell, “Tesla Plans $5 Billion Battery Factory for Mass-Market Electric Car,” New York Times, February 27, 2014. 77. Tim Worstall, “Google To Build Its Own Driverless Cars,” Forbes, August 24, 2013, http://www.forbes.com/sites/timworstall/2013/08/24/google-to-build-its-own-driverless-cars; Jennifer Booton, “Google, Apple Tackle the Next Digital Frontier: Cars,” FOX Business, January 06, 2014, http://www.foxbusiness.com/technology/2014/01/06/google-apple-tackle-next-digital-frontier-cars; Neal E. Boudette and Daisuke Wakabayashi, “Google, Apple Forge Auto Ties,” Wall Street Journal, December 29, 2013. 78.

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As they gather huge technical and financial resources, firms such as Google have begun to utilize their dominance of critical software niches, including but not limited to Internet search, to gather enormous financial and technical resources. This has allowed them to move into a host of fields, such as robotics, energy, mapping, driverless cars, and aging, that have great potential but which are not directly related to their core business. Other tech firms, such as Apple, have made similar forays.67 In many ways, the shift of resources from social media and advertising to such fields as robotics and space travel has to be considered a basically positive development.

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Other tech firms, such as Apple, have made similar forays.67 In many ways, the shift of resources from social media and advertising to such fields as robotics and space travel has to be considered a basically positive development. The social media revolution may thrill people with games and virtual community, but it appears to have done relatively little to enhance the overall productivity of the economy. Viewed this way, investing in more significant breakthroughs, such as space travel or driverless cars, certainly represents a significant improvement in the potential returns to society of the top tech firms.68 This is not to say that these firms’ investments will all work out well. Microsoft and Google, for example, have had their share of failures as they have sought to break out of their core businesses.

The Internet Is Not the Answer by Andrew Keen

"World Economic Forum" Davos, 3D printing, A Declaration of the Independence of Cyberspace, Airbnb, AltaVista, Andrew Keen, AOL-Time Warner, augmented reality, Bay Area Rapid Transit, Berlin Wall, Big Tech, bitcoin, Black Swan, Bob Geldof, Boston Dynamics, Burning Man, Cass Sunstein, Charles Babbage, citizen journalism, Clayton Christensen, clean water, cloud computing, collective bargaining, Colonization of Mars, computer age, connected car, creative destruction, cuban missile crisis, data science, David Brooks, decentralized internet, DeepMind, digital capitalism, disintermediation, disruptive innovation, Donald Davies, Downton Abbey, Dr. Strangelove, driverless car, Edward Snowden, Elon Musk, Erik Brynjolfsson, fail fast, Fall of the Berlin Wall, Filter Bubble, Francis Fukuyama: the end of history, Frank Gehry, Frederick Winslow Taylor, frictionless, fulfillment center, full employment, future of work, gentrification, gig economy, global village, Google bus, Google Glasses, Hacker Ethic, happiness index / gross national happiness, holacracy, income inequality, index card, informal economy, information trail, Innovator's Dilemma, Internet of things, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, John Perry Barlow, Joi Ito, Joseph Schumpeter, Julian Assange, Kevin Kelly, Kevin Roose, Kickstarter, Kiva Systems, Kodak vs Instagram, Lean Startup, libertarian paternalism, lifelogging, Lyft, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Martin Wolf, Mary Meeker, Metcalfe’s law, military-industrial complex, move fast and break things, Nate Silver, Neil Armstrong, Nelson Mandela, Network effects, new economy, Nicholas Carr, nonsequential writing, Norbert Wiener, Norman Mailer, Occupy movement, packet switching, PageRank, Panopticon Jeremy Bentham, Patri Friedman, Paul Graham, peer-to-peer, peer-to-peer rental, Peter Thiel, plutocrats, Potemkin village, power law, precariat, pre–internet, printed gun, Project Xanadu, RAND corporation, Ray Kurzweil, reality distortion field, ride hailing / ride sharing, Robert Metcalfe, Robert Solow, San Francisco homelessness, scientific management, Second Machine Age, self-driving car, sharing economy, Sheryl Sandberg, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Skype, smart cities, Snapchat, social web, South of Market, San Francisco, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, subscription business, TaskRabbit, tech bro, tech worker, TechCrunch disrupt, Ted Nelson, telemarketer, The future is already here, The Future of Employment, the long tail, the medium is the message, the new new thing, Thomas L Friedman, Travis Kalanick, Twitter Arab Spring, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, urban planning, Vannevar Bush, warehouse robotics, Whole Earth Catalog, WikiLeaks, winner-take-all economy, work culture , working poor, Y Combinator

According to the consultancy Booz, the market for connected cars is about to explode, with demand expected to quadruple between 2015 and 2020 and generate revenues of $113 billion by 2020.27 But even today’s connected car is a data machine, with the onboard cameras from Mercedes-Benz’s new S-Class saloon already generating 300 gigabytes of data per hour about the car’s location and speed and the driver’s habits.28 And then there’s Google’s driverless car, an artificially intelligent, networked car that is driven by software called Google Chauffeur. The idea of driverless cars might sound as science fictional as the idea of augmented reality glasses—but Nevada and Florida have already passed laws permitting their operation and the sight of trial versions of Google’s automated cars driving themselves up and down Route 101 between San Jose and San Francisco is not an uncommon one. While there’s no doubt that driverless cars do have enormous potential benefits, particularly in terms of safety and convenience, not to mention the potential environmental benefits of much lighter and thus more energy-efficient vehicles, Google’s pioneering role in them is deeply problematic.

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In the mid-1990s, it was the original Web 1.0 revolution of free websites like Netscape, Yahoo, and Craigslist. In 2005, it was Tim O’Reilly’s Web 2.0 user-generated-content revolution of Google, Wikipedia, and YouTube. And today, in 2014, it’s the “Internet of Things” revolution of 3-D printing, wearable computing, driverless cars, and intelligent drones. To learn more about today’s revolution, I had returned to the scene of my original disenchantment with the Internet. I’d once again come to the O’Reilly Media offices in Sebastopol, the little town up in Sonoma County, California, some fifty miles north of San Francisco.

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The software that powers their cars, Google Chauffeur, is essentially the automotive version of Google Glass, a “free” product designed to track everywhere we go and to feed all that data back to the main Google database so that it can connect the dots of our lives. As the Wall Street Journal columnist Holman Jenkins notes about these so-called autonomous driverless vehicles, “they won’t be autonomous at all,” and they may “pose a bigger threat to privacy than the NSA ever will.”29 After all, if Google links the data collected from its driverless cars with data amassed from the rest of its ubiquitous products and platforms—such as the smartphone it is developing that uses 3-D sensors to automatically map our physical surroundings so that Google always knows where we are30—then you have a surveillance architecture that exceeds even anything that Erich Mielke, in his wildest imagination, ever dreamed up.

Restarting the Future: How to Fix the Intangible Economy by Jonathan Haskel, Stian Westlake

"Friedman doctrine" OR "shareholder theory", activist fund / activist shareholder / activist investor, Andrei Shleifer, Big Tech, Black Lives Matter, book value, Boris Johnson, Brexit referendum, business cycle, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, central bank independence, Charles Lindbergh, charter city, cloud computing, cognitive bias, cognitive load, congestion charging, coronavirus, corporate governance, COVID-19, creative destruction, cryptocurrency, David Graeber, decarbonisation, Diane Coyle, Dominic Cummings, Donald Shoup, Donald Trump, Douglas Engelbart, Douglas Engelbart, driverless car, Edward Glaeser, equity risk premium, Erik Brynjolfsson, Estimating the Reproducibility of Psychological Science, facts on the ground, financial innovation, Francis Fukuyama: the end of history, future of work, general purpose technology, gentrification, Goodhart's law, green new deal, housing crisis, income inequality, index fund, indoor plumbing, industrial cluster, inflation targeting, intangible asset, interchangeable parts, invisible hand, job-hopping, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, Kenneth Arrow, knowledge economy, knowledge worker, lockdown, low interest rates, low skilled workers, Marc Andreessen, market design, Martin Wolf, megacity, mittelstand, new economy, Occupy movement, oil shock, patent troll, Peter Thiel, Phillips curve, postindustrial economy, pre–internet, price discrimination, quantitative easing, QWERTY keyboard, remote working, rent-seeking, replication crisis, risk/return, Robert Gordon, Robert Metcalfe, Robert Shiller, Ronald Coase, Sam Peltzman, Second Machine Age, secular stagnation, shareholder value, Silicon Valley, six sigma, skeuomorphism, social distancing, superstar cities, the built environment, The Rise and Fall of American Growth, The Spirit Level, The Wealth of Nations by Adam Smith, Thorstein Veblen, total factor productivity, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber for X, urban planning, We wanted flying cars, instead we got 140 characters, work culture , X Prize, Y2K

Why doesn’t productivity growth remain unchanged until the new wave boosts it again? The answer lies in how we measure productivity. Think of the driverless car. Very large amounts of money have been spent on the software, hardware, and testing of driverless cars. But at the time of this writing, driverless cars are not in common use. Now consider the measurement of productivity, which is defined as output per unit of input. In developing driverless cars we have greatly increased our inputs, but we have not yet reaped the benefits of the output. According to the mismeasurement hypothesis, this phenomenon is fairly widespread.

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For some, another troubling aspect of competition in the modern economy is the overwhelmingly conglomerate nature of some of our new firms. Amazon started as a bookseller. It now produces movies and sells web hosting; Google has moved from a search engine to online advertising to an email service to driverless cars. This increasingly conglomerate nature of the economy reminds many of the industrial structure of the 1960s, when large conglomerates dominated many industries. That story did not end well: conglomerates were sluggish and unproductive, and most ended up being broken up by market forces. In sum, many people see increased concentration, increased insulation of the leaders from the laggards, and the trend towards conglomerates as indications of a lack of competition.

Trees on Mars: Our Obsession With the Future by Hal Niedzviecki

"World Economic Forum" Davos, Ada Lovelace, agricultural Revolution, Airbnb, Albert Einstein, Alvin Toffler, Amazon Robotics, anti-communist, big data - Walmart - Pop Tarts, big-box store, business intelligence, Charles Babbage, Colonization of Mars, computer age, crowdsourcing, data science, David Brooks, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, Flynn Effect, Ford Model T, Future Shock, Google Glasses, hive mind, Howard Zinn, if you build it, they will come, income inequality, independent contractor, Internet of things, invention of movable type, Jaron Lanier, Jeff Bezos, job automation, John von Neumann, knowledge economy, Kodak vs Instagram, life extension, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Marshall McLuhan, Neil Armstrong, One Laptop per Child (OLPC), Peter H. Diamandis: Planetary Resources, Peter Thiel, Pierre-Simon Laplace, Ponzi scheme, precariat, prediction markets, Ralph Nader, randomized controlled trial, Ray Kurzweil, ride hailing / ride sharing, rising living standards, Robert Solow, Ronald Reagan, Salesforce, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, TaskRabbit, tech worker, technological singularity, technological solutionism, technoutopianism, Ted Kaczynski, TED Talk, Thomas L Friedman, Tyler Cowen, Uber and Lyft, uber lyft, Virgin Galactic, warehouse robotics, working poor

“People in tech, when they talk about why they started their company, they tend to talk about changing the world,” notes Joe Green, a roommate of Mark Zuckerberg at Harvard who now leads a Silicon Valley–funded political advocacy group. Reid Hoffman, founder of LinkedIn, describes it like this: “I can make a multibillion-dollar company with a little bit of investment. Why can’t the whole world do that?”50 Speaking about his company’s research into the driverless car, Google cofounder Sergey Brin says: “We want to fundamentally change the world with this.”51 New Yorker writer Jill Lepore notes, “The eighteenth century embraced the idea of progress; the nineteenth century had evolution; the twentieth century had growth and then innovation. Our era has disruption.”52 Or, as Jeff Bezos once told Charlie Rose: “Amazon is not happening to book selling.

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And exhibit two is, as Mark Winter mentioned, the astonishing number of diverse departments of the United States government now running various XPrize-like challenges. At the forefront is the once-secretive but increasingly public federal agency known as the Defense Advanced Research Projects Agency (DARPA). DARPA has run a bunch of challenges already, including one on the subject of driverless cars. As I write this, the agency is developing a new challenge with a two million dollar prize. The goal of the challenge is to develop “fully automatic network defense systems. DARPA envisions teams creating automated systems that would compete against each other to evaluate software, test for vulnerabilities, generate security patches and apply them to protected computers on a network.”3 Okay, it hardly seems surprising that government departments developed expressly to be experimental in their approach and aggressive in their timelines would eagerly adopt these kinds of competitions.

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Google has continued to acquire tech companies at a frantic pace and seems to be showing a particular interest in robotics—they have bought at least seven robotics companies so far and have put the man behind the development of their Android mobile operating system in charge of their robot division. But, argues one tech commentator, the goal isn’t really robotics and driverless cars, it’s data: “it’s all about gaining a dominant position in markets where data is about to explode. . . . Whoever provides the software that controls and manages these robots not only stands to make a fortune by selling that software; they will have access to a vast new repository of data about how we live and work.”18 Another article, about the big-data-enabled race—currently being led by Google—to map every road, river, and even footpath in the world makes the same point: “Tomorrow’s map, integrally connected to everything that moves (the keys, the tools, the car), will be so fundamental to their operation that the map will, in effect, be their operating system. . . .

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

,” New Yorker, November 25, 2013, http://www.newyorker.com/reporting/2013/11/25/131125fa_fact_bilger?currentPage=all. 16. John Markoff, “Google’s Next Phase in Driverless Cars: No Steering Wheel or Brake Pedals,” New York Times, May 27, 2014, http://www.nytimes.com/2014/05/28/technology/googles-next-phase-in-driverless-cars-no-brakes-or-steering-wheel.html. 17. Kevin Drum, “Driverless Cars Will Change Our Lives. Soon.,” Mother Jones (blog), January 24, 2013, http://www.motherjones.com/kevin-drum/2013/01/driverless-cars-will-change-our-lives-soon. 18. Lila Shapiro, “Car Wash Workers Unionize in Los Angeles,” Huffington Post, February 23, 2012, http://www.huffingtonpost.com/2012/02/23/car-wash-workers-unionize_n_1296060.html. 19.

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Tom Simonite, “Data Shows Google’s Robot Cars Are Smoother, Safer Drivers Than You or I,” Technology Review, October 25, 2013, http://www.technologyreview.com/news/520746/data-shows-googles-robot-cars-are-smoother-safer-drivers-than-you-or-i/. 10. See ibid. for Chris Urmson’s comments. 11. “The Self-Driving Car Logs More Miles on New Wheels” (Google corporate blog), August 7, 2012, http://googleblog.blogspot.co.uk/2012/08/the-self-driving-car-logs-more-miles-on.html. 12. As quoted in Heather Kelly, “Driverless Car Tech Gets Serious at CES,” CNN, January 9, 2014, http://www.cnn.com/2014/01/09/tech/innovation/self-driving-cars-ces/. 13. For US accident statistics, see http://www.census.gov/compendia/statab/2012/tables/12s1103.pdf; for global accident statistics, see http://www.who.int/gho/road_safety/mortality/en/. 14.

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., 10 Delta Cost Project, 140 demand, 196–197 in China, 223–227 productivity and, 207–208 Democrats, income distribution preferred by, 47n design philosophy, 254–255 developing countries, 10–12, 25, 78–79 diagnostic tool, repurposing Watson as, 102–103 digital computer, effect of, 33–34 digital divide, 78 digital economy, long-tail opportunities in, 76–78 DiNardo, Courtney, 148 disruptive technology, xviii, 66 division of labour, 73 Dow News Service, 113–114 Drexler, K. Eric, 241–242, 243, 244–245, 246, 247 driverless cars, See autonomous cars drone-based delivery, 190n drug prices, 170–171 Drum, Kevin, 188 Dunning, David, 18–19 dystopian future, automation and predictions of, 31–32, 219–220 Earned Income Tax Credit (EITC), 271, 277 eBay, 16, 76 economic argument for guaranteed income, 264–267 economic growth, 65, 212–215 economic mobility, decrease in, 46–47 economic policy, 57–58, 217–218 Economic Policy Institute, 127, 158 economic recoveries, jobs created during, 49–50 economics, mathematical models and, x, 205–206 economic trends bear market for labor/bull market for corporations, 38–41 declining incomes and underemployment for college graduates, 48–49 effect of information technology on, 58–61 income inequality, 46–48 job creation, jobless recoveries, and long-term unemployment, 43–46 labor force participation, 41–43 polarization and part-time jobs, 49–51 stagnant wages, 34–38 economists on impact of automation, 60 on income inequality, 202–206 economy complexity of, 211–212 defined, 266n effect of climate change on, 282–283 post-scarcity, 247 e-Discovery software, 124 Edison, Thomas, 234 education basic income guarantee and, 263 collaboration with machines and, 121–128 diminishing returns to, 250–253 effect on income, 48–49 nature of unemployment problem and, 249–250 See also higher education educational robots, 7 edX, 132, 133, 137 Egypt, 46 EITC.

Impact: Reshaping Capitalism to Drive Real Change by Ronald Cohen

"World Economic Forum" Davos, asset allocation, benefit corporation, biodiversity loss, carbon footprint, carbon tax, circular economy, commoditize, corporate governance, corporate social responsibility, crowdsourcing, decarbonisation, diversification, driverless car, Elon Musk, family office, financial independence, financial innovation, full employment, high net worth, housing crisis, impact investing, income inequality, invisible hand, Kickstarter, lockdown, Mark Zuckerberg, microbiome, minimum viable product, moral hazard, performance metric, risk-adjusted returns, risk/return, Silicon Valley, sovereign wealth fund, Steve Ballmer, Steve Jobs, tech worker, TED Talk, The Wealth of Nations by Adam Smith, transaction costs, zero-sum game

In April 2018, Zipline unveiled a new model that ‘flies farther, faster and with more cargo than was ever before possible – even in high altitude, heavy wind, or rain’.7 The company’s long-term mission is ‘to build instant delivery for the planet, allowing on-demand delivery of medicines and other products at low cost without using a drop of gasoline’.8 In May 2019, Zipline secured $190 million in funding from US venture capitalists and achieved a $1.2 billion valuation.9 It announced that it would expand across Africa, the Americas, South Asia and Southeast Asia, with the goal of serving 700 million people in the next five years.10 ‘Zipline wants to establish a new model for success in Silicon Valley,’ Rinaudo said, ‘by showing the world that the right technology company with the right mission and the best team can help improve the lives of every person on the planet.’11 While Rinaudo and his team have reimagined drone technology, another entrepreneurial tech-for-good venture, OrCam in Israel, has repurposed advanced technology in artificial intelligence, initially developed to guide driverless cars, to help the 39 million blind and the 250 million visually impaired people around the world. From Driverless Cars to Helping the Blind In 2016, 27-year-old Luke Hines was able to imagine going to college for the first time.12 In 2018, war veteran Scotty Smiley was finally able to read with his three sons.13 In 2019, Naim Bassa was empowered to cast his vote for the first time without someone having to accompany him.14 These three people were all visually impaired but had access to OrCam’s assistive technology, which uses a camera, computing, machine learning and deep networks to process visual information and relay it to users phonetically.

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From Driverless Cars to Helping the Blind In 2016, 27-year-old Luke Hines was able to imagine going to college for the first time.12 In 2018, war veteran Scotty Smiley was finally able to read with his three sons.13 In 2019, Naim Bassa was empowered to cast his vote for the first time without someone having to accompany him.14 These three people were all visually impaired but had access to OrCam’s assistive technology, which uses a camera, computing, machine learning and deep networks to process visual information and relay it to users phonetically. But the story of this wearable technology began in 1999, when co-founders Professor Amnon Shashua and Ziv Aviram started Mobileye, a technology company that uses cameras and artificial intelligence to replace the human eye in driverless cars.15 Eighteen years later they sold the company to Intel for over $15 billion, the largest acquisition in Israel’s history.16 At this point, Shashua began thinking of applying the technology he had invented to assist his aunt, whose vision was worsening.17 He and Aviram co-founded OrCam in 2010 to help visually impaired people process their surroundings.

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Aboyeji, Iyinoluwa ‘E’ 48 ABP (Dutch civil service pension fund) 74 accelerators, start-up 175–7, 197 Accenture 90 Access Foundation 175 accounts, impact-weighted financial v, 29–30, 67, 84, 107–17, 159, 183, 186, 190–1, 201 Acumen 84 Adidas 93, 101–4, 107 Adidas x Parley 102–3 Adie 162–3 Africa 1, 27, 39, 48, 49, 83, 84, 136, 148, 167–8 agriculture 36, 45–6, 78, 83, 84, 94–5 Ahrens, Andreas 105 Ailman, Christopher 77–8 Align17 80 Aliko Dangote Foundation 136 Alpert, Sharon 145 Amazon (online retailer) 14 Andela 46, 47–9 AOL 15, 48 Apax Partners 1, 2–3, 15, 18, 35, 174–5 AP funds (pension fund) 74 APG (pension fund) 73 Apple 14, 15, 77 Apposite Capital 142 Argentina 125, 164, 173, 176, 177 artificial intelligence (AI) 39, 40 Ashoka 58, 59 Asian Women’s Impact Fund 144 asset classes 7, 13, 27, 67, 187, 191 asset management 33, 63, 64, 65, 69, 70, 71, 72, 73, 74, 79–85, 172 Ather Energy 56 Australia 6, 22, 40, 124, 158, 176, 204 Avanath 143 Aviram, Ziv 40 Avishkaar 84 Bain Capital 81 Baldinger, Michael 79 Ballmer, Steve 127 Bangladesh 94 Bank of America 81, 115 Bank of Palestine 163 Bannick, Matt 148 Barby, Clara 29 Barclays 169 Bassa, Naim 39–40 Bayer 150 B Corp (Benefit Corporation) 57, 92, 96, 149, 177, 189, 197 Bedouin community, Israel 52–3, 163 Beijing, US embassy 28 Ben & Jerry’s 57 Bertelsmann Stiftung 153 Bhatia, Amit 7 Big Lottery Fund 175 Big Society Capital (BSC) v, 19, 72, 76, 160, 169–70, 171–2, 175 Big Win, The 136 Bill and Melinda Gates Foundation 149–50 Strategic Investment Fund (SIF) 149–50 biodiversity loss 16 biotechnology 45 BlackRock 32, 62, 82 Blair, Tony 160, 179 B Lab 56–7, 107 blended finance 197 Blood, David 19, 26, 49, 82 Blue Orchard Finance 80, 145 BNP Paribas 163 Bolton, Emily 20 Bombas 54 bond markets 71, 191 Bono 79 BP 111 Branson, Richard 88 Brazil 7, 41, 42, 94, 160–1, 177 Bridges Fund Management v, 18–19, 29, 53, 83, 125, 126, 127, 165 Bridges Ways to Wellness SIB 126 British Asian Trust 27, 137, 191 Brodin, Jesper 104, 106 Brown, Gordon 25, 27, 136, 179 B Team 88 Buffet, Warren 117 Burberry 54 Business for Inclusive Growth 88 Business Roundtable 87–8 Buycott 89–90 Cabinet Office 160, 161, 169 California Public Employees’ Retirement System (CalPERS) 76, 77 California State Teachers’ Retirement System (CalSTRS) 77–8 Calouste Gulbenkian Foundation 144, 153 Cameron, David 6, 19, 160, 169, 179 Camfed 137 Canada 6, 124, 144, 158, 160, 171 Capital Impact Partners 143 capitalism 3, 4, 5, 82, 117, 146, 148, 154, 181, 182, 186, 191, 193, 194, 200 impact capitalism 4, 180, 181–94, 200 see also impact investment Capricorn Investment Group 149 carbon emissions 18, 36, 55–6, 62, 66, 74, 76, 83, 90, 91, 97, 105, 106, 107, 111, 112–13, 139, 149 Carlyle Group 81 Carney, Mark 106–7 Cartigny, Gerald 73 Casa de Depositi e Prestiti (CDP) 171 Case, Steve 48, 52 Catalyst Strategies initiative 167 catalytic capital 151 Catalytic Capital Consortium 151–2 CDC (UK’s development finance institution (DFI)) 167 Central Outcome Funds 165–6 Chang, Alan 149 Chan, Priscilla 48–9, 151 Chan Zuckerberg Initiative (CZI) 48–9, 151 Charities Act (2016) 141 Charles, Prince 21–2, 191 Chiang, John 77 Children’s Investment Fund Foundation 132 Chobani 93, 98–101, 107 Clevedon Pier 173 Climate Action 100+ 76 climate change 16–17, 28, 45, 66, 73, 74–5, 76, 77, 81, 95, 104, 105, 106, 107, 139, 177, 191 Coca-Cola 90, 91, 110–11 Commission on Unclaimed Assets (2005–7) 19, 169 Community Interest Companies (CIC) 57 Cone Communications Millennial Employee Engagement Study (2016) 92 Conservative Party 160, 179 Consumer Goods Forum, Berlin (2017) 86–7, 97 contraceptive implants 150 coronary heart disease 113 corporate social responsibility (CSR) 92–3, 96 corporation, redefining purpose of 57, 87–8, 93 see also benefit corporation CRE Venture Capital 49 credit 43–4, 80, 83, 95, 144, 163 Crédit Agricole 95 crop production 45–6 CureVac 150 Daimler AG 112 Danone 57, 86, 87, 91, 93, 94–8, 107 David and Lucile Packard Foundation 144 DBL Partners 83–4 Delaney, John 179 Department for International Development (DFID) 137, 167, 168 Department for Work and Pensions (DWP) 164 Designated Utilization Foundation 170 development aid 134, 136, 166–8, 198 Development Finance Institutions (DFI) 166, 167, 198 DIBs (Development Impact Bonds) 8, 27, 68, 80, 124, 130–5, 137, 138, 153, 157, 163, 165, 167–8, 184, 198, 203, 204 Dimon, Jamie 87 dormant accounts 170, 171, 198, 205 Dormant Account Utilization Bill (2016), US 170 Dormant Assets Commission, UK 170 ‘double bottom line’ investment strategy 83–4 Dove 90, 91 Drayton, Bill 58 driverless cars 39, 40 drones 37–9, 149 Dutch SDG Investing Agenda 73 Dyson, Tasha 126 Eccles, Toby 20 Echoing Green 58–9 Edmond de Rothschild Foundation 153 Educate Girls Development Impact Bond 80, 132–4, 137 Education Commission 27, 134, 136 Education Outcomes Funds 26–7, 136, 148 Education Outcomes Fund for Africa and the Middle East (EOF) 136, 148 electric vehicles 55–6 ELMA 136 Elvis & Kresse 54–5 emerging markets 7, 13, 131, 167 Employee Retirement Income Security Act (ERISA) 157 Endeavor 59 endowment 9, 138–46, 149, 152, 153, 173, 187, 188, 202, 204 entrepreneurship 3, 4, 5, 9, 14, 15, 17, 31, 33, 83, 131, 146, 153, 154, 156, 158, 174, 175, 176, 177, 178, 183, 186, 191, 193, 194, 197 age of impact entrepreneurship 34–61, 188–9 government support for 60, 70, 174–5, 176, 177, 178 micro-entrepreneurs 83 social entrepreneurship 8, 19, 21, 82, 137, 144, 148., 176–7 tech entrepreneurship 5, 17, 55, 61, 138 environmental challenges 3, 4, 5, 8, 9, 12–13, 16, 17, 18, 27, 32, 181–2, 183, 191, 192, 193, 194, 197, 198, 199, 200, 201, 202, 203, 205 asset management and 83, 84 B Lab and 56–7 business and 88, 89, 90, 93, 97, 101, 102, 107, 108, 109–13, 114, 116 climate change 16–17, 28, 45, 66, 73, 74–5, 76, 77, 82, 95, 104, 105, 106, 107, 139, 177, 191 government and 154, 156, 159, 162, 163, 164, 166, 171, 172, 174, 176, 177–8, 179 green bonds 63, 173, 191, 199 impact philanthropy and 121, 131, 138, 139, 140, 141, 142, 143, 153 measuring impact on 28 pension funds and 72, 78 SDGs and see United Nations: Sustainable Development Goals SIBs and 68 Environmental Sustainable Governance (ESG) 63, 64, 70, 71, 72, 75, 76, 77, 78–9, 80, 81, 82, 83, 115, 145, 159, 187, 190, 198–9, 200, 203, 205 Esmée Fairbairn Foundation 23 European Bank for Reconstruction and Development 163 European Union 1, 6, 159–60, 171, 173, 176 Everplans 47 externalities 28 Exxon Mobil 111 Faber, Emmanuel 86–7, 94, 95, 96, 97 Facebook 14, 48, 83 family offices 71, 81, 146 Fawcett, Mark 74–5 FC United of Manchester 172–3 Ferrari, Sara 81 fiduciary duty 75, 145, 199 financial crisis (2008) 158–9, 185–6 financial markets, size of 71 Fink, Larry 33, 62, 82 Fink, Lord (Stanley) 19 Finland 124, 163 Fintech 42–5, 199 Flint, Michigan water crisis 54 FMO (Dutch development finance institution) 163 Fondece 176 Ford 111–13, 136, 140, 142–4, 153 Foundation 140, 142–4, 153 France 1, 6, 22, 58, 63, 75–6, 86, 88, 95, 124, 158, 159, 160, 161, 162–3, 173–4, 175–6, 177, 184, 187, 204–5 Francis, Pope 6 Freedom Bakery, Glasgow 172 Friedman, Milton 185 FTSE Blossom Japan Index 78 Fusion Housing SIB, The 126–7 Gates, Bill 55, 149–50 Gaudio, Paul 103 G8 Social Impact Investment Taskforce (G8T) v, 6, 7, 148, 160, 235 gender bond 191 generally accepted accounting principles (GAAP) 110, 115, 117 generally accepted impact principles (GAIP) 110, 114 General Mills 113 General Motors 112 Generation Investment Management 49–50, 82–3 Georgieva, Kristalina 68 Ghislane 37 Giddens, Michele 18 Gilbert, Jay Coen 108 Glencore 76 Global Family Office Report (2017) 81 Global Impact Investing Network (GIIN) 108 Global Impact Investing Rating System (GIIRS) 108 globalization 13, 16 Global Reporting Initiative (GRI): Sustainability Reporting Standards 108 Global Steering Group for Impact Investment (GSG) v, 7, 27, 29, 109, 136, 148, 158, 171, 206 Global Value Exchange 31, 162 Goldberg, Randy 54 Goldman Sachs 80, 127 Goldstein, John 80 Google 14, 49 Gore, Al 28, 49, 83 government 3–4, 30–1, 70, 154–80, 189–90 how impact investment can help governments do their job 155–8 local government 26, 125–6 measuring and valuing impact of 30–1 nine things for governments to do 158–77 Government Pension Investment Fund (GPIF), Japan 78 Grameen Danone Foods Social Business Enterprise 94–5 Great Depression vii, 185 green bond 63, 173, 191, 199 G7 166 gut microbiome, human 45 Guy’s and St Thomas’ Charity 142 GV (formerly Google Ventures) 49 Haas School of Business, University of California, Berkeley 51 Harrison, Peter 64 Harvard Business School 2, 25, 26, 29, 92–3, 109 Hawkes, Richard 191 Hayes, Lisa 40 Heath, David 54 hedge funds 13 Heron Foundation 139, 145 Hewlett Foundation 137, 153 high-net-worth individuals (HNWIs) 70, 71, 81, 145, 200 high-sustainability investments 74 Hines, Luke 39–40 Hohn, Sir Christopher 66 homelessness 30, 31, 54, 124, 125, 126, 127, 166, 170 Horn, Bernard 20 Houlahan, Bart 108 HSBC 75, 169 Hulme, Philip 19 Husain, Safeena 132 Hutchison, David 20 IBM 47, 189 IKEA 93, 103, 104–6, 107 impact capitalism 4, 180, 181–94, 200 impact economies 68, 85, 155, 157, 159, 161, 178, 180, 184, 186, 192, 200 impact investment birth/origins of 17–28 business, embedding in 86–117 defined 11–13 entrepreneurs, rising generation of 34–61 future of 9–10, 32–3, 186–94 government, role of see government impact capitalism, move from selfish capitalism to 181–94 ‘impact investing’ term coined 11 market growth 64 measuring/valuing 10, 13, 14, 28–31, 64, 67–8, 69, 107–17, 119, 120–1, 159, 183, 186, 190–1, 201 philanthropy and 9, 70, 118–53, 187–8 risk and see risk specialist impact investing firms 82–3 wholesalers 168–72, 184, 201 ‘Impact Investment: The Invisible Heart of Markets’ report (2014) 6 Impact Management Project (IMP) v, 29, 109 impact philanthropy 9, 70, 118–53, 187–8 Impact Revolution v, 1, 5, 8, 9, 10, 14, 28, 32, 33, 85, 152, 180, 182, 186, 188, 189, 235 impact unicorn 35, 188–9 impact ventures/impact enterprises 34–61, 63, 66 impact washing 30, 64 Impact-Weighted Accounts Initiative (IWAI) v, 29–30, 109–17, 201 impact-weighted financial accounts v, 29–30, 67, 107–17, 159, 183, 186, 190–1, 201 Impresa Sociale 177 Imprint Capital 80 incubator (collaborative program designed to help new start-ups grow their business) 175, 176, 202 India 7, 27, 43, 47, 53, 56, 80, 84, 94, 124, 131–4, 153 Indigo Agriculture 45–6 inequality vii, 1, 3, 14, 15–16, 58, 88, 116, 140, 154, 155, 177, 179, 181, 182, 186, 192–3, 194, 200 Ingka Group 104 inheritance, generational 65, 81 Instiglio 132 institutional investors 63, 76, 81, 85, 95, 125, 174, 202 insurance companies 69, 71, 73, 157, 170, 202 Intarcia Therapeutics 150–1 Intel 40 intermediary 22, 202 International Committee of the Red Cross Program for Humanitarian Impact Investment (PHII) 133 International Finance Corporation (IFC) 64, 68–9 ‘Operating Principles for Impact Management’ 68–9 ‘The Promise of Impact’ report 69 Investing with Impact Platform 81–2 Invest Palestine 163 ‘invisible hand’ of markets 10, 97, 184, 185 invisible heart of markets 6, 10, 185 iPhone 77 Ireland 171 Israel 7, 39, 40, 50, 52, 53, 83, 124, 152, 158, 163, 177 Italy 6, 11, 58, 133, 158, 159, 171, 173, 177 Jana Partners 77 Japan 6, 63, 78–9, 124, 144, 158, 159, 170, 171 Japan National Advisory Board 171 Jewish Vocational Services (JVS) 128–9 Jobs, Steve 55 Joby Aviation 149 Johnson, Jeremy 48 Jonathan Rose 143 JP Morgan 87, 169 J.W.

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos

Albert Einstein, Amazon Mechanical Turk, Arthur Eddington, backpropagation, basic income, Bayesian statistics, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Charles Babbage, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, creative destruction, crowdsourcing, Danny Hillis, data is not the new oil, data is the new oil, data science, deep learning, DeepMind, double helix, Douglas Hofstadter, driverless car, Erik Brynjolfsson, experimental subject, Filter Bubble, future of work, Geoffrey Hinton, global village, Google Glasses, Gödel, Escher, Bach, Hans Moravec, incognito mode, information retrieval, Jeff Hawkins, job automation, John Markoff, John Snow's cholera map, John von Neumann, Joseph Schumpeter, Kevin Kelly, large language model, lone genius, machine translation, mandelbrot fractal, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, Narrative Science, Nate Silver, natural language processing, Netflix Prize, Network effects, Nick Bostrom, NP-complete, off grid, P = NP, PageRank, pattern recognition, phenotype, planetary scale, power law, pre–internet, random walk, Ray Kurzweil, recommendation engine, Richard Feynman, scientific worldview, Second Machine Age, self-driving car, Silicon Valley, social intelligence, speech recognition, Stanford marshmallow experiment, statistical model, Stephen Hawking, Steven Levy, Steven Pinker, superintelligent machines, the long tail, the scientific method, The Signal and the Noise by Nate Silver, theory of mind, Thomas Bayes, transaction costs, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, white flight, yottabyte, zero-sum game

Scientists everywhere use linear regression because that’s what they know, but more often than not the phenomena they study are nonlinear, and a multilayer perceptron can model them. Linear models are blind to phase transitions; neural networks soak them up like a sponge. Another notable early success of neural networks was learning to drive a car. Driverless cars first broke into the public consciousness with the DARPA Grand Challenges in 2004 and 2005, but a over a decade earlier, researchers at Carnegie Mellon had already successfully trained a multilayer perceptron to drive a car by detecting the road in video images and appropriately turning the steering wheel.

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Most importantly, inference includes making the best decisions, guided not just by the probabilities of different outcomes but also by the corresponding costs (or utilities, to use the technical term). The cost of ignoring an e-mail from your boss asking you to do something by tomorrow is much greater than the cost of seeing a piece of spam, so often it’s better to let an e-mail through even if it does seem fairly likely to be spam. Driverless cars and other robots are a prime example of probabilistic inference in action. As the car drives around, it simultaneously builds up a map of the territory and figures out its location on it with increasing certainty. According to a recent study, London taxi drivers grow a larger posterior hippocampus, a brain region involved in memory and map making, as they learn the layout of the city.

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Like Bayesian networks, Markov networks can be represented by graphs, but they have undirected arcs instead of arrows. Two variables are connected, meaning they depend directly on each other, if they appear together in some feature, like Ballad and By a hip-hop artist in Ballad by a hip-hop artist. Markov networks are a staple in many areas, such as computer vision. For instance, a driverless car needs to segment each image it sees into road, sky, and countryside. One option is to label each pixel as one of the three according to its color, but this is not nearly good enough. Images are very noisy and variable, and the car will hallucinate rocks strewn all over the roadway and patches of road in the sky.

The Rise and Fall of American Growth: The U.S. Standard of Living Since the Civil War (The Princeton Economic History of the Western World) by Robert J. Gordon

3D printing, Affordable Care Act / Obamacare, airline deregulation, airport security, Apple II, barriers to entry, big-box store, blue-collar work, business cycle, Capital in the Twenty-First Century by Thomas Piketty, carbon tax, Charles Lindbergh, classic study, clean water, collective bargaining, computer age, cotton gin, creative destruction, deindustrialization, Detroit bankruptcy, discovery of penicillin, Donner party, Downton Abbey, driverless car, Edward Glaeser, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, feminist movement, financial innovation, food desert, Ford Model T, full employment, general purpose technology, George Akerlof, germ theory of disease, glass ceiling, Glass-Steagall Act, Golden age of television, government statistician, Great Leap Forward, high net worth, housing crisis, Ida Tarbell, immigration reform, impulse control, income inequality, income per capita, indoor plumbing, industrial robot, inflight wifi, interchangeable parts, invention of agriculture, invention of air conditioning, invention of the sewing machine, invention of the telegraph, invention of the telephone, inventory management, James Watt: steam engine, Jeff Bezos, jitney, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, labor-force participation, Les Trente Glorieuses, Lewis Mumford, Loma Prieta earthquake, Louis Daguerre, Louis Pasteur, low skilled workers, manufacturing employment, Mark Zuckerberg, market fragmentation, Mason jar, mass immigration, mass incarceration, McMansion, Menlo Park, minimum wage unemployment, mortgage debt, mortgage tax deduction, new economy, Norbert Wiener, obamacare, occupational segregation, oil shale / tar sands, oil shock, payday loans, Peter Thiel, Phillips curve, pink-collar, pneumatic tube, Productivity paradox, Ralph Nader, Ralph Waldo Emerson, refrigerator car, rent control, restrictive zoning, revenue passenger mile, Robert Solow, Robert X Cringely, Ronald Coase, school choice, Second Machine Age, secular stagnation, Skype, Southern State Parkway, stem cell, Steve Jobs, Steve Wozniak, Steven Pinker, streetcar suburb, The Market for Lemons, The Rise and Fall of American Growth, Thomas Malthus, total factor productivity, transaction costs, transcontinental railway, traveling salesman, Triangle Shirtwaist Factory, undersea cable, Unsafe at Any Speed, Upton Sinclair, upwardly mobile, urban decay, urban planning, urban sprawl, vertical integration, warehouse robotics, washing machines reduced drudgery, Washington Consensus, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, working poor, working-age population, Works Progress Administration, yellow journalism, yield management

As shown in figure 17–2, there has been no response at all of TFP growth to the 2007 introduction of the smartphone or the 2010 introduction of the smart tablet.62 Driverless Cars. This category of future progress is demoted to last place because it offers benefits that are minor compared to the invention of the car itself or the improvements in safety that have created a tenfold improvement in fatalities per vehicle mile since 1950. The most important distinction is between cars and trucks. People are in cars to go from A to B, much of it for essential aspects of life such as commuting or shopping. Thus the people must be inside the driverless car to achieve their objective of getting from point A to point B.

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Instead of listening to the current panoply of options, including Bluetooth phone calls, radio news, or Internet-provided music, drivers will be able to look at a computer screen or their smartphones, read a book, or keep up with their e-mail. The use of driverless cars is predicted to reduce the incidence of automobile accidents, continuing the steady decline in automobile accidents and fatalities that has already occurred. Driverless car technology may also help to foster a shift from nearly universal car ownership to widespread car sharing in cities and perhaps suburbs, leading to reductions in gasoline consumption, air pollution, and the amount of land devoted to parking, all of which should have positive effects on quality of life if not on productivity growth.

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Driverless delivery trucks will not save labor unless work is reorganized so that unloading and placement of goods from the driverless trucks is taken over by workers at the destination location. The enthusiasm of techno-optimists for driverless cars leaves numerous issues unanswered. As pointed out by Autor, the experimental Google car “does not drive on roads” but rather proceeds by comparing data from its sensors with “painstakingly hand-curated maps.” Any deviation of the actual environment from the pre-processed maps, such as a road detour or a crossing guard in place of the expected traffic signal, causes the driving software to blank out and requires instant resumption of control by the human driver.63 At present, tests of driverless cars are being carried out on multilane highways, but test models so far are unable to judge when it is safe to pass on a two-lane road or to navigate winding rural roads in the dark.

The 100-Year Life: Living and Working in an Age of Longevity by Lynda Gratton, Andrew Scott

"World Economic Forum" Davos, 3D printing, Airbnb, asset light, assortative mating, behavioural economics, carbon footprint, carbon tax, classic study, Clayton Christensen, collapse of Lehman Brothers, creative destruction, crowdsourcing, deep learning, delayed gratification, disruptive innovation, diversification, Downton Abbey, driverless car, Erik Brynjolfsson, falling living standards, financial engineering, financial independence, first square of the chessboard, first square of the chessboard / second half of the chessboard, future of work, gender pay gap, gig economy, Google Glasses, indoor plumbing, information retrieval, intangible asset, Isaac Newton, job satisfaction, longitudinal study, low skilled workers, Lyft, Nelson Mandela, Network effects, New Economic Geography, old age dependency ratio, pattern recognition, pension reform, Peter Thiel, Ray Kurzweil, Richard Florida, Richard Thaler, risk free rate, Second Machine Age, sharing economy, Sheryl Sandberg, side project, Silicon Valley, smart cities, Stanford marshmallow experiment, Stephen Hawking, Steve Jobs, tacit knowledge, The Future of Employment, uber lyft, warehouse robotics, women in the workforce, young professional

The increase in processing power going from the 32nd chess square to the 35th is worth four times the cumulative sum of processing power across the first 32 squares. In other words, if Moore’s Law continues to function (discussed further below), then over the next eight years computational power will increase fourfold over and above the level of technology embedded, for example, in driverless cars. The hollowing out of work What will be the impact of this extraordinary trajectory? Discussions of robots and AI inevitably have a science fiction flavour to them and seem rapidly to veer into Terminator-like scenarios or lead to Bladerunner-style metaphysical considerations of the very nature of consciousness.

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So far technology has replaced simple routine tasks, and limits to computational power have restricted the loss of jobs. Driving a car is a routine task – it is just that the list of instructions is very long and complicated. With the innovations associated with the dramatic increase in low-cost computational power, it is now possible to develop driverless cars. When this happens it will threaten a significant number of jobs in the logistics industry. Diagnosing medical conditions is another routine task which has required knowledge and pattern recognition skills that have to date proved beyond computers. However, yet again, the implications of the second half of the chessboard is that this is no longer the case.

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So rather than worrying that robots are going to take our jobs, we should be delighted that they are arriving just in time to boost a flagging working population and maintain output, productivity and living standards. Implementation challenges There is also a view that while frontier technology is likely to advance rapidly, there will be substantial lags in its implementation. Driverless cars, for instance, face a significant number of regulatory and legal hurdles before they become an everyday reality. It is likely that there will come a day, almost certainly in Jane’s lifetime, when cars are driverless, but it will be a while before it happens. Some technologists also believe that, after fifty years, Moore’s Law is beginning to reach physical constraints and is no longer operable.

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

They seem to be taking a long time to arrive as a commercial proposition. Today, many new cars have computer assistants for parking, lane-keeping and collision avoidance. But that’s a rather limited version of driverless vehicles. Automated trucks have driven cross country, solo and in automated convoys. Driverless cars have even been used commercially by the car hire company Uber. Meanwhile, driverless cars are racking up millions of research miles on the roads and soaking up millions of dollars in investment from car and tech giants alike. Much of this driving, however, has been on broad highways in good weather. Navigating the twisting, cluttered roads of a European city in a downpour is another matter altogether.

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Navigating the twisting, cluttered roads of a European city in a downpour is another matter altogether. But eventually that problem will be solved—better sensors and more powerful computers should do the trick. There’s an even bigger problem, one that’s really hard to code for: the unpredictability of other road users. In trials, driverless cars have found themselves being ‘bullied’ by human road users. These might in any case be aggressive, or risk-loving drivers, regardless of who is in control of the other vehicle. But machines are conservative drivers, by design. They don’t speed, and they respect braking distance. Above all, they don’t engage in a struggle of wills with other road users.

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Might such an illusion work for players in the RAND wargame, or even in the real-life strategy games that human-machine teams may soon play? There are certainly parallels between the Jaguar AI car and military strategy—with its dance of minds, and its element of coercion. But there is an important difference too. A military AI system isn’t trying to keep everyone safe, like the driverless car. Its priority is to get its way, at an acceptable cost, whatever that might be. Unlike the car, a warbot strategist might refuse to be intimidated by humans. Perhaps if you escalate against it (like the pedestrian stepping out), it will in turn respond by escalating back. Its automated response could be devastating and happen faster than the ability of its managers to intercede.

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

Covert tracking devices help law enforcement quickly recover stolen cargo. Smart cargo packages can ensure that valuable cargo arrives on time and undamaged. And, if that’s not enough, we can talk about tracking endangered animals, Tweeting plants, body sensors as small as temporary tattoos, connected contact lenses, driverless cars, and more. All these things are possible because of M2M technology, and all these examples represent the Internet of Things. As Glenn Lurie, president of AT&T Emerging Devices Organization (EDO), says, “Any device that is connected is smart. Any device that is not connected is dumb. In the future, everything’s going to be smart.”

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It will become irresponsible and antiquated for people to drive cars. 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.

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In addition, modern cars are filled with sensors: detecting light for the mirrors and headlights and rain for the windshield wipers, tire pressure monitors, accelerometers, gyroscopes, and compasses. Going further down this path, the digitalization of mechanics in cars allows for the arrival of driverless cars or self-driving cars, which Google has successfully tested over the past few years. But connectivity offers even more: communication between cars to optimize traffic flow and make better decisions on behalf of the driver. Volvo, for example, has successfully demonstrated road trains as part of the EU’s SARTRE (Safe Road Trains for the Environment) Project, which has several cars following one another in a platoon formation; the lead car has a professional driver taking responsibility for the platoon, while following vehicles operate in a semi-autonomous mode, reducing the distance between the vehicles, and reducing drag and fuel consumption, while getting to their destination faster.23 You may be familiar with the crowd-sourced navigation application Waze, which is one of the most accurate personal navigation applications today because it uses real-time traffic and construction information provided by users.

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

In particular, it requires vast shifts in subsidies to green and affordable travel and planning decisions that prohibit all new car-based activity. How we choose to get around the city is connected to a set of choices about the very future of the city itself. Driverless cars may yield some marginal emissions gains. But cities full of Google and Tesla driverless cars will not stop the descent into alienated street life, status anxiety and debt-fuelled, corporate-controlled consumerism. The driverless-car city is the next step in the great car take-over of the urban world. Putting ourselves front and centre of the car-free city is one of the most crucial but difficult tasks ahead.

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

This is often expressed through postponement, rejection, or opposition to new products or ideas.28 There are many manifestations of such fear, but economic ones are among the most enduring. For example, in 2015 California recently proposed a law—Senate Bill 1298—that would require driverless cars to have a licensed driver present and capable of taking over if needed.29 This necessitates the presence of a steering wheel—a major drawback for Google’s cars that “famously do not include a steering wheel or pedals for human operation.”30 The bill was introduced despite the fact that driverless cars demonstrated the potential for a greater safety record than human-driven cars. The bill seemed to have been inspired by economic rather than safety concerns.

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A similar scenario is feasible with markets without incumbent automobile industries and infrastructure leapfrogging into the driverless age for a diversity of land, air, and water vehicles. Crewless boats, for example, could become new modes of transportation in remote waterways around the world. In both cases, the intent was to stall the adoption of the new technology, mostly due to its economic ramifications. The safety record of driverless cars will likely wreck the car insurance industry. The technology also poses new challenges for policing due to uncertainties over the locus of liability. Even though the basic concerns regarding radical innovations are economic, additional fuel is provided by intellectual and sociopsychological factors.

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See also Creative destruction Distrust communication and, 312 of gas industry, 147–148 of ice industry, 185 of institutions, 5, 8 Doctrines, established, vs. technological innovation, 26 Domestic fat laws, 114 Draft animals, economic system surrounding, 128–129 Dressed meat, shipping of, 189 Driverless cars, 13, 295, 366n29 Drones, 32 Drug delivery, technological innovation in, 300 Drunkenness, coffee drinking and, 51 DuPont, 235 Durston, Mr., 163 Dutch East India Company, 53 Dynamic social systems, nature of, 22 E. coli, 255 Earth Summit (1992), 239 East India Ice Company, 176 Eastman Chemical Company, 353n29 Eastman Kodak, 353n29 Ecogen, 232–233 Economic crisis (1873), 101 Economic renewal, 280 Economic Research Service (USDA), 140 Economics economic equilibrium, 17 economic evolution, 1, 27 economic impact studies, 37–38, 39–40 economic interests, resistance to commercialization of transgenic salmon and, 271–272 of fish farming, 260 innovation as economic transformation, 23 role in Germany’s response to coffee, 61 Economy agriculture’s contribution to GDP, 140 dependence on technological assemblies, 23 farm mechanization’s impact on GDP, 339n48 impact of differing technologies on, 142–143 inclusive economies, 284 Schumpeter on, 27 as self-organizing systems, 28 Ecosystem degradation, 257 Edison, Thomas AC, actions against, 168–169, 170–171, 297–298 on alternating current, 144, 164–165 DC adoption, attempted delays to, 144 electrical distribution system, 150–152 gas industry, emulation of, 169–170 incandescent light bulbs, improvements to, 144, 149–150 Kemmler case and, 164 new technology, stigmatization of, 169 NY State Death Commission and, 161 phonograph, creation of, 207 social acceptance, sensitivity to, 168 as technological pioneer, 145, 148 Westinghouse competition with, 144, 146, 152–158, 167 Edison Illuminating Companies, 157–158 Education by HAA, 132 importance for open societies, 290 improved support for innovation and, 307–315 role in adoption of new technologies, 23–24, 93 role in technological controversies, 119 by USDA, on cold storage, 195–196 Efendi, Sai’d, 84 Eggs, preservation of, 186 Egypt Arab Spring in, 91 Cairo, coffee in, 50, 53, 54 printed Korans in, 88 transgenic technology, opposition to, 241 Electrical engineers, 30, 170 Electric chair, 158–159, 162 Electricity, 144–173.

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

That’s the subtext to the by-now perennial headline declaiming this is the year for virtual reality. 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.

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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. The real drama is the one nobody wrote about: the absence of questions. It is hard to see how the blind would use driverless cars if, as most believe, safety requires that the driver remain capable of last-minute intervention. It remains unclear why, if drivers already on the road are joined by those who currently can’t drive, the streets would be clearer. More cars in use sits oddly with the notion of needing fewer parking spaces, while promises of 100 per cent safety left thoughtful consumers wondering if there had ever, in history, been a 100 per cent safe product.

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We call it salesmanship because there are products and money attached – automobiles are the largest manufacturing business in the world – but if the technology were correctly seen as a campaign we’d recognise it as propaganda: we are being recruited into an army of believers. Quashing pragmatic questions is not the only consequence of such grandstanding. It also discourages policy questions about the impact of AVs on the labour market, on civil liberties and on public transportation.18 In a perfect world, where everyone has easy access to a driverless car, there’s apparently no need to invest in infrastructure or to address the geographic segregation that plagues so many cities. The more we believe, the less we question, the more probable the forecast becomes. A simplistic commercial view of the future is being forced onto a world as though there are no alternative possibilities, when in fact there are many.

GDP: A Brief but Affectionate History by Diane Coyle

Alan Greenspan, Asian financial crisis, Berlin Wall, big-box store, Bletchley Park, Bretton Woods, BRICs, business cycle, clean water, computer age, conceptual framework, crowdsourcing, Diane Coyle, double entry bookkeeping, driverless car, en.wikipedia.org, endogenous growth, Erik Brynjolfsson, Fall of the Berlin Wall, falling living standards, financial intermediation, global supply chain, happiness index / gross national happiness, hedonic treadmill, income inequality, income per capita, informal economy, Johannes Kepler, John von Neumann, Kevin Kelly, Les Trente Glorieuses, Long Term Capital Management, Mahbub ul Haq, mutually assured destruction, Nathan Meyer Rothschild: antibiotics, new economy, Occupy movement, Phillips curve, purchasing power parity, Robert Shiller, Robert Solow, Ronald Reagan, shareholder value, Silicon Valley, Simon Kuznets, The Wealth of Nations by Adam Smith, Thorstein Veblen, University of East Anglia, working-age population

It also fails to record at all another increasingly important category, namely, preventive goods or services. Take driverless cars for example. One of these will increase GDP by the same amount as any other kind of car, or perhaps by more if statisticians calculate a hedonic price index to adjust for its improved quality—after all, the human can sit back and relax in a driverless car. But GDP will not capture at all the benefit of a reduction in the number of accidents as driverless cars spread, assuming they live up to the high hopes that this will be the result. In chapter 5, I noted the distinction between GDP and welfare and yet the strong link between the two.

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

In the smart kitchen, the toaster will remind you that today is a no-carbs day. The smart toilet will assess the contents of your initial evacuation. (I am not making this up.) * * * And what about cars? Driverless cars are a lovely idea – we can go to the pub, act like we’ve got our own chauffeur, work in the back seat. There’s no cab driver giving us his world-view of American politics, just peace and quiet, and our own private pod. But what happens when you realise that this lovely private pod isn’t private at all? Driverless cars track your movements. Random becomes predictable becomes dossier. The pods can be redirected. Police station? Your scary ex’s home? Enforced trip to hospital as your implant registers your blood-pressure risk?

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Not the next 250 years but the next 25 years will take us into a world where intelligent machines and non-embodied AI are as much a part of everyday life as humans are. Many of the separate strings we are developing now – the Internet of Things, blockchain, genomics, 3D printing, VR, smart homes, smart fabrics, smart implants, driverless cars, voice-activated AI assistants – will work together. Google calls it ambient computing: it’s all around you. It’s inside you. This future isn’t about tools or operating systems; the future is about co-operating systems. The technology is moving fast. The Data Age will be the biggest change yet seen on Planet Earth – bigger than the first Industrial Revolution – when we came blinking out of a centuries-old rural economy, and wide-awake into the daily nightmare of the industrial economy.

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Your scary ex’s home? Enforced trip to hospital as your implant registers your blood-pressure risk? Hijacked? Kidnapped? Autonomous isn’t anonymous. And driverless cars will have to be programmed for tricky situations. Try this game: an unavoidable crash scenario means you are going to run down a little kid or an old lady – which one? 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.

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

Their vehicles also collected huge volumes of real-time data using video, radar, and LIDAR (light detection and ranging) gear mounted on the car; these data were fed into software that takes into account the rules of the road, the presence, trajectory, and likely identity of all objects in the vicinity, driving conditions, and so on. This software controls the car and probably provides better awareness, vigilance, and reaction times than any human driver could. The Google vehicles’ only accident came when the driverless car was rear-ended by a car driven by a human driver as it stopped at a traffic light. None of this is easy. But in a world of plentiful accurate data, powerful sensors, and massive storage capacity and processing power, it is possible. This is the world we live in now. It’s one where computers improve so quickly that their capabilities pass from the realm of science fiction into the everyday world not over the course of a human lifetime, or even within the span of a professional’s career, but instead in just a few years.

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After the chat session ended, both customers and employees were asked whether the automatically translated messages were useful—whether they were clear enough to allow the people to take meaningful action. Approximately 90% reported that they were. In this case, automatic translation was good enough for business purposes. The Google driverless car shows how far and how fast digital pattern recognition abilities have advanced recently. Lionbridge’s GeoFluent shows how much progress has been made in computers’ ability to engage in complex communication. Another technology developed at IBM’s Watson labs, this one actually named Watson, shows how powerful it can be to combine these two abilities and how far the computers have advanced recently into territory thought to be uniquely human.

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.

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., 59–60 Darago, Vincent, 150, 152 Davis, California, 67–76, 146–148 Davis Tweed Run, 76 De Beauvoir Association, 200–201 De Pijp area (Amsterdam), 198 De Silva, Harry Reginald, 52 deferential paralysis, 15 Delft redesign, 137 Delta Works, 183–184 Denington, Evelyn, 96, 170 Department of Transportation (DOT), 136–139, 147, 148–149, 153 Design & Layout of Roads in Built-up Areas report, 79–80 Dobbins, Horace Murrell, 22 Dower, Michael, 106 Dowlin, John, 133 Drees, William, 192 driverless cars, 13–15 Driving Under the Influence, 54 Dubinsky, David, 132 Dutch Cycling Embassy, 209–210 Dutch Cyclists’ Union, 192 Dutch intersections, 71–72 Dutch system, 179–180. See also Netherlands Earth Day (1970), 112, 113 Ebert, Anne-Katrin, 186 Eckhardt, Robert C., 65 Effective Cycling (Forester), 146, 151, 151–152 Eisenhower, Dwight D., 54, 55–56, xv Ellen Fletcher Boulevard, 146 energy efficiency, 94–96 Environmental Protection Agency (EPA), 127–128, 138 environmentalism, 9–10, 89, 92–95, 111–116, 130–131 Epperson, Bruce, 72, 152, 156 Falbo, Nick, 71–72 Far to the Right law (FTR), 27, 153 Farnsworth, Dana L., 56 Fascell, Dante B., 55 Federal-Aid Highway Acts, 54, 111 Fee, Julie Anna, 137 Feilden, Richard, 96, 101 Ferguson, George, 108 Fichter, George, 57 filtered permeability, 146 Finney, Albert, 83 Fischman, Barry, 131 Fisher, Gary, 148 Flanigan, Peter, 109 Fletcher, Ellen, 146 “FoE Cycleways Campaign,” 100 Ford Maverick burial, 112–113 Forester, John, 143, 145–148, 147, 151–152, 155–160 France, 32 Frankel, Godfrey and Lillian, 17 freeways, 85, 181 Friends of Bikeology, 10, 120–122 Friends of the Earth (FoE), 9–10, 92, 94, 97–100 Fulbright, William, 62 Gaffney, Mary, 229–230 Gelinck, W.

Infinite Detail by Tim Maughan

3D printing, augmented reality, bitcoin, Black Lives Matter, Buckminster Fuller, Burning Man, cognitive dissonance, driverless car, fake news, Free Software Foundation, friendly fire, gentrification, global supply chain, hydroponic farming, Internet of things, Mason jar, messenger bag, off grid, Panamax, post-Panamax, ransomware, RFID, rolling blackouts, security theater, self-driving car, Skype, smart cities, South China Sea, surveillance capitalism, the built environment, urban decay, urban planning

Then they’re off the ramp, and Neal finally seems to cut his speed for the first time since they left Wales, easing them around pillars and parked cars—most of them looking to Anika like they’ve been here awhile, abandoned. Cars and vans much newer than the antique Ford they’re sitting in now: driverless cars, fuel-cell-powered, digitally controlled. Chinese, Korean, Brazilian designs. All useless now. Some of them have been stripped for parts, some have been burned out, others converted into what looks like living spaces; she catches the glimpse of curtain fabric in windows, notices how some of the cars have been moved to create walls, defenses.

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For a second Rush thinks his legs will give way beneath him. Scott unhooks his legs, playfully pushes him away. “C’mon, then. We can finish this later. Let’s get you to your protest, boyfriend.” * * * They move quickly and purposefully through the city, thousands strong, shutting down traffic as they flow around it. Streets full of driverless cars are paralyzed, unable to react to this many human bodies flooding their space. The few remaining yellow cabs, artifacts from a dying age, honk in support, their human drivers reaching out of wound-down windows to high-five protestors as they pass. At Rush’s insistence both he and Scott have got their scarves and hoods up to try to mask their faces from the police drones that float constantly above their heads.

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Jobs came and went, and then just stopped coming altogether. Even in the few short months he’d been in prison so much had changed. Shops were closing down, retail jobs disappearing. He spent a couple of weeks standing in the rain in lime-green-and-white waterproof overalls outside the service station at Tesco, plugging recharging cables into driverless cars. Then one day he came into work late to find a robot, some egg-shaped thing with a single stupid fucking arm, doing it for him. It bleeped angrily and told him it was calling the cops when he kicked it with his split, leaking, limited-edition trainers. Instead he found himself hanging around the still-standing towers, putting a new crew together, building up his rep again.

A Hacker's Mind: How the Powerful Bend Society's Rules, and How to Bend Them Back by Bruce Schneier

4chan, Airbnb, airport security, algorithmic trading, Alignment Problem, AlphaGo, Automated Insights, banking crisis, Big Tech, bitcoin, blockchain, Boeing 737 MAX, Brian Krebs, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, computerized trading, coronavirus, corporate personhood, COVID-19, cryptocurrency, dark pattern, deepfake, defense in depth, disinformation, Donald Trump, Double Irish / Dutch Sandwich, driverless car, Edward Thorp, Elon Musk, fake news, financial innovation, Financial Instability Hypothesis, first-past-the-post, Flash crash, full employment, gig economy, global pandemic, Goodhart's law, GPT-3, Greensill Capital, high net worth, Hyman Minsky, income inequality, independent contractor, index fund, information security, intangible asset, Internet of things, Isaac Newton, Jeff Bezos, job automation, late capitalism, lockdown, Lyft, Mark Zuckerberg, money market fund, moral hazard, move fast and break things, Nate Silver, offshore financial centre, OpenAI, payday loans, Peter Thiel, precautionary principle, Ralph Nader, recommendation engine, ride hailing / ride sharing, self-driving car, sentiment analysis, Skype, smart cities, SoftBank, supply chain finance, supply-chain attack, surveillance capitalism, systems thinking, TaskRabbit, technological determinism, TED Talk, The Wealth of Nations by Adam Smith, theory of mind, TikTok, too big to fail, Turing test, Uber and Lyft, uber lyft, ubercab, UNCLOS, union organizing, web application, WeWork, When a measure becomes a target, WikiLeaks, zero day

The company hasn’t had a single profitable year. In 2019, the company lost $8.5 billion worldwide, or 58 cents per dollar on each of its 5.2 billion rides. The only reason Uber exists at all is that there are still investors willing to pour capital into this sinkhole, probably waiting for the time when driverless car technology allows the company to fire all of its drivers and operate a fully autonomous fleet. WeWork has also never turned a profit, losing over $10 billion in the past three years. That particular VC-fueled bubble burst when the company tried and failed to go public in 2019. The COVID-19 work-from-home rules further hurt WeWork’s chances of success, and the company’s co-founder was ousted as CEO and chairman of the board.

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In addition to decision-making, the relevant qualities of the AI systems I’ll be discussing are autonomy (the ability to act independently), automation (the ability to act on preset responses to specific triggers), and physical agency (the ability to alter the physical environment). A thermostat has limited automation and physical agency, and no autonomy. A system that predicts criminal recidivism has no physical agency; it just makes recommendations to a judge. A driverless car has some of all three. R2-D2 has a lot of all three, although for some reason its designers left out English speech synthesis. Def: Robot /bät/ (noun) - 1. Physically embodied objects that can sense, think, and act on their environments through physical motion. Robotics also has developed a popular mythology and a less-flashy reality.

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AIs initially capture this understanding through the data with which they’re trained, and they continue to improve as they are used. Modern AIs are constantly evolving as they acquire new data and adjust their own internal workings accordingly. The constant flow of data continues to train the AI as it operates and adds to its expertise. This is why designers of driverless car systems brag about the number of road hours their creations have clocked. The development of AIs capable of hacking other systems gives rise to two different but related problems. First, an AI might be instructed to hack a system. Someone might feed an AI the world’s tax codes or the world’s financial regulations, in order to create a slew of profitable hacks.

The Retreat of Western Liberalism by Edward Luce

"World Economic Forum" Davos, 3D printing, affirmative action, Airbnb, Alan Greenspan, basic income, Berlin Wall, Bernie Sanders, Boris Johnson, Branko Milanovic, bread and circuses, Bretton Woods, Brexit referendum, business cycle, call centre, carried interest, centre right, Charles Lindbergh, cognitive dissonance, colonial exploitation, colonial rule, computer age, corporate raider, cuban missile crisis, currency manipulation / currency intervention, disinformation, Dissolution of the Soviet Union, Doha Development Round, Donald Trump, double entry bookkeeping, driverless car, Erik Brynjolfsson, European colonialism, everywhere but in the productivity statistics, Evgeny Morozov, fake news, Fall of the Berlin Wall, Francis Fukuyama: the end of history, future of work, gentrification, George Santayana, gig economy, Gini coefficient, global pandemic, global supply chain, Great Leap Forward, illegal immigration, imperial preference, income inequality, independent contractor, informal economy, Internet of things, Jaron Lanier, knowledge economy, lateral thinking, Les Trente Glorieuses, liberal capitalism, Marc Andreessen, Mark Zuckerberg, Martin Wolf, mass immigration, means of production, meritocracy, microaggression, Monroe Doctrine, moral panic, more computing power than Apollo, mutually assured destruction, new economy, New Urbanism, Norman Mailer, offshore financial centre, one-China policy, opioid epidemic / opioid crisis, Peace of Westphalia, Peter Thiel, plutocrats, precariat, purchasing power parity, reserve currency, reshoring, Richard Florida, Robert Gordon, Robert Solow, Ronald Reagan, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Silicon Valley billionaire, Skype, Snapchat, software is eating the world, South China Sea, Steve Bannon, Steve Jobs, superstar cities, telepresence, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas L Friedman, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, unpaid internship, Washington Consensus, We are the 99%, We wanted flying cars, instead we got 140 characters, white flight, World Values Survey, Yogi Berra

His book The Rise and Fall of American Growth makes a startling forecast that did not go down well in Silicon Valley. The future is not what it used to be, he says. The peak age of high growth and disruptive technology is behind us. Forget the power of the iPhone. Stop exulting about Google’s driverless car. Such wonders pale beside the changes felt by earlier generations. They are unlikely to be matched by our age. Gordon’s thesis is not entirely new. Tyler Cowen made a similar argument with his sparkling monograph The Great Stagnation (ironically first published as an ebook). Nor is it as counterintuitive as it sounds to our app-crowded, WiFi-saturated twenty-first-century brains.

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He pointed to the efflorescence of tech unicorns – private start-ups valued at more than $1 billion – that are working on virtual reality, artificial intelligence, gene-splicing medicine and the like. At least one will be as transformative as the iPhone, he said. In fact, the pessimists’ gloom makes ample room for the emergence of virtual headsets, driverless cars and robotic disruptions. The impact of new technologies will be significant but not game-changing, they say. Today’s medical progress is only adding to economic stagnation. Most recent advances are in the treatment of physical ailments, thus prolonging life for those who can afford them. There have been no comparable breakthroughs in curing mental illness, such as Alzheimer’s.

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Karl Marx predicted that capitalism would push the workers of the world to unite. He got it back to front. It is the elites who are loosening their allegiances and workers who are reaching for national flags. This is hardly a vision of social peace. ‘The rich will live in gated communities, and secure compounds, that are protected by drones and connected by driverless cars,’ predict Yascha Mounk and Lee Drutman, two of the sharpest political scientists around. ‘Ever smarter surveillance technologies will help to monitor the activities of the malcontents outside . . . ’70 Elites of the world unite! You have everything to lose. —— Every January, the Davos gathering sounds a little more bemused about what is happening in the world outside.

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

Bring It On: How Pittsburgh Became Uber’s Testing Ground,” New York Times, September 10, 2016, www.nytimes.com/2016/09/11/technology/no-driver-bring-it-on-how-pittsburgh-became-ubers-testing-ground.html. 36. Cecilia Kang, “Pittsburgh Welcomed Uber’s Driverless Car Experiment; Not Anymore,” New York Times, May 21, 2017, www.nytimes.com/2017/05/21/technology/pittsburgh-ubers-driverless-car-experiment.html. 37. Complaint for Permanent Injunction and Other Equitable Relief at 10–11, FTC v. Uber Techs., Inc., No. 17–261 (N.D. Cal. Jan. 19, 2017); Calo and Rosenblat, “The Taking Economy.” 38. The original blog post was located here: https://newsroom.uber.com/an-uber-impact-20000-jobs-created-on-the-uber-platformevery-month-2/.

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You can either put up red tape or roll out the red carpet. If you want to be a 21st-century laboratory for technology, you put out the carpet.”35 The municipal government of Pittsburgh later complained that Uber failed to keep its word to the city as part of their agreement, not following through on offering driverless-car rides for free or creating jobs as promised in a high-unemployment neighborhood.36 UBER’S DOUBLESPEAK: JOB CREATION WITHOUT WORKER RIGHTS Uber is a chameleon: it utilizes the art of doublespeak, which allows it to simultaneously maintain seemingly contradictory political stances. The company’s reputation as a darling of the technology world lost some of its shine by 2015, when the aggressiveness of its tactics was beginning to give many people pause.

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While drivers may have a viable legal claim to employee status under labor law, the culture of technology is pushing us past those considerations in more diffuse ways. Interrelated issues draw in different sets of actors, and Uber can break promises to one without necessarily hindering its relationship with another, because Uber represents more than a single actor. For instance, Uber may never create jobs in a high-unemployment neighborhood near its driverless car track in Pittsburgh, even though it promised to do so when it began operations there. Other regions, like New York State, may continue to welcome the company’s expansion as a sign of certain progression in the digital economy. Despite Uber’s mixed track record as a sometimes friend, sometimes foe, Governor Andrew Cuomo signed a bill to legalize ridehailing in the state on June 5, 2017.

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