Freestyle chess

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pages: 291 words: 81,703

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

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Chapter 4: New Work, Old Game For Gobet on the Herbert Simon quotation, see “Herbert Simon,” Chess Programming Wiki CPW, http://chessprogramming.wikispaces.com/Herbert+Simon. Chapter 5: Our Freestyle Future For the Kasparov quotation, see “Dark horse ZackS wins Freestyle Chess Tournament,” ChessBase News, June 19, 2005, http://chessbase.com/newsdetail.asp ?new sid=2461, which is also the source for the information on the 2005 Freestyle tournaments. For some information on Anson Williams, see Daaim Shabazz, “Anson Williams . . . King of Freestyle Chess,” http://www.thechessdrum.net/blog/2007/12/21/anson-williams-king-of-freestyle-chess/, in addition to my interview with him. The Nelson Hernandez quotation comes from the same source. By the way, in Freestyle chess, Anson Williams and Nelson Hernandez have been part of a team for years, but they have never met, instead using the internet and Skype. For estimates on the strength of some Freestyle teams over the machines, see Vasik Rajlich, “Interviews with Freestylers,” http://www.rybkachess.com/docs/free stylers_version_2.htm.

For estimates on the strength of some Freestyle teams over the machines, see Vasik Rajlich, “Interviews with Freestylers,” http://www.rybkachess.com/docs/free stylers_version_2.htm. The Arno Nickel quotation is from that same source. The Nakamura quotation is from Arno Nickel, “Freestyle Chess,” http://www.free webs.com/freestyle-chess/gmarnonickel.htm. For a discussion of how opening books work, see this useful piece by Dagh Nielsen, untitled, at http://www.spaghettichess.com/Dagh%20Nielsen_tips.txt. See http://youtu.be/JSOw1Yk_RQU for an Accenture talk by Vishy Anand on finding something new in chess and the importance of memory. In addition to Freestyle chess there is Correspondence chess. In the old days, pre-computer, chess players frequently played by mail, with lags of two to three days between moves. It was understood that each player would consult books and study the position in depth by moving around the pieces, although it was forbidden to consult other chess players.

By the late 1990s, there were collaborative efforts between computer programs and top grandmasters—the human competitor would consult the program midgame. So was born “Freestyle chess.” A top-level collaborative man–machine Freestyle competition meant that a top grandmaster sat down with a computer and the grandmaster thought through the strategy of the game long and hard. The programs still had significant strategic gaps in their play, so a grandmaster supplemented or guided the strategic thinking of the machine but would rely on the machine for accurate short-run tactical calculation. As the programs improved, Freestyle chess circa 2004–2007 favored players who understood very well how the computer programs worked. These individuals did not have to be great chess players and very often they were not, although they were very swift at processing information and figuring out which lines of chess play required a deeper look with the most powerful programs.

 

pages: 400 words: 94,847

Reinventing Discovery: The New Era of Networked Science by Michael Nielsen

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And it’s normal human prejudice to undervalue the problems on the left, the domain where data-driven intelligence really shines. But we’ll put aside this prejudice, and think about the problems on the left. What problems can computers solve that we can’t? And how, when we put that ability together with human intelligence, can we combine the two to do more than either is capable of alone? As an example of the latter, in 2005 the chess website Playchess.com ran what they called a freestyle chess tournament, meaning a tournament where humans and computers could enter together as hybrid teams. To put it another way, the tournament allowed human intelligence to team up with data-driven intelligence, in the form of chess-playing computers, which rely on enormous opening and endgame databases, and which analyze myriad possible combinations of moves in the midgame. One of the entrants in the tournament was the team behind the Hydra series of chess computers.

Overviews of some of the progress and challenges in mapping the human connectome may be found in [119] and [125]. p 108: Bioinformatics and cheminformatics are now well-established fields, with a significant literature, and I won’t attempt to single out any particular reference for special mention. Astroinformatics has emerged more recently. See especially [24] for a manifesto on the need for astroinformatics. p 113: A report on the 2005 Playchess.com freestyle chess tournament may be found at [37], with follow-up commentary on the winners at [39]. Garry Kasparov’s comments on the result are in the fascinating article [106], which contains much of interest on the subject of computers and chess. Additional commentary on Hydra’s involvement may be found at [38]. Interestingly, Hydra has played and lost twice in games of correspondence chess, against correspondence chess grandmaster Arno Nickel.

[34] Nicholas Carr. Is Google making us stupid? Atlantic Monthly, July/August, 2008. [35] Nicholas Carr. The Shallows: What the Internet Is Doing to Our Brains. New York: W. W. Norton & Company, 2010. [36] Henry William Chesbrough. Opennovation: The new Imperative for Creating and Profiting from Technology. Boston: Harvard Business Press, 2006. [37] Chess Base. Dark horse ZackS wins Freestyle chess tournament, June 19, 2005. http://www.chessbase.com/newsdetail.asp?newsid=2461. [38] Chess Base. Hydra misses the quarter-finals of Freestyle tournament, June 11, 2005. http://www.chessbase.com/newsdetail.asp?newsid=2446. [39] Chess Base. PAL / CSS report from the dark horse’s mouth, June 22, 2005. http://www.chessbase.com/newsdetail.asp?newsid=2467. [40] The chess games of Hydra (Computer). http://www.chessgames.com/perl/chessplayer?

 

pages: 484 words: 104,873

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

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Nevertheless, we should be very skeptical that this latest iteration will prove to be an adequate solution as information technology continues on its relentless exponential path. The poster child for the machine-human symbiosis idea has come to be the relatively obscure game of freestyle chess. More than a decade after IBM’s Deep Blue computer defeated world chess champion Garry Kasparov, it is generally accepted that, in one-on-one contests between computers and humans, the machines now dominate absolutely. Freestyle chess, however, is a team sport. Groups of people, who are not necessarily world-class chess players individually, compete against each other and are allowed to freely consult with computer chess programs as they evaluate each move. As things stand in 2014, human teams with access to multiple chess algorithms are able to outmatch any single chess-playing computer.

As things stand in 2014, human teams with access to multiple chess algorithms are able to outmatch any single chess-playing computer. There are a number of obvious problems with the idea that human-machine collaboration, rather than full automation, will come to dominate the workplaces of the future. The first is that the continued dominance of human-machine teams in freestyle chess is by no means assured. To me, the process that these teams use—evaluating and comparing the results from different chess algorithms before deciding on the best move—seems uncomfortably close to what IBM Watson does when it fires off hundreds of information-seeking algorithms and then succeeds in ranking the results. I don’t think it is much of a stretch to suggest that a “meta” chess-playing computer with access to multiple algorithms may ultimately defeat the human teams—especially if speed is an important factor.

Businesses will make the investment in areas that are critical to their core competency—in other words, the activities that give the business a competitive advantage. Again, this scenario is nothing new. And, more importantly, it doesn’t really involve any new people. The individuals that businesses are likely to hire and then couple with the best available technology are the same people who are largely immune to unemployment today. It is a small population of elite workers. Economist Tyler Cowen’s 2013 book Average Is Over quotes one freestyle chess insider who says that the very best players are “genetic freaks.”54 That hardly makes the machine collaboration idea sound like a systemic solution for masses of people pushed out of routine jobs. And, as we have just seen, there is also the problem of offshoring. A great many of those 2.6 billion people in India and China are going to be pretty eager to grab one of those elite jobs. There are also good reasons to expect that many machine collaboration jobs will be relatively short-lived.

 

pages: 347 words: 97,721

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

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AI winter, Andy Kessler, artificial general intelligence, asset allocation, Automated Insights, autonomous vehicles, Baxter: Rethink Robotics, business intelligence, business process, call centre, carbon-based life, Clayton Christensen, clockwork universe, conceptual framework, dark matter, David Brooks, deliberate practice, deskilling, Edward Lloyd's coffeehouse, Elon Musk, Erik Brynjolfsson, estate planning, 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 Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Khan Academy, knowledge worker, labor-force participation, 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

If you’re a knowledge worker hoping to keep your job (and prosper) in the age of smart machines, you’ve got to learn a lot, change what you do, and sometimes swallow your pride at the prospect of becoming their helper. Learning from Freestyle Chess Several writers who touch on what we are calling mutual augmentation do so with reference to chess. It’s definitely a realm in which some humility on the part of humans is called for. In one-on-one matches, we know the best chess players are computers these days. Yet the trouncing isn’t so complete as you might have been led to believe. The economist Tyler Cowen (not surprisingly, a chess champion in his youth) and The Second Machine Age authors Erik Brynjolfsson and Andrew McAfee use the example of “freestyle chess,” in which human chess players are free to use as much help from computers as they wish.11 The two of us personally don’t play chess much (we like to get paid for thinking that hard), but we gather that under these rules, people often manage to beat the best programs.

We know when the boundary conditions for a system have changed. Because this type of thinking is not very structured, computers aren’t good at it. • Integrate and synthesize across multiple systems and results We humans know that any one system or decision approach is likely not to provide the only possible answer. We’re pretty good at assessing which of several sources is most likely to be correct, or at triangulating across multiple answers. Freestyle chess players choose among several different systems for each move, as we’ll discuss below. Analytics experts try a variety of different models, and take the best combination of explanatory power and reasonability. Some users of IBM’s Watson have decided to develop alternative systems to see if they can perform particular cognitive tasks better. It is true that some machines are programmed to try out multiple methods and see which works best (often called ensemble methods in machine learning).

The economist Tyler Cowen (not surprisingly, a chess champion in his youth) and The Second Machine Age authors Erik Brynjolfsson and Andrew McAfee use the example of “freestyle chess,” in which human chess players are free to use as much help from computers as they wish.11 The two of us personally don’t play chess much (we like to get paid for thinking that hard), but we gather that under these rules, people often manage to beat the best programs. And although freestyle chess is a unique situation, the particulars of why that is true do seem to suggest possibilities for other forms of augmentation: • Different computer programs are good at different chess situations, so the humans can bring awareness of each program’s strengths and how to integrate them. (Computer chess programs aren’t very good at noticing that there are better programs than themselves, and recusing themselves in that situation.) • Humans are better at the contextual knowledge of when a move is easy and when it is hard, so they can urge their computers to make a quick move when it’s feasible. • It appears to be quite possible to excel at computer chess even if you are not an expert chess player—you just have to know a good move when you see it

 

pages: 397 words: 110,130

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

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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, Edward Glaeser, en.wikipedia.org, experimental subject, Filter Bubble, Freestyle chess, Galaxy Zoo, Google Earth, Google Glasses, Henri Poincaré, hindsight bias, hive mind, Howard Rheingold, information retrieval, iterative process, jimmy wales, Kevin Kelly, Khan Academy, knowledge worker, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Netflix Prize, Nicholas Carr, patent troll, pattern recognition, pre–internet, Richard Feynman, Richard Feynman, Ronald Coase, Ronald Reagan, 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

If you go eight moves out in a game of chess: Kasparov, “The Chess Master and the Computer.” “One, the best one”: Diego Rasskin-Gutman, Chess Metaphors: Artificial Intelligence and the Human Mind, trans. Deborah Klosky (Cambridge, MA: MIT Press, 2009), 50. Together, they would form what chess players later called a centaur . . . fought Kasparov to a 3–3 draw: Kasparov, How Life Imitates Chess, Kindle edition. In 2005, there was a “freestyle” chess tournament: My account of the 2005 “freestyle” chess tournament comes from personal interviews with Steven Cramton and Zackary Stephen, as well as these reports: Kasparov, How Life Imitates Chess; Kasparov, “The Chess Master and the Computer”; Steven Cramton and Zackary Stephen, “The Dark Horse Theory,” Chess Horizons, October–December 2005, 17–20, 40, accessed March 19, 2013, masschess.org/Chess_Horizons/Articles/2005-10_sample.pdf; “PAL / CSS report from the dark horse’s mouth,” ChessBase, June 6, 2005, accessed March 19, 2013, en.chessbase.com/home/TabId/211/PostId/4002467.

“Just as a good Formula One driver really knows his own car, so did we have to learn the way the computer program worked,” he later wrote. Topalov, as it turns out, appeared to be an even better Formula One “thinker” than Kasparov. On purely human terms, Kasparov was a stronger player; a month before, he’d trounced Topalov 4–0. But the centaur play evened the odds. This time, Topalov fought Kasparov to a 3–3 draw. In 2005, there was a “freestyle” chess tournament in which a team could consist of any number of humans or computers, in any combination. Many teams consisted of chess grand masters who’d won plenty of regular, human-only tournaments, achieving chess scores of 2,500 (out of 3,000). But the winning team didn’t include any grand masters at all. It consisted of two young New England men, Steven Cramton and Zackary Stephen (who were comparative amateurs, with chess rankings down around 1,400 to 1,700), and their computers.

But even that laptop-equipped grand master could be beaten by (3) relative newbies, if the amateurs were extremely skilled at integrating machine assistance. “Human strategic guidance combined with the tactical acuity of a computer,” Kasparov concluded, “was overwhelming.” Better yet, it turned out these smart amateurs could even outplay a supercomputer on the level of Deep Blue. One of the entrants that Cramton and Stephen trounced in the freestyle chess tournament was a version of Hydra, the most powerful chess computer in existence at the time; indeed, it was probably faster and stronger than Deep Blue itself. Hydra’s owners let it play entirely by itself, using raw logic and speed to fight its opponents. A few days after the advanced chess event, Hydra destroyed the world’s seventh-ranked grand master in a man-versus-machine chess tournament.

 

pages: 371 words: 108,317

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

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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, 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, Kevin Kelly, Kickstarter, linked data, Lyft, M-Pesa, Marshall McLuhan, means of production, megacity, Minecraft, multi-sided market, natural language processing, Netflix Prize, Network effects, new economy, Nicholas Carr, 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

he dubbed “deep learning”: Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, “Deep Learning,” Nature 521, no. 7553 (2015): 436–44. the network effect: Carl Shapiro and Hal R. Varian, Information Rules: A Strategic Guide to the Network Economy (Boston: Harvard Business Review Press, 1998). famous man-versus-machine match: “Deep Blue,” IBM 100: Icons of Progress, March 7, 2012. rather than competes against them: Owen Williams, “Garry Kasparov—Biography,” KasparovAgent.com, 2010. freestyle chess matches: Arno Nickel, Freestyle Chess, 2010. centaurs won 53 games: Arno Nickel, “The Freestyle Battle 2014,” Infinity Chess, 2015. several different chess programs: Arno Nickel, “‘Intagrand’ Wins the Freestyle Battle 2014,” Infinity Chess, 2015. grand master rating of all time: “FIDE Chess Profile (Carlsen, Magnus),” World Chess Federation, 2015. AI that can view a photo portrait of any person: Personal interview at Facebook, September 2014. 70 percent of American workers: U.S.

You might think that was the end of the story (if not the end of human history), but Kasparov realized that he could have performed better against Deep Blue if he’d had the same instant access to a massive database of all previous chess moves that Deep Blue had. If this database tool was fair for an AI, why not for a human? Let the human mastermind be augmented by a database just as Deep Blue’s was. To pursue this idea, Kasparov pioneered the concept of man-plus-machine matches, in which AI augments human chess players rather than competes against them. Now called freestyle chess matches, these are like mixed martial arts fights, where players use whatever combat techniques they want. You can play as your unassisted human self, or you can act as the hand for your supersmart chess computer, merely moving its board pieces, or you can play as a “centaur,” which is the human/AI cyborg that Kasparov advocated. A centaur player will listen to the moves suggested by the AI but will occasionally override them—much the way we use the GPS navigation intelligence in our cars.

 

pages: 138 words: 40,787

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

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3D printing, Airbnb, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, business intelligence, call centre, Clayton Christensen, cloud computing, connected car, crowdsourcing, data acquisition, en.wikipedia.org, Erik Brynjolfsson, first square of the chessboard, first square of the chessboard / second half of the chessboard, Freestyle chess, Google X / Alphabet X, Internet of things, Network effects, Paul Graham, Ray Kurzweil, RFID, self-driving car, Silicon Valley, smart cities, smart grid, software as a service, Steve Jobs, web application, Y Combinator, yield management

Considering how much decision-making ability has already been given to machines and how much more is going to go that way, and considering the speed at which information flows from sensors and devices to the cloud, will humans be able to comprehend? Are humans the major limiting factor in the development of the Internet of Things today? And, more importantly, will humans be able to cope with all this information? When we spoke with Astro Teller of Google, he reminded us of an interesting story. In 2005, there was a freestyle chess tournament hosted by the website PlayChess.com. “Freestyle” meant any humans or computers, or any combination of humans and computers, could participate in the tournament. Who do you think won? One would expect a grand master or a supercomputer, or perhaps a grand master with an average computer, or an amateur with a supercomputer. One would hardly expect a couple of amateurs with laptops to win a global chess tournament.

In addition, this vast amount of data will have to be processed and analyzed, and decisions will have to be made based on this data and the data that will be looped back into the whole — the feedback loop — and communicated to either consumers who are using the device, medical professionals, or anybody else. The company that figures it out might become the first trillion-dollar company. However, a lot needs to happen in terms of regulation and standardization to get there. In the meantime, humans will have to do a lot of strategic thinking and planning, in a not too dissimilar way from the winners of the freestyle chess tournament. In the next chapter we will look into the key areas of the Internet of Things. 15 Bill Gates, Business @ the Speed of Thought: Succeeding in the Digital Economy (New York: Grand Central Publishing, 1999). 16 Garry Kasparov, “New in Chess,” Chess 2.0, May 2005. Also Garry Kasparov, “The Chess Master and the Computer,” New York Review of Books, February 11, 2010. http://www.nybooks.com/articles/archives/2010/feb/11/the-chess-master-and-the-computer/. 17 Second Life is an online virtual world developed by Linden Lab.

 

pages: 339 words: 88,732

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

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Even Though It’s Checkmate, It’s Not Game Over After the reigning world champion Garry Kasparov lost to the IBM computer Deep Blue in 1997, head-to-head contests between people and chess computers lost much of their allure; it was clear that future competitions would be increasingly one-sided. Dutch grandmaster Jan Hein Donner summed up the current attitude of human chess masters. When asked how he would prepare for a match against a computer, he replied, “I would bring a hammer.”2 It might seem, then, that humans no longer have anything to contribute to the game of chess. But the invention of ‘freestyle’ chess tournaments shows how far this is from the truth. In these events, teams can include any combination of human and digital players. As Kasparov himself explains when discussing the results of a 2005 freestyle contest, The teams of human plus machine dominated even the strongest computers. The chess machine Hydra, which is a chess-specific supercomputer like Deep Blue, was no match for a strong human player using a relatively weak laptop.

Their skill at manipulating and “coaching” their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants. Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.3 The key insight from freestyle chess is that people and computers don’t approach the same task the same way. If they did, humans would have had nothing to add after Deep Blue beat Kasparov; the machine, having learned how to mimic human chess-playing ability, would just keep riding Moore’s Law and racing ahead. But instead we see that people still have a great deal to offer the game of chess at its highest levels once they’re allowed to race with machines, instead of purely against them.

 

pages: 271 words: 77,448

Humans Are Underrated: What High Achievers Know That Brilliant Machines Never Will by Geoff Colvin

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Ada Lovelace, autonomous vehicles, Baxter: Rethink Robotics, Black Swan, call centre, capital asset pricing model, computer age, corporate governance, deskilling, en.wikipedia.org, Freestyle chess, future of work, Google Glasses, Grace Hopper, industrial robot, interchangeable parts, job automation, knowledge worker, low skilled workers, meta analysis, meta-analysis, Narrative Science, new economy, rising living standards, self-driving car, sentiment analysis, Silicon Valley, Skype, Steve Jobs, Steve Wozniak, Steven Levy, Steven Pinker, theory of mind, Tim Cook: Apple, transaction costs

Movellan, “The Faces of Engagement: Automatic Recognition of Student Engagement from Facial Expressions,” IEEE Transactions on Affective Computing, vol. 5, no. 1(2014), pp. 86–98. MIT’s stress-monitoring car . . . http://affect.media.mit.edu/pdfs/14.Hernandez_et_al-DIS.pdf. For Cowen’s observations on competition in chess, see his blog entry at http://marginalrevolution.com/marginalrevolution/2013/11/what-are-humans-still-good-for-the-turning-point-in-freestyle-chess-may-be-approaching.html. CHAPTER THREE The case in Arizona Superior Court . . . The research is described in D. A. Krauss, J. G. McCabe, and J. D. Lieberman, “Dangerously Misunderstood: Representative Jurors’ Reactions to Expert Testimony on Future Dangerousness in a Sexually Violent Predator Trial,” Psychology, Public Policy, and Law, 25 July 2011. Advance online publication, doi: 10.1037/a0024550.

 

pages: 829 words: 186,976

The Signal and the Noise: Why So Many Predictions Fail-But Some Don't by Nate Silver

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airport security, availability heuristic, 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, 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, new economy, Norbert Wiener, PageRank, pattern recognition, pets.com, 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 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

Chess, however, makes for a happy ending. Kasparov and Deep Blue’s programmers saw each other as antagonists, but they each taught us something about the complementary roles that computer processing speed and human ingenuity can play in prediction. In fact, the best game of chess in the world right now might be played neither by man nor machine.47 In 2005, the Web site ChessBase.com, hosted a “freestyle” chess tournament: players were free to supplement their own insight with any computer program or programs that they liked, and to solicit advice over the Internet. Although several grandmasters entered the tournament, it was won neither by the strongest human players nor by those using the most highly regarded software, but by a pair of twentysomething amateurs from New Hampshire, Steven Cramton and Zackary “ZakS” Stephen, who surveyed a combination of three computer programs to determine their moves.48 Cramton and Stephen won because they were neither awed nor intimidated by technology.

Larry Page, “PageRank: Bringing Order to the Web,” Stanford Digital Library Project, August 18, 1997. http://web.archive.org/web/20020506051802/www-diglib.stanford.edu/cgi-bin/WP/get/SIDL-WP-1997-0072?1. 46. “How Search Works,” by Google via YouTube, March 4, 2010. http://www.youtube.com/watch?v=BNHR6IQJGZs. 47. Per interview with Vasik Rajlich. 48. “Amateurs beat GMs in PAL / CSS Freestyle,” ChessBase News. http://www.chessbase.com/newsdetail.asp?newsid=2467. 49. Kasparov, “The Chess Master and the Computer.” CHAPTER 10. THE POKER BUBBLE 1. “Chris Moneymaker Ranking History” in The Mob Poker Database, thehendonmob.com. http://pokerdb.thehendonmob.com/player_graphs/chris_moneymaker_18826. 2. I first played in one of the smaller events at the World Series of Poker in 2005, although I did not participate in the $10,000 main event of the tournament until 2009. 3.

 

pages: 317 words: 100,414

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

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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, Edward Lorenz: Chaos theory, forward guidance, Freestyle chess, fundamental attribution error, germ theory of disease, hindsight bias, index fund, Jane Jacobs, Jeff Bezos, Mikhail Gorbachev, Mohammed Bouazizi, Nash equilibrium, Nate Silver, obamacare, pattern recognition, performance metric, 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, Watson beat the top human players on Jeopardy!

Machines may get better at “mimicking human meaning,” and thereby better at predicting human behavior, but “there’s a difference between mimicking and reflecting meaning and originating meaning,” Ferrucci said. That’s a space human judgment will always occupy. In forecasting, as in other fields, we will continue to see human judgment being displaced—to the consternation of white-collar workers—but we will also see more and more syntheses, like “freestyle chess,” in which humans with computers compete as teams, the human drawing on the computer’s indisputable strengths but also occasionally overriding the computer. The result is a combination that can (sometimes) beat both humans and machines. To reframe the man-versus-machine dichotomy, combinations of Garry Kasparov and Deep Blue may prove more robust than pure-human or pure-machine approaches. What Ferrucci does see becoming obsolete is the guru model that makes so many policy debates so puerile: “I’ll counter your Paul Krugman polemic with my Niall Ferguson counterpolemic, and rebut your Tom Friedman op-ed with my Bret Stephens blog.”

 

pages: 566 words: 163,322

The Rise and Fall of Nations: Forces of Change in the Post-Crisis World by Ruchir Sharma

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3D printing, Asian financial crisis, backtesting, bank run, banking crisis, Berlin Wall, Bernie Sanders, BRICs, business climate, business process, call centre, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, central bank independence, centre right, colonial rule, Commodity Super-Cycle, corporate governance, crony capitalism, currency peg, dark matter, debt deflation, deglobalization, deindustrialization, demographic dividend, demographic transition, Deng Xiaoping, Doha Development Round, Donald Trump, Edward Glaeser, Elon Musk, eurozone crisis, failed state, Fall of the Berlin Wall, falling living standards, Francis Fukuyama: the end of history, Freestyle chess, Gini coefficient, hiring and firing, income inequality, indoor plumbing, industrial robot, inflation targeting, Internet of things, Jeff Bezos, job automation, Joseph Schumpeter, Kenneth Rogoff, knowledge economy, labor-force participation, Malacca Straits, Mark Zuckerberg, market bubble, megacity, Mexican peso crisis / tequila crisis, mittelstand, moral hazard, New Economic Geography, North Sea oil, oil rush, oil shale / tar sands, oil shock, pattern recognition, Peter Thiel, pets.com, Plutocrats, plutocrats, Ponzi scheme, price stability, Productivity paradox, purchasing power parity, quantitative easing, Ralph Waldo Emerson, random walk, rent-seeking, reserve currency, Ronald Coase, Ronald Reagan, savings glut, secular stagnation, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Simon Kuznets, smart cities, Snapchat, South China Sea, sovereign wealth fund, special economic zone, spectrum auction, Steve Jobs, The Wisdom of Crowds, Thomas Malthus, total factor productivity, trade liberalization, trade route, tulip mania, Tyler Cowen: Great Stagnation, unorthodox policies, Washington Consensus, WikiLeaks, women in the workforce, working-age population

China’s Economic Development: Institutions, Growth and Imbalances. Northampton, MA: Edward Elgar Publishing, 2013. Lund, Susan, et al. “Financial Globalization: Retreat or Reset?” McKinsey Global Institute, March 2013. Manyika, James, et al. “Global Growth: Can Productivity Save the Day in an Aging World?” McKinsey Global Institute, January 2015. Mauboussin, Michael J., and Dan Callahan. “Learning from Freestyle Chess.” Credit Suisse Research, September 10, 2014. Miller, Arthur. “The Year It Came Apart.” New York, Dececember 30, 1974. O’Neill, Jim. “Building Better Global Economic BRICs.” Goldman Sachs Global Economics Paper no. 66, November 30, 2001. Peters, Heiko, and Stefan Schneider. “Sluggish Global Trade—Cyclical or Structural?” Deutsche Bank Research, November 25, 2014. “Picking Apart the Productivity Paradox,” Goldman Sachs Research, October 5, 2015.