Watson beat the top human players on Jeopardy!

111 results back to index


pages: 502 words: 107,657

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

Albert Einstein, algorithmic trading, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, backtesting, Black Swan, book scanning, bounce rate, business intelligence, business process, butter production in bangladesh, call centre, Charles Lindbergh, commoditize, computer age, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data is the new oil, en.wikipedia.org, Erik Brynjolfsson, Everything should be made as simple as possible, experimental subject, Google Glasses, happiness index / gross national happiness, job satisfaction, Johann Wolfgang von Goethe, lifelogging, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mass immigration, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, Norbert Wiener, personalized medicine, placebo effect, prediction markets, Ray Kurzweil, recommendation engine, risk-adjusted returns, Ronald Coase, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, Shai Danziger, software as a service, speech recognition, statistical model, Steven Levy, text mining, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Davenport, Turing test, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra, zero-sum game

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. The top-rated quiz show in syndication, Jeopardy! attracts nearly nine million viewers every day and would draw an audience of 34.5 million for this special man-versus-machine match.

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/. IBM, “The DeepQA Project,” IBM Jeopardy! Challenge, April 22, 2009. www.research.ibm.com/deepqa/deepqa.shtml.

Citibank and PayPal detect the customer sentiment about their products, and one researcher’s machine can tell which Amazon.com book reviews are sarcastic. Student essay grade prediction has been developed for possible use to automatically grade. The system grades as accurately as human graders. There’s a machine that can participate in the same capacity as humans in the United States’ most popular broadcast celebration of human knowledge and cultural literacy. On the TV quiz show Jeopardy!, IBM’s Watson computer triumphed. This machine learned to work proficiently enough with English to predict the answer to free-form inquiries across an open range of topics and defeat the two all-time human champs. Computers can literally read your mind. Researchers trained systems to decode a scan of your brain and determine which type of object you’re thinking about—such as certain tools, buildings, and food—with over 80 percent accuracy for some human subjects.


pages: 238 words: 77,730

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

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

They considered creating an enormous wall of Watson. It would take over much of the Jeopardy set, perhaps in the form of a projected brain, with neurons firing, or maybe a virtual sketchpad, dancing with algorithms and formulas as the machine cogitated. “They were pretty grand ideas,” said David Korchin, the project’s creative director. In talking to Jeopardy executives, though, it quickly became clear that they’d have to think smaller. If IBM’s Watson passed muster, it would be a guest on the show. It would not take it over. Its branding space, like that of any other contestant, would be limited to the face behind the podium—or whatever fit there. Jeopardy held the power and exercised it. If IBM’s computer was to benefit from an appearance on Jeopardy, the quiz show would lay down the rules. Now that Watson was reduced from a possible Jumbotron to a human-sized space, what sort of creature would occupy it?

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. One Final Jeopardy technique, Marktiple Choice, involves writing down a number of conceivable answers and then eliminating the unlikely ones. Formulated by a 2003 champion, Mark Dawson, it prods players to extend the search beyond the first response that pops into their mind.

and appeared to pay tribute to its creators, responding: “What is IBM?” Watson’s greatest weakness was in Final Jeopardy. According to the statistics, after the first sixty clues, Watson was leading an astounding 91 percent on the games. Yet that final clue, with its more difficult wording and complex wagering dynamics, lowered its winning percentage to 67 percent. Final Jeopardy turned Watson from a winner to a loser in one-quarter of the games. This was its vulnerability going into the match, and it would no doubt rise against the likes of Ken Jennings and Brad Rutter. The average human got Final Jeopardy right about half the time, according to Gondek. Watson hovered just below 50 percent. Ken Jennings, by contrast, aced Final Jeopardy clues at a 68 percent rate. That didn’t bode well for the machine. Brad Rutter, undefeated in his Jeopardy career, walked into the cavernous Wheel of Fortune studio.


pages: 118 words: 35,663

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

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

With this book, which is aimed at a broad audience rather than just the technical community, we hope to greatly expand the search for answers by stimulating new thinking within industry, government, and academia. 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. Their ingenuity made it possible for a system to excel at a game that requires both encyclopedic knowledge and lightning-quick recall.

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.

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. Expert systems (computer science) 2. Artificial intelligence. 3. Watson (Computer) I. Hamm, Steve. II. Title. QA76.76.E95K434 2014 006.3'3—dc23 2013026154 A Columbia University Press E-book.


pages: 252 words: 74,167

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

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

I was following a recipe created by IBM’s Watson, the Jeopardy!-winning AI described in the previous chapter. ‘The ideas for the recipes in this book weren’t generated by your average chef,’ reads the introduction in a cookbook entitled Cognitive Cooking with Chef Watson. ‘What kind of eccentric would ever dream up a Turkish-Korean Caesar salad or a Cuban lobster bouillabaisse? 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.

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. In all, Jennings’ seventy-four-show streak had netted him an impressive $2,520,700. Elementary, My Dear Watson Among the people who watched Ken Jennings’ astonishing Jeopardy! streak was a man named Charles Lickel. Lickel was a senior manager at IBM Research. He wasn’t a regular Jeopardy! viewer by any means, but in the summer of 2004 it was a hard show to ignore. One evening, Lickel and his team were eating dinner at a steakhouse.

DeepQA was capable of using natural language processing to find the structured information contained in each Jeopardy! clue. After working out what was meant by a question, DeepQA would next work out a list of possible answers – giving each one a different weighting according to the type of information, its reliability, its chances of being right, and the computer’s own learned experiences. These possible answers were then ranked, and the winning entry became the computer’s official response. The project began to gain momentum. Inside IBM it was nicknamed Blue J, before being renamed Watson after IBM’s first CEO, Thomas Watson. It became better and better at answering questions. During initial tests in 2006, Watson was given 500 clues from past Jeopardy! episodes. Of these, it managed to get just 15 per cent correct. By February 2010, the system had been improved sufficiently that it could defeat human players on a regular basis.


pages: 350 words: 98,077

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

Ada Lovelace, AI winter, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, Bernie Sanders, Claude Shannon: information theory, cognitive dissonance, computer age, computer vision, dark matter, Douglas Hofstadter, Elon Musk, en.wikipedia.org, 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, Mark Zuckerberg, natural language processing, Norbert Wiener, ought to be enough for anybody, 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, theory of mind, There's no reason for any individual to have a computer in his home - Ken Olsen, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!

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. The story goes that one of IBM’s vice presidents, Charles Lickel, was having dinner in a restaurant and noticed that the other patrons had suddenly become quiet. Everyone in the restaurant was focused on a television showing an episode of Jeopardy! in which the mega-champion Ken Jennings was competing.

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. History” and “At the Movies”). The host then reads a “clue” from that category, and the contestants compete to be the first to “buzz in” (push a buzzer). The first contestant to buzz in gets to respond with a “question” that corresponds to the clue.

Virtual assistants such as Apple’s Siri and Amazon’s Alexa were installed on our phones and in our homes and could deal with many of our spoken requests. 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.


pages: 239 words: 70,206

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

"Robert Solow", 23andMe, Affordable Care Act / Obamacare, Albert Einstein, big data - Walmart - Pop Tarts, bioinformatics, business cycle, business intelligence, call centre, cloud computing, computer age, conceptual framework, Credit Default Swap, crowdsourcing, Daniel Kahneman / Amos Tversky, Danny Hillis, data is the new oil, David Brooks, East Village, Edward Snowden, Emanuel Derman, Erik Brynjolfsson, everywhere but in the productivity statistics, Frederick Winslow Taylor, Google Glasses, impulse control, income inequality, indoor plumbing, industrial robot, informal economy, Internet of things, invention of writing, Johannes Kepler, John Markoff, John von Neumann, lifelogging, Mark Zuckerberg, market bubble, meta analysis, meta-analysis, money market fund, natural language processing, obamacare, pattern recognition, payday loans, personalized medicine, precision agriculture, pre–internet, Productivity paradox, RAND corporation, rising living standards, Robert Gordon, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, 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, unbanked and underbanked, underbanked, Von Neumann architecture, Watson beat the top human players on Jeopardy!

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. Yet at the end of 2012, Ferrucci joined Bridgewater Associates, a giant hedge fund, after what he describes as “a great, great career” at IBM, spanning twenty years.

In a technical sense, the law, formulated by Intel’s cofounder Gordon Moore in 1965, is the observation that transistor density on computer chips doubles about every two years and that computing power improves at that exponential pace. 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.

Forty thousand IBM consultants, engineers, sales people, and scientists working in the data business are spread across the company’s services, software, and research divisions. 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. The company hopes that Watson can become the equivalent of an operating system for artificial intelligence software.


pages: 484 words: 104,873

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

"Robert Solow", 3D printing, additive manufacturing, Affordable Care Act / Obamacare, AI winter, algorithmic trading, Amazon Mechanical Turk, artificial general intelligence, assortative mating, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Bernie Madoff, Bill Joy: nanobots, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chris Urmson, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, commoditize, computer age, creative destruction, debt deflation, deskilling, disruptive innovation, diversified portfolio, Erik Brynjolfsson, factory automation, financial innovation, Flash crash, Fractional reserve banking, Freestyle chess, full employment, 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, knowledge worker, labor-force participation, liquidity trap, low skilled workers, low-wage service sector, Lyft, 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, Plutocrats, post scarcity, precision agriculture, price mechanism, Ray Kurzweil, rent control, rent-seeking, reshoring, RFID, Richard Feynman, Rodney Brooks, Sam Peltzman, secular stagnation, self-driving car, Silicon Valley, 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, Thomas L Friedman, too big to fail, Tyler Cowen: Great Stagnation, uber lyft, union organizing, Vernor Vinge, very high income, Watson beat the top human players on Jeopardy!, women in the workforce

At the time, the story of IBM’s “Deep Blue” computer and how it had defeated world chess champion Garry Kasparov in 1997, was perhaps the most impressive demonstration of AI in action. 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. It’s a good bet that nearly all of us will be surprised by the progress that occurs in the coming years and decades.

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!

This WorkFusion information is based on a telephone conversation between the author and Adam Devine, vice president of Product Marketing & Strategic Partnerships at WorkFusion, on May 14, 2014. 14. 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. Baker, Final Jeopardy: Man vs. Machine and the Quest to Know Everything, p. 30. 17. Ibid., pp. 9 and 26. 18.


pages: 742 words: 137,937

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

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

Instead, by capturing and reusing huge bodies of past experience, this technology provides an approach to professional work that simply was not possible in the past. 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’. Previously, IBM had developed Deep Blue, a computer system that beat the world chess champion Garry Kasparov in 1997.

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.

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.


pages: 339 words: 88,732

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

"Robert Solow", 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, 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, British Empire, business cycle, business intelligence, business process, call centre, Charles Lindbergh, Chuck Templeton: OpenTable:, clean water, combinatorial explosion, computer age, computer vision, congestion charging, corporate governance, creative destruction, crowdsourcing, David Ricardo: comparative advantage, digital map, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, 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, global village, 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, 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, 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, pattern recognition, Paul Samuelson, payday loans, post-work, price stability, Productivity paradox, profit maximization, Ralph Nader, Ray Kurzweil, recommendation engine, Report Card for America’s Infrastructure, Robert Gordon, Rodney Brooks, Ronald Reagan, 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 Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Tyler Cowen: Great Stagnation, Vernor Vinge, Watson beat the top human players on Jeopardy!, winner-take-all economy, Y2K

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. Right now human diagnosticians reign supreme, but just as Watson soon got good enough to beat Ken Jennings, Brad Rutter, and all other human Jeopardy! players, we predict that Dr. Watson will soon be able to beat Dr. Welby, Dr. House, and real human doctors at their own game.

The translation services company Lionbridge has partnered with IBM to offer GeoFluent, an online application that instantly translates chats between customers and troubleshooters who do not share a language. 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.

title=History_of_Wikipedia&oldid=561664870 (accessed June 27, 2013); “Blogger (service),” Wikipedia, http://en.wikipedia.org/w/index.php?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.


pages: 309 words: 114,984

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

"Robert Solow", 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, collapse of Lehman Brothers, computer age, creative destruction, crowdsourcing, deskilling, disruptive innovation, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, Firefox, Frank Levy and Richard Murnane: The New Division of Labor, Google Glasses, Ignaz Semmelweis: hand washing, Internet of things, job satisfaction, Joseph Schumpeter, Kickstarter, knowledge worker, lifelogging, 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, Productivity paradox, Ralph Nader, RAND corporation, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, six sigma, Skype, Snapchat, software as a service, Steve Jobs, Steven Levy, 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

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

—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. Paul Grundy, IBM’s global director of healthcare transformation, told me: “Certainly none of us on the clinical side ever talked about this being Dr. Watson. That’s not what it does.” Added Michael Weiner, who runs IBM’s healthcare strategies: “Sure, we said, ‘Look, a machine beat a man at a quiz show,’ but I don’t think that’s the power of this conversation.

,” 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. Watson” headline is from J. Jackson, “Paging Dr. Watson, IBM’s Medical Advisor for the Future,” PC World, August 28, 2014. 105 Paul Grundy … told me Interview of Grundy by the author, July 21, 2014. 105 Added Michael Weiner, who runs IBM’s healthcare strategies Interview of Weiner by the author, July 28, 2014. 106 The name of the child, and the software, is Isabel www.isabelhealthcare.com. 106 It wasn’t in Jason Maude’s life plan Interview of Maude by the author, July 21, 2014, as well as L.


pages: 296 words: 78,631

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

23andMe, 3D printing, Air France Flight 447, Airbnb, airport security, augmented reality, autonomous vehicles, Brixton riot, chief data officer, computer vision, crowdsourcing, DARPA: Urban Challenge, Douglas Hofstadter, Elon Musk, Firefox, Google Chrome, Gödel, Escher, Bach, Ignaz Semmelweis: hand washing, John Markoff, Mark Zuckerberg, meta analysis, meta-analysis, 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, speech recognition, Stanislav Petrov, statistical model, Stephen Hawking, Steven Levy, Tesla Model S, The Wisdom of Crowds, Thomas Bayes, Watson beat the top human players on Jeopardy!, web of trust, William Langewiesche

To be transparent about why they came to a particular decision, rather than just inform us of the result. 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. And perhaps if facial recognition algorithms presented a number of possible matches, rather than just homing in on a single face, misidentification might be less of an issue.

Can we dare to imagine a machine that has a mastery of every scrap of cutting-edge medical research? 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. For the past few years, ever since Deep Blue had beaten Garry Kasparov at chess, the IBM bosses had been nagging Charles to find a new challenge worthy of the company’s attention.

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. Casey Ross and Ike Swetlitz, ‘IBM pitched its Watson supercomputer as a revolution in cancer care. It’s nowhere close’, STAT, 5 Sept. 2017, https://www.statnews.com/2017/09/05/watson-ibm-cancer/. 38.


pages: 237 words: 64,411

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

Affordable Care Act / Obamacare, Amazon Web Services, asset allocation, autonomous vehicles, bank run, bitcoin, Bob Noyce, Brian Krebs, business cycle, buy low sell high, Capital in the Twenty-First Century by Thomas Piketty, combinatorial explosion, computer vision, corporate governance, crowdsourcing, en.wikipedia.org, Erik Brynjolfsson, estate planning, 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, Loebner Prize, Mark Zuckerberg, mortgage debt, natural language processing, Own Your Own Home, pattern recognition, Satoshi Nakamoto, school choice, Schrödinger's Cat, Second Machine Age, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Skype, software as a service, The Chicago School, The Future of Employment, Turing test, Watson beat the top human players on Jeopardy!, winner-take-all economy, women in the workforce, working poor, Works Progress Administration

Moore, “Cramming More Components onto Integrated Circuits,” Electronics 38, no. 8 (1965). 10. 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. I’m indebted to my friend Carl Hewitt, known for his early logic programming language Planner, for his eyewitness report on this incident.

They are best understood as developing their own intuitions and acting on instinct: a far cry from the old canard that they “can only do what they are programmed to do.” 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. Check back in two years, and the price will likely be around $75.

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. The announcement says, “Watson can sift through an equivalent of about one million books or roughly 200 million pages of data, and analyze this information and provide precise responses in less than three seconds.


pages: 133 words: 42,254

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

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

However, the advance of Big Data technology doesn’t stop with tomorrow. 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.

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. To drive the Big Data–crunching message home, IBM has stated that 90 percent of the world’s data was created in the last two years, and 80 percent of that data is unstructured.

To drive the Big Data–crunching message home, IBM has stated that 90 percent of the world’s data was created in the last two years, and 80 percent of that data is unstructured. 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. Health care (or in this context, “Big Medicine”) has some specific challenges to overcome and some specific goals to achieve to realize the potential of Big Data: Big Medicine is drowning in information while also dying of thirst.


pages: 472 words: 117,093

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

"Robert Solow", 3D printing, additive manufacturing, AI winter, Airbnb, airline deregulation, airport security, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, backtesting, barriers to entry, bitcoin, blockchain, 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, crony capitalism, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, Dean Kamen, discovery of DNA, disintermediation, disruptive innovation, distributed ledger, double helix, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, everywhere but in the productivity statistics, family office, fiat currency, financial innovation, George Akerlof, global supply chain, Hernando de Soto, hive mind, information asymmetry, Internet of things, inventory management, iterative process, Jean Tirole, Jeff Bezos, jimmy wales, John Markoff, joint-stock company, Joseph Schumpeter, Kickstarter, law of one price, longitudinal study, Lyft, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Marc Andreessen, Mark Zuckerberg, meta analysis, meta-analysis, Mitch Kapor, moral hazard, multi-sided market, Myron Scholes, natural language processing, Network effects, new economy, Norbert Wiener, Oculus Rift, PageRank, pattern recognition, peer-to-peer lending, performance metric, plutocrats, Plutocrats, precision agriculture, prediction markets, pre–internet, price stability, principal–agent problem, Ray Kurzweil, Renaissance Technologies, Richard Stallman, ride hailing / ride sharing, risk tolerance, Ronald Coase, 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, TaskRabbit, Ted Nelson, The Market for Lemons, The Nature of the Firm, Thomas Davenport, Thomas L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, ubercab, Watson beat the top human players on Jeopardy!, winner-take-all economy, yield management, zero day

Phase two, which we believe we’re in now, has a start date that’s harder to pin down. It’s the time when science fiction technologies—the stuff of movies, books, and the controlled environments of elite research labs—started to appear in the real world. In 2010, Google unexpectedly announced that a fleet of completely autonomous cars had been driving on US roads without mishap. In 2011, IBM’s Watson supercomputer beat two human champions at the TV quiz show Jeopardy! By the third quarter of 2012, there were more than a billion users of smartphones, devices that combined the communication and sensor capabilities of countless sci-fi films. And of course, the three advances described at the start of this chapter happened in the past few years. As we’ll see, so did many other breakthroughs. They are not flukes or random blips in technological progress.

‡ Watson doesn’t (yet) understand language the way humans do, but it does find patterns and associations in written text that it can use to populate its knowledge base. § 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. # Or it might not be a good idea.

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. But over time, the intent is for the system to “learn the history of past payment assessment to inherit the experience and expertise of assessor workers.”


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

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

In many ways this result is a tribute to the genius of Kasparov that his human brain could effectively compete with such a powerful machine. 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!

There are also some general purpose game-playing programs that can learn to play any minimax style board game, but they do not play very well. 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. At the end of the game, Watson had $35,734 against Rutter’s $10,400 and Jenning’s $4,800.

But a complete understanding is also not necessary, as a superficial understanding can still be very useful. 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.


pages: 372 words: 101,174

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, Claude Shannon: information theory, cloud computing, computer age, Dean Kamen, discovery of DNA, double helix, en.wikipedia.org, epigenetics, George Gilder, Google Earth, Isaac Newton, iterative process, Jacquard loom, John von Neumann, Law of Accelerating Returns, linear programming, Loebner Prize, mandelbrot fractal, Norbert Wiener, optical character recognition, 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. It needed to master virtually every area of human intellectual endeavor, including history, science, literature, the arts, culture, and more.

Secondly, they provide a solid basis for the lower levels of the conceptual hierarchy so that the automated learning can begin to learn higher conceptual levels. 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. I also predicted that when that happened, we would either downgrade our opinion of human intelligence, upgrade our opinion of machine intelligence, or downplay the importance of chess, and that if history was a guide, we would minimize chess.

The design and analysis of algorithms, and the study of the inherent computational complexity of problems, are fundamental subfields of computer science. 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.”


pages: 392 words: 108,745

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

Albert Einstein, AltaVista, Amazon Mechanical Turk, Amazon Web Services, augmented reality, Automated Insights, autonomous vehicles, Chuck Templeton: OpenTable:, cloud computing, computer age, Donald Trump, Elon Musk, information retrieval, Internet of things, Jacques de Vaucanson, Jeff Bezos, lateral thinking, Loebner Prize, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Mark Zuckerberg, Menlo Park, natural language processing, 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, 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. If ten of its documents stated that Martin Luther King Jr. was born in 1929 and two of them had 1930 as the date, Watson would go with 1929.

Another business deployment of neural network-generated writing happened in the realm of advertising. 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.” In settings where creativity is valued over strict literal correctness, the potential of neurally generated responses can shine.

, 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?”


pages: 347 words: 97,721

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

AI winter, Andy Kessler, artificial general intelligence, asset allocation, Automated Insights, autonomous vehicles, basic income, Baxter: Rethink Robotics, business intelligence, business process, call centre, carbon-based life, Clayton Christensen, clockwork universe, commoditize, conceptual framework, dark matter, David Brooks, deliberate practice, deskilling, digital map, disruptive innovation, Douglas Engelbart, Edward Lloyd's coffeehouse, Elon Musk, Erik Brynjolfsson, estate planning, fixed income, 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, 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, knowledge worker, labor-force participation, lifelogging, longitudinal study, loss aversion, Mark Zuckerberg, Narrative Science, natural language processing, Norbert Wiener, nuclear winter, pattern recognition, performance metric, Peter Thiel, precariat, quantitative trading / quantitative finance, Ray Kurzweil, Richard Feynman, risk tolerance, Robert Shiller, Robert Shiller, 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, transaction costs, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, Works Progress Administration, Zipcar

They don’t initiate analyses on their own, they don’t understand the larger purpose of what they’re doing, and they don’t tell you when they aren’t up to the task at hand. As Mike Rhodin, the head of IBM’s Watson business unit, noted, “Watson doesn’t have the ability to think on its own,” and neither does any other intelligent system thus far created.13 There is some progress, however, in the area of telling humans whether the output of smart machines should be used and trusted. The statistically based systems used for analyzing words and images are increasingly capable of this latter task. In fact, it should be a requirement that all such systems tell you when you should and shouldn’t trust their results, and some already do this. You may recall, for example, that when Watson dominated the Jeopardy! game in 2011, the program displayed a “confidence bar” ranking the top three answers and its confidence level in each one.

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. Most scientists would struggle to identify one such protein in a year; Watson took only a few weeks to find six (although, to be fair, it took several years to prepare Watson to do this).6 Other organizations are using similar technologies to glean insights from natural-language content that exists in enormous volume.

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.


pages: 360 words: 85,321

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, Benoit Mandelbrot, butterfly effect, call centre, Chance favours the prepared mind, Claude Shannon: information theory, collateralized debt obligation, 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.” Washington Post, April 15, 2015. http://www.washingtonpost.com/blogs/innovations/wp/2015/04/15/how-ibm-watson-helped-me-to-create-a-tastier-burrito-than-chipotle/. 167“Let’s try poker”: Wise, Gary.

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. In contrast, human professionals are able to evaluate other players much more quickly.

Afterward, the human player told the bot’s creators, “You have a very strong program. 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. It didn’t take long for the newcomer to edge into the lead.


pages: 424 words: 114,905

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

23andMe, Affordable Care Act / Obamacare, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, augmented reality, autonomous vehicles, bioinformatics, blockchain, cloud computing, cognitive bias, Colonization of Mars, computer age, computer vision, conceptual framework, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, dark matter, David Brooks, digital twin, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, fault tolerance, George Santayana, Google Glasses, ImageNet competition, Jeff Bezos, job automation, job satisfaction, Joi Ito, Mark Zuckerberg, medical residency, meta analysis, meta-analysis, microbiome, natural language processing, new economy, Nicholas Carr, nudge unit, pattern recognition, performance metric, personalized medicine, phenotype, placebo effect, randomized controlled trial, recommendation engine, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, speech recognition, Stephen Hawking, text mining, the scientific method, Tim Cook: Apple, War on Poverty, Watson beat the top human players on Jeopardy!, working-age population

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. It turns out all Watson did to beat humans in the game show was to essentially ingest Wikipedia, from which more than 95 percent of the show’s questions were sourced.

., “Doctors Get Their Own Second Opinions,” Atlantic. 2017. 27. “Doctor Evidence Brings Valuable Health Data to IBM Watson Ecosystem,” IBM Press Release. 2015. 28. Ross, C., and I. Swetlitz, “IBM Pitched Its Watson Supercomputer as a Revolution in Cancer Care: It’s Nowhere Close,” Stat News. 2017. 29. Patel, N. M., et al., “Enhancing Next-Generation Sequencing-Guided Cancer Care Through Cognitive Computing.” Oncologist, 2018. 23(2): pp. 179–185. 30. Patel, et al., “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.

The founding of ImageNet by Fei-Fei Li in 2007 had historic significance. 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.


pages: 397 words: 110,130

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

4chan, A Declaration of the Independence of Cyberspace, augmented reality, barriers to entry, Benjamin Mako Hill, butterfly effect, citizen journalism, Claude Shannon: information theory, conceptual framework, corporate governance, crowdsourcing, Deng Xiaoping, discovery of penicillin, disruptive innovation, Douglas Engelbart, Douglas Engelbart, drone strike, Edward Glaeser, Edward Thorp, en.wikipedia.org, experimental subject, Filter Bubble, Freestyle chess, Galaxy Zoo, Google Earth, Google Glasses, Gunnar Myrdal, Henri Poincaré, hindsight bias, hive mind, Howard Rheingold, information retrieval, iterative process, jimmy wales, Kevin Kelly, Khan Academy, knowledge worker, lifelogging, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Netflix Prize, Nicholas Carr, Panopticon Jeremy Bentham, patent troll, pattern recognition, pre–internet, Richard Feynman, Ronald Coase, Ronald Reagan, Rubik’s Cube, sentiment analysis, Silicon Valley, Skype, Snapchat, Socratic dialogue, spaced repetition, superconnector, telepresence, telepresence robot, The Nature of the Firm, the scientific method, The Wisdom of Crowds, theory of mind, transaction costs, 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. “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?

“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. 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?

But the Internet has enabled us to exercise new powers, new ways to talk to each other, to pass things around, to quickly broadcast a hunch—“hey, wasn’t there a joke involving Toto in that movie?”—and get feedback. Using the mammoth stores of knowledge online, people were able to scrutinize and pick apart the reasoning of the world’s most sophisticated artificial intelligence. For serious fun, imagine if we emulated Kasparov fully here and used Watson to create an entirely new game show—a sort of Advanced Jeopardy!, as it were. Imagine two teams facing one another, a human paired with a Watson-style AI. What type of fiendish, seemingly impossible clues could you throw at a cyborg composed of Ken Jennings and Watson—the brute force of a machine paired with the fuzzy intuition of a human? What puzzles could that combination tackle in everyday life? How should you respond when you get powerful new tools for finding answers?


pages: 389 words: 119,487

21 Lessons for the 21st Century by Yuval Noah Harari

1960s counterculture, accounting loophole / creative accounting, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, algorithmic trading, augmented reality, autonomous vehicles, Ayatollah Khomeini, basic income, Bernie Sanders, bitcoin, blockchain, Boris Johnson, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon-based life, cognitive dissonance, computer age, computer vision, cryptocurrency, cuban missile crisis, decarbonisation, deglobalization, Donald Trump, failed state, 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, meta-analysis, Mohammed Bouazizi, mutually assured destruction, Naomi Klein, obamacare, pattern recognition, post-work, purchasing power parity, race to the bottom, RAND corporation, Ronald Reagan, Rosa Parks, Scramble for Africa, self-driving car, Silicon Valley, Silicon Valley startup, transatlantic slave trade, 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. It is true that for several years after Deep Blue defeated Kasparov, human–computer cooperation flourished in chess.

., ‘Algorithmic Trading’, Computer 44:11 (2011), 61–9; financial planning, portfolio management etc.: 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.

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?


pages: 385 words: 111,113

Augmented: Life in the Smart Lane by Brett King

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

This will also dramatically change the way we view diagnosis. 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.

Imagine having a dedicated concierge, a personal trainer, a tutor for your children and such, not as a dedicated human resource, but built into technology around us. 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. Watson is composed of 90 IBM Power 750 servers, each of which uses a 3.5 GHz POWER7 8-core processor, with four threads per core.

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.


pages: 308 words: 84,713

The Glass Cage: Automation and Us by Nicholas Carr

Airbnb, Airbus A320, Andy Kessler, Atul Gawande, autonomous vehicles, Bernard Ziegler, business process, call centre, Captain Sullenberger Hudson, Charles Lindbergh, Checklist Manifesto, cloud computing, computerized trading, David Brooks, deliberate practice, deskilling, digital map, Douglas Engelbart, drone strike, Elon Musk, Erik Brynjolfsson, Flash crash, Frank Gehry, Frank Levy and Richard Murnane: The New Division of Labor, Frederick Winslow Taylor, future of work, global supply chain, Google Glasses, Google Hangouts, High speed trading, indoor plumbing, industrial robot, Internet of things, 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, Lyft, 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, 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, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley ideology, software is eating the world, Stephen Hawking, Steve Jobs, TaskRabbit, technoutopianism, The Wealth of Nations by Adam Smith, turn-by-turn navigation, US Airways Flight 1549, Watson beat the top human players on Jeopardy!, William Langewiesche

Extrapolations based on recent computing trends have a way of turning into fantasies. 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. It was, essentially, searching a vast database of documents for potential answers and then, by working simultaneously through a variety of prediction routines, determining which answer had the highest probability of being the correct one.

., 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.

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.


pages: 337 words: 103,522

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

3D printing, Ada Lovelace, Albert Einstein, Alvin Roth, Andrew Wiles, Automated Insights, Benoit Mandelbrot, Claude Shannon: information theory, computer vision, correlation does not imply causation, crowdsourcing, data is the new oil, Donald Trump, double helix, Douglas Hofstadter, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, Flash crash, Gödel, Escher, Bach, Henri Poincaré, Jacquard loom, John Conway, Kickstarter, Loebner Prize, mandelbrot fractal, Minecraft, music of the spheres, 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, 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. What makes Jeopardy! more of a challenge is the style of the questions. They are posed in a sort of inverted manner, where the quizmaster reads something that sounds like the answer to a question and the contestant has to respond with the question.

While some at IBM thought that dedicating time to winning such a trivial game show was a waste of resources, others insisted that success would signal a step change in the ability of machines to parse meaning in language. If Kasparov was the champion to beat at chess, the Jeopardy! kings were Brad Rutter and Ken Jennings, both of whom had notched up extraordinary winning streaks. 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.

They provide great publicity stunts for a company that needs to sell its product because everyone loves the drama of human versus machine. 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. This shift from scheduling to training is part of why IBM calls this effort cognitive computing.


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, 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, wikimedia commons

." /> <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. 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.

The final chapter will demonstrate step-by-step use cases for a variety of real-life situations. 214 Chapter 8 ■ Big Data Applications References 1. 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?lang=en. Accessed 23 April 2015. 5. Le Hors, A. (2012) Interview: IBM on the Linked Data Platform. www.w3.org/ blog/2012/05/interview-ibm-on-a-linked-data/.

You are capable of describing and modeling Semantic Web Services with OWL-S, WSDL, WSML, and WS-BPEL. You can run complex SPARQL queries on large LOD datasets, such as DBpedia and Wikidata, and even encourage data reuse with your own easy-to-access OpenLink Virtuoso, Fuseki, or 4store SPARQL endpoint. Finally, you learned about Big Data applications leveraging Semantic Web technologies, such as the Google Knowledge Vault, the Facebook Social Graph, IBM Watson, and the Linked Data Service of the largest library in the world. References 226 1. Das, S., Sundara, S., Cyganiak, R. (eds.) (2012) R2RML Processors and Mapping Documents. In: R2RML: RDB to RDF Mapping Language. www.w3.org/TR/ r2rml/#dfn-r2rml-mapping. Accessed 1 May 2015. 2. Arenas, A., Bertails, A., Prud’hommeaux, E., Sequeda, J. (eds.) (2012) Direct Mapping of Relational Data to RDF. www.w3.org/TR/rdb-direct-mapping/.


pages: 677 words: 206,548

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

23andMe, 3D printing, active measures, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, algorithmic trading, 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, Brian Krebs, business process, butterfly effect, call centre, Charles Lindbergh, Chelsea Manning, cloud computing, cognitive dissonance, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, data acquisition, data is the new oil, Dean Kamen, disintermediation, don't be evil, double helix, Downton Abbey, drone strike, Edward Snowden, Elon Musk, Erik Brynjolfsson, Filter Bubble, Firefox, Flash crash, future of work, game design, global pandemic, Google Chrome, Google Earth, Google Glasses, Gordon Gekko, high net worth, High speed trading, hive mind, Howard Rheingold, hypertext link, illegal immigration, impulse control, industrial robot, 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, knowledge worker, Kuwabatake Sanjuro: assassination market, Law of Accelerating Returns, Lean Startup, license plate recognition, lifelogging, litecoin, low earth orbit, M-Pesa, 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, move fast and break things, Nate Silver, national security letter, natural language processing, obamacare, Occupy movement, Oculus Rift, off grid, offshore financial centre, optical character recognition, Parag Khanna, pattern recognition, peer-to-peer, personalized medicine, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, RAND corporation, ransomware, Ray Kurzweil, refrigerator car, RFID, ride hailing / ride sharing, Rodney Brooks, Ross Ulbricht, Satoshi Nakamoto, Second Machine Age, security theater, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart grid, smart meter, Snapchat, social graph, software as a service, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, supply-chain management, technological singularity, telepresence, telepresence robot, Tesla Model S, The Future of Employment, The Wisdom of Crowds, Tim Cook: Apple, trade route, uranium enrichment, Wall-E, Watson beat the top human players on Jeopardy!, Wave and Pay, We are Anonymous. We are Legion, web application, Westphalian system, WikiLeaks, Y Combinator, 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.” He might want to rethink that. 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.”

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. These moves are putting supercomputerlevel artificial intelligence into the hands of both small companies and individuals—and in the future likely Crime, Inc. as well.

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?


pages: 181 words: 52,147

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

23andMe, 3D printing, Airbnb, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, Bernie Sanders, bitcoin, blockchain, clean water, correlation does not imply causation, distributed ledger, Donald Trump, double helix, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Google bus, Hyperloop, income inequality, Internet of things, job automation, Kevin Kelly, Khan Academy, Kickstarter, Law of Accelerating Returns, license plate recognition, life extension, longitudinal study, Lyft, M-Pesa, Menlo Park, microbiome, mobile money, new economy, personalized medicine, phenotype, precision agriculture, RAND corporation, Ray Kurzweil, recommendation engine, Ronald Reagan, Second Machine Age, self-driving car, Silicon Valley, Skype, smart grid, stem cell, Stephen Hawking, Steve Wozniak, Stuxnet, supercomputer in your pocket, Tesla Model S, 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 narrow and practical stuff that is going to change our lives. 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. A.I. technologies will also be able to analyze a continual flow of data on millions of patients and on the medications they have taken to determine which truly had a positive effect on them, which ones created adverse reactions and new ailments, and which did both.

Dewar, The Information Age and the Printing Press: Looking Backward to See Ahead, Santa Monica, California: RAND Corporation, 1998, http://www.rand.org/pubs/papers/P8014.html (accessed 21 October 2016). PART TWO CHAPTER FIVE 1. 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. Daniela Hernandez, “Artificial intelligence is now telling doctors how to treat you,” WIRED 6 February 2014, https://www.wired.com/2014/06/ai-healthcare (accessed 21 October 2016). 5.

The ability of patients to take regular tests in the comfort of their homes and upload data to shared servers will make it possible to dramatically increase the quality, and lower the cost, of the health care they receive. 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.


pages: 236 words: 50,763

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, Johannes Kepler, John von Neumann, linear programming, new economy, NP-complete, Occam's razor, P = NP, Paul Erdős, Richard Feynman, Rubik’s Cube, 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. The 2,880 simultaneous cores will seem a tiny number in the near future as Moore’s law continues to put more on each machine.

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!)

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?”


pages: 588 words: 131,025

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

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

The more data fed into the program or computer, the more it learns, the better the algorithms, and supposedly the smarter it gets. 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.

House model is ideally suited for computer automation in medicine and it is precisely the output from IBM Watson.70,71 The pretest probability includes all of the medical literature that has been published, up to date. When you submit to IBM Watson all the pieces of evidence about a particular patient in search of the diagnosis, you get a list of the possible ones. Attached to each is a weight or probability (likelihood ratio). Further, the Bayesian model for computer-assisted diagnosis is quickly becoming part of clinical care and can extend to treatment recommendations. A web-based information resource known as Modernizing Medicine has collective knowledge from over fifteen million patient visits and four thousand physicians with treatments and outcomes of each patient.72 So added to IBM Watson’s differential diagnosis capability, a list of treatments with weighted assignments of probability could be generated that matches the patient at hand to all the patients in the database.

“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. M. van Rijmenam, “How Machine Learning Could Result in Great Applications for Your Business,” Big Data-Startups Blog, January 10, 2014, http://www.bigdata-startups.com/machine-learning-result-great-applications-business/. 35.


pages: 293 words: 78,439

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

activist fund / activist shareholder / activist investor, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Amazon Web Services, autonomous vehicles, barriers to entry, Ben Horowitz, blockchain, business process, business process outsourcing, call centre, Clayton Christensen, cloud computing, commoditize, corporate governance, creative destruction, crowdsourcing, death of newspapers, disintermediation, disruptive innovation, distributed ledger, diversified portfolio, Internet of things, invention of hypertext, inventory management, Jeff Bezos, job automation, job satisfaction, Joseph Schumpeter, Kickstarter, late fees, Lean Startup, Lyft, M-Pesa, Marc Andreessen, Mark Zuckerberg, Minecraft, obamacare, Parag Khanna, Paul Graham, peer-to-peer lending, pez dispenser, recommendation engine, self-driving car, shareholder value, side project, Silicon Valley, Skype, software as a service, software is eating the world, Steve Jobs, 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

In our view that capability is very likely to remain science fiction, but there will be ways to teach people what they need, when they need it, in a way that maximizes their ability to learn it. 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? Education and health care are massive, complex industries, and Netflix would need a serious dose of outside capabilities to succeed in either one.

., 17, 18, 19 Hall, Taddy, 38 The Haloid Photographic Company, 13 Hancock, Jake, 84 Hardless, Edgar, 143 Harvard Business School MBA program, 103, 110–113, 176 Hastings, Reed, 23, 32–36 decision making by, 93–95 education and, 69–70 HBO, 5, 35 HBX, 113 health care industry acquisitions in, 22–23 Aetna, 23, 87, 99–102 constrained market in, 59–61 illusory nonconsumption in, 62–63 Johnson and Johnson, 16–22 medical devices, 203–204 Medtronic, 72–73 Netflix and IBM in, 70 retailization of, 101 Healthy Family, 127 Healthy Heart for All, 73, 74 Hill, Andy, 131 Hill, Linda, 146 Hinckley, Gordon B., 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.


pages: 602 words: 177,874

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

3D printing, additive manufacturing, affirmative action, Airbnb, AltaVista, Amazon Web Services, autonomous vehicles, Ayatollah Khomeini, barriers to entry, Berlin Wall, Bernie Sanders, bitcoin, blockchain, Bob Noyce, business cycle, business process, call centre, centre right, Chris Wanstrath, Clayton Christensen, clean water, cloud computing, corporate social responsibility, creative destruction, crowdsourcing, David Brooks, demand response, demographic dividend, demographic transition, Deng Xiaoping, Donald Trump, Erik Brynjolfsson, failed state, Fall of the Berlin Wall, Ferguson, Missouri, first square of the chessboard / second half of the chessboard, Flash crash, game design, gig economy, global pandemic, global supply chain, 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 skilled workers, Lyft, Marc Andreessen, Mark Zuckerberg, mass immigration, Maui Hawaii, Menlo Park, Mikhail Gorbachev, mutually assured destruction, Nelson Mandela, pattern recognition, planetary scale, pull request, Ralph Waldo Emerson, ransomware, Ray Kurzweil, Richard Florida, ride hailing / ride sharing, Robert Gordon, Ronald Reagan, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, smart cities, South China Sea, Steve Jobs, supercomputer in your pocket, TaskRabbit, The Rise and Fall of American Growth, Thomas L Friedman, 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

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. It meant that “every ten milliseconds was gold,” said Kelly. But what made Watson so fast, and eventually so accurate, was not that it was actually “learning” per se, but its ability to self-improve by using all its big data capacities and networking to make faster and faster statistical correlations over more and more raw material.

Today, the IBM team notes, you can get genetic sequencing of your tumor with a lab test in an hour and the doctor, using Watson, can pinpoint those drugs to which that particular tumor is known to best respond—also in an hour. Today, IBM will feed a medical Watson 3,000 images, 200 of which are of melanomas and 2,800 are not, and Watson then uses its algorithm to start to learn that the melanomas have these colors, topographies, and edges. And after looking at tens of thousands and understanding the features they have in common, it can, much quicker than a human, identify particularly cancerous ones. That capability frees up doctors to focus where they are most needed—with the patient. In other words, the magic of Watson happens when it is combined with the unique capabilities of a human doctor—such as intuition, empathy, and judgment. The synthesis of the two can lead to the creation and application of knowledge that is far superior to anything either could do on their own. The Jeopardy! game, said Kelly, pitted two human champions against a machine; the future will be all about Watson and doctors—man and machine—solving problems together.

—Katarn to Skywalker Yeah—and I sense it, too. 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.


pages: 391 words: 71,600

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

"Robert Solow", 3D printing, 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, Deng Xiaoping, Donald Trump, Douglas Engelbart, Edward Snowden, Elon Musk, en.wikipedia.org, equal pay for equal work, everywhere but in the productivity statistics, fault tolerance, Gini coefficient, global supply chain, Google Glasses, Grace Hopper, industrial robot, Internet of things, Jeff Bezos, job automation, John Markoff, John von Neumann, knowledge worker, Mars Rover, Minecraft, Mother of all demos, NP-complete, Oculus Rift, pattern recognition, place-making, Richard Feynman, Robert Gordon, Ronald Reagan, Second Machine Age, self-driving car, side project, Silicon Valley, Skype, Snapchat, special economic zone, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, telepresence, telerobotics, The Rise and Fall of American Growth, 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

Headlines were made in 1996 when IBM’s Deep Blue demonstrated that a computer could win a champion-level chess game against a human. 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. Ultimately, humans and machines will work together—not against one another.

., 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.

., 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.


pages: 322 words: 88,197

Wonderland: How Play Made the Modern World by Steven Johnson

Ada Lovelace, Alfred Russel Wallace, Antoine Gombaud: Chevalier de Méré, Berlin Wall, bitcoin, Book of Ingenious Devices, Buckminster Fuller, Claude Shannon: information theory, Clayton Christensen, colonial exploitation, computer age, conceptual framework, crowdsourcing, cuban missile crisis, Drosophila, Edward Thorp, Fellow of the Royal Society, game design, global village, 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, 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, probability theory / Blaise Pascal / Pierre de Fermat, profit motive, QWERTY keyboard, Ray Oldenburg, 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, 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. There is something lovely about the idea of the world’s most advanced thinking machine learning about the world by browsing a crowdsourced encyclopedia.

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. But when you look at Watson in the context of the history of computer science, the Jeopardy! element becomes much more than just a public relations stunt. Consider how many watershed moments in the history of computation have involved games: Babbage tinkering with the idea of a chess-playing “analytic engine”; Turing’s computer chess musings; Thorp and Shannon at the roulette table in Vegas; the interface innovations introduced by Spacewar!

As Jennings later described it in an essay: The computer’s techniques for unraveling Jeopardy! clues sounded just like mine. That machine zeroes in on key words in a clue, then combs its memory (in Watson’s case, a 15-terabyte data bank of human knowledge) for clusters of associations with those words. It rigorously checks the top hits against all the contextual information it can muster: the category name; the kind of answer being sought; the time, place, and gender hinted at in the clue; and so on. And when it feels “sure” enough, it decides to buzz. This is all an instant, intuitive process for a human Jeopardy! 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.


pages: 720 words: 197,129

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, Apple II, augmented reality, back-to-the-land, beat the dealer, Bill Gates: Altair 8800, bitcoin, Bob Noyce, Buckminster Fuller, Byte Shop, c2.com, call centre, citizen journalism, Claude Shannon: information theory, Clayton Christensen, commoditize, computer age, crowdsourcing, cryptocurrency, Debian, desegregation, Donald Davies, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dynabook, El Camino Real, Electric Kool-Aid Acid Test, en.wikipedia.org, Firefox, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Hacker Ethic, Haight Ashbury, Howard Rheingold, Hush-A-Phone, HyperCard, hypertext link, index card, Internet Archive, Jacquard loom, Jaron Lanier, Jeff Bezos, jimmy wales, John Markoff, John von Neumann, Joseph-Marie Jacquard, Leonard Kleinrock, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Mitch Kapor, Mother of all demos, new economy, New Journalism, Norbert Wiener, Norman Macrae, packet switching, PageRank, Paul Terrell, pirate software, popular electronics, pre–internet, RAND corporation, Ray Kurzweil, 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, technological singularity, technoutopianism, Ted Nelson, The Coming Technological Singularity, The Nature of the Firm, The Wisdom of Crowds, Turing complete, Turing machine, Turing test, 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

“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.

Steve Case’s AOL offers direct access to the Internet. 1994 Justin Hall launches Web log and directory. 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. Instead, most of the innovations of the digital age were done collaboratively.

“In event of an enemy attack, your Regency TR-1 will become one of your most valued possessions,” the first owner’s manual declared. 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.


pages: 586 words: 186,548

Architects of Intelligence by Martin Ford

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

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. That’s almost always the answer. In Watson’s case it’s because about 95% of the answers in Jeopardy turn out to be the titles of Wikipedia pages.

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. But these systems are nowhere near the human capacity for language, and you see that when you try to engage in a dialogue with them.

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? What’s the path that led you to AI? DAVID FERRUCCI: I started back before computers were an everyday term.


pages: 413 words: 119,587

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

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

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. In practice, Watson had an overwhelming mechanical advantage that had little to do with artificial intelligence.

Watson had originally been designed as a “question-answering” system, making progress toward the fundamental goals in artificial intelligence. With Cognea, Watson gained the ability to carry on a conversation. 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.

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.


pages: 344 words: 104,077

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

agricultural Revolution, Airbnb, Albert Einstein, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, Asperger Syndrome, Baxter: Rethink Robotics, bitcoin, blockchain, business process, call centre, clean water, creative destruction, crowdsourcing, Donald Trump, Douglas Engelbart, Douglas Engelbart, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, experimental economics, Exxon Valdez, future of work, Galaxy Zoo, gig economy, happiness index / gross national happiness, 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, Marshall McLuhan, Occupy movement, Pareto efficiency, pattern recognition, prediction markets, price mechanism, Ray Kurzweil, Rodney Brooks, Ronald Coase, 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, 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.

And, of course, the human stylists can relate to the human customers in a more personal way than the machine does. 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.

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 result is a very robust system for taking into account many different kinds of knowledge while learning along the way how best to combine all these different points of view.


pages: 501 words: 114,888

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

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

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. To understand what these neural net-powered AIs make possible, consider the service economy, which now accounts for over 80 percent of the US GDP.

For example, by analyzing word choice and vocal style, Beyond Verbal’s system can tell what kind of shopper the person on the line actually is. 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.

The most unusual development isn’t just the uber-empowerment of creators, it’s also the kinds of creators being empowered. 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.


pages: 345 words: 84,847

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

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

Photo by Kevin Sprague Chapter 8 Velasquez: La Meninas Museo National del Prado, Spain Pablo Picasso: five variations on “Las Meninas,” 1957, oil on canvas Museo Picasso, Barcelona, Spain/ Bridgeman Images. © 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.

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.


pages: 533

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

3D printing, additive manufacturing, affirmative action, agricultural Revolution, Airbnb, airport security, Andrew Keen, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, bitcoin, blockchain, brain emulation, British Empire, business process, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Cass Sunstein, cellular automata, cloud computing, computer age, computer vision, continuation of politics by other means, correlation does not imply causation, crowdsourcing, cryptocurrency, digital map, distributed ledger, Donald Trump, easy for humans, difficult for computers, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, Filter Bubble, future of work, Google bus, Google X / Alphabet X, Googley, industrial robot, informal economy, intangible asset, Internet of things, invention of the printing press, invention of writing, Isaac Newton, Jaron Lanier, John Markoff, Joseph Schumpeter, Kevin Kelly, knowledge economy, lifelogging, Metcalfe’s law, mittelstand, more computing power than Apollo, move fast and break things, move fast and break things, natural language processing, Network effects, new economy, night-watchman state, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, payday loans, price discrimination, price mechanism, RAND corporation, ransomware, Ray Kurzweil, Richard Stallman, ride hailing / ride sharing, road to serfdom, Robert Mercer, Satoshi Nakamoto, Second Machine Age, selection bias, self-driving car, sexual politics, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart contracts, Snapchat, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, technological singularity, the built environment, The Structural Transformation of the Public Sphere, The Wisdom of Crowds, Thomas L Friedman, universal basic income, urban planning, Watson beat the top human players on Jeopardy!, working-age population

‘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.

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. The system made its discovery after digesting all the published literature on ALS and parsing every gene in the human genome.This took Watson a matter of months; humans would have taken years.23 In early 2017, Fukoku Mutual Life Insurance in Japan sacked thirty-four of its staff and replaced them with Watson’s ‘Explorer’ platform, which OUP CORRECTED PROOF – FINAL, 26/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS Increasingly Capable Systems 33 will chomp through tens of thousands of medical records and certificates, data on hospital stays, and surgical information to calculate payouts to policyholders.

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).


pages: 479 words: 144,453

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

23andMe, agricultural Revolution, algorithmic trading, Anne Wojcicki, anti-communist, Anton Chekhov, autonomous vehicles, Berlin Wall, call centre, Chris Urmson, cognitive dissonance, Columbian Exchange, computer age, Deng Xiaoping, don't be evil, 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, Intergovernmental Panel on Climate Change (IPCC), invention of writing, invisible hand, Isaac Newton, job automation, John Markoff, Kevin Kelly, lifelogging, means of production, Mikhail Gorbachev, Minecraft, Moneyball by Michael Lewis explains big data, Monkeys Reject Unequal Pay, mutually assured destruction, new economy, pattern recognition, Peter Thiel, placebo effect, Ray Kurzweil, self-driving car, Silicon Valley, Silicon Valley ideology, stem cell, Steven Pinker, telemarketer, 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

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. 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.

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.

Goode’ 257–61, 358, 387, 388 Bible 46; animal kingdom and 76–7, 93–5; Book of Genesis 76–8, 77, Dataism and 381; 93–4, 97; composition of, research into 193–5; evolution and 102; homosexuality and 192–3, 195, 275; large-scale human cooperation and 174; Old Testament 48, 76; power of shaping story 172–3; scholars scan for knowledge 235–6; self-absorption of monotheism and 173, 174; source of authority 275–6; unique nature of humanity, promotes 76–8 biological poverty line 3–6 biotechnology 14, 43–4, 46, 98, 269, 273, 375 see also individual biotech area Bismarck, Otto von 31, 271 Black Death 6–8, 6, 7, 11, 12 Borges, Jorge Luis: ‘A Problem’ 299–300 Bostrom, Nick 327 Bowden, Mark: Black Hawk Down 255 bowhead whale song, spectrogram of 358, 358 brain: Agricultural Revolution and 156–7, 160; artificial intelligence and 278, 278; biological engineering and 44; brain–computer interfaces 48, 54, 353, 359; consciousness and 105–13, 116, 118–19, 121–4, 125; cyborg engineering and 44–5; Dataism and 368, 393, 395; free will and 282–8; happiness and 37, 38, 41; self and 294–9, 304–5; size of 131, 132; transcranial stimulators and manipulation of 287–90; two hemispheres 291–4 brands 156–7, 159–60, 159, 162 Brezhnev, Leonid 273 Brin, Sergey 28, 336 Buddhism 41, 42, 94, 95, 181, 185, 187, 221, 246, 356 Calico 24, 28 Cambodia 264 Cambridge Declaration on Consciousness, 2012 122 capitalism 28, 183, 206, 208–11, 216–17, 218–19, 251–2, 259, 273–4, 369–73, 383–6, 396 see also economics/economy Caporreto, Battle of, 1917 301 Catholic Church 147, 183; Donation of Constantine 190–2, 193; economic and technological innovations and 274; marriage and 26; papal infallibility principle 147, 190, 270–1; Protestant revolt against 185–7; religious intolerance and 198; Thirty Years War and 242, 243, 246; turns from creative into reactive force 274–5 see also Bible and Christianity Ceauçescu, Nicolae 133–4, 134, 135–6, 137 Charlie Hebdo 226 Château de Chambord, Loire Valley, France 62, 62 Chekhov Law 17, 18, 55 child mortality 10, 33, 175 childbirth, narration of 297–8, 297 China 1, 269; biotech and 336; Civil War 263; economic growth and 206, 207, 210; famine in 5, 165–6; Great Leap Forward 5, 165–6, 376; Great Wall of 49, 137–8, 178; liberalism, challenge to 267–8; pollution in 213–14; Taiping Rebellion, 1850–64 271; Three Gorges Dam, building of 163, 188, 196 Chinese river dolphin 188, 196, 395 Christianity: abortion and 189; animal welfare and 90–6; change from creative to reactive force 274–6; economic growth and 205; homosexuality and 192–3, 225–6, 275–6; immortality and 22 see also Bible and Catholic Church Chukwu 47 CIA 57, 160, 293–4 Clever Hans (horse) 128–30, 129 climate change 20, 73, 151, 213, 214–17, 376, 377, 397 Clinton, Bill 57 Clovis, King of France 227, 227 Cognitive Revolution 156, 352, 378 Cold War 17, 34, 149, 206, 266, 372 cold water experiment (Kahneman) 294–5, 338 colonoscopy study (Kahneman and Redelmeier) 296–7 Columbus, Christopher 197, 359, 380 Communism 5, 56, 57, 98, 149, 165, 166, 171, 181; cooperation and 133–7, 138; Dataism and 369, 370–3, 394, 396; economic growth and 206, 207, 208, 217, 218; liberalism, challenge to 264–6, 271–4; religion and 181, 182, 183; Second World War and 263 computers: algorithms and see algorithms; brain–computer interfaces 48, 54, 287, 353, 359; consciousness and 106, 114, 117–18, 119, 120; Dataism and 368, 375, 388, 389 Confucius 46, 267, 391–2; Analects 269, 270 Congo 9, 10, 15, 19, 168, 206, 257–61, 387, 388 consciousness: animal 106–7, 120–32; as biologically useless by-product of certain brain processes 116–17; brain and locating 105–20; computer and 117–18, 119, 120, 311–12; current scientific thinking on nature of 107–17; denying relevance of 114–16; electrochemical signatures of 118–19; intelligence decoupling from 307–50, 352, 397; manufacturing new states of 360, 362–3, 393; positive psychology and 360; Problem of Other Minds 119–20; self and 294–5; spectrum of 353–9, 359, 360; subjective experience and 110–20; techno-humanism and 352, 353–9 cooperation, intersubjective meaning and 143–51, 155–77; power of human 131–51, 155–77; revolution and 132–7; size of group and 137–43 Cope, David 324–5 credit 201–5 Crusades 146–8, 149, 150–1, 190, 227–8, 240, 305 Csikszentmihalyi, Mihaly 360 customer-services departments 317–18 cyber warfare 16, 17, 59, 309–10 Cyborg 2 (movie) 334 cyborg engineering 43, 44–5, 66, 275, 276, 310, 334 Cyrus, King of Persia 172, 173 Daoism 181, 221 Darom, Naomi 231 Darwin, Charles: evolutionary theory of 102–3, 252, 271, 372, 391; On the Origin of Species 271, 305, 367 data processing: Agricultural Revolution and 156–60; Catholic Church and 274; centralised and distributed (communism and capitalism) 370–4; consciousness and 106–7, 113, 117; democracy, challenge to 373–7; economy and 368–74; human history viewed as a single data-processing system 377–81, 388; life as 106–7, 113, 117, 368, 377–81, 397; stock exchange and 369–70; value of human experience and 387–9; writing and 157–60 see also algorithms and Dataism Dataism 366, 367–97; biological embracement of 368; birth of 367–8; computer science and 368; criticism of 393–5; economy and 368–74; humanism, attitude towards 387–8; interpretation of history and 377–80; invisible hand of the data flow, belief in the 385–7; politics and 370–4, 375–6; power/control over data 373–7; privacy and 374, 384–5; religion of 380–5; value of experience and 387–9 Dawkins, Richard 305 de Grey, Aubrey 24, 25, 27 Deadline Corporation 331 death, 21–9 see also immortality Declaration of the Rights of Man and of the Citizen, The 308–9 Deep Blue 320, 320 Deep Knowledge Ventures 322, 323 DeepMind 321 Dehaene, Stanislas 116 democracy: Dataism and 373–5, 376, 377, 380, 391, 392, 396; evolutionary humanism and 253–4, 262–3; humanist values and 226–8; liberal humanism and 248–50, 262–7, 268; technological challenge to 306, 307–9, 338–41 Dennett, Daniel 116 depression 35–6, 39, 40, 49, 54, 67, 122–4, 123, 251–2, 287, 357, 364 Descartes, René 107 diabetes 15, 27 Diagnostic and Statistical Manual of Mental Disorders (DSM) 223–4 Dinner, Ed 360 Dix, Otto 253; The War (Der Krieg) (1929–32) 244, 245, 246 DNA: in vitro fertilisation and 52–4; sequencing/testing 52–4, 143, 332–4, 336, 337, 347–8, 392; soul and 105 doctors, replacement by artificial intelligence of 315, 316–17 Donation of Constantine 190–2, 193 drones 288, 293, 309, 310, 310, 311 drugs: computer-assisted methods for research into 323; Ebola and 203; pharmacy automation and 317; psychiatric 39–41, 49, 124 Dua-Khety 175 dualism 184–5, 187 Duchamp, Marcel: Fountain 229–30, 233, 233 Ebola 2, 11, 13, 203 economics/economy: benefits of growth in 201–19; cooperation and 139–40; credit and 201–5; Dataism and 368–73, 378, 383–4, 385–6, 389, 394, 396, 397; happiness and 30, 32, 33, 34–5, 39; humanism and 230, 232, 234, 247–8, 252, 262–3, 267–8, 269, 271, 272, 273; immortality and 28; paradox of historical knowledge and 56–8; technology and 307–8, 309, 311, 313, 318–19, 327, 348, 349 education 39–40, 168–71, 231, 233, 234, 238, 247, 314, 349 Eguía, Francisco de 8 Egypt 1, 3, 67, 91, 98, 141, 142, 158–62, 170, 174–5, 176, 178–9, 206; Lake Fayum engineering project 161–2, 175, 178; life of peasant in ancient 174–5, 176; pharaohs 158–60, 159, 174, 175, 176; Revolution, 2011 137, 250; Sudan and 270 Egyptian Journalists Syndicate 226 Einstein, Albert 102, 253 electromagnetic spectrum 354, 354 Eliot, Charles W. 309 EMI (Experiments in Musical Intelligence) 324–5 Engels, Friedrich 271–2 Enki 93, 157, 323 Epicenter, Stockholm 45 Epicurus 29–30, 33, 35, 41 epilepsy 291–2 Erdoğan, Recep Tayyip 207 eugenics 52–3, 55 European Union 82, 150, 160, 250, 310–11 evolution 37–8, 43, 73–4, 75, 78, 79–83, 86–7, 89, 102–5, 110, 131, 140, 150, 203, 205, 252–3, 260, 282, 283, 297, 305, 338, 359, 360, 388, 391 evolutionary humanism 247–8, 252–7, 260–1, 262–3, 352–3 Facebook 46, 137, 340–1, 386, 387, 392, 393 famine 1–6, 19, 20, 21, 27, 32, 41, 55, 58, 166, 167, 179, 205, 209, 219, 350 famine, plague and war, end of 1–21 First World War, 1914–18 9, 14, 16, 52, 244, 245, 246, 254, 261–2, 300–2, 301, 309, 310 ‘Flash Crash’, 2010 313 fMRI scans 108, 118, 143, 160, 282, 332, 334, 355 Foucault, Michel: The History of Sexuality 275–6 France: famine in, 1692–4 3–4, 5; First World War and 9, 14, 16; founding myth of 227, 227; French Revolution 155, 308, 310–11; health care and welfare systems in 30, 31; Second World War and 164, 262–3 France, Anatole 52–3 Frederick the Great, King 141–2 free will 222–3, 230, 247, 281–90, 304, 305, 306, 338 freedom of expression 208, 382, 383 freedom of information 382, 383–4 Freudian psychology 88, 117, 223–4 Furuvik Zoo, Sweden 125–6 Future of Employment, The (Frey/Osborne) 325–6 Gandhi, Indira 264, 266 Gazzaniga, Professor Michael S. 292–3, 295 GDH (gross domestic happiness) 32 GDP (gross domestic product) 30, 32, 34, 207, 262 genetic engineering viii, 23, 25, 41, 44, 48, 50, 52–4, 212, 231, 274, 276, 286, 332–8, 347–8, 353, 359, 369 Germany 36; First World War and 14, 16, 244, 245, 246; migration crisis and 248–9, 250; Second World War and 255–6, 262–3; state pensions and social security in 31 Gilgamesh epic 93 Gillies, Harold 52 global warming 20, 213, 214–17, 376, 377, 397 God: Agricultural Revolution and 95, 96, 97; Book of Genesis and 77, 78, 93–4, 97, 98; Dataism and 381, 382, 386, 389, 390, 393; death of 67, 98, 220, 234, 261, 268; death/immortality and 21, 22, 48; defining religion and 181, 182, 183, 184; evolutionary theory and 102; hides in small print of factual statements 189–90, 195; homosexuality and 192–3, 195, 226, 276; humanism and 220, 221, 222, 224, 225, 226, 227, 228, 229, 234–7, 241, 244, 248, 261, 268, 270, 271, 272, 273, 274, 276, 305, 389, 390–1; intersubjective reality and 143–4, 145, 147–9, 172–3, 179, 181, 182, 183, 184, 189–90, 192–3, 195; Middle Ages, as source of meaning and authority in 222, 224, 227, 228, 235–7, 305; Newton myth and 97, 98; religious fundamentalism and 220, 226, 268, 351; Scientific Revolution and 96, 97, 98, 115; war narratives and 241, 244 gods: Agricultural Revolution and theist 90–6, 97, 98, 156–7; defining religion and 180, 181, 184–5; disappearance of 144–5; dualism and 184–5; Epicurus and 30; humans as (upgrade to Homo Deus) 21, 25, 43–9, 50, 55, 65, 66, 98; humanism and 98; intersubjective reality and 144–5, 150, 155, 156–7, 158–60, 161–3, 176, 178–80, 323, 352; modern covenant and 199–200; new technologies and 268–9; Scientific Revolution and 96–7, 98; spirituality and 184–5; war/famine/plague and 1, 2, 4, 7, 8, 19 Google 24, 28, 114, 114, 150, 157, 163, 275, 312, 321, 322, 330, 334–40, 341, 384, 392, 393; Google Baseline Study 335–6; Google Fit 336; Google Flu Trends 335; Google Now 343; Google Ventures 24 Gorbachev, Mikhail 372 Götze, Mario 36, 63 Greece 29–30, 132, 173, 174, 228–9, 240, 265–6, 268, 305 greenhouse gas emissions 215–16 Gregory the Great, Pope 228, 228 guilds 230 hackers 310, 313, 344, 382–3, 393 Hadassah Hospital, Jerusalem 287 Hamlet (Shakespeare) 46, 199 HaNasi, Rabbi Yehuda 94 happiness 29–43 Haraway, Donna: ‘A Cyborg Manifesto’ 275–6 Harlow, Harry 89, 90 Harris, Sam 196 Hassabis, Dr Demis 321 Hattin, Battle of, 1187 146, 147 Hayek, Friedrich 369 Heine, Steven J. 354–5 helmets: attention 287–90, 362–3, 364; ‘mind-reading’ 44–5 Henrich, Joseph 354–5 Hercules 43, 176 Herodotus 173, 174 Hinduism 90, 94, 95, 181, 184, 187, 197, 206, 261, 268, 269, 270, 348, 381 Hitler, Adolf 181, 182, 255–6, 352–3, 375 Holocaust 165, 257 Holocene 72 Holy Spirit 227, 227, 228, 228 Homo deus: Homo sapiens upgrade to 43–9, 351–66; techno-humanism and 351–66 Homo sapiens: conquer the world 69, 100–51; end famine, plague and war 1–21; give meaning to the world 153–277; happiness and 29–43; Homo deus, upgrade to 21, 43–9; immortality 21–9; loses control, 279–397; problems with predicting history of 55–64 homosexuality 120, 138–9, 192–3, 195, 225–6, 236, 275 Hong Xiuquan 271 Human Effectiveness Directorate, Ohio 288 humanism 65–7, 98, 198, 219; aesthetics and 228–9, 228, 233, 233, 241–6, 242, 245; economics and 219, 230–1, 232, 232; education system and 231, 233, 233, 234; ethics 223–6, 233; evolutionary see evolutionary humanism; formula for knowledge 237–8, 241–2; homosexuality and 225–6; liberal see liberal humanism; marriage and 223–5; modern industrial farming, justification for 98; nationalism and 248–50; politics/voting and 226–7, 232, 232, 248–50; revolution, humanist 220–77; schism within 246–57; Scientific Revolution gives birth to 96–9; socialist see socialist humanism/socialism; value of experience and 257–61; techno-humanism 351–66; war narratives and 241–6, 242, 245, 253–6; wars of religion, 1914–1991 261–7 hunter-gatherers 34, 60, 75–6, 90, 95, 96–7, 98, 140, 141, 156, 163, 169, 175, 268–9, 322, 355, 360, 361, 378 Hussein, Saddam 18, 310 IBM 315–16, 320, 330 Iliescu, Ion 136, 137 ‘imagined orders’ 142–9 see also intersubjective meaning immigration 248–50 immortality 21–9, 30, 43, 47, 50, 51, 55, 56, 64, 65, 67, 138, 179, 268, 276, 350, 394–5 in vitro fertilisation viii, 52–3 Inanna 157, 323 India: drought and famine in 3; economic growth in modern 205–8, 349; Emergency in, 1975 264, 266; Hindu revival, 19th-century 270, 271, 273; hunter-gatherers in 75–6, 96; liberalism and 264, 265; population growth rate 205–6; Spanish Flu and 9 individualism: evolutionary theory and 103–4; liberal idea of undermined by twenty-first-century science 281–306; liberal idea of undermined by twenty-first-century technology 327–46; self and 294–304, 301, 303 Industrial Revolution 57, 61, 270, 274, 318, 319, 325, 374 inequality 56, 139–43, 262, 323, 346–50, 377, 397 intelligence: animal 81, 82, 99, 127–32; artificial see artificial intelligence; cooperation and 130–1, 137; decoupling from consciousness 307–50, 352, 397; definition of 130–1; development of human 99, 130–1, 137; upgrading human 348–9, 352 see also techo-humanism; value of consciousness and 397 intelligent design 73, 102 internet: distribution of power 374, 383; Internet-of-All-Things 380, 381, 382, 388, 390, 393, 395; rapid rise of 50 intersubjective meaning 143–51, 155–77, 179, 323, 352 Iraq 3, 17, 40, 275 Islam 8, 18, 21, 22, 64, 137, 188, 196, 205, 206, 207, 221, 226, 248, 261, 268, 269, 270, 271, 274, 275, 276, 351, 392; fundamentalist 18, 196, 226, 268, 269, 270, 275, 351 see also Muslims Islamic State (IS) 275, 351 Isonzo battles, First World War 300–2, 301 Israel 48, 96, 225–6, 249 Italy 262, 300–2, 301 Jainism 94–5 Jamestown, Virginia 298 Japan 30, 31, 33, 34, 207, 246, 349 Jefferson, Thomas 31, 192, 249, 282, 305 Jeopardy! (game show) 315–16, 315 Jesus Christ 91, 155, 183, 187, 271, 274, 297 Jews/Judaism: ancient/biblical 60, 90–1, 94, 172–3, 174, 181, 193, 194–5, 268, 390; animal welfare and 94; expulsions from early modern Europe 197, 198; Great Jewish Revolt (AD 70) 194; homosexuality and 225–6; Second World War and 164–5, 165, 182 Jolie, Angelina 332–3, 335, 347 Jones, Lieutenant Henry 254 Journal of Personality and Social Psychology 354–5 Joyce, James: Ulysses 240 JSTOR digital library 383 Jung, Carl 223–4 Kahneman, Daniel 294, 295–6, 338–9 Kasparov, Garry 320–1, 320 Khmer Rouge 264 Khrushchev, Nikita 263, 273–4 Kurzweil, Ray 24, 25, 27; The Singularity is Near 381 Kyoto protocol, 1997 215–16 Lake Fayum engineering project, Egypt 161–2, 175, 178 Larson, Professor Steve 324–5 Law of the Jungle 14–21 lawns 58–64, 62, 63 lawyers, replacement by artificial intelligence of 314 Lea, Tom: That 2,000 Yard Stare (1944) 244, 245, 246 Lenin Academy for Agricultural Sciences 371–2 Lenin, Vladimir 181, 207, 251, 271, 272, 273, 375 Levy, Professor Frank 322 liberal humanism/liberalism 98, 181, 247; contemporary alternatives to 267–77; free will and 281–90, 304; humanism and see humanism; humanist wars of religion, 1914– 1991 and 261–7; individualism, belief in 290–304, 305; meaning of life and 304, 305; schism within humanism and 246–57; science undermines foundations of 281–306; technological challenge to 305–6, 307–50; value of experience and 257–9, 260, 387–8; victory of 265–7 life expectancy 5, 25–7, 32–4, 50 ‘logic bombs’ (malicious software codes) 17 Louis XIV, King 4, 64, 227 lucid dreaming 361–2 Luther, Martin 185–7, 275, 276 Luther King, Martin 263–4, 275 Lysenko, Trofim 371–2 MAD (mutual assured destruction) 265 malaria 12, 19, 315 malnutrition 3, 5, 6, 10, 27, 55 Mao Zedong 27, 165, 167, 251, 259, 263, 375 Maris, Bill 24 marriage: artificial intelligence and 337–8, 343; gay 275, 276; humanism and 223–5, 275, 276, 291, 303–4, 338, 364; life expectancy and 26 Marx, Karl/Marxism 56–7, 60, 183, 207, 247–8, 271–4; Communist Manifesto 217; Das Kapital 57, 274 Mattersight Corporation 317–18 Mazzini, Giuseppe 249–50 meaning of life 184, 222, 223, 299–306, 338, 386 Memphis, Egypt 158–9 Mendes, Aristides de Sousa 164–5, 164 Merkel, Angela 248–9 Mesopotamia 93 Mexico 8–9, 11, 263 Michelangelo 27, 253; David 260 Microsoft 15, 157, 330–1; Band 330–1; Cortana 342–3 Mill, John Stuart 35 ‘mind-reading’ helmet 44–5 Mindojo 314 MIT 322, 383 modern covenant 199–219, 220 Modi, Narendra 206, 207 money: credit and 201–5; Dataism and 352, 365, 379; intersubjective nature of 144, 145, 171, 177; invention of 157, 158, 352, 379; investment in growth 209–11 mother–infant bond 88–90 Mubarak, Hosni 137 Muhammad 188, 226, 270, 391 Murnane, Professor Richard 322 Museum of Islamic Art, Qatar 64 Muslims: Charlie Hebdo attack and 226; Crusades and 146, 147, 148, 149; economic growth, belief in 206; evaluating success of 174; evolution and 103; expulsions of from early modern Europe 197, 198; free will and 285; lawns and 64; LGBT community and 225 see also Islam Mussolini, Benito 302 Myanmar 144, 206 Nagel, Thomas 357 nanotechnology 23, 25, 51, 98, 212, 269, 344, 353 National Health Service, UK 334–5 National Salvation Front, Romania 136 NATO 264–5 Naveh, Danny 76, 96 Nayaka people 75–6, 96 Nazism 98, 164–5, 181, 182, 247, 255–7, 262–3, 375, 376, 396 Ne Win, General 144 Neanderthals 49, 156, 261, 273, 356, 378 Nebuchadnezzar, King of Babylonia 172–3, 310 Nelson, Shawn 255 New York Times 309, 332–4, 347, 370 New Zealand: Animal Welfare Amendment Act, 2015 122 Newton, Isaac 27, 97–8, 143, 197 Nietzsche, Friedrich 234, 254, 268 non-organic beings 43, 45 Norenzayan, Ara 354–5 Novartis 330 nuclear weapons 15, 16, 17, 17, 131, 149, 163, 216, 265, 372 Nyerere, Julius 166 Oakland Athletics 321 Obama, President Barack 313, 375 obesity 5–6, 18, 54 OncoFinder 323 Ottoman Empire 197, 207 ‘Our Boys Didn’t Die in Vain’ syndrome 300–3, 301 Page, Larry 28 paradox of knowledge 55–8 Paris Agreement, 2015 216 Pathway Pharmaceuticals 323 Petsuchos 161–2 Pfungst, Oskar 129 pharmacists 317 pigs, domesticated 79–83, 82, 87–8, 90, 98, 99, 100, 101, 231 Pinker, Steven 305 Pius IX, Pope 270–1 Pixie Scientific 330 plague/infectious disease 1–2, 6–14 politics: automation of 338–41; biochemical pursuit of happiness and 41; liberalism and 226–7, 229, 232, 232, 234, 247–50, 247n, 252; life expectancy and 26–7, 29; revolution and 132–7; speed of change in 58 pollution 20, 176, 213–14, 215–16, 341–2 poverty 3–6, 19, 33, 55, 205–6, 250, 251, 262, 349 Presley, Elvis 159–60, 159 Problem of Other Minds 119–20, 126–7 Protestant Reformation 185–7, 198, 242–4, 242, 243 psychology: evolutionary 82–3; focus of research 353–6, 360–2; Freudian 117; humanism and 223–4, 251–2; positive 360–2 Putin, Vladimir 26, 375 pygmy chimpanzees (bonobos) 138–9 Quantified Self movement 331 quantum physics 103, 170, 182, 234 Qur’an 170, 174, 269, 270 rats, laboratory 38, 39, 101, 122–4, 123, 127–8, 286–7 Redelmeier, Donald 296 relativity, theory of 102, 103, 170 religion: animals and 75–8, 90–8, 173; animist 75–8, 91, 92, 96–7, 173; challenge to liberalism 268; Dataism 367–97 see also Dataism; defining 180–7; ethical judgments 195–7; evolution and see evolution; formula for knowledge 235–7; God, death of 67, 234, 261, 268; humanist ethic and 234–5; monotheist 101–2, 173; science, relationship with 187–95, 197–8; scriptures, belief in 172–4; spirituality and 184–7; theist religions 90–6, 98, 274 revolutions 57, 60, 132–7, 155, 263–4, 308, 310–11 Ritalin 39, 364 robo-rat 286–7 Roman Empire 98, 191, 192, 194, 240, 373 Romanian Revolution, 1989 133–7, 138 Romeo and Juliet (Shakespeare) 365–6 Rousseau, Jean-Jacques 223, 282, 305 Russian Revolution, 1917 132–3, 136 Rwanda 15 Saarinen, Sharon 53 Saladin 146, 147, 148, 150–1 Santino (chimpanzee) 125–7 Saraswati, Dayananda 270, 271, 273 Scientific Revolution 96–9, 197–8, 212, 236–7, 379 Scotland 4, 303–4, 303 Second World War, 1939–45 21, 34, 55, 115, 164, 253, 262–3, 292 self: animal self-consciousness 124–7; Dataism and 386–7, 392–3; evolutionary theory and 103–4; experiencing and narrating self 294–305, 337, 338–9, 343; free will and 222–3, 230, 247, 281–90, 304, 305, 306, 338; life sciences undermine liberal idea of 281–306, 328–9; monotheism and 173, 174; single authentic self, humanist idea of 226–7, 235–6, 251, 281–306, 328–41, 363–6, 390–1; socialism and self-reflection 251–2; soul and 285; techno-humanism and 363–6; technological challenge to liberal idea of 327–46, 363–6; transcranial stimulator and 289 Seligman, Martin 360 Senusret III 161, 162 September 11 attacks, New York, 2011 18, 374 Shavan, Shlomi 331 Shedet, Egypt 161–2 Silico Medicine 323 Silicon Valley 15, 24, 25, 268, 274, 351, 381 Sima Qian 173, 174 Singapore 32, 207 smallpox 8–9, 10, 11 Snayers, Pieter: Battle of White Mountain 242–4, 243, 246 Sobek 161–2, 163, 171, 178–9 socialist humanism/socialism 247–8, 250–2, 256, 259–60, 261–2, 263, 264, 265, 266–7, 271–4, 325, 351, 376 soul 29, 92, 101–6, 115–16, 128, 130, 132, 138, 146, 147, 148, 150, 160, 184–5, 186, 189, 195, 229, 272, 282, 283, 285, 291, 324, 325, 381 South Korea 33, 151, 264, 266, 294, 349 Soviet Union: communism and 206, 208, 370, 371–2; data processing and 370, 370, 371–2; disappearance/collapse of 132–3, 135, 136, 145, 145, 266; economy and 206, 208, 370, 370, 371–2; Second World War and 263 Spanish Flu 9–10, 11 Sperry, Professor Roger Wolcott 292 St Augustine 275, 276 Stalin, Joseph 26–7, 256, 391 stock exchange 105–10, 203, 210, 294, 313, 369–70, 371 Stone Age 33–4, 60, 74, 80, 131, 155, 156, 157, 163, 176, 261 subjective experience 34, 80, 82–3, 105–17, 143–4, 155, 179, 229, 237, 312, 388, 393 Sudan 270, 271, 273 suicide rates 2, 15, 33 Sumerians 156–8, 159, 162–3, 323 Survivor (TV reality show) 240 Swartz, Aaron 382–3; Guerilla Open Access Manifesto 383 Sylvester I, Pope 190–1 Syria 3, 19, 149, 171, 220, 275, 313 Taiping Rebellion, 1850–64 271 Talwar, Professor Sanjiv 286–7 techno-humanism: definition of 352–3; focus of psychological research and 353–9; human will and 363–6; upgrading of mind 359–66 technology: Dataism and see Dataism; inequality and future 346–50; liberal idea of individual challenged by 327–46; renders humans economically and militarily useless 307–27; techno-humanism and see techno-humanism Tekmira 203 terrorism 14, 18–19, 226, 288, 290, 311 Tesla 114, 322 Thatcher, Margaret 57, 372 Thiel, Peter 24–5 Third Man, The (movie) 253–4 Thirty Years War, 1618–48 242–3 Three Gorges Dam, 163, 188, 196 Thucydides 173, 174 Toyota 230, 294, 323 transcranial stimulators 44–5, 287–90, 362–3, 364 Tree of Knowledge, biblical 76–7, 77, 97, 98 tuberculosis 9, 19, 23, 24 Turing, Alan 120, 367 Turing Machine 367 Turing Test 120 23andMe 336 Twitter 47, 137, 313, 387 US Army 287–90, 362–3, 364 Uganda 192–3, 195 United States: Dataism and 374; energy usage and happiness levels in 34; evolution, suspicion of within 102; Kyoto protocol, 1997 and 215–16; liberalism, view of within 247n; nuclear weapons and 163; pursuit of happiness and 31; value of life in compared to Afghan life 100; Vietnam War and 264, 265; well-being levels 34 Universal Declaration of Human Rights 21, 24, 31 Urban II, Pope 227–8 Uruk 156–7 Valla, Lorenzo 192 Valle Giulia, Battle of, 1968 263 vampire bats 204–5 Vedas 170, 181, 270 Vietnam War, 1954–75 57, 244, 264, 265 virtual-reality worlds 326–7 VITAL 322–3 Voyager golden record 258–9 Waal, Frans de 140–1 Walter, Jean-Jacques: Gustav Adolph of Sweden at the Battle of Breitenfeld (1631) 242, 243, 244–5 war 1–3, 14–19; humanism and narratives of 241–6, 242, 245, 253–6 Warsaw Pact 264–5 Watson (artificial intelligence system) 315–17, 315, 330 Watson, John 88–9, 90 Waze 341–2 web of meaning 143–9 WEIRD (Western, educated, industrialised, rich and democratic) countries, psychology research focus on 354–5, 359, 360 West Africa: Ebola and 11, 13, 203 ‘What Is It Like to Be a Bat?’


pages: 331 words: 104,366

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

3D printing, Ada Lovelace, AI winter, Albert Einstein, AltaVista, barriers to entry, Berlin Wall, business process, call centre, Charles Lindbergh, clean water, computer age, Daniel Kahneman / Amos Tversky, David Brooks, Donald Trump, Douglas Hofstadter, Drosophila, Elon Musk, Erik Brynjolfsson, factory automation, Freestyle chess, Gödel, Escher, Bach, job automation, Leonard Kleinrock, low earth orbit, Mikhail Gorbachev, Nate Silver, 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

Computers are excellent tools for producing answers, but they don’t know how to ask questions, at least not in the sense humans do. 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.

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?” But this odd convention is simple protocol with no bearing on the machine’s ability to find the answers in its fifteen petabytes of data.

Smarter computers are one key to success, but doing a smarter job of humans and machines working together turns out to be far more important. 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. I’ve been speaking to business audiences for many years, usually on subjects like strategy and how to improve the decision-making process.


pages: 245 words: 83,272

Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard

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

AI enjoyed a popularity bump in 2017 in contrast to many years of what people call an AI winter. In the mainstream, people mostly ignored AI for the first decade of the 2000s. 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. What had happened was that scientists had redefined the term machine learning so that it referred to their work.

I gave him the three-sentence explanation (you’ll read the longer explanation in chapter 11). 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. This interaction stuck with me because it helps me remember the difference between how computer scientists think about AI and how members of the public—including highly informed undergraduates working on tech—think about AI.

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? Or would this particular group of strangers be able to create something new and valuable, something that could shake up the tech industry?


pages: 371 words: 108,317

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

A Declaration of the Independence of Cyberspace, AI winter, Airbnb, Albert Einstein, 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, connected car, crowdsourcing, dark matter, dematerialisation, Downton Abbey, Edward Snowden, Elon Musk, Filter Bubble, Freestyle chess, game design, 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, Kevin Kelly, Kickstarter, lifelogging, linked data, Lyft, M-Pesa, Marc Andreessen, Marshall McLuhan, means of production, megacity, Minecraft, Mitch Kapor, multi-sided market, natural language processing, Netflix Prize, Network effects, new economy, Nicholas Carr, old-boy network, peer-to-peer, peer-to-peer lending, personalized medicine, placebo effect, planetary scale, postindustrial economy, recommendation engine, RFID, ride hailing / ride sharing, 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, the scientific method, transport as a service, two-sided market, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, Whole Earth Review, zero-sum game

Consumers can tap into that always-on intelligence directly, but also through third-party apps that harness the power of this AI cloud. 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.

In 2006, Geoff Hinton, then at the University of Toronto, made a key tweak to this method, which he dubbed “deep learning.” 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. As it does, this cloud-based AI will become an increasingly ingrained part of our everyday life.

And this was before smartphones became the norm. We are just starting to get good at giving great answers. 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.


pages: 232 words: 71,024

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

AltaVista, Bernie Madoff, business cycle, business process, cloud computing, commoditize, compound rate of return, corporate raider, full employment, if you build it, they will come, immigration reform, interchangeable parts, invention of the telephone, Khan Academy, knowledge worker, low skilled workers, 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, Toyota Production System, Watson beat the top human players on Jeopardy!, web application

More IBM customers are probably unhappy with Big Blue right now than are happy. 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. The company has probably been doomed since 2010. It’s just that nobody knew it. These are harsh words, I know, and I don’t write them lightly.

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. The response invited me to a local IBM research facility to discuss my plan.

Out of necessity, because quoting them directly would imperil their jobs, these heroes must go unnamed. 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. Twenty years ago, IBM was demographically most like Kuwait, but temperamentally IBM was like Switzerland.


pages: 301 words: 85,126

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

Air France Flight 447, Albert Einstein, Amazon Web Services, Atul Gawande, autonomous vehicles, availability heuristic, basic income, Bayesian statistics, business cycle, Cepheid variable, Checklist Manifesto, cloud computing, combinatorial explosion, computer age, computer vision, Daniel Kahneman / Amos Tversky, Donald Trump, Douglas Hofstadter, Edward Charles Pickering, Elon Musk, epigenetics, Flash crash, Grace Hopper, Gödel, Escher, Bach, Harvard Computers: women astronomers, index fund, Isaac Newton, John von Neumann, late fees, low earth orbit, Lyft, 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, p-value, pattern recognition, Pierre-Simon Laplace, ransomware, recommendation engine, Ronald Reagan, self-driving car, sentiment analysis, side project, Silicon Valley, Skype, smart cities, speech recognition, statistical model, survivorship bias, 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. Still, we encourage you to keep in mind two facts. First, people also make mistakes with language.

Think of hooking up a cheap stethoscope to your phone, so that a neural network can listen to your heartbeat. 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.

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.  C. R. Cardwell et al., “Exposure to Oral Bisphosphonates and Risk of Esophageal Cancer,” JAMA 304, no. 6 (August 11, 2010): 657–63.   3.  


pages: 239 words: 56,531

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, butterfly effect, computer age, 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, Fall of the Berlin Wall, Francis Fukuyama: the end of history, Frank Gehry, Grace Hopper, gravity well, Guggenheim Bilbao, Honoré de Balzac, Howard Rheingold, invention of movable type, Isaac Newton, Jacquard loom, Jane Jacobs, Jeff Bezos, John Markoff, John von Neumann, Kickstarter, Mark Zuckerberg, Marshall McLuhan, Mercator projection, Metcalfe’s law, Mother of all demos, mutually assured destruction, Nelson Mandela, Network effects, new economy, Norbert Wiener, PageRank, pattern recognition, peer-to-peer, planetary scale, plutocrats, Plutocrats, post-materialism, Potemkin village, RFID, Richard Feynman, Richard Stallman, Robert Metcalfe, Robert X Cringely, Schrödinger's Cat, Search for Extraterrestrial Intelligence, SETI@home, Silicon Valley, Skype, social software, spaced repetition, Steve Ballmer, Steve Jobs, Steve Wozniak, Ted Nelson, the built environment, 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

It manufactured scales, time cards, and most famously, punch card tabulators that allowed for the storage and analysis of ever-larger amounts of manufacturing, distribution, and sales data. 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. During the Second World War, the company cofunded the development of Harvard University’s Mark I computer, and IBM scientists and engineers established and solidified linkages with the military that became increasingly critical to the company in the postwar era.

., was positioned to take over the company in the early 1950s, the biggest question they both faced was how to confront the changes that digital technologies would have on their company. 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. In the 1950s, he managed the development and release of the 650 series of computers, which was the first major commercial computing endeavor.

Just as no one will download mindfully at all times, it is an impossible request to ask people to only upload meaningfully. 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

Airbnb, Albert Einstein, algorithmic trading, barriers to entry, cloud computing, collaborative economy, commoditize, corporate governance, crony capitalism, crowdsourcing, Daniel Kahneman / Amos Tversky, David Graeber, demand response, disintermediation, disruptive innovation, double helix, Downton Abbey, Erik Brynjolfsson, experimental economics, Firefox, framing effect, Google Chrome, 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, offshore financial centre, pattern recognition, prediction markets, price discrimination, 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, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, smart meter, Snapchat, social graph, Steve Jobs, 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, 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. Antonio Regalado, “Is Google Cornering the Market on Deep Learning?”

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. Octo Telematics, for example, uses IBM’s real-time weather data “as a critical input to its driver behav ior scoring app.”51 Octo’s free mobile app offers personalized insurance quotes based on the driver’s behav ior.52 Octo’s algorithm assesses not only the driver’s speed, braking, and acceleration, but also “outside variables often directly affected by weather, such as road and traffic conditions, to determine driver scoring.”53 Drivers with good New Economic Reality 17 scores, as determined by Octo’s algorithm, are rewarded with the option of a discounted insurance quote from a panel of insurers, which they can choose to accept at their discretion.

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/.


pages: 294 words: 96,661

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

agricultural Revolution, AI winter, artificial general intelligence, basic income, Buckminster Fuller, business cycle, business process, Claude Shannon: information theory, clean water, cognitive bias, computer age, crowdsourcing, dark matter, Elon Musk, Eratosthenes, estate planning, financial independence, first square of the chessboard, first square of the chessboard / second half of the chessboard, full employment, 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 skilled workers, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Mary Lou Jepsen, Moravec's paradox, On the Revolutions of the Heavenly Spheres, pattern recognition, profit motive, Ray Kurzweil, recommendation engine, Rodney Brooks, Sam Altman, self-driving car, Silicon Valley, Skype, spinning jenny, Stephen Hawking, Steve Wozniak, Steven Pinker, strong AI, technological singularity, telepresence, telepresence robot, The Future of Employment, the scientific method, Turing machine, Turing test, universal basic income, Von Neumann architecture, Wall-E, Watson beat the top human players on Jeopardy!, women in the workforce, working poor, Works Progress Administration, Y Combinator

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. 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.

In fact, anything a computer can do today, you could theoretically do on a Turing machine. 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. While Turing machines are purely theoretical, designed to frame the question of what computers can do, the von Neumann architecture is about how to build actual computers.

But is this really possible? As mechanization and automation increase, surely there are some people who are left behind. Eventually some people can’t compete for work, right? No. Assuming that a person is not afflicted with a debilitating physical or mental condition, there are no low-skilled humans. The difference between a human with an IQ of 90 and one with an IQ of 130 seems quite stark if they are playing Jeopardy!, but in reality, in the grand scheme of things, the difference is trivial. This idea is the basis of the well-known Polanyi paradox. In 1966, Michael Polanyi argued that there is a vast realm of human knowledge that consists of learning and skills that lie below our conscious thoughts. Think, for instance, about all the steps involved in baking a cake. Getting the dishes and pans out, melting the butter, cracking the eggs, mixing the batter, frosting the cake, and so on.


pages: 389 words: 87,758

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

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, access to a mobile phone, additive manufacturing, Airbnb, Amazon Mechanical Turk, American Society of Civil Engineers: Report Card, autonomous vehicles, Bakken shale, barriers to entry, business cycle, business intelligence, Carmen Reinhart, central bank independence, cloud computing, corporate governance, creative destruction, crowdsourcing, demographic dividend, deskilling, disintermediation, disruptive innovation, distributed generation, 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, 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 skilled workers, Lyft, M-Pesa, mass immigration, megacity, mobile money, Mohammed Bouazizi, Network effects, new economy, New Urbanism, oil shale / tar sands, oil shock, old age dependency ratio, openstreetmap, peer-to-peer lending, pension reform, private sector deleveraging, purchasing power parity, quantitative easing, recommendation engine, Report Card for America’s Infrastructure, RFID, ride hailing / ride sharing, 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, supply-chain management, 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

In the years ahead, cloud technology will continue to spur growth of new business models that are asset light, flexible, highly mobile, and scalable. 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. At its most basic, digitization is a simple proposition: converting information into 1s and 0s so that it can be processed, communicated, and stored in machines.


pages: 265 words: 74,000

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, Isaac Newton, job automation, job satisfaction, McMansion, Myron Scholes, natural language processing, PageRank, personalized medicine, recommendation engine, RFID, Silicon Valley, Skype, statistical model, Watson beat the top human players on Jeopardy!

If the company is marketing the deodorant to Jeff's teenage kids, the "Ugh" from their father might not even be a negative. 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. Once the company has sorted bloggers into tribes, it can start digging for correlations between tribes and products.

And it's a matter of time before management starts recording such behavior. 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. The idea, he said, was to piece together inventories of all of their skills and then to calculate, mathematically, how best to deploy them.

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.


pages: 266 words: 86,324

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

Albert Einstein, Alfred Russel Wallace, Antoine Gombaud: Chevalier de Méré, Atul Gawande, Brownian motion, butterfly effect, correlation coefficient, Daniel Kahneman / Amos Tversky, Donald Trump, feminist movement, forensic accounting, Gerolamo Cardano, Henri Poincaré, index fund, Isaac Newton, law of one price, 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!

Yet had he had different credentials—and not revealed his method—he could have been hailed as the most clever analyst since Charles H. Dow. 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.

Naipaul just another struggling author, and somewhere out there roam the equals of Bill Gates and Bruce Willis and Roger Maris who are not rich and famous, equals on whom Fortune did not bestow the right breakthrough product or TV show or year. 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. It may come as an epiphany merely to recognize the ubiquitous role of random processes in our lives; the true power of the theory of random processes, however, lies in the fact that once we understand the nature of random processes, we can alter the way we perceive the events that happen around us.

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. The show’s main draw was its handsome, amiable host, Monty Hall, and his provocatively clad assistant, Carol Merrill, Miss Azusa (California) of 1957.


pages: 121 words: 36,908

Four Futures: Life After Capitalism by Peter Frase

Airbnb, basic income, bitcoin, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, cryptocurrency, deindustrialization, Edward Snowden, Erik Brynjolfsson, Ferguson, Missouri, fixed income, full employment, future of work, high net worth, income inequality, industrial robot, informal economy, Intergovernmental Panel on Climate Change (IPCC), iterative process, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, litecoin, mass incarceration, means of production, Occupy movement, pattern recognition, peak oil, plutocrats, 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 of Employment, Thomas Malthus, Tyler Cowen: Great Stagnation, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, We are the 99%, Wolfgang Streeck

As I noted above, anxiety about labor-saving technology is actually a constant through the whole history of capitalism. 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. But it is also being released as the “Watson Engagement Advisor,” which is intended for customer service and technical support applications.

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. On the other hand, perhaps an equilibrium will arise where every individual needs to keep a lawyer on retainer, because no one can afford the cost of auto-lawyer software but they must still fight off lawsuits from firms attempting to win big damages for alleged infringement.


pages: 294 words: 81,292

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

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

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.

However, language recognition is not something Watson specializes in. It cannot understand the spoken word. 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. One day not long from now you may have an appointment with a virtual assistant who’ll pepper you with questions, and provide your physician with a diagnosis.

We’ll find out if the challenge of creating software architectures that match human-level intelligence is just too difficult to conquer, and whether or not all that stretches out ahead is a perpetual AI winter. 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.


pages: 405 words: 117,219

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

3D printing, Ada Lovelace, agricultural Revolution, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, animal electricity, anthropic principle, Asperger Syndrome, autonomous vehicles, barriers to entry, battle of ideas, Berlin Wall, bioinformatics, British Empire, business process, carbon-based life, cellular automata, Claude Shannon: information theory, combinatorial explosion, complexity theory, continuous integration, Conway's Game of Life, cosmological principle, dark matter, dematerialisation, double helix, Douglas Hofstadter, Edward Snowden, epigenetics, Flash crash, Google Glasses, Gödel, Escher, Bach, income inequality, index card, industrial robot, 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, millennium bug, Moravec's paradox, natural language processing, Norbert Wiener, off grid, On the Economy of Machinery and Manufactures, packet switching, pattern recognition, Paul Erdős, post-industrial society, prediction markets, Ray Kurzweil, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, social intelligence, speech recognition, stem cell, Stephen Hawking, Steven Pinker, strong AI, technological singularity, The Coming Technological Singularity, The Future of Employment, the scientific method, theory of mind, Turing complete, Turing machine, Turing test, 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.

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. You asked it a question, it understood it, and returned an answer. For this to happen, Watson’s designers exploited the whole arsenal of AI tools and techniques, including machine learning, natural language processing and knowledge representation.

Based largely on this trend, I believe that the creation of greater than human intelligence will occur during the next thirty years.’24 Ray Kurzweil adopted Vinge’s argument in a series of popular science books that explore the technological drivers, and potentially devastating impact, of superhuman Artificial Intelligence. 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.


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

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, illegal immigration, Internet of things, mandatory minimum, millennium bug, move fast and break things, move fast and break things, payday loans, Ralph Nader, RAND corporation, randomized controlled trial, self-driving car, Skype, Snapchat, subscription business, 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.

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.”

A few hours later, the police delivered the news: Dylan had been murdered in his classroom. Shot multiple times. 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. Downstream efforts restore the previous state. My ankle hurts—can you make it stop?


pages: 611 words: 130,419

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

agricultural Revolution, Albert Einstein, algorithmic trading, Andrei Shleifer, autonomous vehicles, bank run, banking crisis, basic income, 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, disintermediation, Donald Trump, Edmond Halley, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, full employment, George Akerlof, germ theory of disease, German hyperinflation, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, implied volatility, income inequality, inflation targeting, 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, market bubble, money market fund, moral hazard, Northern Rock, nudge unit, Own Your Own Home, Paul Samuelson, Philip Mirowski, plutocrats, Plutocrats, Ponzi scheme, publish or perish, random walk, Richard Thaler, Robert Shiller, 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 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.

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.


pages: 406 words: 109,794

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

Airbnb, Albert Einstein, 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, deliberate practice, Exxon Valdez, Flynn Effect, Freestyle chess, functional fixedness, game design, Isaac Newton, Johannes Kepler, knowledge economy, lateral thinking, longitudinal study, Louis Pasteur, Mark Zuckerberg, medical residency, meta analysis, meta-analysis, Mikhail Gorbachev, 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, Stanford marshmallow experiment, Steve Jobs, Steve Wozniak, Steven Pinker, 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.

“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. Ginsberg et al., “Detecting Influenza Epidemics Using Search Engine Query Data,” Nature 457 (2009): 1012–14.

“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.


pages: 565 words: 151,129

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

"Robert Solow", 3D printing, active measures, additive manufacturing, Airbnb, autonomous vehicles, back-to-the-land, big-box store, bioinformatics, bitcoin, business process, Chris Urmson, clean water, cleantech, cloud computing, collaborative consumption, collaborative economy, Community Supported Agriculture, Computer Numeric Control, computer vision, crowdsourcing, demographic transition, distributed generation, en.wikipedia.org, Frederick Winslow Taylor, global supply chain, global village, Hacker Ethic, industrial robot, informal economy, Intergovernmental Panel on Climate Change (IPCC), intermodal, Internet of things, invisible hand, Isaac Newton, James Watt: steam engine, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Julian Assange, Kickstarter, knowledge worker, longitudinal study, Mahatma Gandhi, manufacturing employment, Mark Zuckerberg, market design, mass immigration, means of production, meta analysis, meta-analysis, natural language processing, new economy, New Urbanism, nuclear winter, Occupy movement, off 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, RFID, Richard Stallman, risk/return, Ronald Coase, 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 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, transaction costs, urban planning, Watson beat the top human players on Jeopardy!, web application, Whole Earth Catalog, Whole Earth Review, WikiLeaks, working poor, 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. 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.

pagewanted=all (accessed October 20, 2013). 35. Ibid. 36. 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.

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. IBM says that this is the first time artificial intelligence is graduating from a simple question-and-answer mode to a conversational mode, allowing for more personal interaction and customized answers to individual queries.40 AI scientists will tell you that the most challenging hurdle for their industry is breaking through the language barrier. Comprehending the rich meaning of complex metaphors and phrases in one language and simultaneously retelling the story in another language is perhaps the most difficult of all cognitive tasks and the most unique of all human abilities.


pages: 222 words: 53,317

Overcomplicated: Technology at the Limits of Comprehension by Samuel Arbesman

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

The composer David Cope of the University of California, Santa Cruz, has developed software that generates novel musical compositions in the style of a given composer. 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. So, too, should it be with our machines for all humanity: we can root for what humans have created, even if it wasn’t our own personal achievement and even if we can’t fully understand it.

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. In response, we need to cultivate generalists, individuals who not only can see the lay of the land—the abstract physics style of thinking—but can also delight in the details of a system without necessarily understanding them all—the more miscellaneous biological style of thinking.

., 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.


pages: 413 words: 106,479

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

4chan, book scanning, British Empire, citation needed, Donald Trump, en.wikipedia.org, Firefox, Flynn Effect, Google Hangouts, Internet Archive, invention of the printing press, invention of the telephone, moral panic, multicultural london english, natural language processing, pre–internet, QWERTY keyboard, Ray Oldenburg, Silicon Valley, Skype, Snapchat, social web, Steven Pinker, telemarketer, The Great Good Place, upwardly mobile, Watson beat the top human players on Jeopardy!

Tangleofrainbows. tangleofrainbows.tumblr.com/post/126889100409/re-how-teens-and-adults-text-i-would-be-super. 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. (No author cited.)

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.


pages: 499 words: 144,278

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

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

Most of the contestants were coders who worked at local high-tech firms or computer science students at nearby universities. But the winners, it turns out, were . . . a group of high school girls from New Jersey. In only 24 hours, the trio created reVIVE, a virtualreality app that tests kids for signs of ADHD by having them play a series of games, while also checking their emotional state using IBM’s Watson AI. After the students were handed their winnings onstage—a huge trophy-sized check for $5,000—they flopped into chairs in a nearby room to recuperate. They’d been coding almost nonstop since noon the day before and were bleary with exhaustion. “Lots of caffeine,” said then 17-year-old Amulya Balakrishnan, clad in a blue T-shirt that read “WHO HACK THE WORLD? GIRLS.” They told me that they’d even impressed themselves by how much they pulled off in 24 hours.

And, as with any software craze, the hunt for warm bodies exploded. Silicon Valley and China in particular grew ravenous for coders fluent in deep learning, with salaries reaching well into the six figures for anyone adept at teaching computers to see, hear, read, and predict. What type of coder gets obsessed with making AI? As you’d imagine, many were entranced by the savant robots of sci-fi. Dave Ferrucci—the computer scientist who led an IBM team to create Watson—hankered to make a machine that could converse like the one on the Star Trek Enterprise system. “It understands what you’re asking and provides just the right chunk of response that you needed,” he told me. “When is the computer going to get to a point where the computer knows how to talk to you? That’s my question!” Others come to neural nets via neuroscience, when they start wondering whether these crazy neural nets actually mimic how the brain works.

Flatiron is comparatively transparent about their hiring rates; in an audited report, they found that of the students who graduated between November 2015 and December 2016 and looked for work, 97 percent found some sort of software engineering work in a six-month window around graduation—about 40 percent getting full-time work (with an average salary of $67,607 a year), half winding up with contract work, paid internships, or paid apprenticeship work that averaged $27 an hour, and the rest freelancing. One student who’d enjoyed some success is Luis De Castro, a 29-year-old who lives in San Francisco. When I saw him at a GitHub conference in the fall of 2016, he was hunched over his laptop, his thick dark dreads spilling out of his hoodie as he frantically patched together a demo that used IBM’s Watson AI to take text messages and identify their emotional state. He’d recently finished a stint at Dev Bootcamp, a since-shuttered school nearby, and was trying to get the demo working to show some fellow students at an event later that evening. It wasn’t working, at least not yet. “Oh man, I have no idea what’s going on here,” he said, and laughed, as the code coughed up error after error, before he finally quashed the bug with minutes to go.


pages: 268 words: 75,850

The Formula: How Algorithms Solve All Our Problems-And Create More by Luke Dormehl

3D printing, algorithmic trading, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, big data - Walmart - Pop Tarts, call centre, Cass Sunstein, Clayton Christensen, commoditize, computer age, death of newspapers, deferred acceptance, disruptive innovation, Edward Lorenz: Chaos theory, Erik Brynjolfsson, Filter Bubble, Flash crash, Florence Nightingale: pie chart, Frank Levy and Richard Murnane: The New Division of Labor, Google Earth, Google Glasses, High speed trading, Internet Archive, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Kevin Kelly, Kodak vs Instagram, lifelogging, Marshall McLuhan, means of production, Nate Silver, natural language processing, Netflix Prize, Panopticon Jeremy Bentham, pattern recognition, price discrimination, recommendation engine, Richard Thaler, Rosa Parks, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Slavoj Žižek, social graph, speech recognition, Steve Jobs, Steven Levy, Steven Pinker, Stewart Brand, the scientific method, The Signal and the Noise by Nate Silver, upwardly mobile, Wall-E, Watson beat the top human players on Jeopardy!, Y Combinator

The algorithm surprised the study’s authors by correctly predicting 75 percent of the verdicts (based on only a handful of different metrics), as compared to the team of legal scholars, who guessed just 59 percent, despite having access to far more specialized information.65 In its own way, the “Supreme Court Forecasting Project” was the legal profession’s equivalent of IBM’s Watson supercomputer winning $1 million on Jeopardy! in 2011—marking, as it did, the culmination of a long-held techno dream first proposed by the jurimetrics movement. In 1897, Oliver Wendell Holmes Jr. wrote enthusiastically of his belief that the legal system, as with science’s natural laws, should be quantifiably predictable. “The object of our study . . . is prediction,” he observed, “the prediction of the incidence of the public force through the instrumentality of the courts.”66 But if anything, the Supreme Court Forecasting Project was a bastardization of the jurimetrics’ utopian vision.

In the aftermath of Iamus’s concert, a staff writer for the Columbia Spectator named David Ecker put pen to paper (or rather finger to keyboard) to write a polemic taking aim at the new technology. “I use computers for damn near everything, [but] there’s something about this computer that I find deeply troubling,” Ecker wrote. I’m not a purist by any stretch. I hate overt music categorization, and I hate most debates about “real” versus “fake” art, but that’s not what this is about. This is about the very essence of humanity. Computers can compete and win at Jeopardy!, beat chess masters, and connect us with people on the other side of the world. When it comes to emotion, however, they lack much of the necessary equipment. We live every day under the pretense that what we do carries a certain weight, partly due to the knowledge of our own mortality, and this always comes through in truly great music. Iamus has neither mortality nor the urgency that comes with it.

Jr. 157 Iamus 206–7 IfIDie 96 In The Plex (Levy) 41 Industrial Revolution 21, 40 info-aesthetics 182 Information Age, coming of 21 Instagram 216 Interactive Telecommunications Program 15 Internet: and cookies 17 dating 71, 75–79, 81; see also eHarmony; FindYourFaceMate; GenePartner; love and sex growing access to 76–77 and scavenger-class customers 49–50 shopping via 16–17, 20; see also Amazon tracking of user movements on 18 as transactional machine 46 and web analytics 18–19, 41; see also Google iPhone 36 Is That a Fish in Your Ear? (Bellos) 215 Isaacson, Walter 36 Islendiga-App 88–89 Jackson, Joe 70 James, Henry 70 James, William 17 Jastrow, Robert 96 Jeopardy! 158 Jobs, Steve 36, 87 John Carter 163, 172 Jolie, Angelina 69 Jonze, Spike 103 JPod (Coupland) 16 judges 156–58 judicial behavior, predicting 156–59 and automated judges 160 jurimetrics 139–40, 158 Kahler Communications 24–25 Kahler, Dr. Taibi 22–24 Kardashian, Khloe 68 Kari 99–103, 105 Kasparov, Garry 29 Keillor, Garrison 28 Kelly, John 127–29 Kelly, Kevin 12 Kelvin, Lord 31 Kerckhoff, Alan 77 Kindle 180, 197–98, 203 Kinect 132 Kipman, Alex 132 Kirke, Alexis 190–92, 194, 197 Knack 32–34 Knowledge Acquiring and Response Intelligence 99 Kodak 129, 216 Koppleman, Lee 134 Kranzberg, Melvin 151, 222 lactoferrin 10 Lake Wobegone Strategy 28–29 Lanier, Jaron 90–91, 199, 216, 239 LargeAndLovely 78 Lasswell, Harold 5 Late Age of Print, The (Stiphas) 221 Latour, Bruno 136, 235–36 law and law enforcement 106–33, 137–60 and age and gender 121–23 and Ambient Law 132, 137, 143–44 and automated judges 160 and bail and parole 119–21 and crime hotspots 110–112 and drunk-driving detection 131–33 and legal discovery 125–28 and “PreCrime” 118–19, 123–25 and predicting judicial behavior 155–60 and predictive policing 107–9, 119 and PredPol 113 and “RealCog” 120–21 and relative poverty 117 and rules vs. standards 141–43 and school students 125 Lawrence, Jennifer 169 LegalZoom 130 Leibniz, Gottfried 139–40 Lessig, Lawrence 139 Levitt, Theodore 217 Levy, David 104–5 Levy, Frank 212–13 Levy, Steven 41 Lewis, Sinclair 186–87 Li, Jiwei 35 Life & Times of Michael K (Coetzee) 203 Life on Screen (Turkle) 57 LinkedIn 27 Liquid Love (Bauman) 82 Liu, Benjamin 89 LivesOn 96–97 London Symphony Orchestra 206 Long Tail, The (Anderson) 56, 191n Lorenz, Edward 171 Love in the Time of Algorithms (Slater) 81 love and sex: algorithms and technology for 61–95 passim, 98–105, 239; see also ALikeWise; BeautifulPeople; Bedpost; eHarmony; FindYourFaceMate; FitnessSingles; Kari; LargeAndLovely; love and sex; “Match”; Match.com; OKCupid; SeaCaptainDate; Serendipity; UniformDating; VeggieDate and celebrity marriages, see celebrity marriages, predicting breakup of genetic matching for 77–78 Warren’s researches into 72–74 and wearable tech 94–5 see also divorce; “Match”; PlentyOfFish Love and Sex with Robots (Levy) 104–5 Lovegety 87–88 Lucky You 167–68 Lust in Space 100 McAfee, Andrew 217 Macbeth (Shakespeare) 191 McBride, Joseph 164n MacCormick, John 212, 222 McCue, Colleen 106–7 McLuhan, Marshall 88 Malinowski, Sean 107–14 Manovich, Lev 177–78 Many Worlds 190–92, 194, 197 maps 134–36 Marx, Karl 11, 137n Massachusetts Institute of Technology (MIT) 28 Human Dynamics group in 85 Serendipity project of 85–87 “Match” 62–66 tabulated example of 64 see also love and sex Mattersight Corporation 22–24 Mayer, Marissa 228 Meaney, Nick 166–67, 170–72, 176, 205 Measure of Fidget 32 Medavoy, Mike 162 Medicine and the Reign of Technology (Reiser) 142 Meehl, Paul E. 208–9 Meiklejohn, Alexander 231 Merleau-Ponty, Maurice 98–99 Michael (Quantified Self devotee) 13 see also Quantified Self movement Microserfs (Coupland) 16 Microsoft 51–52, 132, 192–93, 237 MIDI 199 Mill, John Stuart 118 Ming, Vivienne 25–27, 29–30 Miniscript 22–23 Minority Report 118–20, 123 Mismeasure of Man, The (Gould) 33–34 Mohler, George 111–12 money laundering 19 Morozov, Evgeny 201–2, 226, 243 Moses, Robert 134 movies, see art and entertainment Mozart, Wolfgang 172, 203–4 Mumford, Lewis 5 Murnane, Richard 212–13 musical dice game 204 Myhrvold, Nathan 182 Nara 46–47, 136 NASA 24 NASCAR 37 Nautilus 14 Negobot 240 Net Delusion, The (Morozov) 226 Netflix 52, 127, 176, 188–89, 228, 236 neural networks 166 illustration of 168 neuroscience 159 new algorithmic identity 55, 58 New Division of Labor, The (Levy, Murnane) 212–13 New Statesman 55 New York Times 40–41, 52, 58, 67, 71 Newton, Isaac 114 Nietzsche, Friedrich 70 Nightingale, Florence 118 Nine Algorithms That Changed the Future (MacCormick) 222 Nineteen Eighty-Four (Orwell) 138, 198 Nudge (Thaler, Sunstein) 137–38 Obama, Barack 189, 225 Odom, Lamar 68 “(Of the) Standard of Taste” (Hume) 199–200 OKCupid 77 On Love (de Botton) 87 On Love (Stendhal) 70 On Man and the Development of his Faculties (Quetelet) 117 online dating, see Internet: dating online shopping, see Internet: shopping via Onomatics 130–31 OptimEyes 20 Orwell 138 panopticon 55 Parada, Sergio 99–100, 102–3 paradox of choice 82–83, 156 Paradox of Choice, The (Winchester) 82–83 Pariser, Eli 47 Parks, Rosa 59 Pascal, Blaise 70 Patterson, James 203 Pentland, Alex 85 personality types 23–25 tabulated 23 Pfizer 58 Pinker, Steven 80–81 Pinkett, Jada 69 Pitt, Brad 69 Plan of Scientific Operations . . .


pages: 573 words: 157,767

From Bacteria to Bach and Back: The Evolution of Minds by Daniel C. Dennett

Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Andrew Wiles, Bayesian statistics, bioinformatics, bitcoin, Build a better mousetrap, Claude Shannon: information theory, computer age, computer vision, double entry bookkeeping, double helix, Douglas Hofstadter, Elon Musk, epigenetics, experimental subject, Fermat's Last Theorem, Gödel, Escher, Bach, information asymmetry, information retrieval, invention of writing, Isaac Newton, iterative process, John von Neumann, Menlo Park, Murray Gell-Mann, Necker cube, Norbert Wiener, pattern recognition, phenotype, Richard Feynman, Rodney Brooks, self-driving car, social intelligence, sorting algorithm, speech recognition, Stephen Hawking, Steven Pinker, strong AI, The Wealth of Nations by Adam Smith, theory of mind, Thomas Bayes, trickle-down economics, Turing machine, Turing test, Watson beat the top human players on Jeopardy!, Y2K

As Domingos stresses, learning machines are (very intelligently) designed to avail themselves of Darwinesque, bottom-up processes of self-redesign. For IBM’s Watson, the program that beat champion contestants Ken Jennings and Brad Rutter in the Jeopardy television quiz program in 2011, the words it was competent to string together into winning answers were not thinking tools but just nodes located in a multidimensional space of other nodes, not so much memes as fossil traces of human memes, preserving stupendous amounts of information about human beliefs and practices without themselves being active participants in those practices. Not yet, but maybe someday. In short, Watson doesn’t yet think thoughts using the words about which it has so much statistical information. Watson can answer questions (actually, thanks to Jeopardy’s odd convention, Watson can compose questions to which the Jeopardy clues are the answers: Jeopardy: “The capital of Illinois,” contestant: “What is Springfield?”)

.… We are not necessarily attributing any conscious calculation to the animals, although this ‘anthropomorphic’ way of describing the ritualization of the signal is convenient” (p. 41). 82Among explorers making valuable forays into this terra incognita, the best starting from what we know about language and thinking are Jackendoff (2002, 2007, 2007b, 2012) and Millikan (1984, 1993, 2000, 2000b, 2002, 2004, 2005, and forthcoming). 83Is this conscious wondering? Not necessarily; it can be mere epistemic hunger, of the free-floating variety that “motivates” explorations in all animals. 84The feasibility of such a process has been known since the invention of “latent semantic analysis” by Landauer and Dumais (1998), a forerunner of the “deep learning” algorithms being applied these days, by IBM’s Watson, Google Translate, and a host of other impressive applications (see chapter 15). 85See also Christiansen and Chater, “Language as Shaped by the Brain” (a Behavioral and Brain Sciences Target article, 2008) and the extensive commentaries thereupon for a vigorous debate on whether genetic evolution or cultural evolution has played a greater role in creating our linguistic competence. Christiansen and Chater defend a position largely consonant with mine, but they misconstrue the memetic approach in some regards (see Blackmore 2008, for the details), and overstate the case against genetic evolution playing a major role.

But we are entering a new era where the filters and second-guessers and would-be trendsetters may not be people at all, but artificial agents. This will not suit everybody, as we will see in the next section. But that may not stop hierarchical layers of such differential replicators from burgeoning, and then we may indeed face the calamity encountered by the Sorcerer’s Apprentice and the multiplying brooms. In an IBM television advertisement, Watson, “in conversation” with Bob Dylan, says that it can “read 800 million pages a second.” Google Translate, another icon among learning machines, has swept aside the GOFAI systems that were top-down attempts to “parse” and interpret (and thereby understand, in at least a pale version of human comprehension) human language; Google Translate is an astonishingly swift, good—though still far from perfect—translator between languages, but it is entirely parasitic on the corpus of translation that has already been done by human bilinguals (and by volunteer bilingual informants who are invited to assist on the website).


pages: 72 words: 21,361

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

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

Lionbridge’s GeoFluent shows how much progress has been made in computers’ ability to engage in complex communication. Another technology developed at IBM’s Watson labs, this one actually named Watson, shows how powerful it can be to combine these two abilities and how far the computers have advanced recently into territory thought to be uniquely human. Watson is a supercomputer designed to play the popular game show Jeopardy! in which contestants are asked questions on a wide variety of topics that are not known in advance.1 In many cases, these questions involve puns and other types of wordplay. It can be difficult to figure out precisely what is being asked, or how an answer should be constructed. Playing Jeopardy! well, in short, requires the ability to engage in complex communication. The way Watson plays the game also requires massive amounts of pattern matching.

In January of 2011, however, the translation services company Lionbridge announced pilot corporate customers for GeoFluent, a technology developed in partnership with IBM. GeoFluent takes words written in one language, such as an online chat message from a customer seeking help with a problem, and translates them accurately and immediately into another language, such as the one spoken by a customer service representative in a different country. GeoFluent is based on statistical machine translation software developed at IBM’s Thomas J. Watson Research Center. This software is improved by Lionbridge’s digital libraries of previous translations. This “translation memory” makes GeoFluent more accurate, particularly for the kinds of conversations large high-tech companies are likely to have with customers and other parties. One such company tested the quality of GeoFluent’s automatic translations of online chat messages. These messages, which concerned the company’s products and services, were sent by Chinese and Spanish customers to English-speaking employees.

Although multiplying five-digit numbers is an unnatural and difficult skill for the human mind to master, the visual cortex routinely does far more complex mathematics each time it detects an edge or uses parallax to locate an object in space. Machine computation has surpassed humans in the first task but not yet in the second one. As digital technologies continue to improve, we are skeptical that even these skills will remain bastions of human exceptionalism in the coming decades. The examples in Chapter 2 of Google’s self-driving car and IBM’s Watson point to a different path going forward. The technology is rapidly emerging to automate truck driving in the coming decade, just as scheduling truck routes was increasingly automated in the last decade. Likewise, the high end of the skill spectrum is also vulnerable, as we see in the case of e-discovery displacing lawyers and, perhaps, in a Watson-like technology, displacing human medical diagnosticians.


pages: 411 words: 114,717

Breakout Nations: In Pursuit of the Next Economic Miracles by Ruchir Sharma

3D printing, affirmative action, Albert Einstein, American energy revolution, anti-communist, Asian financial crisis, banking crisis, Berlin Wall, BRICs, British Empire, business climate, business cycle, business process, business process outsourcing, call centre, capital controls, Carmen Reinhart, central bank independence, centre right, cloud computing, collective bargaining, colonial rule, corporate governance, creative destruction, crony capitalism, deindustrialization, demographic dividend, Deng Xiaoping, eurozone crisis, Gini coefficient, global supply chain, housing crisis, income inequality, indoor plumbing, inflation targeting, informal economy, Kenneth Rogoff, knowledge economy, labor-force participation, land reform, M-Pesa, Mahatma Gandhi, Marc Andreessen, market bubble, mass immigration, megacity, Mexican peso crisis / tequila crisis, Nelson Mandela, new economy, oil shale / tar sands, oil shock, open economy, Peter Thiel, planetary scale, quantitative easing, reserve currency, Robert Gordon, Shenzhen was a fishing village, Silicon Valley, software is eating the world, sovereign wealth fund, The Great Moderation, Thomas L Friedman, trade liberalization, Watson beat the top human players on Jeopardy!, working-age population, zero-sum game

Not everyone is fully aware that the next step—cloud computing—will allow home PCs to tap the computing power of an army of warehouse-size supercomputers. It’s hard to imagine just what gains will emerge from this awesome capacity, but as a demonstration to provoke interest, Google recently used its cloud to decode the human genome . . . in eleven seconds. This shift—from merely crunching data to analyzing information—was illustrated in a viewer-friendly way by an IBM computer named “Watson” when, in early 2011, it dominated the most successful human champion of the popular American TV quiz show Jeopardy! However large the impact of digital technology will be on economic productivity (and I believe it will be significant), it is likely to be disproportionately large in the leading economies, particularly the United States. As wages rise in emerging nations, they are starting to automate and digitize their manufacturing plants, but nations like Brazil, Russia, India, and China remain well below the global average on automation measures, such as number of robots per employee.


pages: 340 words: 97,723

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

Ada Lovelace, AI winter, Airbnb, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, Asilomar, autonomous vehicles, Bayesian statistics, Bernie Sanders, bioinformatics, blockchain, Bretton Woods, business intelligence, Cass Sunstein, Claude Shannon: information theory, cloud computing, cognitive bias, complexity theory, computer vision, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, Deng Xiaoping, distributed ledger, don't be evil, Donald Trump, Elon Musk, Filter Bubble, Flynn Effect, gig economy, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Inbox Zero, Internet of things, Jacques de Vaucanson, Jeff Bezos, Joan Didion, job automation, John von Neumann, knowledge worker, Lyft, Mark Zuckerberg, Menlo Park, move fast and break things, move fast and break things, natural language processing, New Urbanism, one-China policy, optical character recognition, packet switching, pattern recognition, personalized medicine, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Rodney Brooks, Rubik’s Cube, Sand Hill Road, Second Machine Age, self-driving car, SETI@home, side project, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart cities, South China Sea, sovereign wealth fund, speech recognition, Stephen Hawking, strong AI, superintelligent machines, technological singularity, The Coming Technological Singularity, theory of mind, Tim Cook: Apple, trade route, Turing machine, Turing test, uber lyft, Von Neumann architecture, Watson beat the top human players on Jeopardy!, zero day

CHINOOK won when Tinsley withdrew from the match and relinquished his championship title.34 In 1997, IBM’s Deep Blue supercomputer beat world chess champion Garry Kasparov, who buckled under the stress of a six-game match against a seemingly unconquerable opponent. In 2004, Ken Jennings won a statistically improbable 74 consecutive games on Jeopardy!, setting a Guinness World Record at that time for the most cash ever won on a game show. So when he accepted a match against IBM’s Watson in 2011, he felt confident he was going to win. He’d taken classes on AI and assumed that the technology wasn’t advanced enough to make sense of context, semantics, and wordplay. Watson crushed Jennings, who started to lose confidence early on in the game. What we knew by 2011 was that AI now outperformed humans during certain thinking tasks because it could access and process massive amounts of information without succumbing to stress.

If you wanted to build a health AI to spot anomalies in blood work and oncology scans, the problem isn’t the AI, it’s data—humans are complicated, our bodies have tons of possible variants, and there isn’t a big enough data set ready to be deployed. A decade ago, in the early 2010s, the IBM Watson Health team partnered with different hospitals to see if its AI could supplement the work of doctors. Watson Health had some stunning early wins, including a case involving a very sick nine-year-old boy. After specialists weren’t able to diagnose and treat him, Watson assigned a probability to possible health issues—the list included common ailments as well as outliers, including a rare childhood illness called Kawasaki disease. Once word got out that Watson was performing miracle diagnoses and saving peoples’ lives, the Watson team was under pressure to commercialize and sell the platform, and incomprehensibly unrealistic targets were set. IBM projected that Watson Health would grow from a $244 million business in 2015 to a $5 billion business by 2020.1 That was an anticipated 1,949% growth in under five years.

Good, “Speculations Concerning the First Ultraintelligent Machine,” Advances in Computers 6 (1965): 31–88. 18. Gill A. Pratt, “Is a Cambrian Explosion Coming for Robotics?,” Journal of Economic Perspectives 29, no. 3 (Summer 2015): 51–60, https://www.aeaweb.org/articles?id=10.1257/jep.29.3.51. CHAPTER 6: LEARNING TO LIVE WITH MILLIONS OF PAPER CUTS: THE PRAGMATIC SCENARIO 1. Casey Ross and Ike Swetlitz, “IBM Watson Health Hampered by Internal Rivalries and Disorganization, Former Employees Say,” STAT, June 14, 2018, https://www.statnews.com/2018/06/14/ibm-watson-health-rivalries-disorganization/. 2. Ibid. 3. Gamaleldin F. Elsayed, Ian Goodfellow, and Jascha Sohl-Dickstein, “Adversarial Reprogramming of Neural Networks,” preprint edition accessed, https://arxiv.org/pdf/1806.11146.pdf. 4. Orange Wang, “Chinese Mobile Payment Giants Alipay, Tenpay fined US$88,000 for Breaking Foreign Exchange Rules,” South China Morning Post, July 25, 2018, https://www.scmp.com/news/china/economy/article/2156858/chinese-mobile-payment-giants-alipay-tenpay-fined-us88000.


pages: 419 words: 109,241

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

3D printing, agricultural Revolution, AI winter, Airbnb, Albert Einstein, algorithmic trading, artificial general intelligence, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, blue-collar work, British Empire, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, computer age, computer vision, computerized trading, creative destruction, David Graeber, David Ricardo: comparative advantage, demographic transition, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, drone strike, Edward Glaeser, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, financial innovation, future of work, gig economy, Gini coefficient, Google Glasses, Gödel, Escher, Bach, income inequality, income per capita, industrial robot, interchangeable parts, invisible hand, Isaac Newton, Jacques de Vaucanson, James Hargreaves, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joi Ito, Joseph Schumpeter, Kenneth Arrow, Khan Academy, Kickstarter, low skilled workers, lump of labour, Marc Andreessen, Mark Zuckerberg, means of production, Metcalfe’s law, natural language processing, Network effects, Occupy movement, offshore financial centre, Paul Samuelson, Peter Thiel, pink-collar, precariat, purchasing power parity, Ray Kurzweil, ride hailing / ride sharing, road to serfdom, Robert Gordon, Sam Altman, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, social intelligence, software is eating the world, sovereign wealth fund, spinning jenny, Stephen Hawking, Steve Jobs, strong AI, telemarketer, The Future of Employment, The Rise and Fall of American Growth, the scientific method, The Wealth of Nations by Adam Smith, Thorstein Veblen, Travis Kalanick, Turing test, Tyler Cowen: Great Stagnation, universal basic income, upwardly mobile, Watson beat the top human players on Jeopardy!, We are the 99%, wealth creators, working poor, working-age population, Y Combinator

Douglas Hofstadter, the computer scientist and writer, called its first victory “a watershed event” but dismissed it as something that “doesn’t have to do with computers becoming intelligent.”4 He had “little intellectual interest” in IBM’s machine because “the way brute-force chess programs work doesn’t bear the slightest resemblance to genuine human thinking.”5 John Searle, the philosopher, dismissed Deep Blue as “giving up on A.I.”6 Kasparov himself effectively agreed, writing off the machine as a “$10 million alarm clock.7 Or take Watson, another IBM computer system. Its claim to fame is that in 2011, it appeared on the US quiz show Jeopardy! and beat the two top human champions of the show. In the aftermath, Hofstadter again agreed that the system’s performance was “impressive” but also said that it was “absolutely vacuous.”8 Searle, in a sharp Wall Street Journal editorial, wryly noted, “Watson didn’t know it won on Jeopardy!”9 Nor did the machine want to call up its parents to say how well it had done, or go to the pub to celebrate with its friends. Hofstadter, Kasparov, Searle, and those who make similar observations are all correct, as we saw in the last chapter.

As one of Andreessen’s partners notes, they can use WeChat to “hail a taxi, order a food delivery, buy movie tickets, play casual games, check in for a flight, send money to friends, access fitness tracker data, book a doctor appointment, get banking statements, pay the water bill, find geo-targeted coupons, recognize music, search for a book at the local library, meet strangers … follow celebrity news, read magazine articles, and even donate to charity.”6 But again, we should remember that the technology companies that populate the future might not be today’s most familiar ones. Dominance today does not imply dominance in years to come. Back in 1995, for example, it was unthinkable that Microsoft’s technological rule would ever come to an end, yet now they are being talked about as the “underdog” in the sector.7 Nor do striking contemporary achievements necessarily mean that further remarkable successes will follow. For one cautionary tale, consider IBM’s Watson, the celebrated Jeopardy!-winning computer system. Over the last few years, there has been great excitement about its broad potential. But despite their best efforts, a recent high-profile partnership between the Watson team and MD Anderson, a large American cancer hospital, ended in conspicuous failure: the $60 million system designed to help treat cancer was deemed “not ready for human investigational or clinical use.”8 Indeed, the companies behind the health care technologies that really change our lives may not exist yet.

You’re not trying to imitate humans on processing levels,” and p. 58: “In a way, Deep Blue is giving up on A.I. because it doesn’t say, ‘Well we’re going to try to do what human beings do,’ but it says ‘We’re just going to overpower them with brute force.’”   7.  Garry Kasparov, “The Chess Master and the Computer,” New York Review of Books, 11 February 2010.   8.  Quoted in William Herkewitz, “Why Watson and Siri Are Not Real AI,” Popular Mechanics, 10 February 2014.   9.  John Searle, “Watson Doesn’t Know It Won on ‘Jeopardy!’,” Wall Street Journal, 23 February 2011. 10.  Douglas Hofstadter, Gödel, Escher, Bach: An Eternal Golden Braid (London: Penguin, 2000), p. 601: “There is a related ‘Theorem’ about progress in AI: once some mental function is programmed, people soon cease to consider it as an essential ingredient of ‘real thinking.’ The ineluctable core of intelligence is always in that next thing which hasn’t yet been programmed.


pages: 481 words: 125,946

What to Think About Machines That Think: Today's Leading Thinkers on the Age of Machine Intelligence by John Brockman

agricultural Revolution, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, artificial general intelligence, augmented reality, autonomous vehicles, basic income, bitcoin, blockchain, clean water, cognitive dissonance, Colonization of Mars, complexity theory, computer age, computer vision, constrained optimization, corporate personhood, cosmological principle, cryptocurrency, cuban missile crisis, Danny Hillis, dark matter, discrete time, Douglas Engelbart, Elon Musk, Emanuel Derman, endowment effect, epigenetics, Ernest Rutherford, experimental economics, Flash crash, friendly AI, functional fixedness, global pandemic, Google Glasses, hive mind, income inequality, information trail, Internet of things, invention of writing, iterative process, Jaron Lanier, job automation, Johannes Kepler, John Markoff, John von Neumann, Kevin Kelly, knowledge worker, loose coupling, microbiome, Moneyball by Michael Lewis explains big data, natural language processing, Network effects, Norbert Wiener, pattern recognition, Peter Singer: altruism, phenotype, planetary scale, Ray Kurzweil, recommendation engine, Republic of Letters, RFID, Richard Thaler, Rory Sutherland, Satyajit Das, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, social intelligence, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, superintelligent machines, supervolcano, the scientific method, The Wisdom of Crowds, theory of mind, Thorstein Veblen, too big to fail, Turing machine, Turing test, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Y2K

The “outcompute them” strategy is not frightening, because the computer really has no idea what it’s doing. It can count things fast without understanding what it’s counting. It has counting algorithms—that’s it. We saw this with IBM’s Watson program on Jeopardy! One Jeopardy! question was, “It was the anatomical oddity of U.S. gymnast George Eyser, who won a gold medal on the parallel bars in 1904.” A human opponent answered that Eyser was missing a hand (wrong). And Watson answered, “What is a leg?” Watson lost too, for failing to note that the leg was “missing.” Try a Google search on “Gymnast Eyser.” Wikipedia comes up first with a long article about him. Watson depends on Google. If Jeopardy! contestants could use Google, they’d do better than Watson. Watson can translate “anatomical” into “body part,” and Watson knows the names of the body parts. Watson doesn’t know what an “oddity” is, however.

Leave the map reading and navigation to your GPS; it isn’t conscious, it can’t think in any meaningful sense, but it’s much better than you are at keeping track of where you are and where you want to go. Much farther up the staircase, doctors are becoming increasingly dependent on diagnostic systems that are provably more reliable than any human diagnostician. Do you want your doctor to overrule the machine’s verdict when it comes to making a lifesaving choice of treatment? This may prove to be the best—most provably successful, most immediately useful—application of the technology behind IBM’s Watson, and the issue of whether or not Watson can properly be said to think (or be conscious) is beside the point. If Watson turns out to be better than human experts at generating diagnoses from available data, we’ll be morally obliged to avail ourselves of its results. A doctor who defies it will be asking for a malpractice suit. No area of human endeavor appears to be clearly off-limits to such prosthetic performance-enhancers, and wherever they prove themselves, the forced choice will be reliable results over the human touch, as it always has been.

When we stop someone to ask for directions, there’s usually an explicit or implicit “I’m sorry to bring you down to the level of Google temporarily, but my phone is dead, see, and I require a fact.” It’s a breach of etiquette, on a spectrum with asking someone to temporarily serve as a paperweight or a shelf. I’ve seen this breach, also, in brief conversational moments when someone asks a question of someone else—a number, a date, a surname, the kind of question you could imagine being on a quiz show, some obscure point of fact—and the other person grimaces or waves off the query. They’re saying, “I don’t know. You have a phone, don’t you? You have the entire Internet, and you’re disrespecting me, wasting my time, using me.” Not for nothing do we now have the sarcastic catchphrase, “Here, let me Google that for you.” As things stand, there are still a few arenas in which only a human brain will do the trick—in which the relevant information and experience lives only in humans’ brains and so we have no choice but to trouble those brains when we want something.


pages: 345 words: 75,660

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

"Robert Solow", Ada Lovelace, AI winter, Air France Flight 447, Airbus A320, artificial general intelligence, autonomous vehicles, basic income, Bayesian statistics, Black Swan, blockchain, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, collateralized debt obligation, computer age, creative destruction, Daniel Kahneman / Amos Tversky, data acquisition, data is the new oil, deskilling, disruptive innovation, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, Google Glasses, high net worth, ImageNet competition, income inequality, information retrieval, inventory management, invisible hand, job automation, John Markoff, Joseph Schumpeter, Kevin Kelly, Lyft, Minecraft, Mitch Kapor, Moneyball by Michael Lewis explains big data, Nate Silver, new economy, On the Economy of Machinery and Manufactures, pattern recognition, performance metric, profit maximization, QWERTY keyboard, race to the bottom, randomized controlled trial, Ray Kurzweil, ride hailing / ride sharing, Second Machine Age, self-driving car, shareholder value, Silicon Valley, statistical model, Stephen Hawking, Steve Jobs, Steven Levy, strong AI, The Future of Employment, The Signal and the Noise by Nate Silver, Tim Cook: Apple, Turing test, Uber and Lyft, uber lyft, US Airways Flight 1549, Vernor Vinge, Watson beat the top human players on Jeopardy!, William Langewiesche, Y Combinator, zero-sum game

Machine-learning techniques are increasingly good at predicting missing information, including identification and recognition of items in images. Given a new set of images, the techniques can efficiently compare millions of past examples with and without disease and predict whether the new image suggests the presence of a disease. This kind of pattern recognition to predict disease is what radiologists do.4 IBM, with its Watson system, and many startups have already commercialized AI tools in radiology. Watson can identify a pulmonary embolism and a wide range of other heart issues. One startup, Enlitic, uses deep learning to detect lung nodules (a fairly routine exercise) but also fractures (more complex). These new tools are at the heart of Hinton’s forecast but are a subject for discussion among radiologists and pathologists.5 What does our approach suggest about the future of radiologists?

., 93–94 Duke University Teradata Center, 35 earthquakes, 59–60 eBay, 199 economics, 3 of AI, 8–9 of cost reductions, 9–11 data collection and, 49–50 on externalities, 116–117 New Economy and, 10–11 economies of scale, 49–50, 215–217 Edelman, Ben, 196–197 education, income inequality and, 214 electricity, cost of light and, 11 emergency braking, automatic, 111–112 error, tolerance for, 184–186 ethical dilemmas, 116 Etzioni, Oren, 220 Europe, privacy regulation in, 219–220 exceptions, prediction by, 67–68 Executive Office of the US President, 222–223 experience, 191–193 experimentation, 88, 99–100 AI tool development and, 159–160 expert prediction, 55–58 externalities, 116–117 Facebook, 176, 190, 195–196, 215, 217 facial recognition, 190, 219–220 Federal Aviation Administration, 185 Federal Trade Commission, 195 feedback data, 43, 46 in decision making, 74–76, 134–138 experience and, 191–193 risks with, 204–205 financial crisis of 2008, 36–37 flexibility, 36 Forbes, Silke, 168–169 Ford, 123–134, 164 Frankston, Bob, 141, 164 fraud detection, 24–25, 27, 84–88, 91 Frey, Carl, 149 fulfillment industry, 105, 143–145 Furman, Jason, 213 Gates, Bill, 163, 210, 213, 221 gender discrimination, 196–198 Gildert, Suzanne, 145 Glozman, Ron, 53–54 Goizueta, Robert, 43 Goldin, Claudia, 214 Goldman Sachs, 125 Google, 7–8, 43, 50, 187, 215, 223 advertising, 176, 195–196, 198–199 AI-first strategy at, 179–180 AI tool development at, 160 anti-spam team sting, 202–203 bias in ads and, 195–196 China, 219 Inbox, 185, 187 market share of, 216–217 Now, 106 privacy policy, 190 search engine optimization and, 64 search tool, 19 translation service, 25–26 video content algorithm, 200 Waymo, 95 Waze and, 89–90 Grammarly, 96 Greece, ancient, 23 Griliches, Zvi, 159 Grove, Andy, 155 hackers, 200 Hacking, Ian, 40 Hammer, Michael, 123–134 Harford, Tim, 192–193 Harvard Business School cases, 141 Hawking, Stephen, 8, 210–211, 221 Hawkins, Jeff, 39 health insurance, 28 heart disease, diagnosing, 44–45, 47–49 Heifets, Abraham, 135, 136 Hemingway, Ernest, 25–26 heuristics, 55 Hinton, Geoffrey, 145 hiring, 58, 98 ZipRecruiter and, 93–94, 100 Hoffman, Mitchell, 58 homogeneity, data, 201–202 hotel industry, 63–64 Houston Astros, 161 Howe, Kathryn, 14 human resource (HR) management, 172–173 IBM’s Watson, 146 identity verification, 201, 219–220 iFlytek, 26–27 if-then logic, 91, 104–109 image classification, 28–29 ImageNet, 7 ImageNet Challenge, 28–29 imitation of algorithms, 202–204 income inequality, 19, 212–214 independent variables, 45 inequality, 19, 212–214 initial public offerings (IPOs), 9–10, 125 innovation, 169–170, 171, 218–219 innovator’s dilemma, 181–182 input data, 43 in decision making, 74–76, 134–138 identifying required, 139 Integrate.ai, 14 Intel, 15, 215 intelligence churn prediction and, 32–36 human, 39 prediction as, 2–3, 29, 31–41 internet advertising, 175–176 browsers, 9–10 delivery time uncertainty and commerce via, 157–158 development of the commercial, 9–10 inventory management, 28, 105 Iowa, hybrid corn adoption in, 158–160, 181 iPhone, 129–130, 155 iRobot, 104 James, Bill, 56 Jelinek, Frederick, 108 jobs, 19.

Companies deploy them in situations where they really matter: that is, where we expect them to have a real impact on decisions. Without such decision embeddedness, why go to the trouble of making a prediction in the first place? Sophisticated bad actors in this context would understand that by altering a prediction, they could adjust the decisions. For instance, a diabetic using an AI to optimize insulin intake could end up in serious jeopardy if the AI has incorrect data about that person and then offers predictions that suggest lowering insulin intake when it should be increased. If harming a person is someone’s objective, then this is one way to do it effectively. We are most likely to deploy prediction machines in situations where prediction is hard. A bad actor might not find precisely what data is needed to manipulate a prediction.


pages: 574 words: 164,509

Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

agricultural Revolution, AI winter, Albert Einstein, algorithmic trading, anthropic principle, anti-communist, artificial general intelligence, autonomous vehicles, barriers to entry, Bayesian statistics, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, demographic transition, different worldview, Donald Knuth, Douglas Hofstadter, Drosophila, Elon Musk, en.wikipedia.org, endogenous growth, epigenetics, fear of failure, Flash crash, Flynn Effect, friendly AI, Gödel, Escher, Bach, income inequality, industrial robot, informal economy, information retrieval, interchangeable parts, iterative process, job automation, John Markoff, John von Neumann, knowledge worker, longitudinal study, Menlo Park, meta analysis, meta-analysis, mutually assured destruction, Nash equilibrium, Netflix Prize, new economy, Norbert Wiener, NP-complete, nuclear winter, optical character recognition, pattern recognition, performance metric, phenotype, prediction markets, price stability, principal–agent problem, race to the bottom, random walk, Ray Kurzweil, recommendation engine, reversible computing, social graph, speech recognition, Stanislav Petrov, statistical model, stem cell, Stephen Hawking, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, The Coming Technological Singularity, The Nature of the Firm, Thomas Kuhn: the structure of scientific revolutions, transaction costs, Turing machine, Vernor Vinge, Watson beat the top human players on Jeopardy!, World Values Survey, zero-sum game

The gap between a dumb and a clever person may appear large from an anthropocentric perspective, yet in a less parochial view the two have nearly indistinguishable minds.9 It will almost certainly prove harder and take longer to build a machine intelligence that has a general level of smartness comparable to that of a village idiot than to improve such a system so that it becomes much smarter than any human. Consider a contemporary AI system such as TextRunner (a research project at the University of Washington) or IBM’s Watson (the system that won the Jeopardy! quiz show). These systems can extract certain pieces of semantic information by analyzing text. Although these systems do not understand what they read in the same sense or to the same extent as a human does, they can nevertheless extract significant amounts of information from natural language and use that information to make simple inferences and answer questions. They can also learn from experience, building up more extensive representations of a concept as they encounter additional instances of its use.

It completes perfectly the puzzle rated most difficult by humans, yet is stumped by a couple of nonstandard puzzles that involved spelling backwards or writing answers diagonally.)48 Scrabble Superhuman As of 2002, Scrabble-playing software surpasses the best human players.49 Bridge Equal to the best By 2005, contract bridge playing software reaches parity with the best human bridge players.50 Jeopardy! Superhuman 2010: IBM’s Watson defeats the two all-time-greatest human Jeopardy! champions, Ken Jennings and Brad Rutter.51 Jeopardy! is a televised game show with trivia questions about history, literature, sports, geography, pop culture, science, and other topics. Questions are presented in the form of clues, and often involve wordplay. Poker Varied Computer poker players remain slightly below the best humans for full-ring Texas hold ‘em but perform at a superhuman level in some poker variants.52 FreeCell Superhuman Heuristics evolved using genetic algorithms produce a solver for the solitaire game FreeCell (which in its generalized form is NP-complete) that is able to beat high-ranking human players.53 Go Very strong amateur level As of 2012, the Zen series of go-playing programs has reached rank 6 dan in fast games (the level of a very strong amateur player), using Monte Carlo tree search and machine learning techniques.54 Go-playing programs have been improving at a rate of about 1 dan/year in recent years.

There are both moral and prudential reasons for favoring outcomes in which everybody gets a share of the bounty. We will not say much about the moral case, except to note that it need not rest on any egalitarian principle. The case might be made, for example, on grounds of fairness. A project that creates machine superintelligence imposes a global risk externality. Everybody on the planet is placed in jeopardy, including those who do not consent to having their own lives and those of their family imperiled in this way. Since everybody shares the risk, it would seem to be a minimal requirement of fairness that everybody also gets a share of the upside. The fact that the total (expected) amount of good seems greater in collaboration scenarios is another important reason such scenarios are morally preferable.


pages: 918 words: 257,605

The Age of Surveillance Capitalism by Shoshana Zuboff

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

, “Predicting Individual Well-Being.” 67. CaPPr, Interview with Michal Kosinski. 68. “IBM Cloud Makes Hybrid a Reality for the Enterprise,” IBM, February 23, 2015, https://www-03.ibm.com/press/us/en/pressrelease/46136.wss. 69. “IBM Watson Personality Insights,” IBM Watson Developer Cloud, October 14, 2017, https://personality-insights-livedemo.mybluemix.net; “IBM Personality Insights—Needs,” IBM Watson Developer Cloud, October 14, 2017, https://console.bluemix.net/docs/services/personality-insights/needs.html#needs; “IBM Personality Insights—Values,” IBM Watson Developer Cloud, October 14, 2017, https://console.bluemix.net/docs/services/personality-insights/values.html#values. 70. “IBM Personality Insights—Use Cases,” IBM Cloud Docs, November 8, 2017, https://console.bluemix.net/docs/services/personality-insights/usecases.html #usecases. 71.

For those who seek surveillance revenues, dark data represent lucrative and necessary territories in the dynamic universal jigsaw constituted by surveillance capitalism’s urge toward scale, scope, and action. Thus, the technology community casts dark data as the intolerable “unknown unknown” that threatens the financial promise of the “internet of things.”25 It is therefore understandable that Green portrays machine intelligence—and specifically IBM’s anthropomorphized artificial intelligence system called “Watson”—as the authoritative savior of an apparatus threatened by waste and incomprehensibility. Machine intelligence is referred to as “cognitive computing” at IBM, presumably to avoid the uneasy connotations of inscrutable power associated with words like machine and artificial. Under the leadership of CEO Ginni Rometty, the corporation invested heavily in “Watson,” heralded by the company as “the brains of the ‘internet of things.’”

.… We should basically grow up finally and stop it.”67 In capitalism, though, latent demand summons suppliers and supplies. Surveillance capitalism is no different. The prediction imperative unleashes the surveillance hounds to stalk behavior from the depths, and well-intentioned researchers unwittingly oblige, leaving a trail of cheap, push-button raw meat for surveillance capitalists to hunt and devour. It did not take long. By early 2015, IBM announced that its Watson Personality Service was open for business.68 The corporation’s machine intelligence tools are even more complex and invasive than those used in most academic studies. In addition to the five-factor personality model, IBM assesses each individual across twelve categories of “needs,” including “Excitement, Harmony, Curiosity, Ideal, Closeness, Self-expression, Liberty, Love, Practicality, Stability, Challenge, and Structure.”


pages: 317 words: 100,414

Superforecasting: The Art and Science of Prediction by Philip Tetlock, Dan Gardner

Affordable Care Act / Obamacare, Any sufficiently advanced technology is indistinguishable from magic, availability heuristic, Black Swan, butterfly effect, buy and hold, cloud computing, cuban missile crisis, Daniel Kahneman / Amos Tversky, desegregation, drone strike, Edward Lorenz: Chaos theory, forward guidance, Freestyle chess, fundamental attribution error, germ theory of disease, hindsight bias, index fund, Jane Jacobs, Jeff Bezos, Kenneth Arrow, Laplace demon, longitudinal study, Mikhail Gorbachev, Mohammed Bouazizi, Nash equilibrium, Nate Silver, Nelson Mandela, obamacare, pattern recognition, performance metric, Pierre-Simon Laplace, place-making, placebo effect, prediction markets, quantitative easing, random walk, randomized controlled trial, Richard Feynman, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Reagan, Saturday Night Live, scientific worldview, Silicon Valley, Skype, statistical model, stem cell, Steve Ballmer, Steve Jobs, Steven Pinker, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Watson beat the top human players on Jeopardy!

But spectacular advances in information technology suggest we are approaching a historical discontinuity in humanity’s relationship with machines. In 1997 IBM’s Deep Blue beat chess champion Garry Kasparov. Now, commercially available chess programs can beat any human. In 2011 IBM’s Watson beat Jeopardy! champions Ken Jennings and Brad Rutter. That was a vastly tougher computing challenge, but Watson’s engineers did it. Today, it’s no longer impossible to imagine a forecasting competition in which a supercomputer trounces superforecasters and superpundits alike. After that happens, there will still be human forecasters, but like human Jeopardy! contestants, we will only watch them for entertainment. So I spoke to Watson’s chief engineer, David Ferrucci. I was sure that Watson could easily field a question about the present or past like “Which two Russian leaders traded jobs in the last ten years?”

When I asked Joshua Frankel what he reads for fun, the young Brooklyn filmmaker rattled off the names of highbrow authors like Thomas Pynchon, thought for a moment, and added that he’d recently read a biography of the German rocket scientist Wernher von Braun and various histories of New York, although Frankel was careful to note that the books about New York are also for his work: he is producing an opera about the legendary clash between Robert Moses, New York’s great urban planner, and the free-spirited antiplanner Jane Jacobs. Frankel is not someone to tangle with on Jeopardy! Are superforecasters better simply because they are more knowledgeable and intelligent than others? That would be flattering for them but deflating for the rest of us. Knowledge is something we can all increase, but only slowly. People who haven’t stayed mentally active have little hope of catching up to lifelong learners. Intelligence feels like an even more daunting obstacle. There are believers in cognitive enhancement pills and computer puzzles who may someday be vindicated, but most people feel that adult intelligence is relatively fixed, a function of how well you did in the DNA lottery at conception and the lottery for loving, wealthy families at birth.

Sailors knew they would be doomed if they strayed too far in either direction. Forecasters should feel the same about under- and overreaction to new information, the Scylla and Charybdis of forecasting. Good updating is all about finding the middle passage. Captain Minto In the third season of the IARPA tournament, Tim Minto won top spot with a final Brier score of 0.15, an amazing accomplishment, almost in the league of Ken Jennings’s winning Jeopardy! seventy-four games in a row. A big reason why the forty-five-year-old Vancouver software engineer did so well is his skill at updating. For his initial forecasts, Tim takes less time than some other top forecasters. “I typically spend five to fifteen minutes, which means maybe an hour or so total when a new batch of six or seven questions come out,” he said. But the next day, he’ll come back, take another look, and form a second opinion.


Microserfs by Douglas Coupland

car-free, computer age, El Camino Real, game design, hive mind, Kevin Kelly, Maui Hawaii, means of production, Menlo Park, postindustrial economy, RAND corporation, Ronald Reagan, Sand Hill Road, Silicon Valley, Stephen Hawking, Steve Ballmer, Steve Jobs, telemarketer, Watson beat the top human players on Jeopardy!, white picket fence

I got his machine's message, cobbled together from old Learn how to speak Japanese tapes: [Resonant Berlitzian voice:] Japanese at a glance [Befuddled U.S. tourist:] I can't find my luggage [Japanese bimbette voice:] Nimotsu ga mitsukarimasen [Candice Bergen-type female:] My luggage is here [Studly Toho Studios leading male voice:] Nimotsu wa, koko desu [Game show host voice:] Is there a good disco nearby? [Japanese nerdy male voice:] Chikaku ni, ii disco ga arimasu ka? [Game show host:] I have cramps [Candice:] I have diarrhea [Studly male:] There's something wrong with this camera [Bimbette:] Cauliflower [Game show host] Eggplant [Candice:] Prosciutto with melon [Studly guy:] Shrimp cocktail BEEP... * * * I told Todd to dial Michael's number and he did, and we had to agree that Michael's messages always indeed rocked the Free World. Todd, I should add, like many 1990s people, equates his self-worth with the number of messages on his phone answering machine.

When this aerospace generation grew old enough, they chose to make those dreams in metal. TUESDAY January 4,1994 Woke up sick this morning - finally got the flu. I thought it might be a hangover, but no. In spite of the fact that I think I feel like death-on-a-stick, I want to write down what happened today. * * * First, Michael bounced through the sliding doors around noon in a shiny happy mood, and invited us all out to see our (game show tone of voice) . . . new office! Ethan sold his Ferrari to do the lease. "Farewell 1980s!" he said. (He drives a 1987 Honda Civic now. "I feel like I'm in high school.") Uncharacteristically brash, he yelled, "Convoy! Everybody . . . down to our new office. You, too, Mrs. Underwood . . . we've been liberated from the Habitrail." We stuffed ourselves into two cars and drove through the vine-covered suburbs and carefully mowed, Frisbee-free lawns of Palo Alto's tech parks, to Hamilton Street, a block south of University Street downtown.

We all said, "Ooooh . . ." expecting Todd to freak out, and he did get huffy. "I know, I know," she said preemptively, "the Russians are supposed to be our friends now. But face it, Todd - they'll never get it right. Capitalism is something that's ingrained in you from birth. There's more to developing a market economy than pulling a switch and suddenly being a capitalist overnight. As a child you need to read about Lucy's 5-cent psychiatry booth in Charlie Brown; game shows; mailing away for Sea Monkeys - it's all a part of being 'encapitalized.' " She removed the Barbie head from the lineup of objects: "Probably too good." * * * Later on, Susan and Karla were cackling together. I asked them what about and they shot guilty looks at each other. "Barbies," said Karla. Susan added, "It's like every girl I know did all this incredibly sick sex shit with their Barbies, and in the end the head and/or limbs would fall off and you'd have to hide her but your Mom always found the dismembered Barbie and would say, 'Gee, honey - what happened to Barbie?'"


pages: 428 words: 121,717

Warnings by Richard A. Clarke

active measures, Albert Einstein, algorithmic trading, anti-communist, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, Bernie Madoff, cognitive bias, collateralized debt obligation, complexity theory, corporate governance, cuban missile crisis, data acquisition, discovery of penicillin, double helix, Elon Musk, failed state, financial thriller, fixed income, Flash crash, forensic accounting, friendly AI, Intergovernmental Panel on Climate Change (IPCC), Internet of things, James Watt: steam engine, Jeff Bezos, John Maynard Keynes: Economic Possibilities for our Grandchildren, knowledge worker, Maui Hawaii, megacity, Mikhail Gorbachev, money market fund, mouse model, Nate Silver, new economy, Nicholas Carr, nuclear winter, pattern recognition, personalized medicine, phenotype, Ponzi scheme, Ray Kurzweil, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, Ronald Reagan, Sam Altman, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, smart grid, statistical model, Stephen Hawking, Stuxnet, technological singularity, The Future of Employment, the scientific method, The Signal and the Noise by Nate Silver, Tunguska event, uranium enrichment, Vernor Vinge, Watson beat the top human players on Jeopardy!, women in the workforce, Y2K

Experts have given a name to this era of the hyperintelligent computer: the “intelligence explosion.” Nearly every computer and neural scientist with expertise in the field believes that the intelligence explosion will happen in the next seventy years; most predict it will happen by 2040. In 2015, more than $8.5 billion was invested in the development of new AI technologies. IBM’s Watson supercomputer is hard at work performing tasks ranging from playing (and winning at) Jeopardy! to diagnosing cancer. What will Earth be like when humans are no longer the most intelligent things on the planet? As science fiction writer and computer scientist Vernor Vinge wrote, “The best answer to the question, ‘Will computers ever be as smart as humans?’ is probably ‘Yes, but only briefly.’”5 As the excitement grows, so too does fear. The astrophysicist and Nobel laureate Dr.

He wrote in 2007, “Lonely dissent doesn’t feel like going to school dressed in black. It feels like going to school wearing a clown suit.”14 Bullish tech experts point to how AI has already and will continue to benefit society. Ginni Rometty, the CEO of IBM, says, “In the future, every decision that humankind makes is going to be informed by a cognitive system like Watson, and our lives will be better for it.” Another IBM executive discounts the idea that Watson could become a threat, because “the only data [Watson] has access to is the data we provide it with. It is not capable of going out on its own and creating—in some iRobot-type of form—its own data construct.”15 IBM also has good reason for touting the safety and promise of its technology: Watson is anticipated to generate $10 billion in revenue for IBM by 2023. Noted futurist and AI cheerleader Ray Kurzweil welcomes the advance of superintelligence and believes man and machine will become one in a happy marriage he calls “the singularity.”

What will be the role of humankind when machines can do the vast majority of jobs? What does a society look like when the labor force can no longer earn?29 In 1932, every fourth U.S. household had no breadwinner,30 and the unemployment figures in Europe and Russia were just as glum. Franklin D. Roosevelt saw unemployment as the greatest threat to the nation since the Civil War. “There had never been a time when our institutions were in such jeopardy.”31 FDR was right. Unemployment is corrosive to government stability and calls for remarkably deft leadership, lest the nation collapse. In 1932, the U.S. responded with the New Deal. Western Europe responded with Fascism and the imminent rise of Nazism, Russia deepened into Stalinism and five-year plans. Large-scale unemployment in the current era is no less disruptive and dangerous. The rise of radical Islam throughout the Middle East, the rise of narco-terror in Latin America, and spikes in inner-city gun violence in the United States all have strong correlations with the very low employment rates of young men in those areas.


pages: 396 words: 117,149

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

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

Some things, try as we might, are just unpredictable. For the vast middle ground between the two, there’s machine learning. Paradoxically, even as they open new windows on nature and human behavior, learning algorithms themselves have remained shrouded in mystery. Hardly a day goes by without a story in the media involving machine learning, whether it’s Apple’s launch of the Siri personal assistant, IBM’s Watson beating the human Jeopardy! champion, Target finding out a teenager is pregnant before her parents do, or the NSA looking for dots to connect. But in each case the learning algorithm driving the story is a black box. Even books on big data skirt around what really happens when the computer swallows all those terabytes and magically comes up with new insights. At best, we’re left with the impression that learning algorithms just find correlations between pairs of events, such as googling “flu medicine” and having the flu.

For example, if a fever can be caused by influenza or malaria, and you should take Tylenol for a fever and a headache, this can be expressed as follows: By combining many such operations, we can carry out very elaborate chains of logical reasoning. People often think computers are all about numbers, but they’re not. Computers are all about logic. Numbers and arithmetic are made of logic, and so is everything else in a computer. Want to add two numbers? There’s a combination of transistors that does that. Want to beat the human Jeopardy! champion? There’s a combination of transistors for that too (much bigger, naturally). It would be prohibitively expensive, though, if we had to build a new computer for every different thing we want to do. Rather, a modern computer is a vast assembly of transistors that can do many different things, depending on which transistors are activated. Michelangelo said that all he did was see the statue inside the block of marble and carve away the excess stone until the statue was revealed.

Since then, learning-based methods have swept the field, to the point where it’s hard to find a paper devoid of learning in a computational linguistics conference. Statistical parsers analyze language with accuracy close to that of humans, where hand-coded ones lagged far behind. Machine translation, spelling correction, part-of-speech tagging, word sense disambiguation, question answering, dialogue, summarization: the best systems in these areas all use learning. Watson, the Jeopardy! computer champion, would not have been possible without it. To this Chomsky might reply that engineering successes are not proof of scientific validity. On the other hand, if your buildings collapse and your engines don’t run, perhaps something is wrong with your theory of physics. Chomsky thinks linguists should focus on “ideal” speaker-listeners, as defined by him, and this gives him license to ignore things like the need for statistics in language learning.


pages: 294 words: 80,084

Tomorrowland: Our Journey From Science Fiction to Science Fact by Steven Kotler

Albert Einstein, Alexander Shulgin, autonomous vehicles, barriers to entry, Burning Man, carbon footprint, Colonization of Mars, crowdsourcing, Dean Kamen, epigenetics, gravity well, haute couture, interchangeable parts, Kevin Kelly, life extension, Louis Pasteur, low earth orbit, North Sea oil, Oculus Rift, oil shale / tar sands, peak oil, personalized medicine, Peter H. Diamandis: Planetary Resources, private space industry, RAND corporation, Ray Kurzweil, Richard Feynman, Ronald Reagan, self-driving car, stem cell, Stephen Hawking, Stewart Brand, theory of mind, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, WikiLeaks

Sure, there would be thousands of sniffling people on campus, but the Secret Service probably wouldn’t think anything was amiss. It was December, after all — cold and flu season. 2. Does the scenario we’ve just sketched sound like nothing beyond science fiction? If so, consider that since the turn of the twenty-first century, rapidly accelerating technology has shown a distinct tendency to turn the impossible into the everyday in no time at all. A few years back, IBM’s Watson, an artificial intelligence, whipped the human champion, Ken Jennings, on Jeopardy. As we write this, soldiers with bionic limbs are fighting our enemies and autonomous cars are driving down our streets. Yet most of these advances are small in comparison to the great leap forward currently underway in the biosciences — a leap with consequences we’ve only begun to imagine. More to the point, consider that the Secret Service is already taking extraordinary steps to protect presidential DNA.

See International Genetically Engineered Machines (iGEM) Inception (film), 27 incest, 257–60 India, thorium in, 121 Infertility Network UK, 255 insecticides, 133, 134 Institute for Cancer Stem Cell Biology and Medicine, 216–17 Integral Fast Reactors (IFR), 118–19 International Cryogenics, 255 International Genetically Engineered Machines (iGEM), 234–35 International Thermonuclear Experimental Reactor (ITER), xvii In the President’s Secret Service (Kessler), 238 Intoxication: The Universal Drive for Mind-Altering Substances (Siegel), 167 iPhone, 226 iridium, 150 Iron Man. See Rozelle, David iWalk, 19 Jacob, Francis, 53 Jacobs-Lorena, Marcelo, 138–39 Jacobson, Cecil, 258 Jaeger, William, 257 James, Anthony, 137, 138 James, William, 168 Janiger, Oscar, 169 Japan asteroid mining missions by, 146–47 nuclear power in, 117, 122–23 Jekot, Walter, 195, 196, 197 Jennings, Ken, 223 Jeopardy (TV show), 223 Jet Propulsion Laboratory, 149 John of God, 159 Johns Hopkins University, 162 Johnson, Brittany, 254 Johnson, Diane, 254 Johnson, Ronald, 254 Johnson Space Center, 148–49 Journal of Psychoactive Drugs, 176 The Journey of a Parisian in the 21st Century (Béliard), 87 JoVE (Journal of Visualized Experiments), 229 Juiced (Canseco), 187–88 jumping genes, 136–37, 138–40 Jupiter, mining, 151 Kamen, Dean, 18 Kanada, 109 Kargel, Jeffrey, 144, 149 Kass, Leon, 214–15, 217 Keasling, Jay, 231 Kelly, Kevin, xvi–xviii Kesey, Ken, 169 Kessler, Ronald, 238 ketamine, 39, 162 Kievenaar, Butch, 7, 15 Killer 6.


pages: 168 words: 50,647

The End of Jobs: Money, Meaning and Freedom Without the 9-To-5 by Taylor Pearson

"side hustle", Airbnb, barriers to entry, Ben Horowitz, Black Swan, call centre, cloud computing, commoditize, creative destruction, David Heinemeier Hansson, Elon Musk, en.wikipedia.org, Frederick Winslow Taylor, future of work, Google Hangouts, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, loss aversion, low skilled workers, Lyft, Marc Andreessen, Mark Zuckerberg, market fragmentation, means of production, Oculus Rift, passive income, passive investing, Peter Thiel, remote working, Ronald Reagan: Tear down this wall, sharing economy, side project, Silicon Valley, Skype, software as a service, software is eating the world, Startup school, Steve Jobs, Steve Wozniak, Stewart Brand, telemarketer, Thomas Malthus, Uber and Lyft, uber lyft, unpaid internship, Watson beat the top human players on Jeopardy!, web application, Whole Earth Catalog

Instead of choosing from a set of available options, we can create our own. It’s the triumph of design over choice. Instead of ordering from the menu, we are more empowered than any prior generation to become the cooks. Are You Structuring Your Reality or Having It Structured for You? The least free are those whose reality is structured for them. “Stay tuned,” they are told before each Jeopardy commercial break, and they do so. At work they are assigned tasks and roles that are clearly defined. They exert very little freedom over their reality. The middle class has created a greater degree of independence and structure a greater deal of their own realities. They are more likely to structure their families and hobbies in ways they find more meaningful and not just as the mass media may instruct them.


pages: 356 words: 105,533

Dark Pools: The Rise of the Machine Traders and the Rigging of the U.S. Stock Market by Scott Patterson

algorithmic trading, automated trading system, banking crisis, bash_history, Bernie Madoff, butterfly effect, buttonwood tree, buy and hold, Chuck Templeton: OpenTable:, cloud computing, collapse of Lehman Brothers, computerized trading, creative destruction, Donald Trump, fixed income, Flash crash, Francisco Pizarro, Gordon Gekko, Hibernia Atlantic: Project Express, High speed trading, Joseph Schumpeter, latency arbitrage, Long Term Capital Management, Mark Zuckerberg, market design, market microstructure, pattern recognition, pets.com, Ponzi scheme, popular electronics, prediction markets, quantitative hedge fund, Ray Kurzweil, Renaissance Technologies, Sergey Aleynikov, Small Order Execution System, South China Sea, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stochastic process, transaction costs, Watson beat the top human players on Jeopardy!, zero-sum game

The system might track respected blogs about Apple such as Mac Rumors, speeches by industry experts, shipping data out of China (where iPhones were built), employment sites measuring the number of workers with Apple experience looking for jobs (an uptick would indicate a round of layoffs, hence trouble and possibly an earnings miss). The system would scour SEC filings, data on Amazon.com or other retail sites that indicated sales performance, and Twitter feeds that mentioned Apple products. Collectively, the AI program crunched the information like a magical data grinder and spit out a buy or sell recommendation with a certain probability, much like a Wall Street analyst—or IBM’s Watson submitting a response on Jeopardy! That, at least, was the theory. The goal: predict a company’s performance before it became public. Effectively, they were building from scratch an AI financial analyst. Ideally, Kinetic would be able to detect a company’s fortunes even before the company’s own executives and employees knew what was happening. Sales trends, buzz on a product, a pricing war coming from a tough competitor—they were crystal balls into the future, if only you could find the right data and make sense of it.

But now programmers were attempting to build computers that could beat humans at the trading game itself, buying and selling stocks based on fundamentals such as sales trends and economic variables. While the effort seemed almost quixotic, there were indications that it could be done. IBM, after all, had recently built an AI computer system called Watson that had defeated the world’s elite Jeopardy! players. The system Kinetic deployed resembled Watson in certain ways. Kinetic’s task, however, was in reality far harder than cracking Jeopardy! Kinetic was trying to hack the market by mining endless terabytes of information stored on databases throughout the world. Its hacker in chief, Ladopoulos, was a charismatic, intense character with a shaved head, rimless glasses, a fondness for vintage tennis shoes, and a million stories. Many of those stories went back to his previous incarnation in the early 1990s as an infamous hacker, back when being a hacker was actually cool, the closest a computer nerd could get to rock-star status.


pages: 374 words: 111,284

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

"Robert Solow", 3D printing, agricultural Revolution, AI winter, Albert Einstein, anti-work, autonomous vehicles, basic income, Ben Bernanke: helicopter money, Bernie Sanders, blockchain, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chris Urmson, computer age, conceptual framework, corporate governance, correlation does not imply causation, creative destruction, David Ricardo: comparative advantage, deindustrialization, deskilling, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, facts on the ground, financial intermediation, full employment, future of work, income inequality, income per capita, industrial robot, Internet of things, invention of the wheel, Isaac Newton, James Watt: steam engine, Jeff Bezos, job automation, job satisfaction, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, Kevin Kelly, license plate recognition, Marc Andreessen, Mark Zuckerberg, market bubble, mega-rich, natural language processing, Network effects, new economy, Nicholas Carr, Paul Samuelson, Peter Thiel, positional goods, quantitative easing, RAND corporation, Ray Kurzweil, Richard Florida, ride hailing / ride sharing, rising living standards, road to serfdom, Robert Gordon, Robert Shiller, Robert Shiller, Second Machine Age, secular stagnation, self-driving car, Silicon Valley, Simon Kuznets, Skype, social intelligence, spinning jenny, Stanislav Petrov, Stephen Hawking, Steven Pinker, technological singularity, The Future of Employment, The Wealth of Nations by Adam Smith, Thomas Malthus, trade route, universal basic income, US Airways Flight 1549, Vernor Vinge, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, wealth creators, winner-take-all economy, Y2K, Yogi Berra

It used to be thought that the game of chess would be beyond even the most capable computer. But in 1997 IBM’s Deep Blue defeated the world’s best player, Gary Kasparov. Deep Blue was able to evaluate between 100 and 200 million positions per second. Kasparov said: “I had played a lot of computers but had never experienced anything like this. I could feel – I could smell – a new kind of intelligence across the table.” In 2001 an IBM machine called Watson beat the best human players at the TV quiz game Jeopardy! In 2013 a DeepMind AI system taught itself to play Atari video games like Breakout and Pong, which involve hand–eye coordination. This was much more significant that it might have seemed. The AI system wasn’t taught how to play video games, but rather how to learn to play the games. Kevin Kelly thinks that AI has now made a decided leap forward, but its significance is still not fully appreciated.

For example, bats interpret sonar signals better than humans. But I have yet to meet anyone who thinks that, given sufficient time for evolution to play out, bats are going to surpass humans in general intelligence. And the philosopher John Searle penned an opinion piece in the Wall Street Journal that wittily put the achievement of Watson in winning Jeopardy! into perspective. It appeared under the headline: “Watson Doesn’t Know It Won on ‘Jeopardy!’” Searle pointed out that Watson did not dream about it beforehand or celebrate it afterward. No chats with friends, no commiserations with vanquished opponents.33 Murray Shanahan has recognized the limitations. He has said: “A chatbot that is programed to crack a few jokes or a humanoid robot whose eyes can follow you around a room can easily give a contrary impression.


pages: 370 words: 94,968

The Most Human Human: What Talking With Computers Teaches Us About What It Means to Be Alive by Brian Christian

4chan, Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Bertrand Russell: In Praise of Idleness, carbon footprint, cellular automata, Claude Shannon: information theory, cognitive dissonance, commoditize, complexity theory, crowdsourcing, David Heinemeier Hansson, Donald Trump, Douglas Hofstadter, George Akerlof, Gödel, Escher, Bach, high net worth, Isaac Newton, Jacques de Vaucanson, Jaron Lanier, job automation, l'esprit de l'escalier, Loebner Prize, Menlo Park, Ray Kurzweil, RFID, Richard Feynman, Ronald Reagan, Skype, Social Responsibility of Business Is to Increase Its Profits, starchitect, statistical model, Stephen Hawking, Steve Jobs, Steven Pinker, Thales of Miletus, theory of mind, Thomas Bayes, Turing machine, Turing test, Von Neumann architecture, Watson beat the top human players on Jeopardy!, zero-sum game

He asked me about myself, and I explained that I’m a nonfiction writer of science and philosophy, specifically of the ways in which science and philosophy intersect with daily life, and that I’m fascinated by the idea of the Turing test and of the “Most Human Human.” For one, there’s a romantic notion as a confederate of defending the human race, à la Garry Kasparov vs. Deep Blue—and soon, Ken Jennings of Jeopardy! fame vs. the latest IBM system, Watson. (The mind also leaps to other, more Terminator– and The Matrix–type fantasies, although the Turing test promises to involve significantly fewer machine guns.) When I read that the machines came up shy of passing the 2008 test by just one single vote, and realized that 2009 might be the year they finally cross the threshold, a steely voice inside me rose up seemingly out of nowhere.

Okuno, “Enabling a User to Specify an Item at Any Time During System Enumeration: Item Identification for Barge-In-Able Conversational Dialogue Systems,” Proceedings of the International Conference on Spoken Language Processing (2009). 18 Brian Ferneyhough, in Kriesberg, “Music So Demanding.” 19 David Mamet, Glengarry Glen Ross (New York: Grove, 1994). 20 For more on back-channel feedback and the (previously neglected) role of the listener in conversation, see, e.g., Bavelas, Coates, and Johnson, “Listeners as Co-narrators.” 21 Jack T. Huber and Dean Diggins, Interviewing America’s Top Interviewers: Nineteen Top Interviewers Tell All About What They Do (New York: Carol, 1991). 22 Clark and Fox Tree, “Using Uh and Um.” 23 Clive Thompson, “What Is I.B.M.’s Watson?” New York Times, June 14, 2010. 24 Nikko Ström and Stephanie Seneff, “Intelligent Barge-In in Conversational Systems,” Proceedings of the International Conference on Spoken Language Processing (2000). 25 Jonathan Schull, Mike Axelrod, and Larry Quinsland, “Multichat: Persistent, Text-as-You-Type Messaging in a Web Browser for Fluid Multi-person Interaction and Collaboration” (paper presented at the Seventh Annual Workshop and Minitrack on Persistent Conversation, Hawaii International Conference on Systems Science, Kauai, Hawaii, January 2006). 26 Deborah Tannen, That’s Not What I Meant!

“Speakers can use these announcements,” linguists Clark and Fox Tree write, “to implicate, for example, that they are searching for a word, are deciding what to say next, want to keep the floor, or want to cede the floor.” We are told by speaking coaches, teachers, parents, and the like just to hold our tongue. The fact of the matter is, however, filling pauses in speech with sound is not simply a tic, or an error—it’s a signal that we’re about to speak. (Consider, as an analogue, your computer turning its pointer into an hourglass before freezing for a second.) A big part of the skill it takes to be a Jeopardy! contestant is the ability to buzz in before you know the answer, but as soon as you know you know the answer—that buzz means, roughly, “Oh! Uh …,” and its successful deployment is part of what separates champions from average players. (By the way, this is part of what has been giving IBM researchers such a hard time preparing their supercomputer Watson for serious competition against humans, especially for short questions that only take Alex Trebek a second or two to read.)


pages: 280 words: 74,559

Fully Automated Luxury Communism by Aaron Bastani

"Robert Solow", autonomous vehicles, banking crisis, basic income, Berlin Wall, Bernie Sanders, Bretton Woods, capital controls, cashless society, central bank independence, collapse of Lehman Brothers, computer age, computer vision, David Ricardo: comparative advantage, decarbonisation, dematerialisation, Donald Trump, double helix, Elon Musk, energy transition, Erik Brynjolfsson, financial independence, Francis Fukuyama: the end of history, future of work, G4S, housing crisis, income inequality, industrial robot, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Isaac Newton, James Watt: steam engine, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Kuiper Belt, land reform, liberal capitalism, low earth orbit, low skilled workers, M-Pesa, market fundamentalism, means of production, mobile money, more computing power than Apollo, new economy, off grid, pattern recognition, Peter H. Diamandis: Planetary Resources, post scarcity, post-work, price mechanism, price stability, private space industry, Productivity paradox, profit motive, race to the bottom, RFID, rising living standards, Second Machine Age, self-driving car, sensor fusion, shareholder value, Silicon Valley, Simon Kuznets, Slavoj Žižek, stem cell, Stewart Brand, technoutopianism, the built environment, the scientific method, The Wealth of Nations by Adam Smith, Thomas Malthus, transatlantic slave trade, Travis Kalanick, universal basic income, V2 rocket, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, working-age population

While that was an iconic moment in the unfolding story of humans and machines, it paled in comparison to Watson, also built by IBM, when it later defeated Ken Jennings and Brad Rutter – two of the greatest Jeopardy! players in the history of the TV quiz show. Chess is a uniquely challenging game, but Jeopardy!, which demands real-time pattern recognition and creative thinking, more closely resembles the features associated with distinctively human intelligence. Not long after, Ken Jennings neatly summed up what that defeat might mean for white-collar work – which values pattern recognition and creative thinking – over the coming decades. Just as factory jobs were eliminated in the twentieth 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.


pages: 360 words: 100,991

Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence by Richard Yonck

3D printing, AI winter, artificial general intelligence, Asperger Syndrome, augmented reality, Berlin Wall, brain emulation, Buckminster Fuller, call centre, cognitive bias, cognitive dissonance, computer age, computer vision, crowdsourcing, Elon Musk, en.wikipedia.org, epigenetics, friendly AI, ghettoisation, industrial robot, Internet of things, invention of writing, Jacques de Vaucanson, job automation, John von Neumann, Kevin Kelly, Law of Accelerating Returns, Loebner Prize, Menlo Park, meta analysis, meta-analysis, Metcalfe’s law, neurotypical, Oculus Rift, old age dependency ratio, pattern recognition, RAND corporation, Ray Kurzweil, Rodney Brooks, self-driving car, Skype, social intelligence, software as a service, Stephen Hawking, Steven Pinker, superintelligent machines, technological singularity, telepresence, telepresence robot, The Future of Employment, the scientific method, theory of mind, Turing test, twin studies, undersea cable, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Review, working-age population, zero day

In time, Andrew desires to be granted full recognition as a human, a goal it eventually attains near the story’s end. Today, in the twenty-first century, we find ourselves facing a future in which our machines are consistently and repeatedly besting us in all manner of intellectual pursuits. IBM’s Deep Blue beat world chess champion Garry Kasparov in a six-game match in 1997. In 2011, IBM’s Watson (DeepQA) defeated the two all-time Jeopardy champions Brad Rutter and Ken Jennings in a two-day contest of general knowledge. Google’s AlphaGo soundly trounced the longtime world Go grandmaster, Lee Sedol, in four games out of five in March 2016. Given all this, it seems one of the few remaining aspects of machine intelligence left to explore in fiction is how they interact with the world emotionally. In A.I. Artificial Intelligence, Steven Spielberg tells the story of David, a mecha or highly advanced robot in the form of an eleven-year-old child who wants to become a “real boy” so that his mother will love him.

(It’s tempting to say “adult” supervision, but unfortunately that adjective has a very different meaning on the Internet that could result in all kinds of trouble as well!) This is hardly the first time something like this has happened. In 2011, Eric Brown, the head of IBM’s DeepQA project, decided to teach its AI Watson using the Urban Dictionary, an online resource intended to capture modern slang and street talk. This was soon after the AI’s famous win on the game show Jeopardy. Brown’s reasoning was that this would be an excellent way to learn the intricacies of informal conversation. Shortly thereafter, the AI began swearing up a storm. The DeepQA team was forced to remove the new input from Watson’s vocabulary and design a swear filter for it as well.2 Something similarly unexpected occurred in 2012 when Google’s secretive X Lab decided to let its best artificial neural network loose on the web without any defined instructions or guidance.

Of course, there are other ways to incorporate artificial emotional intelligence into teaching. In May 2016, the Wall Street Journal carried a story about a teacher’s assistant for an artificial intelligence course at the Georgia Institute of Technology.9 One of nine assistants for more than three hundred students, Jill Watson was conversational, knowledgeable, and efficient. Jill was also an AI developed by professor of computer science Ashok Goel. Built on IBM’s Watson platform, Jill operates at a 97 percent confidence level.10 Goel estimated that within a year, Jill would be able to handle 40 percent of all online student questions. Though in its current form Jill is devoid of an emotion channel, she fooled Goel’s students because the role mostly didn’t call for the AI to be affectively aware. However, as this technology advances, adding that extra dimension should make such digital assistants even more acceptable and engaging.


pages: 252 words: 72,473

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O'Neil

Affordable Care Act / Obamacare, Bernie Madoff, big data - Walmart - Pop Tarts, call centre, carried interest, cloud computing, collateralized debt obligation, correlation does not imply causation, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, Emanuel Derman, housing crisis, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, illegal immigration, Internet of things, late fees, mass incarceration, medical bankruptcy, Moneyball by Michael Lewis explains big data, new economy, obamacare, Occupy movement, offshore financial centre, payday loans, peer-to-peer lending, Peter Thiel, Ponzi scheme, prediction markets, price discrimination, quantitative hedge fund, Ralph Nader, RAND corporation, recommendation engine, Rubik’s Cube, Sharpe ratio, statistical model, Tim Cook: Apple, too big to fail, Unsafe at Any Speed, Upton Sinclair, Watson beat the top human players on Jeopardy!, working poor

an Arkansas resident named Catherine Taylor: Ylan Q. Mui, “Little-Known Firms Tracking Data Used in Credit Scores,” Washington Post, July 16, 2011, www.​washingtonpost.​com/​business/​economy/​little-​known-​firms-​tracking-​data-​used-​in-​credit-​scores/​2011/​05/​24/​gIQAXHcWII_​story.​html. a butterfly’s diet was “Kosher”: Stephen Baker, “After ‘Jeopardy,’ ” Boston Globe, February 15, 2011, www.​boston.​com/​bostonglobe/​editorial_​opinion/​oped/​articles/​2011/​02/​15/​after_​jeopardy/. labeled them as gorillas: Alistair Barr, “Google Mistakenly Tags Black People as ‘Gorillas,’ Showing Limits of Algorithms,” Wall Street Journal, July 1, 2015, http://​blogs.​wsj.​com/​digits/​2015/​07/​01/​google-​mistakenly-​tags-​black-​people-​as-​gorillas-​showing-​limits-​of-​algorithms/. Facebook, for example, has patented: Robinson Meyer, “Could a Bank Deny Your Loan Based on Your Facebook Friends?

Errors are inevitable, as in any statistical program, but the quickest way to reduce them is to fine-tune the algorithms running the machines. Humans on the ground only gum up the works. This trend toward automation is leaping ahead as computers make sense of more and more of our written language, in some cases processing thousands of written documents in a second. But they still misunderstand all sorts of things. IBM’s Jeopardy!-playing supercomputer Watson, for all its brilliance, was flummoxed by language or context about 10 percent of the time. It was heard saying that a butterfly’s diet was “Kosher,” and it once confused Oliver Twist, the Charles Dickens character, with the 1980s techno-pop band the Pet Shop Boys. Such errors are sure to pile up in our consumer profiles, confusing and misdirecting the algorithms that manage more and more of our lives.


Deep Work: Rules for Focused Success in a Distracted World by Cal Newport

8-hour work day, Albert Einstein, barriers to entry, business climate, Cal Newport, Capital in the Twenty-First Century by Thomas Piketty, Clayton Christensen, David Brooks, David Heinemeier Hansson, deliberate practice, disruptive innovation, Donald Knuth, Donald Trump, Downton Abbey, en.wikipedia.org, Erik Brynjolfsson, experimental subject, follow your passion, Frank Gehry, informal economy, information retrieval, Internet Archive, Jaron Lanier, knowledge worker, Mark Zuckerberg, Marshall McLuhan, Merlin Mann, Nate Silver, new economy, Nicholas Carr, popular electronics, remote working, Richard Feynman, Ruby on Rails, Silicon Valley, Silicon Valley startup, Snapchat, statistical model, the medium is the message, Watson beat the top human players on Jeopardy!, web application, winner-take-all economy, zero-sum game

Jack Dorsey’s success without depth is common at this elite level of management. Once we’ve stipulated this reality, we must then step back to remind ourselves that it doesn’t undermine the general value of depth. Why? Because the necessity of distraction in these executives’ work lives is highly specific to their particular jobs. A good chief executive is essentially a hard-to-automate decision engine, not unlike IBM’s Jeopardy!-playing Watson system. They have built up a hard-won repository of experience and have honed and proved an instinct for their market. They’re then presented inputs throughout the day—in the form of e-mails, meetings, site visits, and the like—that they must process and act on. To ask a CEO to spend four hours thinking deeply about a single problem is a waste of what makes him or her valuable.

To do so, she did something extreme: She forced each member of the team to take one day out of the workweek completely off—no connectivity to anyone inside or outside the company. “At first, the team resisted the experiment,” she recalled about one of the trials. “The partner in charge, who had been very supportive of the basic idea, was suddenly nervous about having to tell her client that each member of her team would be off one day a week.” The consultants were equally nervous and worried that they were “putting their careers in jeopardy.” But the team didn’t lose their clients and its members did not lose their jobs. Instead, the consultants found more enjoyment in their work, better communication among themselves, more learning (as we might have predicted, given the connection between depth and skill development highlighted in the last chapter), and perhaps most important, “a better product delivered to the client.” This motivates an interesting question: Why do so many follow the lead of the Boston Consulting Group and foster a culture of connectivity even though it’s likely, as Perlow found in her study, that it hurts employees’ well-being and productivity, and probably doesn’t help the bottom line?

In particular, identify a deep task (that is, something that requires deep work to complete) that’s high on your priority list. Estimate how long you’d normally put aside for an obligation of this type, then give yourself a hard deadline that drastically reduces this time. If possible, commit publicly to the deadline—for example, by telling the person expecting the finished project when they should expect it. If this isn’t possible (or if it puts your job in jeopardy), then motivate yourself by setting a countdown timer on your phone and propping it up where you can’t avoid seeing it as you work. At this point, there should be only one possible way to get the deep task done in time: working with great intensity—no e-mail breaks, no daydreaming, no Facebook browsing, no repeated trips to the coffee machine. Like Roosevelt at Harvard, attack the task with every free neuron until it gives way under your unwavering barrage of concentration.


pages: 317 words: 84,400

Automate This: How Algorithms Came to Rule Our World by Christopher Steiner

23andMe, Ada Lovelace, airport security, Al Roth, algorithmic trading, backtesting, big-box store, Black-Scholes formula, call centre, cloud computing, collateralized debt obligation, commoditize, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, Donald Trump, Douglas Hofstadter, dumpster diving, Flash crash, G4S, Gödel, Escher, Bach, High speed trading, Howard Rheingold, index fund, Isaac Newton, John Markoff, John Maynard Keynes: technological unemployment, knowledge economy, late fees, Marc Andreessen, Mark Zuckerberg, market bubble, medical residency, money market fund, Myron Scholes, Narrative Science, PageRank, pattern recognition, Paul Graham, Pierre-Simon Laplace, prediction markets, quantitative hedge fund, Renaissance Technologies, ride hailing / ride sharing, risk tolerance, Robert Mercer, Sergey Aleynikov, side project, Silicon Valley, Skype, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, transaction costs, upwardly mobile, Watson beat the top human players on Jeopardy!, Y Combinator

But what’s become clear in the years since Deep Blue’s victory is that algorithms will continue to invade professions and skill areas that we have always assumed will remain inherently human. Chess was just the beginning. In early 2011, IBM’s newest creation, Watson, bested all human contestants on the game show Jeopardy!—including Ken Jennings, the most prolific champion in the show’s history. That a bot could be so intellectually nimble in the way it processed random questions, speedily consulted raw stores of data, and issued answers was impressive. Whereas chess is a game played on a limited board with rigid rules, Jeopardy! is chaotic, arbitrary, and offers almost no guidelines on the content or nature of its queries, which can be pocked with humor, puns, and irony. To do it, IBM stored 200 million pages of content on four terabytes of disk drives that were read by twenty-eight hundred processor cores (the newish Apple computer I used to write this book has two) assisted by sixteen terabytes of memory (RAM).

No willy-nilly tests, no gut feelings, just data in, data out. Watson won’t miss clues on those rare cases because he’s simply not prejudiced to rely on the easy answers. Soon after its Jeopardy! triumph, IBM began working with doctors and researchers at Columbia University to develop a version of Watson that won’t be a mere novelty in health care but a true caregiver and diagnostic authority. In September 2011, the giant health insurer WellPoint announced plans to give Watson a job assisting doctors in their offices with diagnoses, providing a valuable and legitimate second opinion. WellPoint’s main purpose in using Watson is saving money, but in paying IBM for Watson’s time, patients also receive the benefit of more correct initial diagnoses. Herbert Chase, a clinical medicine professor at Columbia, tested Watson with a vexing case from earlier in his career when he had to treat a woman in her midthirties who complained of fleeting strength and limp muscles.24 The woman’s blood tests revealed low phosphate levels and strangely high readings of alkaline phosphatase, an enzyme.

The tryst gave us what Peter Parham, an immunogeneticist at Stanford, calls “hybrid vigor,” endowing us with a powerful immune system that allowed humans to colonize the world.23 A generation from now, algorithms like Patterson’s will scan our DNA and tell us what diseases we’re likely to get and even when they may come. Treating those maladies will be handled by a computer the world knows well: IBM’s Watson. When you head to the doctor’s office with a health quandary, your appointment usually goes something like this: Your doctor asks a question, you answer; your doctor asks another question, you answer. This pattern goes on until your caregiver can suss out what she thinks is your exact problem. She bases her diagnosis on your answers, which lead her through a tree of possibilities within her head.


pages: 179 words: 43,441

The Fourth Industrial Revolution by Klaus Schwab

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

, 17 September 2013 Positive impacts – Cost reductions – Efficiency gains – Unlocking innovation, opportunities for small business, start-ups (smaller barriers to entry, “software as a service” for everything) Negative impacts – Job losses – Accountability and liability – Change to legal, financial disclosure, risk – Job automation (refer to the Oxford Martin study) The shift in action Advances in automation were reported on by FORTUNE: “IBM’s Watson, well known for its stellar performance in the TV game show Jeopardy!, has already demonstrated a far more accurate diagnosis rate for lung cancers than humans – 90% versus 50% in some tests. The reason is data. Keeping pace with the release of medical data could take doctors 160 hours a week, so doctors cannot possibly review the amount of new insights or even bodies of clinical evidence that can give an edge in making a diagnosis.

Because of this, the ability to determine our individual genetic make-up in an efficient and cost-effective manner (through sequencing machines used in routine diagnostics) will revolutionize personalized and effective healthcare. Informed by a tumour’s genetic make-up, doctors will be able to make decisions about a patient’s cancer treatment. While our understanding of the links between genetic markers and disease is still poor, increasing amounts of data will make precision medicine possible, enabling the development of highly targeted therapies to improve treatment outcomes. Already, IBM’s Watson supercomputer system can help recommend, in just a few minutes, personalized treatments for cancer patients by comparing the histories of disease and treatment, scans and genetic data against the (almost) complete universe of up-to-date medical knowledge.11 The ability to edit biology can be applied to practically any cell type, enabling the creation of genetically modified plants or animals, as well as modifying the cells of adult organisms including humans.

– Blurring the lines between man and machine Unknown, or cuts both ways – Cultural shift – Disembodiment of communication – Improvement of performance – Extending human cognitive abilities will trigger new behaviours The shift in action – Cortical computing algorithms have already shown an ability to solve modern CAPTCHAs (widely used tests to distinguish humans from machines). – The automotive industry has developed systems monitoring attention and awareness that can stop cars when people are falling asleep while driving. – An intelligent computer program in China scored better than many human adults on an IQ test. – IBM’s Watson supercomputer, after sifting through millions of medical records and databases, has begun to help doctors choose treatment options for patients with complex needs. – Neuromorphic image sensors, i.e. inspired how the eye and brain communicate, will have impact ranging from battery usage to robotics – Neuroprosthetics are allowing disabled people to control artificial members and exoskeletons.


pages: 327 words: 103,336

Everything Is Obvious: *Once You Know the Answer by Duncan J. Watts

active measures, affirmative action, Albert Einstein, Amazon Mechanical Turk, Black Swan, business cycle, butterfly effect, Carmen Reinhart, Cass Sunstein, clockwork universe, cognitive dissonance, coherent worldview, collapse of Lehman Brothers, complexity theory, correlation does not imply causation, crowdsourcing, death of newspapers, discovery of DNA, East Village, easy for humans, difficult for computers, edge city, en.wikipedia.org, Erik Brynjolfsson, framing effect, Geoffrey West, Santa Fe Institute, George Santayana, happiness index / gross national happiness, high batting average, hindsight bias, illegal immigration, industrial cluster, interest rate swap, invention of the printing press, invention of the telescope, invisible hand, Isaac Newton, Jane Jacobs, Jeff Bezos, Joseph Schumpeter, Kenneth Rogoff, lake wobegon effect, Laplace demon, Long Term Capital Management, loss aversion, medical malpractice, meta analysis, meta-analysis, Milgram experiment, natural language processing, Netflix Prize, Network effects, oil shock, packet switching, pattern recognition, performance metric, phenotype, Pierre-Simon Laplace, planetary scale, prediction markets, pre–internet, RAND corporation, random walk, RFID, school choice, Silicon Valley, social intelligence, statistical model, Steve Ballmer, Steve Jobs, Steve Wozniak, supply-chain management, The Death and Life of Great American Cities, the scientific method, The Wisdom of Crowds, too big to fail, Toyota Production System, ultimatum game, urban planning, Vincenzo Peruggia: Mona Lisa, Watson beat the top human players on Jeopardy!, X Prize

Taylor, Carl C. 1947. “Sociology and Common Sense.” American Sociological Review 12 (1):1–9. Tetlock, Philip E. 2005. Expert Political Judgment: How Good Is It? How Can We Know? Princeton, NJ: Princeton University Press. Thaler, Richard H., and Cass R. Sunstein. 2008. Nudge: Improving Decisions about Health, Wealth, and Happiness. New Haven, CT: Yale University Press. Thompson, Clive. 2010. “What Is I.B.M.’s Watson?” New York Times Magazine (June 20):30–45. Thorndike, Edward L. 1920. “A Constant Error on Psychological Rating.” Journal of Applied Psychology 4:25–9. Tomlinson, Brian, and Clive Cockram. 2003. “SARS: Experience at Prince of Wales Hospital, Hong Kong.” The Lancet 361 (9368):1486–87. Tuchman, Barbara W. 1985. The March of Folly: From Troy to Vietnam. New York: Ballantine Books. Tucker, Nicholas. 1999.

Rather than trying to crack the problem, therefore, AI researchers took a different approach entirely—one that emphasized statistical models of data rather than thought processes. This approach, which nowadays is called machine learning, was far less intuitive than the original cognitive approach, but it has proved to be much more productive, leading to all kinds of impressive breakthroughs, from the almost magical ability of search engines to complete queries as you type them to building autonomous robot cars, and even a computer that can play Jeopardy!18 WE DON’T THINK THE WAY WE THINK WE THINK The frame problem, however, isn’t just a problem for artificial intelligence—it’s a problem for human intelligence as well. As the psychologist Daniel Gilbert describes in Stumbling on Happiness, when we imagine ourselves, or someone else, confronting a particular situation, our brains do not generate a long list of questions about all the possible details that might be relevant.

In fact, one can always do this trivially by exhaustively including every item and concept in the known universe in the basket of potentially relevant factors, thereby making what at first seems to be a global problem local by definition. Unfortunately, this approach succeeds only at the expense of rendering the computational procedure intractable. 18. For an introduction to machine learning, see Bishop (2006). See Thompson (2010) for a story about the Jeopardy-playing computer. 19. For a compelling discussion of the many ways in which our brains misrepresent both our memories of past events and our anticipated experience of future events, see Gilbert (2006). As Becker (1998, p. 14) has noted, even social scientists are prone to this error, filling in the motivations, perspectives, and intentions of their subjects whenever they have no direct evidence of them.


pages: 420 words: 98,309

Mistakes Were Made (But Not by Me): Why We Justify Foolish Beliefs, Bad Decisions, and Hurtful Acts by Carol Tavris, Elliot Aronson

Ayatollah Khomeini, cognitive dissonance, cuban missile crisis, desegregation, Donald Trump, false memory syndrome, fear of failure, Lao Tzu, longitudinal study, medical malpractice, medical residency, meta analysis, meta-analysis, Milgram experiment, moral panic, Nelson Mandela, placebo effect, psychological pricing, Richard Feynman, Ronald Reagan, social intelligence, telemarketer, the scientific method, trade route, transcontinental railway, Watson beat the top human players on Jeopardy!

At the heart of it, Festinger's theory is about how people strive to make sense out of contradictory ideas and lead lives that are, at least in their own minds, consistent and meaningful. The theory inspired more than 3,000 experiments that, taken together, have transformed psychologists' understanding of how the human mind works. Cognitive dissonance has even escaped academia and entered popular culture. The term is everywhere. The two of us have heard it in TV newscasts, political columns, magazine articles, bumper stickers, even on a soap opera. Alex Trebek used it on Jeopardy, Jon Stewart on The Daily Show, and President Bartlet on The West Wing. Although the expression has been thrown around a lot, few people fully understand its meaning or appreciate its enormous motivational power. In 1956, one of us (Elliot) arrived at Stanford University as a graduate student in psychology. Festinger had arrived that same year as a young professor, and they immediately began working together, designing experiments to test and expand dissonance theory.3 Their thinking challenged many notions that were gospel in psychology and among the general public, such as the behaviorist's view that people do things primarily for the rewards they bring, the economist's view that human beings generally make rational decisions, and the psychoanalyst's view that acting aggressively gets rid of aggressive impulses.

., who sued her father, Joel Hungerford, in the state of New Hampshire in 1995. She lost. 3 Two of the earliest and still best books on the day-care scandals and claims of widespread cults that were promoting ritual Satanic sexual abuse are Debbie Nathan and Michael Snedeker (1995), Satan's Silence: Ritual Abuse and the Making of a Modern American Witch Hunt, New York: Basic Books; and Stephen J. Ceci and Maggie Bruck (1995), Jeopardy in the Courtroom: A Scientific Analysis of Children's Testimony, Washington, DC: American Psychological Association. Dorothy Rabinowitz, a Wall Street Journal editorial writer, was the first to publicly question the conviction of Kelly Michaels and get her case reopened; see also Rabinowitz (2003), No Crueler Tyrannies: Accusation, False Witness, and Other Terrors of Our Times. New York: Wall Street Press Books/ Free Press.

Meehl (1954), Clinical versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence, Minneapolis: University of Minnesota Press; and Robyn Dawes, David Faust, and Paul E. Meehl (1989), "Clinical versus Actuarial Judgment," Science, 243, pp. 1668–1674. Meehl's findings have been repeatedly reconfirmed. See Howard Grob (1998), Studying the Clinician: Judgment Research and Psychological Assessment. Washington, DC: American Psychological Association. 26 Our account of the Kelly Michaels case is based largely on Ceci and Bruck, Jeopardy in the Courtroom (note 3); and Pendergrast, Victims of Memory (note 2). See also Maggie Bruck and Stephen Ceci (1995), "Amicus Brief for the Case of State of New Jersey v. Margaret Kelly Michaels, Presented by Committee of Concerned Social Scientists," Psychology, Public Policy, & Law, 1(2) [entire issue]. 27 Quoted in Pendergrast, Victims of Memory, p. 423; note 2. 28 Jason J. Dickinson, Debra A.


pages: 416 words: 112,268

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

3D printing, Ada Lovelace, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Alfred Russel Wallace, Andrew Wiles, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, basic income, blockchain, brain emulation, Cass Sunstein, Claude Shannon: information theory, complexity theory, computer vision, connected car, crowdsourcing, Daniel Kahneman / Amos Tversky, delayed gratification, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ernest Rutherford, Flash crash, full employment, future of work, Gerolamo Cardano, ImageNet competition, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the wheel, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Nash: game theory, John von Neumann, Kenneth Arrow, Kevin Kelly, Law of Accelerating Returns, Mark Zuckerberg, Nash equilibrium, Norbert Wiener, NP-complete, openstreetmap, P = NP, Pareto efficiency, Paul Samuelson, Pierre-Simon Laplace, positional goods, probability theory / Blaise Pascal / Pierre de Fermat, profit maximization, RAND corporation, random walk, Ray Kurzweil, recommendation engine, RFID, Richard Thaler, ride hailing / ride sharing, Robert Shiller, Robert Shiller, Rodney Brooks, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, smart cities, smart contracts, social intelligence, speech recognition, Stephen Hawking, Steven Pinker, superintelligent machines, Thales of Miletus, The Future of Employment, Thomas Bayes, Thorstein Veblen, transport as a service, Turing machine, Turing test, universal basic income, uranium enrichment, Von Neumann architecture, Wall-E, Watson beat the top human players on Jeopardy!, web application, zero-sum game

A machine that really understands human language would be in a position to quickly acquire vast quantities of human knowledge, allowing it to bypass tens of thousands of years of learning by the more than one hundred billion people who have lived on Earth. It seems simply impractical to expect a machine to rediscover all this from scratch, starting from raw sensory data. At present, however, natural language technology is not up to the task of reading and understanding millions of books—many of which would stump even a well-educated human. Systems such as IBM’s Watson, which famously defeated two human champions of the Jeopardy! quiz game in 2011, can extract simple information from clearly stated facts but cannot build complex knowledge structures from text; nor can they answer questions that require extensive chains of reasoning with information from multiple sources. For example, the task of reading all available documents up to the end of 1973 and assessing (with explanations) the probable outcome of the Watergate impeachment process against then president Nixon would be well beyond the current state of the art.

The second elaboration is to allow for some probability of human error—that is, Harriet might sometimes switch Robbie off even when his proposed action is reasonable, and she might sometimes let Robbie go ahead even when his proposed action is undesirable. We can put this probability of human error into the mathematical model of the assistance game and find the solution, as before. As one might expect, the solution to the game shows that Robbie is less inclined to defer to an irrational Harriet who sometimes acts against her own best interests. The more randomly she behaves, the more uncertain Robbie has to be about her preferences before deferring to her. Again, this is as it should be—for example, if Robbie is an autonomous car and Harriet is his naughty two-year-old passenger, Robbie should not allow himself to be switched off by Harriet in the middle of the freeway.

See artificial intelligence (AI) intelligent personal assistants, 67–71, 101 commonsense modeling and, 68–69 design template for, 69–70 education systems, 70 health systems, 69–70 personal finance systems, 70 privacy considerations, 70–71 shortcomings of early systems, 67–68 stimulus–response templates and, 67 understanding content, improvements in, 68 International Atomic Energy Agency, 249 Internet of Things (IoT), 65 interpersonal services as the future of employment, 122–24 algorithmic bias and, 128–30 decisions affecting people, use of machines in, 126–28 robots built in humanoid form and, 124–26 intractable problems, 38–39 inverse reinforcement learning, 191–93 IQ, 48 Ishiguro, Hiroshi, 125 is-ought problem, 167 “it’s complicated” argument, 147–48 “it’s impossible” argument, 149–50 “it’s too soon to worry about it” argument, 150–52 jellyfish, 16 Jeopardy! (tv show), 80 Jevons, William Stanley, 222 JiaJia (robot), 125 jian ai, 219 Kahneman, Daniel, 238–40 Kasparov, Garry, 62, 90, 261 Ke Jie, 6 Kelly, Kevin, 97, 148 Kenny, David, 153, 163 Keynes, John Maynard, 113–14, 120–21, 122 King Midas problem, 136–40 Kitkit School (software system), 70 knowledge, 79–82, 267–72 knowledge-based systems, 50–51 Krugman, Paul, 117 Kurzweil, Ray, 163–64 language/common sense problem, 79–82 Laplace, Pierre-Simon, 54 Laser-Interferometer Gravitational-Wave Observatory (LIGO), 82–84 learning, 15 behavior, learning preferences from, 190–92 bootstrapping process, 81–82 culture and, 19 cumulative learning of concepts and theories, 82–87 data-driven view of, 82–83 deep learning, 6, 58–59, 84, 86–87, 288–93 as evolutionary accelerator, 18–20 from experience, 285–93 explanation-based learning, 294–95 feature engineering and, 84–85 inverse reinforcement learning, 191–93 reinforcement learning, 17, 47, 55–57, 105, 190–91 supervised learning, 58–59, 285–93 from thinking, 293–95 LeCun, Yann, 47, 165 legal profession, 119 lethal autonomous weapons systems (LAWS), 110–13 Life 3.0 (Tegmark), 114, 138 LIGO (Laser-Interferometer Gravitational-Wave Observatory), 82–84 living standard increases, and AI, 98–100 Lloyd, Seth, 37 Lloyd, William, 31 Llull, Ramon, 40 Lodge, David, 1 logic, 39–40, 50–51, 267–72 Bayesian, 54 defined, 267 first-order, 51–52, 270–72 formal language requirement, 267 ignorance and, 52–53 programming, development of, 271 propositional (Boolean), 51, 268–70 lookahead search, 47, 49–50, 260–61 loophole principle, 202–3, 216 Lovelace, Ada, 40, 132–33 loyal AI, 215–17 Luddism accusation, 153–54 machines, 33 “Machine Stops, The” (Forster), 254–55 machine translation, 6 McAfee, Andrew, 117 McCarthy, John, 4–5, 50, 51, 52, 53, 65, 77 malice, 228–29 malware, 253 map navigation, 257–58 mathematical proofs for beneficial AI, 185–90 mathematics, 33 matrices, 33 Matrix, The (film), 222, 235 MavHome project, 71 mechanical calculator, 40 mental security, 107–10 “merge with machines” argument, 163–65 metareasoning, 262 Methods of Ethics, The (Sidgwick), 224–25 Microsoft, 250 TrueSkill system, 279 Mill, John Stuart, 217–18, 219 Minsky, Marvin, 4–5, 76, 153 misuses of AI, 103–31, 253–54 behavior modification, 104–7 blackmail, 104–5 deepfakes, 105–6 governmental reward and punishment systems, 106–7 intelligence agencies and, 104 interpersonal services, takeover of, 124–31 lethal autonomous weapons systems (LAWS), 110–13 mental security and, 107–10 work, elimination of, 113–24 mobile phones, 64–65 monotonicity and, 24 Moore, G.


pages: 374 words: 89,725

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

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

For the most part, it is better suited to responding to questions—not so good at asking them. Picasso was onto this truth fifty years ago when he commented, “Computers are useless—they only give31 you answers.” On the other hand, technology can serve up amazing, innovative, life-changing answers—if we know how to ask for them. The potential is mind-boggling,32 as IBM’s Watson system demonstrates. Its winning appearance in 2011 on the TV quiz show Jeopardy! proved it could answer questions better than any human. Today, IBM is feeding the system a steady diet of, among other things, medical information—so that it can answer just about any question a doctor might throw at it (If patient exhibits symptoms A, B, and C, what might this indicate?). But the doctor still has to figure out what to ask—and then must be able to question Watson’s response, which might be technically accurate but not commonsensical.


pages: 97 words: 31,550

Money: Vintage Minis by Yuval Noah Harari

23andMe, agricultural Revolution, algorithmic trading, Anne Wojcicki, autonomous vehicles, British Empire, call centre, credit crunch, European colonialism, Flash crash, greed is good, job automation, joint-stock company, joint-stock limited liability company, lifelogging, pattern recognition, Ponzi scheme, self-driving car, telemarketer, The Future of Employment, The Wealth of Nations by Adam Smith, trade route, transatlantic slave trade, Watson beat the top human players on Jeopardy!, zero-sum game

Alas, not even the most diligent doctor can remember all my previous ailments and check-ups. 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 sometimes 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. It can then update these databanks daily, not only with the findings of new researches, but also with medical statistics gathered from every linked-in clinic and hospital in the world.

For this to happen there is no need of an external algorithm that knows me perfectly and never makes a mistake; it is enough that the algorithm will know me better than I know myself and will make fewer mistakes than I do. It will then make sense to trust this algorithm with more and more of my decisions and life choices. We have already crossed this line as far as medicine is concerned. In hospitals we are no longer individuals. It is highly likely that during your lifetime many of the most momentous decisions about your body and your health will be taken by computer algorithms such as IBM’s Watson. And this is not necessarily bad news. Diabetics already carry sensors that automatically check their sugar level several times a day, alerting them whenever it crosses a dangerous threshold. In 2014 researchers at Yale University announced the first successful trial of an ‘artificial pancreas’ controlled by an iPhone. Fifty-two diabetics took part in the experiment. Each patient had a tiny sensor and a tiny pump implanted in his or her abdomen.


Falter: Has the Human Game Begun to Play Itself Out? by Bill McKibben

23andMe, Affordable Care Act / Obamacare, Airbnb, American Legislative Exchange Council, Anne Wojcicki, artificial general intelligence, Bernie Sanders, Bill Joy: nanobots, Burning Man, call centre, carbon footprint, Charles Lindbergh, clean water, Colonization of Mars, computer vision, David Attenborough, Donald Trump, double helix, Edward Snowden, Elon Musk, ending welfare as we know it, energy transition, Flynn Effect, Google Earth, Hyperloop, impulse control, income inequality, Intergovernmental Panel on Climate Change (IPCC), Jane Jacobs, Jaron Lanier, Jeff Bezos, job automation, life extension, light touch regulation, Mark Zuckerberg, mass immigration, megacity, Menlo Park, moral hazard, Naomi Klein, Nelson Mandela, obamacare, off grid, oil shale / tar sands, pattern recognition, Peter Thiel, plutocrats, Plutocrats, profit motive, Ralph Waldo Emerson, Ray Kurzweil, Robert Mercer, Ronald Reagan, Sam Altman, self-driving car, Silicon Valley, Silicon Valley startup, smart meter, Snapchat, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, supervolcano, technoutopianism, The Wealth of Nations by Adam Smith, traffic fines, Travis Kalanick, urban sprawl, Watson beat the top human players on Jeopardy!, Y Combinator, Y2K, yield curve

In 2017 he announced plans to spend $100 million to send a robot weighing less than a sheet of paper to Alpha Centauri with a giant space sail and a hundred-billion-watt laser. If it works, it will take only twenty years to get the featherweight probe there. In fact, the very mission I was watching lift off at Cape Canaveral carried the first artificial intelligence into space, an orb called CIMON (Crew Interactive Mobile CompaniON) that had been equipped with the same Watson AI gear that IBM used to win on Jeopardy! and beat the world’s best Go players. CIMON looks a lot like the original iMac, and in weightlessness, it would float around the space station until summoned, and then use little fans to fly across the capsule and face the astronaut, who could then ask it various technical questions. Before liftoff, a team of chipper Teutonic gents from Airbus, who had developed the orb, talked at some length about how it would offer “partnership and even companionship,” and how it would display “infinite patience,” and how it would be “like a buddy, like a good friend working together.”

See also glaciers; sea ice iGen immune systems Inconvenient Truth, An (film) Inconvenient Truth … or Convenient Fiction, An (film) India individualism Indonesia inequality inertia infant mortality Ingraffea, Tony insects InsideClimateNews (website) Institute for Justice Intel Intergovernmental Panel on Climate Change (IPCC) Interior, Department of the International Congress of Genetics, Sixteenth International Organization for Migration International Space Station internet Inuit Iowa IQ scores Iran Iraq Ireland irrigation Italy IVF treatment Jackson, Jesse Jacobs, Jane Jacobson, Mark Jaeger, John Jakarta Japan Java Sea jellyfish Jenner, Kylie Jeopardy! (TV show) Jetnil-Kijiner, Kathy Jobs, Steve John Birch Society Johnson, Lyndon B. Journal of Mathematical Biology Journal of Physical Therapy Science Joy, Bill Joyce, James Kac, Eduardo Kaepernick, Colin Kalanick, Travis Kansas Kasparov, Gary Kavanaugh, Brett Kempf, Hervé Kennedy, John F. Kennedy Space Center Kepler (satellite) Kerry, James Kerry, John Keynes, John Maynard Keystone XL Pipeline King, Martin Luther, Jr.


Crypto: How the Code Rebels Beat the Government Saving Privacy in the Digital Age by Steven Levy

Albert Einstein, Claude Shannon: information theory, cognitive dissonance, computer age, Donald Knuth, Eratosthenes, Extropian, invention of the telegraph, John Markoff, Kevin Kelly, knapsack problem, Marc Andreessen, Mitch Kapor, MITM: man-in-the-middle, Network effects, new economy, NP-complete, Ronald Reagan, Saturday Night Live, Silicon Valley, Simon Singh, Stephen Hawking, Steven Levy, Watson beat the top human players on Jeopardy!, web of trust, Whole Earth Catalog, zero-sum game, Zimmermann PGP, éminence grise

“They probably don’t exist.” Undaunted, Diffie kept on, desperate for someone who could provide him with more clues. He and Fischer went to see a friend in Cambridge who mentioned a fellow named Alan Tritter. Tritter supposedly had done work in cryptography. He now worked for IBM. So during that same summer of 1974, Diffie tracked him down at the major center of cryptographic activity outside the government, IBM’s T. J. Watson Labs, in Westchester County, New York. Even in a field littered with brilliant oddballs, Tritter stood out. Due to a rare disease that generated a massive volume of body fat, he weighed what friends estimated as a minimum of 400 pounds. Rumor had it that his grandfather had been a wealthy man who had left Tritter only enough money to attend school. Though some regarded him as a mathematical genius, others felt that his reputation was unearned.

No geeky chatter: the NSA people were formally prohibited “from entering into technical discussions with IBM representatives in regard to the information presented.” Afterward, the NSA folks would hold postmortems to determine whether the IBM scientists might have stumbled on information or techniques “of a sensitive nature.” In that case NSA would then formally notify the company, and IBM would keep the information under wraps. The NSA certainly did know its stuff. It was particularly interested in a technique discovered by the IBM researchers that was referred to at Watson labs as the “T Attack.” Later it would be known as “differential cryptanalysis.” This was a complicated series of mathematical assaults that required lots of chosen plaintext (meaning that the attacker needed to have matched sets of original dispatches and encrypted output). Sometime that year, the Watson researchers had discovered that, under certain conditions, the IBM cipher could fall prey to a T Attack—a successful foray could actually allow a foe to divine the bits of the key.

Page 38 key size Whitfield Diffie, “Preliminary Remarks on the National Bureau of Standards Proposed Standard Encryption Algorithm for Computer Data Protection,” May 1975. 39 Feistel Biographical information on this seminal figure is sparse. Diffie’s Privacy on the Line does the best job. 40 during the war David Kahn, unpublished notes on an interview with Feistel, March 29, 1976. 40 told Whit Diffie Diffie, Privacy on the Line, p. 57. 40 a co-worker Alan Konheim 41 Computers now constitute Horst Feistel, “Cryptography and Computer Privacy,” Scientific American, Vol. 228, No. 5, May 1973, pp. 15–23. 41 IBM colleague Feistel told Diffie that the Watson Labs researcher John Lynn Smith came up with the name. 49 his report “A Study of the Lucifer Crypto-Algorithm,” August 18, IBM Memorandum, 1973. 52 dez While the Kingston engineers commonly used this single syllable, the mathematicians at Watson fussily referred to it as Dee-Ee-Ess. 55 technical article “The Data Encryption Standard and Its Strength Against Attacks,” IBM Research Journal, Vol. 38, No. 3, May 1994. 63 summary U.S.


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

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

I was a speaker at an IBM-sponsored cognitive computing conference in San Francisco in 2015.2 IBM was making a big investment in Watson, a program based on collections of large databases of facts about everything from history to popular culture that could be interrogated with a wide range of algorithms using a natural language interface. Ken Jennings had won 74 games in a row over 192 days on Jeopardy!, the longest winning streak in the history of the game show. When Watson nonetheless beat Jennings on Jeopardy! in 2011, the world took notice. In the taxi from my hotel to the conference, I overheard two IBM executives in the back of the car talking shop. IBM was rolling out a platform around Watson that could be used to organize and answer questions from unstructured databases in specialized areas such as health and financial services.

Could the Hopfield net solve this constraint satisfaction problem? The energy function was a measure of how well the network satisfied all the constraints (See box 7.1). The vision problem required a solution that was the global energy minimum, the best solution, whereas the Hopfield net, by design, found only local minima of the energy. I had recently come across a paper in the journal Science by Scott Kirkpatrick, then at IBM’s Thomas J. Watson Research Center in Yorktown Heights, New York, that I thought could help.13 Kirkpatrick used a method called “simulated annealing” to get around local minima. Suppose you had a bunch of components in an electrical circuit that had to be mounted onto two circuit boards. What would be the best placement of the parts to minimize the number of wires needed to connect them? Poor solutions are found by initially randomizing the placement of the parts, then moving them back and forth one at a time to see which placem