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To assemble Watson, IBM crated in a mammoth configuration of hardware, about 10 refrigerators’ worth. Watson didn’t go to Jeopardy!; Jeopardy! came to Watson, setting up a temporary game show studio within IBM’s T. J. Watson Research Center. Double Jeopardy!—Would Watson Win? Watson was not sure to win. During sparring games against human champions, Watson had tweaked its way up to a 71 percent win record. It didn’t always win, and these trial runs didn’t pit it against the lethal competition it was preparing to face on the televised match: all-time leading Jeopardy! champions Ken Jennings and Brad Rutter. Reproduced with permission. The Jeopardy! match was to gain full-scale media publicity, exposing IBM’s analytical prowess or failure. 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.
Grockit: This test preparation company predicts which GMAT, SAT, and ACT questions a test taker will get wrong in order to target areas for which he or she needs more study. Table 8 Predictive Analytics in Human Language Understanding, Thought, and Psychology What’s predicted: Example organizations that use predictive analytics: Answers to questions () IBM: Developed with predictive modeling the Watson question-answering computer, which defeated the two all-time human champions of the TV quiz show Jeopardy! on a televised standoff (more details in Chapter 6). Lies () University at Buffalo: Researchers trained a system to detect lies with 82 percent accuracy by observing eye movements alone. Researchers: Predict deception with 76 percent accuracy within written statements by persons of interest in military base criminal investigations.
Final Jeopardy: Man vs. Machine and the Quest to Know Everything by Stephen Baker
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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.
AI winter, call centre, carbon footprint, crowdsourcing, demand response, discovery of DNA, 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, 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.
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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.
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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.
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson, Andrew McAfee
2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, 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 intelligence, business process, call centre, 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, 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, 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.
The Future of the Professions: How Technology Will Transform the Work of Human Experts by Richard Susskind, Daniel Susskind
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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.
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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.
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, 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, 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.
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.
Machine, Platform, Crowd: Harnessing Our Digital Future by Andrew McAfee, Erik Brynjolfsson
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, book scanning, British Empire, 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, distributed ledger, double helix, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, 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, 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, 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 L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, two-sided market, Uber and Lyft, Uber for X, 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.”
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, 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.”
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, 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, 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, Khan Academy, knowledge worker, labor-force participation, lifelogging, 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, Richard Feynman, risk tolerance, Robert Shiller, Robert Shiller, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, six sigma, Skype, 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.
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, 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, 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.
3D printing, 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, 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, patent troll, pattern recognition, pre–internet, Richard Feynman, Richard Feynman, Ronald Coase, Ronald Reagan, Rubik’s Cube, sentiment analysis, Silicon Valley, Skype, Snapchat, Socratic dialogue, spaced repetition, 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?
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, Law of Accelerating Returns, license plate recognition, life extension, 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, Turing test, Uber and Lyft, Uber for X, 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.
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, 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, 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.
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, John von Neumann, linear programming, new economy, NP-complete, Occam's razor, P = NP, Paul Erdős, Richard Feynman, 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?”
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, 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, Joseph Schumpeter, 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, Watson beat the top human players on Jeopardy!, 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.
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, 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, 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, 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, M-Pesa, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Metcalfe’s law, 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, 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?
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, 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.
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, 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, Mikhail Gorbachev, Nate Silver, Norbert Wiener, packet switching, pattern recognition, Ray Kurzweil, Richard Feynman, 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.
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 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 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, 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, Thomas L Friedman, transaction costs, Transnistria, 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.
3D printing, 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, 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, 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.
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, 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?’
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.
Four Futures: Life After Capitalism by Peter Frase
3D printing, Airbnb, basic income, bitcoin, 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, Norbert Wiener, Occupy movement, pattern recognition, peak oil, Plutocrats, plutocrats, 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.
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, 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.
Albert Einstein, Andrew Keen, Apple II, Berlin Wall, British Empire, Brownian motion, Buckminster Fuller, Burning Man, 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, Jacquard loom, Jane Jacobs, Jeff Bezos, John Markoff, John von Neumann, Mark Zuckerberg, Marshall McLuhan, Mercator projection, Metcalfe’s law, Mother of all demos, mutually assured destruction, Network effects, new economy, Norbert Wiener, PageRank, pattern recognition, peer-to-peer, planetary scale, Plutocrats, plutocrats, Post-materialism, post-materialism, Potemkin village, RFID, Richard Feynman, 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.
3D printing, AI winter, 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.
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, labour mobility, 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.
Overcomplicated: Technology at the Limits of Comprehension by Samuel Arbesman
3D printing, 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, 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.
3D printing, Ada Lovelace, agricultural Revolution, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, 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, Jacquard loom, Jacques de Vaucanson, James Watt: steam engine, job automation, John von Neumann, Joseph-Marie Jacquard, 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, 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.
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, 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, 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 . . .
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, probability theory / Blaise Pascal / Pierre de Fermat, RAND corporation, random walk, Richard Feynman, 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.
Amazon Mechanical Turk, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, 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, labour mobility, 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.
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, 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, 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.
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, 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, Mikhail Gorbachev, Mohammed Bouazizi, Nash equilibrium, Nate Silver, obamacare, pattern recognition, performance metric, Pierre-Simon Laplace, place-making, placebo effect, prediction markets, quantitative easing, random walk, randomized controlled trial, Richard Feynman, Richard Feynman, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Reagan, Saturday Night Live, 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.
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, North Sea oil, Oculus Rift, oil shale / tar sands, peak oil, personalized medicine, Peter H. Diamandis: Planetary Resources, RAND corporation, Ray Kurzweil, Richard Feynman, 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.
algorithmic trading, automated trading system, banking crisis, bash_history, Bernie Madoff, butterfly effect, buttonwood tree, 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.
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 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, labour market flexibility, land reform, M-Pesa, Mahatma Gandhi, Marc Andreessen, market bubble, mass immigration, megacity, Mexican peso crisis / tequila crisis, 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.
3D printing, 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, Google Glasses, hive mind, income inequality, information trail, Internet of things, invention of writing, iterative process, Jaron Lanier, job automation, 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, 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.
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, Richard Feynman: Challenger O-ring, risk tolerance, Ronald Reagan, 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.
The End of Jobs: Money, Meaning and Freedom Without the 9-To-5 by Taylor Pearson
Airbnb, barriers to entry, 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, 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.
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, Richard Feynman, Ronald Reagan, Skype, statistical model, Stephen Hawking, Steve Jobs, Steven Pinker, 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.)
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.
3D printing, 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, Richard Feynman, Second Machine Age, self-driving car, Silicon Valley, speech recognition, 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.
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, 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, 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.
Everything Is Obvious: *Once You Know the Answer by Duncan J. Watts
active measures, affirmative action, Albert Einstein, Amazon Mechanical Turk, Black Swan, butterfly effect, Carmen Reinhart, Cass Sunstein, clockwork universe, cognitive dissonance, 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, 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, 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.
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, 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, 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.
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, disintermediation, 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, 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, The Future of Employment, The Spirit Level, total factor productivity, transaction costs, Uber and 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.
50 Future Ideas You Really Need to Know by Richard Watson
23andMe, 3D printing, access to a mobile phone, Albert Einstein, artificial general intelligence, augmented reality, autonomous vehicles, BRICs, Buckminster Fuller, call centre, clean water, cloud computing, collaborative consumption, computer age, computer vision, crowdsourcing, dark matter, dematerialisation, digital Maoism, digital map, Elon Musk, energy security, failed state, future of work, Geoffrey West, Santa Fe Institute, germ theory of disease, happiness index / gross national happiness, hive mind, hydrogen economy, Internet of things, Jaron Lanier, life extension, Mark Shuttleworth, Marshall McLuhan, megacity, natural language processing, Network effects, new economy, oil shale / tar sands, pattern recognition, peak oil, personalized medicine, phenotype, precision agriculture, profit maximization, RAND corporation, Ray Kurzweil, RFID, Richard Florida, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Skype, smart cities, smart meter, smart transportation, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, supervolcano, telepresence, The Wisdom of Crowds, Thomas Malthus, Turing test, urban decay, Vernor Vinge, Watson beat the top human players on Jeopardy!, web application, women in the workforce, working-age population, young professional
Second, even if machines do not reach this level of sophistication, it’s likely that they’ll become very smart indeed, so what happens to the people who previously did the things that machines will do in the future? Welcome to the future. It’s metallic and uses lots of batteries. Hopefully, it’s not angry and it won’t work out a way to enslave the human race. the condensed idea The machines wake up timeline 1990 iRobot Corporation founded to manufacture industrial and domestic robots 2011 Watson, an IBM computer, wins Jeopardy!, a US TV show 2027 A $79 toaster passes the Turing test 2040 $750 smartphone contains as much processing power as a human brain 2042 Software virus disables 90 percent of machines 2050 Intelligent robots outnumber human beings 2054 Machines start to paint and compose music 2069 Machines demand equal rights 21 Personalized genomics It’s now possible to sequence, then analyze, the genome of individuals to predict specific human traits or to forecast the probability that an individual will suffer from certain conditions or diseases.
Becoming Steve Jobs: The Evolution of a Reckless Upstart Into a Visionary Leader by Brent Schlender, Rick Tetzeli
Albert Einstein, Apple II, Apple's 1984 Super Bowl advert, Bill Gates: Altair 8800, Bob Noyce, Byte Shop, computer age, corporate governance, El Camino Real, Isaac Newton, John Markoff, Jony Ive, Marc Andreessen, market design, McMansion, Menlo Park, Paul Terrell, popular electronics, QWERTY keyboard, Ronald Reagan, Sand Hill Road, side project, Silicon Valley, Silicon Valley startup, skunkworks, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Tim Cook: Apple, Wall-E, Watson beat the top human players on Jeopardy!, Whole Earth Catalog
Gates and Grove knew that eventually—and it wasn’t going to take very long at all—the expensive, customized guts of engineering workstations would become juiced-up PC circuit boards, and that the same evolution would ultimately subsume business minicomputers, mainframes, and even supercomputers, those rare and superexpensive machines used for everything from modeling weather patterns to controlling nuclear devices. (For example, IBM’s Watson, the machine that in 2011 beat Jeopardy! phenomenon Ken Jennings, is one such computer based on a PC-like architecture.) As a result, pretty much every computer that companies relied on to manage their most critical operations would adopt the internal electronic architecture of a PC writ large. All were much, much cheaper and easier to program and operate than unwieldy mainframes, because they were built out of the very same semiconductor components as PCs, and usually used a variation of the Windows operating system software.
3D printing, Airbnb, carbon footprint, Clayton Christensen, clean water, fear of failure, Google X / Alphabet X, Isaac Newton, Jeff Bezos, jimmy wales, 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, 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.
Adaptive Markets: Financial Evolution at the Speed of Thought by Andrew W. Lo
Albert Einstein, Alfred Russel Wallace, algorithmic trading, Andrei Shleifer, Arthur Eddington, Asian financial crisis, asset allocation, asset-backed security, backtesting, bank run, barriers to entry, Berlin Wall, Bernie Madoff, bitcoin, Bonfire of the Vanities, bonus culture, break the buck, Brownian motion, business process, butterfly effect, capital asset pricing model, Captain Sullenberger Hudson, Carmen Reinhart, Chance favours the prepared mind, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, corporate governance, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, Daniel Kahneman / Amos Tversky, delayed gratification, Diane Coyle, diversification, diversified portfolio, double helix, easy for humans, difficult for computers, Ernest Rutherford, Eugene Fama: efficient market hypothesis, experimental economics, experimental subject, Fall of the Berlin Wall, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, Fractional reserve banking, framing effect, Gordon Gekko, greed is good, Hans Rosling, Henri Poincaré, high net worth, housing crisis, incomplete markets, index fund, interest rate derivative, invention of the telegraph, Isaac Newton, James Watt: steam engine, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, Joseph Schumpeter, Kenneth Rogoff, London Interbank Offered Rate, Long Term Capital Management, loss aversion, Louis Pasteur, mandelbrot fractal, margin call, Mark Zuckerberg, market fundamentalism, martingale, merger arbitrage, meta analysis, meta-analysis, Milgram experiment, money market fund, moral hazard, Myron Scholes, Nick Leeson, old-boy network, out of africa, p-value, paper trading, passive investing, Paul Lévy, Paul Samuelson, Ponzi scheme, predatory finance, prediction markets, price discovery process, profit maximization, profit motive, quantitative hedge fund, quantitative trading / quantitative ﬁnance, RAND corporation, random walk, randomized controlled trial, Renaissance Technologies, Richard Feynman, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, Robert Shiller, Robert Shiller, short selling, sovereign wealth fund, statistical arbitrage, Steven Pinker, stochastic process, survivorship bias, The Great Moderation, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Malthus, Thorstein Veblen, Tobin tax, too big to fail, transaction costs, Triangle Shirtwaist Factory, ultimatum game, Upton Sinclair, US Airways Flight 1549, Walter Mischel, Watson beat the top human players on Jeopardy!, WikiLeaks, Yogi Berra, zero-sum game
Paradoxically enough, the early primitive computers could deal with the pinnacles of human thought—chess, logic, mathematics—much more easily than with the fundamentals of human life. As computer science progressed, computers were able to imitate many more basic human abilities, such as voice recognition and speech synthesis. Today we have integrated expert systems like the iPhone’s Siri, or IBM’s Jeopardy-winning supercomputer Watson, which answer questions as well as any reasonably smart human—but in a way completely unlike any human. Artificial intelligence has achieved many milestones, but the biggest challenge still remains unmet: to produce truly intelligent behavior. However, artificial intelligence may be getting closer to its goal as several different research paths converge. In 1987, one of the founding fathers of artificial intelligence, the late MIT professor Marvin Minsky, published an important book called The Society of Mind.38 This was Minsky’s sweeping view of human intelligence, in which he laid out his vision for how to reproduce human consciousness and intelligence in machine form.
First, they want a seat at the table, giving them timely access to the facts as they’re discovered. Second, they want to be able to offer their interpretation of those facts, or correct mistakes made by others that could reflect poorly on them. And finally, the NTSB’s accident report isn’t admissible as evidence in lawsuits for civil damages, which allows the stakeholders to be much more candid about their role in the accident than they might be if they faced legal jeopardy. 382 • Chapter 11 Once all the facts are collected and agreed on by the various parties, the second phase of the investigation begins. Only the NTSB’s internal staff conducts the analysis, to reduce the chances of conflict of interest. This analysis presents a theory of the probable cause of the accident and rules out opposing theories. The final accident report contains the facts of the accident, the analysis of the accident, and policy recommendations regarding the accident.16 Here’s an example of how it works in practice.
Lund, in which he wrote: This letter is written to insure that management is fully aware of the seriousness of the current O-ring erosion problem in the SRM joints from an 422 • Notes to Chapter 1 engineering standpoint. . . . The result would be a catastrophe of the highest order—loss of human life. . . . It is my honest and very real fear that if we do not take immediate action to dedicate a team to solve the problem with the field joint having the number one priority, then we stand in jeopardy of losing a flight along with all the launch pad facilities. And on a teleconference call during the evening prior to the January 28 launch, a number of Morton Th iokol engineers, including Boisjoly, raised concerns about the cold temperature and argued for postponing the launch but were overruled by Morton Thiokol and NASA senior management. Many studies of the decision-making processes and management structures that led to this tragic event have been completed since 1986, and NASA, Morton Thiokol, and other organizations have changed a number of their operating procedures in response.
The Elusive Quest for Growth: Economists' Adventures and Misadventures in the Tropics by William R. Easterly
Andrei Shleifer, business climate, Carmen Reinhart, central bank independence, clean water, colonial rule, correlation does not imply causation, creative destruction, endogenous growth, financial repression, Gini coefficient, Gunnar Myrdal, Hernando de Soto, income inequality, income per capita, inflation targeting, interchangeable parts, inventory management, invisible hand, Isaac Newton, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, large denomination, manufacturing employment, Network effects, New Urbanism, open economy, Productivity paradox, purchasing power parity, rent-seeking, Ronald Reagan, selection bias, Silicon Valley, Simon Kuznets, The Wealth of Nations by Adam Smith, Thomas Malthus, total factor productivity, trade liberalization, urban sprawl, Watson beat the top human players on Jeopardy!, Yogi Berra, Yom Kippur War
“After nearly half a century of continuous population growth,” the news release dolefully continues, ”the demand in many countries for food, water, and forest products is simply outrunning the capacity of local life support systems.”8State of the World 2000 from the World Watch Institute warns that population growth ”may more directly affect economic progress than any other single trend, exacerbating nearly all other environmental and social problem^."^ And Pakistan is imperiled again: ”Pakistan’s projected growth from 146 million today to 345 million by 2050 will shrink its grainland per person from 0.08 hectares at present to 0.03 hectares, an area scarcely the size of a tennis court.”1o The organization Population Action International notes that ”the capacity of farmers to feed the world’s future population is also in jeopardy.”’l The Population Institute warns bluntly of ”The Four Cash for Condoms? 89 Horsemen of the 21st Century Apocalypse: Overpopulation. Deforestation. Water Scarcity. Famine.” As a a result, ”Developed countries will be lookingat staggering disasterrelief budgets as a result ...and only a few years from now.’’12 Not only that but, according to Lester Brown, population grows faster than jobs: ”Inthe absence of an accelerated effort toslow population growth in the years ahead, unemployment could soar to unmanageable levels.”
Rise of the Rocket Girls: The Women Who Propelled Us, From Missiles to the Moon to Mars by Nathalia Holt
Bill Gates: Altair 8800, British Empire, computer age, cuban missile crisis, desegregation, financial independence, Grace Hopper, Isaac Newton, labor-force participation, Mars Rover, music of the spheres, new economy, operation paperclip, Richard Feynman, Richard Feynman, Richard Feynman: Challenger O-ring, Steve Jobs, V2 rocket, Watson beat the top human players on Jeopardy!, women in the workforce, Works Progress Administration, Yogi Berra
It took a room with the same square footage as most of the computers’ houses to contain the IBM 701. It wasn’t just one big box but eleven separate components that, together, weighed a whopping 20,516 pounds. Notwithstanding its size, the 701 moved IBM into the computer business. At first the company didn’t think it would have many customers for the machine. At a stockholder meeting, IBM’s president, Thomas Watson Jr., explained that they were expecting to sell only five of them, but “we came home with orders for eighteen.” One of those orders was for JPL. Despite a monthly rental price starting at $11,900, the 701 came with no instruction manual. To use the machine one had to learn an obscure numerical code. Even the simplest of operations, such as obtaining a square root, involved an incredible amount of programming.
I pressed the pen to my lips and concentrated, trying to balance my pregnant belly while perched on the wobbly edge of a bar stool. It was the summer of 2010, and my husband and I were trying to come up with names for our daughter’s December arrival. Sitting in a bar in Cambridge, Massachusetts, we brainstormed names, each writing them down privately on a napkin before showing the other, as if we were on some bizarre game show: Name Your Baby! We weren’t having much luck. We both have unusual first names—Nathalia and Larkin—so we wanted to find one that wouldn’t subject our daughter to a lifetime of odd nicknames. When Larkin wrote down Eleanor, I immediately rejected it. It sounded so old-fashioned. I couldn’t imagine naming my daughter that. But as the months went by and my belly grew, the name grew on me too. We started coming up with middle names.
The problem lay in a midflight ignition that had caused an electrical short in the camera system. With the design issues resolved, everyone looked ahead to Ranger 7. Surely, after six miserable failures, this would be the one. It had to be. The launch of Ranger 7 took place on a hot, humid afternoon in July 1964. The control room at JPL was tense. Everyone knew that their jobs, and even the fate of the lab, were in jeopardy. To lighten the mood and distract everyone from the pressure, one of the engineers, Richard Wallace, known as Dick, decided to pass out peanuts. Whether it was the good-luck peanuts or simply the hard-won lessons from six failed missions, the launch went off flawlessly. But it wasn’t time to celebrate yet; the ship had to successfully reach the moon’s surface. A few days later, in the early morning of July 31, Helen sat in the gallery of the new SFOF.
Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer-Schonberger, Kenneth Cukier
23andMe, Affordable Care Act / Obamacare, airport security, AltaVista, barriers to entry, Berlin Wall, big data - Walmart - Pop Tarts, Black Swan, book scanning, business intelligence, business process, call centre, cloud computing, computer age, correlation does not imply causation, dark matter, double entry bookkeeping, Eratosthenes, Erik Brynjolfsson, game design, IBM and the Holocaust, index card, informal economy, intangible asset, Internet of things, invention of the printing press, Jeff Bezos, lifelogging, Louis Pasteur, Mark Zuckerberg, Menlo Park, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, obamacare, optical character recognition, PageRank, performance metric, Peter Thiel, Post-materialism, post-materialism, random walk, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, smart grid, smart meter, social graph, speech recognition, Steve Jobs, Steven Levy, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, Turing test, Watson beat the top human players on Jeopardy!
Spam filters are designed to automatically adapt as the types of junk email change: the software couldn’t be programmed to know to block “via6ra” or its infinity of variants. Dating sites pair up couples on the basis of how their numerous attributes correlate with those of successful previous matches. The “autocorrect” feature in smartphones tracks our actions and adds new words to its spelling dictionary based on what we type. Yet these uses are just the start. From cars that can detect when to swerve or brake to IBM’s Watson computer beating humans on the game show Jeopardy!, the approach will revamp many aspects of the world in which we live. At its core, big data is about predictions. Though it is described as part of the branch of computer science called artificial intelligence, and more specifically, an area called machine learning, this characterization is misleading. Big data is not about trying to “teach” a computer to “think” like humans.
For example, using voice-recognition software to characterize complaints to a call center, and comparing that data with the time it takes operators to handle the calls, may yield an imperfect but useful snapshot of the situation. Messiness can also refer to the inconsistency of formatting, for which the data needs to be “cleaned” before being processed. There are a myriad of ways to refer to IBM, notes the big-data expert DJ Patil, from I.B.M. to T. J. Watson Labs, to International Business Machines. And messiness can arise when we extract or process the data, since in doing so we are transforming it, turning it into something else, such as when we perform sentiment analysis on Twitter messages to predict Hollywood box office receipts. Messiness itself is messy. Suppose we need to measure the temperature in a vineyard. If we have only one temperature sensor for the whole plot of land, we must make sure it’s accurate and working at all times: no messiness allowed.
The Industries of the Future by Alec Ross
23andMe, 3D printing, Airbnb, algorithmic trading, AltaVista, Anne Wojcicki, autonomous vehicles, banking crisis, barriers to entry, Bernie Madoff, bioinformatics, bitcoin, blockchain, Brian Krebs, British Empire, business intelligence, call centre, carbon footprint, cloud computing, collaborative consumption, connected car, corporate governance, Credit Default Swap, cryptocurrency, David Brooks, disintermediation, Dissolution of the Soviet Union, distributed ledger, Edward Glaeser, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, fiat currency, future of work, global supply chain, Google X / Alphabet X, industrial robot, Internet of things, invention of the printing press, Jaron Lanier, Jeff Bezos, job automation, John Markoff, knowledge economy, knowledge worker, lifelogging, litecoin, M-Pesa, Marc Andreessen, Mark Zuckerberg, Mikhail Gorbachev, mobile money, money: store of value / unit of account / medium of exchange, new economy, offshore financial centre, open economy, Parag Khanna, peer-to-peer, peer-to-peer lending, personalized medicine, Peter Thiel, precision agriculture, pre–internet, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Rubik’s Cube, Satoshi Nakamoto, selective serotonin reuptake inhibitor (SSRI), self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, social graph, software as a service, special economic zone, supply-chain management, supply-chain management software, technoutopianism, The Future of Employment, underbanked, Vernor Vinge, Watson beat the top human players on Jeopardy!, women in the workforce, Y Combinator, young professional
Moreover, while weak artificial intelligence, whereby robots simply specialize in a specific function, is currently advancing exponentially, strong artificial intelligence, whereby robots demonstrate humanlike cognition and intelligence, is advancing only linearly. While inventions like IBM’s Watson (the computer designed by IBM that beat Jeopardy! champions Ken Jennings and Brad Rutter) are exciting, scientists need a better understanding of the brain before these advances progress beyond winning a game show. Watson didn’t actually “think”; it was basically a very comprehensive search engine querying a large database. As robotics expert and UC Berkeley professor Ken Goldberg explains, “Robots are going to become increasingly human. But the gap between humans and robots will remain—it’s so large that it will be with us for the foreseeable future.”
3D printing, Affordable Care Act / Obamacare, airline deregulation, airport security, Apple II, barriers to entry, big-box store, blue-collar work, Capital in the Twenty-First Century by Thomas Piketty, clean water, collective bargaining, computer age, creative destruction, deindustrialization, Detroit bankruptcy, discovery of penicillin, Donner party, Downton Abbey, Edward Glaeser, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, feminist movement, financial innovation, full employment, George Akerlof, germ theory of disease, glass ceiling, high net worth, housing crisis, immigration reform, impulse control, income inequality, income per capita, indoor plumbing, industrial robot, inflight wifi, interchangeable parts, invention of agriculture, invention of air conditioning, invention of the telegraph, invention of the telephone, inventory management, James Watt: steam engine, Jeff Bezos, jitney, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, labor-force participation, Loma Prieta earthquake, Louis Daguerre, Louis Pasteur, low skilled workers, manufacturing employment, Mark Zuckerberg, market fragmentation, Mason jar, mass immigration, mass incarceration, McMansion, Menlo Park, minimum wage unemployment, mortgage debt, mortgage tax deduction, new economy, Norbert Wiener, obamacare, occupational segregation, oil shale / tar sands, oil shock, payday loans, Peter Thiel, pink-collar, Productivity paradox, Ralph Nader, Ralph Waldo Emerson, refrigerator car, rent control, Robert X Cringely, Ronald Coase, school choice, Second Machine Age, secular stagnation, Skype, stem cell, Steve Jobs, Steve Wozniak, Steven Pinker, The Market for Lemons, Thomas Malthus, total factor productivity, transaction costs, transcontinental railway, traveling salesman, Triangle Shirtwaist Factory, Unsafe at Any Speed, Upton Sinclair, upwardly mobile, urban decay, urban planning, urban sprawl, washing machines reduced drudgery, Washington Consensus, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, working poor, working-age population, Works Progress Administration, yellow journalism, yield management
Nouriel Roubini writes, “[T]here is a new perception of the role of technology. Innovators and tech CEOs both seem positively giddy with optimism.”44 The well-known pair of techno-optimists Erik Brynjolfsson and Andrew McAfee assert that “we’re at an inflection point” between a past of slow technological change and a future of rapid change.45 They appear to believe that Big Blue’s chess victory and Watson’s victory on the TV game show Jeopardy presage an age in which computers outsmart humans in every aspect of human work effort. They remind us that Moore’s Law predicts endless exponential growth of the performance capability of computer chips—but they ignore that chips have fallen behind the predicted pace of Moore’s Law after 2005. The decline in the price of ICT equipment relative to performance was most rapid in the late 1990s, and there has been hardly any decline at all in the past few years.
Google’s Ngram Viewer is a delightful tool that allows one to look at cultural trends based on the prevalence of key words and phrases that a user can search. 78. O’Brien (2012), p. 189. 79. Munos (2009), p. 962. 80. Vijg (2011), p. 65. 81. Fuchs and Garber (2003), p. 46. 82. The previous two paragraphs summarize and quote Vijg (2011, pp. 63–75). 83. See Cohen (2013) for an account of how data-mining by artificial intelligence such as IBM’s Watson could become a new diagnostic tool. 84. Stevens (1989), pp. 204–20. 85. Stevens (1989), p. 231, 296–301. 86. Starr (1982), p. 368. 87. Yount (2001), p. 9. 88. http://managedhealthcareexecutive.modernmedicine.com/managed-healthcare-executive/content/higher-costs-resulting-medical-arms-race?page=full. 89. Stevens (1989), p. 252, 306–8. The previous paragraph comes from pages 252 and 308, and the quotation comes from pages 306–7. 90.
airport security, availability heuristic, Bayesian statistics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, big-box store, Black Swan, Broken windows theory, Carmen Reinhart, Claude Shannon: information theory, Climategate, Climatic Research Unit, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, complexity theory, computer age, correlation does not imply causation, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, Daniel Kahneman / Amos Tversky, diversification, Donald Trump, Edmond Halley, Edward Lorenz: Chaos theory, en.wikipedia.org, equity premium, Eugene Fama: efficient market hypothesis, everywhere but in the productivity statistics, fear of failure, Fellow of the Royal Society, Freestyle chess, fudge factor, George Akerlof, haute cuisine, Henri Poincaré, high batting average, housing crisis, income per capita, index fund, Intergovernmental Panel on Climate Change (IPCC), Internet Archive, invention of the printing press, invisible hand, Isaac Newton, James Watt: steam engine, John Nash: game theory, John von Neumann, Kenneth Rogoff, knowledge economy, locking in a profit, Loma Prieta earthquake, market bubble, Mikhail Gorbachev, Moneyball by Michael Lewis explains big data, Monroe Doctrine, mortgage debt, Nate Silver, negative equity, new economy, Norbert Wiener, PageRank, pattern recognition, pets.com, Pierre-Simon Laplace, prediction markets, Productivity paradox, random walk, Richard Thaler, Robert Shiller, Robert Shiller, Rodney Brooks, Ronald Reagan, Saturday Night Live, savings glut, security theater, short selling, Skype, statistical model, Steven Pinker, The Great Moderation, The Market for Lemons, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transaction costs, transfer pricing, University of East Anglia, Watson beat the top human players on Jeopardy!, wikimedia commons
It was like when HAL 9000 took over the spaceship. Like the moment when, exactly thirteen seconds into “Love Will Tear Us Apart,” the synthesizer overpowers the guitar riff, leaving rock and roll in its dust.43 Except it wasn’t true. Kasparov had been the victim of a large amount of human frailty—and a tiny software bug. How to Make a Chess Master Blink Deep Blue was born at IBM’s Thomas J. Watson Center—a beautiful, crescent-shaped, retro-modern building overlooking the Westchester County foothills. In its lobby are replicas of early computers, like the ones designed by Charles Babbage. While the building shows a few signs of rust—too much wood paneling and too many interior offices—many great scientists have called it home, including the mathematician Benoit Mandelbrot, and Nobel Prize winners in economics and physics.
The Weather of Supercomputers The supercomputer labs at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado, literally produce their own weather. They are hot: the 77 trillion calculations that the IBM Bluefire supercomputer makes every second generate a substantial amount of radiant energy. They are windy: all that heat must be cooled, lest the nation’s ability to forecast its weather be placed into jeopardy, and so a series of high-pressure fans blast oxygen on the computers at all times. And they are noisy: the fans are loud enough that hearing protection is standard operating equipment. The Bluefire is divided into eleven cabinets, each about eight feet tall and two feet wide with a bright green racing stripe running down the side. From the back, they look about how you might expect a supercomputer to look: a mass of crossed cables and blinking blue lights feeding into the machine’s brain stem.