Google X / Alphabet X

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pages: 302 words: 74,350

I Hate the Internet: A Novel by Jarett Kobek

Anne Wojcicki, Burning Man, disruptive innovation, East Village, Edward Snowden, Golden Gate Park, Google bus, Google Glasses, Google X / Alphabet X, immigration reform, indoor plumbing, informal economy, Jeff Bezos, liberation theology, Mark Zuckerberg, MITM: man-in-the-middle, Norman Mailer, nuclear winter, packet switching, PageRank, Peter Thiel, quantitative easing, Ray Kurzweil, rent control, Ronald Reagan, Silicon Valley, Steve Jobs, technological singularity, Triangle Shirtwaist Factory, union organizing, V2 rocket, Vernor Vinge, wage slave, Whole Earth Catalog

Sergey Brin, the other co-founder, was like Dionysius, the god of sex and drugs and revelry. Sergey Brin had rebranded himself as the head of Google X, Google’s nonsense experimental lab which developed faddish technologies like wearable computers and cars that could drive themselves and dogs that didn’t need to clean their genitals. These technologies would amount to nothing. They were banal visions of the future as imagined by the fans of Science Fiction. Google was an advertising company. Every time the company released a physical commodity, that commodity failed. Google X’s real purpose was an advertisement for the mythical vision of Google as a company of innovation. Google X was Google lying about the company’s actual function, using the methods of advertising to obfuscate its revenues derived from advertising. Google X was the pointless indulgence of one of the world’s richest men.

He was married to Anne Wojcicki. Over the summer, news had broken that Sergey Brin was having an affair with an underling at Google X. Google X was an experimental lab that developed products like driverless cars, dogs that don’t need to lick their own genitals, and Google Glass. Google Glass was a wearable computer built into a pair of ugly eyeglasses. Google Glass allowed its wearers to act out their social inadequacies. They could record videos with Google Glass and alienate everyone in their surrounding vicinity. Sergey Brin’s sexual dalliance was with the Marketing Manager for Google Glass. He had internalized his company’s business model. “I told you,” said Christine. “Google X is just picking up chicks.” Adeline decided to go home. Christine saw Adeline to the door. Adeline was in the hallway.


pages: 292 words: 85,151

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

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

Recommendation: Hire both internal and external Black Ops teams and have them establish startups with a combined goal of defeating one another and disrupting the mother ship. Copy Google[X] At a Singularity University event three years ago, Larry Page told Salim he’d heard good things about Brickhouse and asked whether Google should set up something similar. Salim’s recommendation was no; he believed it would only evoke the same immune system response he’d experienced at Yahoo. Page’s response was cryptic: “What would a Brickhouse for atoms look like?” he asked. We now know what he meant. In launching the Google[X] lab, Google has taken the classic skunkworks approach to new product development further than anyone ever imagined. Google[X] offers two fascinating new extensions to the traditional approach. First, it aims for moonshot-quality ideas (e.g., life extension, autonomous vehicles, Google Glass, smart contact lenses, Project Loon, etc.).

Step 2: Join or Create Relevant MTP Communities Step 3: Compose a Team Step 4: Breakthrough Idea Step 5: Build a Business Model Canvas Step 6: Find a Business Model Step 7: Build the MVP Step 8: Validate Marketing and Sales Step 9: Implement SCALE and IDEAS Step 10: Establish the Culture Step 11: Ask Key Questions Periodically Step 12: Building and Maintaining a Platform In Concert Lessons for Enterprise ExOs (EExOs) Chapter Seven: ExOs and Mid-Market Companies Example 1: TED Example 2: GitHub Example 3: Coyote Logistics Example 4: Studio Roosegaarde Retrofitting an ExO Example 5: GoPro Chapter Eight: ExOs for Large Organizations Transform Leadership Education Board Management Implement Diversity Skills and Leadership Partner with, Invest in or Acquire ExOs Disrupt[X]—set up Edge ExOs Inspire ExOs at the Edge Hire a Black Ops Team Copy Google[X] Partner with Accelerators, Incubators and Hackerspaces ExO Lite (The Gentle Cycle) Migrate towards an MTP Community & Crowd Algorithms Engagement Dashboards Experimentation Social Technologies Conclusion Chapter Nine: Big Companies Adapt The Coca-Cola Company – Exponential Pop Haier – Higher and Higher Xiaomi – Showing You and Me The Guardian – Guarding Journalism General Electric – General Excellence Amazon – Clearing the Rainforest of “No” Zappos – Zapping Boredom ING Direct Canada (now Tangerine) – BankING Autonomy Google Ventures – The Almost Perfect EExO Growing with the Crowd Chapter Ten: The Exponential Executive CEO – Chief Executive Officer CMO – Chief Marketing Officer CFO – Chief Financial Officer CTO/CIO – Chief Technology Officer/Chief Information Officer CDO – Chief Data Officer CIO – Chief Innovation Officer COO – Chief Operating Officer CLO - Chief Legal Officer CHRO - Chief Human Resources Officer The World’s Most Important Job Epilogue: A New Cambrian Explosion Afterword Appendix A: What is your Exponential Quotient?

Leading startups in this space are DeepMind, bought by Google in early 2014 for $500 million, back when DeepMind had just thirteen employees, and Vicarious, funded with investment from Elon Musk, Jeff Bezos and Mark Zuckerberg. Twitter, Baidu, Microsoft and Facebook are also heavily invested in this area. Deep Learning algorithms rely on discovery and self-indexing, and operate in much the same way that a baby learns first sounds, then words, then sentences and even languages. As an example: In June 2012, a team at Google X built a neural network of 16,000 computer processors with one billion connections. After allowing it to browse ten million randomly selected YouTube video thumbnails for three days, the network began to recognize cats, without actually knowing the concept of “cats.” Importantly, this was without any human intervention or input. In the two years since, the capabilities of Deep Learning have improved considerably.


pages: 389 words: 112,319

Think Like a Rocket Scientist by Ozan Varol

Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, Amazon Web Services, Andrew Wiles, Apple's 1984 Super Bowl advert, Arthur Eddington, autonomous vehicles, Ben Horowitz, Cal Newport, Clayton Christensen, cloud computing, Colonization of Mars, dark matter, delayed gratification, different worldview, discovery of DNA, double helix, Elon Musk, fear of failure, functional fixedness, Gary Taubes, George Santayana, Google Glasses, Google X / Alphabet X, Inbox Zero, index fund, Isaac Newton, James Dyson, Jeff Bezos, job satisfaction, Johannes Kepler, Kickstarter, knowledge worker, late fees, lateral thinking, lone genius, longitudinal study, Louis Pasteur, low earth orbit, Marc Andreessen, Mars Rover, meta analysis, meta-analysis, move fast and break things, move fast and break things, multiplanetary species, obamacare, Occam's razor, out of africa, Peter Thiel, Pluto: dwarf planet, Ralph Waldo Emerson, Richard Feynman, Richard Feynman: Challenger O-ring, Ronald Reagan, Sam Altman, Schrödinger's Cat, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, Simon Singh, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, Upton Sinclair, Vilfredo Pareto, We wanted flying cars, instead we got 140 characters, Whole Earth Catalog, women in the workforce, Yogi Berra

Land-based cell towers often have limited ranges and don’t make economic sense for many rural, sparsely populated areas—even in developed countries like New Zealand. Challenging geography, like mountains and jungles, can also prevent cell tower signals from reaching their destinations. Nimmo was the first test subject for an audacious project intended to lift the internet blackout that covers much of the world. The project is the brainchild of X, formerly known as Google X. The notoriously secretive company is dedicated to researching and developing breakthrough technologies. X doesn’t innovate for Google. X creates the next Google. To solve the internet access problem, Xers (as they’re called) came up with a loony thought experiment: What if we used balloons? They imagined balloons the size of tennis courts, shaped like giant jellyfish, hovering in the stratosphere at around sixty thousand feet—above the weather and air traffic.

c_id=137&objectid=10890750; “Google Tests Out Internet-Beaming Balloons in Skies Over New Zealand,” (San Francisco) SFist, June 16, 2013, http://sfist.com/2013/06/16/google_tests_out_internet-beaming_b.php; Derek Thompson, “Google X and the Science of Radical Creativity,” Atlantic, November 2017, www.theatlantic.com/magazine/archive/2017/11/x-google-moonshot-factory/540648/; Loon.com, “Loon: The Technology,” video, YouTube, uploaded June 14, 2013, www.youtube.com/watch?v=mcw6j-QWGMo&feature=youtu.be; Alex Davies, “Inside X, the Moonshot Factory Racing to Build the Next Google,” Wired, July 11, 2018, www.wired.com/story/alphabet-google-x-innovation-loon-wing-graduation; Steven Levy, “The Untold Story of Google’s Quest to Bring the Internet Everywhere—by Balloon,” Wired, August 13, 2013, www.wired.com/2013/08/googlex-project-loon. 2.

Chuck Salter, “Failure Doesn’t Suck,” Fast Company, May 1, 2007, www.fastcompany.com/59549/failure-doesnt-suck. 11. Hans C. Ohanian, Einstein’s Mistakes: The Human Failings of Genius (New York: W.W. Norton & Company, 2009). 12. Jillian D’Onfro, “Jeff Bezos: Why It Won’t Matter If the Fire Phone Flops,” Business Insider, December 2, 2014, www.businessinsider.com/jeff-bezos-on-big-bets-risks-fire-phone-2014-12. 13. D’Onfro, “If the Fire Phone Flops.” 14. Derek Thompson, “Google X and the Science of Radical Creativity,” Atlantic, November 2017, www.theatlantic.com/magazine/archive/2017/11/x-google-moonshot-factory/540648. 15. Astro Teller, “The Head of ‘X’ Explains How to Make Audacity the Path of Least Resistance,” Wired, April 15, 2016, www.wired.com/2016/04/the-head-of-x-explains-how-to-make-audacity-the-path-of-least-resistance. 16. Adele Peters, “Why Alphabet’s Moonshot Factory Killed Off a Brilliant Carbon-Neutral Fuel,” Fast Company, October 13, 2016, www.fastcompany.com/3064457/why-alphabets-moonshot-factory-killed-off-a-brilliant-carbon-neutral-fuel. 17.


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

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

Strictly speaking, a neural network is a biological entity, and the models that are used in machine learning are artificial neural networks—ANNies. However, in this book a “neural network” means an artificial one unless otherwise indicated. 2. Conor Dougherty, “Astro Teller, Google’s ‘Captain of Moonshots,’ on Making Profits at Google X,” New York Times, February 6, 2015, https://bits.blogs.nytimes. com/2015/02/16/googles-captain-of-moonshots-on-making-profits-at-google-x. Deep learning has reduced the energy costs of running data centers by 15 percent, which amounts to hundreds of millions of dollars in savings per year. 3. Although the 1943 Watson quotation has never been confirmed, it reflects the almost universal failure to imagine the future of computers at that time. Chapter 1 1. “O brave new world that has such people in’t!”

Chips 205 Inside Information 219 Consciousness 233 Nature Is Cleverer Than We Are 245 Deep Intelligence 261 169 viii Acknowledgments 269 Recommended Reading 275 Glossary 281 Notes 285 Index 321 Contents Preface P P r r e e f f a a c c e e © Massachusetts Institute of TechnologyAll Rights Reserved If you use voice recognition on an Android phone or Google Translate on the Internet, you have communicated with neural networks1 trained by deep learning. In the last few years, deep learning has generated enough profit for Google to cover the costs of all its futuristic projects at Google X, including self-driving cars, Google Glass, and Google Brain.2 Google was one of the first Internet companies to embrace deep learning; in 2013, it hired Geoffrey Hinton, the father of deep learning, and other companies are racing to catch up. The recent progress in artificial intelligence (AI) was made by reverse engineering brains. Learning algorithms for layered neural network models are inspired by the way that neurons communicate with one another and are modified by experience.

The 132-mile course had narrow tunnels and sharp turns, including Beer Bottle Pass, a winding mountain road with a sheer drop-off on one side and a rock face on the other (figure 1.1). Rather than follow the traditional AI approach by writing a computer program to anticipate every contingency, Thrun drove Stanley around the desert (figure 1.2), and it learned for itself to predict how to steer based on sensory inputs from its vision and distance sensors. Thrun later founded Google X, a skunk works for high-tech projects, where the technology for self-driving cars was developed further. Google’s self-driving cars have since logged 3.5 million miles driving around the San Francisco Bay Area. Uber has deployed a fleet of self-driving cars in Pittsburgh. Apple is moving into self-driving cars to extend the range of Figure 1.1 Sebastian Thrun with Stanley, the self-driving automobile that won the 2005 DARPA Grand Challenge.


pages: 294 words: 81,292

Our Final Invention: Artificial Intelligence and the End of the Human Era by James Barrat

AI winter, AltaVista, Amazon Web Services, artificial general intelligence, Asilomar, Automated Insights, Bayesian statistics, Bernie Madoff, Bill Joy: nanobots, brain emulation, cellular automata, Chuck Templeton: OpenTable:, cloud computing, cognitive bias, commoditize, computer vision, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, drone strike, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Isaac Newton, Jaron Lanier, John Markoff, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, Loebner Prize, lone genius, mutually assured destruction, natural language processing, Nicholas Carr, optical character recognition, PageRank, pattern recognition, Peter Thiel, prisoner's dilemma, Ray Kurzweil, Rodney Brooks, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, smart grid, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, superintelligent machines, technological singularity, The Coming Technological Singularity, Thomas Bayes, traveling salesman, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, zero day

Even the Google camera cars that take “Street View” photographs for Google Maps are part of the plan—for three years, Google used its photo-taking fleet to grab data from private Wi-Fi networks in the United States and elsewhere. Passwords, Internet usage history, personal e-mails—nothing was off limits. It’s clear they’ve put once loyal customers in our place, and it’s not first place. So it seemed inconceivable that Google did not have AGI in mind. Then, about a month after my last correspondence with Freidenfelds, The New York Times broke a story about Google X. Google X was a stealth company. The secret Silicon Valley laboratory was initially headed by AI expert and developer of Google’s self-driving car, Sebastian Thrun. It is focused on one hundred “moon-shot” projects such as the Space Elevator, which is essentially a scaffolding that would reach into space and facilitate the exploration of our solar system. Also onboard at the stealth facility is Andrew Ng, former director of Stanford University’s Artificial Intelligence Lab, and a world-class roboticist.

As we’ll discuss in chapter 9, Kurzweil has a long track record of achievements in AI, and has promoted brain research as the most direct route to achieving AGI. It doesn’t take Google glasses to see that if Google employs at least two of the world’s preeminent AI scientists, and Ray Kurzweil, AGI likely ranks high among its moon-shot pursuits. Seeking a competitive advantage in the marketplace, Google X and other stealth companies may come up with AGI away from public view. * * * Stealth companies may represent a surprise track to AGI. But according to Vassar the quickest path to AGI will be very public, and cost serious money. That route calls for reverse engineering the human brain, using a combination of programming skill and brute force technology. “Brute force” is the term for overpowering a problem with sheer hardware muscle—racks of fast processors, petabytes of memory—along with clever programming.

climate change cloud computing cognitive architectures OpenCog cognitive bias Cognitive Computing Coherent Extrapolated Volition (CEV) Colossus “Coming Technological Singularity, The” (Vinge) computational neuroscience computers, computing cloud detrimental effects from exponential growth in power of see also programming; software computer science consciousness creativity cybercrime Cyc Cycle Computing Cycorp DARPA (Defense Advanced Research Projects Agency) Darwin Machine Deep Blue de Garis, Hugo Dennett, Daniel Dijkstra, Edger DNA-related research Dongarra, Jack Drake, Francis drives creativity efficiency resource acquisition self-preservation Dugan, Regina Duqu Dyson, George ecophagy efficiency Einstein, Albert emotions energy grid Enigma Enron Eurisko evil extropians Fastow, Andrew Ferrucci, David financial scandals financial system Flame Foreign Affairs Freidenfelds, Jason Friendly AI Coherent Extrapolated Volition and definition of intelligence explosion and SyNAPSE and Future of Humanity Institute genetic algorithms genetic engineering genetic programming George, Dileep global warming Global Workspace Theory Goertzel, Benjamin Golden Rule Good, I. J. Google search engine Google X Granger, Richard Grassie, William Greaves, Mark Grossberg, Steven grounding problem GUI (Graphical User Interface) hackers Hawking, Stephen Hawkins, Jeff Hebb, Donald heuristics Hibbard, Bill high-frequency trading systems (HFTs) Hillis, Danny Holtzman, Golde Horvitz, Eric Hughes, James I, Robot (Asimov) IA, see intelligence augmentation IBM Blue Brain Deep Blue SyNAPSE Watson immortality incomprehensibility Ings, Simon inscrutability paradox Institute for Ethics and Emerging Technologies (IEET) integrated circuits intelligence embodiment and emerging from Internet knowledge and intelligence augmentation (IA) intelligence explosion economics and financial markets and hard takeoff in limiting factors to Moore’s law and risks of, see risks of artificial intelligence self-awareness and, see self-awareness self-improvement and, see self-improvement software complexity and system space for Internet intelligence emerging from security and iPad iPhone Siri Iran iRobot Jennings, Ken Jeopardy!


pages: 285 words: 58,517

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

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

The hard work of operationalizing the vision—to transform Enterprise from an asset financier and service provider to a digitally enabled network orchestrator—was still ahead. OPERATE Enact Your Network Business Model When you innovate, you’ve got to be prepared for people to tell you that you are nuts. —Larry Ellison, former CEO, Oracle WHAT DOES IT TAKE FOR A LARGE FIRM TO INNOVATE? Let’s look at Google, one of the most innovative companies of the past decade, and its secret lab, X, previously known as Google X. Google is well known for its capability with digital technology and for its ability to innovate, and it has developed a specific structure and process for nurturing its most innovative projects, the ones it calls “moonshots.” In 2010, Google created X to develop a self-driving car. Since then, new projects have been added, such as Google Glass, a wearable computer with an optical head-mounted display; and Project Loon, which aims to bring the internet to everyone via a network of high-altitude balloons.

Barry Libert, Yoram (Jerry) Wind, and Megan Beck Fenley, “What Apple, Lending Club, and Airbnb Know about Collaborating with Customers,” HBR.org, July 3, 2015, https://hbr.org/2015/07/what-apple-lending-club-and-airbnb-know-about-collaborating-with-customers; and “What Airbnb, Uber, and Alibaba Have in Common,” HBR.org, November 20, 2014, https://hbr.org/2014/11/what-airbnb-uber-and-alibaba-have-in-common. 3. “Did You Know? Facts from Our Executive Compensation and Benefits (ECB) Proprietary Databases,” Alvarez & Marsal, issue 8, November 10, 2014, http://www.alvarezandmarsal.com/sites/default/files/files/Age-CEO-CFO-COO.pdf. Operate 1. Claire Cain Miller and Nick Bilton, “Google’s Lab of Wildest Dreams,” New York Times, November 13, 2011, http://www.nytimes.com/2011/11/14/technology/at-google-x-a-top-secret-lab-dreaming-up-the-future.html?_r=0. Leaders Need to Think and Act Differently 1. David McRaney, You Are Not So Smart (New York: Avery, 2011). 2. “2015 Social CEO Report,” CEO.com, http://www.ceo.com/social-ceo-report-2015/. 3. “How Millennials Use and Control Social Media,” American Press Institute, March 16, 2015, http://www.americanpressinstitute.org/publications/reports/survey-research/millennials-social-media/. 4.

., 184 Inventory step and, 152–153 Operating platforms and network step and, 175–176 overview of, 127–128 Pinpointing business model and mental model step and, 140–141 questions asked by, 128 Tracking step and, 183–184 Visualizing step and, 163–165 Ernst & Young, 85 Etsy, 10, 15, 81, 91 Everything Store, The: Jeff Bezos and the Age of Amazon (Stone), 119 Facebook, 6, 12, 15, 21, 22, 32, 33, 36, 42 Fenwick, Nigel, 5–6 Fidor, 130 financial services, 129–130 Forbes, 46, 47, 190 Ford Motor Company, 133 Forrester Research, 5–6, 24 Gallup, 90 General Electric (GE), 199–200 generally accepted accounting principles (GAAP), 97 General Mills Worldwide Innovation Network (G-WIN), 73 General Motors (GM), 113–114, 197 Gerstner, Lou, 47 GlossyBox, 76 goals for big data collection, 99–100 for boards, 109 for capital allocation, 53 Google, 3, 43, 91, 101, 110, 114, 118, 119, 148, 167–168, 183, 190 Google Glass, 167 Google Labs, 190 Google+, 33 Google Ventures, 101 Google X, 167, 168, 190 governance, 104, 107–109 Granular, 101 growth of networks, and law of increasing returns, 12, 17 guiding principles, of network leaders, 192–193 Guru, 87 Gutierrez, Carlos, 103 Hastings, Reed, 196–197 Hazelbaker, Jill, 168 Hertz, 4 Hicks, Angie, 197 Hollywood model of employment, 86, 87 Homeaway, 156 human capital business model based on, 15, 132 inventory of, 126, 145, 146, 147–148, 163 mental model values on, 138 network platforms and, 159 network team talent and, 171 IBM, 47–48, 50, 86, 88, 190 ideas.


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Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days by Jake Knapp, John Zeratsky, Braden Kowitz

23andMe, 3D printing, Airbnb, Anne Wojcicki, Google Earth, Google Hangouts, Google X / Alphabet X, self-driving car, side project, Silicon Valley, Wall-E

I created a rough schedule for my first sprint: a day of sharing information and sketching ideas, followed by four days of prototyping. Once again, Google teams welcomed the experiment. I led sprints for Chrome, Google Search, Gmail, and other projects. It was exciting. The sprints worked. Ideas were tested, built, launched, and best of all, they often succeeded in the real world. The sprint process spread across Google from team to team and office to office. A designer from Google X got interested in the method, so she ran a sprint for a team in Ads. The Googlers from the Ads sprint told their colleagues, and so on. Soon I was hearing about sprints from people I’d never met. I made some mistakes along the way. My first sprint involved forty people—a ridiculously high number that nearly derailed the sprint before it began. I adjusted the amount of time spent on developing ideas and the time spent on prototyping.

Heidi Qiao volunteered to sit for the customer test photos on pages 203 to 204. All other photos are by either Jake Knapp, Braden Kowitz, or John Zeratsky. Image postproduction by Braden Kowitz. Illustrations by Jake Knapp. JAKE KNAPP created the Google Ventures sprint process and has run more than a hundred sprints with startups such as 23andMe, Slack, Nest, and Foundation Medicine. Previously, Jake worked at Google, leading sprints for everything from Gmail to Google X. He is among the world’s tallest designers. JOHN ZERATSKY has designed mobile apps, medical reports, and a daily newspaper (among other things). Before joining Google Ventures, he was a design lead at YouTube and an early employee of FeedBurner, which Google acquired in 2007. John writes about design and productivity for The Wall Street Journal, Fast Company, and Wired. He studied journalism at the University of Wisconsin.

., 169–70 finance experts, 34 Fitbit, 171 fitness training, automated, 171–74 FitStar sprint, 171–74, 189, 206 Flatiron Health sprint, 60–64, 76, 85, 88, 100–101, 153, 176, 224 Flickr, 143 focus, sprint process emphasis on, 32 Foundation Medicine sprint, 16, 176–77, 185 FoundationOne, 176 Freeman, James, 21–25, 30, 103 Gebbia, Joe, 210–11 genetic analysis, in cancer treatments, 176 George Mason University, 38 Getting Things Done (Allen), 108–9 Giarusso, Serah, 24, 103 Glitch (video game), 128–29, 143 Gmail, 2, 4 goals, ambitious, 229 goals, long-term, 55–57, 61, 67, 110, 138, 141, 147 dangerous assumptions and, 56–57 in Flatiron Health sprint, 62–63 Goldilocks quality, 170, 207 Gonzalez, Tony, 171–72 Google, 60 experimentation culture of, 1 self-driving car of, 16 Google Earth, 83 Google Forms, 121 Google Hangouts, 3 Google Search, 4 Google Ventures (GV), 4–6, 7, 12, 15, 16, 60, 85, 113, 130, 171, 176, 201, 231 Google X, 4 Grace, Merci, 130, 131, 143–44, 152, 156, 175, 216–17, 221, 222 Graco sprint, 27–28 Green, Bobby, 76, 85, 86 Grijalva, Dave, 171–74 Harry Potter and the Philosopher’s Stone (Rowling), 196, 196n heat map, in deciding process, 131, 132–35 high stakes, as challenge, 26 honesty, in deciding process, 139–40 hotels, guest satisfaction and, 10, 56 Howard, Ron, 53 How Might We notes, 68, 73–82, 110 in Blue Bottle sprint, 73–74 challenges and, 77–78 in Flatiron Health sprint, 76–78 maps and, 81–82 organizing, 79–80 prioritizing, 80–81 target and, 87 HTML, 184 Hurley, Chad, 6 IdeaPaint, 44 IDEO, 73 illusion, 165–66 see also façades Incredibles, The (film), 149 Indian Ocean, 84 industrial companies, sprints and, 27–28 Ingram, Alex, 62, 76 interruptions, productivity and, 38–39 Interviewer, 188, 190, 204–5, 217, 225 tips for, 212–15 interviews, 196–200, 201–15 being a good host in, 212 broken questions in, 214–15 context questions in, 202, 205–6 curiosity mindset in, 215 debriefing in, 202, 209–10 detailed tasks in, 202, 208–9 as emotional roller coaster for sprint team, 197 feedback in, 207 in FitStar sprint, 197, 206 in FitStar test, 208 five-act structure of, 202 ideal number of customers for, 197–99 introducing prototypes in, 202, 206–7 in One Medical sprint, 199–200 open-ended vs. leading questions in, 212–13 power of, 210–11 schedule of, 199 in Slack sprint, 217 team observation of, see interviews, learning from thinking aloud in, 207–8 welcome in, 202, 204–5 “why” questions in, 199–200 interviews, learning from: in Blue Bottle sprint, 223–24 in Flatiron Health sprint, 224 group note-taking in, 219–21 importance of real-time team observation in, 202–4, 218–19 looking for patterns in, 222 in Savioke sprint, 223 in Slack sprint, 220–21, 223 sprint questions and, 222–23 Invite Media, 60 iPads, 171–73, 178, 189 as banned from sprint room, 41 JavaScript, 184 Keynote, 171, 173, 175, 176, 177, 178, 184–85, 186 Knapp, Jake, 24, 27–28, 30, 47, 48, 60, 62, 76, 77, 85, 107n, 109 Kowitz, Braden, 5, 22, 23–24, 30, 43, 60, 76, 156, 216 Kranz, Gene, 53, 55, 85 Lachapelle, Serge, 3 Lancelotta, Mary Pat, 176 Landauer, Thomas K., 198n laptops, as banned from sprint room, 41 Lau, Tessa, 11, 12, 178 lean development, 17 learning, see interviews, learning from Lightning Demos, 96–101, 110 Lord of the Rings, The (Tolkien), 59, 60 Lowe, David, 27 McKinsey & Company, 230 Makers, 187, 188 mapping the problem, 16, 59–67, 110, 230 in Blue Bottle sprint, 23–24, 65, 66 division of labor and, 101–2 experts and, 69–70, 76, 77 in Flatiron Health sprint, 62–63 How Might We notes and, 81–82, 85 in Savioke sprint, 10, 64–65, 66 steps in, 66 as story, 65–66 target and, 84, 85–86 Margolis, Michael, 5, 12, 60, 62, 201–2, 203, 204, 206, 208, 209, 212, 214, 216, 217 Maris, Bill, 4–5 markers, dry-erase, 75 marketing experts, 34 Maser, Mike, 171–73 “Mathematical Model of the Finding of Usability Problems, A” (Nielsen and Landauer), 198n mechanics, of product or service, 70–71 Medium, 6 Medium sprint, 224 Meehan, Bryan, 22 meetings, frustrations of, 127–28, 230 Microsoft Word, 186 Mid-Ocean Ridge, 83–84, 87 “Mind Reader, The” (Blue Bottle solution sketch), 104–6, 115 Mission Control, 53–54, 225 momentum, regaining, 26 Move Loot sprint, 113 movies, façades in, 165–66, 173 My Neighbor Totoro (film), 98 NASA, 54 Nest, 16 Newton, Alice, 195–96 Newton, Nigel, 195–96 New York Times, 15, 130, 152, 153, 188 Nielsen, Jakob, 197–98, 198n no-devices rule, 41, 110 Note-and-Vote, 146–47 note-taking: on interviews, 219–21 sketching and, 109, 110 see also How Might We notes Ocean’s Eleven (film), 29–30, 36, 37, 225 office supplies, for sprint rooms, 45 One Medical Group sprint, 180–82, 185–86, 199 opening scene, 188 OstrichCo, 139–40 paper, for sprint rooms, 44 paper coffee filters, 95–96 patterns, in customer reactions to prototypes, 222 permission, Facilitators and, 89 personal trainers, 171 phones, as banned from sprint room, 41 Photoshop, 184 Pitt, Brad, 29, 36 Pixar, 149 plate tectonics, 84 PlayStation, 178 Porter, Josh, 89 Post-It notes, see sticky notes PowerPoint, 184, 186 previous efforts, see existing solutions priorities, setting, 54–55 “Priority Inbox” project, 2–3 Procter & Gamble, 73 productivity, interruptions and, 38–39 progress, rapid, from sprint process, 31 prototype mindset, 168–69, 230 prototypes, prototyping, 16, 60, 183–90 actors and scripts in, 186 appearance of reality in, 169–70 Asset Collector in, 188 in Blue Bottle sprint, 25, 28, 104–6 Brochure Façades in, 185 Deciders and, 31, 32 deciding on, see deciding as disposable, 169 division of labor in, 183, 187 façades and, see façades Facilitator and, 187 in FitStar sprint, 189 focus on learning from, 169 in Foundation Medicine sprint, 185 Goldilocks quality in, 170 in Graco sprint, 27–28 Interviewer in, 188, 190 Makers in, 187 mindset and, 168–69 in One Medical sprint, 199 picking right tools for, 183–86 in Priority Inbox sprint, 3 Rumbles and, 143–47 in Savioke sprint, 9, 10, 11–12, 185 sketching and, 104–6 in SquidCo sprint, 30–31 Stitcher in, 183, 187, 189 storyboard scenes and, 188, 189–90 trial run in, 183, 189–90 universal application of, 169 using existing objects or spaces in, 186 Writer in, 187–88 questions: in interviews, 212–14 obvious, Facilitators and, 90 questions, finding answers to, 138, 141, 147 in Blue Bottle sprint, 23 in FitStar sprint, 171 in Flatiron Health sprint, 62–63, 88 in Foundation Medicine sprint, 176–77 in Graco sprint, 27–28 and learning from interviews, 222–23 in One Medical sprint, 180 role of sprints in, 15, 16–17, 67 in Savioke sprint, 9, 10, 178 in Slack sprint, 175, 216–17, 222–23 Starting at the End and, 55–58 surface and, 28 see also How Might We notes reaction, feedback vs., 169–70 Relay robot, 7, 14, 56 eyes of, 97–98 guest satisfaction and, 10 guests’ responses to, 13 “personality” of, 11, 13, 71, 178, 179 risk-taking, 156, 166 robot helpers, human interaction with, 8–9, 10 Rogers, Jan, 46–47 Rogers, Loran, 46, 48 rooms, for sprints, 41–45 Rumbles, 143–47, 223 in Blue Bottle sprint, 146 Deciders in, 145, 146 fake brands in, 145–46 Note-and-Vote in, 146–47 single-prototype vs., 145, 147 in Slack sprint, 144, 145 Savioke Labs sprint, 7–15, 26, 33, 64, 66, 71, 119, 145, 153, 157, 178–79, 185, 223 better guest experience as goal of, 56, 84 schedule, clearing space for sprints in, 10, 39, 40–41 screener surveys, in recruiting test customers, 119–21 Scribe, in speed critique, 135–36 Seattle, Wash., 229 Sharpies, 75n simplicity, in maps, 66 sketching, 16, 60, 102, 103–18 abstract ideas and, 106–7 in Blue Bottle sprint, 24, 103–4, 108, 113 Crazy 8s exercise in, 109, 111–13 in Move Loot sprint, 113 prototypes and, 104–6 of rough ideas, 109, 111 solution sketches in, see solution sketches taking notes in, 109, 110 as working alone together, 107–9 Slack sprint, 129–31, 143–44, 149–58, 175, 216, 217, 220–21, 222, 223 expansion into new markets as challenge for, 129–30 Smithsonian Institute, 228 snacks, for sprints, 45 solution sketches, 109, 114–18 anonymity of, 114–15 in Blue Bottle sprint, 116–17 deciding on, see deciding as explanatory, 114 importance of words in, 115 maybe-laters in, 142, 155 single-scene, 114, 117 in Slack sprint, 130 sticky notes and, 114 storyboard format in, 114, 116 titles for, 115 winners in, 141–42 speed critique: in deciding process, 131, 135–37 Scribe in, 135–36 sprints: checklists for, 232–49 clearing calendars for, 10, 39, 40–41 concept of, 3 daily schedule in, 39, 40–41, 90–91 deciding process in, see deciding façades in, see façades as five-day process, 5–6, 9, 16, 40–41 frequently asked questions about, 251–57 learning from, see interviews, learning from no-devices rule in, 41, 110 origin of, 2–5 prototypes in, see prototypes, prototyping questions to be answered in, see questions, finding answers to; tests, real-world risk-taking in, 166 Rumbles in, 143–47 setting priorities in, 54–55 storyboards in, see storyboarding time allocation in, 38–41 timers for, 46–48 uncovering dangerous assumptions through, 56–57 universal application of, 229–30 versatility of, 5–6, 229–30 wide application of, 5–6 working alone together in, 107–9 work rooms for, 41–45 Squarespace, 186 SquidCo sprint, 30–31, 32, 139 Starting at the End, 5, 53–58 in Apollo 13 rescue, 53–54 in Blue Bottle sprint, 55–56, 57 in Flatiron Health sprint, 62–63 long-term goals and, 55–57, 61, 62–63, 67 questions to be answered in, 55–58, 62–63, 67 in Savioke sprint, 56 setting priorities in, 54–55 startups, 231 sprints and, 4–5, 15–16, 27 Starwood, 9 sticky notes: poster-size, 43, 44 solution sketches and, 114 see also How Might We notes Stitcher, 187, 189 storyboarding, 125, 148–58 “artist” for, 151, 154–55, 156 assigning prototyping tasks from, 188, 189–90 in Blue Bottle sprint, 153, 157, 188 competitors’ products in, 154 copywriting in, 155–56 Decider in, 156 detail in, 156 in Flatiron Health sprint, 153 maybe-laters in, 155 opening scene in, 152–53 resisting new ideas in, 155 risk-taking in, 156 in Savioke sprint, 153, 157 in Slack sprint, 149–53, 156 solution sketches as, 114, 116 test-time limits and, 157 story-centered design, 5 strategy, 70 straw polls, 87–88 in deciding process, 131, 138–40 successes, flawed, 223–24 supervotes, 143, 144 in deciding process, 131, 140–42, 143 surface, as contact point between product and customer, 28 target, 82, 83–88 in Blue Bottle sprint, 84–85, 101 Decider and, 31, 32, 85–88 in Flatiron Health sprint, 85–87, 88 How Might We notes and, 87 key customers in, 85–86 key event in, 85–86 maps and, 84, 85–86 in Savioke sprint, 84 straw polls and, 87–88 Tcho, 97 team processes, 1 teams, 29–37, 218 in Blue Bottle sprint, 22–24, 33 challenges and, 68 choosing members of, 33, 34–36 Deciders in, see Deciders division of labor in, 101–2 experts and, see Ask the Experts Facilitators in, see Facilitators ideal size of, 33 interviews observed by, see interviews, learning from in Ocean’s Eleven, 29–30 in Savioke sprint, 9–11, 33 in SquidCo sprint, 30–31 troublemakers in, 35 tech/logistic experts, 34 “Tenacious Tour, The” (Slack solution sketch), 144, 175, 217, 220–21, 222 tests, real-world, 5, 16, 231 in Blue Bottle sprint, 25 competitors’ products in, 154 Deciders and, 31, 32 in FitStar sprint, 173–74 in Graco sprint, 27–28 interview in, see interviews recruiting customers for, 119–23, 197 in Savioke sprint, 10, 11–13, 15 time units in, 157 Tharp, Marie, 83–84 3D printing, 27, 185, 186 tight deadlines, 109 time, allocation of, for sprints, 38–41 timers, in deciding process, 136, 138 Time Timers, 46–48 Tolkien, J.


pages: 278 words: 70,416

Smartcuts: How Hackers, Innovators, and Icons Accelerate Success by Shane Snow

3D printing, Airbnb, Albert Einstein, attribution theory, augmented reality, barriers to entry, conceptual framework, correlation does not imply causation, David Heinemeier Hansson, deliberate practice, disruptive innovation, Elon Musk, Fellow of the Royal Society, Filter Bubble, Google X / Alphabet X, hive mind, index card, index fund, Isaac Newton, job satisfaction, Khan Academy, Kickstarter, lateral thinking, Law of Accelerating Returns, Lean Startup, Mahatma Gandhi, meta analysis, meta-analysis, pattern recognition, Peter Thiel, popular electronics, Ray Kurzweil, Richard Florida, Ronald Reagan, Ruby on Rails, Saturday Night Live, self-driving car, side project, Silicon Valley, Steve Jobs, superconnector

To use a baseball analogy: instead of trying to get on base—or even aiming for a home run—it’s trying to hit the ball into the next town. No amount of weight lifting or swing practice will get you there. Such a goal requires you to think radically different. The apostle of 10x Thinking is a man with perhaps the coolest name ever: Astro Teller. Teller is the goatee-and-ponytailed head of a rather secret Google laboratory in California called Google[x]. He holds a PhD in artificial intelligence. Teller’s job is to dream big. 10x big. Google’s founders have endowed him with an engineer-filled building and a mandate to blow their minds. His team has built self-driving cars, augmented reality glasses, and WiFi balloons meant to roam the stratosphere. He’s hired some brilliant minds onto his team, but that’s not the secret of their success. The secret sounds a bit crazy.

You have to break some of the basic assumptions and, of course, you can’t know ahead of time. It’s by definition counterintuitive.” Incremental progress, he says, depends on working harder. More resources, more effort. 10x progress is built on bravery and creativity instead. Working smarter. In other words, 10x goals force you to come up with smartcuts. “I joke that this is a moon-shot factory,” Teller says, of Google[x]. “Our belief is that if you can get people to let go of their fear, and to be more intellectually open, intellectually honest, more dispassionate about being creative, trying new things, and then being honest about what the results are instead of having all these other issues cloud their judgment, you can get to radically better solutions in honestly about the same amount of time, about the same amount of resources, as making the 10-percent improvement.”

Kennedy, “Moon Speech,” Rice Stadium, Houston, September 12, 1962, http://er.jsc.nasa.gov/seh/ricetalk.htm (accessed February 15, 2014). 176 “The Internet taught me nearly everything”: Kosta Grammatis, Kosta.is, http://kosta.is/ (accessed December 20, 2013). 177 to provide free Internet: Kosta Grammatis’s “Buy This Satellite” campaign was featured in an article by Jim Fields, “Q&A: As Egypt Shuts Down the Internet, One Group Wants Online Access for All,” Time, January 31, 2011, http://content.time.com/time/health/article/0,8599,2045428,00.html (accessed February 17, 2014). His satellite tablet project in Dadaab, Kenya, was just commencing as this book entered production. 177 “It’s often easier”: Astro Teller wrote this direct quote in an opinion piece for Wired at “Google X Head on Moonshots: 10X Is Easier Than 10 Percent,” Wired, February 11, 2013, http://www.wired.com/opinion/2013/02/moonshots-matter-heres-how-to-make-them-happen/ (accessed February 17, 2014), and then quoted it nearly verbatim to me during a phone interview. 179 the N-Effect: The effect is documented in this fascinating study: Stephen M. Garcia and Avishalom Tor, “The N-Effect: More Competitors, Less Competition,” Psychological Science 20, no. 7 (2009): 871–77.


pages: 477 words: 75,408

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

3D printing, additive manufacturing, agricultural Revolution, AI winter, Airbnb, artificial general intelligence, augmented reality, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Berlin Wall, Bernie Sanders, bitcoin, blockchain, call centre, Chris Urmson, congestion charging, credit crunch, David Ricardo: comparative advantage, Douglas Engelbart, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Flynn Effect, full employment, future of work, gender pay gap, gig economy, Google Glasses, Google X / Alphabet X, ImageNet competition, income inequality, industrial robot, Internet of things, invention of the telephone, invisible hand, James Watt: steam engine, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, knowledge worker, lifelogging, lump of labour, Lyft, Marc Andreessen, Mark Zuckerberg, Martin Wolf, McJob, means of production, Milgram experiment, Narrative Science, natural language processing, new economy, Occupy movement, Oculus Rift, PageRank, pattern recognition, post scarcity, post-industrial society, post-work, precariat, prediction markets, QWERTY keyboard, railway mania, RAND corporation, Ray Kurzweil, RFID, Rodney Brooks, Sam Altman, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, software is eating the world, speech recognition, Stephen Hawking, Steve Jobs, TaskRabbit, technological singularity, The Future of Employment, Thomas Malthus, transaction costs, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, universal basic income, Vernor Vinge, working-age population, Y Combinator, young professional

But producing maps for the roads outside California doesn't sound like an insurmountable obstacle, and in any case, systems like SegNet from Cambridge University enable cars to produce maps on the fly.[clxxxvii] A fully autonomous bus made in France has been serving the centre of the Greek city of Trikala since February 2015. It travels at a top speed of 20 mph along a pre-determined route which is also used by pedestrians, cyclists and cars.[clxxxviii] In December 2015 Bloomberg reported that Google was preparing to move its self-driving cars unit from its Google X research arm to become a stand-alone business unit within the Alphabet holding company.[clxxxix] At the same time, Elon Musk, CEO of Tesla, remarked that he was revising his estimate of the time when fully automated cars would be available from three years down to two.[cxc] In January 2016 he announced that within about two years, Tesla owners would be able to “summon” their driverless car from New York to pick them up in Los Angeles.

He is dismissive of artificial intelligence because it has not yet produced a conscious mind, but he thinks that augmented reality might turn out to be a new platform for innovation, just as the smartphone did a decade ago. But in conclusion he believes that “2045... is going to look more like it looks today than you think.” It is tempting to think that Markoff was to some extent playing to the gallery, wallowing self-indulgently in sexagenarian nostalgia about the passing of old glories. His critique blithely ignores the arrival of social media and much else, and dismisses the basic research that goes on at Google X, DeepMind, the Human Brain Project and elsewhere. Nevertheless, Markoff does articulate a fairly widespread point of view. Many people believe that the industrial revolution had a far greater impact on everyday life than anything produced by the information revolution. Before the arrival of railroads and then cars, most people never travelled outside their town or village, much less to a foreign country.

[lxxx] http://searchengineland.com/faq-all-about-the-new-google-rankbrain-algorithm-234440?utm_campaign=socialflow&utm_source=facebook&utm_medium=social [lxxxi] http://www.wired.com/2016/02/ai-is-changing-the-technology-behind-google-searches/ [lxxxii] http://www.thedrum.com/opinion/2016/02/08/why-artificial-intelligence-key-google-s-battle-amazon [lxxxiii] http://www.wired.com/2012/06/google-x-neural-network/ [lxxxiv] They are the Pembroke and the Cardigan Corgi. http://research.microsoft.com/en-us/news/features/dnnvision-071414.aspx [lxxxv] http://image-net.org/challenges/LSVRC/2015/index#news [lxxxvi] http://www.eetimes.com/document.asp?doc_id=1325712 [lxxxvii] https://youtu.be/U_Wgc1JOsBk?t=33 [lxxxviii] http://news.sciencemag.org/social-sciences/2015/02/facebook-will-soon-be-able-id-you-any-photo [lxxxix] http://www.computerworld.com/article/2941415/data-privacy/is-facial-recognition-a-threat-on-facebook-and-google.html [xc] http://www.wired.com/2016/01/2015-was-the-year-ai-finally-entered-the-everyday-world/ [xci] At the time of writing, April 2016, Aipoly is impressive, but far from perfect.


pages: 245 words: 83,272

Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard

1960s counterculture, A Declaration of the Independence of Cyberspace, Ada Lovelace, AI winter, Airbnb, Amazon Web Services, autonomous vehicles, availability heuristic, barriers to entry, Bernie Sanders, bitcoin, Buckminster Fuller, Chris Urmson, Clayton Christensen, cloud computing, cognitive bias, complexity theory, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, digital map, disruptive innovation, Donald Trump, Douglas Engelbart, easy for humans, difficult for computers, Electric Kool-Aid Acid Test, Elon Musk, Firefox, gig economy, global supply chain, Google Glasses, Google X / Alphabet X, Hacker Ethic, Jaron Lanier, Jeff Bezos, John von Neumann, Joi Ito, Joseph-Marie Jacquard, life extension, Lyft, Mark Zuckerberg, mass incarceration, Minecraft, minimum viable product, Mother of all demos, move fast and break things, move fast and break things, Nate Silver, natural language processing, PageRank, payday loans, paypal mafia, performance metric, Peter Thiel, price discrimination, Ray Kurzweil, ride hailing / ride sharing, Ross Ulbricht, Saturday Night Live, school choice, self-driving car, Silicon Valley, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, Tesla Model S, the High Line, The Signal and the Noise by Nate Silver, theory of mind, Travis Kalanick, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, women in the workforce

However, public transportation funding is a complex issue that requires massive, collaborative effort over a period of years. It involves government bureaucracy. This is exactly the kind of project that tech people don’t want to attack because it takes a really long time and it’s complicated and there aren’t easy fixes. Meanwhile, the self-driving car remains a fantasy. In 2011, Sebastian Thrun launched Google X, the company’s “moonshot” division. In 2012, he founded Udacity, which also failed. “I’d aspired to give people a profound education—to teach them something substantial. But the data was at odds with this idea,” Thrun told Fast Company. “We have a lousy product.”11 Thrun has been honest about things he’s tried that don’t work—but it seems nobody’s listening. Why not? The simplest explanation may be greed.

Kamal told Kalanick about his struggle. Kalanick replied: “Some people don’t like to take responsibility for their own shit. They blame everything in their life on somebody else. Good luck!” The company launched self-driving cars in California in defiance of state regulations. They were shut down after a legal fight. Kalanick personally hired Anthony Levandowski, a DARPA Grand Challenge participant who worked with Thrun at Google X and later Waymo. Levandowski was fired from Uber in May 2017 for failing to cooperate with an investigation into whether he stole intellectual property from Waymo and used it to further Uber’s technology interests.16 In May 2016, Joshua D. Brown of Canton, Ohio, became the first person to die in a self-driving car. Brown, forty years old, was a Navy SEAL and master Explosive Ordinance Disposal (EOD) technician turned technology entrepreneur.

., 180 FEC.gov, 178–179 Film, AI in, 31, 32, 198 FiveThirtyEight.com, 47 Foote, Tully, 122–123, 125 Ford Motor Company, 140 Fowler, Susan, 74 Fraud campaign finance, 180 Internet advertising, 153–154 Free press, role of, 44 Free speech, 82 Fuller, Buckminster, 74 Futurists, 89–90 Games, AI and, 33–37 Gates, Bill, 61 Gates, Melinda, 157–158 Gawker, 83 Gender equality, hostility toward, 83 Gender gap, 5, 84–85, 115, 158 Genius, cult of, 75 Genius myth, 83–84 Ghost-in-the-machine fallacy, 32, 39 Giffords, Gabby, 19–20 GitHub, 135 Go, 33–37 Good Old-Fashioned Artificial Intelligence (GOFAI), 10 Good vs. popular, 149–152, 160 Google, 72 Google Docs, 25 Google Maps API, 46 Google Street View, 131 Google X, 138, 151, 158 Government campaign finance, 177–186, 191 cyberspace activism, antigovernment ideology, 82–83 tech hostility toward, 82–83 Graphical user interface (GUI), 25, 72 Greyball, 74 Guardian, 45, 46 Hackathons, 165–174 Hackers, 69–70, 82, 153–154, 169, 173 Halevy, Alon, 119 Hamilton, James T., 47 Harley, Mike, 140 Harris, Melanie, 58–59 Harvard, Andrew, 184 Harvard University Berkman Klein Center, 195 Data Privacy Lab, 195 mathematics department, 84 “Hello, world” program, 13–18 Her, 31 Hern, Alex, 159 Hernandez, Daniel, Jr., 19 Heuristics, 95–96 Hillis, Danny, 73 Hippies, 5, 82 HitchBOT, 69 Hite, William, 58 Hoffman, Brian, 159 Holovaty, Adrian, 45–46 Home Depot, 46, 115, 155 Hooke, Robert, 88 Houghton Mifflin Harcourt (HMH) HP, 157 Hugo, Christoph von, 145 Human-centered design, 147, 177 Human computers, 77–78, 198 Human error, 136–137 Human-in-the-loop systems, 177, 179, 187, 195 Hurst, Alicia, 164 Illinois quarter, 153–154 Imagination, 89–90, 128 Imitation Game, The (film), 74 Information industry, annual pay, 153 Injury mortality, 137 Innovation computational, 25 disruptive, 163, 171 funding, 172–173 hackathons and, 166 Instacart, 171 Intelligence in machine learning Interestingness threshold, 188 International Foundation for Advanced Study, 81 Internet advertising model, 151 browsers, 25, 26 careers, annual pay rates, 153 core values, 150 drug marketplace, 159–160 early development of the, 5, 81 fraud, 153–154 online communities, technolibertarianism in culture of, 82–83 rankings, 72, 150–152 Internet Explorer, 25 Internet pioneers, inspiration for, 5, 81–82 Internet publishing industry, annual pay, 153 Internet search, 72, 150–152 Ito, Joi, 147, 195 Jacquard, Joseph Marie, 76 Java, 89 JavaScript, 89 Jobs, Steve, 25, 70, 72, 80, 81 Jones, Paul Tudor, 187–188 Journalism.


pages: 387 words: 119,409

Work Rules!: Insights From Inside Google That Will Transform How You Live and Lead by Laszlo Bock

Airbnb, Albert Einstein, AltaVista, Atul Gawande, Black Swan, book scanning, Burning Man, call centre, Cass Sunstein, Checklist Manifesto, choice architecture, citizen journalism, clean water, correlation coefficient, crowdsourcing, Daniel Kahneman / Amos Tversky, deliberate practice, en.wikipedia.org, experimental subject, Frederick Winslow Taylor, future of work, Google Earth, Google Glasses, Google Hangouts, Google X / Alphabet X, Googley, helicopter parent, immigration reform, Internet Archive, longitudinal study, Menlo Park, mental accounting, meta analysis, meta-analysis, Moneyball by Michael Lewis explains big data, nudge unit, PageRank, Paul Buchheit, Ralph Waldo Emerson, Rana Plaza, random walk, Richard Thaler, Rubik’s Cube, self-driving car, shareholder value, side project, Silicon Valley, six sigma, statistical model, Steve Ballmer, Steve Jobs, Steven Levy, Steven Pinker, survivorship bias, TaskRabbit, The Wisdom of Crowds, Tony Hsieh, Turing machine, winner-take-all economy, Y2K

These insights translated directly into billions of dollars of value for our shareholders, hundreds of billions of dollars in new business for our advertisers, and happier users who could now find exactly what they wanted across the entire Web. Other exceptional Googlers include Diane Tang—one of only a handful of engineers to earn the accolade of Google Fellow, an honorific reserved only for those who have had the greatest technical contributions—who for years led the team focused on making sure ad quality continued to improve and recently took on a confidential project at Google[x]. Dr. Hal Varian, who literally wrote the book on microeconomics, leads our economics team. Charlotte Monico, a London-based member of our people operations team, is one of over a dozen Googlers to have taken part in the Olympic games. Vint Cerf, known as “the co-father of the Internet” for his seminal work co-inventing the Internet, is our lead evangelist. The inventor of the optical mouse (Dick Lyon) and founders or cofounders of Excite (Joe Kraus and Graham Spencer), Ushahidi (a crowdsourcing utility that allows citizen journalists and eyewitnesses to report violence in Africa, created by Ory Okolloh), Chrome (Sundar Pichai and Linus Upson), and Digg (Kevin Rose) work alongside one another and tens of thousands of other remarkable people.

This might mean an email or call, or even meeting at a conference. Recruiters typically start the relationship, but sometimes the best contact is one of our engineers or executives. And while there may not be an opportunity today, it’s always possible the candidate has a bad day a year later and remembers that great conversation they had with a Google recruiter. Jeff Huber, our longtime SVP of engineering for Ads and Apps and now a part of Google[x], where he works on Google’s next big bets, personally recruited more than twenty-five senior engineers, one of whom he’d cultivated for ten years across three companies before finally convincing her to join him at Google. Today our own Google Careers website is one of our best sources of candidates, though we’re hard at work making it even better. Corporate job sites are awful. They are difficult to search, filled with generic job descriptions that don’t tell you anything about what the job really is or what the team you’ll be part of is like, and provide no feedback on whether you’d be good for a role or not.

It’s important to have both a quality and an efficiency measure, because otherwise engineers could just solve for one at the expense of the other. It’s not enough to give you a perfect result if it takes three minutes. We have to be both relevant and fast. We deliberately set ambitious goals that we know we won’t be able to achieve in all cases. If you’re achieving all your goals, you’re not setting them aggressively enough. Astro Teller,xlii who oversees Google[x], our team that developed Glass (an eyeglass-mounted computer with a viewscreen the size of your fingernail) and our self-driving cars, describes it this way: “If you want your car to get fifty miles per gallon, fine. You can retool your car a little bit. But if I tell you it has to run on a gallon of gas for five hundred miles, you have to start over.” We don’t set all our goals quite that aggressively, but there’s wisdom in his approach.


pages: 468 words: 124,573

How to Build a Billion Dollar App: Discover the Secrets of the Most Successful Entrepreneurs of Our Time by George Berkowski

Airbnb, Amazon Web Services, barriers to entry, Black Swan, business intelligence, call centre, crowdsourcing, disruptive innovation, en.wikipedia.org, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, iterative process, Jeff Bezos, Jony Ive, Kickstarter, knowledge worker, Lean Startup, loose coupling, Marc Andreessen, Mark Zuckerberg, minimum viable product, MITM: man-in-the-middle, move fast and break things, move fast and break things, Network effects, Oculus Rift, Paul Graham, QR code, Ruby on Rails, self-driving car, Silicon Valley, Silicon Valley startup, Skype, Snapchat, social graph, software as a service, software is eating the world, Steve Jobs, Steven Levy, Travis Kalanick, ubercab, Y Combinator

.’, article and video interview on FirstRound.com, firstround.com/article/how-dave-goldberg-of-surveymonkey-built-a-billion-dollar-business-and-still-gets-home-by-5-30. 4 Ibid. 5 Mike Rose, ‘Supercell’s Secret Sauce’, article on Gamasutra.com, 7 December 2012, www.gamasutra.com/view/feature/183064/supercells_secret_sauce.php. 6 Ibid. 7 Alyson Shontell and Andrea Huspeni, ‘15 Incredible Employee Perks That Will Make You Wish You Worked at a Startup’, article on BusinessInsider.com, 31 May 2012, www.BusinessInsider.com/killer-startup-perks-2012-5. 8 Heather Leonard, ‘Facebook Generates Over $1 Million in Revenue Per Employee’, article on BusinessInsider.com, 19 March 2013, www.BusinessInsider.com/facebook-has-high-revenue-per-employee-2013-3. 9 Megan Rose Dickey, ‘“Clash of Clans” Maker Had a Monster Year in 2013: Revenue Increased Nearly Ninefold’, article on BusinessInsider.com, 12 February 2014, www.BusinessInsider.com/gaming-startup-supercell-2013-revenue-2014-2. 10 Steven Levy, ‘Google’s Larry Page on Why Moon Shots Matter’, article on Wired.com, 17 January 2013, www.wired.com/business/2013/01/ff-qa-larry-page/all/. 11 Peter Murray, ‘Google’s Self-Driving Car Passes 300,000 Miles’, article on Forbes.com, 15 August 2012, www.forbes.com/sites/singularity/2012/08/15/googles-self-driving-car-passes-300000-miles/. 12 For more information about Project Loon, visit www.google.com/loon/. 13 ‘Google X’, entry on Wikipedia, en.wikipedia.org/wiki/Google_X. Chapter 38: Advice from Billion-Dollar CEOs 1 Will Oremus, ‘Google’s Big Break’, article on Slate.com, 13 October 2013, www.slate.com/articles/business/when_big_businesses_were_small/2013/10/google_s_big_break_how_bill_gross_goto_com_inspired_the_adwords_business.html. 2 Ibid. 3 ‘Drew Houston’s Morph from Hacker to Hyper-Growth CEO’, article on FirstRound.com, www.firstround.com/article/Drew-Houstons-morph-from-hacker-to-hyper-growth-CEO. 4 Peter Kafka, ‘Larry Page on Speed: “There are no companies that have good slow decisions”’, article on AllThingsD.com, 27 September 2011, allthingsd.com/20110927/larry-page-on-speed-there-are-no-companies-that-have-good-slow-decisions/. 5 Glen Cathey, ‘LinkedIn Traffic Statistics and User Demographics 2013’, article on BooleanBlackBelt.com, 24 July 2013, booleanblackbelt.com/2013/07/linkedin-traffic-statistics-and-user-demographics-2013/. 6 Juhana Hietala, ‘Rovio Mobile Company Presentation – Dynamic World of Mobile Game Business’, 1 April 2005, www.soberit.hut.fi/T-76.640/Slides/T-76.640_Rovio2005_04_01HUT.pdf. 7 ‘The 30 Best Pieces of Advice for Entrepreneurs’, article on FirstRound.com, firstround.com/article/30-Best-Pieces#ixzz2pRF5EZ8a. 8 Ibid. 9 ‘Drew Houston’s Morph from Hacker to Hyper-Growth CEO’, op. cit. 10 Ibid. 11 ‘The 30 Best Pieces of Advice for Entrepreneurs’, op. cit. 12 Ibid. 13 Eric Savitz, ‘Jack Dorsey: Leadership Secrets of Twitter and Square’, article for Forbes, 5 November 2012 issue, www.forbes.com/sites/ericsavitz/2012/10/17/jack-dorsey-the-leadership-secrets-of-twitter-and-square/. 14 ‘The 30 Best Pieces of Advice for Entrepreneurs’, op. cit.

And my point from earlier is that Google’s revenue – and profit – per employee allows it to pursue this strategy. Companies that don’t have robust business models will not be able to invest in these kinds of activities, which will make it increasingly harder for them to retain the best people, who in turn, once salary is taken care of, will be looking for a job with meaning. And that comes from a company that has a culture of pure innovation and solving meaningful problems. Google X is the division of Google that is home to the company’s moonshots. Since 2010 it has delivered a variety of seemingly impossible fantasies, such as the self-driving car (which has travelled over 500,000 km without a single accident11), Google Glass (a wearable computer with an optical head-mounted display), Project Loon (which provides rural Internet connectivity via high-altitude autonomous balloons12) and more than 100 other projects.13 So, when you think about the future of your app, it’s important to think about how big your ambition and vision are – and how you are going to take people on that journey.

Index Note: page numbers in bold refer to illustrations, page numbers in italics refer to information contained in tables. 99designs.com 111 500 Startups accelerator 136, 160 Accel Partners 3, 158, 261, 304, 321, 336, 383 accelerators 136, 159–60, 160 accountants 164, 316 accounting software 164 acquisition (of users) costs 148–9, 184, 236–7, 275–9, 282 and Facebook 271, 272, 273–4 for five hundred-million-dollar apps 327, 341–3 for hundred-million-dollar apps 252, 259, 266, 267–74, 275–84, 295–307 and incentive-based networks 270–1 international 295–307 for million dollar apps 136–7, 139, 140–51, 148–9, 153 and mobile social media channels 271–3, 272 and mobile user-acquisition channels 269–70 strategy 222–31 for ten-million-dollar apps 211–12, 213, 222–31, 236–7, 248–9 and traditional channels 268–9 and ‘viral’ growth 225, 278, 279–84 zero-user-acquisition cost 278 acquisitions 414–25 buying sustained growth 417–18 by non-tech corporations 418–20 initial public offerings 420–2 Waze 415–16 activation (user) 136, 137, 139, 153–4, 211–12, 213 Acton, Brian 54, 394 addiction, smartphone 30–1 Adler, Micah 269 administrators 409 AdMob 414–15 advertising 43 business model 67, 89–90 costs 140 and Facebook 271, 272, 273–4 mobile 148–9, 268–70, 272–3, 272 mobile social media 272–3, 272 mobile user-acquisition channels 269–70 outdoor 264 shunning of 42, 54–6 video ads 273 aesthetics 131 after product–market fit (APMF) 180 agencies 195–7, 264, 343 ‘agile coaches’ see scrum masters agile software development 192–3, 299, 315, 357, 377 Ahonen, Tomi 45 ‘aiming high’ 40–1 Airbnb 160, 301 alarm features 48 Albion 111 alerts 293 Alexa.com 146 Alibaba 227 ‘ALT tags’ 147 Amazon 7, 29, 131, 164, 227, 276, 366, 374–5, 401, 406 Amazon Web Services 374 American Express 347 Amobee 149 analytics 134–5, 149, 199, 205, 210, 212, 217–21, 294 and cohort analysis 287–8 Flurry 135, 149, 220 function 217–18 Google Analytics 135, 219–20, 345 limitations 284 Localytics 135, 221 and marketing 263 mistakes involving 218–19 Mixpanel.com tool 135, 217–18, 220–1, 287, 290–1, 345 Andreessen, Marc 180, 418–19 Andreessen Horowitz 72, 80, 180, 321, 383, 385, 418–19 Android (mobile operating system) 6, 23–4, 38, 415 advertising 274 audience size 119 beta testing 202 building apps for 116–22 and international apps 296 in Japan 306 scaling development and engineering 357–8 time spent on 26 and WhatsApp 55 Angel Capital Association 162 angel investors 154, 155–6, 323 AngelList 99, 131, 155, 159, 233 Angry Birds (game) 6, 42, 47, 57–8, 87, 89, 97 and application programming interface 36 delivering delight 207 design 131 funding 321 game in game 348–9 international growth 297–9 platform 117, 118 product extension 356 virality 282 annual offsites 379 annual revenue per user (ARPU) 215, 219, 232, 236 anonymity 43, 56–7 anti-poaching clauses 247 antidilution rights 245 API see application programming interface app descriptions 143 app development billion-dollar app 8, 389–425 CEO advice 406–13 getting acquired 414–25 people 395–405 process 390–1 five-hundred-million-dollar app 325–87 funding 328, 383–7 hiring staff 334–6, 337–40 killer product expansion 350–63 process 326–8 scaling 326, 330–6, 331–2 scaling marketing 341–9 scaling people 364–72, 377–9 scaling process 373–82 scaling product development 357–63 hundred-million-dollar app 251–324 international growth 295–307 process 252–4 product-market fit 255–6 retention of users 286–94 revenue engines 257–66, 275–85 user acquisition 267–74 million-dollar app 81–171 app Version 0.1 123–35 coding 133–4 design 129–33 feedback 127, 134–5 funding 152–60, 161–71, 176, 235–49 identity of the business 106–14 lean companies 115–22 metrics 136–9, 139 process 82–4 startup process 85–105 testing 126–8 user acquisition 140–51 ten-million-dollar app 173–249 growth engine 222–31, 235–49 metrics 211–21 new and improved Version 1.0 198–210 process 174–6 product–market fit 180–97 revenue engine 232–4 venture capital 235–49 app stores 22, 27–8, 33–4 see also Apple App Store; Google Play app-store optimisation (ASO) 142, 225 AppAnnie 205 Apple 19, 20, 31–2, 393 application programming interface 35–6 designers 129 Facetime app 46 iWatch 38–9 profit per employee 402–3 revenue per employee 401 visual voicemail 50 Worldwide Developers Conference (WWDC) 313 see also iPad; iPhone Apple App Store 22, 27, 32–3, 75, 88, 89, 117, 226 finding apps in 140, 141, 142–5 international apps 297–9 making submissions to 152–3 and profit per employee 403 ratings plus comments 204–5 Apple Enterprise Distribution 201–2 application programming interface (API) 35–6, 185, 360, 374 ARPU see annual revenue per user articles of incorporation 169 ASO see app-store optimisation Atari 20 Atomico 3, 261, 321, 383 attribution 227–31 for referrals 230–1 average transaction value (ATV) 214–15, 219, 232, 236, 387 Avis 95 backlinking to yourself 146 ‘bad leavers’ 247 Balsamiq.com 128 Banana Republic 352 bank accounts 164 banking 156–7 Bardin, Noam 43 Barr, Tom 338 Barra, Hugo 120, 306 Baseline Ventures 72 Baudu 226 beauty 131 BeeJiveIM 33 before product–market fit (BPMF) 180 ‘below the fold’ 143 Beluga Linguistics 297 Benchmark 75 benefits 398–400 beta testing 201–4 Betfair 358 Bezos, Jeff 366, 374 Bible apps 45 billion 9–10 Billion-Dollar Club 5 billionaires 9 Bing 226 ‘black-swan’ events 54 BlackBerry 23 Blank, Steve 257 Blogger 41 blood sugar monitoring devices 38 board seats 242, 243–4 board-member election consent 169 Bolt Peters 363 Booking.com 320 Bootstrap 145 Botha, Roelof 76, 77, 80 Box 7, 90, 276, 396–7, 411 brains 10 brainstorming 108 branding 111–13, 143, 263–4 Braun 129 Bregman, Jay xiii, 14–16, 95, 124, 209, 303 bridge loans 323 Brin, Sergey 366 Bring Your Own Infrastructure (BYOI) 17–18 Brougher, Francoise 340 Brown, Donald 44 Brown, Reggie 104–5 Bubble Witch 421 Buffet, Warren 4 build-measure-learn cycle 116 Burbn.com 72–4, 80 business advisors/coaches 103 business analysts 343 business culture 395–8 business goal setting 310–11 business models 67, 83, 87, 88–91, 175, 253, 259, 327, 351–2, 391, 400, 423–4 business success, engines of 183–4, 423–4 Business Wire 150 CAC see Customer Acquisition Cost Cagan, Marty 314 calendars 49 calorie measurement sensors 38 Cambridge Computer Scientists 160 camera feature 48 Camera+ app 48 Candy Crush Saga 6, 47, 87, 89, 131, 278–81, 318, 349, 421–2 card-readers 41–2 cash flow 164 CEOs see Chief Executive Officers CFOs see Chief Financial Officers channels incentive-based networks 270–1 mobile social-media 271–3, 272 mobile user-acquisition 269–70 source attribution 227–31 testing 224–7 traditional 268–9 viral 280–2 charging phones 49–50 Chartboost 149 chauffer hire see Uber app check-ins, location-based 72, 74 Chief Executive Officers (CEOs) 309, 380 advice from 406–13 and the long haul 68 and product centricity 185–6 role 337 Chief Financial Officers (CFOs) 316 Chief Operations Officers (COOs) 309, 326, 337–40, 380 Chief Technology Officers (CTOs) 186–7, 195 Chillingo 298 China 24–5, 146, 226, 306–7 Cisco 402 Clash of Clans (game) 6, 28, 36, 47, 87, 89, 97, 118, 227, 348–9, 398 Clements, Dave 120 Climate Corporation 412, 419 clock features 47 cloud-based software 67, 90 Clover 419 coding 133–4 cofounders 85, 91–105, 188, 191 chemistry 92–3 complementary skills 93 finding 96–9 level of control 94 passion 93–4 red flags 102–3 successful matches 104–5 testing out 100–2 cohort analysis 237, 287–8 Color.com (social photo-sharing) app 113, 255 colour schemes 111 Commodore 20 communication open 412–13 team 194 with users 208–9 Companiesmadesimple.com 163–4 computers 20–1, 29 conferences 97–8, 202, 312–13 confidentiality provisions 244 connectedness 30 ConnectU 105 consumer audience apps 233–4 content, fresh 147 contracts 165–6 convertible loans 163 Cook, Daren 112 cookies 228–9 Coors 348 COOs see Chief Operations Officers Cost Per Acquisition (CPA) 148–9 Cost Per Download (CPD) 148 Costolo, Dick 77–8, 79–80 costs, and user acquisition 148–9, 184, 236–7, 275–9, 282 Crash Bandicoot 33 crawlers 146–7 Cray-1 supercomputer 20 CRM see customer-relationship management CrunchBase 238 CTOs see Chief Technology Officers Customer Acquisition Cost (CAC) 148–9, 184, 236–7, 275–9 customer lifecycle 212–14 customer segments 346–7 customer-centric approach 344 customer-relationship management (CRM) 290–4, 343 customer-support 208–9 Cutright, Alyssa 369 daily active users (DAUs) 142 D’Angelo, Adam 75–6 data 284–5, 345–7 data engineers 284 dating, online 14, 87–8, 101–2, 263 decision making 379–82, 407–8 defining apps 31–4 delegation 407 delight, delivery 205–7 design 82, 129–33, 206–7 responsive 144 designers 132, 189–91, 363, 376 developer meetups 97 developers see engineers/developers development see app development; software development development agencies 196 ‘development sprint’ 192 Devine, Rory 358–9 Digital Sky Technologies 385 directors of finance 316–17 Distimo 205 DLD 97 Doerr, John 164, 310 Doll, Evan 42–3, 105 domain names 109–10 international 146 protection 145–6 Domainnamesoup.com 109 Dorsey, Jack 41, 58, 72, 75–7, 79–80, 104, 112, 215–16, 305, 312, 412–13 ‘double-trigger’ vesting 247 DoubleClick 414 Dow Jones VentureSource 64 down rounds 322–3 downloads, driving 150–1 drag along rights 245 Dribbble.com 132 Dropbox 7, 90, 131, 276 CEO 407, 410–11 funding 160 scaling 336 staff 399 Dunbar, Robin 364–5 Dunbar number 365 e-commerce/marketplace 28–9, 67, 89, 213–14 Chinese 306 Flipboard and 351–2 and revenue engines 232, 233–4, 276 social media generated 271–2 and user retention 288, 289 eBay 7, 28–9, 131, 180, 276 economic models 275 economies of scale 331–2, 331–2 eCourier 15, 95 education 68–9 edX 69 Ek, Daniel 357 Ellis, Sean 182 emails 291–3 emotion effects of smartphones on 29–30, 30 inspiring 223–4 employees see staff employment contracts 246–7 engagement 236, 278, 283 engineering VPs 337, 358–9 engineers/developers 190–1, 194–5, 361–2, 362, 370, 375–7, 405 enterprise 90, 233–4 Entrepreneur First programme 160 entrepreneurs 3–5, 7–8, 65, 262, 393–4, 409, 424 Ericsson 21 Etsy 107, 109, 110, 358 Euclid Analytics 149 Evernote 7, 90, 131, 399 ExactTarget 291 excitement 30 executive assistants 367 Exitround 419 experience 67–8, 264, 397 Fab.com 352 Facebook 7, 10, 26, 32, 48, 76, 226, 394, 422 and acquisition of users 271, 272, 273–4 acquisitions 416–18, 417 agile culture 375 alerts 293 and application programming interface 36 board 180 and business identity 114 and Candy Crush 280–1 Chief Executive Officer 406 cofounders 100–1 and Color 255–6 design 131, 206, 363 Developer Garage 97 driving downloads on 151 and e-commerce decisions 271, 272 and FreeMyApps.com 271 funding 419 and getting your app found 147 and the ‘hacker way’ 375 initial public offering 420–1 and Instagram 29, 51, 76–80, 90, 117 name 110 ‘No-Meeting Wednesday’ 376 product development 187 profit per employee 403 revenue per employee 401 scaling 336 and Snapchat 57 staff 339, 362, 363, 398, 401, 403 and virality 281 WhatsApp purchase 42, 54–6, 416–17, 417 zero-user-acquisition cost 278 and Zynga 279, 281 Facetime app 46 fanatical users 294 feedback 86, 127, 134–5, 182, 192–3, 198–201, 256, 396 loops 204, 211 qualitative 199 quantitative 199 see also analytics Feld, Brad 170, 241 Fenwick and West 168 Fiksu 264, 269–70 finance, VP of 317–18 finding apps 140–8, 148–9 FireEye 90 First Data 419 first impressions 107–10 Fitbit 38 fitness bracelets 38 flat rounds 322–3 Flipboard 6, 29, 42–3, 49, 51, 89–90 and application programming interface 36 Catalogs 351–2 cofounders 105 design 131, 207 funding 164 growth 351–2 platform choice 119 product innovation 351–2 user notifications 292 virality 281 zero-user-acquisition cost 278 Flurry 135, 149, 220 Fontana, Ash 233 Forbes magazine 40 Ford Motors 419 Founder Institute, The 168 founder vesting 166–7, 244 Foursquare 419 France Telecom 13 franchising 354 FreeMyApps.com 270–1 Friedberg, David 412 Froyo (Android mobile software) 7 Fujii, Kiyotaka 304 full service agencies 195–6 functionality 25–6, 45–50, 131 funding 72, 75–6, 84, 87–8, 152–60, 161–71, 179 accelerators 159–60 angel investors 154, 155–6, 323 for billion-dollar apps 391 convertible loans 163 core documents 169–70 for five-hundred-million-dollar apps 328, 383–7 founder vesting 166–7 for hundred-million-dollar apps 254, 258, 316–17, 318–24 incubators 159–60 legal aspects 163–4 and revenue engines 233–4 Series A 234, 238–40, 238, 240, 241, 242–6, 255, 319–21, 385 Series B 238, 241, 253, 260, 284, 319–21, 322, 384 Series C 384 signing a deal 167–8 for ten-million-dollar apps 152–60, 161–71, 176, 235–49 venture capital 72, 75, 156–8, 165–6, 235–49, 261–2, 383–5, 385, 418–19 game in game 348–9 gaming 42, 47, 318, 355 business model 67, 89 and revenue engines 232, 278–9 and user retention 288, 289 see also specific games Gandhi, Sameer 336 Gartner 271 Gates, Bill 4 general managers (GMs) 300–3 Gladwell, Malcolm 424 Glassdoor 361–2 Global Positioning System (GPS) 23 Gmail 72 GMs see general managers goal setting 40–1, 310–11 Goldberg, Dave 397 Goldman Sachs 385 ‘good leavers’ 247 Google 7, 19, 23, 27, 72, 88, 164, 226 acquisitions 43, 414–16, 418 application programming interface 35–6 beta testing 202 Chief Executive Officer 406–8 developer meetups 97 finding your app on 144, 147 Hangouts app 46 meetings 381–2 mission 404, 408–9 and the OKR framework 310 profit per employee 403, 405 revenue per employee 401, 405 scaling 332 and Snapchat 57 and source attribution 228–9 staff 339, 340, 361–2, 366, 401, 403, 404–5, 412 Thank God It’s Friday (TGIF) meetings 311–12 transparency 413 value 78 Waze app purchase 43 and WhatsApp 56 zero-user-acquisition cost 278 see also Android (mobile operating system) Google Ad Mob 149 Google AdSense 149 Google Analytics 135, 219–20, 345 Google Glass 38–9, 405 Google I/O conference 313 Google Maps 33, 35, 414, 416 Google Now 37 Google Play 88, 89, 117, 120, 226 and beta testing 202 finding apps in 141–5 profit per employee 403 ratings plus comments 204–5 Google Reader 72 Google Ventures 384 Google X 405 Google+ and business identity 114 and virality 281 Google.org 339 GPS see Global Positioning System Graham, Paul 184–5, 211 Graphical User Interface (GUI) 20 Greylock 321, 383 Gross, Bill 406–7, 409–10 Groupon 7, 51–2, 227, 344–5, 419 Grove, Andy 310 growth 267, 308–17 buying sustained 417–18 engines 184, 210, 222–31, 259, 265 and five-hundred-million-dollar apps 329–36 and Friday update meetings 311–12 and goal setting 310–11 and hiring staff 308–9, 411–12 and product and development teams 313–14 and staff conferences 312–13 targets 234, 260 see also acquisition (of users); international growth; scaling Growth Hackers 182 GUI see Graphical User Interface hackathons 99 Haig, Patrick 143 Hailo app xiii–xiv, 5, 36, 89, 386 big data 284–5 branding 112–13 cofounders 94–6 customer segments 346–7 customer-support 208–9 design 131, 132, 133, 206–7 development 123–7, 153–4 Friday update meetings 311 funding 162, 242 goal setting 310 growth 296–7, 299, 302–4, 308–11, 313, 315–17, 329–30, 334–6 hiring staff 308–9, 334–6, 338, 366–7 idea for 14–18 international growth 296, 297, 299, 302–4 market research 182 marketing 263, 264, 268, 270, 273, 341, 347–8 meetings 381 metrics 137–9, 216 name 107 organisational culture 396 platform choice 117, 120, 121 premises xiii–xiv, 177–8, 329–30, 371–2, 386 product development 189, 191, 196 retention 293–4 revenue engine 276 scaling development and engineering 357 scaling people 365–7 scaling process 377 team 258 testing 177–8, 201–4 and user emotionality 224 virality 280, 282 Hangouts app 46 Harris Interactive 31 HasOffers 149 Hay Day 47, 97 head of data 342 Heads Up Display (HUD) 38 heart rate measurement devices 37–8 Hed, Niklas 42 hiring staff 308–9, 334–6, 337–40, 365–70 history of apps 31–2 HMS President xiii–xiv, 177–9, 329, 371, 386 HockeyApp 202 HootSuite 151 Houston, Drew 407, 410–11 HP 180, 402 HTC smartphone 121 HUD see Heads Up Display human universals 44–5 Humedica 419 hyperlinks 147 hypertext markup language (HTML) 147 I/O conference 2013 202 IAd mobile advertising platform 149 IBM 20, 402 icons 143 ideas see ‘thinking big’ identity of the business 86 branding 111–13 identity crises 106–14 names 106–11 websites 113–14 image descriptions 147 in Mobi 149 in-app purchases 28 incentive-based networks 270–1 incorporation 163–4, 179 incubators 159–60 Index Ventures 3, 261 initial public offerings (IPOs) 64, 67–9, 78, 80, 246, 420–2 innovation 404–5 Instagram 6, 29, 48, 51, 67, 71–80, 88–90, 114, 117, 226, 278, 340, 417–18 cofounders 73–4 design 131 funding 75–6, 77–8 X-Pro II 75 zero-user-acquisition cost 278 instant messaging 46 Instantdomainsearch.com 109 integrators 410 Intel 310 intellectual property 165–6, 244, 247 international growth 295–307 Angry Birds 297–9 Hailo 296, 297, 299, 302–4 language tools 297 Square 295, 299, 304–6 strings files 296 Uber 299–302 International Space Station 13 Internet bubble 13 investment see funding iOS software (Apple operating system) 7, 23–4, 46, 75, 104 advertising 274 audience size 119 building apps for 116–22 and international apps 296 scaling development and engineering 357–8 time spent on 26 iPad 42–3, 118–20, 351 iPhone 6, 19, 22–3, 32, 38–9, 183, 351 advertising on 274 camera 48 designing apps for 117–18, 120 finding apps with 145 games 42, 47, 58 and Instagram 74–6 in Japan 306 and Square 104, 306 and Uber 301 user spend 117 and WhatsApp app 54–5 iPod 22 IPOs see initial public offerings Isaacson, Walter 32 iTunes app 22, 47, 88, 143 iTunes U app 69 Ive, Jony 129 iZettle 304 Jackson, Eric 40 Jain, Ankit 142 Japan 227, 304–6 Jawbone Up 38 Jelly Bean (Android mobile software) 7 Jobs, Steve 4, 22, 32, 323, 393, 425 journalists 150–1 Jun, Lei 306 Kalanick, Travis 299–300, 384, 422 Kayak 336 Keret, Samuel 43 Keyhole Inc. 414 keywords 143, 146 Kidd, Greg 104 King.com 349, 421–2 see also Candy Crush Saga KISSmetrics 291 KitKat (Android mobile software) 7 Klein Perkins Caulfield Byers (KPCB) 158, 261, 321, 383 Kontagent 135 Koolen, Kees 320, 339 Korea 30 Koum, Jan 42, 54, 55–6, 154, 321, 394, 416 Kreiger, Mike 73–6 language tools 297 Launchrock.com 113–14, 145, 202 Lawee, David 415 lawyers 103, 169, 170, 242 leadership 410–11 see also Chief Executive Officers; managers lean companies 69, 115–22, 154, 257, 320–1 Lee, Bob 340 legalities 163–70, 242–7, 301 letting go 406–7 Levie, Aaron 396–7, 411 Levinson, Art 32 LeWeb 97 Libin, Phil 399 licensing 356 life experience 67–8, 264 lifetime value (LTV) 184, 215, 219, 220–1, 232, 275–7, 279, 291, 342 Line app 46, 226 Lingo24 297 LinkedIn 97, 226, 406, 408–9 links 147 liquidation preference 242, 243, 245 non-participating 245 Livio 419 loans, convertible 163 Localytics 135, 221 locations 69 logos 111–14 LTV see lifetime value luck 412 Luckey, Palmer 39 LVMH 304 Lyons, Carl 263 Maiden 95 makers 375–7 see also designers; engineers/developers managers 189–90, 300–3, 375–7, 405 MapMyFitness 419 market research 115, 127, 182 marketing data 345–7 and Facebook 271, 272, 273–4 and incentive-based networks 270–1 marketing engineering team 344–5 and mobile social media channels 271–3, 272 and mobile user-acquisition channels 269–70 partner marketing 347–8 scaling 341–9 teams 262–6, 337, 342 and traditional channels 268–9 VPs 262–6, 337, 342 marketplace see e-commerce/marketplace MasterCard 347–8 Matrix Partners 283 McClure, Dave 136, 160, 211, 234 McCue, Mike 42–3, 105, 351 McKelvey, Jim 41, 104 ‘me-too’ products 181 Medium 41 Meebo 73 meetings 379–82, 412–13 annual offsite 379 daily check-ins 381 disruptive nature 376–7 Friday update 311–12 meaningful 381–2 monthly strategic 380 quarterly 380 weekly tactical 380 Meetup.com 98–9 Mendelsen, Jason 170 messaging platforms 226 time spent on 46 and user retention 288, 289 metrics 136–9, 139, 211–21 activation 136, 137, 139, 153–4, 211–12, 213 annual revenue per user (ARPU) 215, 219, 232, 236 average transaction value (ATV) 214–15, 219, 232, 236, 387 consensual 215–16 lifetime value (LTV) 184, 215, 219, 220–1, 232, 275–7, 279, 291, 342 and product-market fit 209–10 referral 137, 138, 139, 153, 154, 211–12, 213, 230–1 revenue 137, 138, 139, 154, 211–12, 213, 214–15, 219, 291 transparency regarding 312 see also acquisition (of users); retention (of users) mice 20 Microsoft application programming interface 35–6 revenue per employee 401 Windows 20, 22, 24 Millennial Media 149 minimum viable product (MVP) 123, 153 MirCorp 13–14 mission 261, 404, 408–9 Mitchell, Jason 51 Mitsui Sumitomo Bank 305 Mixpanel.com tool 135, 217–18, 220–1, 287, 290–1, 345 MMS see Multimedia Messaging Service Mobile Almanac 45 Mobile App Tracking 230, 231 mobile technology, rise of 19–39 MoMo app 306 Monsanto 419 moonshots 404–5 Moore, Jonathan 200 MoPub 149 Moqups.com 128 Mosaic 180 Motorola 21 Moz.com 143 Mullins, Jacob 419 Multimedia Messaging Service (MMS) 47 Murphy, Bobby 43, 104–5, 152–3 music player apps 47 MVP see Metrics into Action; minimum viable product names 106–11, 142 NameStation.com 108 Nanigans 273–4 National Venture Capital Association 64 native apps 33–4 NDA see Non Disclosure Agreement negotiation 265 Net Promoter Score (NPS) 206, 209 net-adding users 206 Netflix 400 Netscape 164, 180 New Enterprise Associates 385 New York Times news app 32–3, 256 news and alerts feature 48–9 Nextstop 72 Nguyen, Bill 255–6 NHN 227 Nike Fuelband 38 Nintendo Game Boy 47 Nokia 21, 35–6 Non Disclosure Agreement (NDA) 165 noncompetition/non-solicitation provision 244, 247 notifications 291–4 NPS see Net Promoter Score Oculus VR 39 OKR (‘objectives and key results’) framework 310–11, 380 OmniGraffle 128 open-source software 23, 34–5, 185 OpenCourseWare 68–9 operating systems 20–4 see also Android; iOS software operations VPs 337 org charts 258, 309 organisational culture 395–8 O’Tierney, Tristan 104 outsourcing 194–7 ownership and founder vesting 166–7 and funding 155, 156, 161–3, 318 oxygen saturation measurement devices 37–8 Paananen, Ilkka 118–19, 397–8 Page, Larry 4, 23, 382, 404, 407–8 Palantir 90 Palihapitiya, Chamath 187 Pandora 7, 47, 67, 131, 410 pay-before-you-download model 28 pay-per-download (PPD) 225 Payleven 304 payment systems 7, 33–4, 227, 304, 305 see also Square app PayPal 7, 227, 304, 305 Pepsi 196 Perka 419 perks 398–400 perseverance 67, 394, 410 personal computers (PCs) 29 perspiration measurement devices 38 Pet Rescue Saga 349, 421 Petrov, Alex 369 phablets 7 Pham, Peter 255 PhoneSaber 33 Photoshop 128 PIN technology 305 Pincus, Mark 311 Pinterest app 48, 226 and business identity 114 and e-commerce decisions 271, 272 and getting your app found 147 name 107 and virality 281 Pishevar, Shervin 300 pivoting 73–4 population, global 9–10 portfolio companies 261–2 PowerPoint 128 PPD see pay-per-download preferential return 243 premises 370–2 preparation 412 press kits 148, 150 press releases 150 Preuss, Dom 98 privacy issues 43, 56–7 private vehicle hire see Uber pro-rata rights 242, 243 producers 409 product chunks 360 product development scaling 357–63 scope 199 team building for 188–91 and team location 193–4 and vision 186–8, 191 see also app development; testing product expansion 350–63 product extension 354 product managers 189–90, 405 product-centricity 185–6, 314, 360 product-market fit 9, 180–97, 235–6, 248, 256–7 measurement 209–10, 212, 286–8 profit 267, 320, 342 profit margin 258–9, 318, 321 profit per employee 402–4, 403, 405 profitability 260, 277, 400 Project Loon 405 proms 12 proto.io tool 133 prototype apps 86, 174 app Version 0.1 123–35, 174 new and improved Version 1.0 198–210 rapid-design prototyping 132–3 PRWeb 150 PSP 47 psychological effects of smartphones 29–30, 30 pttrns.com 131 public-relations agencies 343 publicity 150–1, 225, 313 putting metrics into action 138–9 Puzzles and Dragons 47, 131 QlikView 221, 284–5 QQ 307 quality assurance (QA) 190–1, 196 Quora 76 QZone 307 Rabois, Keith 368, 369 Rakuten 227 Rams, Dieter 129 rapid-design prototyping 132–3 ratings plus comments 204–5 Red Bull 223 redemption codes 230 referrals (user) 137, 138, 139, 153, 154, 211–12, 213 attribution for referrals 230–1 referral codes 230 religious apps 45 remuneration 361–2, 362, 363 Renault 13 restated certification 169 retention (of users) 136–9, 153, 154 for five hundred-million-dollar apps 327, 341–3 for hundred-million-dollar apps 286–94, 288–9 measurement 286–8 for ten-million-dollar apps 206, 211–12, 213, 278 revenue 137–8, 139, 154, 211–12, 213, 214–15, 219, 236, 239–40, 267, 291, 331–2, 341–2, 354 revenue engines 184, 210, 232–4, 257–66, 265, 275–85 revenue per employee 400–2, 402, 405 revenue streams 27–9 Ries, Eric, The Lean Startup 115–16 Rockefeller, John D. 9 Rocket Internet 304 Rolando 33 Rosenberg, Jonathan 413 Rovio 58, 97, 118, 297–9, 318, 320–1, 336, 354, 409 see also Angry Birds Rowghani, Ali 77 Rubin, Andy 23 Runa 419 SaaS see software as a service Sacca, Chris 75–6 sacrifice 86–7 Safari Web browser 32 salaries 361–2, 362, 363 sales VPs 337 Salesforce 291 Samsung 23 Galaxy Gear smartwatch 38 smartphones 121 Sandberg, Sheryl 4, 100–1, 339, 397 SAP 304 scaling 259, 308, 312, 323–4, 326, 330–6, 331–2, 384–5 decision making 379–81 international growth 295–307 marketing 341–9 and organisational culture 396–8 people 338–9, 364–72 premature 334–5 process 373–82 product development and engineering 357–63 and product innovation 350–6 reasons for 333–4 skill set for 335–6 Schmidt, Eric 120 scope 199 screenshots 131, 144, 206 scrum masters (‘agile coaches’) 315, 359, 360 search functions 49 organic 141–2, 141, 145 search-engine optimisation (SEO) 142, 145–8, 225 Sedo.com 109 Seed Fund 136 Seedcamp 160 Sega Game Gear 47 segmentation 220, 287, 290, 346–7 self-empowered squads/units 360 SEO see search-engine optimisation Sequoia Capital 76, 77–80, 158, 255, 321, 383, 385 Series A funding 234, 238–40, 238, 240, 241, 242–6, 255, 261, 262, 319–21, 385 Series B funding 238, 241, 253, 260, 319–21, 322, 384 Series C funding 384 Series Seed documents 168 Sesar, Steven 263 sex, smartphone use during 31 Shabtai, Ehud 43 shares 156, 166–8, 244 ‘sharing big’ 51–2, 52 Shinar, Amir 43 Shopzilla 263 Short Message Service (SMS) 21, 46–7 Silicon Valley 71–4, 77, 79, 99, 162, 168, 180, 184, 255, 340, 361, 411, 422 Sina 227 sitemaps 146–7 skills sets complementary 93 diverse 409–10 for scaling 335–6 Skok, David 283 Skype app 7, 46, 111, 200–1, 226, 357, 419 Sleep Cycle app 48 Smartling 297 smartwatches 7, 38–9 SMS see Short Message Service Snapchat app 6, 43, 46, 56–7, 88, 89, 223, 226, 416, 418 cofounders 104–5 design 131 funding 152–3, 307, 320 name 107 platform 117 staff 340 valuations 333 virality 280, 283 zero-user-acquisition cost 278 social magazines 42–3 see also Flipboard social media 48 driving downloads through 151 and getting your app found 147 mobile channels 271–3, 272 and user retention 288, 289 Sofa 363 SoftBank 227 software development agile 192–3, 299, 315, 357, 377 outsourcing 194–5 see also app development software as a service (SaaS) 67, 90, 208, 214, 233, 276–7 Somerset House 329–30, 371 Sony 21, 47 SoundCloud 358 source attribution 227–31 space tourism 13–14 speech-to-text technology 50 speed 20 Spiegel, Evan 43, 56–7, 104–5, 152–3 Spinvox 50 Splunk 90 Spotify app 47, 357–8 SQL 284 Square app 6, 41–2, 58–9, 87, 89, 333, 350 branding 112 Chief Executive Officer 412–13 cofounders 104 design 131, 363 funding 320–1 international growth 295, 299, 304–6 marketing 348 metrics 215–16 name 107, 110 product–market fit 183 revenue engine 276 scaling people 367–8 scaling product innovation 352–3 staff 340, 367–8 transparency 312 virality 282 Square Cash 353 Square Market 353 Square Register 350, 352–3 Square Wallet 348, 350, 353 Squareup.com 144 staff at billion-dollar app scale 395–405, 423 attracting the best 91 benefits 398–400 conferences 312–13 conflict 334, 378 employee agreements 244 employee legals 246–7 employee option pool 244 employee-feedback systems 378 firing 370, 378 hiring 308–9, 334–6, 411–12 induction programmes 370 investment in 360 mistakes 369–70, 411–12 and premises 370–2 profit per employee 402–4, 403 revenue per employee 400–2, 402 reviews 370 scaling people 364–72, 377–9 scrum masters 315, 359, 360 training programmes 370 see also cofounders; specific job roles; teams Staples 419 Starbucks 338, 348 startup weekends 98 startups, technology difficulties of building 63–80 failure 63–5, 73–4 identity 106–14 lean 115–22, 154 process 82–4, 85–105 secrets of success 66–9 step sensors 38 stock markets 420–1 straplines 111 strings files 296 Stripe 160 style 111 subscriptions 90 success, engines of 183–4, 423–4 SumUp 304 Supercell 28, 47, 97, 118–19, 318, 336, 397–8, 401, 403 see also Clash of Clans; Hay Day SurveyMonkey 397 surveys 206, 209 synapses 10 Systrom, Kevin 71–80 tablets 7 Tableau Software 90 Taleb, Nicholas Nassim 54 Tamir, Diana 51 Tap Tap Revolution (game) 42 Target 419 taxation 164 taxi hailing apps see Hailo app TaxiLight 16 team builders 264 team building 188–91 teams 82, 174, 252, 390 complementary people 409–10 for five-hundred-million-dollar apps 326, 342–5, 357–63, 374, 386 growth 313–14, 326, 342–4 for hundred-million-dollar apps 258–61 located in one place 193–4 marketing 262–6, 342–4 marketing engineering 344–5 product development and engineering 357–63 ‘two-pizza’ 374 TechCrunch Disrupt 97, 99 technology conferences 97–8, 202, 312–13 Techstars 159, 160, 168 Tencent 307 Tencent QQ 226 term sheets 168, 169, 170, 243–4 testing 126–8, 177–8, 187–8, 192–3, 199–201 beta 201–4 channels 224–7 text messaging 21 unlimited packages 42 see also Short Message Service ‘thinking big’ 40–59, 82, 85 big problem solutions 41–3 disruptive ideas 53–9 human universals 44–5 sharing big 51–2, 52 smartphones uses 45–50 Thoughtworks 196 time, spent checking smartphones 25–6, 26, 45–50 Tito, Dennis 13 tone of voice 111 top-down approaches 311 traction 233, 252 traffic information apps 43 traffic trackers 146 translation 296–7 transparency 311–12, 412–13 Trilogy 13 Tumblr 110, 226, 399, 418 Twitter 41, 48, 54, 72, 226, 394 acquisitions 418 and application programming interface 36 and Bootstrap 145 and business identity 114 delivering delight 206 and e-commerce decisions 272 and FreeMyApps.com 271 funding 419, 421 and getting your app found 147 initial public offering 421 and Instagram 51, 76–7, 79–80 name 110 and virality 281 ‘two-pizza’ teams 374 Uber 6, 36, 87, 89, 333, 350 and attribution for referrals 231 design 131 funding 320, 384, 422 international growth 295, 299–302 name 107, 110 revenue engine 276 revenue per employee 401 scaling product innovation 355–6 staff 339, 399 user notifications 292 virality 280 Under Armour 419 Union Square Ventures (USV) 3, 158, 242, 261, 262, 288, 321, 323, 377, 383 unique propositions 198 UnitedHealth Group 419 URLs 110 ‘user experience’ (UX) experts 190 user journeys 127–8, 213–14 user notifications 291–4 user stories 193 users 83, 175, 252, 327, 390 activation 136, 137, 139, 153–4, 211–12, 213 annual revenue per user (ARPU) 215, 219, 232, 236 communication with 208–9 definition 137 emotional response of 223–4 fanatical 294 finding apps 140–8 lifetime value (LTV) 184, 215, 219, 220–1, 232, 275–7, 279, 291 metrics 136–9 net-adding of 206 ratings plus comments 204–5 referrals 137, 138, 139, 153, 154, 211–12, 213, 230–1 target 83, 115, 127 wants 180–97 see also acquisition (of users); retention (of users) Usertesting.com 200–1 USV see Union Square Ventures valuations 83, 161–3, 175, 237–8, 238, 253, 318, 319, 322, 327, 333, 391 venture capital 72, 75, 156–8, 165–6, 235–49, 261–2, 383–5, 385, 418–19 Viber app 6, 46, 1341 video calls 46, 47 viral coefficient 282–4 ‘viral’ growth 225, 278, 279–84 Communication virality 281 and cycle time 283–4 incentivised virality 280–1 inherent virality 280 measurement 282–4 social-network virality 281 word-of-mouth virality 281–2 virtual reality 39 vision 261, 393–4, 408–9, 414, 415 voice calls 46–7 voice-over-Internet protocol (VOIP) 46 voicemail 50 Wall Street Journal 43, 55 warranties 246 Waze app 6, 43, 97 acquisition 415–16 design 131 name 107 zero-user-acquisition cost 278 web browsing 49 Web Summit 97 websites 113–14, 144–8 WebTranslateIt (WTI) 297 WeChat app 46, 226, 306 Weibo 48 Weiner, Jeff 408–9 Wellington Partners 4 Weskamp, Marcos 207 Westergren, Tim 410 WhatsApp 6, 42, 46, 54–6, 87, 90, 226, 394 acquisition 42, 54–6, 416, 416–17, 417 cofounders 96 design 131, 144 funding 154, 320–1 platform 117–18 valuations 333 virality 280 White, Emily 340 Williams, Evan 41, 65 Williams, Rich 344 Wilson, Fred 110, 242, 288, 323, 377 Windows (Microsoft) 20–1, 22, 24, 24 Winklevoss twins 105 wireframes 127–8 Woolley, Caspar 15–16, 95, 124, 338 WooMe.com 14, 87–8, 101–2, 263 Workday 90 world population 9–10 Worldwide Developers Conference (WWDC) 313 wowing people 8–9 WTI see WebTranslateIt Xiaomi 306 Y Combinator 159–60, 184–5, 211, 407, 410–11 Yahoo!


pages: 444 words: 127,259

Super Pumped: The Battle for Uber by Mike Isaac

"side hustle", activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, always be closing, Amazon Web Services, Andy Kessler, autonomous vehicles, Ayatollah Khomeini, barriers to entry, Bay Area Rapid Transit, Burning Man, call centre, Chris Urmson, Chuck Templeton: OpenTable:, citizen journalism, Clayton Christensen, cloud computing, corporate governance, creative destruction, don't be evil, Donald Trump, Elon Musk, family office, gig economy, Google Glasses, Google X / Alphabet X, high net worth, Jeff Bezos, John Markoff, Kickstarter, Lyft, Marc Andreessen, Mark Zuckerberg, mass immigration, Menlo Park, Mitch Kapor, money market fund, moral hazard, move fast and break things, move fast and break things, Network effects, new economy, off grid, peer-to-peer, pets.com, Richard Florida, ride hailing / ride sharing, Sand Hill Road, self-driving car, shareholder value, side project, Silicon Valley, Silicon Valley startup, skunkworks, Snapchat, software as a service, software is eating the world, South China Sea, South of Market, San Francisco, sovereign wealth fund, special economic zone, Steve Jobs, TaskRabbit, the payments system, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, ubercab, union organizing, upwardly mobile, Y Combinator

Traffic in major urban areas, especially the San Francisco Bay Area, was abysmal. People clogged the streets with inefficiently operated cars. One person to every vehicle on the road was inefficient and wasteful. A fleet of self-driving cars, used only when necessary, would be far cleaner and more cost-effective. Once Google had bought Levandowski’s startup, he dove headlong into mapping and self-driving tech for his superiors, joining the secretive Google X division. Colleagues said Levandowski deserved much of the credit for convincing Google’s top brass, especially Larry Page, to pour millions into self-driving research. And by virtue of working on a project dear to the CEO’s heart, Levandowski began to develop a special relationship with Page. But he was also shrewd. When Google bought 510 Systems, Levan­dowski sold it for just under the amount that would have required him to share the profits with the fifty or so employees under him, depriving dozens of his colleagues of a rich payday.

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

Page wanted to make his childhood dreams of the future come true in his own lifetime. Although Google was the first Big Tech company to devote substantial resources and money to self-driving-car research, executives admitted they were slow to move and test the cars more aggressively. Competitors like Apple and Tesla were gaining traction in the space. After Levandowski left, Page made changes to how the self-driving wing would operate. Formerly operating under the Google “X” wing of “Moonshots,” Page spun self-driving research out into its own, separate company. It was called Waymo, derived from the idea that the work will create “a new way forward in mobility.” Page tapped John Krafcik, a former president of Hyundai Motor America, to be Waymo’s CEO. Waymo had a years-long head start on the competition. The new company planned to capitalize on that lead, before they were overtaken.


pages: 285 words: 86,853

What Algorithms Want: Imagination in the Age of Computing by Ed Finn

Airbnb, Albert Einstein, algorithmic trading, Amazon Mechanical Turk, Amazon Web Services, bitcoin, blockchain, Chuck Templeton: OpenTable:, Claude Shannon: information theory, commoditize, Credit Default Swap, crowdsourcing, cryptocurrency, disruptive innovation, Donald Knuth, Douglas Engelbart, Douglas Engelbart, Elon Musk, factory automation, fiat currency, Filter Bubble, Flash crash, game design, Google Glasses, Google X / Alphabet X, High speed trading, hiring and firing, invisible hand, Isaac Newton, iterative process, Jaron Lanier, Jeff Bezos, job automation, John Conway, John Markoff, Just-in-time delivery, Kickstarter, late fees, lifelogging, Loebner Prize, Lyft, Mother of all demos, Nate Silver, natural language processing, Netflix Prize, new economy, Nicholas Carr, Norbert Wiener, PageRank, peer-to-peer, Peter Thiel, Ray Kurzweil, recommendation engine, Republic of Letters, ride hailing / ride sharing, Satoshi Nakamoto, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, social graph, software studies, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, supply-chain management, TaskRabbit, technological singularity, technoutopianism, The Coming Technological Singularity, the scientific method, The Signal and the Noise by Nate Silver, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, transaction costs, traveling salesman, Turing machine, Turing test, Uber and Lyft, Uber for X, uber lyft, urban planning, Vannevar Bush, Vernor Vinge, wage slave

Minneapolis: University of Minnesota Press, 2006. http://site.ebrary.com/lib/alltitles/docDetail.action?docID=10151343. Galloway, Alexander R. Protocol: How Control Exists after Decentralization. Leonardo. Cambridge, Mass.: MIT Press, 2004. http://hdl.handle.net/2027/heb.31968. Galloway, Alexander R. The Interface Effect. 1st ed. Cambridge, UK: Polity, 2012. Gertner, Jon. “The Truth About Google X: An Exclusive Look Behind the Secretive Lab’s Closed Doors.” Fast Company, April 15, 2014. http://www.fastcompany.com/3028156/united-states-of-innovation/the-google-x-factor. Gibson, William. Neuromancer. 1st ed. New York: Ace, 1984. Gillespie, Tarleton. The Relevance of Algorithms. In Media Technologies: Essays on Communication, Materiality, and Society, edited by Tarleton Gillespie, Pablo J. Boczkowski and Kirsten A. Foot, 167–193. Cambridge, Mass.: MIT Press, 2014. http://www.myilibrary.com?

Of course, word order can still shape Siri’s interpretation, but only through probabilities, not a formal grammatical structure. 19. Bosker, “SIRI RISING.” 20. Poeter, “Siri, Are You Anti-Abortion?” 21. “Shit That Siri Says—Baby Stores.” 22. Journalist Mat Honan made this argument with passion in the early months after Siri’s release: Honan, “Siri Is Apple’s Broken Promise.” 23. Labovitz, “Google Sets New Internet Record.” 24. Vaidhyanathan, The Googlization of Everything, 16. 25. Gertner, “The Truth About Google X.” 26. Tsukayama, “FAQ.” 27. Jenkins, “Google and the Search for the Future.” 28. Manjoo, “Where No Search Engine Has Gone Before.” 29. Kolbe, “Evolution.” 30. Bleecker, “Design Fiction: A Short Essay on Design, Science, Fact and Fiction.” 31. Thanks to Sam Arbesman for this insight: Arbesman, “Email Correspondence.” 32. Darnton, The Business of the Enlightenment, 520. 33. Schwab, “Translator’s Introduction.” 34.


pages: 49 words: 12,968

Industrial Internet by Jon Bruner

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

“Each silo has achieved its highest possible level of efficiency,” says Alok Batra, the CTO and chief architect for GE Global Research.[3] “If we don’t break down silos, we can’t generate more efficiency. Nothing operates in isolation anymore. If you operate a manufacturing plant, you need to know about wind and power supplies.” Substitution of software for assets The industrial internet will, as Astro Teller[4], Captain of Moonshots at Google[x], suggests, “trade away physical complexity for control-system problems.” As machines deliver their work more efficiently, we’ll need fewer of them and the machines themselves will become simpler. Consider, for instance, that California’s state-wide electricity demand stays below 30 gigawatts about 80% of the time. For about 20 hours every year, though, it surges past 47 gigawatts.[5] Utilities must build out massive capacity that’s only used during peak hours a few days each summer.


pages: 252 words: 74,167

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

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

‘The rule of thumb is that if there’s a task you want to do and you know it involves huge amounts of knowledge, that means that if you’re going to learn to do it you need huge numbers of parameters. If that’s the case, then deep learning is the way to do that.’ Impressive applications are everywhere. In 2011, the summer before Hinton joined Google, Google engineers Jeff Dean and Greg Corrado and Stanford computer scientist Andrew Ng launched what is known as the Google Brain project. Housed at Google’s semi-secret research laboratory, Google X, Google Brain used a deep learning net to recognise high-level concepts such as cats by analysing still images from YouTube videos – without ever having been told what a cat is. (Incidentally, this is virtually the exact same feat Frank Rosenblatt told the New Yorker that neural networks would one day perform, half a century earlier.) Having a computer that knows what a cat is may not sound like a particularly useful achievement, but the ability to use deep learning for computer vision has a host of real-world uses.

To find a specific word or phrase from the index, please use the search feature of your ebook reader. 2001: A Space Odyssey (1968) 2, 228, 242–4 2045 Initiative 217 accountability issues 240–4, 246–8 Active Citizen 120–2 Adams, Douglas 249 Advanced Research Projects Agency (ARPA) 19–20, 33 Affectiva 131 Age of Industry 6 Age of Information 6 agriculture 150–1, 183 AI Winters 27, 33 airlines, driverless 144 algebra 20 algorithms 16–17, 59, 67, 85, 87, 88, 145, 158–9, 168, 173, 175–6, 183–4, 186, 215, 226, 232, 236 evolutionary 182–3, 186–8 facial recognition 10–11, 61–3 genetic 184, 232, 237, 257 see also back-propagation AliveCor 87 AlphaGo (AI Go player) 255 Amazon 153, 154, 198, 236 Amy (AI assistant) 116 ANALOGY program 20 Analytical Engine 185 Android 59, 114, 125 animation 168–9 Antabi, Bandar 77–9 antennae 182, 183–5 Apple 6, 35, 56, 65, 90–1, 108, 110–11, 113–14, 118–19, 126–8, 131–2, 148–9, 158, 181, 236, 238–9, 242 Apple iPhone 108, 113, 181 Apple Music 158–9 Apple Watch 66, 199 architecture 186 Artificial Artificial Intelligence (AAI) 153, 157 Artificial General Intelligence (AGI) 226, 230–4, 239–40, 254 Artificial Intelligence (AI) 2 authentic 31 development problems 23–9, 32–3 Good Old-Fashioned (Symbolic) 22, 27, 29, 34, 36, 37, 39, 45, 49–52, 54, 60, 225 history of 5–34 Logical Artificial Intelligence 246–7 naming of 19 Narrow/Weak 225–6, 231 new 35–63 strong 232 artificial stupidity 234–7 ‘artisan economy’ 159–61 Asimov, Isaac 227, 245, 248 Athlone Industries 242 Atteberry, Kevan J. 112 Automated Land Vehicle in a Neural Network (ALVINN) 54–5 automation 141, 144–5, 150, 159 avatars 117, 193–4, 196–7, 201–2 Babbage, Charles 185 back-propagation 50–3, 57, 63 Bainbridge, William Sims 200–1, 202, 207 banking 88 BeClose smart sensor system 86 Bell Communications 201 big business 31, 94–6 biometrics 77–82, 199 black boxes 237–40 Bletchley Park 14–15, 227 BMW 128 body, machine analogy 15 Bostrom, Nick 235, 237–8 BP 94–95 brain 22, 38, 207–16, 219 Brain Preservation Foundation 219 Brain Research Through Advanced Innovative Neurotechnologies 215–16 brain-like algorithms 226 brain-machine interfaces 211–12 Breakout (video game) 35, 36 Brin, Sergey 6–7, 34, 220, 231 Bringsjord, Selmer 246–7 Caenorhabditis elegans 209–10, 233 calculus 20 call centres 127 Campbell, Joseph 25–6 ‘capitalisation effect’ 151 cars, self-driving 53–56, 90, 143, 149–50, 247–8 catering 62, 189–92 chatterbots 102–8, 129 Chef Watson 189–92 chemistry 30 chess 1, 26, 28, 35, 137, 138–9, 152–3, 177, 225 Cheyer, Adam 109–10 ‘Chinese Room, the’ 24–6 cities 89–91, 96 ‘clever programming’ 31 Clippy (AI assistant) 111–12 clocks, self-regulating 71–2 cognicity 68–9 Cognitive Assistant that Learns and Organises (CALO) 112 cognitive psychology 12–13 Componium 174, 176 computer logic 8, 10–11 Computer Science and Artificial Intelligence Laboratory (CSAIL) 96–7 Computer-Generated Imagery (CGI) 168, 175, 177 computers, history of 12–17 connectionists 53–6 connectomes 209–10 consciousness 220–1, 232–3, 249–51 contact lenses, smart 92 Cook, Diane 84–6 Cook, Tim 91, 179–80 Cortana (AI assistant) 114, 118–19 creativity 163–92, 228 crime 96–7 curiosity 186 Cyber-Human Systems 200 cybernetics 71–4 Dartmouth conference 1956 17–18, 19, 253 data 56–7, 199 ownership 156–7 unlabelled 57 death 193–8, 200–1, 206 Deep Blue 137, 138–9, 177 Deep Knowledge Ventures 145 Deep Learning 11–12, 56–63, 96–7, 164, 225 Deep QA 138 DeepMind 35–7, 223, 224, 245–6, 255 Defense Advanced Research Projects Agency (DARPA) 33, 112 Defense Department 19, 27–8 DENDRAL (expert system) 29–31 Descartes, René 249–50 Dextro 61 DiGiorgio, Rocco 234–5 Digital Equipment Corporation (DEC) 31 Digital Reasoning 208–9 ‘Digital Sweatshops’ 154 Dipmeter Advisor (expert system) 31 ‘do engines’ 110, 116 Dungeons and Dragons Online (video game) 197 e-discovery firms 145 eDemocracy 120–1 education 160–2 elderly people 84–6, 88, 130–1, 160 electricity 68–9 Electronic Numeric Integrator and Calculator (ENIAC) 12, 13, 92 ELIZA programme 129–30 Elmer and Elsie (robots) 74–5 email filters 88 employment 139–50, 150–62, 163, 225, 238–9, 255 eNeighbor 86 engineering 182, 183–5 Enigma machine 14–15 Eterni.me 193–7 ethical issues 244–8 Etsy 161 Eurequa 186 Eve (robot scientist) 187–8 event-driven programming 79–81 executives 145 expert systems 29–33, 47–8, 197–8, 238 Facebook 7, 61–2, 63, 107, 153, 156, 238, 254–5 facial recognition 10–11, 61–3, 131 Federov, Nikolai Fedorovich 204–5 feedback systems 71–4 financial markets 53, 224, 236–7 Fitbit 94–95 Flickr 57 Floridi, Luciano 104–5 food industry 141 Ford 6, 230 Foxbots 149 Foxconn 148–9 fraud detection 88 functional magnetic resonance imaging (fMRI) 211 Furbies 123–5 games theory 100 Gates, Bill 32, 231 generalisation 226 genetic algorithms 184, 232, 237, 257 geometry 20 glial cells 213 Go (game) 255 Good, Irving John 227–8 Google 6–7, 34, 58–60, 67, 90–2, 118, 126, 131, 155–7, 182, 213, 238–9 ‘Big Dog’ 255–6 and DeepMind 35, 245–6, 255 PageRank algorithm 220 Platonic objects 164, 165 Project Wing initiative 144 and self-driving cars 56, 90, 143 Google Books 180–1 Google Brain 61, 63 Google Deep Dream 163–6, 167–8, 184, 186, 257 Google Now 114–16, 125, 132 Google Photos 164 Google Translate 11 Google X (lab) 61 Government Code and Cypher School 14 Grain Marketing Adviser (expert system) 31 Grímsson, Gunnar 120–2 Grothaus, Michael 69, 93 guilds 146 Halo (video game) 114 handwriting recognition 7–8 Hank (AI assistant) 111 Hawking, Stephen 224 Hayworth, Ken 217–21 health-tracking technology 87–8, 92–5 Healthsense 86 Her (film, 2013) 122 Herd, Andy 256–7 Herron, Ron 89–90 High, Rob 190–1 Hinton, Geoff 48–9, 53, 56, 57–61, 63, 233–4 hive minds 207 holograms 217 HomeChat app 132 homes, smart 81–8, 132 Hopfield, John 46–7, 201 Hopfield Nets 46–8 Human Brain Project 215–16 Human Intelligence Tasks (HITs) 153, 154 hypotheses 187–8 IBM 7–11, 136–8, 162, 177, 189–92 ‘IF THEN’ rules 29–31 ‘If-This-Then-That’ rules 79–81 image generation 163–6, 167–8 image recognition 164 imagination 178 immortality 204–7, 217, 220–1 virtual 193–8, 201–4 inferences 97 Infinium Robotics 141 information processing 208 ‘information theory’ 16 Instagram 238 insurance 94–5 Intellicorp 33 intelligence 208 ambient 74 ‘intelligence explosion’ 228 top-down view 22, 25, 246 see also Artificial Intelligence internal combustion engine 140–1, 150–1 Internet 10, 56 disappearance 91 ‘Internet of Things’ 69, 70, 83, 249, 254 invention 174, 178, 179, 182–5, 187–9 Jawbone 78–9, 92–3, 254 Jennings, Ken 133–6, 138–9, 162, 189 Jeopardy!


pages: 280 words: 71,268

Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World With OKRs by John Doerr

Albert Einstein, Bob Noyce, cloud computing, collaborative editing, commoditize, crowdsourcing, Firefox, Frederick Winslow Taylor, Google Chrome, Google Earth, Google X / Alphabet X, Haight Ashbury, Jeff Bezos, job satisfaction, Khan Academy, knowledge worker, Menlo Park, meta analysis, meta-analysis, PageRank, Paul Buchheit, Ray Kurzweil, risk tolerance, self-driving car, side project, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, Steven Levy, subscription business, web application, Yogi Berra, éminence grise

But by no means, as Andy Grove made clear, is it the place to stop: You know, in our business we have to set ourselves uncomfortably tough objectives, and then we have to meet them. And then after ten milliseconds of celebration we have to set ourselves another [set of] highly difficult-to-reach objectives and we have to meet them. And the reward of having met one of these challenging goals is that you get to play again. 13 Stretch: The Google Chrome Story Sundar Pichai CEO Stretch goals were beautifully defined by the leader of the Google X team that developed Project Loon and self-driving cars. Says Astro Teller: “ If you want your car to get fifty miles per gallon, fine. You can retool your car a little bit. But if I tell you it has to run on a gallon of gas for five hundred miles, you have to start over.” In 2008, Sundar Pichai was Google’s vice president of product development. When Sundar and his team took their Chrome browser to market, they were most definitely starting over.

See also Chrome; YouTube author’s slide show on OKRs at, 3 –4, 13 –14, 156 –57 board of directors, 3 , 154 , 159 Campbell at, 251 –52 check-in cycle, 119 compensation and OKRs, 181 –82 gospel of 10x, 138 –40 market cap, 5 , 15 marriage of OKRs and, 3 –6, 11 –12, 13 –15 mission statement, 48 –49 moonshot goals, 140 , 141 , 148 , 149 OKR baskets, 135 –36 OKR Playbook, 255 –65 OKR scoring, 120 , 120 n Project Aristotle, 215 self-assessments, 123 “20 percent time,” 87 Google Docs, 110 Google Health, 70 Google Research Group, 161 Google Search, 145 , 152 , 156 , 161 –62 Google Toolbar, 145 –46, 149 Google Videos, 158 –59 Google X, 143 gospel of 10x, 138 –40, 277 Grand Engineering Challenges, 134 Granger, Kay, 242 green zone (“on track”), 118 , 130 , 168 , 231 Gróf, András István, 20 –21 Grove, Andy, 6 , 16 , 19 , 20 –34, 21 , 39 basic OKR hygiene, 33 –34 bottom-up ideas, 87 culture and, 145 , 213 –15 genesis of OKRs, 22 –29 goal setting, 56 iOPEC seminar, 22 –23, 27 , 34 , 213 legacy of, 32 –34 measuring output, 25 –27 OKR incarnate, 29 –32 OKR scoring, 121 –22 one-on-one conversations, 182 –83 Operation Crush, 35 , 36 –39, 43 –46 personality of, 30 –31 stretch goals, 136 –37, 142 Grove, Eva, 28 hard goals, 9 –10, 134 Harvard Business Review, 79 Harvard Business School, 8 –9, 19 , 29 , 124 Hastings, Tom, 232 Healthcare.gov, 71 –72 Hewlett-Packard, 25 high-motivation cultures, 216 , 280 High Output Management (Grove), 51 , 53 –54, 56 , 77 , 213 Hippocrates, 225 Hobson, Mellody, 133 horizontal connections, 111 –12 House, Dave, 137 HOW: Why HOW We Do Anything Means Everything (Seidman), 219 –21, 246 How Google Works (Schmidt and Rosenberg), 13 Iacocca, Lee, 52 IBM, 5 , 19 , 28 Ibrahim, Hadeel, 241 –42 Ibrahim, Lila, 217 –19, 219 Ibrahim, Mo, 241 –42 Imagine K12, 62 implementation kinks, 104 , 273 incentives, 117 , 137 –38, 224 , 275 individual OKRs, 15 , 16 , 24 –25, 33 , 56 , 95 , 107 insufficient KRs, 260 –61 integration meetings, 96 Intel, xi , 5 , 6 , 19 –34.


pages: 588 words: 131,025

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, 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, disintermediation, disruptive innovation, don't be evil, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Firefox, global village, Google Glasses, Google X / Alphabet X, Ignaz Semmelweis: hand washing, information asymmetry, interchangeable parts, Internet of things, Isaac Newton, job automation, Julian Assange, Kevin Kelly, license plate recognition, lifelogging, Lyft, Mark Zuckerberg, Marshall McLuhan, meta analysis, meta-analysis, microbiome, Nate Silver, natural language processing, Network effects, Nicholas Carr, obamacare, pattern recognition, personalized medicine, phenotype, placebo effect, RAND corporation, randomized controlled trial, 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, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, WikiLeaks, X Prize

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//?print=1. 29. R. 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.

Underlying its predictive capabilities was quite a portfolio of machine learning systems, including Bayesian nets, Markov chains, support vector machine algorithms, and genetic algorithms.33 I won’t go into any more depth; my brain is not smart enough to understand it all, and fortunately it’s not particularly relevant to where we are going here. Another subtype of AI and machine learning,2,20,34–48 known as deep learning, has deep importance to medicine. Deep learning is behind Siri’s ability to decode speech as well as Google Brain experiments to recognize images. Researchers at Google X extracted ten million still images from YouTube videos and fed them to the network of one thousand computers to see what the Brain, with its one million simulated neurons and one billion simulated synapses would come up with on its own.35,36 The answer—cats. That the Internet, at least the YouTube segment (which occupies a lot of it), is chock full of cat videos. More than the cat diagnosis, this revelation exemplified the operation of cognitive, or what is also known as neuromorphic, computing.49a For if computers can emulate the human brain, as the theory goes, they can be taken to the next level of performance for perception, action, and cognition.


pages: 567 words: 122,311

Lean Analytics: Use Data to Build a Better Startup Faster by Alistair Croll, Benjamin Yoskovitz

Airbnb, Amazon Mechanical Turk, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, barriers to entry, Bay Area Rapid Transit, Ben Horowitz, bounce rate, business intelligence, call centre, cloud computing, cognitive bias, commoditize, constrained optimization, en.wikipedia.org, Firefox, Frederick Winslow Taylor, frictionless, frictionless market, game design, Google X / Alphabet X, Infrastructure as a Service, Internet of things, inventory management, Kickstarter, lateral thinking, Lean Startup, lifelogging, longitudinal study, Marshall McLuhan, minimum viable product, Network effects, pattern recognition, Paul Graham, performance metric, place-making, platform as a service, recommendation engine, ride hailing / ride sharing, rolodex, sentiment analysis, skunkworks, Skype, social graph, social software, software as a service, Steve Jobs, subscription business, telemarketer, transaction costs, two-sided market, Uber for X, web application, Y Combinator

Less than six months later, working in a closely guarded circus tent, they built the first plane.[151] This group became known as the Skunk Works, a title that’s synonymous with an independent, autonomous group charged with innovation inside a bigger, slower-moving organization. Such groups are often immune to the restrictions and budget oversight that guides the rest of the company, and have the specific goal of working “out of the box” to mitigate the inertia of large businesses. Companies like Google and Apple adopt this same approach, creating their own advanced research groups such as the Google X Lab.[152] Making things change quickly is hard, and if you’re going to do it, you need authority commensurate with responsibility. If you’re trying to disrupt from within, you have a lot of work to do. Many of the lessons learned from the startup world apply, but they need to be tweaked to survive in a corporate setting. Span of Control and the Railroads If you work in a company of any significant size, you owe your organizational chart to an enterprising general superintendent of the railroad era named Daniel C.

That means you really have two customers: the external one buying the product, and the internal one that has to make, sell, and support it. Ultimately, the intrapreneur must manage the relationship with the host organization as well as the relationship with the target market. Initially, this can be intentionally distant, but as the disruptive product becomes part of the host, the handoff must be graceful. * * * [151] http://en.wikipedia.org/wiki/Skunkworks_project [152] http://www.nytimes.com/2011/11/14/technology/at-google-x-a-top-secret-lab-dreaming-up-the-future.html?_r=2 [153] http://en.wikipedia.org/wiki/Daniel_McCallum [154] http://beforeitsnews.com/banksters/2012/08/the-stanford-lectures-so-is-software-really-eating-the-world-2431478.html [155] Richard Templar, The Rules of Work (Upper Saddle River, New Jersey: Pearson Education, 2003), 142. [156] Larry Bossidy and Ram Charan, Confronting Reality (New York: Crown Business, 2004), 22–24

Google Adwords, LikeBright “Mechanical Turks” Its Way into TechStars Google Analytics, The Long Funnel, Abandonment, What DuProprio Watches Google Consumer Surveys, LikeBright “Mechanical Turks” Its Way into TechStars Google Maps, The Minimum Viable Vision Google Now, Value of Created Content Google Play, Model Three: Free Mobile App Google search engine, WineExpress Increases Revenue by 41% Per Visitor Google Voice, LikeBright “Mechanical Turks” Its Way into TechStars Google Wave, Measuring Engagement Google X Lab, Lean from Within: Intrapreneurs Goward, Chris, WineExpress Increases Revenue by 41% Per Visitor, WineExpress Increases Revenue by 41% Per Visitor Graham, Paul on startup growth, What Business Are You In?, Growth Rate, How Coradiant Found a Market Y Combinator accelerator, Growth Rate, Bottom Line Greenfield, Mike, Exploratory Versus Reporting Metrics, Engagement Funnel Changes, Correlation Predicts Tomorrow growth hacking, Ash Maurya’s Lean Canvas, Instrumenting the Viral Pattern guerilla marketing, data-driven, Instrumenting the Viral Pattern gut instinct, Finding a Problem to Fix (or, How to Validate a Problem), Running Lean and How to Conduct a Good Interview, Get Executive Buy-in H Haidt, Jonathan, How to Avoid Leading the Witness Hawken game (Meteor Entertainment), Virality Engine Herrmann, Bjoern Lasse, Average Isn’t Good Enough hibernation breakdown, Breakeven on Variable Costs HighScore House case study, HighScore House Defines an “Active User”, A “Day in the Life” of Your Customer Hillstrom, Kevin, Model One: E-commerce, What Makes a Good Leading Indicator?


pages: 309 words: 81,975

Brave New Work: Are You Ready to Reinvent Your Organization? by Aaron Dignan

"side hustle", activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, bitcoin, Black Swan, blockchain, Buckminster Fuller, Burning Man, butterfly effect, cashless society, Clayton Christensen, clean water, cognitive bias, cognitive dissonance, corporate governance, corporate social responsibility, correlation does not imply causation, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, David Heinemeier Hansson, deliberate practice, DevOps, disruptive innovation, don't be evil, Elon Musk, endowment effect, Ethereum, ethereum blockchain, Frederick Winslow Taylor, future of work, gender pay gap, Geoffrey West, Santa Fe Institute, gig economy, Google X / Alphabet X, hiring and firing, hive mind, income inequality, information asymmetry, Internet of things, Jeff Bezos, job satisfaction, Kevin Kelly, Kickstarter, Lean Startup, loose coupling, loss aversion, Lyft, Marc Andreessen, Mark Zuckerberg, minimum viable product, new economy, Paul Graham, race to the bottom, remote working, Richard Thaler, shareholder value, Silicon Valley, six sigma, smart contracts, Social Responsibility of Business Is to Increase Its Profits, software is eating the world, source of truth, Stanford marshmallow experiment, Steve Jobs, TaskRabbit, the High Line, too big to fail, Toyota Production System, uber lyft, universal basic income, Y Combinator, zero-sum game

—but they’re inherently incremental. We tweak the recipe without ever questioning whether we should make an entirely different dish. But incremental improvement can be a trap. Scientific Management reveals only the best way to do what we’re already doing. True innovation often requires a departure from the safety of the status quo. Astro Teller, captain of moonshots at Alphabet’s X (formerly Google X), puts it this way: “It’s often easier to make something 10 times better than it is to make it 10 percent better. . . . Because when you’re working to make things 10 percent better, you inevitably focus on the existing tools and assumptions, and on building on top of an existing solution that many people have already spent a lot of time thinking about . . . But when you aim for a 10x gain, you lean instead on bravery and creativity—the kind that, literally and metaphorically, can put a man on the moon.”

Madrian, “Plan Design and 401(k) Savings Outcomes,” working paper 10486 (Cambridge, MA.: National Bureau of Economic Research, 2004): www.nber.org/papers/w10486.pdf. We prefer to stick with what we’ve got: Daniel Kahneman, Jack L. Knetsch, and Richard H. Thaler, “Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias,” Journal of Economic Perspectives 5, no. 1 (1991), doi:10.1257/jep.5.1.193. “put a man on the moon”: Astro Teller, “Google X Head on Moonshots: 10x Is Easier Than 10 Percent,” Wired, February 11, 2013, www.wired.com/2013/02/moonshots-matter-heres-how-to-make-them-happen. “All models are wrong”: George E. P. Box and Norman R. Draper, Empirical Model-Building and Response Surfaces (New York: John Wiley & Sons, 1987), 440. PART TWO: THE OPERATING SYSTEM human-centric design principles: Gary Hamel, “First, Let’s Fire All the Managers,” Harvard Business Review, December 2011, https://hbr.org/2011/12/first-lets-fire-all-the-managers.


pages: 247 words: 81,135

The Great Fragmentation: And Why the Future of All Business Is Small by Steve Sammartino

3D printing, additive manufacturing, Airbnb, augmented reality, barriers to entry, Bill Gates: Altair 8800, bitcoin, BRICs, Buckminster Fuller, citizen journalism, collaborative consumption, cryptocurrency, David Heinemeier Hansson, disruptive innovation, Elon Musk, fiat currency, Frederick Winslow Taylor, game design, Google X / Alphabet X, haute couture, helicopter parent, illegal immigration, index fund, Jeff Bezos, jimmy wales, Kickstarter, knowledge economy, Law of Accelerating Returns, lifelogging, market design, Metcalfe's law, Minecraft, minimum viable product, Network effects, new economy, peer-to-peer, post scarcity, prediction markets, pre–internet, profit motive, race to the bottom, random walk, Ray Kurzweil, recommendation engine, remote working, RFID, Rubik’s Cube, self-driving car, sharing economy, side project, Silicon Valley, Silicon Valley startup, skunkworks, Skype, social graph, social web, software is eating the world, Steve Jobs, survivorship bias, too big to fail, US Airways Flight 1549, web application, zero-sum game

It needs to be about building new methods with which to go to market, not just new things to put into the market. The change we’re living through is environmental, rather than about the species that live in the environment. So it requires much more than improvements on the existing; it requires new systems. What smart companies are doing is creating external environments for radical innovation. The Google X lab, for example, carries out research and development that’s breaking totally new ground. The aim of the Google X lab is to create 10-fold improvements in the technology they release. As with all classic skunkworks, they’re in a different building so that the existing business culture doesn’t infect their purpose. skunkworks: a small, independent, loosely structured group who research and develop a project primarily for the purpose of radical innovation (Wikipedia) Internal venture capital If the new players in the market are consistently being beaten by startups, why not join them?


pages: 282 words: 81,873

Live Work Work Work Die: A Journey Into the Savage Heart of Silicon Valley by Corey Pein

23andMe, 4chan, affirmative action, Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, Anne Wojcicki, artificial general intelligence, bank run, barriers to entry, Benevolent Dictator For Life (BDFL), Bernie Sanders, bitcoin, Build a better mousetrap, California gold rush, cashless society, colonial rule, computer age, cryptocurrency, data is the new oil, disruptive innovation, Donald Trump, Douglas Hofstadter, Elon Musk, Extropian, gig economy, Google bus, Google Glasses, Google X / Alphabet X, hacker house, hive mind, illegal immigration, immigration reform, Internet of things, invisible hand, Isaac Newton, Jeff Bezos, job automation, Kevin Kelly, Khan Academy, Law of Accelerating Returns, Lean Startup, life extension, Lyft, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Menlo Park, minimum viable product, move fast and break things, move fast and break things, mutually assured destruction, obamacare, passive income, patent troll, Paul Graham, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, Peter Thiel, platform as a service, plutocrats, Plutocrats, Ponzi scheme, post-work, Ray Kurzweil, regulatory arbitrage, rent control, RFID, Robert Mercer, rolodex, Ronald Reagan, Ross Ulbricht, Ruby on Rails, Sam Altman, Sand Hill Road, Scientific racism, self-driving car, sharing economy, side project, Silicon Valley, Silicon Valley startup, Singularitarianism, Skype, Snapchat, social software, software as a service, source of truth, South of Market, San Francisco, Startup school, stealth mode startup, Steve Jobs, Steve Wozniak, TaskRabbit, technological singularity, technoutopianism, telepresence, too big to fail, Travis Kalanick, tulip mania, Uber for X, uber lyft, ubercab, upwardly mobile, Vernor Vinge, X Prize, Y Combinator

Tech companies in particular—thanks to the deep pools of financing available to them and, more important, their invaluable data hoard—approach politics from a position of strength. Yet they also possess the obsessive mentality of tinkerers, and cling to the aggrievement of self-declared outsiders. The combination of power and geekery has produced some bizarre outcomes, and some even weirder ventures in practical change. I saw this firsthand when I attended a conference talk featuring a man named Tom Chi, who had recently left a high-level position at Google X, the company’s far-out research and development division. “My first day,” he recalled, “all I had was a one-page document from Larry and Sergey. And all it said was: ‘What would it take to get Google inside your brain?’” “Don’t be scared,” he added. Cue nervous laughter. Chi’s latest project was a startup focused on “radically shifting large organizations.” It was a semisecret project. “This has been stealth for a while,” he said.

Fiverr Fiverr Success (Ferreira) Fleetzen Foodpanda Forbes Ford Foresight Institute Forrest, Katherine Bolan Fortune Founders Floor Founders Fund 4chan Foursquare French, David Friedman, Milton Friedman, Patri Friedman, Thomas Froomkin, A. Michael Fusion Fwd.us Galvanize Gamergate Gandhi, Mahatma Gates, Bill Gawker Genentech General Dynamics General Electric Getty Images Ghostruck Girard, René Glassdoor GM Gmail Goldman Sachs Google Google AdWords Google Express Google Maps Googleplex Google X Gore, Al Government Proposal Solutions Graham, Paul Green, Joe Greender Greyball Greylock Partners GRiD Computers Grossman, Terry Groupon Guardian Hacker News Hagel, John Harper-Mercer, Chris Harvard University Hennessy, John Hewlett-Packard Heyer, Heather Hitler, Adolf Hoffa, Jimmy Hoffman, Reid Hofstadter, Douglas Hogan, Hulk Holmes, Elizabeth Hudson Pacific Properties Humanity+ Hunter, Duncan, Jr.


pages: 391 words: 105,382

Utopia Is Creepy: And Other Provocations by Nicholas Carr

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

Jazzed by a Googler’s TED Talk on driverless cars, MIT automation expert Andrew McAfee says that the Googlemobile will “free us from a largely tedious task.” Writes Wired transport reporter Alex Davies, “Liberated from the need to keep our hands on the wheel and eyes on the road, drivers will become riders with more time for working, leisure, and staying in touch with loved ones”—in other words, they’ll be able to spend more time on their phones. When Astro Teller, head of the Google X lab, watches people drive by in their cars, all he hears is a giant sucking sound, as potentially productive minutes pour down the drain of a vast time sink: “There’s over a trillion dollars of wasted time per year we could collectively get back if we didn’t have to pay attention while the car took us from one place to another.” Driving on a private track may be pleasantly meditative, even joy-inducing, but driving on public thoroughfares is just a drag.

(FTC), 280, 284 feedback loops, 67 Feldman, Morton, 216 Ferriero, David, 272 fiction, effect on brain of, 248–52 filters, information overload and, 90–92 Finnegans Wake (Joyce), 106 first nature, 179–80 Fitbit, 119, 197 Flickr, xvi flight, human quest for, 329–30, 340–42 Fonda, Jane, Wikipedia entry on, 6–7 Food and Drug Administration, 332 Foreman, Richard, 241–42 forensic imagination, 326 45 rpm singles, 44–46, 121 Four-Second Rule, 205 Foursquare, 257 Fox, Justin, 116 France, Google and, 264 Franklin, Benjamin, 325 Friedman, Bruce, 232–33 Friedman, Thomas, 133 Frost, Robert, 145–46, 182, 247–48, 296–99, 302, 304–5, 313 Fuller, Buckminster, 171 Galbraith, John Kenneth, xix Gates, Bill, xvii Jobs compared to, 32–33 Wikipedia entry on, 5–6 Gelernter, David, xvii gender reassignment, 337–38 generational change, 230 generativity, 76–78 gene therapy, 335 genetic engineering, 334–35 geology, 326 Germany, Google and, 283–84 GIFs, 203 Gil, Sandrine, 203–4 Gilligan’s Island, fact-mongering about, 58–62 Gillmor, Dan, 7 Gleick, James, 204 Go, GeForce (avatar), 25 Goldsmith, Kenneth, 216–17 Google, 13, 67, 79, 86–89, 112, 115, 144–46, 162, 181, 195, 199, 204, 205, 226, 253, 257, 321 in AI, 136–37 competition for, 284–85 corporate management of, 16–17 customized searching on, 264–66 early days of, xvii, 279–81 effect on memory of, 98–101 ethical criticism of, 283 failed projects of, 269, 283 goals of, 23–24, 87, 145–46, 239–40, 268 growth and evolving hegemony of, 279–85 international projects of, 283–84 investigations into, 280, 284–85 music streaming by, 207, 209 in online privacy case, 190–94 philosophy of, 279–80, 283 political use of, 319 social stream management by, 166–67 universal book project of, 267–72, 275–77, 283 Google Apps, 283 Google Blog Search, 66 Google Book Search, 268–72, 275–77, 283 Google bus, 170–71, 173 “Google Effects on Memory” (Sparrow, Liu, and Wegner), 98 Google Glass, 131–32, 160–61, 164 Google Maps, 153 Google News, 315, 320 Google Now, 145 Google Play Music, 207 Googleplex, 17, 238 restrooms of, 23–24 Google Reader, 67 Google Serendipity, 13, 15 Google Suggest, 264–65 Google X lab, 195 Gordon, Robert J., 116–17 Gothic High-Tech, 113–15 GPS systems, 56–57, 226, 304 Graham, Lindsay, 314 Grand Theft Auto, 262 Gray, John, 36 Great Man theory, 28–29 Green, Shawn, 93–94 Greenfield, Patricia, 95 Grimmelmann, James, 277 Grossman, Lev, 28–29 Grover, Monte, 185 Guitar Hero (game), 64–65 Gutenberg Galaxy, The (McLuhan), 102–3 hackability, 76–78 HAL (computer), 231, 239, 242 Haldane, J.


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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, Boris Johnson, Burning Man, call centre, Chuck Templeton: OpenTable:, collaborative consumption, East Village, fixed income, Google X / Alphabet X, housing crisis, inflight wifi, Jeff Bezos, Justin.tv, Kickstarter, Lyft, Marc Andreessen, Mark Zuckerberg, Menlo Park, Mitch Kapor, Necker cube, obamacare, Paul Graham, peer-to-peer, Peter Thiel, race to the bottom, rent control, ride hailing / ride sharing, Ruby on Rails, Sand Hill Road, self-driving car, semantic web, sharing economy, side project, Silicon Valley, Silicon Valley startup, Skype, South of Market, San Francisco, Startup school, Steve Jobs, TaskRabbit, Tony Hsieh, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, ubercab, Y Combinator, Y2K, Zipcar

In San Francisco, engineers retrofitted code from an Uber marketing stunt at the recent South by Southwest Conference, which had allowed attendees to order barbecue and pedicabs, and created a new feature to give riders in Chicago a choice between black cars and taxis. On the Chicago streets, Penn and his team started taking taxis, inviting cabbies to the Uber offices, and showing off the app. Uber rolled out its taxi service on April 18, 2012. Because Kalanick was still nervous about the reception, he framed Uber Taxi as coming from a wing of the still-tiny startup, an entirely fictitious department that he dubbed Uber Garage.9 “Google has Google X, and we have the Uber Garage,” Kalanick told me that year. “If we have an idea we don’t like, we put it in the parking lot.” Uber beat Hailo to Chicago’s taxi fleet by a mile. The London startup wouldn’t open for business there for five more months. But that wasn’t the only reason Uber drove circles around its first major international competitor. There was also a sharp difference in strategy, which was on stark display a few weeks later when Kalanick and Jay Bregman shared a stage at the LeWeb conference in London’s Westminster Central Hall, in a panel billed by organizers as a taxi-app smackdown between the CEOs and their backers.

Kalanick was intrigued by the idea of aligning himself with Google but wanted reassurances from the top and asked for a meeting with founder and CEO Larry Page. 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. Page assured Kalanick that the companies could work together to develop Google Maps, which Uber relied on for navigation in its apps, but he didn’t say much or stay very long.


pages: 501 words: 114,888

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

Ada Lovelace, additive manufacturing, Airbnb, Albert Einstein, Amazon Mechanical Turk, augmented reality, autonomous vehicles, barriers to entry, bitcoin, blockchain, blood diamonds, Burning Man, call centre, cashless society, Charles Lindbergh, Clayton Christensen, clean water, cloud computing, Colonization of Mars, computer vision, creative destruction, crowdsourcing, cryptocurrency, Dean Kamen, delayed gratification, dematerialisation, digital twin, disruptive innovation, Edward Glaeser, Edward Lloyd's coffeehouse, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Ethereum, ethereum blockchain, experimental economics, food miles, game design, Geoffrey West, Santa Fe Institute, gig economy, Google X / Alphabet X, gravity well, hive mind, housing crisis, Hyperloop, indoor plumbing, industrial robot, informal economy, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the telegraph, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Kickstarter, late fees, Law of Accelerating Returns, life extension, lifelogging, loss aversion, Lyft, M-Pesa, Mary Lou Jepsen, mass immigration, megacity, meta analysis, meta-analysis, microbiome, mobile money, multiplanetary species, Narrative Science, natural language processing, Network effects, new economy, New Urbanism, Oculus Rift, out of africa, packet switching, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, Peter Thiel, QR code, RAND corporation, Ray Kurzweil, RFID, Richard Feynman, Richard Florida, ride hailing / ride sharing, risk tolerance, Satoshi Nakamoto, Second Machine Age, self-driving car, Silicon Valley, Skype, smart cities, smart contracts, smart grid, Snapchat, sovereign wealth fund, special economic zone, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, supercomputer in your pocket, supply-chain management, technoutopianism, Tesla Model S, Tim Cook: Apple, transaction costs, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, urban planning, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, X Prize

With 3G, it takes forty-five minutes to download a high-definition movie. 4G shrinks that to twenty-one seconds. But 5G? It takes longer to read this sentence than it takes to download that movie. Yet, even as these cell networks kudzu the planet, others are sprouting in the territory far over our heads. Alphabet is now rolling out Project Loon—which, when they first proposed it, could have been short for “Project Loony.” Born a decade back out of Google X, the tech giant’s skunk works, the idea was to replace terrestrial cell towers with stratospherically located balloons. That idea is now a reality. Light and durable enough to cruise the slipstreams some twenty kilometers above the Earth’s surface, Google’s fifteen by twelve-meter balloons are providing 4G-LTE connections to users on the ground. Each balloon covers five thousand square kilometers, and Google’s plan is a network of thousands, wiring the unwired, providing continuous coverage for anyone, anywhere on Earth.

The list of once multimillion-dollar medical machines now being dematerialized, demonetized, democratized, and delocalized—that is, made into portable and even wearable sensors—could fill a textbook. Consider the spectrum of possibilities. On the whiz-bang side, there’s Exo Imaging’s AI-enabled, cheap, handheld ultrasound 3-D imager—meaning you will soon be able to track anything from wound healing to fetus growth from the comfort of your home. Or former Google X project leader Mary Lou Jepsen’s startup, Openwater, which is using red laser holography to create a portable MRI equivalent, turning what is today a multimillion-dollar machine into a wearable consumer electronics device and giving three-quarters of the world access to medical imaging they currently lack. Yet simpler developments might be more revolutionary. In less than two decades, wearables have gone from step-counting first-generation self-trackers to Apple’s fourth-generation iWatch that includes an FDA-approved ECG scanner capable of real-time cardiac monitoring.


pages: 138 words: 40,787

The Silent Intelligence: The Internet of Things by Daniel Kellmereit, Daniel Obodovski

Airbnb, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, business intelligence, call centre, Clayton Christensen, cloud computing, commoditize, connected car, crowdsourcing, data acquisition, 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, self-driving car, Silicon Valley, smart cities, smart grid, software as a service, Steve Jobs, web application, Y Combinator, yield management

Says Astro Teller: Self-driving cars in the not-too-distant future are just going to be meaningfully better than people. 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. Astro says, “I bet you there are a lot of places where that’s going to be one of the big pieces of news — the radically changing rhythms, styles, interaction modalities between people and computers for solving hard, big, distributed problems.”


pages: 413 words: 119,587

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

"Robert Solow", A Declaration of the Independence of Cyberspace, AI winter, airport security, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, basic income, Baxter: Rethink Robotics, Bill Duvall, bioinformatics, Brewster Kahle, Burning Man, call centre, cellular automata, Chris Urmson, Claude Shannon: information theory, Clayton Christensen, clean water, cloud computing, collective bargaining, computer age, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data acquisition, Dean Kamen, deskilling, don't be evil, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dynabook, Edward Snowden, Elon Musk, Erik Brynjolfsson, factory automation, From Mathematics to the Technologies of Life and Death, future of work, Galaxy Zoo, Google Glasses, Google X / Alphabet X, Grace Hopper, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, haute couture, hive mind, hypertext link, indoor plumbing, industrial robot, information retrieval, Internet Archive, Internet of things, invention of the wheel, Jacques de Vaucanson, Jaron Lanier, Jeff Bezos, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, knowledge worker, Kodak vs Instagram, labor-force participation, loose coupling, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, Mitch Kapor, Mother of all demos, natural language processing, new economy, Norbert Wiener, PageRank, pattern recognition, pre–internet, RAND corporation, Ray Kurzweil, Richard Stallman, Robert Gordon, Rodney Brooks, Sand Hill Road, Second Machine Age, self-driving car, semantic web, 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, strong AI, superintelligent machines, technological singularity, Ted Nelson, telemarketer, telepresence, telepresence robot, Tenerife airport disaster, The Coming Technological Singularity, the medium is the message, Thorstein Veblen, Turing test, Vannevar Bush, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, William Shockley: the traitorous eight, zero-sum game

A variety of new neural net and other machine-learning techniques have led to a dramatic revival of interest in AI in Silicon Valley and elsewhere. Combining the new approach to AI with the Internet has meant that it is now possible to create a new service based on computer vision or speech recognition and then use the Internet and tens of millions of smartphone users to immediately reach a global audience. In 2010 Sebastian Thrun had come to Google to start the Google X Laboratory, which was initially framed inside the company as Google’s version of Xerox’s Palo Alto Research Center. It had a broad portfolio of research projects, stretching from Thrun’s work in autonomous cars to efforts to scale up neural networks, loosely identified as “brain” projects, evoking a new wave of AI. The Human Brain Project was initially led by Andrew Ng, who had been a colleague with Thrun at the resurrected Stanford Artificial Intelligence Laboratory.

., 240–241 DARPA Advanced Research Projects Agency as precursor to, 30, 110, 111–112, 164, 171 ARPAnet, 164, 196 autonomous cars and Grand Challenge, 24, 26, 27–36, 40 CALO and, 31, 297, 302–304, 310, 311 Dugan and, 236 Engelbart and, 6 Licklider and, 11 LRASM, 26–27 Moravec and, 119 Pratt and, 235–236 Robotics Challenge, 227–230, 234, 236–238, 244–254, 249, 333–334 Rosen and, 102 Taylor and, 160 Darrach, Brad, 103–105 Dartmouth Summer Research Project on Artificial Intelligence, 105, 107–109, 114, 143 DataLand, 307 Davis, Ruth, 102–103 “Declaration of the Independence of Cyberspace, A” (Barlow), 173 DeepMind Technologies, 91, 337–338 Defense Science Board, 27 de Forest, Lee, 98 “demons,” 190 Dendral, 113–114, 127 Diebold, John, 98 Diffie, Whitfield, 8, 112 Digital Equipment Corporation, 112, 285 direct manipulation, 187 Djerassi, Carl, 113 Doerr, John, 7 Dompier, Steve, 211–212 Dreyfus, Hubert, 177–178, 179 drone delivery research, 247–248 Dubinsky, Donna, 154 Duda, Richard, 128, 129 Dugan, Regina, 236 Duvall, Bill, 1–7 Earnest, Les, 120, 199 Earth Institute, 59 Edgerton, Germeshausen, and Grier (EG&G), 127 e-discovery software, 78 E-Groups, 259 elastic actuation, 236–237 electronic commerce, advent of, 289, 301–302 electronic stability control (ESC), 46 Elementary Perceiver and Memorizer (EPAM), 283 “Elephants Don’t Play Chess” (Brooks), 201 Eliza, 14, 113, 172–174, 221 email, advent of, 290, 310 End of Work, The (Rifkin), 76–77 Engelbart, Doug. see also SRI International on exponential power of computers, 118–119 IA versus AI debate and, 165–167 on intelligence augmentation (IA), xii, 5–7, 31 Minsky and, 17 “Mother of All Demos” (1968) by, 62 NLS, 5–7, 172, 197 Rosen and, 102 Siri and, 301, 316–317 Engineers and the Price System, The (Veblen), 343 Enterprise Integration Technologies, 289, 291 ethical issues, 324–344. see also intelligence augmentation (IA) versus AI; labor force of autonomous cars, 26–27, 60–61 decision making and control, 341–342 Google on, 91 human-in-the-loop debates, 158–165, 167–169, 335 of labor force, 68–73, 325–332 scientists’ responsibility and, 332–341, 342–344 “techno-religious” issues, 116–117 expert systems, defined, 134–141, 285 Facebook, 83, 156–158, 266–267 Fast-SLAM, 37 Feigenbaum, Ed, 113, 133–136, 167–169, 283, 287–288 Felsenstein, Lee, 208–215 Fernstedt, Anders, 71 “field robotics,” 233–234 Fishman, Charles, 81 Flextronics, 68 Flores, Fernando, 179–180, 188 Foot, Philippa, 60 Ford, Martin, 79 Ford Motor Company, 70 Forstall, Scott, 322 Foxconn, 93, 208, 248 Friedland, Peter, 292 Galaxy Zoo, 219–220 Gates, Bill, 305, 329–330 General Electric (GE), 68–69 General Magic, 240, 315 General Motors (GM), 32–35, 48–50, 52, 53, 60 Genetic Finance, 304 Genghis (robot), 202 Geometrics, 127 George, Dileep, 154 Geraci, Robert, 85, 116–117 Gerald (digital light field), 271 Giant Brains, or Machines That Think (Berkeley), 231 Gibson, William, 23–24 Go Corp., 141 God & Golem, Inc. (Wiener), 75, 211 GOFAI (Good Old-Fashioned Artificial Intelligence), 108–109, 186 “Golemic Approach, The” (Felsenstein), 212–213 “golemics,” 75, 208–215 Google Android, 43, 239, 248, 320 autonomous cars and, 35–45, 51–52, 54–59, 62–63 Chauffeur, 43 DeepMind Technologies and, 91, 337–338 Google Glass, 23, 38 Google Now, 12–13, 341 Google X Laboratory, 152–153 Human Brain Project, 153–154 influence of early AI history on, 99 Kurzweil and, 85 PageRank algorithm, 62, 92, 259 robotic advancement by, 241–244, 248–255, 256, 260–261 70-20-10 rule of, 39 Siri’s development and, 314–315 Street View cars, 39, 42–43, 54 X Lab, 38, 55–56 Gordon, Robert J., 87–89 Gou, Terry, 93, 248 Gowen, Rhia, 277–279 Granakis, Alfred, 70 Grand Challenge (DARPA), 24, 26, 27–36, 40 “Grand Traverse,” 234 Green, David A., 80 Grendel (rover), 203 Grimson, Eric, 47 Gruber, Tom, xiii–xiv, 277–279, 278, 282–297, 310–323, 339 Grudin, Jonathan, 15, 170, 193, 342 Guzzoni, Didier, 303 hacker culture, early, 110–111, 174 Hart, Peter, 101–102, 103, 128, 129 Hassan, Scott, 243, 259–260, 267, 268, 271 Hawkins, Jeff, 85, 154 Hayon, Gaby, 50 Hearsay-II, 282–283 Heartland Robotics (Rethink Robotics), 204–208 Hecht, Lee, 135, 139 Hegel, G.


pages: 504 words: 126,835

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

"Robert Solow", Airbnb, Albert Einstein, American ideology, asset allocation, autonomous vehicles, barriers to entry, Basel III, Bernie Madoff, bitcoin, Black Swan, blockchain, BRICs, Burning Man, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, Clayton Christensen, Colonization of Mars, commoditize, corporate governance, corporate social responsibility, creative destruction, crony capitalism, dark matter, David Graeber, David Ricardo: comparative advantage, discounted cash flows, distributed ledger, Donald Trump, Elon Musk, Erik Brynjolfsson, fear of failure, first square of the chessboard / second half of the chessboard, Francis Fukuyama: the end of history, George Gilder, global supply chain, global value chain, Google Glasses, Google X / Alphabet X, Gordon Gekko, high net worth, hiring and firing, 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, Lyft, manufacturing employment, Mark Zuckerberg, market design, Martin Wolf, mass affluent, means of production, Mont Pelerin Society, Network effects, new economy, offshore financial centre, pensions crisis, Peter Thiel, Potemkin village, price mechanism, principal–agent problem, Productivity paradox, QWERTY keyboard, RAND corporation, Ray Kurzweil, rent-seeking, risk tolerance, risk/return, Robert Gordon, 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, technological singularity, 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: Great Stagnation, uber lyft, University of East Anglia, unpaid internship, Vanguard fund, Yogi Berra

The optical head-mounted display was like the Ericsson Cordless Web Screen that had been invented 15 years earlier – “the next big thing.” And if you have tried a pair, you will know they are pretty cool. Google went much further than Ericsson ever did when it started to sell the glasses in May 2014. Yet Google still failed and production was halted less than a year after release for the simple reason that sales were not good enough. In the aftermath of this public mishap, Astro Teller – the head of what was then called the Google X research lab – explained that the company had failed by “not making clear to everyone else that what was out was really just a prototype of the smart glassware, and too much bad publicity was really what killed Google Glass.”13 Failure happens – every day – and premature scaling is a common recipe for disaster. But this was Google, a company with near limitless resources for planning and preparation.

A peek into the history books reveals that leakages have been a familiar problem to the ballooning community for over 100 years, if not longer. One spectacular incident occurred in 1897 when Swedish adventurer S.A. Andrée and his crew failed to cross the North Pole in a hydrogen balloon because of this problem. Had it not, they would have been pioneers of balloon journeys across the Pole. Instead they may have been killed by polar bears as they were trying to make it back home by foot. Google X staff fortunately did not have to worry about hungry bears and they were still around to draw the right conclusions from the failure. To Astro Teller the leaking balloons were a good experiment and a lesson in the trial-and-error process of innovation and business development. “Sometimes the most interesting failures,” he concluded, “are the ones that you don’t expect. Particularly, when they are something that you think will be the easiest part of the project and it turns out to be the hardest part of the project instead.”14 Tech failures are just one of the problems faced by new innovations.


pages: 165 words: 45,397

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

3D printing, augmented reality, autonomous vehicles, Berlin Wall, Buckminster Fuller, Cass Sunstein, computer age, corporate governance, David Attenborough, en.wikipedia.org, Fall of the Berlin Wall, game design, global village, Google X / Alphabet X, haute couture, life extension, Mark Zuckerberg, mouse model, New Urbanism, Peter Eisenman, RAND corporation, Richard Thaler, Ronald Reagan, self-driving car, Silicon Valley, social software, technoutopianism, Wall-E

One can't help but wonder if ideology12 is at the source of true innovation in the sense that new ideas and thinking come from new and different ways of viewing the world. The Lun - class Ekranoplan, Kaspiysk, 2009. Photograph by Igor Kolokolov. With a few governmental exceptions, such as DARPA-where some of the most imaginative, boldest, and admirably ridiculous thinking is to be found, invisibility cloaks and holes in time, for example-and commercial exceptions such as Google's X Lab, which is currently working on space elevators and asteroid mining, it feels today as if the era of big ideas and fantastic dreams has passed. Much of today's dreaming around technology is shaped by military priorities or a short-term, market-led view of the world based on standardized consumer dreams and desires. And bold visions put forward by architects such as Terreform ONE and BIG (Bjarke Ingels Group) don't seem to include the underlying alternative worldviews and ideology of earlier big thinkers.


Moon Rush: The New Space Race by Leonard David

agricultural Revolution, Colonization of Mars, cuban missile crisis, data acquisition, Donald Trump, Elon Musk, Google X / Alphabet X, gravity well, Jeff Bezos, life extension, low earth orbit, multiplanetary species, out of africa, self-driving car, Silicon Valley, telepresence, telerobotics

Collectively, these Apolloesque idioms imply aspiring for a lofty goal, and nowadays we recognize that we can do it. British science fiction writer and futurist Arthur C. Clarke wrote that every revolutionary idea passes through three stages: It’s completely impossible. It’s possible, but it’s not worth doing. I said it was a good idea all along. Today we use the word “moonshot” to connote an ambitious, exploratory, and groundbreaking project. Google X’s “Moonshot Factory” has embraced the term for inventive initiatives like driverless cars, robots for manufacturing purposes, even life extension. They define “moonshot” as a task or idea that addresses a huge problem, proposes a radical solution, and makes use of breakthrough technology. The National Cancer Institute at the National Institutes of Health is sponsoring the “Cancer Moonshot” to accelerate cancer study and make more therapies available to more patients, while also advancing the capacity to prevent cancer and detect it at an early stage.


pages: 222 words: 53,317

Overcomplicated: Technology at the Limits of Comprehension by Samuel Arbesman

algorithmic trading, Anton Chekhov, Apple II, Benoit Mandelbrot, citation needed, combinatorial explosion, Danny Hillis, David Brooks, digital map, discovery of the americas, en.wikipedia.org, Erik Brynjolfsson, Flash crash, friendly AI, game design, Google X / Alphabet X, Googley, HyperCard, Inbox Zero, Isaac Newton, iterative process, Kevin Kelly, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mandelbrot fractal, Minecraft, Netflix Prize, Nicholas Carr, Parkinson's law, Ray Kurzweil, recommendation engine, Richard Feynman, Richard Feynman: Challenger O-ring, Second Machine Age, self-driving car, software studies, statistical model, Steve Jobs, Steve Wozniak, Steven Pinker, Stewart Brand, superintelligent machines, Therac-25, Tyler Cowen: Great Stagnation, urban planning, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, Y2K

Systems we build to reflect the world: That the complexity of the world is reflected in the complexity of our systems is also discussed in Vikram Chandra, Geek Sublime: The Beauty of Code, the Code of Beauty (Minneapolis: Graywolf Press, 2014). One need not always end up with messy code because the world is messy, but it does often happen. Fortunately, there are ways to mitigate it. See Steve McConnell, Code Complete: A Practical Handbook of Software Construction, 2nd ed. (Redmond, WA: Microsoft Press, 2004), 583. building a self-driving vehicle: The complexity of building self-driving cars was discussed by Google[x]’s “Captain of Moonshots” in his closing keynote address at South by Southwest Interactive (SXSW) 2015: Astro Teller, “How to Make Moonshots,” Backchannel, March 17, 2015, https://medium.com/backchannel/how-to-make-moonshots-65845011a277. the exceptions that nonetheless have to be dealt with: One solution is to use humans to manually troubleshoot, or at least hard-code, the exceptions. For example, here’s how Google does this for Maps: “This is a Google-y approach to the problem of ultra-reliability.


pages: 190 words: 62,941

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

"side hustle", Airbnb, always be closing, Amazon Web Services, autonomous vehicles, Ayatollah Khomeini, business process, Chuck Templeton: OpenTable:, cognitive dissonance, corporate governance, DARPA: Urban Challenge, Donald Trump, Elon Musk, gig economy, Golden Gate Park, Google X / Alphabet X, information retrieval, Jeff Bezos, Lyft, Marc Andreessen, Mark Zuckerberg, megacity, Menlo Park, new economy, pattern recognition, price mechanism, ride hailing / ride sharing, Sand Hill Road, self-driving car, Silicon Valley, Silicon Valley startup, Skype, Snapchat, South of Market, San Francisco, sovereign wealth fund, statistical model, Steve Jobs, TaskRabbit, Tony Hsieh, transportation-network company, Travis Kalanick, turn-by-turn navigation, Uber and Lyft, Uber for X, uber lyft, ubercab, young professional

So suffice it to say they paid keen attention to scientific breakthrough prizes that engaged the computer science departments from which they continued to recruit as well as the government agency that sponsored the competitions. Several years after the DARPA Grand Challenge, Larry Page and Sergey Brin decided they wanted to build a self-driving car. Never mind that it had little to do with Google’s information-quest mission. It was “moonshot” technology they wanted to advance. They persuaded Thrun to leave Stanford in 2010 to help start an in-house research arm called Google X. The group would go on to develop diverse technology such as antiaging drugs and computers that could be printed on eyeglasses and contact lenses. Its first project would be a self-driving car. Thrun helped develop software called Street View and sent cars driven by Google engineers onto city streets to map everything from street signs to the placement of barriers. Google also bought a small company called 510 Systems, founded by an engineer named Anthony Levandowski, that specialized in self-driving technology.


pages: 313 words: 84,312

We-Think: Mass Innovation, Not Mass Production by Charles Leadbeater

1960s counterculture, Andrew Keen, barriers to entry, bioinformatics, c2.com, call centre, citizen journalism, clean water, cloud computing, complexity theory, congestion charging, death of newspapers, Debian, digital Maoism, disruptive innovation, double helix, Douglas Engelbart, Edward Lloyd's coffeehouse, frictionless, frictionless market, future of work, game design, Google Earth, Google X / Alphabet X, Hacker Ethic, Hernando de Soto, hive mind, Howard Rheingold, interchangeable parts, Isaac Newton, James Watt: steam engine, Jane Jacobs, Jaron Lanier, Jean Tirole, jimmy wales, Johannes Kepler, John Markoff, John von Neumann, Joi Ito, Kevin Kelly, knowledge economy, knowledge worker, lateral thinking, lone genius, M-Pesa, Mark Shuttleworth, Mark Zuckerberg, Marshall McLuhan, Menlo Park, microcredit, Mitch Kapor, new economy, Nicholas Carr, online collectivism, planetary scale, post scarcity, Richard Stallman, Shoshana Zuboff, Silicon Valley, slashdot, social web, software patent, Steven Levy, Stewart Brand, supply-chain management, The Death and Life of Great American Cities, the market place, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Whole Earth Catalog, Zipcar

WikiHistory counter.li.org/ english.ohmynews.com/ www.fark.com www.ige.com www.plastic.com portal.eatonweb.com www.slashdot.org www.technorati.com/about www.worldofwarcraft.com INDEX 42 Entertainment 10, 11 A ABC 173 academia, academics 6, 27, 48, 59 Acquisti, Alessandro 210 Adam, James 95 adaptation 109, 110, 121 advertising 104, 105, 129, 173, 180, 219 Aegwynn US Alliance server 99 Afghanistan 237 Africa broadband connections 189 mobile phones 185, 207 science 196 use of Wikipedia 18 Aids 193, 206, 237 al-Qaeda 237 Alka-Seltzer 105 Allen, Paul 46 Altair BASIC 46 Amadeu, Sérgio 202 amateurism 105 Amazon 86 America Speaks 184 American Chemical Society 159 anarchy cultural 5 Wikipedia 16 Anderson, Chris: The Long Tail 216 Apache program 68 Apple 42, 103, 104, 135, 182 iPhone 134 iPods 46 Arendt, Hannah 174, 176 Argentina 203 Arrayo, Gloria 186 Arseblog 29, 30 Arsenal Football Club 29, 30 Arsenal.com 29 arXiv 160 Asia access to the web 5, 190 attitude to open-source 203 and democracy 189 mobile phones 166, 185 and open-source design communities 166–7 Ask a Ninja 57, 219 assembly line 93, 130 assets 224 astronomy 155, 162–3 authority 110, 115, 233 authorship and folk culture 57, 58 and mapping of the human genome 62 Azerbaijan 190 B bacteria, custom-made 164 Baker, Steve 148 Banco do Brazil 201 Bangladesh 205–6 banking 115, 205–6 Barber, Benjamin: Strong Democracy 174 Barbie, Klaus 17 Barbie dolls 17 Barefoot College 205 barefoot thinking 205–6 Barthes, Roland 45 Batchelor, Charles 95 Bath University 137 BBC 4, 17, 127, 142 news website 15 beach, public 49, 50, 51 Beach, The (think-tank) xi Bebo 34, 85, 86 Bedell, Geraldine x, xii–xiii Beekeepers 11, 15 Benkler, Yochai 174 The Wealth of Networks 194 Berger, Jorn 33 Bermuda principles 160 Billimoria, Jeroo 206 BioBrick Foundation 164 biology 163 open-source 165 synthetic 164–5 BioMedCentral 159 biotechnology 154, 163–4, 196–7, 199 black fever (visceral leishmaniasis) 200 Blackburn Rovers Football Club 29 Blades, Joan 188 Blizzard Entertainment 100 Bloc 8406 191 Blogger.com 33 blogs, blogging 1, 3, 20, 29–35, 57, 59, 74, 75, 78, 86, 115, 159, 170, 171, 176, 179, 181–2, 183, 191, 192, 214, 219, 229 BMW 140 Bohr, Neils 93 bookshops 2 Boulton, Matthew 54–5 Bowyer, Adrian 139, 140, 232 Boyd, Danah 213, 214 Bradley, Bill 180 Brand, Stewart 39–40, 43, 63 brands 104, 109 Brazil 201–2 Brenner, Sydney 62–5, 70, 77, 118, 231 Brief History of Time, A (Hawking) 163 Brindley, Lynne 141, 142, 144–5 British Library, London 141, 142, 144, 145 British Medical Journal 159 British National Party 169 Brooks, Fred 77–8 Brooks Hall, San Francisco 38 BT 112 bugs, software 70, 72, 165 bulletin boards 34, 40, 68, 77 Burma 190, 191 Bush, President George W. 18, 33–4, 180, 183 business services 130, 132, 166 C C. elegans (Caenorhabditis elegans) 62–5 Cambia 197 Cambridge University Press 159 camcorders 11 Campbell, Anne 176 Cancer Genome Atlas 160 capital 224 capitalism 224 commune 121, 125 managerial 24 modern 91, 121 social dimension of 90 Carlson, Rob 164 Carnegie Mellon University 210 cars manufacture 135–6 sharing 153 CBS 173 Center for Bits and Atoms, MIT 139 CERN (European Organization for Nuclear Research) 30–31, 159 Chan, Timothy 106, 107 chat rooms 165 Chavez, President Hugo 203 Cheney, Dick 180 Chevrolet 105 Chicago: Full Circle council project 184 China based on privileged access to information 236 creative and cultural sectors 129–30 hackers 234 Internet connection 190, 204 makes available genetic data 199 motor-cycle production 136–7 online games market 106 open-access scientific data 159–60 open-source designs 141 politics 171, 192 power struggle in 235 spending on R & D 96, 159 web censorship 190–91 Chinese Communist Party 171, 235 Chongquing, China 136 Cisco 190 Citibank 207 Citizendium 14 climate change 170, 239 Clinton, Bill 174, 188 Clinton, Senator Hillary 181, 182, 183 CNN 15 co-operatives 121, 122, 123, 188 co-ordination 109, 110–11 coffee houses, London 95 Coke 109–10, 239 Cold War 169, 235 Coles, Polly xiii collaboration 9, 22, 31, 32, 36, 67, 79–80, 81, 82 collaborative innovation 65, 70, 75 and commerce 227 computer game 99, 100 Cornish tin-mining 55 and healthcare 150 and the library of the future 145 new technologies for 227–8 open 126, 128 peer 239 public services 145, 146, 152, 153 scientific 154, 155–6 We-Think 21, 23, 24, 146 Collis, Charles 134 Columbia University 212 commerce 25, 38, 48, 52, 57, 98, 227 commons 49, 50, 51–3, 79, 80, 124, 191, 226 communes 39–40, 46, 90, 121, 122, 128 communication(s) 130, 168, 174, 206, 239 mobile 186 Communism, collapse of 6 communities collaborative 117 and commerce 48 and commons 52 conversational 63 Cornish tin-mining 55 creative 70, 95 diverse 79–80 egalitarian 27, 48, 59, 63, 64 hacker 232 healthcare 151, 152 independence of 23 of innovation 54 libertarian, voluntaristic 45 Linux 65, 227 and loss of market for local newspapers 3 meritocratic 63 open-source 45, 68, 75, 80, 83, 95–6, 102, 109, 110, 111 open-source design 166–7 of scientists 53, 228 self-governing 59, 79, 80, 97, 104, 232 sharing and developing ideas 25 web 21, 23 worm-genome researchers 62–5 community councils 77, 80, 82 Community Memory project 42–3 companies computer-games 128 employee-owned 121, 122 shareholder-owned 122, 123, 125 see also corporations; organisations computer games 60, 127, 218 children and 147 created by groups on the web 7, 23, 87 modularity 78 multi-player 7, 204 success of World of Warcraft 98–9 tools for creating content 74 and We-Think 23 computer-aided design 134 computers democratising how information is accessed 139 distrust of 39 Goa School Computers Project 200–201 laptop 5, 36, 82, 155 mini- 135 personal 39, 46, 203 punch-cards 38 and science 154, 155 viruses 3, 4 connect 67, 75–9 Connectiva 201 consumer spending 131 consumers 98–108 consumer innovators 101–3 consumption constraints 25–6 engaging 89 fans 103–4 freedom 218 and innovation risk 100–101 participant 98–108 urban 124 contribute 67, 70, 71, 72, 73, 74–5 conversation 53, 54, 63, 69, 77, 93, 95, 113, 118, 174 Copernicus, Nicolaus 162 copyright 124, 157, 196 core 66, 67, 68–9, 70 Cornell University 233 ‘Cornish’ engines 55–6, 136, 229 Cornish tin-mining industry 54–6, 63, 125, 136 corporations centralisation of power 110 closed 128 and collaborative approaches to work 109 the cost of corporate efficiency 89–90 difficulty in making money from the web 7 hierarchies 88, 110 industrial-era 88 leadership 115, 117–19 loss of stability 122 restructuring and downsizing 88–9 see also companies; organisations counter-culture (1960s) 6, 27, 39, 45, 46, 59 Counts, David 183 Craigslist 3, 40, 118, 128, 218 Creative Commons 124 creative sector 129–30 creativity 1–2, 3, 5, 6, 9, 67, 82–3 collaborative 7, 20, 58, 86, 154 collective 39, 57–8 consumers 89 corporate 91–2 emergence of 93, 96 enabled by the web 1–2, 3, 5, 19, 26, 218–21, 222, 227 freedom to create 218–21 and interaction 119 and open innovation 93 origin of 112–13 social 5, 7, 58, 59, 82, 83, 86 tools for 218, 219 Crick, Francis 52, 62, 76 crime 153, 169, 183 criminality 1, 3 crowds 23, 61, 70, 72, 77 Crowdspirit 134 cultural élite 2 cultural sector 129–30 culture academic 38 anti-industrial 27, 28 basis of 4 collaborative 135 consumerist 172 corrosion of 4 cultural anarchy 5 folk 6, 27, 56–9, 220, 226 hippie 38 individual participation 6 political 171 popular 102 post-industrial 27, 28 pre-industrial 27, 28 We-Think 28, 59, 62, 169, 194, 230, 232–3, 238 Web 2.0 45 web-inflected 27 Western 239 wiki 14 work 114 YouTube cultural revolution 3 Cunningham, Ward 35–6 cyber cafés 107, 190, 192, 201, 204 Cyworld 34, 85, 86 D Dali, Salvador 105 Darby, Newman 102 Darpa 164 David, Paul 53 de Soto, Hernando 224–5 The Mystery of Capital 224 de Vellis, Phil 182 Dean, Howard 176–7, 178, 180, 185 Dean Corps 177 Debian 66 Debord, Guy 45, 46 decentralisation 7, 13, 39, 46, 59, 78, 226, 232 decision-making 78, 82, 84, 115, 173, 174 del.i.cious 86 democracy 1, 3, 5, 6, 7, 16, 24, 170–74, 175, 176–92 basis of 174 conversational democracy at a national level 184 ‘craftsmen of democracy’ 174 Dean campaign 178 democratic advances 184 depends on public sovereignty 172 formal 195 geek 65 Homebrew 176 public debate 170, 171 and We-Think 170, 221, 239 Department for International Development (DFID) 207 Descartes, René 19–20 design 166 modular 136–7 open-source 133–5, 140, 141, 162–3, 166–7 developing world Fab Labs in 166 government attitudes to the Internet 190 impact of the web on 166 mobile phones 185–6 and open-access publishing 166 and open-source design communities 166–7 and open-source software 200–203 research and development 196 and We-Think’s style of organisation 204 diabetes 150 Digg 33 discussion forums 77 diversity 9, 23, 72, 76, 77, 79–80, 112, 121 division of labour 111 DNA description of the double helix (Watson and Crick) 52, 62, 76 DNA-sequencing 164–5 Dobson, John 102, 162–3 Doritos 105 dot.com boom 106 Dupral 68 Dyson (household-goods company) 134 Dyson, Freeman 163, 164 E E-Lagda.com 186 Eaton, Brigitte 33 Eatonweb 33 eBay 40, 44, 102, 128, 152, 165, 216–18, 221, 229, 235 Ebola virus 165 Eccles, Nigel xi economies of scale 137 economy digital 124, 131, 216 gift 91, 226 global 192 global knowledge 239 of ideas 6 individual participation 6 industrial 122 market 91, 221 a mass innovation economy 7 networked 227 of things 6 UK 129, 130 and We-Think 129 Edison, Thomas 72, 93, 95 EditMe 36 education 130, 146–50, 167, 183, 194, 239 among the poorest people in the world 2, 193 civic 174 a more convivial system 44 Edwards, John 181 efficiency 109, 110 Einstein, Albert: theory of relativity 52 elderly, care of 170 Electronic Arts 105, 106, 128, 177 Electronic Frontier Foundation 40 electronics 93, 135 Eli Lilly (drugs company) 77 Ellis, Mark: The Coffee House: a social history 95 enclosures 124 Encyclopaedia Britannica, The 15–18, 126 encyclopaedias 1, 4, 7, 12–19, 21, 23, 36, 53, 60, 61, 79, 161, 231 Encyclopedia of Life (EOL) 161, 226 Endy, Drew 164, 165 energy 166, 232, 238 Engelbart, Doug 38–9, 59 engineering 133, 166 Environmental Protection Agency 152 epic poems 58, 60 equality 2, 24, 192–7, 198, 199–208 eScholarship repository, University of California 160 Estonia 184, 234 Estrada, President Joseph 186 ETA (Basque terrorist group) 187 European Union (EU) 130 Evans, Lilly x Evolt 68, 108 F Fab Labs 139, 166, 232 fabricators 139 Facebook 2, 34–5, 53, 142, 152, 191, 193, 210 factories 7, 8, 24 families, and education 147 Fanton, Jonathan 161 Fark 33 Feinstein, Diane 176 Felsenstein, Lee 42, 43, 44 fertilisers 123 Field Museum of Natural History, Harvard University 161 file-sharing 51, 58, 135, 144, 233 film 2, 3, 4, 47, 86, 129, 216, 218, 220–21 film industry 56 filters, collaborative 36, 86 financial services 130, 132 Financial Times 118 First International Computer (FIC), Inc. 136, 141 flash mobbing 10, 11 Flickr 34, 85, 86, 210, 218–19 Food and Drug Administration (US) 92 Ford, Henry 24, 93, 96 Fortune 500 company list 122 Frank, Ze (Hosea Jan Frank) 57, 219 freedom 1, 2, 6, 24, 208, 209, 210–21, 226 French, Gordon 41, 42 friendly societies 188 Friends Reunited 34 friendship 5, 233 combinatorial 95 Friendster 34, 35 fundamentalists 232 G Gaia Online 35 Galileo Galilei 154 gambling 169 GarageBand software 57, 135, 148 Gates, Bill 46, 47, 51, 227 Gates Foundation 160 geeks 27, 29–36, 37, 38, 48, 59, 65, 179 gene-sequencing machines, automated 64 genetic engineering 164, 196–7, 235 Georgia: ’colour revolution’ 187 Gershenfeld, Neil 139–40, 166, 232 GetFrank 108 Ghana, Fab Lab in 139 Gil, Gilberto 202 Gjertsen, Lasse 56, 218 Gland Pharma 200 global warming 238 globalisation 202, 228, 239 Gloriad 155 GM 135 Goa School Computers Project 200–201 Goffman, Erving 103–4 Goldcorp Inc. 132–3, 153 Golden Toad 40 GoLoco scheme 153 Google x, 1, 29, 32, 33, 47, 66, 97, 104, 113–14, 128, 141, 142, 144, 212 Google Earth 161 Gore, Al 64 governments in developing countries 190 difficulty in controlling the web 7 GPS systems 11 Grameen Bank 205–6, 208 ‘grey’ sciences 163 grid computing 155 Gross, Ralph 210 group-think 23, 210–11 groups 230–31 of clever people with the same outlook and skills 72 decision-making 78 diverse 72, 80, 231 and tools 76–7 Guthrie, Woody 58 H Habermas, Jurgen 174 hackers 48, 74, 104, 140, 232, 234 Hale, Victoria 199 Halo 2 science fiction computer game 8 Hamilton, Alexander 17–18 Hampton, Keith 183–4 Hanson, Matt xi health 130, 132, 146, 150–52, 167, 183, 239 Heisenberg, Werner 93 Henry, Thierry 29 Hewlett Packard 47 hierarchies 88, 110, 115 hippies 27, 48, 59, 61 HIV 193 Homebrew Computer Club 42, 46–7, 51, 227 Homebrew Mobile Phone Club 136 Homer Iliad 58 Odyssey 58 Homer-Dixon, Thomas: The Upside of Down 238–9 Hubble, Edwin 162 Human Genome Project 62, 64, 78, 155, 160, 161, 226 human rights 206 Hurricane Katrina 184 Hyde, Lewis: The Gift 226 hypertext 35, 39 I I Love Bees game 8, 10–12, 15–16, 19, 20, 69, 231 IBM 47, 66, 97 System/360 computer 77 idea-sharing 37, 94, 237, 239 as the biggest change the web will bring about 6 with colleagues 27 and consumer innovators 103 dual character of 226 gamers 106 Laboratory of Molecular Biology 63 through websites and bulletin boards 68 tools 222 We-Think-style approach to 97 and the web’s underlying culture 7 ideas combining 77 and creative thinking 87 from creative conversations 93, 95 gifts of 226 growth of 222, 239 and the new breed of leaders 117–18 ratifying 84 separating good from bad 84, 86 testing 74 the web’s growing domination 1 identity sense of 229 thieves 213–14 Illich, Ivan 43–5, 48 Deschooling Society 43, 44, 150 Disabling Professions 43 The Limits to Medicine 43, 152 Tools for Conviviality 44 independence 9, 72, 231 India Barefoot College 205 creative and cultural sectors 129–30 Fab Lab in 139 Internet connection 190, 204 mobile phones 207 and One World Health 200 spending on R & D 96 telephone service for street children 206 individuality 210, 211, 215, 216, 233 industrialisation 48, 150, 188 information barriers falling fast 2 computers democratise how it is accessed 139 effect of We-Think 129 large quantities on the web 31–2 libraries 141, 142, 143, 145 looking for 8 privileged access to 236 sharing 94, 136 the web’s growing domination 1 Wikipedia 19 Innocentive 77 innovation 5, 6, 91–3, 94, 95–8, 109 among the poorest people in the world 2 biological 194 collaborative 65, 70, 75, 90, 119, 146, 195 collective 170, 238 and competition/co-operation mix 137 Cornish mine engines 54–6 corporate 89, 109, 110 and creative conversations 93, 95 creative interaction with customers 113 cumulative 125, 238 decentralised 78 and distributed testing 74 and diverse thinking 79 and education 147 independent but interconnected 78 and interaction 119 and Linux 66 local 139 a mass innovation economy 7 medical 194 open 93, 96–7, 125, 195 in open-source communities 95–6 and patents 124 pipeline model 92, 93, 97 R & D 92, 96 risks of 100–101 social 170, 238 successful 69 user-driven 101 and We-Think 89, 93, 95, 125, 126 the web 2, 5, 7, 225 Institute for One World Health 199–200 Institute for Politics, Democracy & the Internet (IPDI) 179 Institute of Fiscal Studies 131 institutions convivial 44 industrial-era 234 and knowledge 103 and professionals 3, 5 public 142, 145 Instructables site 134 Intel 97 intellectual property 75, 122, 124, 125, 234 law 124–5 intelligence, collective bloggers 33 getting the mix right 23 Google’s search system 32 I Love Bees and Wikipedia examples 8, 10–19 milked by Google 47 the need to collaborate 32 self-organisation of 8 and social-networking sites 35 the web’s potential 3, 5 International Polar Year (IPY) 156, 226 Internet broadband connection 178, 189, 192 combined with personal computers (mid-1990s) 39 cyber cafés 107, 190, 192, 201, 204 Dean campaign 177 in developing countries 190 draws young people into politics 179, 180 an early demonstration (1968) 38 and Linux 66 news source 178–9 open-source software 68 openness 233 and political funding 180 pro-am astronomers 163 used by groups with a grievance 168 in Vietnam 189–90, 191 investment 119, 121, 133, 135 Iran 190, 191 Iraq war 18, 134, 191 Israel 18 Ito, Joi 99 J Japan politics 171 technology 171 JBoss 68 Jefferson, Richard 197, 199 Jodrell Bank Observatory, Macclesfield, Cheshire 162 JotSpot 36 journalism 3, 74, 115, 170–71 Junker, Margrethe 206 K Kampala, Uganda 206 Kazaa music file-sharing system 144 Keen, Andrew 208 The Cult of the Amateur 208 Kelly, Kevin 211 Kennedy, John F. 176 Kenya 207 Kepler, Johannes 162 Kerry, John 180 Khun, Thomas 69 knowledge access to 194, 196 agricultural 194 barriers falling fast 2 collaborative approach to 14, 69 encyclopaedia 79 expanding 94 gifts of 226 individual donation of 25 and institutions 103 and networking 193 and pro-ams 103 professional, authoritative sources of 222 sharing 27, 44, 63, 70, 199 spread by the web 2, 3 Wikipedia 16, 18, 19, 195 Korean War 203 Kotecki, James (’EmergencyCheese’) 182 Kraus, Joe 36 Kravitz, Ben 13 Kuresi, John 95 Kyrgyzstan: ’colour revolution’ 187 L Laboratory of Molecular Biology, Cambridge 62–3, 77 labour movement 188 language 52–3 Lanier, Jaron 16, 210–11, 213 laptop computers 5, 36, 82, 155 lateral thinking 113 leadership 89, 115, 116, 117–19 Lean, Joel 55 Lean’s Engine Reporter 55, 63, 77 Lee, Tim Berners 30–31 Lego: Mindstorms products 97, 104, 140 Lewandowska, Marysia 220, 221 libraries 2, 141–2, 143, 144–5, 227 life-insurance industry (US) 123 limited liability 121 Linked.In 35 Linux 65–6, 68, 70, 74, 80, 85, 86, 97, 98, 126, 127, 128, 136, 201, 203, 227 Lipson Community College, Plymouth 148 literacy 194 media 236 Lloyd, Edward 95 SMS messaging (texting)"/>London coffee houses 95 terrorist bombings (July 2005) 17 Lott, Trent 181–2 Lula da Silva, President Luiz Inacio 201 M M-PESA 207, 208 MacArthur Foundation 161 McCain, John 180 MacDonald’s 239 McGonigal, Jane 11, 69 McHenry, Robert 17 McKewan, Rob 132–3, 153 McLuhan, Marshall: Understanding the Media 45 Madrid bombings (March 2004) 186–7 Make magazine 165 management authoritative style of 117 and creative conversation 118 hierarchies 110 manufacturing 130, 132, 133–7, 138, 139–41, 166, 232 niche 139 Marcuse, Herbert 43 Marin 101 Mark, Paul xi market research 101 market(s) 77, 90, 93, 102, 123, 216, 226–7 Marsburg virus 165 Marx, Karl 224 mass production 7, 8, 24, 56, 96, 227, 232, 238 Massachusetts Institute of Technology (MIT) 139, 164, 233 Matsushita 135 media 129, 130, 156, 172, 173, 182, 211 literacy 236 Meetup 179, 185 Menlo Park laboratory, New Jersey 95 Merholz, Peter 33 meritocracy 16, 63 Microsoft 46, 47, 51, 56, 75, 109–10, 126, 127, 144, 202, 203, 204, 239 Office 201 Windows 200 Windows XP 66 Middle East 170, 189, 190, 192 Milovich, Dimitry 102 ‘minihompy’ (mini homepage) 204 Minnesota Mining and Materials 121 mobile phones 5 in Africa 185, 207 in Asia 166, 185 camera phones 74, 115, 210 children and 147 in developing-world markets 207–8 with digital cameras 36 flash mobs 10 I Love Bees 11 in India 207 open-source 136, 203 politics 185–9 SMS messaging (texting) 101–2, 185, 187, 214, 215 mobs 23, 61 flash 10, 11 modularity 77, 84 Moore, Fred 41–2, 43, 46, 47, 59, 227 More, Thomas: Utopia 208 Morris, Dick 174 Morris, Robert Tappan 233 Mosaic 33 motivation 109–12, 148 Mount Wilson Observatory, California 162 mountain bikes 101 MoveOn 188–9 Mowbray, Miranda xi music 1, 3, 4, 47, 51, 52, 57, 102, 135, 144, 218, 219, 221 publishing 130 social networking test 212–13 mutual societies 90, 121 MySpace 34, 44, 57, 85, 86, 152, 187, 193, 214, 219 MySQL 68 N National Football League (US) 105 National Health Service (NHS) 150, 151 National Public Radio (NPR) 188 Natural History Museum, London 161 Nature magazine 17 NBC 173 neo-Nazis 168 Netflix 216, 218 Netherlands 238 networking by geeks 27 post-industrial networks 27 social 2–7, 20, 23, 34–5, 36, 53, 57, 86, 95, 147, 149, 153, 159, 171, 183–4, 187, 193, 208, 210, 212, 213–15, 230, 233 New Economy 40 New Orleans 184 New York Magazine 214 New York Review of Books 164 New York Stock Exchange 95 New York Times 15, 182, 191 New Yorker magazine 149 Newmark, Craig 118 news services 60, 61, 171, 173, 178–9 newspapers 2, 3, 30, 32, 34, 171, 172, 173 Newton, Sir Isaac 25, 154 niche markets 216 Nixon, Richard 176 NLS (Online System) 39 Nokia 97, 104, 119, 140 non-profits 123 Nooteboom, Bart 74 Noronha, Alwyn 200–201 Norris, Pippa 189 North Africa, and democracy 189 Nosamo 35, 186 Noyes, Dorothy 58 Nupedia 13, 14 Nussbaum, Emily 214–15 O Obama, Barack 181, 191 Ofcom (Office of Communications) 31 OhmyNews 34, 87, 204, 231 oil companies 115 Oldenburg, Henry 25, 53–4, 156 Ollila, Jorma 119 Online System (NLS) 39 Open Architecture Network (OAN) 133–4 Open Net Initiative 190 Open Office programme 201 Open Prosthetics 134 Open Source Foundation 97 OpenMoko project 136 OpenWiki 36 O’Reilly, Tim 31 organisation commons as a system of organisation 51 pre-industrial ideas of 27, 48 social 20, 64, 165 We-Think’s organisational recipe 21 collaboration 21, 23 participation 21, 23 recognition 21 Organisation for Economic Co-operation and Development (OECD) 196 organisations civic 189 open/collaborative vs. closed/hierarchical models 89, 126, 127, 128 public 152 successful 228 see also companies; corporations Orwell, George: 1984 182 Ostrom, Elinor 51–2, 80 ownership 6, 119, 120, 121–6, 127, 128, 225 Oxford University 234 P paedophiles 3, 168, 213–14 Page, Scott xi, 72 Pakistan 237 Palace of Fine Arts, San Francisco 40 parallel universes 7 participation 23, 216, 223, 230, 232 consumers 98, 100 public services 145, 146, 150, 152, 153 a We-Think ingredient 21, 24 Partido Populaire (PP) (Spain) 187 patents 55, 56, 92, 97, 102, 124, 154, 196, 197, 199 Paul, Ron 185 Pawson, Dave x–xi Pax, Salam 57 peasants 27, 48, 59 peer recognition 54, 106, 111, 156, 228–9 peer review 53, 54, 156, 165, 236 peer-to-peer activity 53–4, 135, 148, 151 People’s Computer Company 41 People’s Democratic Party (Vietnam) 191 performance art/artists 2, 10 performance management 110 Perl 68 Peruvian Congress 202 Pew Internet & American Life 31, 179 pharmaceutical industry 92–3, 195–6, 197, 199, 200 Phelps, Edmund 114–15, 220 Philippines: mobile phones 185–6 Philips, Weston 105 photographs, sharing of 34, 75, 86, 218–19 Pitas.com 33 Plastic 33 Playahead 35 podcasts 142 Poland 220–21 polar research 156 politics bloggers able to act as public watchdog 181–2, 183 decline in political engagement 171–2 democratic 173 donations 179 funding 180–81 and journalism 170–71 and mobile phones 185–9 online 183 the online political class 179 and online social networks 35, 86 political advocates of the web 173–4 racist groups on the web 169 and television 173, 183 ultra-local 183, 184 US presidential elections 173, 179 videos 182 the web enters mainstream politics 176 young people drawn into politics by the Internet 179 Popper, Karl 155 Popular Science magazine 102 pornography 169, 214 Post-it notes 121 Potter, Seb 108–9 Powell, Debbie ix power and networking 193 technological 236 of the We-Think culture 230 of the web 24–5, 185, 233 PowerPoint presentations 140, 142, 219 privacy 210, 211 private property 224, 225 Procter and Gamble (P & G) 96–7, 98 productivity 112, 119, 121, 151, 227, 232 agricultural 124 professionals, and institutions 3, 5 property rights 224 public administration 130 Public Broadcasting Service 188 Public Intellectual Property Research for Agriculture initiative 199 Public Library of Science 159 public services 132, 141–2, 143, 144–53, 183 public spending 146 publishing 130, 166 science 156–7, 159–60 Putnam, Robert 173, 184 Python 68 Q quantum mechanics 93 ‘quick-web’ 35 R racism 169, 181–2 radio 173, 176 RapRep (Rapid Replicator) machines 137, 138, 139, 140, 141, 232 Rawls, John: A Theory of Justice 194 Raymond, Eric 64 recognition 21, 223 peer 54, 106, 111, 156 record industry 56, 102 recycling 111 Red Hat 66, 227 Red Lake, Ontario 132, 133 research 166 market 101 pharmaceutical 195–6 research and development (R & D) 92, 96, 119, 196 scientific 154–7, 159–65 retailing 130, 132 Rio Grande do Sul, Brazil 201 Roh Moo-hyun, President of South Korea 35, 186 Roosevelt, Franklin 176 Roy, Bunker 205 Royal Botanic Gardens, Kew, Surrey 161 Royal Society 54 Philosophical Transactions 25, 156 Ryze.com 34 S Sacca, Chris 113, 114 Safaricom 207 St Louis world fair (1904) 75–6 Samsung xi, 203 Sanger, Larry 13, 14, 16 Sanger Centre, Cambridge 155 Sao Paolo, Brazil 201 SARS virus 165 Sass, Larry 139 satellite phones 11 Saudi Arabia 190 scanners 11 Schumacher, E.


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

activist fund / activist shareholder / activist investor, Airbnb, Amazon Web Services, autonomous vehicles, bitcoin, blockchain, Bob Noyce, business intelligence, Chuck Templeton: OpenTable:, cloud computing, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, database schema, discounted cash flows, Elon Musk, Firefox, forensic accounting, George Gilder, global pandemic, Google Hangouts, Google X / Alphabet X, hydraulic fracturing, Hyperloop, inventory management, Isaac Newton, Jeff Bezos, Joi Ito, Khan Academy, late fees, Lean Startup, Lyft, M-Pesa, Marc Andreessen, margin call, Mark Zuckerberg, minimum viable product, move fast and break things, move fast and break things, Network effects, Oculus Rift, oil shale / tar sands, Paul Buchheit, Paul Graham, Peter Thiel, pre–internet, recommendation engine, ride hailing / ride sharing, Sam Altman, Sand Hill Road, Saturday Night Live, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart grid, social graph, software as a service, software is eating the world, speech recognition, stem cell, Steve Jobs, subscription business, Tesla Model S, thinkpad, transaction costs, transport as a service, Travis Kalanick, Uber for X, uber lyft, web application, winner-take-all economy, Y Combinator, yellow journalism

This purchase intent proved to be far more valuable per unit of traffic, enabling Google to earn fat margins. Google has since used the financial power of its gross margins to place big bets that other companies might shy away from, such as investing in Android and Chrome, two products that were going up against dominant competitors (Apple’s iOS in mobile phone software and Microsoft and Firefox in Web browsers). Google has also used its margins to fund radical experiments like X (formerly Google X) and Waymo (self-driving cars). These bets may or may not pay off, but even if they fail, Google’s margins give it the ability to recover quickly and keep going. Network Effects Google has leveraged network effects quite a bit in its major business lines, though not, ironically enough, in its core search product! The mobile traffic app Waze is a classic example of a direct network effect.


pages: 305 words: 79,303

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

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

And like the other horsemen, it sucks the profits out of its sector. The irony is that Google’s victims invited the company in, letting Google crawl their data. Now Google’s extraordinary market cap is equal to the next eight biggest media companies combined.17 Few people can explain how Google works. Or what Alphabet exactly is. Alphabet incorporated in 2015, and Google is one of its subsidiaries in addition to Google Ventures, Google X, and Google Capital.18 People have an idea about Apple: it builds beautiful objects around computer chips. People understand Amazon: you buy a bunch of stuff at a low price, then people (robots) in a big warehouse pick, pack, and get it to you, fast. Facebook? A network of friends linked to ads. But few people understand what happens inside a holding company that happens to “hold” a gigantic search engine.


The Buddha and the Badass: The Secret Spiritual Art of Succeeding at Work by Vishen Lakhiani

Buckminster Fuller, Burning Man, call centre, Colonization of Mars, crowdsourcing, deliberate practice, Elon Musk, fundamental attribution error, future of work, Google Glasses, Google X / Alphabet X, iterative process, Jeff Bezos, meta analysis, meta-analysis, microbiome, performance metric, Peter Thiel, profit motive, Ralph Waldo Emerson, Silicon Valley, Silicon Valley startup, skunkworks, Skype, Steve Jobs, Steven Levy, web application, white picket fence

Sometimes it’s simply the act of saying no or walking away from a situation. People who live from a place of making a positive difference for the world in what they do, have a higher bar of integrity. Like my friend Tom Chi. He has always inspired me with his commitment to using business as a vehicle for good in the world. Tom is an inventor, author, speaker, and cofounder of X Development, sometimes called Google X, Google’s semi-secret skunkworks lab (now a subsidiary of Google’s parent company Alphabet). A few years back Tom ran a think tank in Silicon Valley in which I was an investor. Large companies hired him and his team to help solve their tactical problems. At one point, a major beverage manufacturer approached Tom to solve its marketing hurdle. Teenagers were not buying enough of their product.


pages: 374 words: 89,725

A More Beautiful Question: The Power of Inquiry to Spark Breakthrough Ideas by Warren Berger

Airbnb, carbon footprint, Clayton Christensen, clean water, disruptive innovation, fear of failure, Google X / Alphabet X, Isaac Newton, Jeff Bezos, jimmy wales, Joi Ito, Kickstarter, late fees, Lean Startup, Mark Zuckerberg, minimum viable product, new economy, Paul Graham, Peter Thiel, Ray Kurzweil, self-driving car, sharing economy, side project, Silicon Valley, Silicon Valley startup, Stanford marshmallow experiment, Stephen Hawking, Steve Jobs, Steven Levy, Thomas L Friedman, Toyota Production System, Watson beat the top human players on Jeopardy!, Y Combinator, Zipcar

It was popularized a couple of decades35 ago by the American pastor Robert Schuller. The full version of his question was What would you attempt to do if you knew you could not fail? In the past few years, the question has had another surge in popularity that seems to have been jump-started by the former DARPA director Regina Dugan, who used it in a widely circulated TED speech.36 The question has also been picked up and championed by the influential Google X founder Sebastian Thrun, who has quoted it on Reddit and elsewhere. What if a TV drama could inspire real-life change?37 When the gritty television series The Wire ended in 2008, lead actress Sonja Sohn wasn’t ready to say good-bye to urban Baltimore, where the show was set. Sohn’s own hard-knock upbringing had given her empathy for the troubled lives depicted in The Wire; she wanted to help in some way.


pages: 319 words: 90,965

The End of College: Creating the Future of Learning and the University of Everywhere by Kevin Carey

Albert Einstein, barriers to entry, Bayesian statistics, Berlin Wall, business cycle, business intelligence, carbon-based life, Claude Shannon: information theory, complexity theory, David Heinemeier Hansson, declining real wages, deliberate practice, discrete time, disruptive innovation, double helix, Douglas Engelbart, Douglas Engelbart, Downton Abbey, Drosophila, Firefox, Frank Gehry, Google X / Alphabet X, informal economy, invention of the printing press, inventory management, John Markoff, Khan Academy, Kickstarter, low skilled workers, Lyft, Marc Andreessen, Mark Zuckerberg, meta analysis, meta-analysis, natural language processing, Network effects, open borders, pattern recognition, Peter Thiel, pez dispenser, ride hailing / ride sharing, Ronald Reagan, Ruby on Rails, Sand Hill Road, self-driving car, Silicon Valley, Silicon Valley startup, social web, South of Market, San Francisco, speech recognition, Steve Jobs, technoutopianism, transcontinental railway, uber lyft, Vannevar Bush

Unlike a professor of comparative literature, Sebastian Thrun had another organization he could work for that was just as wealthy and famous and prestigious in its way: Google. Also unlike a professor of comparative literature, Thrun had money from his various tech-related activities. Before CS221 even launched, he had withdrawn $300,000 from his bank account and created a start-up company to produce new online courses. Thrun had already resigned his tenured faculty position earlier that year to run Google X, the company’s research center. So when the university said no to credits, he finished out the course, awarded the not-Stanford statements of accomplishment, and walked away. At this point, everyone in Palo Alto was paying close attention to Sebastian Thrun and CS221. The venture capitalist business model depends on backing a lot of companies with the hope that a few pay off with spectacular returns.


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

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

The atmosphere of the conference was reminiscent of a poker game at an exclusive men’s club: lots of cheerful bonhomie among the players cloaking the fact that no one knew the content of one another’s poker hands. Our first lesson was that players in the autonomous vehicle industry keep their cards close to their chests. Of the employees we contacted at half a dozen car companies, not a single one responded to our email requests for an interview. When we reached out to Google X, the division of the company developing the driverless car, after several repeated queries, an administrative assistant politely pointed us to an exhibit on the history of driverless cars taking place in the Computer History Museum in nearby Palo Alto. Our second lesson was that cars can be deadly overlords. For three days, we scurried back and forth from the conference hotel to where were staying, a harrowing pedestrian experience involving crossing several ten-lane highways that slice apart the parched landscape outside Silicon Valley.


pages: 401 words: 93,256

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

3D printing, Alfred Russel Wallace, barriers to entry, basic income, Black Swan, butterfly effect, California gold rush, call centre, Captain Sullenberger Hudson, Cass Sunstein, cognitive dissonance, Daniel Kahneman / Amos Tversky, Dava Sobel, delayed gratification, Donald Trump, double helix, Downton Abbey, Elon Musk, Firefox, George Akerlof, gig economy, Google Chrome, Google X / Alphabet X, Grace Hopper, Hyperloop, Ignaz Semmelweis: hand washing, IKEA effect, information asymmetry, James Dyson, John Harrison: Longitude, loss aversion, low cost airline, Mason jar, Murray Gell-Mann, 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, 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

.* A similar placebo effect may mean that ersatz smoking only works for ex-smokers. 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.


pages: 411 words: 98,128

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

activist fund / activist shareholder / activist investor, AI winter, Airbnb, Amazon Web Services, Atul Gawande, autonomous vehicles, basic income, Bernie Sanders, Black Swan, call centre, Chris Urmson, cloud computing, corporate raider, creative destruction, Danny Hillis, Donald Trump, Elon Musk, Erik Brynjolfsson, future of work, gig economy, Google Glasses, Google X / Alphabet X, income inequality, industrial robot, Internet of things, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Lyft, Marc Andreessen, Mark Zuckerberg, money market fund, natural language processing, pets.com, plutocrats, Plutocrats, race to the bottom, ride hailing / ride sharing, Sand Hill Road, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Snapchat, speech recognition, Steve Jobs, Stewart Brand, supply-chain management, Tim Cook: Apple, too big to fail, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, wealth creators, web application, Whole Earth Catalog

The health-care industry also operates inside an intricate web of long-standing contracts between hospitals, suppliers, drug benefit managers, insurers, and physicians that’s hard for an outsider like Amazon to infiltrate. Despite the long odds, Bezos has decided to double down on health care. The company’s logistics have become more sophisticated and its pockets deeper—and, besides, if Amazon wants to maintain its rapid growth pace, it will need new markets to penetrate. As a first step, Bezos in 2014 hired Babak Parviz, an Iranian immigrant who previously headed Google X, a respected research facility (now a division of Alphabet called X) that worked on various moonshot projects, including kites that gather wind energy, the Google Glass virtual-reality headset, and self-driving cars—an initiative that eventually became the Alphabet subsidiary Waymo. Just as at Google, Parviz’s innovation lab at Amazon, which is named Grand Challenge, will have, as its name suggests, a broad mandate to take the long view and to tinker creatively on some of the world’s biggest problems.


pages: 364 words: 99,897

The Industries of the Future by Alec Ross

23andMe, 3D printing, Airbnb, algorithmic trading, AltaVista, Anne Wojcicki, autonomous vehicles, banking crisis, barriers to entry, Bernie Madoff, bioinformatics, bitcoin, blockchain, Brian Krebs, British Empire, business intelligence, call centre, carbon footprint, cloud computing, collaborative consumption, connected car, corporate governance, Credit Default Swap, cryptocurrency, David Brooks, disintermediation, Dissolution of the Soviet Union, distributed ledger, Edward Glaeser, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, fiat currency, future of work, global supply chain, Google X / Alphabet X, industrial robot, Internet of things, invention of the printing press, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Joi Ito, Kickstarter, knowledge economy, knowledge worker, lifelogging, litecoin, M-Pesa, Marc Andreessen, Mark Zuckerberg, Mikhail Gorbachev, mobile money, money: store of value / unit of account / medium of exchange, Nelson Mandela, new economy, 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, The Future of Employment, Travis Kalanick, underbanked, Vernor Vinge, Watson beat the top human players on Jeopardy!, women in the workforce, Y Combinator, young professional

His Google colleague Anthony Levandowski described the shortcomings of the earlier electric models in this way: “We don’t have the money to fix potholes. Why would we invest in putting wires in the road?” Today, however, almost every major car company is researching and building its own version of a driverless car. But the company at the forefront is not a traditional car company at all: it’s Google. For the past six years, the tech giant’s moon shot development lab, Google X, has been working on the driverless Google car. While much of the technology is proprietary and secret, the company has disclosed a few of its most prominent features. Among other technologies, the Google car includes radar, cameras to ensure that cars stay within lanes, and a light detection and ranging system. Infrared, 3D imaging, an advanced GPS system, and wheel sensors are also being incorporated.


pages: 334 words: 104,382

Brotopia: Breaking Up the Boys' Club of Silicon Valley by Emily Chang

23andMe, 4chan, Ada Lovelace, affirmative action, Airbnb, Apple II, augmented reality, autonomous vehicles, barriers to entry, Bernie Sanders, Burning Man, California gold rush, Chuck Templeton: OpenTable:, David Brooks, Donald Trump, Elon Musk, equal pay for equal work, Ferguson, Missouri, game design, gender pay gap, Google Glasses, Google X / Alphabet X, Grace Hopper, high net worth, Hyperloop, Jeff Bezos, job satisfaction, Khan Academy, Lyft, Marc Andreessen, Mark Zuckerberg, Maui Hawaii, Menlo Park, meta analysis, meta-analysis, microservices, paypal mafia, Peter Thiel, post-work, pull request, ride hailing / ride sharing, rolodex, Saturday Night Live, shareholder value, side project, Silicon Valley, Silicon Valley startup, Skype, Snapchat, Steve Jobs, Steve Wozniak, Steven Levy, subscription business, Tim Cook: Apple, Travis Kalanick, uber lyft, women in the workforce

“I’ve worked at Google for about six years, and I just haven’t been surrounded by women who are managers. I’ve just worked with so many men, and I’ve had crappy male bosses. Crappy and rude.” It wasn’t until she arrived at Google, Evans told me, that she realized how isolated she was as a woman in technology. In 2015, Larry Page rebranded the company as Alphabet, now made up of twelve different divisions including Google, Google Ventures (now called GV), and Google X, each of them with its own CEO. At the same time, he set out to find a female CFO for Alphabet and succeeded, hiring the longtime Morgan Stanley executive Ruth Porat. Page also brought in Diane Greene, the co-founder of VMware, to run Google’s cloud efforts. The management team of Google’s CEO, Sundar Pichai (who was promoted to the role after Page made himself CEO of Alphabet), is some 40 percent women.


pages: 385 words: 111,113

Augmented: Life in the Smart Lane by Brett King

23andMe, 3D printing, additive manufacturing, Affordable Care Act / Obamacare, agricultural Revolution, Airbnb, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, Apple II, artificial general intelligence, asset allocation, augmented reality, autonomous vehicles, barriers to entry, bitcoin, blockchain, business intelligence, business process, call centre, chief data officer, Chris Urmson, Clayton Christensen, clean water, congestion charging, crowdsourcing, cryptocurrency, deskilling, different worldview, disruptive innovation, distributed generation, distributed ledger, double helix, drone strike, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, fiat currency, financial exclusion, Flash crash, Flynn Effect, future of work, gig economy, Google Glasses, Google X / Alphabet X, Hans Lippershey, Hyperloop, income inequality, industrial robot, information asymmetry, Internet of things, invention of movable type, invention of the printing press, invention of the telephone, invention of the wheel, James Dyson, Jeff Bezos, job automation, job-hopping, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, Kodak vs Instagram, Leonard Kleinrock, lifelogging, low earth orbit, low skilled workers, Lyft, M-Pesa, Mark Zuckerberg, Marshall McLuhan, megacity, Metcalfe’s law, Minecraft, mobile money, money market fund, more computing power than Apollo, Network effects, new economy, obamacare, Occupy movement, Oculus Rift, off grid, packet switching, pattern recognition, peer-to-peer, Ray Kurzweil, RFID, ride hailing / ride sharing, Robert Metcalfe, Satoshi Nakamoto, Second Machine Age, selective serotonin reuptake inhibitor (SSRI), self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart grid, smart transportation, Snapchat, social graph, software as a service, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, TaskRabbit, technological singularity, telemarketer, telepresence, telepresence robot, Tesla Model S, The Future of Employment, Tim Cook: Apple, trade route, Travis Kalanick, Turing complete, Turing test, uber lyft, undersea cable, urban sprawl, V2 rocket, Watson beat the top human players on Jeopardy!, white picket fence, WikiLeaks

Dr Garth Webb, inventor of the Ocumetrics Bionic Lens By treating such lenses with technology like Valspar’s EnChroma coating, the new lens could even theoretically correct colour blindness. Webb says that the Ocumetrics Bionic Lens could be available to the public within a few years, but we’ll just have to wait and see. It is clear, however, that materials science, manufacturing processes and new medical techniques will allow us to make these sort of advancements in the next two decades. It won’t take long before such a lens will have other capabilities embedded in it, too. Google X announced early in 2014 a project with the eye care division of Novartis to develop a contact lens that can monitor glucose levels and automatically adjust focus. For instance, one of the Novartis-Google prototype lenses contains a device about the size of a speck of glitter that measures glucose in tears. A wireless antenna then transmits the measurements to an external device. It’s designed to ease the burden of diabetics who otherwise have to prick their fingers to test their blood sugar levels.


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

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

To understand that a page titled “Tomato-Free Salsa” probably does match the query “salsa recipes NOT tomato” even though it contains the word “Tomato”. To this end Google recently built the Knowledge Graph semantic network that incorporated some 570 million objects and more than 18 billion facts about the world based on the earlier Freebase ontology. Corporate, Fair use. Google has recently invested heavily in much more ambitious artificial intelligence projects. Their secretive Google X division is developing autonomous self-driving cars as well as advanced image understanding programs. Google recently hired Ray Kurzweil, who promoted the idea of the Singularity, as well as Peter Norvig, the much more conservative co-author of the major text-book on artificial intelligence. Norvig estimated that Google employed well over 5% of the world’s experts in machine learning some time ago.


pages: 742 words: 137,937

The Future of the Professions: How Technology Will Transform the Work of Human Experts by Richard Susskind, Daniel Susskind

23andMe, 3D printing, additive manufacturing, AI winter, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, Andrew Keen, Atul Gawande, Automated Insights, autonomous vehicles, Big bang: deregulation of the City of London, big data - Walmart - Pop Tarts, Bill Joy: nanobots, business process, business process outsourcing, Cass Sunstein, Checklist Manifesto, Clapham omnibus, Clayton Christensen, clean water, cloud computing, commoditize, computer age, Computer Numeric Control, computer vision, conceptual framework, corporate governance, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, death of newspapers, disintermediation, Douglas Hofstadter, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, full employment, future of work, Google Glasses, Google X / Alphabet X, Hacker Ethic, industrial robot, informal economy, information retrieval, interchangeable parts, Internet of things, Isaac Newton, James Hargreaves, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Khan Academy, knowledge economy, lifelogging, lump of labour, Marshall McLuhan, Metcalfe’s law, Narrative Science, natural language processing, Network effects, optical character recognition, Paul Samuelson, personalized medicine, pre–internet, Ray Kurzweil, Richard Feynman, Second Machine Age, self-driving car, semantic web, Shoshana Zuboff, Skype, social web, speech recognition, spinning jenny, strong AI, supply-chain management, telepresence, The Future of Employment, the market place, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, transaction costs, Turing test, Watson beat the top human players on Jeopardy!, WikiLeaks, young professional

Nanomedicine, the use of nanotechnology in a medical setting, is another field. Nobel Laureate Richard Feynman’s seventy-year-old prediction that we might one day ‘swallow the surgeon’50 has come true—there are already small nanobots that are able to swim through our bodies, relaying images, delivering targeted drugs, and attacking particular cells with a precision that makes even the finest of surgeons’ blades look blunt. (At Google X, one of Google’s research facilities, they are said to be developing a version of this.51) Non-humans are also playing a role. Engineers are developing a large number of sophisticated robotic systems that support patients (sometimes called ‘assistive robotics’).52 There are, for example, robotics that help paraplegics to walk, and prosthetics, controlled by patients, to replace lost limbs.53 Some systems also help practitioners.


pages: 499 words: 144,278

Coders: The Making of a New Tribe and the Remaking of the World by Clive Thompson

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 4chan, 8-hour work day, Ada Lovelace, AI winter, Airbnb, Amazon Web Services, Asperger Syndrome, augmented reality, Ayatollah Khomeini, barriers to entry, basic income, Bernie Sanders, bitcoin, blockchain, blue-collar work, Brewster Kahle, Brian Krebs, Broken windows theory, call centre, cellular automata, Chelsea Manning, clean water, cloud computing, cognitive dissonance, computer vision, Conway's Game of Life, crowdsourcing, cryptocurrency, Danny Hillis, David Heinemeier Hansson, don't be evil, don't repeat yourself, Donald Trump, dumpster diving, Edward Snowden, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Ethereum, ethereum blockchain, Firefox, Frederick Winslow Taylor, game design, glass ceiling, Golden Gate Park, Google Hangouts, Google X / Alphabet X, Grace Hopper, Guido van Rossum, Hacker Ethic, HyperCard, illegal immigration, ImageNet competition, Internet Archive, Internet of things, Jane Jacobs, John Markoff, Jony Ive, Julian Assange, Kickstarter, Larry Wall, lone genius, Lyft, Marc Andreessen, Mark Shuttleworth, Mark Zuckerberg, Menlo Park, microservices, Minecraft, move fast and break things, move fast and break things, Nate Silver, Network effects, neurotypical, Nicholas Carr, Oculus Rift, PageRank, pattern recognition, Paul Graham, paypal mafia, Peter Thiel, pink-collar, planetary scale, profit motive, ransomware, recommendation engine, Richard Stallman, ride hailing / ride sharing, Rubik’s Cube, Ruby on Rails, Sam Altman, Satoshi Nakamoto, Saturday Night Live, self-driving car, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, single-payer health, Skype, smart contracts, Snapchat, social software, software is eating the world, sorting algorithm, South of Market, San Francisco, speech recognition, Steve Wozniak, Steven Levy, TaskRabbit, the High Line, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, universal basic income, urban planning, Wall-E, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y Combinator, Zimmermann PGP, éminence grise

The ImageNet challenge, as it’s known, is an annual competition among AI researchers to see whose system is best at recognizing images. That year, Hinton’s deep-learning neural net got only 15.3 percent of the images wrong. The next-best competitor had an error rate almost twice as high, of 26.2 percent. It was an AI moon shot. Another of Dean’s colleagues was equally impressed: a Stanford professor named Andrew Ng, then a part-time consultant for Google X. Like Dean, he’d tinkered with neural code while young, then set it aside during deep learning’s long AI winter. But in 2011, over a dinner with Dean, the two got excited about the idea of using Google’s enormous phalanxes of computers to see how powerful a neural net they could build. “When people think of AI, they think of sentience,” Ng tells me. “But when I think of AI, I think of automation.


pages: 554 words: 149,489

The Content Trap: A Strategist's Guide to Digital Change by Bharat Anand

Airbnb, Benjamin Mako Hill, Bernie Sanders, Clayton Christensen, cloud computing, commoditize, correlation does not imply causation, creative destruction, crowdsourcing, death of newspapers, disruptive innovation, Donald Trump, Google Glasses, Google X / Alphabet X, information asymmetry, Internet of things, inventory management, Jean Tirole, Jeff Bezos, John Markoff, Just-in-time delivery, Khan Academy, Kickstarter, late fees, Mark Zuckerberg, market design, Minecraft, multi-sided market, Network effects, post-work, price discrimination, publish or perish, QR code, recommendation engine, ride hailing / ride sharing, selection bias, self-driving car, shareholder value, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Skype, social graph, social web, special economic zone, Stephen Hawking, Steve Jobs, Steven Levy, Thomas L Friedman, transaction costs, two-sided market, ubercab, WikiLeaks, winner-take-all economy, zero-sum game

Thrun had been a computer science professor, at Carnegie Mellon and then Stanford, for more than a decade. He specialized in artificial intelligence (AI) and in far-out projects that Google later called “moon shots.” Like many Stanford computer scientists, he was closely involved with Valley start-ups—in his case, Google. Thrun had advised Google since 2007, led its program to develop a driverless car, and started Google X, the lab that developed Google Glass. In 2010 Thrun was about to teach the AI course he offered every fall. But this time he also recorded his lectures and put them online. That would certainly benefit any of his Stanford students who missed a lecture or two. But his real motivation was to make his teaching available to anyone who was interested but would never set foot on Stanford’s campus. Thrun was shocked by what ensued.


pages: 533

Future Politics: Living Together in a World Transformed by Tech by Jamie Susskind

3D printing, additive manufacturing, affirmative action, agricultural Revolution, Airbnb, airport security, Andrew Keen, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, bitcoin, blockchain, brain emulation, British Empire, business process, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Cass Sunstein, cellular automata, cloud computing, computer age, computer vision, continuation of politics by other means, correlation does not imply causation, crowdsourcing, cryptocurrency, digital map, distributed ledger, Donald Trump, easy for humans, difficult for computers, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, Filter Bubble, future of work, 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, lifelogging, Metcalfe’s law, mittelstand, more computing power than Apollo, move fast and break things, move fast and break things, natural language processing, Network effects, new economy, night-watchman state, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, payday loans, 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 Jobs, Steve Wozniak, Steven Levy, technological singularity, the built environment, The Structural Transformation of the Public Sphere, The Wisdom of Crowds, Thomas L Friedman, universal basic income, urban planning, Watson beat the top human players on Jeopardy!, working-age population

Brynjolfsson, McAfee, and Spence, ‘New World Order’. 69. Susskind and Susskind, Future of the Professions, 1. 70. Susskind and Susskind, Future of the Professions, 307. 71. Arun Sundararajan, The Sharing Economy: The End of Employment and the Rise of Crowd-Based Capitalism (Cambridge, Mass: MIT Press, 2017), 3–5. 72. Lauren Goode, ‘Delivery Drones Will Mean the End of Ownership’, The Verge, 8 November 2016 <https://www.theverge.com/a/verge2021/google-x-astro-teller-interview-drones-innovation> (accessed 8 December 2017). This piece imagines the hammer located in a ‘central place’, presumably under common ownership. The general point is the same. 73. Allen, Technology and Inequality, Kindle Locations 2600–2601. 74. Allen, Technology and Inequality, Kindle Locations 2592, 2645–2647. 75. Evgeny Morozov, ‘To Tackle Google’s Power, Regulators Have to Go After its Ownership of Data’, The Guardian, 2 July 2017.


pages: 1,197 words: 304,245

The Invention of Science: A New History of the Scientific Revolution by David Wootton

agricultural Revolution, Albert Einstein, British Empire, clockwork universe, Commentariolus, commoditize, conceptual framework, Dava Sobel, double entry bookkeeping, double helix, en.wikipedia.org, Ernest Rutherford, Fellow of the Royal Society, fudge factor, germ theory of disease, Google X / Alphabet X, Hans Lippershey, interchangeable parts, invention of gunpowder, invention of the steam engine, invention of the telescope, Isaac Newton, Jacques de Vaucanson, James Watt: steam engine, Johannes Kepler, John Harrison: Longitude, knowledge economy, lateral thinking, lone genius, Mercator projection, On the Revolutions of the Heavenly Spheres, Philip Mirowski, placebo effect, QWERTY keyboard, Republic of Letters, social intelligence, spice trade, spinning jenny, the scientific method, Thomas Kuhn: the structure of scientific revolutions

It is easy to think that new knowledge comes from new types of apparatus – Galileo’s telescope, Boyle’s air pump, Newton’s prism – not from new intellectual tools.iv Often this is a mistaken view: in a hundred years time the randomized clinical trial (streptomycin, 1948) may look much more significant than the X-ray (1895) or even the MRI scanner (1973). New instruments are plain as pikestaffs; new intellectual tools are not. As a result we tend to overestimate the importance of new technology and underestimate the rate of production and the impact of new intellectual tools. A good example is Descartes’ innovation of using letters from near the end of the alphabet (x, y, z) to represent unknown quantities in equations, or William Jones’s introduction of the symbol π in 1706. Leibniz believed that the reform of mathematical symbols would improve reasoning just as effectively as the telescope had improved sight.17 Another example is the graph: graphs are now ubiquitous, so it comes as something of a shock to discover that they only began to be put to use in the natural sciences in the 1830s, and in the social sciences in the 1880s.


pages: 586 words: 186,548

Architects of Intelligence by Martin Ford

3D printing, agricultural Revolution, AI winter, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, bitcoin, business intelligence, business process, call centre, cloud computing, cognitive bias, Colonization of Mars, computer vision, correlation does not imply causation, crowdsourcing, DARPA: Urban Challenge, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Fellow of the Royal Society, Flash crash, future of work, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Rosling, ImageNet competition, income inequality, industrial robot, information retrieval, job automation, John von Neumann, Law of Accelerating Returns, life extension, Loebner Prize, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, natural language processing, new economy, optical character recognition, pattern recognition, phenotype, Productivity paradox, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, Ted Kaczynski, 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, zero-sum game, Zipcar

In 2011, I started to work on more machine learning-oriented systems, because I started to get very interested in how we could apply the very large amounts of computation that we had to train very large and powerful neural nets. MARTIN FORD: You’re the head, and one of the founders, of Google Brain, which was one of the first real applications of deep learning and neural networks. Could you sketch out the story of Google Brain, and the role it plays at Google? JEFF DEAN: Andrew Ng was a consultant in Google X for one day a week, and I bumped into him in the kitchen one day, and I said, “What are you up to?” He said, “Oh, I’m still figuring things out here, but at Stanford, my students are starting to look at how neural networks can be applied to different kinds of problems, and they’re starting to work.” I had experience with neural networks from doing my undergraduate thesis 20 years ago, so I said, “That’s cool, I like neural networks.


pages: 669 words: 210,153

Tools of Titans: The Tactics, Routines, and Habits of Billionaires, Icons, and World-Class Performers by Timothy Ferriss

Airbnb, Alexander Shulgin, artificial general intelligence, asset allocation, Atul Gawande, augmented reality, back-to-the-land, Ben Horowitz, Bernie Madoff, Bertrand Russell: In Praise of Idleness, Black Swan, blue-collar work, Boris Johnson, Buckminster Fuller, business process, Cal Newport, call centre, Charles Lindbergh, Checklist Manifesto, cognitive bias, cognitive dissonance, Colonization of Mars, Columbine, commoditize, correlation does not imply causation, David Brooks, David Graeber, diversification, diversified portfolio, Donald Trump, effective altruism, Elon Musk, fault tolerance, fear of failure, Firefox, follow your passion, future of work, Google X / Alphabet X, Howard Zinn, Hugh Fearnley-Whittingstall, Jeff Bezos, job satisfaction, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Kickstarter, Lao Tzu, lateral thinking, life extension, lifelogging, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Mason jar, Menlo Park, Mikhail Gorbachev, MITM: man-in-the-middle, Nelson Mandela, Nicholas Carr, optical character recognition, PageRank, passive income, pattern recognition, Paul Graham, peer-to-peer, Peter H. Diamandis: Planetary Resources, Peter Singer: altruism, Peter Thiel, phenotype, PIHKAL and TIHKAL, post scarcity, post-work, premature optimization, QWERTY keyboard, Ralph Waldo Emerson, Ray Kurzweil, recommendation engine, rent-seeking, Richard Feynman, risk tolerance, Ronald Reagan, selection bias, sharing economy, side project, Silicon Valley, skunkworks, Skype, Snapchat, social graph, software as a service, software is eating the world, stem cell, Stephen Hawking, Steve Jobs, Stewart Brand, superintelligent machines, Tesla Model S, The Wisdom of Crowds, Thomas L Friedman, Wall-E, Washington Consensus, Whole Earth Catalog, Y Combinator, zero-sum game

Exposure to things that capture your ‘shower time’ [those things you can’t stop thinking about in the shower]?” [Peter recommends environments like Singularity University.] TF: Still struggling with a sense of purpose or mission? Roughly half a dozen people in this book (e.g., Robert Rodriguez) have suggested the book Start with Why by Simon Sinek. The Benefits of Thinking 10x Versus 10% “I interviewed Astro Teller [for my book Bold]. Astro is the head of Google X (now called ‘X’), Google skunkworks. . . . He says, ‘When you go after a moonshot—something that’s 10 times bigger, not 10% bigger—a number of things happen. . . .’ “First of all, when you’re going 10% bigger, you’re competing against everybody. Everybody’s trying to go 10% bigger. When you’re trying to go 10 times bigger, you’re there by yourself. For me, [take asteroid mining as an example].


pages: 918 words: 257,605

The Age of Surveillance Capitalism by Shoshana Zuboff

Amazon Web Services, Andrew Keen, augmented reality, autonomous vehicles, barriers to entry, Bartolomé de las Casas, Berlin Wall, bitcoin, blockchain, blue-collar work, book scanning, Broken windows theory, California gold rush, call centre, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, choice architecture, citizen journalism, cloud computing, collective bargaining, Computer Numeric Control, computer vision, connected car, corporate governance, corporate personhood, creative destruction, cryptocurrency, dogs of the Dow, don't be evil, Donald Trump, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, Ford paid five dollars a day, future of work, game design, Google Earth, Google Glasses, Google X / Alphabet X, hive mind, impulse control, income inequality, Internet of things, invention of the printing press, invisible hand, Jean Tirole, job automation, Johann Wolfgang von Goethe, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, knowledge economy, linked data, longitudinal study, low skilled workers, Mark Zuckerberg, market bubble, means of production, multi-sided market, Naomi Klein, natural language processing, Network effects, new economy, Occupy movement, off grid, PageRank, Panopticon Jeremy Bentham, pattern recognition, Paul Buchheit, performance metric, Philip Mirowski, precision agriculture, price mechanism, profit maximization, profit motive, recommendation engine, refrigerator car, RFID, Richard Thaler, ride hailing / ride sharing, Robert Bork, Robert Mercer, Second Machine Age, self-driving car, sentiment analysis, shareholder value, Shoshana Zuboff, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, slashdot, smart cities, Snapchat, social graph, social web, software as a service, speech recognition, statistical model, Steve Jobs, Steven Levy, structural adjustment programs, The Future of Employment, The Wealth of Nations by Adam Smith, Tim Cook: Apple, two-sided market, union organizing, Watson beat the top human players on Jeopardy!, winner-take-all economy, Wolfgang Streeck

Drew FitzGerald, “Now Google Glass Can Turn You into a Live Broadcast,” Wall Street Journal, June 24, 2014, http://www.wsj.com/articles/now-google-glass-can-turn-you-into-a-live-broadcast-1403653079. 77. See Amir Efrati, “Google Glass Privacy Worries Lawmakers,” Wall Street Journal, May 17, 2013, http://www.wsj.com/articles/SB100014241278873247 67004578487661143483672. 78. “We’re Graduating from Google[x] Labs,” Google, January 15, 2015, https://plus.google.com/app/basic/stream/z124trxirsruxvcdp23otv4qerfwghdhv04. 79. Alistair Barr, “Google Glass Gets a New Name and Hires from Amazon,” Wall Street Journal, September 16, 2015. 80. Fred O’Connor, “Google is Making Glass ‘Ready for Users,’ Says Schmidt,” PCWorld, March 23, 2015, http://www.pcworld.com/article/2900632/google-is-making-glass-ready-for-users-says-schmidt.html; “Looking Ahead for WhatsApp,” WhatsApp (blog), August 25, 2016, https://blog.whatsapp.com/10000627/Looking-ahead-for-WhatsApp. 81.