Watson beat the top human players on Jeopardy!

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Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel

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To assemble Watson, IBM crated in a mammoth configuration of hardware, about 10 refrigerators’ worth. Watson didn’t go to Jeopardy!; Jeopardy! came to Watson, setting up a temporary game show studio within IBM’s T. J. Watson Research Center. Double Jeopardy!—Would Watson Win? Watson was not sure to win. During sparring games against human champions, Watson had tweaked its way up to a 71 percent win record. It didn’t always win, and these trial runs didn’t pit it against the lethal competition it was preparing to face on the televised match: all-time leading Jeopardy! champions Ken Jennings and Brad Rutter. Reproduced with permission. The Jeopardy! match was to gain full-scale media publicity, exposing IBM’s analytical prowess or failure.

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Robert Jewell, IBM, “Putting IBM Watson to Work,” Predictive Analytics World Boston Conference, October 2, 2012, Boston, MA. www.predictiveanalyticsworld.com/boston/2012/agenda.php#day2–1kn. Clive Thompson, “What Is IBM’s Watson?” New York Times, June 16, 2010. www.nytimes.com/2010/06/20/magazine/20Computer-t.html. “Smartest Machine on Earth,” Nova, PBS, originally aired May 2, 2012. www.pbs.org/wgbh/nova/tech/smartest-machine-on-earth.html. Roger Craig, the PA practitioner and Jeopardy! winner: Roger Craig, “Data Science Meets the Quiz Show Jeopardy!” Predictive Analytics World Chicago Conference, June 26, 2012, Chicago, IL. www.predictiveanalyticsworld.com/chicago/2012/agenda.php#day2–11. Chris Jones, “If You Could Master All the Data in the World. . .,” Esquire, January 25, 2012. www.esquire.com/features/roger-craig-jeopardy-champ-0212.

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TREC Proceedings. trec.nist.gov/proceedings/proceedings.html. Alex Trebek on IBM’s investment to build Watson: “Trebek Says IBM Computer Could Answer Wrong on Jeopardy!” Bloomberg Television, February 14, 2012. Online video clip, YouTube, accessed March 23, 2012. www.youtube.com/watch?v=tYguL82wk78. IBM’s estimated spend to develop Watson: Don Tennant, “‘Final Jeopardy’ Author Stephen Baker on the Impact of IBM’s Watson,” ITBusinessEdge, February 14, 2011. www.itbusinessedge.com/cm/community/features/interviews/blog/final-jeopardy-author-stephen-baker-on-the-impact-of-ibms-watson/. How Watson works: IBM, “IBM Watson: Ushering in a new era of computing,” IBM Innovations, April 11, 2012. www-03.ibm.com/innovation/us/watson/.

Final Jeopardy: Man vs. Machine and the Quest to Know Everything by Stephen Baker

23andMe, AI winter, Albert Einstein, artificial general intelligence, behavioural economics, business process, call centre, clean water, commoditize, computer age, Demis Hassabis, Frank Gehry, information retrieval, Iridium satellite, Isaac Newton, job automation, machine translation, pattern recognition, Ray Kurzweil, Silicon Valley, Silicon Valley startup, statistical model, The Soul of a New Machine, theory of mind, thinkpad, Turing test, Vernor Vinge, vertical integration, Wall-E, Watson beat the top human players on Jeopardy!

As soon as his shows had aired in mid-September (and he was free to discuss his victories), he e-mailed Ferrucci, asking for a chance to IBM and spar with Watson. Ferrucci’s response, while cordial, was noncommittal. Jeopardy, not IBM, was in charge of selecting Watson’s sparring partners. Before going on Jeopardy, Craig had long relied on traditional strategies. He’d read books on the game, including the 1998 How to Get on Jeopardy—And Win, by Michael DuPee. He’d also gone to Google Scholar, the search engine’s repository of academic works, and downloaded papers on Final Jeopardy betting. Craig was steeped in the history and lore of the games, as well as various strategies, many of them named for players who had made them famous.

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But this, it turned out, wasn’t a bad thing at all. Ratings soared. Jeopardy had hatched its first celebrity. His name was Ken Jennings. Nothing about the man suggested quiz show dominance. Unlike basketball, where a phenom like LeBron James emerged in high school, amid monster dunks, as the Next Big Thing, a Jeopardy champion like Jennings could surprise even himself. A computer programmer from Salt Lake City, Jennings had competed in quiz bowl events during high school and college. A turn on Jeopardy would be a kick. So in the summer of 2003, he and a friend drove from Salt Lake City to the Jeopardy studios in Culver City and took the qualifying exam.

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And for her love, support, and help in maintaining a sense of balance, I give thanks to my wife, Jalaire. She’d see the forty Jeopardy shows stored on TiVo and say, “Let’s watch something else.” Notes [>] It was a September morning: Like Yahoo! and a handful of other businesses, the official name of the quiz show in this story ends in an exclamation point: Jeopardy! Initially, I tried using that spelling, but I thought it made reading harder. People see a word like this! and they think it ends a sentence. Since I use the name Jeopardy more than two hundred times in the book, I decided to eliminate that distraction. My apologies to the Jeopardy! faithful, many of whom are sticklers for this kind of detail. [>] pressing the button: A few months before the final match, I was talking to the Jeopardy champion Ken Jennings in Los Angeles.

Smart Machines: IBM's Watson and the Era of Cognitive Computing (Columbia Business School Publishing) by John E. Kelly Iii

AI winter, book value, call centre, carbon footprint, Computing Machinery and Intelligence, crowdsourcing, demand response, discovery of DNA, disruptive innovation, Erik Brynjolfsson, Fairchild Semiconductor, future of work, Geoffrey West, Santa Fe Institute, global supply chain, Great Leap Forward, Internet of things, John von Neumann, Large Hadron Collider, Mars Rover, natural language processing, optical character recognition, pattern recognition, planetary scale, RAND corporation, RFID, Richard Feynman, smart grid, smart meter, speech recognition, TED Talk, Turing test, Von Neumann architecture, Watson beat the top human players on Jeopardy!

And, just as importantly, we hope to inspire university and high school students to pursue careers in science, technology, engineering, and mathematics. Together, we can drive the exploration and invention that will shape society, the economy, and business for the next fifty years. 1 A NEW ERA OF COMPUTING IBM’s Watson computer created a sensation when it bested two past grand champions on the TV quiz show Jeopardy! Tens of millions of people suddenly understood how “smart” a computer could be. This was no mere parlor trick; the scientists who designed Watson built upon decades of research in the fields of artificial intelligence and natural-language processing and produced a series of breakthroughs.

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We depend on them to produce the surprising advances that knock the world off kilter and, ultimately, have the potential to make it a better place. We will need many of them to make the transition to the era of cognitive systems. In the end, this era is not about machines but about the people who design and use them. 2 BUILDING LEARNING SYSTEMS In November 2009, when IBM’s Watson was under development for its showdown on Jeopardy!, the machine made one laughable mistake after another in test matches. In one particularly funny instance, it was prompted to identify what the “Al” in the company name Alcoa stands for. It fired back, “What is Al Capone?” Everybody in the room cracked up. The machine had confused the first two letters of aluminum with the name of a famous gangster.1 No harm was done.

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SMART MACHINES SMART MACHINES IBM’s Watson and the Era of Cognitive Computing JOHN E. KELLY III STEVE HAMM Columbia Business School Publishing COLUMBIA UNIVERSITY PRESS Publishers Since 1893 New York Chichester, West Sussex cup.columbia.edu Copyright © 2013 IBM Corporation All rights reserved E-ISBN 978-0-231-53727-8 Library of Congress Cataloging-in-Publication Data Kelly, John E., III (John Edward), 1954– IBM’s Watson and the era of cognitive computing / John E. Kelly III and Steve Hamm. pages cm Includes bibliographical references. ISBN 978-0-231-16856-4 (cloth : alk. paper) — ISBN 978-0-231-53727-8 (e-book) 1.

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

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

In this case, it’s one that has never tasted a single morsel of food. This culinary prodigy, in fact, has no taste buds, no nose, nor any sensual experience of food or drink.’ Perhaps surprisingly, Chef Watson is the project IBM settled on after Watson beat Ken Jennings at Jeopardy! As it became evident that Watson was capable of parsing the complex question-as-answer conundrums of Jeopardy! in a way that could defeat even the most skilled of humans, employees at IBM Research decided the next logical challenge was to go beyond answers and develop a system capable of being creative. The suggestions rolled in – covering bases like art, literature and music.

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And all the time Jennings kept winning, smashing every previous record in Jeopardy! history. From being a no-name software engineer from Salt Lake City, suddenly he had a Hollywood agent and a book deal. One day Jennings’ agent phoned to say he had received offers to appear on both Sesame Street and The Tonight Show. ‘It was all totally surreal,’ Jennings says. ‘It had never happened in my lifetime that Americans cared so much about who was on a quiz show.’ Jennings’ streak eventually came to an end following a record seventy-four consecutive shows. He was sad to lose, but Jeopardy! had done him wonders. He was smart, he was in-demand, and – thanks to his winnings – he was rich.

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For a human, this means that we don’t need a language that has billions of unique words. For a computer, it means that it isn’t enough to simply build the quiz show version of Google. A regular search engine can answer around 30 per cent of Jeopardy! questions by looking for statistically likely answers based on keywords, but struggles when it comes to the remaining 70 per cent. IBM’s computer would need to go further than this. The raw data the Jeopardy!-playing computer had available to answer its questions was approximately 200 million pages of information, extracted from a variety of sources. All of these had to be stored locally, since IBM’s machine would be unable to access the Internet during the Grand Challenge.

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

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

Everyone in the restaurant was focused on a television showing an episode of Jeopardy! in which the mega-champion Ken Jennings was competing. This gave Lickel the idea that IBM should develop a computer program that could play Jeopardy! well enough to win against human champions. IBM could then showcase the program in a highly publicized televised tournament.5 This idea helped give rise to a many-year effort, led by natural-language researcher David Ferrucci, which resulted in Watson, an AI system named after IBM’s first chairman, Thomas J. Watson. Jeopardy! is a hugely popular TV game show that first aired in 1964. The game features three contestants, who take turns choosing from a list of categories (for example, “U.S.

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YouTube started providing impressively accurate automated subtitles for videos, and Skype offered simultaneous translation between languages in video calls. Suddenly Facebook could recognize your face eerily well in uploaded photos, and the photo-sharing website Flickr began automatically labeling photos with text describing their content. In 2011, IBM’s Watson program roundly defeated human champions on television’s Jeopardy! game show, adroitly interpreting pun-laden clues and prompting its challenger Ken Jennings to “welcome our new computer overlords.” Just five years later, millions of internet viewers were introduced to the complex game of Go, a longtime grand challenge for AI, when a program called AlphaGo stunningly defeated one of the world’s best players in four out of five games.

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While computers can accurately transcribe our requests, the “final frontier,” if you will, is to get them to understand the meaning of our questions. The Story of Watson Prior to Siri, Alexa, and the like, the most famous question-answering program in the AI landscape was IBM’s Watson. You may remember back in 2011, when Watson thrillingly beat two human champions on the game show Jeopardy! Not long after Deep Blue’s 1997 win against the world chess champion Garry Kasparov, executives at IBM were pushing for another high-profile project that, unlike Deep Blue, could actually lead to a useful product for IBM customers. A question-answering system—indeed, inspired in part by the Star Trek computer—perfectly fit the bill.

Data-Ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else by Steve Lohr

"World Economic Forum" Davos, 23andMe, Abraham Maslow, Affordable Care Act / Obamacare, Albert Einstein, Alvin Toffler, Bear Stearns, behavioural economics, big data - Walmart - Pop Tarts, bioinformatics, business cycle, business intelligence, call centre, Carl Icahn, classic study, cloud computing, computer age, conceptual framework, Credit Default Swap, crowdsourcing, Daniel Kahneman / Amos Tversky, Danny Hillis, data is the new oil, data science, David Brooks, driverless car, East Village, Edward Snowden, Emanuel Derman, Erik Brynjolfsson, everywhere but in the productivity statistics, financial engineering, Frederick Winslow Taylor, Future Shock, Google Glasses, Ida Tarbell, impulse control, income inequality, indoor plumbing, industrial robot, informal economy, Internet of things, invention of writing, Johannes Kepler, John Markoff, John von Neumann, lifelogging, machine translation, Mark Zuckerberg, market bubble, meta-analysis, money market fund, natural language processing, obamacare, pattern recognition, payday loans, personalized medicine, planned obsolescence, precision agriculture, pre–internet, Productivity paradox, RAND corporation, rising living standards, Robert Gordon, Robert Solow, Salesforce, scientific management, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, SimCity, six sigma, skunkworks, speech recognition, statistical model, Steve Jobs, Steven Levy, The Design of Experiments, the scientific method, Thomas Kuhn: the structure of scientific revolutions, Tony Fadell, unbanked and underbanked, underbanked, Von Neumann architecture, Watson beat the top human players on Jeopardy!, yottabyte

The current debate, Berner adds, revives an old one in economics, pointing to a 1947 article, “Measurement Without Theory,” by Tjalling Koopmans, a Dutch-American economist who later won a Nobel Prize. The Koopmans article was a critique of the hard-line “empiricist” approach to the study of business cycles back then. Few people have wielded the power of data with so dramatic effect as David Ferrucci. He led the IBM research team that created Watson the Jeopardy! winner. That contest ended with Ken Jennings, the all-time champion on the TV quiz show, writing on his video screen, in a gesture of genial surrender, “I, for one, welcome our new computer overlords.” The human face of Watson, to the extent that there was one, was Ferrucci, a goateed computer scientist who was always articulate and at ease in front of a camera or microphone.

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But in a practical sense, it also means that seemingly quantitative changes become qualitative, opening the door to new possibilities and doing new things. In computing, you start by calculating the flight trajectory of artillery shells, the task assigned the ENIAC (Electronic Numerical Integrator and Computer) in 1946. And by 2011, you have IBM’s Watson beating the best humans in the question-and-answer game Jeopardy! To a computer, it’s all just the 1’s and 0’s of digital code. Yet the massive quantitative improvement in performance over time drastically changes what can be done. Trained physicists in the data world often compare the quantitative-to-qualitative transformation to a “phase change,” or change of state, as when a gas becomes a liquid or a liquid becomes a solid.

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In early 2014, Rometty announced that its prototype projects with the Watson technology in health care and other industries were sufficiently encouraging to justify creating a new business division. IBM will invest $1 billion in the Watson business and the unit would grow to 2,000 people. Watson has become a “cloud” software service, delivered Google-style over the Internet from remote data centers. IBM is sharing Watson technology with outside software developers and start-ups, so they can write applications that run on top of Watson. IBM has created a $100 million equity fund to jump-start that third-party development by outsiders.

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

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

Once again, I was taken by surprise when IBM introduced Deep Blue’s successor, Watson—a machine that took on a far more difficult challenge: the television game show Jeopardy! Chess is a game with rigidly defined rules; it is the sort of thing we might expect a computer to be good at. Jeopardy! is something else entirely: a game that draws on an almost limitless body of knowledge and requires a sophisticated ability to parse language, including even jokes and puns. Watson’s success at Jeopardy! is not only impressive, it is highly practical, and in fact, IBM is already positioning Watson to play a significant role in fields like medicine and customer service.

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WorkFusion has found that, as the system’s machine learning algorithms incrementally automate the process further, costs typically drop by about 50 percent after one year and still another 25 percent after a second year of operation.13 Cognitive Computing and IBM Watson In the fall of 2004, IBM executive Charles Lickel had dinner with a small team of researchers at a steakhouse near Poughkeepsie, New York. Members of the group were taken aback when, at precisely seven o’clock, people suddenly began standing up from their tables and crowding around a television in the bar area. It turned out that Ken Jennings, who had already won more than fifty straight matches on the TV game show Jeopardy!, was once again attempting to extend his historic winning streak. Lickel noticed that the restaurant’s patrons were so engaged that they abandoned their dinners, returning to finish their steaks only after the match concluded.14 That incident, at least according to many recollections, marked the genesis of the idea to build a computer capable of playing—and beating the very best human champions at—Jeopardy!

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This incident is recounted in Steven Baker, Final Jeopardy: Man vs. Machine and the Quest to Know Everything (New York: Houghton Mifflin Harcourt, 2011), p. 20. The story of the steakhouse dinner is also told in John E. Kelly III, Smart Machines: IBM’s Watson and the Era of Cognitive Computing (New York: Columbia University Press, 2013), p. 27. However, Baker’s book indicates that some IBM employees believe the idea to build a Jeopardy!-playing computer predates the dinner. 15. Rob High, “The Era of Cognitive Systems: An Inside Look at IBM Watson and How it Works,” IBM Redbooks, 2012, p. 2, http://www.redbooks.ibm.com/redpapers/pdfs/redp4955.pdf. 16.

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

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

In the words of Patrick Winston, a leading voice for decades in the world of artificial intelligence, ‘there are lots of ways of being smart that aren't smart like us’.46 IBM’s Watson In the same spirit, IBM’s system Watson, which we regard as a landmark development in artificial intelligence, was not designed to solve problems in the way that human beings do.47 Watson was developed in part to demonstrate that machines could indeed attain exceptional levels of apparently intelligent performance. Named after the founder of IBM, the system was developed to compete on Jeopardy!—a TV quiz show in the United States. This represented IBM’s latest contribution to the branch of AI that in the 1980s was called ‘game-playing’.

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In section 4.6 we point to a variety of existing techniques and technologies that are already achieving remarkable levels of performance in a wide range of tasks. Perhaps the most dramatic is IBM’s Watson, the computer system that was catapulted to fame by its appearance in 2011 on Jeopardy!, a TV quiz show, on which, in a live broadcast, it beat the two best-ever human contestants (see section 4.6). Much has been said and written about this feat, but nothing perhaps as witty and insightful as a headline in the Wall Street Journal to an opinion piece by the philosopher John Searle. It read: ‘Watson Doesn’t Know It Won on “Jeopardy!”2 We could add that Watson, after its great triumph, had no apparent inclination to laugh or cry, to go for a celebratory drink, to share the moment with a close friend, to chat about what it felt like, or to commiserate with its vanquished opponents.

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In contemplating the potential of future machines to outperform human beings, what really matters is not how the systems operate but whether, in terms of the outcome, the end product is superior. In other words, whether machines will replace human professionals is not about the capacity of systems to perform tasks as people do. It is about whether systems can outperform human beings—full stop. And so, when IBM’s Watson beat the best-ever human champions on a TV quiz show, what mattered was not whether Watson had cognitive states in common with its flesh-and-blood opponents, but whether its score was higher. To be more precise, then, the fundamental question to be asked and answered is whether machines and systems can undertake tasks that for human beings require cognitive, affective, manual, and moral capabilities, even if they discharge these tasks by quite different means.

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

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

In an initial trial, approximately 90 percent of GeoFluent users reported that it was good enough for business purposes.14 Human Superiority in Jeopardy! Computers are now combining pattern matching with complex communication to quite literally beat people at their own games. In 2011, the February 14 and 15 episodes of the TV game show Jeopardy! included a contestant that was not a human being. It was a supercomputer called Watson, developed by IBM specifically to play the game (and named in honor of legendary IBM CEO Thomas Watson, Sr.). Jeopardy! debuted in 1964 and in 2012 was the fifth most popular syndicated TV program in America.15 On a typical day almost 7 million people watch host Alex Trebek ask trivia questions on various topics as contestants vie to be the first to answer them correctly.* The show’s longevity and popularity stem from its being easy to understand yet extremely hard to play well.

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In other words, we believe that employers now and for some time to come will, when looking for talent, follow the advice attributed to the Enlightenment sage Voltaire: “Judge a man by his questions, not his answers.”6 Ideation, creativity, and innovation are often described as ‘thinking outside the box,’ and this characterization indicates another large and reasonably sustainable advantage of human over digital labor. Computers and robots remain lousy at doing anything outside the frame of their programming. Watson, for example, is an amazing Jeopardy! player, but would be defeated by a child at Wheel of Fortune, The Price is Right, or any other TV game show unless it was substantially reprogrammed by its human creators. Watson is not going to get there on its own. Instead of conquering other game shows, however, the IBM team behind Watson is turning its attention to other fields such as medicine. Here again, it will be limited by its frame. Make no mistake: we believe that Watson will ultimately make an excellent doctor.

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title=Blogger_(service)&oldid=560541931 (accessed June 27, 2013). 8. “Top Sites,” Alexa: The Web Information Company, http://www.alexa.com/topsites (accessed September 8, 2012). 9. “IBM Watson Vanquishes Human Jeopardy Foes,” PCWorld, February 16, 2011, http://www.pcworld.com/article/219893/ibm_watson_vanquishes_human_jeopardy_foes.html. 10. “IBM’s Watson Memorized the Entire ‘Urban Dictionary,’ Then His Overlords Had to Delete It,” The Atlantic, January 10, 2013, http://www.theatlantic.com/technology/archive/2013/01/ibms-watson-memorized-the-entire-urban-dictionary-then-his-overlords-had-to-delete-it/267047/. 11. Kevin J. O’Brien, “Talk to Me, One Machine Said to the Other,” New York Times, July 29, 2012, http://www.nytimes.com/2012/07/30/technology/talk-to-me-one-machine-said-to-the-other.html. 12.

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

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

If the driverless car weren’t enough of a challenge to human superiority, who could have watched IBM’s Watson supercomputer defeat the Jeopardy Hall of Famers in 2011 and not fretted about the future of physicians, or any highly skilled workers, for that matter? “Just as factory jobs were eliminated in the twentieth century by new assemblyline robots,” wrote all-time (human) Jeopardy champion Ken Jennings soon after the lopsided match ended, “Brad [Rutter, the other defeated champ] and I were the first knowledge-industry workers put out of work by the new generation of ‘thinking’ machines. ‘Quiz show contestant’ may be the first job made redundant by Watson, but I’m sure it won’t be the last.”

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Chapter 10 David and Goliath There is a science in what we do, yes, but also habit, intuition, and sometimes plain old guessing. —Atul Gawande, Complications: A Surgeon’s Notes on an Imperfect Science When IBM announced that Watson’s first post-Jeopardy focus would be healthcare, the media immediately ran with the Man Versus Machine meme, dubbing the computer “Dr. Watson.” “Meet Dr. Watson: Jeopardy Winning Supercomputer Heads into Healthcare,” proclaimed one headline. “Paging Dr. Watson: IBM’s Medical Advisor for the Future,” read another. IBMers immediately steered clear of the “Dr. Watson” narrative, with its implied cockiness and gauntlet throwing.

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,” Artificial Intelligence in Medicine 5:93–106 (1993). 102 He calls today’s medical IT programs “Version 0” Khosla, “20-Percent Doctor Included.” 103 These cases illustrate a perennial debate in AI See, for example, M. van Emden, “Scruffies and Neats in Artificial Intelligence: A Programmer’s Place,” September 11, 2011, available at http://vanemden.wordpress.com/2011/09/11/scruffies-and-neats-inartificial-intelligence/. 103 When he was asked about the difference between human thinking E. Brown, “IBM’s ‘Watson’ in Layman’s Terms by Dr. Eric W. Brown,” available at https://www.youtube.com/watch?v=gRVjFhEnLRQ. Chapter 10: David and Goliath 105 “There is a science in what we do” A. Gawande, Complications: A Surgeon’s Notes on an Imperfect Science (New York: Metropolitan Books, 2002). 105 dubbing the computer “Dr. Watson” The “Meet Dr. Watson” headline is from S. Kliff, “Meet Dr. Watson: ‘Jeopardy’-Winning Super Computer Heads into Health Care,” Washington Post, September 12, 2011; the “Paging Dr.

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

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

In my view, the best algorithms are the ones that take the human into account at every stage. The ones that recognize our habit of over-trusting the output of a machine, while embracing their own flaws and wearing their uncertainty proudly front and centre. This was one of the best features of the IBM Watson Jeopardy!-winning machine. While the format of the quiz show meant it had to commit to a single answer, the algorithm also presented a series of alternatives it had considered in the process, along with a score indicating how confident it was in each being correct. Perhaps if likelihood of recidivism scores included something similar, judges might find it easier to question the information the algorithm was offering.

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One that offers an accurate diagnosis and perfectly tailored treatment plan? In short, how about something a little like IBM’s Watson? Elementary, my dear In 2004, Charles Lickel was tucking into a steak dinner with some colleagues in a New York restaurant. Partway through the meal, the dining area began to empty. Intrigued, Charles followed the crowd of diners and found them huddled around a television, eagerly watching the popular game show Jeopardy!. The famous Jeopardy! champion Ken Jennings had a chance to hold on to his record-breaking six-month winning streak and the diners didn’t want to miss it.34 Charles Lickel was the vice-president of software at IBM.

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Taylor Kubota, ‘Deep learning algorithm does as well as dermatologists in identifying skin cancer’, Stanford News, 25 Jan. 2017, https://news.stanford.edu/2017/01/25/artificial-intelligence-used-identify-skin-cancer/. 34. Jo Best, ‘IBM Watson: the inside story of how the Jeopardy-winning supercomputer was born, and what it wants to do next’, Tech Republic, n.d., https://www.techrepublic.com/article/ibm-watson-the-inside-story-of-how-the-jeopardy-winning-supercomputer-was-born-and-what-it-wants-to-do-next/. 35. Jennings Brown, ‘Why everyone is hating on IBM Watson, including the people who helped make it’, Gizmodo, 14 Aug. 2017, https://www.gizmodo.com.au/2017/08/why-everyone-is-hating-on-watsonincluding-the-people-who-helped-make-it/. 36. https://www.theregister.co.uk/2017/02/20/watson_cancerbusting_trial_on_hold_after_damning_audit_report/ 37.

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

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

Ronda Hauben, “From the ARPANET to the Internet,” last modified June 23, 1998, http://www.columbia.edu/~rh120/other/tcpdigest_paper.txt. 11. For the proverbially impaired, here’s the original: “Give a man a fish and you feed him for a day; teach a man to fish and you feed him for a lifetime.” 12. Joab Jackson, “IBM Watson Vanquishes Human Jeopardy Foes,” PC World, February 16, 2011, http://www.pcworld.com/article/219893/ibm_watson_vanquishes_human_jeopardy_foes.html. 2. TEACHING ROBOTS TO HEEL 1. For a firsthand narrative of some of these events by the inventor himself, see Vic Scheinman’s interview at Robotics History: Narratives and Networks, accessed November 25, 2014, http://roboticshistory.indiana.edu/content/vic-scheinman. 2.

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I’m happy to report that IBM long ago came around to accepting the potential of AI and to recognizing its value to its corporate mission. In 2011, the company demonstrated its in-house expertise with a spectacular victory over the world’s champion Jeopardy! player, Ken Jennings. IBM is now parlaying this victory into a broad research agenda and has, characteristically, coined its own term for the effort: cognitive computing. Indeed, it is reorganizing the entire company around this initiative. It’s worth noting that IBM’s program, named Watson, had access to 200 million pages of content consuming four terabytes of memory.12 As of this writing, three years later, you can purchase four terabytes of disk storage from Amazon for about $150.

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For me, the practice of medicine today conjures the image of a Hieronymus Bosch painting, with tiny, pitchfork-wielding devils inflicting their own unique forms of pain. As a patient, you would ideally prefer to be treated by a superdoc who is expert in all the specialties and is up to date on all of the latest medical information and best practices. But of course no such human exists. Enter IBM’s Watson program. Fresh off its Jeopardy! victory over champions Brad Rutter and Ken Jennings, Watson was immediately redeployed to tackle this new challenge. In 2011, IBM and WellPoint, the nation’s largest healthcare benefits manager, entered into a collaboration to apply Watson technology to help improve patient care.

Big Data Analytics: Turning Big Data Into Big Money by Frank J. Ohlhorst

algorithmic trading, bioinformatics, business intelligence, business logic, business process, call centre, cloud computing, create, read, update, delete, data acquisition, data science, DevOps, extractivism, fault tolerance, information security, Large Hadron Collider, linked data, machine readable, natural language processing, Network effects, pattern recognition, performance metric, personalized medicine, RFID, sentiment analysis, six sigma, smart meter, statistical model, supply-chain management, warehouse automation, Watson beat the top human players on Jeopardy!, web application

Beyond tomorrow probably holds surprises that no one has even imagined yet. As technology marches ahead, so will the usefulness of Big Data. A case in point is IBM’s Watson, an artificial intelligence computer system capable of answering questions posed in natural language. In 2011, as a test of its abilities, Watson competed on the quiz show Jeopardy!, in the show’s only human-versus-machine match to date. In a two-game, combined-point match, broadcast in three episodes aired February 14–16, Watson beat Brad Rutter, the biggest all-time money winner on Jeopardy!, and Ken Jennings, the record holder for the longest championship streak (74 wins). Watson had access to 200 million pages of structured and unstructured content consuming four terabytes of disk storage, including the full text of Wikipedia, but was not connected to the Internet during the game.

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Watson demonstrated that there are new ways to deal with Big Data and new ways to measure results, perhaps exemplifying where Big Data may be headed. So what’s next for Watson? IBM has stated publicly that Watson was a client-driven initiative, and the company intends to push Watson in directions that best serve customer needs. IBM is now working with financial giant Citi to explore how the Watson technology could improve and simplify the banking experience. Watson’s applicability doesn’t end with banking, however; IBM has also teamed up with health insurer WellPoint to turn Watson into a machine that can support the doctors of the world. According to IBM, Watson is best suited for use cases involving critical decision making based on large volumes of unstructured data.

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Furthering the value proposition of Watson and Big Data, IBM has also stated that five new research documents come out of Wall Street every minute, and medical information is doubling every five years. IBM views the future of Big Data a little differently than other vendors do, most likely based on its Watson research. In IBM’s future, Watson becomes a service—as IBM calls it, Watson-as-a-Service—which will be delivered as a private or hybrid cloud service. Watson aside, the health care industry seems ripe as a source of prediction for how Big Data will evolve. Examples abound for the benefits of Big Data and the medical field; however, getting there is another story altogether.

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

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

It’s the time when science fiction technologies—the stuff of movies, books, and the controlled environments of elite research labs—started to appear in the real world. In 2010, Google unexpectedly announced that a fleet of completely autonomous cars had been driving on US roads without mishap. In 2011, IBM’s Watson supercomputer beat two human champions at the TV quiz show Jeopardy! By the third quarter of 2012, there were more than a billion users of smartphones, devices that combined the communication and sensor capabilities of countless sci-fi films. And of course, the three advances described at the start of this chapter happened in the past few years.

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§ Fast Company journalist Mark Wilson loved the “Bengali Butternut” barbecue sauce that Watson came up with (Mark Wilson, “I Tasted BBQ Sauce Made by IBM’s Watson, and Loved It,” Fast Company, May 23, 2014, https://www.fastcodesign.com/3027687/i-tasted-bbq-sauce-made-by-ibms-watson-and-loved-it), but called its “Austrian Chocolate Burrito” the worst he’d ever had (Mark Wilson, “IBM’s Watson Designed the Worst Burrito I’ve Ever Had,” Fast Company, April 20, 2015, https://www.fastcodesign.com/3045147/ibms-watson-designed-the-worst-burrito-ive-ever-had). ¶ A mechanical fillet is a smooth transition from one area of a part to another—for example, a rounded corner between two surfaces that meet at a right angle

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However, the process of eliciting knowledge in interviews would consume a lot of time, would take people away from their job, and probably wouldn’t work very well. The people doing the less routine back-office work are, in all likelihood, not able to accurately and completely tell someone else how to do their job. The Japanese insurer Fukoku Mutual Life is taking a different approach. In December of 2016, it announced an effort to use IBM’s Watson AI technology to at least partially automate the work of human health insurance claim processors. The system will begin by extracting relevant information from documents supplied by hospitals and other health providers, using it to fill in the proper codes for insurance reimbursement, then presenting this information to people.

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

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

Today chess programs running on ordinary personal computers are essentially unbeatable. Chess will be discussed in detail later in the book, but in many ways it presents a constrained mathematical problem that is amenable to automated computation. A far more impressive result is the victory of IBM’s Watson program on the Jeopardy! game show. Jeopardy! set, showing Watson’s guesses. Fair Use. Wikipedia. Jeopardy! requires contestants to answer questions in natural language that cover a wide range of general knowledge topics. In 2011 Watson competed against two former prize winners and received first prize of $1 million. These is a sample of questions that Watson could answer:Wanted for a 12-year crime spree of eating King Hrothgar’s warriors; officer Beowulf has been assigned the case : Grendel Milorad Cavic almost upset this man’s perfect 2008 Olympics, losing to him by one hundredth of a second : Michael Phelps It’s just a bloody nose!

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It is a testament to human cognition that people can learn to play these games with such competence. And it is perhaps an even greater testament that a novice looking at the Go ladder above will quickly see the pattern without being told. Watson and Jeopardy! Jeopardy! game with Watson. Fair Use Wikipedia On the 14th February, 2011 the IBM Watson computer won the Jeopardy! game show against two of the previously most successful contestants on the show, Ken Jennings and Brad Rutter. The wide ranging questions were given in unconstrained natural language, and had to be answered in real time according to the rules of the game.

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The crudest level of understanding is to simply note that the document contains certain words and phrases. This can be used to index documents and so identify which ones might be relevant to some topic. That is exactly what internet search engines do. Systems like IBM’s Watson go much further by partially parsing sentences and interpreting their meaning with reference to a large ontology of phrases and synonyms. That is what enabled it to win the Jeopardy! game show. This technology will become much more advanced over the following decade or two. It will enable search engines to go beyond simply retrieving documents to become active research assistants that can make simple deductions based on large numbers of documents, even though they would not really understand them.

How to Create a Mind: The Secret of Human Thought Revealed by Ray Kurzweil

Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Albert Michelson, anesthesia awareness, anthropic principle, brain emulation, cellular automata, Charles Babbage, Claude Shannon: information theory, cloud computing, computer age, Computing Machinery and Intelligence, Dean Kamen, discovery of DNA, double helix, driverless car, en.wikipedia.org, epigenetics, George Gilder, Google Earth, Hans Moravec, Isaac Newton, iterative process, Jacquard loom, Jeff Hawkins, John von Neumann, Law of Accelerating Returns, linear programming, Loebner Prize, mandelbrot fractal, Nick Bostrom, Norbert Wiener, optical character recognition, PalmPilot, pattern recognition, Peter Thiel, Ralph Waldo Emerson, random walk, Ray Kurzweil, reversible computing, selective serotonin reuptake inhibitor (SSRI), self-driving car, speech recognition, Steven Pinker, strong AI, the scientific method, theory of mind, Turing complete, Turing machine, Turing test, Wall-E, Watson beat the top human players on Jeopardy!, X Prize

Intelligent algorithms automatically detect credit card fraud, fly and land airplanes, guide intelligent weapons systems, help design products with intelligent computer-aided design, keep track of just-in-time inventory levels, assemble products in robotic factories, and play games such as chess and even the subtle game of Go at master levels. Millions of people witnessed the IBM computer named Watson play the natural-language game of Jeopardy! and obtain a higher score than the best two human players in the world combined. It should be noted that not only did Watson read and “understand” the subtle language in the Jeopardy! query (which includes such phenomena as puns and metaphors), but it obtained the knowledge it needed to come up with a response from understanding hundreds of millions of pages of natural-language documents including Wikipedia and other encyclopedias on its own.

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As mentioned above, Watson represents a particularly impressive example of the approach of combining hand-coded rules with hierarchical statistical learning. IBM combined a number of leading natural-language programs to create a system that could play the natural-language game of Jeopardy! On February 14–16, 2011, Watson competed with the two leading human players: Brad Rutter, who had won more money than anyone else on the quiz show, and Ken Jennings, who had previously held the Jeopardy! championship for the record time of seventy-five days. By way of context, I had predicted in my first book, The Age of Intelligent Machines, written in the mid-1980s, that a computer would take the world chess championship by 1998.

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Note that the linear programming that Grötschel cites above as having benefited from an improvement in performance of 43 million to 1 is the mathematical technique that is used to optimally assign resources in a hierarchical memory system such as HHMM that I discussed earlier. I cite many other similar examples like this in The Singularity Is Near.6 Regarding AI, Allen is quick to dismiss IBM’s Watson, an opinion shared by many other critics. Many of these detractors don’t know anything about Watson other than the fact that it is software running on a computer (albeit a parallel one with 720 processor cores). Allen writes that systems such as Watson “remain brittle, their performance boundaries are rigidly set by their internal assumptions and defining algorithms, they cannot generalize, and they frequently give nonsensical answers outside of their specific areas.”

Talk to Me: How Voice Computing Will Transform the Way We Live, Work, and Think by James Vlahos

Albert Einstein, AltaVista, Amazon Mechanical Turk, Amazon Web Services, augmented reality, Automated Insights, autonomous vehicles, backpropagation, Big Tech, Cambridge Analytica, Chuck Templeton: OpenTable:, cloud computing, Colossal Cave Adventure, computer age, deep learning, DeepMind, Donald Trump, Elon Musk, fake news, Geoffrey Hinton, information retrieval, Internet of things, Jacques de Vaucanson, Jeff Bezos, lateral thinking, Loebner Prize, machine readable, machine translation, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Mark Zuckerberg, Menlo Park, natural language processing, Neal Stephenson, Neil Armstrong, OpenAI, PageRank, pattern recognition, Ponzi scheme, randomized controlled trial, Ray Kurzweil, Ronald Reagan, Rubik’s Cube, self-driving car, sentiment analysis, Silicon Valley, Skype, Snapchat, speech recognition, statistical model, Steve Jobs, Steve Wozniak, Steven Levy, TechCrunch disrupt, Turing test, Watson beat the top human players on Jeopardy!

What’s more, many types of information—populations, sporting statistics, celebrity news, emerging technologies—are moving targets, which means that ontologies quickly become dated. So many researchers are trying to move beyond knowledge graphs. They instead deploy systems that hunt for answers in sources of unstructured data: web pages, scanned documents, and digitized books. IBM’s Watson program, which could access 200 million pages of content, famously demonstrated this approach in 2011 when it bested two human competitors to win at the television quiz show Jeopardy! Watson’s success stemmed from clever programming and computational brute force. To bolster its confidence that it was coming up with the right answer, the system sought confirmation from multiple sources.

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In 2017 the Saatchi & Saatchi LA agency was tasked with creating a campaign for the Toyota Mirai, a futuristic vehicle powered by hydrogen fuel cells. The agency enlisted the help of Watson, IBM’s supercomputer. Human copywriters sketched out fifty different scripts that touted various features of the car. IBM then trained Watson on these scripts so it could churn out thousands of short pieces of copy on its own. Toyota then used many of them as taglines that ran with short videos posted to Facebook. The Watson-crafted zingers included “Yes, it’s for fans of possibility,” “Yes, the future is available now,” and “Yes, it will turn heads on the moon.”

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, Branded3, April 24, 2017, https://goo.gl/FEabdG. 204 Typed queries are typically one to three words: “The Humanization of Search,” Microsoft report, 2016, https://goo.gl/SDmGgL. 205 For instance, at the time of Freebase’s acquisition: Xin Luna Dong et al., “Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion,” Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (August 24, 2014): 601–10, https://goo.gl/JYEYUB. 205 IBM’s Watson program: David Ferrucci et al., “Building Watson: An Overview of the DeepQA Project,” AI Magazine 31, no. 3 (Fall 2010), https://goo.gl/RVopVR. 206 Both companies had scored as well as the average human: Allison Linn, “Microsoft creates AI that can read a document and answer questions about it as well as a person,” The AI Blog, January 15, 2018, https://goo.gl/tBKHTu. 206 A much more difficult: Danqi Chen et al., “Reading Wikipedia to Answer Open-Domain Questions,” arXiv:1704.00051v2, March 31, 2017, https://goo.gl/uudGiA. 206 In July 2015 Google was serving up instant answers: Eric Enge, “Featured Snippets: New Insights, New Opportunities,” Stone Temple, May 24, 2017, https://goo.gl/sviB0b. 207 The internet is being upended: discussion in this section additionally informed by author interviews with Adam Marchick, the cofounder and CEO of Alpine.AI, May 21, 2018; James McQuivey, principal analyst, Forrester Research, May 30, 2018; and Greg Hedges, former vice president for emerging experiences, Rain, July 11, 2018. 207 “A searcher’s everyday quest”: Christi Olson, “A brief evolution of Search: out of the search box and into our lives,” Marketing Land, June 27, 2016, https://goo.gl/5kwWZr. 207 For instance, of the $110.9 billion: “Google’s ad revenue from 2001 to 2017,” chart posted on Statista, 2018, https://goo.gl/ncu7da. 207 In 2018 Google and Facebook: Daniel Liberto, “Facebook, Google Digital Ad Market Share Drops as Amazon Climbs,” Investopedia, March 20, 2018, https://goo.gl/LB4nc1. 208 “Start thinking about the types of questions you get”: Sherry Bonelli, “How to optimize for voice search,” Search Engine Land, May 1, 2017, https://goo.gl/B5DpPy. 209 “There’s going to be a battle for shelf space”: Christopher Heine, “Here’s What You Need to Know About Voice AI, the Next Frontier of Brand Marketing,” Adweek, August 6, 2017, https://goo.gl/HdGVcM. 209 In 2017 the market research firm L2: Marty Swant, “Alexa Is More Likely to Recommend Amazon Prime Products, According to New Research,” Adweek, July 7, 2017, https://goo.gl/RbQ77p. 210 They click through from Google search results: Nic Newman, “Digital News Project 2018: Journalism, Media, and Technology Trends and Predictions 2018,” published by the Reuters Institute, 2018, https://is.gd/QYI3po. 210 In one of them, Slovakia: Alexis Madrigal, “When the Facebook Traffic Goes Away,” The Atlantic, October 24, 2017, https://goo.gl/A3Xk4s. 211 “They have billions of dollars in profit every year”: Brian Warner, email to author, August 5, 2018. 211 In a 2018 blog post: Danny Sullivan, “A reintroduction to Google’s featured snippets,” The Keyword, January 30, 2018, https://goo.gl/Kqdmsh. 211 “As websites, we expect to compete”: Asher Elran, “Should You Change Your SEO Strategy Because of Google Hummingbird?”

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

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

But with that strong caveat noted, it’s important to keep feeding the knowledge base that will help you make connections between problems your organization has and the solutions that are out there. For example, it’s been a very recent development that computers have learned to read and make inferences from fast, vast digestion of textual content. If you weren’t part of the AI community, you might have first learned of this when IBM’s Watson won Jeopardy! To come up with each response, Watson (specifically its “Discovery Advisor”) read whole encyclopedias and untold Internet pages. How could you use that power? At the Baylor College of Medicine in Dallas, they used it to read through more than 70,000 scientific articles, looking for accounts of any protein that could modify p53, a protein that regulates cancer growth.

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Doris Day and Melafind, 137–38 insurance underwriting in, 83–84 jobs at risk of automation, 19 medical researchers, 181–82 radiology, 16–18, 19, 24, 25, 41, 166 robotic surgery, 40, 51 Stepping Narrowly in, 157 therapy and robots, 123–24 Watson and cancer research, 46, 53–54 Henry, John, 1, 9, 29 Hernandez, Nelson, 75 Herrell, Shane, 104, 132, 140–42, 146 Holland, 241–42 Holmes, Kimberly, 131 Hoover, Dave, 170 HOPE (Hands On Preservation Experience), 240 Hopkins, Gerard Manley, 36 Houtermans, Fritz, 170 Howe, Michael, 163 “How Schools Kill Creativity” (Robinson), 115 Humana, 83–84 humaniqueness, 112 Humans Are Underrated (Colvin), 127, 244 Humans Need Not Apply (Kaplan), 240, 250 Human Use of Human Beings, The (Wiener), 26, 64 humor, 30, 111, 117, 124–25, 129 Joke Analysis and Production Engine (JAPE), 125 Hyperco, 154 IBM, 194, 203 “Bluemix” cognitive cloud, 45 hiring plans by, 176–77 IBM Watson, 20, 44, 55, 72, 92 Baylor College of Medicine use, 212 broadening applications of, 194 components of, 45 confidence bar, 55, 193 customizing of, 187, 188 gastronomy function, 122–23, 128 hiring related to, 176–77, 178 IP Australia applications, 250 Jeopardy! challenge, 44, 55, 212 “learning” by, 53 marketing of, 44, 155 MD Anderson Cancer Center use, 53, 155, 209–10 Memorial Sloan Kettering Cancer Center use, 46, 209, 215 natural language processing, 34, 44, 212 in other robotic brains, 56 as physician advisor, 15 “Q&A” capability, 45, 218 Watson Health, 176, 209, 215 ideation, 27, 28 i4j (Innovation for Jobs), 248 Industrial Revolution, 228, 230–31, 233 innovation, 5, 27, 84, 170, 179, 228, 229, 246, 247 augmentation and, 206, 251 technology-based, 96, 98, 203, 215, 216 In-Q-Tel, 186 insurance industry adding new sources of data, 197 augmentation in, 203–4 automated actuaries, 128 automated processing of claims (“auto- adjudication”), 48 computerized technologies, 21–22 example, Mike Krans, 134–35 Stepping In to automation, 102–3 transparency of automated systems, 192–93 underwriting augmented, 77–84, 218–19 underwriting automated, 94, 103–4, 105, 136, 139–40, 192–93 interior designers, 111 Internet achieving mastery of a specialty, 165 devices connected to, 43 Engelbart and, 64 niche markets and specialties, 161–62 Pew survey, 226 Internet of Things, 43, 80, 213 Interstellar (film), 125 Intuitive Surgical, 40 IP Australia, 250 IPsoft, 44, 45, 183–85, 195, 216, 217 “Amelia,” 44–45, 183–85, 217 Ito, Joi, 235, 247 Jabil Circuit, 50 Jackson Lewis P.C., 142 Japan, 24, 160, 247 Jobs, Steve, 63–64, 112, 158, 159 Jokes Every Man Should Know (Steinberg), 124–25 journalism, 22, 95–98, 103, 104, 121 Kabbage, 197 Kahneman, Daniel, 236 Kaplan, Jerry, 240, 250 Kay, Alan, 164 Kechley, Aaron, 193, 195–96 Kelley, Kevin, 139–40 Kensho Technologies, 20 Kessler, Andy, 84 Key, Ed, 122 Keynes, John Maynard, 69, 238 Khan Academy, 141 KIGEN, 59 Killinger, Kerry, 90 Kim, Kyung-Hee, 115 Kitaura, Francisco, 59 KMG International, 217 Knewton, 20 knowledge workers, 5, 6, 8, 29–30, 66, 74, 84, 97, 115, 204, 217, 233.

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Using machines will deepen the empathy, or heighten the creativity, or refine the taste that people bring to the table. A good example is the new capability of IBM’s Watson to find novel combinations of food ingredients that add up to palatable dishes. Chefs, of course, are very creative people—and computers don’t even eat. But gastronomy is also, on one level, just chemistry. Lav Varshney, a computer scientist at IBM, explains that Watson entered the world of haute cuisine when a database of already highly rated recipes was fed into it. The next step was, akin to the marketing-message software, to “remix them, substitute things, do all kinds of other modifications and generate millions of new ideas for recipes.”24 But it wouldn’t be feasible to prepare all of those and foist the results onto courageous taste testers.

The Perfect Bet: How Science and Math Are Taking the Luck Out of Gambling by Adam Kucharski

Ada Lovelace, Albert Einstein, Antoine Gombaud: Chevalier de Méré, beat the dealer, behavioural economics, Benoit Mandelbrot, Bletchley Park, butterfly effect, call centre, Chance favours the prepared mind, Claude Shannon: information theory, collateralized debt obligation, Computing Machinery and Intelligence, correlation does not imply causation, diversification, Edward Lorenz: Chaos theory, Edward Thorp, Everything should be made as simple as possible, Flash crash, Gerolamo Cardano, Henri Poincaré, Hibernia Atlantic: Project Express, if you build it, they will come, invention of the telegraph, Isaac Newton, Johannes Kepler, John Nash: game theory, John von Neumann, locking in a profit, Louis Pasteur, Nash equilibrium, Norbert Wiener, p-value, performance metric, Pierre-Simon Laplace, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, random walk, Richard Feynman, Ronald Reagan, Rubik’s Cube, statistical model, The Design of Experiments, Watson beat the top human players on Jeopardy!, zero-sum game

CHAPTER 7 165Thanks to their ability to dissect: Background on Watson comes from: Rashid, Fahmida. “IBM’s Watson Ties for Lead on Jeopardy but Makes Some Doozies.” EWeek, February 14, 2011. http://www.eweek.com/c/a/IT-Infrastructure/IBMs-Watson-Ties-for-Lead-on-Jeopardy-but-Makes-Some-Doozies-237890; and Best, Jo. “IBM Watson: How the Jeopardy-Winning Supercomputer Was Born, and What It Wants to Do Next.” TechRepublic. http://www.techrepublic.com/article/ibm-watson-the-inside-story-of-how-the-jeopardy-winning-supercomputer-was-born-and-what-it-wants-to-do-next/. 166IBM collected some of the results: Basulto, Dominic. “How IBM Watson Helped Me to Create a Tastier Burrito Than Chipotle.”

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Ask the human to perform a calculation, and he’d be much slower, not to mention more error prone, than the computer. Even so, there are still some situations that bots struggle with. When playing Jeopardy! Watson found the short clues the most difficult. If the host read out a single category and a name—such as “first ladies” and Ronald Reagan—Watson would take too long to search through its database to find the correct response (which is “Who is Nancy Reagan?”). Whereas Watson would beat a human contestant in a race to solve a long, complicated clue, the human would prevail if there were only a few words to go by. In quiz shows, it seems that brevity is the enemy of machines. The same is true of poker. Bots need time to study their opponents, learning their betting style so it can be exploited.

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Once you add opponent modeling to it, it will kill everyone.” 7 THE MODEL OPPONENT WHEN IT CAME TO THE GAME SHOW JEOPARDY! KEN JENNINGS and Brad Rutter were the best. It was 2011, and Rutter had netted the most prize money, while Jennings had gone a record seventy-four appearances without defeat. Thanks to their ability to dissect the show’s famous general knowledge clues, they had won over $5 million between them. On Valentine’s Day that year, Jennings and Rutter returned for a special edition of the show. They would face a new opponent, named Watson, who had never appeared on Jeopardy! before. Over the course of three episodes, Jennings, Rutter, and Watson answered questions on literature, history, music, and sports.

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

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

., “Enhancing Next-Generation Sequencing-Guided Cancer Care Through Cognitive Computing.” 31. Mukherjee, S., “A.I. Versus M.D.: What Happens When Diagnosis Is Automated?,” New Yorker. 2017. 32. Ross and Swetlitz, “IBM Pitched Its Watson Supercomputer as a Revolution in Cancer Care.” 33. Herper, M., “MD Anderson Benches IBM Watson in Setback for Artificial Intelligence in Medicine,” Forbes. 2017. 34. Ross and Swetlitz, “IBM Pitched Its Watson Supercomputer as a Revolution in Cancer Care.” 35. Muoio, D., “IBM Watson Manager, Academics Describe Challenges, Potential of Health Care AI,” MobiHealthNews. 2017. 36. Harari, Y. N., Homo Deus. 2016. New York: HarperCollins, p. 448. 37.

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That massive database of 15 million labeled images would help catapult DNN into prominence as a tool for computer vision. In parallel, natural-language processing for speech recognition based on DNN at Microsoft and Google was moving into full swing. More squarely in the public eye was man versus machine in 2011, when IBM Watson beat the human Jeopardy! champions. Despite the relatively primitive AI that was used, which had nothing to do with deep learning networks and which relied on speedy access to Wikipedia’s content, IBM masterfully marketed it as a triumph of AI. The ensuing decade has seen remarkable machine performance. Deep learning got turbocharged in 2012 with the publication of research by Hinton and his University of Toronto colleagues that showed remarkable progress in image recognition at scale.19 Progress in unlabeled image recognition was notable in 2012, when Google Brain’s team, led by Andrew Ng and Jeff Dean, developed a system based on one hundred computers and 10 million images that could recognize cats in YouTube videos.

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It was a bit comical in 2017 to see IBM Watson’s ads claiming that with the system a doctor could read 5,000 studies a day and still see patients. Neither Watson nor any other AI algorithms can support that, at least not yet. What Watson is actually dealing with are only abstracts, the brief encapsulations that appear at the beginning of most published papers. Even then, these data are unstructured, so there’s no way that simple ingestion of all the text automatically translates into an augmented knowledge base. This might be surprising, given that Watson’s ability to outdo humans in Jeopardy! suggests it would have the ability to outsmart doctors, too, and make quick work of the medical literature.

Smarter Than You Think: How Technology Is Changing Our Minds for the Better by Clive Thompson

4chan, A Declaration of the Independence of Cyberspace, Andy Carvin, augmented reality, barriers to entry, behavioural economics, Benjamin Mako Hill, butterfly effect, citizen journalism, Claude Shannon: information theory, compensation consultant, conceptual framework, context collapse, corporate governance, crowdsourcing, Deng Xiaoping, digital rights, discovery of penicillin, disruptive innovation, Douglas Engelbart, Douglas Engelbart, drone strike, Edward Glaeser, Edward Thorp, en.wikipedia.org, Evgeny Morozov, experimental subject, Filter Bubble, folksonomy, Freestyle chess, Galaxy Zoo, Google Earth, Google Glasses, Gunnar Myrdal, guns versus butter model, Henri Poincaré, hindsight bias, hive mind, Howard Rheingold, Ian Bogost, information retrieval, iterative process, James Bridle, jimmy wales, John Perry Barlow, Kevin Kelly, Khan Academy, knowledge worker, language acquisition, lifelogging, lolcat, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Netflix Prize, Nicholas Carr, Panopticon Jeremy Bentham, patent troll, pattern recognition, pre–internet, public intellectual, Richard Feynman, Ronald Coase, Ronald Reagan, Rubik’s Cube, sentiment analysis, Silicon Valley, Skype, Snapchat, Socratic dialogue, spaced repetition, superconnector, telepresence, telepresence robot, The future is already here, The Nature of the Firm, the scientific method, the strength of weak ties, The Wisdom of Crowds, theory of mind, transaction costs, Twitter Arab Spring, Two Sigma, Vannevar Bush, Watson beat the top human players on Jeopardy!, WikiLeaks, X Prize, éminence grise

As Cory Doctorow points out: Cory Doctorow, “We Need a Serious Critique of Net Activism,” The Guardian, January 25, 2011, accessed March 26, 2013, www.guardian.co.uk/technology/2011/jan/25/net-activism-delusion. Chapter 10: Epilogue The Jeopardy! clue popped up on the screen: Parts of my writing here on Watson appeared previously in my story “What Is I.B.M.’s Watson?” The New York Times Magazine, June 16, 2010, accessed March 26, 2013, www.nytimes.com/2010/06/20/magazine/20Computer-t.html. Indeed, some clever experiments by Harvard’s Teresa Amabile and others: Teresa M. Amabile, “Brilliant but Cruel: Perceptions of Negative Evaluators,” Journal of Experimental Social Psychology 19, no. 2 (March 1983): 146–56; Bryan Gibson and Elizabeth Oberlander, “Wanting to Appear Smart: Hypercriticism as an Indirect Impression Management Strategy,” Self and Identity 7, no. 4 (2008): 380–92.

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Amabile, “Brilliant but Cruel: Perceptions of Negative Evaluators,” Journal of Experimental Social Psychology 19, no. 2 (March 1983): 146–56; Bryan Gibson and Elizabeth Oberlander, “Wanting to Appear Smart: Hypercriticism as an Indirect Impression Management Strategy,” Self and Identity 7, no. 4 (2008): 380–92. “the smartest medical student we have ever had”: Joanna Stern, “IBM’s Watson Supercomputer Gets Job as Oncologist at Memorial Sloan-Kettering Cancer Center,” ABC News, March 22, 2012, accessed March 26, 2013, abcnews.go.com/Technology/ibms-watson-supercomputer-job-memorial-sloan-kettering-cancer/story?id=15979580#.UVQxTKt5MhM. A Pew study found that 22 percent of all TV watchers: Aaron Smith and Jan Lauren Boyles, “The Rise of the ‘Connected Viewer’” (Pew Internet & American Life Project, July 17, 2012), 2, accessed March 26, 2013, pewinternet.org/Reports/2012/Connected-viewers.aspx.

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A Pew study found that 22 percent of all TV watchers: Aaron Smith and Jan Lauren Boyles, “The Rise of the ‘Connected Viewer’” (Pew Internet & American Life Project, July 17, 2012), 2, accessed March 26, 2013, pewinternet.org/Reports/2012/Connected-viewers.aspx. rebuslike short forms of expression: I owe this idea to Crystal’s Txtng, 39. Andy Hickl, an AI inventor: Thompson, “What Is I.B.M.’s Watson?” As one of the commenters at the site explained: “Reader’s Comments: What Is IBM’s Watson?” accessed March 26, 2013, community.nytimes.com/comments/www.nytimes.com/2010/06/20/magazine/20Computer-t.html?sort=recommended. to a 2004 discussion-board thread: “Favorite Movie Quotes,” accessed March 26, 2013, www.clemsontalk.com/vb/archive/index.php/t-2926.html.

Tomorrow's Lawyers: An Introduction to Your Future by Richard Susskind

business intelligence, business process, business process outsourcing, call centre, Clayton Christensen, cloud computing, commoditize, crowdsourcing, data science, disruptive innovation, global supply chain, information retrieval, invention of the wheel, power law, pre–internet, Ray Kurzweil, Silicon Valley, Skype, speech recognition, supply-chain management, telepresence, Watson beat the top human players on Jeopardy!

The disruption here is that crucial legal insights, correlations, and even algorithms might come to play a central role in legal practice and legal risk management and yet they will not be generated through the work of mainstream lawyers (unless they choose to collaborate with big data scientists). AI-Based Problem-Solving If IBM’s Watson (an artificially intelligent computer system designed to compete on the US TV quiz show Jeopardy!) is able publicly to beat the two finest human competitors, then the days of online problem-solving by computer are not very far away. And when we enter that era, and we apply the same techniques and technologies in law, then we will have AI-based legal problem-solving.

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We need, as lawyers, to be open-minded because we are living in an era of unprecedented technological change. Consider recent progress in artificial intelligence (AI) and, in particular, the achievements of Watson, IBM’s AI-based system that competed—in a live broadcast in 2011—on the US television general knowledge quiz show Jeopardy! Watson beat the show’s two finest ever human contestants. This is a phenomenal technological feat, combining advanced natural language understanding, machine learning, information retrieval, knowledge processing, speech synthesis, and more. While the remarkable Google retrieves information for us that might be relevant, Watson shows how AI-based systems, in years to come, will actually speak with us and solve our problems.

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This thinking led me in 1996, in my book The Future of Law, to predict a shift in legal paradigm, by which I meant that many or most of our fundamental assumptions about legal service and legal process would be challenged and displaced by IT and the Internet. It was a 20-year prediction, so I can be called fully to account in 2016. I do not think I will be far out—when I look at IBM’s Watson (see Chapter 1) and think of similar technology in law, or reflect on information retrieval systems that are already outperforming human beings engaged in document review, then I feel we are on the brink of a monumental shift. Crucially, I concluded in 1996 that legal service would move from being a one-to-one, consultative, print-based advisory service to a one-to-many, packaged, Internet-based information service.

The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do by Erik J. Larson

AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Alignment Problem, AlphaGo, Amazon Mechanical Turk, artificial general intelligence, autonomous vehicles, Big Tech, Black Swan, Bletchley Park, Boeing 737 MAX, business intelligence, Charles Babbage, Claude Shannon: information theory, Computing Machinery and Intelligence, conceptual framework, correlation does not imply causation, data science, deep learning, DeepMind, driverless car, Elon Musk, Ernest Rutherford, Filter Bubble, Geoffrey Hinton, Georg Cantor, Higgs boson, hive mind, ImageNet competition, information retrieval, invention of the printing press, invention of the wheel, Isaac Newton, Jaron Lanier, Jeff Hawkins, John von Neumann, Kevin Kelly, Large Hadron Collider, Law of Accelerating Returns, Lewis Mumford, Loebner Prize, machine readable, machine translation, Nate Silver, natural language processing, Nick Bostrom, Norbert Wiener, PageRank, PalmPilot, paperclip maximiser, pattern recognition, Peter Thiel, public intellectual, Ray Kurzweil, retrograde motion, self-driving car, semantic web, Silicon Valley, social intelligence, speech recognition, statistical model, Stephen Hawking, superintelligent machines, tacit knowledge, technological singularity, TED Talk, The Coming Technological Singularity, the long tail, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, theory of mind, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, Yochai Benkler

As luck would have it, one popu­lar game in par­tic­u ­lar was already in the news: Jeopardy! The tele­v i­sion quiz show Jeopardy! is a kind of gamified version of a simplified Turing test (or so it seems). It’s a broad test of knowledge of facts, with the “conversation” reversed so that an answer is given to contestants, who then must conjure the correct question. For example, to the prompt “Developed by IBM, it beat Kasparov at chess,” the correct response would be “What is Deep Blue?” In 2004, Jeopardy! ratings ­were soaring b­ ecause of a ­human, Ken Jennings, the returning world champion who had won a record-­breaking seventy-­ four Jeopardy! contests in a row.

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T U R I NG’S I N TELLIGENCE ER ROR A N D NA R ROW A I The problem-­solving view of intelligence helps explain the production of invariably narrow applications of AI throughout its history. Game playing, for instance, has been a source of constant inspiration for the development of advanced AI techniques, but games are simplifications of life that reward simplified views of intelligence. A chess program plays chess, but does rather poorly driving a car. IBM’s Watson system plays Jeopardy!, but not chess or Go, and massive programming or “porting” efforts are required to use the Watson platform to perform other data mining and natu­ral language pro­cessing functions, as with recent (and largely unsuccessful) forays into health care. Treating intelligence as problem-­solving thus gives us narrow applications.

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DeepQA became an effective feedback loop involving dozens of expert h­ umans gradually fine-­t uning the Watson system to play world-­class Jeopardy! (Perhaps less appreciated, the resulting system became narrow—­a consequence itself of this engineering strategy aimed at game-­play specifics.) Inspired by the “society of minds” idea, DeepQA did include innovations in natu­ral language pro­cessing (NLP). Older QA systems like PIQUANT relied on pro­cessing pipelines that ­were similar to IBM Watson: first analyze the question, then search and retrieve pos­ si­ble answers, then score the retrieved answers, returning the best. 224 T he P rob­lem of I nference Yet PIQUANT and other earlier-­generation systems share a common flaw; they can get locked into incorrect answers by m ­ istakes made in early parts of analy­sis.

Augmented: Life in the Smart Lane by Brett King

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

You might recall a few years ago that IBM fielded a computer to compete in the game show Jeopardy! against two of its longtime champions. Watson, as the computer is known, won the game show convincingly, defeating the two previously undefeated human challengers Jennings and Rutter.14 More recently, IBM Watson was board approved by the NY Genome Centre to act as a medical diagnostician.15 As far as we are aware, this is the first time a specific machine intelligence (MI) has been certified academically or professionally to practise medicine. It will certainly not be the last time. What was the driver behind the medical certification? The team behind IBM Watson wondered whether Watson could learn to hypothesise on problems like diagnosing cancer or finding genetic markers for hereditary conditions if they gave it the right data.

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The fact is that human advisers are going to have significant disadvantages competing with their so-called robo-adviser challengers. Here are the most significant disadvantages: The Big Data Theory: AIs Will Analyse much more Data The example used earlier in chapter 3 is that of IBM Watson either competing against humans in the game show Jeopardy! or providing advice to doctors and nurses in the field of cancer research and treatment. Watson has been provided with millions of documents including medical journals, case studies, dictionaries, encyclopedias, literary works, newswire articles and other databases.

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Horvitz, “Toward enhanced pharmacovigilance using patient-generated data on the Internet,” Journal of Clinical Pharmacology & Therapeutics 96, no. 2 (August 2014): 239–46. 12 “All Things Considered,” NPR Radio, aired 8 April 2015. 13 Slang for “Internet” 14 John Markoff, “Computer wins on ‘Jeopardy!’: Trivial, It’s Not!” New York Times, 16 February 2011, http://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html. 15 Irana Ivanova, “IBM’s Watson joins Genome Center to cure cancer,” Crain’s New York Business, 19 March 2014, http://www.crainsnewyork.com/article/20140319/HEALTH_CARE/140319845/ibmswatson-joins-genome-center-to-cure-cancer. 16 p53 is often called a “tumour suppressor protein structure” because of its role in defending the body against the formation of cancer cells. 17 Ian Steadman, “IBM’s Watson is better at diagnosing cancer than human doctors,” Wired, 11 February 2013, http://www.wired.co.uk/news/archive/2013-02/11/ibm-watson-medical-doctor. 18 “IBM and Partners to Transform Person Health with Watson and Open Cloud,” IBM Press Release, 13 April 2015, https://www-03.ibm.com/press/us/en/pressrelease/46580.wss. 19 Friction here refers to the user workload required to use the software.

21 Lessons for the 21st Century by Yuval Noah Harari

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

Similarly, human–computer centaur teams are likely to be characterised by a constant tug of war between the humans and the computers, instead of settling down to a lifelong partnership. Teams made exclusively of humans – such as Sherlock Holmes and Dr Watson – usually develop permanent hierarchies and routines that last decades. But a human detective who teams up with IBM’s Watson computer system (which became famous after winning the US TV quiz show Jeopardy! in 2011) will find that every routine is an invitation for disruption, and every hierarchy an invitation for revolution. Yesterday’s sidekick might morph into tomorrow’s superintendent, and all protocols and manuals will have to be rewritten every year.17 A closer look at the world of chess might indicate where things are heading in the long run.

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.: Arash Bahrammirzaee, ‘A comparative Survey of Artificial Intelligence Applications in Finance: Artificial Neural Networks, Expert System and Hybrid Intelligent Systems’, Neural Computing and Applications 19:8 (2010), 1165–95; analysis of complex data in medical systems and production of diagnosis and treatment: Marjorie Glass Zauderer et al., ‘Piloting IBM Watson Oncology within Memorial Sloan Kettering’s Regional Network’, Journal of Clinical Oncology 32:15 (2014), e17653; creation of original texts in natural language from massive amounts of data: Jean-Sébastien Vayre et al., ‘Communication Mediated through Natural Language Generation in Big Data Environments: The Case of Nomao’, Journal of Computer and Communication 5 (2017), 125–48; facial recognition: Florian Schroff, Dmitry Kalenichenko and James Philbin, ‘FaceNet: A Unified Embedding for Face Recognition and Clustering’, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015), 815–23; and driving: Cristiano Premebida, ‘A Lidar and Vision-based Approach for Pedestrian and Vehicle Detection and Tracking’, 2007 IEEE Intelligent Transportation Systems Conference (2007). 3 Daniel Kahneman, Thinking, Fast and Slow (New York: Farrar, Straus & Giroux, 2011); Dan Ariely, Predictably Irrational (New York: Harper, 2009); Brian D.

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Spreitzer, Lindsey Cameron and Lyndon Garrett, ‘Alternative Work Arrangements: Two Images of the New World of Work’, Annual Review of Organizational Psychology and Organizational Behavior 4 (2017), 473–99; Sarah A. Donovan, David H. Bradley and Jon O. Shimabukuru, ‘What Does the Gig Economy Mean for Workers?’, Congressional Research Service, Washington DC, 2016; ‘More Workers Are in Alternative Employment Arrangements’, Pew Research Center, 28 September 2016. 17 David Ferrucci et al.,‘Watson: Beyond Jeopardy!’, Artificial Intelligence 199–200 (2013), 93–105. 18 ‘Google’s AlphaZero Destroys Stockfish in 100-Game Match’, Chess.com, 6 December 2017; David Silver et al., ‘Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm’, arXiv (2017), https://arxiv.org/pdf/1712.01815.pdf; see also Sarah Knapton, ‘Entire Human Chess Knowledge Learned and Surpassed by DeepMind’s AlphaZero in Four Hours’, Telegraph, 6 December 2017. 19 Cowen, Average is Over, op. cit.; Tyler Cowen, ‘What are humans still good for?

The Glass Cage: Automation and Us by Nicholas Carr

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

But even if we assume, contrary to the extravagant promises of big-data evangelists, that there are limits to the applicability and usefulness of correlation-based predictions and other forms of statistical analysis, it seems clear that computers are a long way from bumping up against those limits. When, in early 2011, the IBM supercomputer Watson took the crown as the reigning champion of Jeopardy!, thrashing two of the quiz show’s top players, we got a preview of where computers’ analytical talents are heading. Watson’s ability to decipher clues was astonishing, but by the standards of contemporary artificial-intelligence programming, the computer was not performing an exceptional feat.

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., 153–76 Human Condition, The (Arendt), 108, 227–28 humanism, 159–61, 164, 165 Human Use of Human Beings, The (Wiener), 37, 38 Huth, John Edward, 216–17 iBeacon, 136 IBM, 27, 118–20, 195 IBM Systems Journal, 194–95 identity, 205–6 IEX, 171 Illingworth, Leslie, 19, 33 imagination, 25, 121, 124, 142, 143, 215 inattentional blindness, 130 industrial planners, 37 Industrial Revolution, 21, 24, 28, 32, 36, 106, 159, 195 Infiniti, 8 information, 68–74, 76–80, 166 automation complacency and bias and, 68–72 health, 93–106, 113 information overload, 90–92 information underload, 90–91 information workers, 117–18 infrastructure, 195–99 Ingold, Tim, 132 integrated development environments (IDEs), 78 Intel, 203 intelligence, 137, 151 automation of, 118–20 human vs. artificial, 11, 118–20 interdependent networks, 155 internet, 12–13, 33n, 176, 188 internet of things, 195 Introduction to Mathematics, An, (Whitehead), 65 intuition, 105–6, 120 Inuit hunters, 125–27, 131, 217–20 invention, 161, 174, 214 iPads, 136, 153, 203 iPhones, 13, 136 Ironstone Group, 116 “Is Drawing Dead?” (symposium), 144 Jacquard loom, 36 Jainism, 185 Jefferson, Thomas, 160, 222 Jeopardy! (quiz show), 118–19, 121 Jobless Future, The (Aronowitz and DiFazio), 27–28 jobs, 14–17, 27–33, 85, 193 automation’s altering of, 67, 112–20 blue-collar, 28, 109 creating, 31, 32, 33 growth of, 28, 30, 32 loss of, 20, 21, 25, 27, 28, 30, 31, 40, 59, 115–18, 227 middle class, 27, 31, 32, 33n white-collar, 28, 30, 32, 40, 109 Jobs, Steve, 194 Jones, Michael, 132, 136–37, 151 Kasparov, Garry, 12 Katsuyama, Brad, 171 Kay, Rory, 58 Kelly, Kevin, 153, 225, 226 Kennedy, John, 27, 33 Kessler, Andy, 153 Keynes, John Maynard, 26–27, 66, 224, 227 Khosla, Vinod, 153–54 killing, robots and, 184, 185, 187–93 “Kitty Hawk” (Frost), 215 Klein, Gary, 123 Knight Capital Group, 156 know-how, 74, 76, 115, 122–23 knowledge, 74, 76, 77, 79, 80–81, 84, 85, 111, 121, 123, 131, 148, 153, 206, 214, 215 design, 144 explicit (declarative), 9, 10–11, 83 geographic, 128 medicine and, 100, 113, 123 tacit (procedural), 9–11, 83, 105, 113, 144 knowledge workers, 17, 148 Kool, Richard, 228–29 Korzybski, Alfred, 220 Kroft, Steve, 29 Krueger, Alan, 30–31 Krugman, Paul, 32–33 Kurzweil, Ray, 181, 200 labor, 227 abridging of, 23–25, 28–31, 37, 96 costs of, 18, 20, 31, 175 deskilling of, 106–12 division of, 106–7, 165 intellectualization of, 118 in “Mowing,” 211–14 strife, 37, 175 see also jobs; work Labor and Monopoly Capital (Braverman), 109–10 Labor Department, U.S., 66 labor unions, 25, 37, 59 Langewiesche, William, 50–51, 170 language, 82, 121, 150 Latour, Bruno, 204, 208 lawn mowers, robotic, 185 lawyers, law, 12, 116–17, 120, 123, 166 learning, 72–73, 77, 82, 84, 88–90, 175 animal studies and, 88–89 medical, 100–102 Lee, John, 163–64, 166, 169 LeFevre, Judith, 14, 15, 18 leisure, 16, 25, 27, 227 work vs., 14–16, 18 lethal autonomous robots (LARs), 188–93 Levasseur, Émile, 24–25 Leveson, Nancy, 155–56 Levesque, Hector, 121 Levinson, Stephen, 101 Levy, Frank, 9, 10 Lewandowsky, Stephan, 74 Lex Machina, 116–17 Licklider, J.

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The FAA had collected evidence, from crash investigations, incident reports, and cockpit studies, indicating that pilots had become too dependent on autopilots and other computerized systems. Overuse of flight automation, the agency warned, could “lead to degradation of the pilot’s ability to quickly recover the aircraft from an undesired state.” It could, in blunter terms, put a plane and its passengers in jeopardy. The alert concluded with a recommendation that airlines, as a matter of operational policy, instruct pilots to spend less time flying on autopilot and more time flying by hand.1 This is a book about automation, about the use of computers and software to do things we used to do ourselves. It’s not about the technology or the economics of automation, nor is it about the future of robots and cyborgs and gadgetry, though all those things enter into the story.

The Creativity Code: How AI Is Learning to Write, Paint and Think by Marcus Du Sautoy

3D printing, Ada Lovelace, Albert Einstein, algorithmic bias, AlphaGo, Alvin Roth, Andrew Wiles, Automated Insights, Benoit Mandelbrot, Bletchley Park, Cambridge Analytica, Charles Babbage, Claude Shannon: information theory, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, data is the new oil, data science, deep learning, DeepMind, Demis Hassabis, Donald Trump, double helix, Douglas Hofstadter, driverless car, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, Flash crash, Gödel, Escher, Bach, Henri Poincaré, Jacquard loom, John Conway, Kickstarter, Loebner Prize, machine translation, mandelbrot fractal, Minecraft, move 37, music of the spheres, Mustafa Suleyman, Narrative Science, natural language processing, Netflix Prize, PageRank, pattern recognition, Paul Erdős, Peter Thiel, random walk, Ray Kurzweil, recommendation engine, Rubik’s Cube, Second Machine Age, Silicon Valley, speech recognition, stable marriage problem, Turing test, Watson beat the top human players on Jeopardy!, wikimedia commons

If that is true, it will be a real challenge for machine learning to pick up language just by being exposed to a huge database of language use. ‘This is Jeopardy!’ One of the most impressive displays of algorithmic negotiation of the vagaries of natural language came some years ago, a little over ten years after IBM’s super computer DeepBlue successfully took the crown from the reigning chess champion Garry Kasparov. In 2011 IBM turned its attention to a very different sort of competition from chess or Go: it decided to take a shot at the American game show Jeopardy!. Jeopardy! is basically a general-knowledge quiz show. Given that a computer can simply trawl through Wikipedia, that doesn’t seem like much of a test for an algorithm.

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They are like an algorithmic catwalk allowing a company to show off its coding prowess. IBM Watson has already changed our perception of what computers may do – it beat the best Jeopardy! champions, and it is being used for medical diagnoses. What sets Watson apart? What makes it different? This capability to take into unstructured data is a big strength for Watson. We train it. Additionally just dumping the text in Watson, humans actually form the system to understand what is most important and reliable inside the text. Watson pulled in all of Wikipedia prior to its Jeopardy! appearance, and stored that data offline. Humans can tell Watson to trust one source of info more than another.

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Jennings had gone seventy-four games in a row unbeaten, while Rutter had earned over four million dollars during his time on the show. Both had cut their teeth on quiz teams at school and university, although Rutter had always been regarded as something of a slacker academically. Jeopardy! generally features three competitors, so the two human champions agreed to take on the algorithm that IBM produced. IBM named its algorithm Watson, not after Sherlock Holmes’s sidekick but the first CEO of the company, Thomas J. Watson. Over two days in January 2011 Rutter and Jennings battled valiantly against Watson and each other. The filming had to be staged at IBM’s research lab in Yorktown Heights in New York State because it was impossible to relocate the computer hardware to a TV studio.

Mastering Structured Data on the Semantic Web: From HTML5 Microdata to Linked Open Data by Leslie Sikos

AGPL, Amazon Web Services, bioinformatics, business process, cloud computing, create, read, update, delete, Debian, en.wikipedia.org, fault tolerance, Firefox, Google Chrome, Google Earth, information retrieval, Infrastructure as a Service, Internet of things, linked data, machine readable, machine translation, natural language processing, openstreetmap, optical character recognition, platform as a service, search engine result page, semantic web, Silicon Valley, social graph, software as a service, SPARQL, text mining, Watson beat the top human players on Jeopardy!, web application, Wikidata, wikimedia commons, Wikivoyage

Twitter Card Annotation in the Markup <meta name="twitter:card" content="summary" /> <meta name="twitter:site" content="@lesliesikos" /> <meta name="twitter:creator" content="@lesliesikos" /> <meta property="og:url" content="http://www.lesliesikos.com/linked-data-platform-1-0  standardized/" /> <meta property="og:title" content="Linked Data Platform 1.0 Standardized" /> <meta property="og:description" content="The Linked Data Platform 1.0 is now a W3C  Recommendation, covering a set of rules for HTTP operations on Web resources, including  RDF-based Linked Data, to provide an architecture for read-write Linked Data on the  Semantic Web." /> <meta property="og:image" content="http://www.lesliesikos.com/img/LOD.svg" /> 211 Chapter 8 ■ Big Data Applications IBM Watson IBM Watson’s DeepQA system is a question-answering system originally designed to compete with contestants of the Jeopardy! quiz show, where three contestants compete against one another in answering open-domain questions. While the system famously won the contest against human grand champions, it has applications well beyond Jeopardy!, namely, natural language content analysis in both questions and knowledge sources in general [3]. Watson’s cognitive computing algorithms are used in health care, to provide insight and decision support, and give personalized and instant responses to any inquiry or service issue in customer service.

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Watson’s cognitive computing algorithms are used in health care, to provide insight and decision support, and give personalized and instant responses to any inquiry or service issue in customer service. Developers can use the IBM Watson Developers Cloud, a collection of REST APIs and SDKs for cognitive computing tasks, such as natural language classification, concept expansion, machine translation, relationship extraction, speech to text, and visual recognition. Among other technologies, Watson uses triplestores such as Sesame, ontologies, and inference. The information resources of DeepQA consist of unstructured, semistructured, and structured data, of which the last one plays an important role, mainly by implementing RDF in IBM’s DB2 database platform [4].

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Google (2015) Introducing the Knowledge Graph. www.google.co.uk/ insidesearch/features/search/knowledge.html. Accessed 9 March 2015. 2. Twitter (2015) Getting Started with Cards. https://dev.twitter.com/cards/. Accessed 12 March 2015. 3. Gliozzo, A., Patwardhan, S., Biran, O., McKeown, K. (2013) Semantic Technologies in IBM Watson. www.cs.columbia.edu/nlp/papers/2013/ watson_class_acl_tnlp_2013.pdf. Accessed 23 April 2015. 4. Gucer, V. (2013) IBM is embracing Semantic technologies in its products. In: 5 Things To Know About Semantic Technologies. www.ibm.com/developerworks/ community/blogs/5things/entry/5_things_to_know_about_the_semantic_ technologies?

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

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

Follow that out further to, say, 2045, we will have multiplied the intelligence, the human biological machine intelligence of our civilization a billion-fold. RAY KURZWEIL In 2011, we all watched with awe when IBM’s Watson supercomputer beat the world champions on the television game show Jeopardy! Using artificial intelligence and natural language processing, Watson digested over 200 million pages of structured and unstructured data, which it processed at a rate of eighty teraflops—that’s eighty trillion operations per second. In doing so, it handily defeated Ken Jennings, a human Jeopardy! contestant who had won seventy-four games in a row. Jennings was gracious in his defeat, noting, “I, for one, welcome our new computer overlords.”

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Just three years after Watson beat Jennings, the supercomputer achieved a 2,400 percent improvement in performance and shrank by 90 percent, “from the size of a master bedroom to three stacked pizza boxes.” Watson has also now shifted careers, using its vast cognitive powers not for quiz shows but for medicine. The M. D. Anderson Cancer Center is using Watson to help doctors match patients with clinical trials, and at the Sloan Kettering Institute, Watson is voraciously reading 1.5 million patient records and hundreds of thousands of oncology journal articles in an effort to help clinicians come up with the best diagnoses and treatments. IBM has even launched the Watson Business Group with a $1 billion investment earmarked to get companies, nonprofits, and governments to take advantage of Watson’s capabilities.

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FBI Issues Warrant for $100M Cybercrime Mastermind,” Mail Online, June 2, 2014. 10 The unparalleled levels: McAfee, Center for Strategic and International Studies, Net Losses: Estimating the Global Cost of Cybercrime, June 2014. 11 There is another way: Jenny Awford, “Student Accused of Murder ‘Asked Siri Where to Hide Body,’ Say Police,” Mail Online, Aug. 13, 2014. 12 Just three years: “IBM Watson,” IBM Web site, http://​www-​03.​ibm.​com/​press/​us/​en/​presskit/​27297.​wss. 13 The M. D. Anderson Cancer Center: “IBM Watson Hard at Work,” Memorial Sloan Kettering Cancer Center, Feb. 8, 2013; Larry Greenemeier, “Will IBM’s Watson Usher in a New Era of Cognitive Computing,” Scientific American, Nov. 13, 2013. 14 Ray Kurzweil has popularized: Ray Kurzweil, The Singularity Is Near: When Humans Transcend Biology (New York: Penguin Books, 2006), 7. 15 In 2014, Google purchased: Catherine Shu, “Google Acquires Artificial Intelligence Startup DeepMind,” TechCrunch, Jan. 26, 2014. 16 “Whereas the short-term impact”: Stephen Hawking et al., “Stephen Hawking: ‘Transcendence Looks at the Implications of Artificial Intelligence—but Are We Taking AI Seriously Enough?

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

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

It’s easy to imagine a future where a powerful diagnostic AI system dramatically increases the productivity of doctors while creating an environment in which even an inexperienced or mediocre physician navigates patient encounters with what amounts to a virtual team of elite specialists looking over his or her shoulder providing continuous advice. We are definitely not there yet, and one of the earliest attempts to move along this path offers a cautionary tale. Immediately after Watson’s triumph in the 2011 Jeopardy! challenge, IBM moved aggressively to repurpose the technology for healthcare and other industries and built a new billion-dollar business unit around Watson. IBM’s vision was that Watson would assimilate knowledge from a wide variety of sources; it would inhale a torrent of information from textbooks, clinical notes, diagnostic and genetic test results and scientific papers, and then leverage a superhuman ability to connect the dots in ways that would elude even the most capable expert.

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Hassabis says that “we know seeing novel things releases dopamine in the brain” and if the brain is wired so that “finding information and structure [is] rewarding in itself, then that’s a highly useful motivation.”23 In other words, the engine powering our continuous drive to understand the world around us could be a reinforcement learning algorithm linked to the production of dopamine. An entirely different approach to building more general machine intelligence is being pursued by David Ferrucci, the CEO and founder of the AI startup company Elemental Cognition. Ferrucci is best known for leading the team that created IBM’s Watson, the system that defeated Ken Jennings and other top Jeopardy! contestants in 2011. After Watson’s triumph, Ferrucci left IBM and joined the Wall Street hedge fund Bridgewater and Associates, where he reportedly worked on using artificial intelligence to make sense of the macroeconomy and helped build Bridgewater founder Ray Dalio’s management and investment philosophies into algorithms deployed throughout the firm.

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IBM triumphed again in 2011 with the advent of Watson, a machine that easily defeated the world’s top contestants on the TV game show Jeopardy! In many ways this was a far more impressive feat because it required an understanding of natural language that included even the ability to navigate jokes and puns. Unlike Deep Blue, Watson was a system that could go beyond the confines of a board game with rigidly defined rules and deal with a seemingly limitless body of information. Watson won at Jeopardy! by simultaneously deploying a swarm of smart algorithms that coursed through reams of data, often drawn from Wikipedia articles, to determine the correct responses as it played the game.

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

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

The fact is that, no matter what the experts say, no one really knows how A.I. will evolve in the long term. How A.I. Will Affect Our Lives—And Take Our Jobs Let’s begin with our bodies. The same type of artificial-intelligence technology that IBM used to defeat champions on the TV show Jeopardy, called Watson, will soon monitor our health data, predict disease, and advise on how to stay fit. Already, IBM Watson has learned about all the advances in oncology and is better at diagnosing cancer than human doctors are.2 Watson and its competitors will soon learn about every other field of medicine and will provide us with better, and better-informed, advice than our doctors do.

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Gustavo Diaz-Jerez, “Composing with melomics: Delving into the computational world for musical inspiration,” LMJ December 2011; 21:13– 14, http://www.mitpressjournals.org/doi/abs/10.1162/LMJ_a_00053 (accessed 21 October 2016). 2. Ian Steadman, “IBM’s Watson is better at diagnosing cancer than human doctors,” WIRED 11 February 2013, http://www.wired.co.uk/article/ibm-watson-medical-doctor (accessed 21 October 2016). 3. Vinod Khosla, “Technology will replace 80% of what doctors do,” Fortune 4 December 2012, http://fortune.com/2012/12/04/technology-will-replace-80-of-what-doctors-do (accessed 21 October 2016). 4.

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Continuous monitoring of health data by artificial intelligence– based applications will enable the prevention of disease, especially lifestyle diseases such as diabetes and cardiovascular illness. Patients able to operate health systems equipped with a smartly designed user interface will also be able to use IBM Watson or other A.I. systems for personal diagnoses, cutting the doctor entirely out of the loop for initial detection and diagnoses (though we certainly will still need doctors to guide us through more-advanced care choices). So the cost of delivering high-quality care will surely plummet, and acute medical treatments in expensive hospitals rife with nasty resistant bugs will give way to preventive care occurring in our communities and, ultimately, our homes.

The Golden Ticket: P, NP, and the Search for the Impossible by Lance Fortnow

Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Andrew Wiles, Claude Shannon: information theory, cloud computing, complexity theory, Donald Knuth, Erdős number, four colour theorem, Gerolamo Cardano, Isaac Newton, James Webb Space Telescope, Johannes Kepler, John von Neumann, Large Hadron Collider, linear programming, new economy, NP-complete, Occam's razor, P = NP, Paul Erdős, quantum cryptography, quantum entanglement, Richard Feynman, Rubik’s Cube, seminal paper, smart grid, Stephen Hawking, traveling salesman, Turing machine, Turing test, Watson beat the top human players on Jeopardy!, William of Occam

Now these chips do more than one computation at a time, working together in parallel, to solve problems faster by doing more than one thing at a time. Let’s look at one machine in particular, IBM’s Watson, which played and won on the game show Jeopardy on episodes first broadcast in February 2011. Watson consisted of ninety IBM POWER 750 servers, each with four POWER7 processors. A POWER7 processor actually has eight processors (called cores) so it can run eight simultaneous computations at once. That’s thirty-two cores per server for a total of 2,880 cores in the entire Watson system. Watson does 2,880 simultaneous computations so it can interpret the Jeopardy “answer” and determine whether to buzz in that fraction of a section before its opponents.

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See P Einstein, Albert, 21 ellipsoid algorithm, 69–70 employment, effect of P = NP on, 27 Enigma machine, 124–26, 125 entertainment, automated creation of, 25 Entscheidungsproblem, 49 Erdős, Paul, 32, 32n Euler, Leonhard, 38–39 Eulerian paths, 39, 39 Exclusive-OR gates, 114, 116 exponential order, versus algebraic order, 76–77 Facebook: encryption on, 128–29; largest clique on, 8–9 face recognition, 22–23 factoring, 67–69, 127–28, 129, 146–47 fantasy sports, 18 Fermat’s Last Theorem, 7, 110 “A Few Words on Secret Writing” (Poe), 124 Feynman, Richard, 143 Fields Medal, 12 films, automated creation of, 25 finite automaton, 75 Floyd-Warshall algorithm, 33 four-color theorem, 42–44, 43, 46, 92–96, 93, 94, 95, 96 Frenemy: cliques in, 36–37, 45, 52, 52–53; description of, 29; grouping in, 44, 44–45, 46; map of, 96, 96; matchmaking in, 33–36, 34, 35; painting houses in, 42–44, 46; Pass the Rod game in, 37–41, 45–46; six degrees rule in, 30–33 friendship diagram: approximation for, 104; in graph isomorphism, 66, 66–67; logical expressions as, 55 Fukushima power station, 161 fully homomorphic cryptography, 138–39 functions, of maximal complexity, 79–80 game protocols, 136–38 gates, in circuits, 113, 113–18, 114, 115, 116 general relativity theory, 21 genetics research, in Soviet Union, 81 Gentry, Craig, 139 Georgia Tech, 25 GE Research Labs, 76 Gödel, Kurt, 6, 9, 49, 85–86, 111, 111n Gödel Prize, 86 Goemans, Michel, 45 “The Gold Bug” (Poe), 124 golden ticket, finding, 1–2 Google: algorithms of, 159; name of, 34; processing power of, 2 googol, 34 GPS, calculations of, 7–8 graph isomorphism, 66, 66–67 grouping, 44, 44–45 Grover, Luv, 146 Guthrie, Francis, 42 hackers, methods of, 141 Haken, Wolfgang, 42 halting problem, 74 Hamilton, William Rowan, 40 Hamiltonian paths, 41, 45–46 hand, controlling, 5–6, 165 handwriting recognition, 21–23, 22 hard functions, generation of, 80 Hartmanis, Juris, 76, 109 Hawking, Stephen, x Hellman, Martin, 126, 127 heuristics, 92–97 Hilbert, David, 49 https, 128–29 Hubble telescope, 158 Hull, Tom, 51 human body, teleporting, 153 human factors, in dealing with innovation, 161 human genome, mapping, 47–48, 158 IBM T. J. Watson Research Center, 78 Icosian game, 40–41, 41, 50 identity spoofing, 136 identity theft, 106–7 IEEE Foundations of Computer Science conference, 148 innovation, dealing with, 160–61 integrated circuits, 113, 113–14 Intel microprocessors, 90–91 interior point algorithm, 70 International Conference on Theory and Applications of Satisfiability, 96 Internet: quantum cryptography over, 149; security on, 128–29; size of, 2, 34; tracking over, 159–60 “The Intrinsic Computational Difficulty of Functions” (Cobham), 76, 77 Jeopardy!, 156–57 job scheduling problem, 56–57, 87 Johnson, Pete, 24 Journal of the Association of Computing Machinery, 118–19 Karmarkar, Narendra, 70 Karp, Richard, 6–7, 55–58, 77–78 Kasner, Edward, 34 Katrina (hurricane), 161 Kempe, Alfred, 42 Kepler, Johannes, 20 Khachiyan, Leonid, 69–70 Khot, Subhash, 104 al-Khwārizmī, Muhammad ibn Mūsā, 32 kidney exchange problem, 64–65 Kiev University, 84 Kleene, Stephen, 75 Knuth, Donald, 58 Kolmogorov, Andrey, 79, 80–83, 84, 167 Kolmogorov complexity, 83 Komsomol, 85 language, sentences in, 75, 75–76 language translation, 18, 23 Large Hadron Collider, 158 law enforcement, and P = NP, 25–26 laws of motion, 20–21 Levin, Leonid, 6, 79, 83–85 LHC Computing Grid, 158 Liapunov, Alexy, 79 liar’s paradox, 110–13 linear programming, 69–70, 70 linguistics, context-free grammar in, 75, 75–76 logical functions: circuit complexity of, 79–80; for cliques, 53–54; as friendship diagram, 55; satisfiable, 54, 79 Loren, Sophia, and Kevin Bacon, 31–32 Los Alamos National Lab, 149 machine learning, 22, 22–23, 159 Major League Baseball, 16–19 map coloring: explanation of problem, 42–43, 43; heuristic for, 92–96, 93, 94, 95, 96; as NP, 46 market equilibrium, 49 marketing, P = NP and, 27 Martian rule, 86–87 Marxism, and probability theory, 81 Massachusetts Institute of Technology, 85 matchmaking, 33–36, 34, 35, 117–18 mathematics, NP problems in, 49–50 math puzzles, usefulness of, 4–5 Matsuoka, Yoky, 5–6, 165 max-cut problem, 45, 46 McCulloch, Warren, 75 McLean, Malcolm, 160 medicine, and P = NP, 14–15 Merkle, Roger, 127 messenger RNA (mRNA), 47–48 Meyer, Albert, 85 microprocessors: capabilities of, 90–92; parallel, 155, 156–57; speed of, 156 microwave attack, 107 Milgram, Stanley, 30–31 Millennium Problems, 7, 14 min-cut problem, 44, 44–45 Minesweeper, 61–62, 62 minimum energy states, 48 MMORPG (massively multiplayer online role-playing game), 66, 66–67 Moore, Gordon, 156 Moore’s law, 91, 92, 156 Moscow State University, 80, 84 Mulmuley, Ketan, 121 music, automated creation of, 24–25 Nash, John, 49 National Center for Supercomputing Applications (NCSA), 13–14 natural proofs, 118 NC (Nick’s Class) problems, 157–58 Neumann, John von, 6, 85–86 neural nets, creation of, 75 Nevada, neighbors of, 42–43, 43 Newton, Isaac, 20–21 New York to Chicago, possible routes, 7–8 Nobel Prize, for Nash, 49 NOT gates, 79, 114, 114, 116–17 Novikov, Pyotr, 79 Novosibirsk State University, 83–84 NP (nondeterministic polynomial time): in biology, 47–48; circuit size in, 116; in economics, 49; examples of, 46; in mathematics, 49–50; meaning of, ix, 4; in physics, 48, 48; reduction to satisfiability, 54–55 NP-complete problems: accepting unsolvability of, 107–8; approximation for, 99–104, 100, 101, 102, 103; brute force approach to, 90–92, 91; categorizing, 59; changing the problem, 105–7; commonality of, 162; examples of, 59–65; and factoring, 140–41; heuristics for, 92–97; Levin’s work with, 84–85; naming of, 58; problems of unknown status, 65–70; quantum algorithms for, 146–47; small solutions for, 97, 97–99, 98; Sudoku as, 135–36 NP = NC, 157–58 number theory, 68 observation: entanglement and, 147; in outcome, 144 Occam’s razor, 19–23 100 factorial (100!)

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Later developments in randomized and quantum computation will show that perhaps we cannot have a fixed notion of efficient computation. Then in 1971 came Steve Cook’s paper that defined NP, the problems we can verify efficiently, the P versus NP problem, and the first NP-complete problem. A year later Richard Karp gave his paper showing that a number of important problems were NP-complete. In 1972 the IBM T. J. Watson Research Center hosted the Symposium on the Complexity of Computer Computations, a meeting mostly remembered for Karp’s presentation of his paper. The organizers of the meeting held a panel discussion at the end of the symposium on the future of the field. One of the questions asked was, “How is the theory developing from originally being a scattering of a few results on lower bounds and some algorithms into a more unified theory?”

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

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

Techniques from machine learning and artificial intelligence are what powered the triumph of the IBM Watson supercomputer over humans on Jeopardy. This relied upon quickly answering complex questions that would not be amenable to a Google search.30–32 IBM Watson was taught through hundreds of thousands of questions from prior Jeopardy shows, armed with all the information in Wikipedia, and programmed to do predictive modeling. There’s no prediction of the future here, just prediction that IBM Watson has the correct answer. 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.

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“Move Over, Siri,” The Economist, November 30, 2013, http://www.economist.com/news/technology-quarterly/21590760-predictive-intelligence-new-breed-personal-assistant-software-tries/print. 30. “A Cure for the Big Blues,” The Economist, January 11, 2014, http://www.economist.com/node/21593489/print. 31. S. E. Ante, “IBM Struggles to Turn Watson Computer into Big Business,” Wall Street Journal, January 7, 2014, http://online.wsj.com/news/articles/SB10001424052702304887104579306881917668654. 32. J. Hempel, “IBM’s Massive Bet on Watson,” CNN Money, September 19, 2013, http://money.cnn.com/2013/09/19/technology/ibm-watson.pr.fortune/index.html?pw_log=in. 33. A. Bari, M. Chaouchi, and T. Jong, Predictive Analytics for Dummies (Hoboken, NJ: John Wiley & Sons, 2014), 129. 34.

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Online symptom checkers66 are getting increasing electronic traffic and attention on the Internet to help people “self” (computer-assisted) diagnose, but they don’t predict an illness. At best, from a cluster of symptoms that a person inputs, a differential diagnosis is offered with the right one on the list. That’s useful and practical, but not predicting anything. Likewise, the Biovideo developers—who are building an app for the IBM Watson supercomputer so that “a mother with a sick child at 4 am will be able to use IBM Watson to ask what is wrong with her baby and get a 100 per cent accurate response”67a—may create something useful, but that something is not about prediction. We have a very serious problem with misdiagnosis—diagnosis of a patient with the wrong disease or the correct one too late—affecting 12 million Americans per year.67b,67c We can look to technology and contextual computing to help alleviate this struggle.

Dual Transformation: How to Reposition Today's Business While Creating the Future by Scott D. Anthony, Mark W. Johnson

activist fund / activist shareholder / activist investor, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Amazon Web Services, Andy Rubin, Apollo 13, asset light, autonomous vehicles, barriers to entry, behavioural economics, Ben Horowitz, Big Tech, blockchain, business process, business process outsourcing, call centre, Carl Icahn, Clayton Christensen, cloud computing, commoditize, corporate governance, creative destruction, crowdsourcing, death of newspapers, disintermediation, disruptive innovation, distributed ledger, diversified portfolio, driverless car, Internet of things, invention of hypertext, inventory management, Jeff Bezos, job automation, job satisfaction, Joseph Schumpeter, Kickstarter, late fees, Lean Startup, long term incentive plan, Lyft, M-Pesa, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Minecraft, obamacare, Parag Khanna, Paul Graham, peer-to-peer lending, pez dispenser, recommendation engine, Salesforce, self-driving car, shareholder value, side project, Silicon Valley, SimCity, Skype, software as a service, software is eating the world, Steve Jobs, subscription business, the long tail, the market place, the scientific method, Thomas Kuhn: the structure of scientific revolutions, transfer pricing, uber lyft, Watson beat the top human players on Jeopardy!, Y Combinator, Zipcar

Education will increasingly move from something that is done at a specific point in people’s lives to something that is truly perpetual. Let’s dream a little bigger. What if Netflix and IBM were to pair up to fundamentally disrupt health care? IBM’s Watson platform is best known to many people for its appearance on (and domination of) the popular quiz show Jeopardy. But IBM is working hard to apply Watson’s supercomputing brainpower to complex problems such as addressing difficult-to-diagnose health conditions. Imagine Netflix tuning its discovery and recommendation power to help individuals make all the day-to-day behavior modifications required to live healthier, happier lives?

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., 41 Hoffman, Reid, 49 HOPE acronym, 65 Horowitz, Ben, 206 House of Cards, 35 House of Payne, 98 Houston, Drew, 151 the how, changing in transformation A, 36–45 Huffington, Arianna, 27 Huffington Post, 27, 28 Humana, 183 Hundred Flowers Campaign, 117 Hurley, Chad, 97 HyperText Markup Language (HTML), 3 IBM, 8, 54, 132 Watson, 70, 204 ibrutinib, 19 Icahn, Carl, 15 ideas, sharing, 149 identity crises, 168–173, 193–197 IDEO, 61 Imbruvica, 19 industry entrant activity, 104–105, 112 Innosight, 61, 77, 93 Innov8, 143–144 innovation business model, 40–42 in business models, 20 catalysts in, 104–105 creating safe spaces for, 143–145 in established companies, 71–72 for improving today and creating tomorrow, 55 incumbents’ failure in, 14–15 pace of disruptions and, 4–5 physical environment and, 148 predictability and, 137–139 sharing ideas and, 149 simplifying experiments and, 148–149 Innovation: The Attacker’s Advantage (Foster), 71 The Innovator’s Dilemma (Christensen), 14–15, 36, 71 The Innovator’s Extinction (Ulmer), 71–72 The Innovator’s Guide to Growth (Anthony, Johnson, Sinfield, and Altman), 62 Instagram, 48 Institute for Health Sciences, 204 Intel, 78–79 InterActive Corp, 49–50 interface management, 75, 80–87 arbitration in, 86–87 exchange teams in, 82–83 transfer pricing in, 85 internet browsers, 2–3, 47 media transformations from, 2–3 Intuit, 132–133 inverse mentors, 150–151 investment curiosity and funding of, 141 at Deseret, 30 estimating potential of existing, 119 at SingPost, 52–53 by venture capitalists, 103–104 iPhone, 4, 92–93, 104 iPod, 92–93 Israel, Simon, 53, 142 iTunes, 92–93 Janssen, Paul, 16 Janssen Pharmaceuticals, 16–22 business model innovation at, 42 postdisruption job to be done at, 39 Jarden Consumer Solutions (JCS), 130–131 Jasper, 143 Jassy, Andy, 53–54 Jensen, Michael, 177 job loss, 7 at Deseret, 30 at Media General, 157 Jobs, Steve, 4, 8 destruction by, 132 focus of, 116 influenced by Xerox, 13 Motorola and, 92–93 transformation journey of, 181–182 job to be done, 21 determining defensible postdisruption, 36–39 Johnson, Lyndon, 116 Johnson, Mark, 36, 53, 62, 109–110 Johnson & Johnson, 16–22, 177–178 Joyce, Jim, 64 Karim, Jawed, 97 Kay, Alan, 154 Kennedy, John F., 24, 115–116, 117, 132 Kickbox, 148–149 Kickstart Ventures, 143–144 Knewton, 56, 67 Knight, Wayne, 95 Knight Ridder, 97 Kodak, 1–2, 4, 11 Kodak Moments, 1–2 Koonin, Steve, 95 KPO, 51 KSL, 8, 9, 29, 68 Kuhn, Thomas, 68 Lafley, A.G., 124, 137 Lasseter, John, 4 Lazarus, Mark, 95 Lead and Disrupt (O’Reilly and Tushman), 53, 54 leaders and leadership commitment to transformation A implementation by, 43–45 conflict arbitration by, 86–87 conviction to persevere and, 24, 155–179 courage in decision making and, 91–113 on crises of commitment, 186–189 on crises of conflict, 189–193 curiosity in, 24, 135–154 discussion questions for, 210 in dual transformation, 23–24 exchange teams and, 83–84 exposing to new thinking, 145–147 focus and, 24, 115–133 greatest challenge facing current, 5, 11 hands-on involvement by, 44 in maintaining transformations, 162–163 mindsets for success in, 23–24 opportunity of disruption and, 11–12 overestimation of alignment by, 119 profiles of transformation, 182–186 purpose and, 176–178 understanding customer problems and the job to be done, 38–39 The Lean Startup (Ries), 65, 153 LeBlanc, Paul, 58 le Carré, John, 153 Lee, Christopher M., 67–68, 86–87 Lee Hsien Yang, 136 Lee Kuan Yew, 136 LegalZoom, 207 Lenovo, 92 Levitt, Ted, 37, 175 Lew, Allen, 144–145 Lim Ho Kee, 53 Linford, Jon, 84 LinkedIn, 49 Lin Media, 156 local maximums, 6 lunar module frame, 131–132 Lyft, 205 Lynch, Kevin, 32 Major League Baseball, 98–99 Manila Water, 117–128, 184–185 determining goals and boundaries at, 121–123 focus at, 142 growth gap determination at, 118–121 outcomes for, 127–128 strategic opportunity areas of, 123–127 Mao Zedong, 116–117 Marcial, Sharon, 127 margins, 122 “Marketing Myopia” (Levitt), 175 markets identifying constrained, 59–63 opened by disruptions, 5 Marriott, 8 Martin, George R.R., 5 Martin, Roger, 124, 140, 177 McClatchy, 97 McGrath, Rita, 65, 146 Meckling, William, 177 media companies founded after disruption in, 47–50 streaming, 33–36, 93–95 transformations in, 2–3 Media General, 155–157 Medicity, 183 Medtronic, 72–73, 74 Merck, 22 metrics, 42–43 microlenders, 73 Microsoft, 4, 49, 54 Mint, 132 mission statements, 177, 178 mobile phones, 3–5, 91–93 banking and, 151–152 shipping industry and, 202–203 Monte Carlo techniques, 98–99 moonshot, 24, 115–116, 131–132 Morton, Marshall, 155–156 motivation, 175–176 leaders on, 194 Motorola, 4–5, 92 M-PESA, 201 Mulally, Alan, 153–154 Mulcahy, Anne, 14, 86 multisystem operators (MSOs), 96, 98–99 Murdoch, Rupert, 97, 109 Myspace, 48, 97, 109 Narayen, Shantanu, 31–33 National Basketball Association, 98–99 National Science Foundation, 56 Navarrete, Minette, 143–144 Nestlé, 204 Netflix, 23, 97, 104 Amazon Web Services and, 54 business model innovation at, 40, 42, 146 business model of, 106 content creation at, 34–35 decision making at, 93–95, 102 early warning signs at, 108 metrics at, 43 postdisruption job to be done at, 39 transformation A at, 32–36 transformation B at, 69–70 transformation journey at, 181 net present value (NPV), 110 net promoter scores, 78 Netscape, 2–3, 47 News Corp, 48, 97 Newspaper Association of America, 3 newspapers.

Thank You for Being Late: An Optimist's Guide to Thriving in the Age of Accelerations by Thomas L. Friedman

3D printing, additive manufacturing, affirmative action, Airbnb, AltaVista, Amazon Web Services, Anthropocene, Apple Newton, autonomous vehicles, Ayatollah Khomeini, barriers to entry, Berlin Wall, Bernie Sanders, Big Tech, biodiversity loss, bitcoin, blockchain, Bob Noyce, business cycle, business process, call centre, carbon tax, centre right, Chris Wanstrath, Clayton Christensen, clean tech, clean water, cloud computing, cognitive load, corporate social responsibility, creative destruction, CRISPR, crowdsourcing, data science, David Brooks, deep learning, demand response, demographic dividend, demographic transition, Deng Xiaoping, digital divide, disinformation, Donald Trump, dual-use technology, end-to-end encryption, Erik Brynjolfsson, fail fast, failed state, Fairchild Semiconductor, Fall of the Berlin Wall, Ferguson, Missouri, first square of the chessboard / second half of the chessboard, Flash crash, fulfillment center, game design, gig economy, global pandemic, global supply chain, Great Leap Forward, illegal immigration, immigration reform, income inequality, indoor plumbing, intangible asset, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the steam engine, inventory management, Irwin Jacobs: Qualcomm, Jeff Bezos, job automation, John Markoff, John von Neumann, Khan Academy, Kickstarter, knowledge economy, knowledge worker, land tenure, linear programming, Live Aid, low interest rates, low skilled workers, Lyft, Marc Andreessen, Mark Zuckerberg, mass immigration, Maui Hawaii, Menlo Park, Mikhail Gorbachev, mutually assured destruction, Neil Armstrong, Nelson Mandela, ocean acidification, PalmPilot, pattern recognition, planetary scale, power law, pull request, Ralph Waldo Emerson, ransomware, Ray Kurzweil, Richard Florida, ride hailing / ride sharing, Robert Gordon, Ronald Reagan, Salesforce, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, smart cities, Solyndra, South China Sea, Steve Jobs, subscription business, supercomputer in your pocket, synthetic biology, systems thinking, TaskRabbit, tech worker, TED Talk, The Rise and Fall of American Growth, Thomas L Friedman, Tony Fadell, transaction costs, Transnistria, uber lyft, undersea cable, urban decay, urban planning, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y2K, Yogi Berra, zero-sum game

“Watson is no longer contained in a box that is unconnected to the Internet, but rather is now of the Internet,” explained David Yaun, an IBM communications vice president. IBM put together the mini-Watson mockup “to illustrate that we could cram all the computing power of the Jeopardy! Watson into a suitcase today. But Watson itself now is literally part of your supernova—unleashed from a twentieth-century paradigm of a box or a standalone server.” And anyway—Watson’s grandson would never waste his time trying to beat humans on Jeopardy! That is so 2011! Today’s Watson is now busy ingesting all known medical research on subjects such as cancer diagnostics and treatments.

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It could happen only after Moore’s law entered the second half of the chessboard and gave us sufficient power to digitize almost everything imaginable—words, photos, data, spreadsheets, voice, video, and music—as well as the capacity to load it all into computers and the supernova, the networking ability to move it all around at high speed, and the software capacity to write multiple algorithms that could teach a computer to make sense of unstructured data, just as a human brain might, and thereby enhance every aspect of human decision making. When IBM designed Watson to play Jeopardy!, Kelly explained to me, it knew from studying the show and the human contestants exactly how long it could take to digest the question and buzz in to answer it. Watson would have about a second to understand the question, half a second to decide the answer, and a second to buzz in to answer first.

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On February 14, 2011, a turning point of sorts in the history of humanity was reached on—of all places—one of America’s longest-running television game shows, Jeopardy! That afternoon one of the contestants, who went by just his last name, Watson, competed against two all-time great Jeopardy! champions, Ken Jennings and Brad Rutter. Mr. Watson did not try to respond to the first clue, but with the second clue he buzzed in first to answer. The clue was: “Iron fitting on the hoof of a horse or a card-dealing box in a casino.” Watson, in perfect Jeopardy! style, responded with the question “What is ‘shoe’?” That response should go down in history with the first words ever uttered on a telephone, on March 10, 1876, when Alexander Graham Bell, the inventor, called his assistant—whose name, ironically, was Thomas Watson—and said, “Mr.

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

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

The following year Deep Blue went a giant step further when it defeated Russian chess legend Garry Kasparov in an entire six-game match. It was stunning to see a computer win a contest in a domain long regarded as representing the pinnacle of human intelligence. By 2011, IBM Watson had defeated two masters of the game show Jeopardy!, and in 2016 Google DeepMind’s AlphaGo outplayed Lee Se-dol, a South Korean master of Go, the ancient, complex strategy game played with stones on a grid of lines, usually nineteen by nineteen. Make no mistake, these are tremendous science and engineering feats. But the future holds far greater promise than computers beating humans at games.

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., 145 Gates, Bill, 4, 12, 21, 28, 64, 46, 67–69, 73–75, 87, 91, 127, 146, 183, 203 Gavasker, Sunil, 36 GE, 3, 126–27, 237 Gelernter, David, 143, 183 Geneva Convention, Fourth (1949), 171 Georgia Pacific, 29 Germany, 220, 223, 227–36 Gervais, Michael, 4–5 Gini, Corrado, 219 Gini coefficient, 219–21 GLEAM, 117 Gleason, Steve, 10–11 global competitiveness, 78–79, 100–102, 215 global information, policy and, 191 globalization, 222, 227, 235–37 global maxima, 221–22 goals, 90, 136 Goethe, J.W. von, 155 Go (game), 199 Goldman Sachs, 3 Google, 26, 45, 70–72, 76, 127, 160, 173–74, 200 partnership with, 125, 130–32 Google DeepMind, 199 Google Glass, 145 Gordon, Robert, 234 Gosling, James, 26 government, 138, 160 cybersecurity and, 171–79 economic growth and, 12, 223–24, 226–28 policy and, 189–92, 223–28 surveillance and, 173–76, 181 Grace Hopper, 111–14 graph coloring, 25 graphical user interfaces (GUI), 26–27 graphics-processing unit (GPU), 161 Great Convergence, the (Baldwin), 236 Great Recession (2008), 46, 212 Greece, 43 Green Card (film), 33 Guardians of Peace, 169 Gutenberg Bible, 152 Guthrie, Scott, 3, 58, 60, 82, 171 H1B visa, 32–33 habeas corpus, 188 Haber, Fritz, 165 Haber process, 165 hackathon, 103–5 hackers, 169–70, 177, 189, 193 Hacknado, 104 Halo, 156 Hamaker, Jon, 157 haptics, 148 Harvard Business Review, 118 Harvard College, 3 Harvey Mudd College, 112 Hawking, Stephen, 13 Hazelwood, Charles, 180 head-mounted computers, 144–45 healthcare, 41–42, 44, 142, 155–56, 159, 164, 198, 218, 223, 225, 237 Healthcare.gov website, 3, 81, 238 Heckerman, David, 158 Hewlett Packard, 63, 87, 127, 129 hierarchy, 101 Himalayas, 19 Hindus, 19 HIV/AIDS, 159, 164 Hobijn, Bart, 217 Hoffman, Reid, 232, 233 Hogan, Kathleen, 3, 80–82, 84 Holder, Eric, 173–74 Hollywood, 159 HoloLens, 69, 89, 125, 144–49, 236 home improvement, 149 Hong Kong, 229 Hood, Amy (CFO), 3, 5, 82, 90 Horvitz, Eric, 154, 208 hospitals, 42, 78, 145, 153, 223 Hosseini, Professor, 23 Huang, Xuedong, 151 human capital, 223, 226 humanistic approach, 204 human language recognition, 150–51, 154–55 human performance, augmented by technology, 142–43, 201 human rights, 186 Hussain, Mumtaz, 36, 37 hybrid computing, 89 Hyderabad, 19, 36–37, 92 Hyderabad Public School (HPS), 19–20, 22, 37–38, 136 hyper-scale, cloud-first services, 50 hypertext, 142 IBM, 1, 160, 174, 198 IBM Watson, 199–200 ideas, 16, 42 Illustrator, 136 image processing, 24 images, moving, 109 Imagine Cup competition, 149 Immelt, Jeff, 237 Immigration and Naturalization Act (1965), 24, 32–33 import taxes, 216 inclusiveness, 101–2, 108, 111, 113–17, 202, 206, 238 independent software vendor (ISV), 26 India, 6, 12, 17–22, 35–37, 170, 186–87, 222–23, 236 immigration from, 22–26, 32–33, 114–15 independence and, 16–17, 24 Indian Administrative Service (IAS), 16–17, 31 Indian Constitution, 187 Indian Institutes of Technology (IIT), 21, 24 Indian Premier League, 36 IndiaStack, 222–23 indigenous peoples, 78 Indonesia, 223, 225 industrial policy, 222 Industrial Revolution, 215 Fourth or future, 12, 239 information platforms, 206 information technology, 191 Infosys, 222 infrastructure, 88–89, 152–53, 213 innovation, 1–2, 40, 56, 58, 68, 76, 102, 111, 120, 123, 142, 212, 214, 220, 224, 234 innovator’s dilemma, 141–42 insurance industry, 60 Intel, 21, 45, 160, 161 intellectual property, 230 intelligence, 13, 88–89, 126, 150, 154–55, 160, 169, 173, 239 intelligence communities, 173 intensity of use, 217, 219, 221, 224–26 International Congress of the International Mathematical Union, 162 Internet, 28, 30, 48, 79, 97–98, 222 access and, 225–26, 240 security and privacy and, 172–73 Internet Explorer, 127 Internet of Things (IoT), 79, 134, 142, 228 Internet Tidal Wave, 203 Intersé, 3 Interview, The (film), 169–71 intimidation, 38 investment strategy, 90, 142 iOS devices, 59, 72, 123, 132 iPad, 70, 141 iPad Pro, 123–25 iPhone, 70, 72, 85, 121–22, 125, 177–79 Irish data center, 176, 184 Islamic State (ISIS), 177 Istanbul, 214 Jaisimha, M.L., 18, 36–37 Japan, 44, 223, 230 Japanese-American internment, 188 JAVA, 26 Jeopardy (TV show), 199 Jha, Rajesh, 82 jobs, 214, 231, 239–40.

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., 145 Gates, Bill, 4, 12, 21, 28, 64, 46, 67–69, 73–75, 87, 91, 127, 146, 183, 203 Gavasker, Sunil, 36 GE, 3, 126–27, 237 Gelernter, David, 143, 183 Geneva Convention, Fourth (1949), 171 Georgia Pacific, 29 Germany, 220, 223, 227–36 Gervais, Michael, 4–5 Gini, Corrado, 219 Gini coefficient, 219–21 GLEAM, 117 Gleason, Steve, 10–11 global competitiveness, 78–79, 100–102, 215 global information, policy and, 191 globalization, 222, 227, 235–37 global maxima, 221–22 goals, 90, 136 Goethe, J.W. von, 155 Go (game), 199 Goldman Sachs, 3 Google, 26, 45, 70–72, 76, 127, 160, 173–74, 200 partnership with, 125, 130–32 Google DeepMind, 199 Google Glass, 145 Gordon, Robert, 234 Gosling, James, 26 government, 138, 160 cybersecurity and, 171–79 economic growth and, 12, 223–24, 226–28 policy and, 189–92, 223–28 surveillance and, 173–76, 181 Grace Hopper, 111–14 graph coloring, 25 graphical user interfaces (GUI), 26–27 graphics-processing unit (GPU), 161 Great Convergence, the (Baldwin), 236 Great Recession (2008), 46, 212 Greece, 43 Green Card (film), 33 Guardians of Peace, 169 Gutenberg Bible, 152 Guthrie, Scott, 3, 58, 60, 82, 171 H1B visa, 32–33 habeas corpus, 188 Haber, Fritz, 165 Haber process, 165 hackathon, 103–5 hackers, 169–70, 177, 189, 193 Hacknado, 104 Halo, 156 Hamaker, Jon, 157 haptics, 148 Harvard Business Review, 118 Harvard College, 3 Harvey Mudd College, 112 Hawking, Stephen, 13 Hazelwood, Charles, 180 head-mounted computers, 144–45 healthcare, 41–42, 44, 142, 155–56, 159, 164, 198, 218, 223, 225, 237 Healthcare.gov website, 3, 81, 238 Heckerman, David, 158 Hewlett Packard, 63, 87, 127, 129 hierarchy, 101 Himalayas, 19 Hindus, 19 HIV/AIDS, 159, 164 Hobijn, Bart, 217 Hoffman, Reid, 232, 233 Hogan, Kathleen, 3, 80–82, 84 Holder, Eric, 173–74 Hollywood, 159 HoloLens, 69, 89, 125, 144–49, 236 home improvement, 149 Hong Kong, 229 Hood, Amy (CFO), 3, 5, 82, 90 Horvitz, Eric, 154, 208 hospitals, 42, 78, 145, 153, 223 Hosseini, Professor, 23 Huang, Xuedong, 151 human capital, 223, 226 humanistic approach, 204 human language recognition, 150–51, 154–55 human performance, augmented by technology, 142–43, 201 human rights, 186 Hussain, Mumtaz, 36, 37 hybrid computing, 89 Hyderabad, 19, 36–37, 92 Hyderabad Public School (HPS), 19–20, 22, 37–38, 136 hyper-scale, cloud-first services, 50 hypertext, 142 IBM, 1, 160, 174, 198 IBM Watson, 199–200 ideas, 16, 42 Illustrator, 136 image processing, 24 images, moving, 109 Imagine Cup competition, 149 Immelt, Jeff, 237 Immigration and Naturalization Act (1965), 24, 32–33 import taxes, 216 inclusiveness, 101–2, 108, 111, 113–17, 202, 206, 238 independent software vendor (ISV), 26 India, 6, 12, 17–22, 35–37, 170, 186–87, 222–23, 236 immigration from, 22–26, 32–33, 114–15 independence and, 16–17, 24 Indian Administrative Service (IAS), 16–17, 31 Indian Constitution, 187 Indian Institutes of Technology (IIT), 21, 24 Indian Premier League, 36 IndiaStack, 222–23 indigenous peoples, 78 Indonesia, 223, 225 industrial policy, 222 Industrial Revolution, 215 Fourth or future, 12, 239 information platforms, 206 information technology, 191 Infosys, 222 infrastructure, 88–89, 152–53, 213 innovation, 1–2, 40, 56, 58, 68, 76, 102, 111, 120, 123, 142, 212, 214, 220, 224, 234 innovator’s dilemma, 141–42 insurance industry, 60 Intel, 21, 45, 160, 161 intellectual property, 230 intelligence, 13, 88–89, 126, 150, 154–55, 160, 169, 173, 239 intelligence communities, 173 intensity of use, 217, 219, 221, 224–26 International Congress of the International Mathematical Union, 162 Internet, 28, 30, 48, 79, 97–98, 222 access and, 225–26, 240 security and privacy and, 172–73 Internet Explorer, 127 Internet of Things (IoT), 79, 134, 142, 228 Internet Tidal Wave, 203 Intersé, 3 Interview, The (film), 169–71 intimidation, 38 investment strategy, 90, 142 iOS devices, 59, 72, 123, 132 iPad, 70, 141 iPad Pro, 123–25 iPhone, 70, 72, 85, 121–22, 125, 177–79 Irish data center, 176, 184 Islamic State (ISIS), 177 Istanbul, 214 Jaisimha, M.L., 18, 36–37 Japan, 44, 223, 230 Japanese-American internment, 188 JAVA, 26 Jeopardy (TV show), 199 Jha, Rajesh, 82 jobs, 214, 231, 239–40. See also labor Jobs, Steve, 69 Johnson, Kevin, 46 Johnson, Peggy, 3, 80, 82, 131–33 Joy, Bill, 26 judgment, 207 Jung, Saad Bin, 37 Juniper, 25, 46 Justice Department, 130, 177, 189 Kamler, Arnold, 232 Kaplan, Steven, 29 Kasich, John, 230 Kasparov, Garry, 198–199 Kay, Alan, 203 Kennedy, John F., 199 Kent International, 232 Kenya, 43, 97–100 Khan, Nusrat Fateh Ali, 10 Kidder, Tracy, 68 Kim Jong-un, 169 Kindle, 141 Kipman, Alex, 145–48 Klawe, Maria, 112, 113 knowledge-based economy, 226–27 Koenigsbauer, Kirk, 124 Korea, 225 labor, 231–33, 239–40.

Wonderland: How Play Made the Modern World by Steven Johnson

"hyperreality Baudrillard"~20 OR "Baudrillard hyperreality", Ada Lovelace, adjacent possible, Alfred Russel Wallace, Antoine Gombaud: Chevalier de Méré, Berlin Wall, bitcoin, Book of Ingenious Devices, Buckminster Fuller, Charles Babbage, Claude Shannon: information theory, Clayton Christensen, colonial exploitation, computer age, Computing Machinery and Intelligence, conceptual framework, cotton gin, crowdsourcing, cuban missile crisis, Drosophila, Edward Thorp, Fellow of the Royal Society, flying shuttle, game design, global village, Great Leap Forward, Hedy Lamarr / George Antheil, HyperCard, invention of air conditioning, invention of the printing press, invention of the telegraph, Islamic Golden Age, Jacquard loom, Jacques de Vaucanson, James Watt: steam engine, Jane Jacobs, John von Neumann, joint-stock company, Joseph-Marie Jacquard, land value tax, Landlord’s Game, Lewis Mumford, lone genius, mass immigration, megacity, Minecraft, moral panic, Murano, Venice glass, music of the spheres, Necker cube, New Urbanism, Oculus Rift, On the Economy of Machinery and Manufactures, pattern recognition, peer-to-peer, pets.com, placebo effect, pneumatic tube, probability theory / Blaise Pascal / Pierre de Fermat, profit motive, QWERTY keyboard, Ray Oldenburg, SimCity, spice trade, spinning jenny, statistical model, Steve Jobs, Steven Pinker, Stewart Brand, supply-chain management, talking drums, the built environment, The Great Good Place, the scientific method, The Structural Transformation of the Public Sphere, trade route, Turing machine, Turing test, Upton Sinclair, urban planning, vertical integration, Victor Gruen, Watson beat the top human players on Jeopardy!, white flight, white picket fence, Whole Earth Catalog, working poor, Wunderkammern

One night, Horn and his colleagues were dining out at a steak house near IBM’s headquarters and noticed that all the restaurant patrons had suddenly gathered around the televisions at the bar. The crowd had assembled to watch Ken Jennings continue his legendary winning streak at the game show Jeopardy!, a streak that in the end lasted seventy-four episodes. Seeing that crowd forming planted the seed of an idea in Horn’s mind: Could IBM build a computer smart enough to beat Jennings at Jeopardy!? The system they eventually built came to be called Watson, named after IBM’s founder Thomas J. Watson. To capture as much information about the world as possible, Watson ingested the entirety of Wikipedia, along with more than a hundred million pages of additional data.

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player, but I felt convinced that under the hood my brain was doing more or less the same thing. IBM, of course, has plans for Watson that extend far beyond Jeopardy!. Already Watson is being employed to recommend cancer treatment plans by analyzing massive repositories of research papers and medical data, and answer technical support questions about complex software issues. But still, Watson’s roots are worth remembering: arguably the most advanced form of artificial intelligence on the planet received its education by training for a game show. You might be inclined to dismiss Watson’s game-playing roots as a simple matter of publicity: beating Ken Jennings on television certainly attracted more attention for IBM than, say, Watson attending classes at Oxford would have.

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From the data sources, and from software prompting by the IBM researchers, Watson developed a nuanced understanding of linguistic structures that let it parse and engage in less rigid conversations with humans. By the end of the process, Watson could understand and answer questions about just about anything in the world, particularly answers that relied on factual statements. And of course, given the Grand Challenge that had inspired its creation, Watson knew how to present those answers in the form of a question. In February of 2011, Watson competed in a special two-round Jeopardy! competition against Jennings and another Jeopardy! champion named Brad Rutter.

The Innovators: How a Group of Inventors, Hackers, Geniuses and Geeks Created the Digital Revolution by Walter Isaacson

1960s counterculture, Ada Lovelace, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, AltaVista, Alvin Toffler, Apollo Guidance Computer, Apple II, augmented reality, back-to-the-land, beat the dealer, Bill Atkinson, Bill Gates: Altair 8800, bitcoin, Bletchley Park, Bob Noyce, Buckminster Fuller, Byte Shop, c2.com, call centre, Charles Babbage, citizen journalism, Claude Shannon: information theory, Clayton Christensen, commoditize, commons-based peer production, computer age, Computing Machinery and Intelligence, content marketing, crowdsourcing, cryptocurrency, Debian, desegregation, Donald Davies, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, driverless car, Dynabook, El Camino Real, Electric Kool-Aid Acid Test, en.wikipedia.org, eternal september, Evgeny Morozov, Fairchild Semiconductor, financial engineering, Firefox, Free Software Foundation, Gary Kildall, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Hacker Ethic, Haight Ashbury, Hans Moravec, Howard Rheingold, Hush-A-Phone, HyperCard, hypertext link, index card, Internet Archive, Ivan Sutherland, Jacquard loom, Jaron Lanier, Jeff Bezos, jimmy wales, John Markoff, John von Neumann, Joseph-Marie Jacquard, Leonard Kleinrock, Lewis Mumford, linear model of innovation, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Mitch Kapor, Mother of all demos, Neil Armstrong, new economy, New Journalism, Norbert Wiener, Norman Macrae, packet switching, PageRank, Paul Terrell, pirate software, popular electronics, pre–internet, Project Xanadu, punch-card reader, RAND corporation, Ray Kurzweil, reality distortion field, RFC: Request For Comment, Richard Feynman, Richard Stallman, Robert Metcalfe, Rubik’s Cube, Sand Hill Road, Saturday Night Live, self-driving car, Silicon Valley, Silicon Valley startup, Skype, slashdot, speech recognition, Steve Ballmer, Steve Crocker, Steve Jobs, Steve Wozniak, Steven Levy, Steven Pinker, Stewart Brand, Susan Wojcicki, technological singularity, technoutopianism, Ted Nelson, Teledyne, the Cathedral and the Bazaar, The Coming Technological Singularity, The Nature of the Firm, The Wisdom of Crowds, Turing complete, Turing machine, Turing test, value engineering, Vannevar Bush, Vernor Vinge, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, Whole Earth Review, wikimedia commons, William Shockley: the traitorous eight, Yochai Benkler

“Their skill at manipulating and coaching their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants,” according to Kasparov.22 In other words, the future might belong to people who can best partner and collaborate with computers. In a similar fashion, IBM decided that the best use of Watson, the Jeopardy!-playing computer, would be for it to collaborate with humans rather than try to top them. One project involved using the machine to work in partnership with doctors on cancer treatment plans. “The Jeopardy! challenge pitted man against machine,” said IBM’s Kelly. “With Watson and medicine, man and machine are taking on a challenge together—and going beyond what either could do on its own.”23 The Watson system was fed more than 2 million pages from medical journals and 600,000 pieces of clinical evidence, and could search up to 1.5 million patient records.

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HotWired and Time Inc.’s Pathfinder become first major magazine publishers on Web. 1995 Ward Cunningham’s Wiki Wiki Web goes online. 1997 IBM’s Deep Blue beats Garry Kasparov in chess. 1998 Larry Page and Sergey Brin launch Google. 1999 Ev Williams launches Blogger. 2001 Jimmy Wales, with Larry Sanger, launches Wikipedia. 2011 IBM’s computer Watson wins Jeopardy! INTRODUCTION HOW THIS BOOK CAME TO BE The computer and the Internet are among the most important inventions of our era, but few people know who created them. They were not conjured up in a garret or garage by solo inventors suitable to be singled out on magazine covers or put into a pantheon with Edison, Bell, and Morse.

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But it quickly became an object of consumer desire and teenage obsession. Its plastic case came, iPod-like, in four colors: black, ivory, Mandarin Red, and Cloud Gray. Within a year, 100,000 had been sold, making it one of the most popular new products in history.40 Suddenly everyone in America knew what a transistor was. IBM’s chief Thomas Watson Jr. bought a hundred Regency radios and gave them to his top executives, telling them to get to work using transistors in computers.41 More fundamentally, the transistor radio became the first major example of a defining theme of the digital age: technology making devices personal. The radio was no longer a living-room appliance to be shared; it was a personal device that allowed you to listen to your own music where and when you wanted—even if it was music that your parents wanted to ban.

Architects of Intelligence by Martin Ford

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

You need all these capabilities to participate in a dialogue, unless you tightly constrain what a person says and does; but that makes it very hard for people to actually do what they want to do! MARTIN FORD: What would you point to as being state-of-the-art right now? I was pretty astonished when I saw IBM Watson win at Jeopardy! I thought that was really remarkable. Was that as much of a breakthrough as it seemed to be, or would you point to something else as really being on the leading edge? BARBARA GROSZ: I was impressed by Apple’s Siri and by IBM’s Watson; they were phenomenal achievements of engineering. I think that what is available today with natural language and speech systems is terrific. It’s changing the way that we interact with computer systems, and it’s enabling us to get a lot done.

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Another researcher later showed that newborns could do the same thing. MARTIN FORD: I know that you have a great interest in IBM’s Watson, and that it drew you back into the field of AI. Could you talk about why Watson reignited your interest in artificial intelligence? GARY MARCUS: I was skeptical about Watson, so I was surprised when it first won at Jeopardy in 2011. As a scientist, I’ve trained myself to pay attention to the things that I get wrong, and I thought natural language understanding was too hard for a contemporary AI to do. Watson should not be able to beat a human in Jeopardy, and yet it did. That made me start thinking about AI again I eventually figured out that the reason Watson won is because it was actually a narrower AI problem than it first appeared to be.

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DAVID FERRUCCI I don’t think, as other people might, that we don’t know how to do [AGI] and we’re waiting for some enormous breakthrough. I don’t think that’s the case, I think we do know how to do it, we just need to prove that. FOUNDER, ELEMENTAL COGNITION DIRECTOR OF APPLIED AI, BRIDGEWATER ASSOCIATES David Ferrucci built and led the IBM Watson team from its inception to its landmark success in 2011 when Watson defeated the greatest Jeopardy! players of all time. In 2015 he founded his own company, Elemental Cognition, focused on creating novel AI systems that dramatically accelerate a computer’s ability to understand language. MARTIN FORD: How did you become interested in computers?

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

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

She raised enough money, changed the company’s name from My Cybertwin to Cognea, and set up shop in both Silicon Valley and New York. In the spring of 2014, she sold her company to IBM. The giant computer firm followed its 1997 victory in chess over Garry Kasparov with a comparable publicity stunt in which one of its robots competed against two of the best human players of the TV quiz show Jeopardy! In 2011, the IBM Watson system triumphed over Brad Rutter and Ken Jennings. Many thought the win was evidence that AI technologies had exceeded human capabilities. The reality, however, was more nuanced. The human contestants could occasionally anticipate the brief window of time in which they could press the button and buzz in before Watson.

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How will Watson be used? The choice faced by IBM and its engineers is remarkable. Watson can serve as an intelligent assistant to any number of professionals, or it can replace them. At the dawn of the field of artificial intelligence IBM backed away from the field. What will the company do in the future? Ken Jennings, the human Jeopardy! champion, saw the writing on the wall: “Just as factory jobs were eliminated in the 20th century by new assembly-line robots, Brad and I were the first knowledge-industry workers put out of work by the new generation of ‘thinking’ machines. ‘Quiz show contestant’ may be the first job made redundant by Watson, but I’m sure it won’t be the last.”23 7|TO THE RESCUE The robot laboratory was ghostly quiet on a weekend afternoon in the fall of 2013.

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Artificial intelligence researchers like to point out that aircraft can fly just fine without resorting to flapping their wings—an argument that asserts that to duplicate human cognition or behavior, it is not necessary to comprehend it. However, the chasm between AI and IA has only deepened as AI systems have become increasingly facile at human tasks, whether it is seeing, speaking, moving boxes, or playing chess, Jeopardy!, or Atari video games. Terry Winograd was one of the first to see the two extremes clearly and to consider the consequences. His career traces an arc from artificial intelligence to intelligence augmentation. As a graduate student at MIT in the 1960s, he focused on understanding human language in order to build a software equivalent to Shakey—a software robot capable of interacting with humans in conversation.

Superminds: The Surprising Power of People and Computers Thinking Together by Thomas W. Malone

Abraham Maslow, agricultural Revolution, Airbnb, Albert Einstein, Alvin Toffler, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, Asperger Syndrome, Baxter: Rethink Robotics, bitcoin, blockchain, Boeing 747, business process, call centre, carbon tax, clean water, Computing Machinery and Intelligence, creative destruction, crowdsourcing, data science, deep learning, Donald Trump, Douglas Engelbart, Douglas Engelbart, driverless car, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, experimental economics, Exxon Valdez, Ford Model T, future of work, Future Shock, Galaxy Zoo, Garrett Hardin, gig economy, happiness index / gross national happiness, independent contractor, industrial robot, Internet of things, invention of the telegraph, inventory management, invisible hand, Jeff Rulifson, jimmy wales, job automation, John Markoff, Joi Ito, Joseph Schumpeter, Kenneth Arrow, knowledge worker, longitudinal study, Lyft, machine translation, Marshall McLuhan, Nick Bostrom, Occupy movement, Pareto efficiency, pattern recognition, prediction markets, price mechanism, radical decentralization, Ray Kurzweil, Rodney Brooks, Ronald Coase, search costs, Second Machine Age, self-driving car, Silicon Valley, slashdot, social intelligence, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, technological singularity, The Nature of the Firm, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Tim Cook: Apple, Tragedy of the Commons, transaction costs, Travis Kalanick, Uber for X, uber lyft, Vernor Vinge, Vilfredo Pareto, Watson beat the top human players on Jeopardy!

In Minsky’s view, a society of mind emerges from the interactions of many smaller “agents,” none of which is very intelligent as an individual but all of which, together, create an overall system that is intelligent.22 A hint of what this might look like comes from IBM’s Watson system. When Watson plays Jeopardy, the system makes use of thousands of smaller agents, many of which work in parallel on different processors.23 Each of these agents is more complex than a single human neuron, but none of them alone is nearly smart enough to be a competitive Jeopardy player. For instance, one of the questions Watson answered was “President under whom the U.S. gave full recognition to Communist China.” To answer this question, some of Watson’s agents went to work proposing the names of US presidents as possible answers.

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So the combination of people and computers together can provide better service than either could alone. In a very different industry, the WatsonPaths software that IBM is developing in collaboration with Cleveland Clinic4 is also an automated assistant. Building on the same basic technology used in the version of Watson that beat the human champions of the TV game show Jeopardy!, WatsonPaths uses knowledge it has culled from the medical literature to identify multiple possible diagnoses that are consistent with a patient’s symptoms and medical history. Then, like the Stitch Fix software, the Watson system shows a number of plausible diagnoses to the doctors. It also shows the doctors the chain of reasoning it used and the degree of confidence it has for each of these different diagnoses.

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But the clear statistical results in this project point the way toward a very intriguing possible future for truth-finding democracies. Perhaps instead of traditional democracies, we should have more sophisticated democracies where carefully designed algorithms combine the opinions of group members to produce much more accurate results. In fact, this is surprisingly similar to the basic architecture that IBM’s Watson system uses. As we saw in chapter 4, Watson includes many different computational agents, each with a different kind of expertise, producing evidence for or against different possible answers. Over time, machine-learning algorithms built into the system refine the weights they give to these “opinions” from the different agents.

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

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

And the result of this relatively minor convergence—big data sets meeting cheap, potent GPUs—sparked one of the fastest invasions in history, with artificial intelligence starting to encroach on every facet of our lives. Machine learning emerged first, using algorithms to analyze data, learn from it, then make predictions about the world. This is Netflix and Spotify suggesting movies and music, but it’s also IBM’s Watson serving as a wealth manager. Next, neural networks came online. Inspired by the biology of the human brain, these nets are capable of unsupervised learning from unstructured data. You no longer need to feed AI information one piece at a time. With neural nets, simply unleash them on the internet and the system will do the rest.

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If they’re an early adopter, the AI alerts the sales agent to offer them the latest and greatest. If they’re more conservative, then it suggests items more tried-and-true. Second, there are companies like New Zealand’s Soul Machines working to replace human customer service agents altogether. Powered by IBM’s Watson, Soul Machines builds lifelike customer service avatars designed for empathy, making them one of many helping to pioneer the field of emotionally intelligent computing. We’ll explore this in depth a little later, but what’s critical here is a single stat: 40 percent. With their technology, 40 percent of all customer service interactions are now resolved with a high degree of satisfaction and without any human intervention.

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In June of 2016, the extremely eerie short film Sunspring was released online, the end result of a neural net–powered AI being fed hundreds of sci-fi film scripts and allowed to take a crack at writing one of its own. Two months later, Twentieth Century Fox debuted the trailer for the upcoming thriller Morgan, also created with the help of an AI—this time, IBM’s Watson. To pull this off, Watson “watched” trailers for a hundred horror movies, then conducted visual, audio, and composition analysis to understand what humans deemed scary. By applying this same kind of analysis to Morgan, the AI identified the film’s critical moments. Although a human was needed to arrange those moments into a coherent order, Watson reduced the amount of time it takes to make a trailer from ten days to one.

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

3D printing, additive manufacturing, affirmative action, agricultural Revolution, Airbnb, airport security, algorithmic bias, AlphaGo, Amazon Robotics, Andrew Keen, Apollo Guidance Computer, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, Big Tech, bitcoin, Bletchley Park, blockchain, Boeing 747, brain emulation, Brexit referendum, British Empire, business process, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Cass Sunstein, cellular automata, Citizen Lab, cloud computing, commons-based peer production, computer age, computer vision, continuation of politics by other means, correlation does not imply causation, CRISPR, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, digital divide, digital map, disinformation, distributed ledger, Donald Trump, driverless car, easy for humans, difficult for computers, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, Erik Brynjolfsson, Ethereum, ethereum blockchain, Evgeny Morozov, fake news, Filter Bubble, future of work, Future Shock, Gabriella Coleman, Google bus, Google X / Alphabet X, Googley, industrial robot, informal economy, intangible asset, Internet of things, invention of the printing press, invention of writing, Isaac Newton, Jaron Lanier, John Markoff, Joseph Schumpeter, Kevin Kelly, knowledge economy, Large Hadron Collider, Lewis Mumford, lifelogging, machine translation, Metcalfe’s law, mittelstand, more computing power than Apollo, move fast and break things, natural language processing, Neil Armstrong, Network effects, new economy, Nick Bostrom, night-watchman state, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, payday loans, Philippa Foot, post-truth, power law, price discrimination, price mechanism, RAND corporation, ransomware, Ray Kurzweil, Richard Stallman, ride hailing / ride sharing, road to serfdom, Robert Mercer, Satoshi Nakamoto, Second Machine Age, selection bias, self-driving car, sexual politics, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart contracts, Snapchat, speech recognition, Steve Bannon, Steve Jobs, Steve Wozniak, Steven Levy, tech bro, technological determinism, technological singularity, technological solutionism, the built environment, the Cathedral and the Bazaar, The Structural Transformation of the Public Sphere, The Wisdom of Crowds, Thomas L Friedman, Tragedy of the Commons, trolley problem, universal basic income, urban planning, Watson beat the top human players on Jeopardy!, work culture , working-age population, Yochai Benkler

‘I . . . was able to get one single win,’ said Lee Sedol rather poignantly; ‘I wouldn’t exchange it for anything in the world.’16 OUP CORRECTED PROOF – FINAL, 26/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS 32 FUTURE POLITICS A year later, a version of AlphaGo called AlphaGo Master thrashed Ke Jie, the world’s finest human player, in a 3–0 clean sweep.17 A radically more powerful version now exists, called AlphaGo Zero. AlphaGo Zero beat AlphaGo Master 100 times in a row.18 As long ago as 2011, IBM’s Watson vanquished the two all-time greatest human champions at Jeopardy!—a TV game show in which the moderator presents general knowledge ‘answers’ relating to sports, science, pop culture, history, art, literature, and other fields and the contestants are required to provide the ‘questions’. Jeopardy! demands deep and wide-ranging knowledge, the ability to process natural language (including wordplay), retrieve relevant information, and answer using an acceptable form of speech—all before the other contestants do the same.19 The human champions were no match for Watson, whose victory marked a milestone in the development of artificial intelligence.

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This was a system that could answer questions ‘on any topic under the sun . . . more accurately and quickly than the best human beings’.20 The version of Watson used on Jeopardy! was said to be the size of a bedroom; by the early 2020s it’s expected that the same technology, vastly improved, will sit comfortably in a smartphone-sized computer.21 Today, what IBM calls ‘Watson’ no longer exists in a single physical space but is distributed across a cloud of servers that can be accessed by commercial customers on their computers and smartphones.22 As IBM is keen to stress, different versions of Watson do more than win game shows. In late 2016, one Watson platform discovered five genes linked to amyotrophic lateral sclerosis (ALS), a degenerative disease that can lead to paralysis and death.

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OUP CORRECTED PROOF – FINAL, 30/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS Notes 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 373 Susskind and Susskind, Future of the Professions, 165. Ibid. Ibid. Kevin Kelly, The Inevitable: Understanding the 12 Technological Forces that Will Shape Our Future (New York:Viking, 2016), 31. Emma Hinchliffe, ‘IBM’s Watson Supercomputer Discovers 5 New Genes Linked to ALS’, Mashable UK, 14 December 2016 <http:// mashable.com/2016/12/14/ibm-watson-als-research/?utm_ cid=mash-com-Tw-tech-link%23sd613jsnjlqd#HJziN5r0aGq5> (accessed 28 November 2017). Murray Shanahan, The Technological Singularity (Cambridge, Mass: MIT Press, 2015), 12. BBC, ‘Google Working on “Common-Sense” AI Engine at New Zurich Base’, BBC News, 17 June 2016 <http://www.bbc.co.uk/news/­ technology-36558829> (accessed 30 November 2017); Blue Brain Project <https://bluebrain.epfl.ch/page-56882-en.html> (accessed 6 December 2017).

The Runaway Species: How Human Creativity Remakes the World by David Eagleman, Anthony Brandt

active measures, Ada Lovelace, agricultural Revolution, Albert Einstein, Andrew Wiles, Apollo 13, Burning Man, cloud computing, computer age, creative destruction, crowdsourcing, Dava Sobel, deep learning, delayed gratification, Donald Trump, Douglas Hofstadter, en.wikipedia.org, Frank Gehry, Gene Kranz, Google Glasses, Great Leap Forward, haute couture, informal economy, interchangeable parts, Isaac Newton, James Dyson, John Harrison: Longitude, John Markoff, Large Hadron Collider, lone genius, longitudinal study, Menlo Park, microbiome, Netflix Prize, new economy, New Journalism, pets.com, pneumatic tube, QWERTY keyboard, Ray Kurzweil, reversible computing, Richard Feynman, risk tolerance, Scaled Composites, self-driving car, Simon Singh, skeuomorphism, Solyndra, SpaceShipOne, stem cell, Stephen Hawking, Steve Jobs, Stewart Brand, synthetic biology, TED Talk, the scientific method, Watson beat the top human players on Jeopardy!, wikimedia commons, X Prize

. © 2016 Estate of Pablo Picasso / Artists Rights Society (ARS), New York Max Kulich’s sketches for the Audi CitySmoother Courtesy of Max Kulich The Architectural Reseasrch Office’s sketches for the Flea Theater in New York Courtesy of Architectural Research Office Joshua Davis’ skethes for IBM Watson Courtesy of Joshua Davis IBM Watson on the Jeopardy set Courtesy of Sony Pictures Television Advent, Thunderbird, Starchaser, Ascender, and Proteus Courtesy of the Ansari X-Prize Scaled Composite’s SpaceShipOne Courtesy of the Ansari X-Prize Chapter 9 Einstein blouses https://www.google.com/patents/USD101756 Sarah Burton: Kate Middleton wedding dress Photo by Kirsty Wigglesworth – WPA Pool/Getty Images Sarah Burton: three dresses from the Autumn/Winter 2011-12 Alexander McQueen ready-to-ware collection Photo by Francois Guillot, AFP, Getty Images Norman Bel Geddes: Motor Coach no. 2, Roadable Airplane, Aerial Restaurant and Walless House Courtesy of the Harry Ransom Center, the University of Texas at Austin © The Edith Lutyens and Norman Bel Geddes Foundation, Inc.

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In a book review titled “The Descent of Edward Wilson,” Richard Dawkins, one of Wilson’s most prestigious peers, was unsparing in his criticism: “I’m reminded of the old Punch cartoon where a mother beams down on a military parade and proudly exclaims, ‘There’s my boy, he’s the only one in step.’ Is Wilson the only evolutionary biologist in step?”10 But being out of step with his colleagues didn’t bother Wilson. Others were amazed that such a venerated figure, the winner of two Pulitzer Prizes, would put his standing in the field in jeopardy. But Wilson, a practiced innovator, was not afraid to radically change his views to match where the science led him – even if it meant overturning his own legacy. The jury is still out on the veracity of Wilson’s proposal (it may turn out to be incorrect), but, right or wrong, there are no pieces that he considers glued into place. *** Humankind constantly renews itself by breaking good: rotary phones turn into push button phones, which turn into brick-like cellphones, then flip-phones, then smartphones.

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

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

Similarly, no doctor can be familiar with every illness and drug, or read every new article published in every medical journal. To top it all, the doctor is sometimes tired or hungry or perhaps even sick, which affects her judgement. No wonder that doctors often err in their diagnoses, or recommend a less-than-optimal treatment. Now consider IBM’s famous Watson – an artificial intelligence system that won the Jeopardy! television game show in 2011, beating human former champions. Watson is currently groomed to do more serious work, particularly in diagnosing diseases. An AI such as Watson has enormous potential advantages over human doctors. Firstly, an AI can hold in its databanks information about every known illness and medicine in history.

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Firstly, an AI can hold in its databanks information about every known illness and medicine in history. It can then update these databanks every day, not only with the findings of new researches, but also with medical statistics gathered from every clinic and hospital in the world. IBM’s Watson defeating its two humans opponents in Jeopardy! in 2011. © Sony Pictures Television. Secondly, Watson can be intimately familiar not only with my entire genome and my day-to-day medical history, but also with the genomes and medical histories of my parents, siblings, cousins, neighbours and friends. Watson will know instantly whether I visited a tropical country recently, whether I have recurring stomach infections, whether there have been cases of intestinal cancer in my family or whether people all over town are complaining this morning about diarrhoea.

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Munoz-Organero, ‘Enhancement of Student Learning Through the Use of a Hinting Computer e-Learning System and Comparison With Human Teachers’, IEEE Transactions on Education 54:1 (2011), 164–7; Mindojo, accessed 14 July 2015, http://mindojo.com/. 8. Steiner, Automate This, 146–62; Ian Steadman, ‘IBM’s Watson Is Better at Diagnosing Cancer than Human Doctors’, Wired, 11 February 2013, accessed 22 December 2014, http://www.wired.co.uk/news/archive/2013-02/11/ibm-watson-medical-doctor; ‘Watson Is Helping Doctors Fight Cancer’, IBM, accessed 22 December 2014, http://www-03.ibm.com/innovation/us/watson/watson_in_healthcare.shtml; Vinod Khosla, ‘Technology Will Replace 80 per cent of What Doctors Do’, Fortune, 4 December 2012, accessed 22 December 2014, http://tech.fortune.cnn.com/2012/12/04/technology-doctors-khosla; Ezra Klein, ‘How Robots Will Replace Doctors’, Washington Post, 10 January 2011, accessed 22 December 2014, http://www.washingtonpost.com/blogs/wonkblog/post/how-robots-will-replace-doctors/2011/08/25/gIQASA17AL_blog.html. 9.

Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins by Garry Kasparov

3D printing, Ada Lovelace, AI winter, Albert Einstein, AlphaGo, AltaVista, Apple Newton, barriers to entry, Berlin Wall, Bletchley Park, business process, call centre, Charles Babbage, Charles Lindbergh, clean water, computer age, cotton gin, Daniel Kahneman / Amos Tversky, David Brooks, DeepMind, Donald Trump, Douglas Hofstadter, driverless car, Drosophila, Elon Musk, Erik Brynjolfsson, factory automation, Freestyle chess, gamification, Gödel, Escher, Bach, Hans Moravec, job automation, Ken Thompson, Leonard Kleinrock, low earth orbit, machine translation, Max Levchin, Mikhail Gorbachev, move 37, Nate Silver, Nick Bostrom, Norbert Wiener, packet switching, pattern recognition, Ray Kurzweil, Richard Feynman, rising living standards, rolodex, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, Skype, speech recognition, stem cell, Stephen Hawking, Steven Pinker, technological singularity, The Coming Technological Singularity, The Signal and the Noise by Nate Silver, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, zero-sum game

In 2014, I got an interesting response to this assertion. I had been invited to speak at the headquarters of the world’s largest hedge fund in Connecticut, Bridgewater Associates. In a revealing turn of events, they had hired Dave Ferrucci, one of the creators of the IBM artificial intelligence project Watson, famous for its triumphs on the American television quiz show Jeopardy. Ferrucci sounded disillusioned by IBM’s focus on a data-driven approach to AI, and how it wanted to exploit the impressive Watson and its sudden celebrity by turning it into a commercial product as quickly as possible. He had been working on more sophisticated “paths” that aimed at explaining the “why” of things, not only finding useful correlations via data mining.

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As with a chess engine crunching through billions of positions to find the best move, language can be broken down into values and probabilities to produce a response. The faster the machine, the more and better quality the data, and the smarter the code, the more accurate the response is likely be. Adding a bit of irony regarding whether or not computers can ask questions, the format of the television game show Jeopardy, where Watson showed off its capabilities by defeating two human former champions, requires contestants to provide their answers in the form of a question. That is, if the show’s host says, “This Soviet program won the first World Computer Chess Championship in 1974,” the player would press the buzzer and answer, “What was Kaissa?”

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These investigations led to visits to places like Google, Facebook, and Palantir, companies for whom algorithms are lifeblood. There have also been some more surprising invitations, including one from the headquarters of the world’s largest hedge fund, where algorithms make or lose billions of dollars every day. There I met one of the creators of Watson, the Jeopardy-playing computer that could be called IBM’s successor to Deep Blue. Another trip was to participate in a debate in front of an executive banking audience in Australia on what impact AI was likely to have on jobs in their industry. Their interests are quite different, but they all want to be on the cutting edge of the machine intelligence revolution, or at least to not be cut by it.

Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard

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

The Internet was the popular thing technologically, then mobile devices, and those were the focus of our collective imagination. In the middle of the 2010s, however, people started talking about machine learning. Suddenly, AI was on fire again. AI startups were founded and acquired. IBM’s Watson beat a human player at Jeopardy!; an algorithm outfoxed a human player at playing Go. Even the words machine learning were cool. A machine could learn! The promise was delivered! At first, I wanted to believe that some genius had figured out the truly hard problem of making a machine think—but when I looked closer, it turned out that the reality was far more nuanced.

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He looked confused and a little disappointed. “So, it’s not real AI?” he asked. “Oh, it’s real,” I said. “And it’s spectacular. But you know, don’t you, that there’s no simulated person inside the machine? Nothing like that exists. It’s computationally impossible.” His face fell. “I thought that’s what AI meant,” he said. “I heard about IBM Watson, and the computer that beat the champion at Go, and self-driving cars. I thought they invented real AI.” He looked depressed. I realized he’d been looking at the laptop because he thought there was something in there—a “real” ghost in the machine. I felt terrible for having burst his bubble, so I steered the conversation toward a neutral topic—an upcoming Star Wars movie—to cheer him up.

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There’s also open innovation, in which people from outside a company develop new tools or products for a variety of altruistic or self-interested reasons.2 Then there’s the innovation competition, in which a company announces a challenge and offers a bounty for the best product or solution. The iconic example of this is the DARPA Grand Challenge, the robot car race in which the winner was offered $2 million, and which I wrote about in chapter 8. Incidentally, $1 million is the bounty offered on the game show Survivor, which has nothing to do with tech and very little to do with innovation, but which requires surviving elimination challenges on a tropical island with very little food or water, among a group of scheming strangers. For thirty-nine days. On camera. Would the Startup Bus be like Survivor, but with computers and on a moving bus?

The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future by Kevin Kelly

A Declaration of the Independence of Cyberspace, Aaron Swartz, AI winter, Airbnb, Albert Einstein, Alvin Toffler, Amazon Web Services, augmented reality, bank run, barriers to entry, Baxter: Rethink Robotics, bitcoin, blockchain, book scanning, Brewster Kahle, Burning Man, cloud computing, commoditize, computer age, Computer Lib, connected car, crowdsourcing, dark matter, data science, deep learning, DeepMind, dematerialisation, Downton Abbey, driverless car, Edward Snowden, Elon Musk, Filter Bubble, Freestyle chess, Gabriella Coleman, game design, Geoffrey Hinton, Google Glasses, hive mind, Howard Rheingold, index card, indoor plumbing, industrial robot, Internet Archive, Internet of things, invention of movable type, invisible hand, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Perry Barlow, Kevin Kelly, Kickstarter, lifelogging, linked data, Lyft, M-Pesa, machine readable, machine translation, Marc Andreessen, Marshall McLuhan, Mary Meeker, means of production, megacity, Minecraft, Mitch Kapor, multi-sided market, natural language processing, Netflix Prize, Network effects, new economy, Nicholas Carr, off-the-grid, old-boy network, peer-to-peer, peer-to-peer lending, personalized medicine, placebo effect, planetary scale, postindustrial economy, Project Xanadu, recommendation engine, RFID, ride hailing / ride sharing, robo advisor, Rodney Brooks, self-driving car, sharing economy, Silicon Valley, slashdot, Snapchat, social graph, social web, software is eating the world, speech recognition, Stephen Hawking, Steven Levy, Ted Nelson, TED Talk, The future is already here, the long tail, the scientific method, transport as a service, two-sided market, Uber for X, uber lyft, value engineering, Watson beat the top human players on Jeopardy!, WeWork, Whole Earth Review, Yochai Benkler, yottabyte, zero-sum game

Like many parents of a bright mind, IBM would like Watson to pursue a medical career, so it should come as no surprise that the primary application under development is a medical diagnosis tool. Most of the previous attempts to make a diagnostic AI have been pathetic failures, but Watson really works. When, in plain English, I give it the symptoms of a disease I once contracted in India, it gives me a list of hunches, ranked from most to least probable. The most likely cause, it declares, is giardia—the correct answer. This expertise isn’t yet available to patients directly; IBM provides Watson’s medical intelligence to partners like CVS, the retail pharmacy chain, helping it develop personalized health advice for customers with chronic diseases based on the data CVS collects.

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He was able to mathematically optimize results from each layer so that the learning accumulated faster as it proceeded up the stack of layers. Deep-learning algorithms accelerated enormously a few years later when they were ported to GPUs. The code of deep learning alone is insufficient to generate complex logical thinking, but it is an essential component of all current AIs, including IBM’s Watson; DeepMind, Google’s search engine; and Facebook’s algorithms. This perfect storm of cheap parallel computation, bigger data, and deeper algorithms generated the 60-years-in-the-making overnight success of AI. And this convergence suggests that as long as these technological trends continue—and there’s no reason to think they won’t—AI will keep improving.

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Siri, the audio phone assistant for the iPhone, delivers spoken answers when you ask her a question in natural English. I use Siri routinely. When I want to know the weather, I just ask, “Siri, what’s the weather for tomorrow?” Android folks can audibly ask Google Now for information about their calendars. IBM’s Watson proved that for most kinds of factual reference questions, an AI can find answers fast and accurately. Part of the increasing ease in providing answers lies in the fact that past questions answered correctly increase the likelihood of another question. At the same time, past correct answers increase the ease of creating the next answer, and increase the value of the corpus of answers as a whole.

The Decline and Fall of IBM: End of an American Icon? by Robert X. Cringely

AltaVista, Bernie Madoff, business cycle, business process, Carl Icahn, cloud computing, commoditize, compound rate of return, corporate raider, financial engineering, full employment, Great Leap Forward, if you build it, they will come, immigration reform, interchangeable parts, invention of the telephone, Khan Academy, knowledge worker, low skilled workers, managed futures, Paul Graham, platform as a service, race to the bottom, remote working, Robert Metcalfe, Robert X Cringely, shareholder value, Silicon Valley, six sigma, software as a service, Steve Jobs, stock buybacks, tech worker, TED Talk, Toyota Production System, Watson beat the top human players on Jeopardy!, web application, work culture

After years of corporate downsizing, employee morale is at an all-time low. Bonuses and even annual raises are rare. But for all that, IBM is still an enormous multinational corporation with high profits, deep pockets, and grand ambitions for new technical initiatives in cloud computing, Big Data analytics, and artificial intelligence as embodied in the company’s Jeopardy game-show-winning Watson technology. Yet for all this, IBM seems to have lost some of its mojo, or at least that’s what Wall Street and the business analysts are starting to think. Just starting to think? The truth is that IBM is in deep trouble and has been since before the Great Recession of 2008.

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My father was a natural scrounger, and using all the cool crap he dragged home from who knows where, I decided to base my voice control work on the amplitude modulation optical sound track technology from 16mm film (we had a projector). If I could paint optical tracks to represent commands then all I’d need was some way of characterizing and analyzing those tracks to tell the computer what to do. But the one thing I didn’t have down in the lab in 1961 was a computer. That’s what took me to IBM. I wrote a letter to IBM CEO T.J. Watson, Jr., pecking it out on an old Underwood manual typewriter. My proposal was simple: a 50/50 partnership between IBM and me to develop and exploit advanced user interface technologies. In a few days I received a letter from IBM. I don’t know if it was from Watson, himself, because neither my parents nor I thought to keep the letter.

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We can all hope their assistance will not have been in vain. CHAPTER ONE Good Old IBM Few of us actually live in the present. Our minds are often in the recent past where judgments are formed and go for long periods of time unchallenged. That’s why the IBM nearly everyone thinks of is the IBM of the Watsons, father and son. That IBM—of the 1960s and 1970s—was less a company than it was a country. In some ways it still is. IBM has a greater gross national product than most countries. It has some 430,000 workers. Throw in spouses and their kids and we’re looking at well over a million citizens of IBM.

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

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

And the running joke among AI experts is that if Stanley Kubrick had made his film 2001: A Space Odyssey today, then the conversation between Dave and the malevolent supercomputer HAL-9000 might have gone like this: Dave Open the pod bay doors, HAL. HAL I’ve searched the web and found some results for iPods, Dave. Would you like to see them? Machines make subtler errors, too. IBM’s Watson supercomputer was once put through a rhyming test ahead of his big match against human contestants on Jeopardy! One test clue was “a boxing term for a hit below the belt.” The right rhyming response was “low blow”—but Watson answered “wang bang,” a phrase that did not appear in his database and that he must have conceived on his own. So go ahead, pile on the insults.

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Or of staring into the camera to allow an algorithm in the cloud to scan your eyes for symptoms of eye disease. Now think of putting those algorithms together with something like Dr. Alexa: a digital assistant trained on vast troves of medical knowledge that’s been programmed to ask questions about your symptoms and respond appropriately. (IBM’s Watson team has already developed something very much like this for the purpose of training medical students.51) A new generation of wearable sensors could boost the effectiveness of AI-based remote medicine even further. If you think your Fitbit is cool, wait until the first person in your office gets a biometric e-tattoo: a small wearable patch, with the same thickness and elasticity as human skin, that can send health data wirelessly to your phone.

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See also Lee Bell, “Nvidia to Train 100,000 Developers in ‘Deep Learning’ AI to Bolster Healthcare Research,” Forbes.com, May 11, 2017, https://www.forbes.com/sites/leebelltech/2017/05/11/nvidia-to-train-100000-developers-in-deep-learning-ai-to-bolster-health-care-research/. 50.  See, e.g., Tom Simonite, “The Recipe for the Perfect Robot Surgeon,” MIT Technology Review, October 14, 2016, https://www.technologyreview.com/s/602595/the-recipe-for-the-perfect-robot-surgeon/. 51.  David Szondy, “IBM’s Watson Adapted to Teach Medical Students and Aid Diagnosis,” New Atlas, October 21, 2013, http://newatlas.com/ibm-supercomputer-watsonpath/29415/. CHAPTER 7   1.  Chris Anderson, “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete,” Wired, June 23, 2008, https://www.wired.com/2008/06/pb-theory/.   2.  

The Secret War Between Downloading and Uploading: Tales of the Computer as Culture Machine by Peter Lunenfeld

Albert Einstein, Andrew Keen, anti-globalists, Apple II, Berlin Wall, British Empire, Brownian motion, Buckminster Fuller, Burning Man, business cycle, business logic, butterfly effect, Charles Babbage, computer age, Computing Machinery and Intelligence, creative destruction, crowdsourcing, cuban missile crisis, Dissolution of the Soviet Union, don't be evil, Douglas Engelbart, Douglas Engelbart, Dynabook, East Village, Edward Lorenz: Chaos theory, Fairchild Semiconductor, Fall of the Berlin Wall, folksonomy, Francis Fukuyama: the end of history, Frank Gehry, Free Software Foundation, Grace Hopper, gravity well, Guggenheim Bilbao, Herman Kahn, Honoré de Balzac, Howard Rheingold, Ian Bogost, invention of movable type, Isaac Newton, Ivan Sutherland, Jacquard loom, Jane Jacobs, Jeff Bezos, John Markoff, John von Neumann, Jon Ronson, Kickstarter, Mark Zuckerberg, Marshall McLuhan, Mercator projection, Metcalfe’s law, Mother of all demos, mutually assured destruction, Neal Stephenson, Nelson Mandela, Network effects, new economy, Norbert Wiener, PageRank, pattern recognition, peer-to-peer, planetary scale, plutocrats, post-materialism, Potemkin village, RFID, Richard Feynman, Richard Stallman, Robert Metcalfe, Robert X Cringely, Schrödinger's Cat, Search for Extraterrestrial Intelligence, seminal paper, SETI@home, Silicon Valley, Skype, social bookmarking, social software, spaced repetition, Steve Ballmer, Steve Jobs, Steve Wozniak, technological determinism, Ted Nelson, the built environment, the Cathedral and the Bazaar, The Death and Life of Great American Cities, the medium is the message, Thomas L Friedman, Turing machine, Turing test, urban planning, urban renewal, Vannevar Bush, walkable city, Watson beat the top human players on Jeopardy!, William Shockley: the traitorous eight

By 1915, a young salesman named Thomas J. Watson had risen from a regional office to take over the company, then named the Computer Tabulating Recording Corporation. Within a decade, he had changed the name of the company to the International Business Machines Corporation, and encouraged the use of its acronym, IBM. Watson Sr.’s famous motto was “Think,” but the driving ethos of the company was “Sell.” In an era when the art of selling was 153 GENERATIONS revered as being next to godliness, Watson was known as the world’s greatest salesman (as well as being one of the world’s richest people).12 Watson Sr. understood that IBM had to keep its eye on computers and help to shape their future.

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Should they invest in digital computing, or would this undercut the profits on their flagship mechanical calculators and paper card tabulating machines? Watson Sr.’s contribution was essentially to hand over the company to his son at the very moment when this decision became central to IBM’s fortunes. In so doing, Watson Sr. ensured that the computer would make it out of the laboratory and into businesses worldwide, with the huge infrastructure of sales and support that IBM had already built up during the prewar years and the postwar boom. It was to Thomas J. Watson Jr.’s credit that he negotiated the smooth transition between the two regimes at IBM.

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But setting the bar too high is preferable to not setting the bar at all. Fifty years ago, the categorizing of meaning was considered to be one of—if not the—chief calling of the critic. The advent of critical theories like poststructuralism, deconstruction, and postmodernism put many of the classic categories 29 CHAPTER 2 in jeopardy: building a canon around the good and the beautiful was “problematized,” high and low ceased to function as viable categories for culture, and progress and truth were discussed as creations of power struggles rather than immutables in the human condition. Theory with a capital T practiced a brilliant negative dialectics, but did not always replace the overthrown concepts with new, more congenial ones.

Virtual Competition by Ariel Ezrachi, Maurice E. Stucke

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

See also Robert McMillan, “IBM Turns Up Heat Under Competition in Artificial Intelligence,” Wall Street Journal, November 24, 2015, http://www.wsj.com /articles/ibm-turns-up-heat-under-competition-in-artificial-intelligence -1448362800. Jo Best, Jo. “IBM Watson: The Inside Story of How the Jeopardy-Winning Supercomputer Was Born, and What It Wants to Do Next,” TechRepublic (2013), http://www.techrepublic.com/article/ibm-watson-the-inside-story-of -how-the-jeopardy-winning-supercomputer-was-born-and-what-it-wants-to -do-next/. Steve Lohr, “IBM’s AI System Watson to Get Second Home, on West Coast,” New York Times, September 24, 2015, http://www.nytimes.com/2015/09/25 Notes to Page 16 41. 42. 43. 44. 45. 46. 47. 259 /technology/ibms-ai-system-watson-to-get-new-west-coast-home.html ?smprod=nytcore-iphone&smid=nytcore-iphone-share& _r = 0.

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As the European Data Protection Supervisor observed, “Deep learning computers teach themselves tasks by crunching large data sets using (among other things) neural networks that appear to emulate the brain.”46 The algorithms’ capacity to learn increases as they process more relevant data.47 The belief is that simple algorithms with lots of data will eventually outperform sophisticated algorithms with little data.48 Part of this is due to the opportunity for algorithms to learn through trial and error. Another is seeing correlations from big data sets. Thus one thing IBM’s Watson and artificial intelligence (AI) generally need in order to “do meaningful work” is data.49 That is why IBM acquired the digital and data assets of Weather Co., owner of the Weather Channel. Watson could analyze the volume of weather data to refine its algorithms.50 Watson’s ser vices, in turn, can be sold to other parties, like insurance apps.

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Tereza Pultarova, “Jaguar Land Rover to Lead Driverless Car Research,” E&T (October 9, 2015), http://eandt.theiet.org /news/2015/oct /jaguar-land -rover-driverless-cars.cfm; David Talbot, “CES 2015: Nvidia Demos a Car Computer Trained with ‘Deep Learning,’ ” MIT Technology Review, January 6, 2015), http://www.technologyreview.com /news/533936/ces-2015 -nvidia-demos-a-car-computer-trained-with-deep-learning /; David Levitin, 2015. “The Sum of Human Knowledge,” Wall Street Journal, September 18, 2015, http://www.wsj.com /articles/the-sum-of-human -knowledge-1442610803. Lohr, “IBM’s AI System Watson to Get Second Home.” European Data Protection Supervisor, Towards a New Digital Ethics. Take, for example, the Rubicon Project, “a leading technology company automating the buying and selling of advertising.” As its website notes, “Relentless in its efforts for innovation, Rubicon Project has engineered one of the largest real-time cloud and Big Data computing systems, processing trillions of transactions within milliseconds each month”; Rubicon Project. (2016), http://rubiconproject.com/whoweare/.

Futureproof: 9 Rules for Humans in the Age of Automation by Kevin Roose

"World Economic Forum" Davos, adjacent possible, Airbnb, Albert Einstein, algorithmic bias, algorithmic management, Alvin Toffler, Amazon Web Services, Atul Gawande, augmented reality, automated trading system, basic income, Bayesian statistics, Big Tech, big-box store, Black Lives Matter, business process, call centre, choice architecture, coronavirus, COVID-19, data science, deep learning, deepfake, DeepMind, disinformation, Elon Musk, Erik Brynjolfsson, factory automation, fake news, fault tolerance, Frederick Winslow Taylor, Freestyle chess, future of work, Future Shock, Geoffrey Hinton, George Floyd, gig economy, Google Hangouts, GPT-3, hiring and firing, hustle culture, hype cycle, income inequality, industrial robot, Jeff Bezos, job automation, John Markoff, Kevin Roose, knowledge worker, Kodak vs Instagram, labor-force participation, lockdown, Lyft, mandatory minimum, Marc Andreessen, Mark Zuckerberg, meta-analysis, Narrative Science, new economy, Norbert Wiener, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, off-the-grid, OpenAI, pattern recognition, planetary scale, plutocrats, Productivity paradox, QAnon, recommendation engine, remote working, risk tolerance, robotic process automation, scientific management, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, social distancing, Steve Jobs, Stuart Kauffman, surveillance capitalism, tech worker, The Future of Employment, The Wealth of Nations by Adam Smith, TikTok, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, warehouse robotics, Watson beat the top human players on Jeopardy!, work culture

In the twentieth century, “process optimization” tools were used to squeeze extra efficiency out of manufacturing workers, and workers in the service industry have decades of experience with “dynamic scheduling” software like Kronos, which is used to set workers’ shifts based on a business’s predicted staffing needs. But AI and machine learning have made it possible to outsource even higher-level management tasks to machines. Amazon uses complex algorithms to track the productivity of its warehouse workers, and can reportedly even automatically generate the paperwork required to fire underperformers. IBM has used Watson, its AI platform, in employee performance reviews—meaning that your bonus might be determined not simply by how you did last year, but by how the algorithm predicts you’ll do next year. On-demand platforms like Uber and Lyft have dispensed with the idea of human supervision altogether, putting decisions like pay, dispatching, and dispute resolution in the hands of algorithms.

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Every weekday, Conor Sprouls Kevin Roose, “A Machine May Not Take Your Job, but One Could Become Your Boss,” New York Times, June 23, 2019. Amazon uses complex algorithms to track the productivity Colin Lecher, “How Amazon Automatically Tracks and Fires Warehouse Workers for ‘Productivity,’ ” The Verge, April 25, 2019. IBM has used Watson, its AI platform Tristan Greene, “IBM Is Using Its AI to Predict How Employees Will Perform,” TheNextWeb, July 10, 2018. Percolata, a Silicon Valley start-up Hazel Sheffield, “The Great Data Leap: How AI Will Transform Recruitment and HR,” Financial Times, November 4, 2019. The company, whose clients include Sweetgreen and OfferUp Daisuke Wakabayashi, “Firm Led by Google Veterans Uses AI to ‘Nudge’ Workers Toward Happiness,” New York Times, December 31, 2018.

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Shane, who expanded AI Weirdness into a book Janelle Shane, You Look Like a Thing and I Love You (New York: Headline, 2019). A trading firm called Knight Capital, for example Nathaniel Popper, “Knight Capital Says Trading Glitch Cost It $440 Million,” New York Times, August 2, 2012. In 2018, internal tests obtained Casey Ross and Ike Swetlitz, “IBM’s Watson Supercomputer Recommended ‘Unsafe and Incorrect’ Cancer Treatments, Internal Documents Show,” Stat, July 25, 2018. decades of systematic overpolicing Rashida Richardson, Jason M. Schultz, and Kate Crawford, “Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice,” New York University Law Review, Online Feature (2019).

The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity by Byron Reese

"World Economic Forum" Davos, agricultural Revolution, AI winter, Apollo 11, artificial general intelligence, basic income, bread and circuses, Buckminster Fuller, business cycle, business process, Charles Babbage, Claude Shannon: information theory, clean water, cognitive bias, computer age, CRISPR, crowdsourcing, dark matter, DeepMind, Edward Jenner, Elon Musk, Eratosthenes, estate planning, financial independence, first square of the chessboard, first square of the chessboard / second half of the chessboard, flying shuttle, full employment, Hans Moravec, Hans Rosling, income inequality, invention of agriculture, invention of movable type, invention of the printing press, invention of writing, Isaac Newton, Islamic Golden Age, James Hargreaves, job automation, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, lateral thinking, life extension, Louis Pasteur, low interest rates, low skilled workers, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Mary Lou Jepsen, Moravec's paradox, Nick Bostrom, On the Revolutions of the Heavenly Spheres, OpenAI, pattern recognition, profit motive, quantum entanglement, radical life extension, Ray Kurzweil, recommendation engine, Rodney Brooks, Sam Altman, self-driving car, seminal paper, Silicon Valley, Skype, spinning jenny, Stephen Hawking, Steve Wozniak, Steven Pinker, strong AI, technological singularity, TED Talk, telepresence, telepresence robot, The Future of Employment, the scientific method, Timothy McVeigh, Turing machine, Turing test, universal basic income, Von Neumann architecture, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, women in the workforce, working poor, Works Progress Administration, Y Combinator

The more you increase your mastery of an ever-smaller domain, the easier it is to instantiate that knowledge in a computer program. A hunter-gatherer is much harder to build a computer replacement for than an X-ray technician, because the technician does just one narrow thing. Ken Jennings, who was famously beaten on Jeopardy! by IBM’s Watson, explains that during that whole experience, the folks at IBM maintained a line graph that showed Watson’s progress on its quest to the dot labeled “Ken Jennings.” Every week, Watson kept inching ever closer. In his TED talk, Jennings explains how it all made him feel: And I saw this line coming for me.

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And Turing not only conceived of the machine but figured all this out. Consider that simple machine, that thought experiment with just a handful of parts: Everything Apollo 11 needed to do to make it to the moon and back could be programmed on a Turing Machine. Everything your smartphone can do can be programmed on a Turing machine, and everything IBM Watson can do can be programmed on a Turing machine. Who could have guessed that such a humble little device could do all that? Well, Turing could, of course. But no one else seems to have had that singular idea. Exit Turing. Enter John von Neumann, whom we call the father of modern computing. In 1945, he developed the von Neumann architecture for computers.

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In his TED talk, Jennings explains how it all made him feel: And I saw this line coming for me. And I realized, this is it. This is what it looks like when the future comes for you. It’s not the Terminator’s gunsight; it’s a little line coming closer and closer to the thing you can do, the only thing that makes you special, the thing you’re best at. Playing Jeopardy! is one narrow thing. Well, actually a few narrow things. And that’s why today’s AI can master it. So far, we have just been exploring the cognitive technical challenges of AI robots. Let’s look now at the equally formidable physical challenges. The physical world is a difficult place for an AI robot unless it is performing purely repetitive motions inside a controlled environment, such as a factory floor.

MegaThreats: Ten Dangerous Trends That Imperil Our Future, and How to Survive Them by Nouriel Roubini

"World Economic Forum" Davos, 2021 United States Capitol attack, 3D printing, 9 dash line, AI winter, AlphaGo, artificial general intelligence, asset allocation, assortative mating, autonomous vehicles, bank run, banking crisis, basic income, Bear Stearns, Big Tech, bitcoin, Bletchley Park, blockchain, Boston Dynamics, Bretton Woods, British Empire, business cycle, business process, call centre, carbon tax, Carmen Reinhart, cashless society, central bank independence, collateralized debt obligation, Computing Machinery and Intelligence, coronavirus, COVID-19, creative destruction, credit crunch, crony capitalism, cryptocurrency, currency manipulation / currency intervention, currency peg, data is the new oil, David Ricardo: comparative advantage, debt deflation, decarbonisation, deep learning, DeepMind, deglobalization, Demis Hassabis, democratizing finance, Deng Xiaoping, disintermediation, Dogecoin, Donald Trump, Elon Musk, en.wikipedia.org, energy security, energy transition, Erik Brynjolfsson, Ethereum, ethereum blockchain, eurozone crisis, failed state, fake news, family office, fiat currency, financial deregulation, financial innovation, financial repression, fixed income, floating exchange rates, forward guidance, Fractional reserve banking, Francis Fukuyama: the end of history, friendshoring, full employment, future of work, game design, geopolitical risk, George Santayana, Gini coefficient, global pandemic, global reserve currency, global supply chain, GPS: selective availability, green transition, Greensill Capital, Greenspan put, Herbert Marcuse, high-speed rail, Hyman Minsky, income inequality, inflation targeting, initial coin offering, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of movable type, Isaac Newton, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, junk bonds, Kenneth Rogoff, knowledge worker, Long Term Capital Management, low interest rates, low skilled workers, low-wage service sector, M-Pesa, margin call, market bubble, Martin Wolf, mass immigration, means of production, meme stock, Michael Milken, middle-income trap, Mikhail Gorbachev, Minsky moment, Modern Monetary Theory, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, Mustafa Suleyman, Nash equilibrium, natural language processing, negative equity, Nick Bostrom, non-fungible token, non-tariff barriers, ocean acidification, oil shale / tar sands, oil shock, paradox of thrift, pets.com, Phillips curve, planetary scale, Ponzi scheme, precariat, price mechanism, price stability, public intellectual, purchasing power parity, quantitative easing, race to the bottom, Ralph Waldo Emerson, ransomware, Ray Kurzweil, regulatory arbitrage, reserve currency, reshoring, Robert Shiller, Ronald Reagan, Salesforce, Satoshi Nakamoto, Savings and loan crisis, Second Machine Age, short selling, Silicon Valley, smart contracts, South China Sea, sovereign wealth fund, Stephen Hawking, TED Talk, The Great Moderation, the payments system, Thomas L Friedman, TikTok, too big to fail, Turing test, universal basic income, War on Poverty, warehouse robotics, Washington Consensus, Watson beat the top human players on Jeopardy!, working-age population, Yogi Berra, Yom Kippur War, zero-sum game, zoonotic diseases

Their study, published in 2013, determined that 47 percent of jobs in America were highly vulnerable to substitution by computer capital in the near future. During the Great Depression, 25 percent of Americans were out of work.11 Unlike board games that adhere to strict rules, the television game show Jeopardy! features puns, slang, red herrings, vernacular, mischievous wordplay, and obscure associations to elicit factual knowledge on topics from pop culture to the esoteric. No human competitor outperformed erstwhile computer programmer and trivia whiz Ken Jennings, who won Jeopardy! seventy-four times, a legendary streak. Under intense pressure, with the speed of a Google search, he named, for instance, the leader whose brother is believed to be the first known European to have died in the Americas and the disease that prompted US surgeon general Walter Wyman to establish a hospital in Hawaii in 1901 (answers: “Who is Leif Erikson?”

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Under intense pressure, with the speed of a Google search, he named, for instance, the leader whose brother is believed to be the first known European to have died in the Americas and the disease that prompted US surgeon general Walter Wyman to establish a hospital in Hawaii in 1901 (answers: “Who is Leif Erikson?” and “What is leprosy?”). Jennings, though, was no match for AI. By his own account in a 2013 TED Talk, IBM’s Watson defeated him handily. He commiserated with Detroit factory workers who became obsolete when robots took their jobs. “I’m not an economist,” Jennings said. “All I know is how it felt to be the guy put out of work and it was freaking demoralizing. It was terrible,” he recalled. “Here’s the one thing that I was ever good at and all it took was IBM pouring tens of millions of dollars and its smartest people and thousands of processors working in parallel and they could do the same thing.

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“Here’s the one thing that I was ever good at and all it took was IBM pouring tens of millions of dollars and its smartest people and thousands of processors working in parallel and they could do the same thing. They could do it a little bit faster and a little better on national TV and I’m sorry Ken, we don’t need you anymore.” He began to wonder, Where would digital outsourcing of jobs stop? “I felt like a quiz show contestant was now the first job that had become obsolete under this new regime of thinking computers and it hasn’t been the last.”12 The philosopher Friedrich Nietzsche envisioned upheaval a century before personal computers arrived. “Every step forward,” he warned in The Genealogy of Morals, “is made at the cost of mental and physical pain to someone.”

System Error by Rob Reich

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

It was not until 1994 that a computer program could defeat the human world checkers champion. Then, in 1997, front-page stories in newspapers around the world announced that IBM’s Deep Blue computer had “unseated humanity” by defeating the reigning chess champion, Garry Kasparov. In 2011, IBM’s Watson system, built to play the TV quiz show Jeopardy, soundly defeated the two former all-time winners, Brad Rutter and Ken Jennings. Final score: Rutter with $21,600, Jennings with $24,000, and Watson with $77,147. In 2017, scientists from Google’s DeepMind group used machine learning to build a program that would go on to beat Ke Jie, the number one Go player in the world.

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See also technological innovation Inrupt, 149–50 Instagram, xii, xxvi, 42, 128, 137, 145, 188, 194, 201, 221, 227, 229 intermediary liability, 222 International Ladies’ Garment Workers’ Union (ILGWU), 54–55 internet and COVID-19, xii–xiv. See also COVID-19 pandemic; free speech and the internet internet service providers (ISPs), 62. See also big tech platforms ISM Connect, 111–12 Jasanoff, Sheila, 231 Jefferson, Thomas, 198 Jeopardy (TV game show), 157 Jiankui, He, 249 JSTOR (scholarly repository), xxiv–xxv justice as fairness, 93–94 Kaiser Permanente, 180 Keller, Daphne, 222 Kennard, William, 61, 67 Kentucky, 97, 102 Keynes, John Maynard, 10, 174 Khan, Lina, 228 Kirchhoff, Christopher, 260–61 Kleinberg, Jon, 95–96, 105–6 Kleiner, Eugene, 26 Knuth, Donald, 15–16 Koop, C.

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

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

The technologies will continue to expand, increasingly accompanied by advances in machine learning, artificial intelligence, and human-machine interaction. These changes make it possible for computers to do jobs that it was assumed only humans could perform. From IBM’s Watson supercomputer—which beat human champions on the TV quiz show Jeopardy!—to automated discovery processes in the legal world and even software that can automatically write sports coverage, knowledge work is being automated on a scale we couldn’t imagine just a few years ago. A DIGITAL, DATA-RICH THREAD RUNS THROUGH IT ALL Digitization is the common thread running through many of these technology disruptions.

The Numerati by Stephen Baker

Berlin Wall, Black Swan, business process, call centre, correlation does not imply causation, Drosophila, full employment, illegal immigration, index card, information security, Isaac Newton, job automation, job satisfaction, junk bonds, McMansion, Myron Scholes, natural language processing, off-the-grid, PageRank, personalized medicine, recommendation engine, RFID, Silicon Valley, Skype, statistical model, surveillance capitalism, Watson beat the top human players on Jeopardy!, workplace surveillance

Jeff makes it easy for Umbria's computer by putting his age and gender on the blog. (We even learn that he's a Leo.) This type of research turns traditional surveying on its head. Unprompted by marketers, bloggers like Jeff volunteer the answers to millions of potential questions. "In a sense, we're very similar to the game show Jeopardy!" Kaushansky says. "People have already said that they like a certain car or dislike a movie. It's our job to formulate the questions." Kaushansky's team is also starting to divide bloggers into different groups, or tribes. Kaushansky envisions nearly endless tribal affiliations. Doritos munchers, bikers for Obama, MINI Cooper enthusiasts.

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The very thought fills me with such regret that I click on the video once more, not so much to laugh at the dog as to soak up the on-the-job freedom it represents. On a late spring morning I drive over the Tappan Zee Bridge, across the wide expanse of the Hudson. Then I hook left, away from New York City and up into the forests of Westchester County, to the headquarters of IBM's Thomas J. Watson Research Laboratory. It sits like a fortress atop a hill, a long, curved wall of glass reflecting the cotton-ball clouds floating above. I have a date there with Samer Takriti, the Syrian-born mathematician who launched me on this entire project. He was the one who described to me early on how his team was building mathematical models of thousands of IBM's tech consultants.

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Story has it, Takriti says after he hangs up, that the original Takritis were warriors who marched from Saddam's native city, Tikrit, in Iraq. His branch of the family, he tells me, eventually settled in Syria. A top engineering student in Damascus, Takriti won a fellowship in the mid-1980s to study at the University of Michigan. He fell head over heels for math. In 1996, by then a Ph.D., he landed a research job at IBM's fabled Watson Research Center, a half-hour drive north of New York City. This son of Tikrit warriors now walked among the gods of math. Takriti's specialty was stochastic analysis. This is the math that attempts to tie predictions to random events. Say it rains in Tucson from zero to six times per month, and you listen to the weather report, which has been right 19 of the past 20 days, only three times a week.

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

AI winter, air gap, AltaVista, Amazon Web Services, artificial general intelligence, Asilomar, Automated Insights, Bayesian statistics, Bernie Madoff, Bill Joy: nanobots, Bletchley Park, brain emulation, California energy crisis, cellular automata, Chuck Templeton: OpenTable:, cloud computing, cognitive bias, commoditize, computer vision, Computing Machinery and Intelligence, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, drone strike, dual-use technology, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Hacker News, Hans Moravec, Isaac Newton, Jaron Lanier, Jeff Hawkins, John Markoff, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, Loebner Prize, lone genius, machine translation, mutually assured destruction, natural language processing, Neil Armstrong, Nicholas Carr, Nick Bostrom, optical character recognition, PageRank, PalmPilot, paperclip maximiser, pattern recognition, Peter Thiel, precautionary principle, prisoner's dilemma, Ray Kurzweil, Recombinant DNA, Rodney Brooks, rolling blackouts, 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 Jurvetson, Steve Wozniak, strong AI, Stuxnet, subprime mortgage crisis, 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

After Siri translates your query into text, its three other main talents come into play: its NLP facility, searching a vast knowledge database, and interacting with Internet search providers, such as OpenTable, Movietickets, and Wolfram|Alpha. IBM's Watson is kind of a Siri on steroids, and a champion at NLP. In February 2011, it employed both brain-derived and brain-inspired systems to achieve an impressive victory against human contestants on Jeopardy! Like chess champion computer Deep Blue, Watson is IBM’s way of showing off its computing know-how while moving the ball down the field for AI. The long-running game show promised a formidable challenge because of its open domain of clues and its wordplay. Contestants must understand puns, similes, and cultural references, and they must phrase answers in the form of questions.

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And since it cannot see or feel, it cannot read, so during the competitions the words of the Jeopardy! clues were hand-entered by Watson’s pit crew. And since Watson cannot hear either, audio and video clues were omitted. Hey, wait a minute, did Watson really win at Jeopardy! or a custom-tailored variation? Since its victory, to get Watson to understand what people say, IBM has paired it with Nuance speech recognition technology. And Watson is reading terabytes of medical literature. One of IBM’s goals is to shrink Watson down from its present size—a roomful of servers—to refrigerator-size and make it the world’s best medical diagnostician.

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Chapter Twelve The Last Complication How can we be so confident that we will build superintelligent machines? Because the progress of neuroscience makes it clear that our wonderful minds have a physical basis, and we should have learned by now that our technology can do anything that’s physically possible. IBM’s Watson, playing Jeopardy! as skillfully as human champions, is a significant milestone and illustrates the progress of machine language processing. Watson learned language by statistical analysis of the huge amounts of text available online. When machines become powerful enough to extend that statistical analysis to correlate language with sensory data, you will lose a debate with them if you argue that they don’t understand language.

The Drunkard's Walk: How Randomness Rules Our Lives by Leonard Mlodinow

Albert Einstein, Alfred Russel Wallace, Antoine Gombaud: Chevalier de Méré, Atul Gawande, behavioural economics, Brownian motion, butterfly effect, correlation coefficient, Daniel Kahneman / Amos Tversky, data science, Donald Trump, feminist movement, forensic accounting, Gary Kildall, Gerolamo Cardano, Henri Poincaré, index fund, Isaac Newton, law of one price, Monty Hall problem, pattern recognition, Paul Erdős, Pepto Bismol, probability theory / Blaise Pascal / Pierre de Fermat, RAND corporation, random walk, Richard Feynman, Ronald Reagan, Stephen Hawking, Steve Jobs, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Bayes, V2 rocket, Watson beat the top human players on Jeopardy!

As a counterpoint to Koppett’s story, consider now the story of a fellow who does have credentials, a fellow named Bill Miller. For years, Miller maintained a winning streak that, unlike Koppett’s, was compared to Joe DiMaggio’s fifty-six-game hitting streak and the seventy-four consecutive victories by the Jeopardy! quiz-show champ Ken Jennings. But in at least one respect these comparisons were not very apt: Miller’s streak earned him each year more than those other gentlemen’s streaks had earned them in their lifetimes. For Bill Miller was the sole portfolio manager of Legg Mason Value Trust Fund, and in each year of his fifteen-year streak his fund beat the portfolio of equity securities that constitute the Standard & Poor’s 500.

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What I’ve learned, above all, is to keep marching forward because the best news is that since chance does play a role, one important factor in success is under our control: the number of at bats, the number of chances taken, the number of opportunities seized. For even a coin weighted toward failure will sometimes land on success. Or as the IBM pioneer Thomas Watson said, “If you want to succeed, double your failure rate.” I have tried in this book to present the basic concepts of randomness, to illustrate how they apply to human affairs, and to present my view that its effects are largely overlooked in our interpretations of events and in our expectations and decisions.

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That may be the case for Marilyn, who is most famous for her response to the following question, which appeared in her column one Sunday in September 1990 (I have altered the wording slightly): Suppose the contestants on a game show are given the choice of three doors: Behind one door is a car; behind the others, goats. After a contestant picks a door, the host, who knows what’s behind all the doors, opens one of the unchosen doors, which reveals a goat. He then says to the contestant, “Do you want to switch to the other unopened door?” Is it to the contestant’s advantage to make the switch?2 The question was inspired by the workings of the television game show Let’s Make a Deal, which ran from 1963 to 1976 and in several incarnations from 1980 to 1991.

Upstream: The Quest to Solve Problems Before They Happen by Dan Heath

"Hurricane Katrina" Superdome, Affordable Care Act / Obamacare, airport security, Albert Einstein, bank run, British Empire, Buckminster Fuller, call centre, cloud computing, cognitive dissonance, colonial rule, correlation does not imply causation, cuban missile crisis, en.wikipedia.org, epigenetics, food desert, high-speed rail, Housing First, illegal immigration, Internet of things, mandatory minimum, millennium bug, move fast and break things, Nick Bostrom, payday loans, Ralph Nader, RAND corporation, randomized controlled trial, self-driving car, Skype, Snapchat, subscription business, systems thinking, urban planning, Watson beat the top human players on Jeopardy!, Y2K

a metric called the ROSC: “Return of Spontaneous Circulation,” EMT Prep, 2018, https://emtprep.com/free-training/post/return-of-spontaneous-circulation-rosc. world’s best early-detection systems for earthquakes: Alex Greer, “Earthquake Preparedness and Response: Comparison of the United States and Japan,” Leadership and Management in Engineering 12, no. 3 (2012): 111–25. TV commercial for IBM: IBM Watson TV commercial, Watson at Work: Engineering, 2017, https://www.ispot.tv/ad/wIha/ibm-watson-watson-at-work-engineering, accessed April 30, 2019. elevator companies today offer “smart” elevators: Oscar Rousseau, “AI, Sensors, and the Cloud Could Make Your Buildings Lift Safer,” Construction Week Online, February 18, 2019, https://www.constructionweekonline.com/products-services/169357-ai-sensors-and-the-cloud-could-make-your-buildings-lifts-safer.

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This is not fantasy.I Many major elevator companies today offer “smart” elevators, which send a smorgasbord of diagnostic data to the cloud—including lighting, noise, speed, temperature, and much more—that can be scoured for early signs of problems. “One of the most important things that an online connection to the cloud gives you is the ability to spot trends in advance before they start creating problems,” John Macleod, an IBM Watson IoT technical specialist, told Computerworld. “Take the time it takes a door to close; normally 5 seconds, but it may gradually extend to 5.1, then 5.2. Nobody’s really noticing it as you get in and out of the lift, but the gradual change in time might well indicate something’s becoming sticky and needs lubrication.… And then you can act in advance to deal with them rather than waiting for the doors to stick shut and catch people inside the lift.”

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He was found in the arms of his special education assistant, who died while trying to protect him. He was in first grade. Hockley wants desperately to stop this moment from happening to another parent. To interrupt another school’s chain of dominoes by rushing into the space between them. I. Although it’s striking how far Watson has slipped: from “the computer who won Jeopardy!” to a black box sitting in a random office building, making predictions out loud to no one. CHAPTER 9 How Will You Know You’re Succeeding? A question that bedevils many upstream interventions is: What counts as success? With downstream work, success can be wonderfully tangible, and that’s partly because it involves restoration.

Four Futures: Life After Capitalism by Peter Frase

Aaron Swartz, Airbnb, Anthropocene, basic income, bitcoin, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, carbon tax, congestion pricing, cryptocurrency, deindustrialization, do what you love, Dogecoin, Donald Shoup, Edward Snowden, emotional labour, Erik Brynjolfsson, Ferguson, Missouri, fixed income, full employment, future of work, green new deal, Herbert Marcuse, high net worth, high-speed rail, income inequality, industrial robot, informal economy, Intergovernmental Panel on Climate Change (IPCC), iterative process, Jevons paradox, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, Kim Stanley Robinson, litecoin, mass incarceration, means of production, military-industrial complex, Occupy movement, pattern recognition, peak oil, plutocrats, post-work, postindustrial economy, price mechanism, private military company, Ray Kurzweil, Robert Gordon, Second Machine Age, self-driving car, sharing economy, Silicon Valley, smart meter, TaskRabbit, technoutopianism, The future is already here, The Future of Employment, Thomas Malthus, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, We are the 99%, Wolfgang Streeck

But we do see many indications that we now have the possibility—although not necessarily the reality—of drastically reducing the need for human labor. A few examples will demonstrate the diverse areas in which human labor is being reduced or eliminated entirely. In 2011, IBM made headlines with its Watson supercomputer, which successfully competed and won against human competitors on the game show Jeopardy. Although this feat was a somewhat frivolous publicity stunt, it also demonstrated Watson’s suitability for other, more valuable tasks. The technology is already being tested to assist doctors in processing the enormous volume of medical literature to better diagnose patients, which in fact was the system’s original purpose.

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In a world where the economy is based on intellectual property, companies will constantly be suing each other for alleged infringements of others’ copyrights and patents, so there will be a need for a lot of lawyers. This will provide employment for some significant fraction of the population, but again it’s hard to see this being enough to sustain an entire economy, particularly because of a theme that we saw in the introductory chapter: just about anything can, in principle, be automated. Watson, IBM’s Jeopardy-playing computer program, is already automating the work of lower-level law firm staff. And it’s easy to imagine big intellectual property firms coming up with procedures for mass-filing lawsuits that rely on fewer and fewer human lawyers, just as there are now systems that detect copyrighted music in online videos and send requests for removal.

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

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

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

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The other seminal event that signalled that something big was changing in the field of Artificial Intelligence took place in February 2011, and was televised. Watson – another computer developed by IBM – beat two former, human, winners of the popular American TV quiz Jeopardy! and won the prize of a million dollars. Watson was a truly amazing machine. It was not a singular entity but a cluster of ninety servers, each one equipped with multiple processors. Its massively parallel hardware architecture was capable of supporting millions of searches into its knowledge base. For the purpose of the TV quiz, the engineers at IBM loaded Watson with 200 million pages of data, including dictionaries, encyclopaedias and literary articles. Moreover, Watson communicated in natural language.

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Kurzweil marks the year 2030 as a watershed by extrapolating, like Vinge, from today’s exponential improvement of computers according to Moore’s Law:25 2030 thus becomes the year that computer complexity will surpass the complexity of information processing in the human brain. Deep Blue, driverless cars crossing the Mojave Desert, and Watson beating humans at Jeopardy! all seem to validate the arguments made by Vinge and Kurzweil. Brute computer power has made computers more ‘intelligent’. Nevertheless, underneath the correlation between powerful computing and intelligent behaviour lurk two fundamental assumptions that deserve closer examination. The first assumption is that our computer technology, whose architecture is different from that of the human brain, is nevertheless capable of exhibiting every aspect of human intelligence, including self-awareness.

Narrative Economics: How Stories Go Viral and Drive Major Economic Events by Robert J. Shiller

agricultural Revolution, Alan Greenspan, Albert Einstein, algorithmic trading, Andrei Shleifer, autism spectrum disorder, autonomous vehicles, bank run, banking crisis, basic income, behavioural economics, bitcoin, blockchain, business cycle, butterfly effect, buy and hold, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, central bank independence, collective bargaining, computerized trading, corporate raider, correlation does not imply causation, cryptocurrency, Daniel Kahneman / Amos Tversky, debt deflation, digital divide, disintermediation, Donald Trump, driverless car, Edmond Halley, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, fake news, financial engineering, Ford Model T, full employment, George Akerlof, germ theory of disease, German hyperinflation, Great Leap Forward, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, implied volatility, income inequality, inflation targeting, initial coin offering, invention of radio, invention of the telegraph, Jean Tirole, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, litecoin, low interest rates, machine translation, market bubble, Modern Monetary Theory, money market fund, moral hazard, Northern Rock, nudge unit, Own Your Own Home, Paul Samuelson, Philip Mirowski, plutocrats, Ponzi scheme, public intellectual, publish or perish, random walk, Richard Thaler, Robert Shiller, Ronald Reagan, Rubik’s Cube, Satoshi Nakamoto, secular stagnation, shareholder value, Silicon Valley, speech recognition, Steve Jobs, Steven Pinker, stochastic process, stocks for the long run, superstar cities, The Rise and Fall of American Growth, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, theory of mind, Thorstein Veblen, traveling salesman, trickle-down economics, tulip mania, universal basic income, Watson beat the top human players on Jeopardy!, We are the 99%, yellow journalism, yield curve, Yom Kippur War

To answer this question, we must consider the advent of Apple’s Siri, the iPhone app launched in 2011 that uses automatic speech recognition (ASR) and natural language understanding (NLU) to (attempt to) answer the questions you’ve asked it.21 To many, Siri’s ability to talk, understand, and provide information looked like the advent of that long-awaited singularity when machines become as smart as, or smarter than, people. That same year, IBM presented its talking computer Watson as a competitor on the television quiz show Jeopardy, and Watson beat the human champions who played against it. Now these are followed by Amazon Echo’s Alexa, Google’s “OK Google,” and other variations and improvements such as Alibaba’s Tmall Genie, LingLong’s DingDong, and Yandex’s Alice. These inventions were amazing; the time prophesied by Star Wars, The Transformers, and The Jetsons seemed finally to have arrived.

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See also labor unions Wagner, Robert, 184 Walker, Edmond, 250 Wall Street Journal “Mansion” section, 224–25 Wanniski, Jude, 44–45 war metaphors, 17 Warner/Chappell Music, 98 wars: inflation during, 265–66. See also Civil War, US; World War I; World War II war to end all wars, 242 Washington, George, 100–101, 102, 117, 177 Washington Mutual (WaMu) bank run, 135 Watson, IBM computer on Jeopardy, 207 The Way the World Works (Wanniski), 44 “We are the 99%” protests of 2011, 8, 225 weather forecasting, 123–25 Weems, Mason Locke, 100 Weiman, Rita, 139 Welch, Ivo, 300 welfare mother, narrative on, 49–50 When Washington Shut Down Wall Street (Silber), 94 Whewell, William, 12 White, Hayden, 37 Whitman, Walt, 165 Wicked (Broadway musical), 172 Wicked (Maguire), 172 Wikipedia, 7 Wikiquotes, 102 wikis, 7 Williams, James D., 147 Wilson, E.

Range: Why Generalists Triumph in a Specialized World by David Epstein

Airbnb, Albert Einstein, Apollo 11, Apple's 1984 Super Bowl advert, Atul Gawande, Checklist Manifesto, Claude Shannon: information theory, Clayton Christensen, clockwork universe, cognitive bias, correlation does not imply causation, Daniel Kahneman / Amos Tversky, deep learning, deliberate practice, Exxon Valdez, fail fast, Flynn Effect, Freestyle chess, functional fixedness, game design, Gene Kranz, Isaac Newton, Johannes Kepler, knowledge economy, language acquisition, lateral thinking, longitudinal study, Louis Pasteur, Mark Zuckerberg, medical residency, messenger bag, meta-analysis, Mikhail Gorbachev, multi-armed bandit, Nelson Mandela, Netflix Prize, pattern recognition, Paul Graham, precision agriculture, prediction markets, premature optimization, pre–internet, random walk, randomized controlled trial, retrograde motion, Richard Feynman, Richard Feynman: Challenger O-ring, Silicon Valley, Silicon Valley billionaire, Stanford marshmallow experiment, Steve Jobs, Steve Wozniak, Steven Pinker, sunk-cost fallacy, systems thinking, Walter Mischel, Watson beat the top human players on Jeopardy!, Y Combinator, young professional

In the rule-bound but messier world of driving, AI has made tremendous progress, but challenges remain. In a truly open-world problem devoid of rigid rules and reams of perfect historical data, AI has been disastrous. IBM’s Watson destroyed at Jeopardy! and was subsequently pitched as a revolution in cancer care, where it flopped so spectacularly that several AI experts told me they worried its reputation would taint AI research in health-related fields. As one oncologist put it, “The difference between winning at Jeopardy! and curing all cancer is that we know the answer to Jeopardy! questions.” With cancer, we’re still working on posing the right questions in the first place. In 2009, a report in the esteemed journal Nature announced that Google Flu Trends could use search query patterns to predict the winter spread of flu more rapidly than and just as accurately as the Centers for Disease Control and Prevention.

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“In narrow enough worlds”: In addition to an interview with Gary Marcus, I used video of his June 7, 2017, lecture at the AI for Good Global Summit in Geneva, as well as several of his papers and essays: “Deep Learning: A Critical Appraisal,” arXiv: 1801.00631; “In Defense of Skepticism About Deep Learning,” Medium, January 14, 2018; “Innateness, AlphaZero, and Artificial Intelligence,” arXiv: 1801.05667. IBM’s Watson: For a balanced take on Watson’s challenges in healthcare—from one critic calling it “a joke,” to others suggesting it falls far short of the original hype but does indeed have value—see: D. H. Freedman, “A Reality Check for IBM’s AI Ambitions,” MIT Technology Review, June 27, 2017, online ed. “The difference between winning at Jeopardy!”: The oncologist is Dr. Vinay Prasad. He said this to me in an interview, and also shared it on Twitter. a report in the esteemed journal Nature: J.

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“using procedures” question types, 82–85, 90 See also learning fast and slow “eduction,” 43 Ehrlich, Paul, 215–18, 220, 221 Einstein, Albert, 229 Einstellung effect, 177 Eli Lilly, 172–73 Ellington, Duke, 9, 69–70 employment and careers changing, 130–32, 131n, 136–37, 154n, 160–61 compensation in, 9 and corporate HR policies, 213 high-risk opportunities, 136, 137 “match quality” in, 128–29, 130–31, 135–36, 138–140 and personal changes, 156 sampling opportunity in, 130 and winding paths of dark horses, 154 error correction, 74, 75 experience-expertise link, 18–21, 53 experimentation, 269–273, 287–89, 290, 291 Exxon Valdez oil tanker disaster, 175–77 fame, 220 Federer, Roger, 3–4, 31 Fermi, Enrico, 52, 52n Fermi problems, 52–53 Feynman, Richard, 249 figlie del coro of Venice, 56–64 and composers, 62–63 multi-instrument approach of, 61–62, 64 music program of, 61 and names of children, 59, 63 financial crisis of 2008, 12, 279 Finster, Howard, 169 firefighting in novel circumstances, 21, 30 pattern recognition in, 19, 20, 30, 112 wilderness firefighting, 245–47, 248 Fischer, Bobby, 16 Fleisher, Leon, 76 flu predictions, 29–30 Flynn, James, 37–40, 44, 45, 47–50, 275, 277 Flynn effect, 37–40, 45–46 Flyvbjerg, Bent, 110–11 Foles, Nick, 8 food preservation, 173–74 Frances Hesselbein Leadership Institute, 152 Freakonomics (Levitt), 131 Gaiman, Neil, 210 Game Boy by Nintendo, 196–97 Garg, Abhimanyu, 188 Gauguin, Paul, 128 Gawande, Atul, 6 Geim, Andre, 273 generation effect, 85–86 Gentner, Dedre, 102–3, 104, 113–14, 115, 119 Geveden, Rex, 261–63, 264 Gigerenzer, Gerd, 226 Gilbert, Dan, 156 Girl Scouts, 148–151, 153 Gleason, Paul, 248 global financial crisis of 2008, 12, 279 Gobet, Fernand, 26, 34 Godin, Seth, 136, 143 golf, 1–2, 5–6, 18, 20, 21, 30 Good Judgment Project, 222–23, 224, 258 Google Flu Trends, 29–30 Gorbachev, Mikhail, 220–21 Gore, Bill, 286 Graham, Paul, 163 Grant, Adam, 77 graphene, 273 Gravity Probe B project, 260–63 Great Britain, 6, 227–28 Greve, Henrich, 208–10 Griffin, Abbie, 211–12, 213 grit, 121–145 in athletics, 141–42, 143 and changing course, 142 and context principle, 159–160 Duckworth’s concept of, 133–35 and marshmallow test, 157–59, 159n and match quality, 135, 143 and officer training, 132–35, 137–140, 142 and spelling bee competitors, 133, 134, 142 of Van Gogh, 121–27, 144 and youthful ambition, 142–43 Gruber, Howard, 212–13 Hamilton (musical), 281 happiness, 131, 131n, 162 Haydn, Joseph, 63 Head Start early childhood education programs, 96–97, 97n head starts and changing course, 162 cult of, 67, 97, 162, 275 in music training, 67 of the Polgar sisters, 15–18 hedgehog/foxes model of expertise, 221, 223, 225, 228, 229–231 Hendrix, Jimi, 73 Henslow, John Stevens, 229 Hernandez, Nelson, 23–24 Hesselbein, Frances, 147–153, 161–62, 264 high-risk opportunities, 136, 137 Hinds, Ciarán, 166–67 hint-giving practices, 82–83, 85, 87 hiring practices, 213 HIV/AIDS, 283–84 Hogarth, Robin, 20–21, 31, 34 Holley, Lonnie, 168 Holmes, Oliver Wendell, 291 household rules, 77 Hutchison, Kay Bailey, 284–85 hypercorrection effect, 86 Ibarra, Herminia, 160–64, 290 Ibn Khaldun, 47 “if-then signatures,” 159 Ig Nobel Prizes, 273 immigration, 281n impressionism, 127 improvisational skills, 74–76 influenza predictions, 29–30 infrastructure projects, 110–11 InnoCentive, 173, 175, 177–78, 189 innovation and innovators in art world, 127–28 comic book creators, 209–10 and corporate hiring practices, 213 disorderly path of, 287–89 and immigration, 281n and medieval guilds, 278 and Nintendo’s Wii, 199–200 outsider advantage in, 178 and R&D spending, 206 routines as an impediment to, 211 and savants, 32 serial, 211–13 of specialists, 205 in teams and individual creators, 209–10 traits of, 211–12 “inside view” concept of Kahneman and Tversky, 108 instinctive decisions, 19 instructors, 91–92 Integrated Science Program at Northwestern University, 114–15, 119 Intelligence Advanced Research Projects Activity (IARPA), 222–23 interdisciplinary approaches, 180–81, 276–77, 278n, 279, 281 “interleaving” (mixed practice), 94–96 inventors failures and breakthroughs of, 288 impacts of specialist vs. generalist, 9, 203–4, 205 See also innovation and innovators IQ scores and the Flynn effect, 37–40, 45–46 Israel Defense Forces, 19–20 Jackson, Kirabo, 132 Japan, 83–84, 281n jazz musicians, 68, 75–76 Jentleson, Katherine, 168–69 Jeopardy! 29 Jobs, Steve, 33 Jordan, Hillary, 166 Junger, Sebastian, 165 Kaggle competitions, 178–79 Kahan, Dan, 227–28, 230 Kahneman, Daniel on experience-expertise link, 19–21, 159–160 high school curriculum project of, 108 “inside view” concept of, 108 on repetitive domains, 32 Kasparov, Garry, 18–19, 22–23, 24, 25 Kepler, Johannes analogies used by, 100–102, 103, 113, 116 and Copernican model of planets, 99–100 documentation of discovery process, 115 and Mars orbit, 116 and planetary motion, 100–102 “kind” learning environments about, 20 domains that benefit from, 32 and early specialization, 21, 32 efficiency of specialization in, 210–11, 213 pattern recognition in, 20–21, 24–25, 112 Klein, Gary, 18–19, 20, 112 Knight, Phil, 154–55 Konnikova, Maria, 143–44 Kornell, Nate, 85–88, 95 Kpelle people in Liberia, 44 Kranz, Gene, 258, 262 Lakhani, Karim, 178, 181 language and abstract thinking, 44–45 lateral thinking, 191–213 about, 193–94 of Darwin, 212–13 and Dyson’s birds/frogs analogy, 200–201 and Game Boy by Nintendo, 196–97 and impact of specialist vs. generalist inventors, 203–4, 205 and Ouderkirk’s multilayer optical film, 201–3 of polymaths, 204–6 and Unusual (or Alternative) Uses Task, 198 with withered technology, 193–94, 197, 271–72 of Yokoi at Nintendo, 192–97, 198–200 late starts/specialization in art world, 121–28 in athletics, 3–4, 6–9, 287, 289 and changing employment, 130–32 as integral to success, 128 in literary world, 128 of Malamud, 128–29 and “match quality” in vocations, 129–130, 157 of Tillman Scholars, 10, 145 learning environments, 20–21, 24, 30, 32.

The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism by Jeremy Rifkin

3D printing, active measures, additive manufacturing, Airbnb, autonomous vehicles, back-to-the-land, benefit corporation, big-box store, bike sharing, bioinformatics, bitcoin, business logic, business process, Chris Urmson, circular economy, clean tech, clean water, cloud computing, collaborative consumption, collaborative economy, commons-based peer production, Community Supported Agriculture, Computer Numeric Control, computer vision, crowdsourcing, demographic transition, distributed generation, DIY culture, driverless car, Eben Moglen, electricity market, en.wikipedia.org, Frederick Winslow Taylor, Free Software Foundation, Garrett Hardin, general purpose technology, global supply chain, global village, Hacker Conference 1984, Hacker Ethic, industrial robot, informal economy, information security, Intergovernmental Panel on Climate Change (IPCC), intermodal, Internet of things, invisible hand, Isaac Newton, James Watt: steam engine, job automation, John Elkington, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Julian Assange, Kickstarter, knowledge worker, longitudinal study, low interest rates, machine translation, Mahatma Gandhi, manufacturing employment, Mark Zuckerberg, market design, mass immigration, means of production, meta-analysis, Michael Milken, mirror neurons, natural language processing, new economy, New Urbanism, nuclear winter, Occupy movement, off grid, off-the-grid, oil shale / tar sands, pattern recognition, peer-to-peer, peer-to-peer lending, personalized medicine, phenotype, planetary scale, price discrimination, profit motive, QR code, RAND corporation, randomized controlled trial, Ray Kurzweil, rewilding, RFID, Richard Stallman, risk/return, Robert Solow, Rochdale Principles, Ronald Coase, scientific management, search inside the book, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, smart cities, smart grid, smart meter, social web, software as a service, spectrum auction, Steve Jobs, Stewart Brand, the built environment, the Cathedral and the Bazaar, the long tail, The Nature of the Firm, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, too big to fail, Tragedy of the Commons, transaction costs, urban planning, vertical integration, warehouse automation, Watson beat the top human players on Jeopardy!, web application, Whole Earth Catalog, Whole Earth Review, WikiLeaks, working poor, Yochai Benkler, zero-sum game, Zipcar

The Big Ten Network uses algorithms to create original pieces posted just seconds after games, eliminating human copywriters.37 Artificial intelligence took a big leap into the future in 2011 when an IBM computer, Watson—named after IBM’s past chairman—took on Ken Jennings, who held the record of 74 wins on the popular TV show Jeopardy, and defeated him. The showdown, which netted a $1 million prize for IBM, blew away TV viewers as they watched their Jeopardy hero crumble in the presence of the “all-knowing” Watson. Watson is a cognitive system that is able to integrate “natural language processing, machine learning, and hypothesis generation and evaluation,” says its proud IBM parent, allowing it to think and respond to questions and problems.38 Watson is already being put to work.

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Christopher Steiner, “Automatons Get Creative,” New York Times, August 17, 2012, http://online.wsj.com/news/articles/SB10000872396390444375104577591304277229534#printprin (accessed June 30, 2013). 37. Ibid. 38. “IBM Watson: Ushering in a New Era of Computing,” IBM, http://www-03.ibm.com/innova tion/us/watson/ (accessed October 22, 2013). 39. Brian T. Horowitz, “IBM, Nuance to Tune Watson Supercomputer for Use in Health Care,” EWeek, February 17, 2011, http://www.eweek.com/c/a/Health-Care-IT/IBM-Nuance-to-Tune -Watson-Supercomputer-for-Use-in-Health-Care-493127/ (accessed October 22, 2013). 40. Associated Press, “Watson’s Medical Expertise Offered Commercially,” Telegram, February 8 2013, http://www.telegram.com/article/20130208/NEWS/102089640/0 (accessed October 22, 2013). 41.

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Watson is a cognitive system that is able to integrate “natural language processing, machine learning, and hypothesis generation and evaluation,” says its proud IBM parent, allowing it to think and respond to questions and problems.38 Watson is already being put to work. IBM Healthcare Analytics will use Watson to assist physicians in making quick and accurate diagnoses by analyzing Big Data stored in the electronic health records of millions of patients, as well as in medical journals.39 IBM’s plans for Watson go far beyond serving the specialized needs of the research industry and the back-office tasks of managing Big Data. Watson is being offered up in the marketplace as a personal assistant that companies and even consumers can converse with by typed text or in real-time spoken words.

Overcomplicated: Technology at the Limits of Comprehension by Samuel Arbesman

algorithmic trading, Anthropocene, Anton Chekhov, Apple II, Benoit Mandelbrot, Boeing 747, Chekhov's gun, citation needed, combinatorial explosion, Computing Machinery and Intelligence, Danny Hillis, data science, David Brooks, digital map, discovery of the americas, driverless car, en.wikipedia.org, Erik Brynjolfsson, Flash crash, friendly AI, game design, Google X / Alphabet X, Googley, Hans Moravec, HyperCard, Ian Bogost, Inbox Zero, Isaac Newton, iterative process, Kevin Kelly, machine translation, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mandelbrot fractal, Minecraft, Neal Stephenson, Netflix Prize, Nicholas Carr, Nick Bostrom, Parkinson's law, power law, Ray Kurzweil, recommendation engine, Richard Feynman, Richard Feynman: Challenger O-ring, Second Machine Age, self-driving car, SimCity, software studies, statistical model, Steve Jobs, Steve Wozniak, Steven Pinker, Stewart Brand, superintelligent machines, synthetic biology, systems thinking, the long tail, Therac-25, Tyler Cowen, Tyler Cowen: Great Stagnation, urban planning, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, Y2K

And they sound really good. A Scott Joplin–style composition sounds like Joplin. While Cope didn’t create these songs directly, he still can take pride in their construction. His computational creations can provide him with naches. Similarly, the creators of IBM’s Watson might shep naches from the machine’s win over its human opponents on Jeopardy! We can broaden this sense of naches still more. Many of us support a sports team and take pride in its wins, even though we had nothing to do with them. Or we become excited when a citizen of our country takes the gold in the Olympics, or makes a new discovery and is awarded a prestigious prize.

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Even though we continue to specialize in order to handle the more complicated systems we are building, seeing this web of interconnections reminds us that each domain does not stand alone; they are all part of a vast connected framework. Since these systems are interconnected in many different ways, we will increasingly require the ability to connect one area of knowledge to another. When constructing a computer program that can play Jeopardy!, for example, you need knowledge of everything from linguistics to computer hardware; specialization alone will not work. We need a certain breadth of knowledge. However, as noted earlier, before too long, we will bump up against the limits to what we can truly understand; we just can’t hold all the relevant knowledge in our heads.

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., 22, 46 HTML, 32 humility: as response to limits of human comprehension, 155–56 as response to technological complexity, 155–56, 158, 165, 167, 170, 174, 176 Huxley, Thomas Henry, 113, 114 HyperCard, 162–63 IBM, 84, 169 IBM 3083 computer, 37 ideas, interconnectivity of, 142 if-then statements, 80–81 Iliad (Homer), 129–30 infield fly rule, 172 infrastructure, 66 accretion in, 42, 100–101 complexity of, 100–101 interconnection of, 2 interconnection of natural world and, 3–4 of Internet, 101–2 replacement of, 46 Ingenuity Gap, The (Homer-Dixon), 2 interaction, 65 in cancer, 126 in catastrophes, 126 in complex systems, 36, 43–51, 62, 65, 146 in financial sector, 126 in legal system, 45–46 of modules, 64 in software, 44–45 interconnectivity, 2, 14–15, 45–46, 103, 128, 135, 146 in financial sector, 24–26, 62, 64 ideas and, 142 and limits of human comprehension, 78–79 modules in, 63–65, 208 in technological complexity, 2, 47–48 unexpected behavior of, see unexpected behavior Internal Revenue Service, 37–38 Internet, 47, 66 evolving function of, 31–32 physical infrastructure of, 101–2 interoperability, 47–48 optimal vs. maximum, 62–63, 64–65 interpreters, of complex systems, 166–67, 229 Ionia, 138–39 iPad, 162 Jeopardy! (TV show), 142, 169 Jobs, Steve, 161 Jones, Benjamin, 90 July 8, 2015, system crashes on, 1, 4 Kant Generator, 74 Kasparov, Garry, 84 Katsuyama, Brad, 189 Kelly, Kevin, 83 Kelly, Sean Dorrance, 173 Kircher, Athanasius, 86 Kirk, Chris, 32–33 kluges: in biological systems, 119 definition of, 33 “good enough” in, 42 as inevitable in complex systems, 34–36, 62–66, 127, 128, 154, 173–74 in legal system, 33–34 and limits of human comprehension, 42 in software, 35 knowledge: burden of, 90, 212 explosion of, 86–88, 89–91, 142–43 generalists and, 142–49 limits to, 153–54 Renaissance man and, 86–89, 93, 144 specialization and, 85–86, 90–91 Knowledge, The, 78 Koopman, Philip, 10, 100, 201 kosmos, 139–40 language: cognitive processing of, 73–74 grammar in, 54, 57–58 hapax legomena in, 54–55, 206 machine translation and, 57–58, 207 power laws in, 55–56 recursion in, 71–72, 75 legacy code, legacy systems, 43, 223 accretion and, 39–40, 198–99 in biological systems, 118, 119–20 inducing new functions from, 126, 198 trauma of replacing, 39–42 legal system: accretion in, 40–41, 46 complexity in, 16, 85 edge cases in, 59–61 interaction in, 45–46 kluges in, 33–34 limits of comprehension and, 22 Leibniz, Gottfried, 89 Leidy, Joseph, 86 Lewis, Michael, 189 liberal arts, 145 Library of Congress, 90 limitative theorems, 175 Linus’s law, 102 logic, computer vs. human, 82–84 logistics, 84 London, cabdrivers in, 78 long-tailed distributions, 55–56, 206 “losing the bubble,” 70–71, 85 Lovecraft, H.

Because Internet: Understanding the New Rules of Language by Gretchen McCulloch

4chan, Black Lives Matter, book scanning, British Empire, Cambridge Analytica, citation needed, context collapse, Day of the Dead, DeepMind, digital divide, disinformation, Donald Trump, emotional labour, en.wikipedia.org, eternal september, Firefox, Flynn Effect, Google Hangouts, Ian Bogost, Internet Archive, invention of the printing press, invention of the telephone, lolcat, machine translation, moral panic, multicultural london english, natural language processing, Neal Stephenson, off-the-grid, pre–internet, QWERTY keyboard, Ray Oldenburg, Silicon Valley, Skype, Snapchat, Snow Crash, social bookmarking, social web, SoftBank, Steven Pinker, tech worker, TED Talk, telemarketer, The Great Good Place, the strength of weak ties, Twitter Arab Spring, upwardly mobile, Watson beat the top human players on Jeopardy!, Wayback Machine

psychologist Jeffrey Hancock: Jeffrey T. Hancock. 2004. “Verbal Irony Use in Face-to-Face and Computer-Mediated Conversations.” Journal of Language and Social Psychology 23(4). pp. 447–463. IBM experimented: Alexis C. Madrigal. January 10, 2013. “IBM’s Watson Memorized the Entire ‘Urban Dictionary,’ Then His Overlords Had to Delete It.” The Atlantic. www.theatlantic.com/technology/archive/2013/01/ibms-watson-memorized-the-entire-urban-dictionary-then-his-overlords-had-to-delete-it/267047/. Chapter 5. Emoji and Other Internet Gestures Second Life made: The most recent statistic that Linden Lab provides is from 2013 and consists of 36 million accounts created in total, with a million monthly active users.

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I’d love to see a proper corpus study comparing postcards and texts from younger and older people, to see what else we can learn by drawing together informal writing across different generations and mediums. POST INTERNET PEOPLE When I was growing up, my family didn’t have a television. This made me a trifle eccentric among my peers, but I nonetheless picked up, by cultural osmosis and glimpses at other people’s houses, the essentials of TV culture: how to operate a remote control, the Jeopardy! theme song, and the social progression of Sesame Street from “the best” to “a thing for babies” to the nostalgia-fueled best again. I grew up in a post-television generation, irrespective of my own (lack of) participation in it. The Pre Internet People don’t feel socially connected to the internet even when they do use it, and the Post Internet People are the inverse: socially influenced by the internet regardless of their own level of use.

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

"Margaret Hamilton" Apollo, "Susan Fowler" uber, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 4chan, 8-hour work day, Aaron Swartz, Ada Lovelace, AI winter, air gap, Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, Andy Rubin, Asperger Syndrome, augmented reality, Ayatollah Khomeini, backpropagation, barriers to entry, basic income, behavioural economics, Bernie Sanders, Big Tech, bitcoin, Bletchley Park, blockchain, blue-collar work, Brewster Kahle, Brian Krebs, Broken windows theory, call centre, Cambridge Analytica, cellular automata, Charles Babbage, Chelsea Manning, Citizen Lab, clean water, cloud computing, cognitive dissonance, computer vision, Conway's Game of Life, crisis actor, crowdsourcing, cryptocurrency, Danny Hillis, data science, David Heinemeier Hansson, deep learning, DeepMind, Demis Hassabis, disinformation, don't be evil, don't repeat yourself, Donald Trump, driverless car, dumpster diving, Edward Snowden, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Ethereum, ethereum blockchain, fake news, false flag, Firefox, Frederick Winslow Taylor, Free Software Foundation, Gabriella Coleman, game design, Geoffrey Hinton, glass ceiling, Golden Gate Park, Google Hangouts, Google X / Alphabet X, Grace Hopper, growth hacking, Guido van Rossum, Hacker Ethic, hockey-stick growth, HyperCard, Ian Bogost, illegal immigration, ImageNet competition, information security, Internet Archive, Internet of things, Jane Jacobs, John Markoff, Jony Ive, Julian Assange, Ken Thompson, Kickstarter, Larry Wall, lone genius, Lyft, Marc Andreessen, Mark Shuttleworth, Mark Zuckerberg, Max Levchin, Menlo Park, meritocracy, microdosing, microservices, Minecraft, move 37, move fast and break things, Nate Silver, Network effects, neurotypical, Nicholas Carr, Nick Bostrom, no silver bullet, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, Oculus Rift, off-the-grid, OpenAI, operational security, opioid epidemic / opioid crisis, PageRank, PalmPilot, paperclip maximiser, 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, scientific management, 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, systems thinking, TaskRabbit, tech worker, techlash, TED Talk, 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!, WeWork, WikiLeaks, women in the workforce, Y Combinator, Zimmermann PGP, éminence grise