Turing test

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pages: 370 words: 94,968

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

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

Part of what’s fascinating about studying the programs that have done well at the Turing test is that it is a (frankly, sobering) study of how conversation can work in the total absence of emotional intimacy. A look at the transcripts of Turing tests past is in some sense a tour of the various ways in which we demure, dodge the question, lighten the mood, change the subject, distract, burn time: what shouldn’t pass as real conversation at the Turing test probably shouldn’t be allowed to pass as real human conversation, either. There are a number of books written about the technical side of the Turing test: for instance, how to cleverly design Turing test programs—called chatterbots, chatbots, or just bots. In fact, almost everything written at a practical level about the Turing test is about how to make good bots, with a small remaining fraction about how to be a good judge.

Certainly it’s true that if language is the judge’s sole means of determining which of his correspondents is which, then any limitations in language use become limitations in the judge’s overall ability to conduct the test. There’s a joke that goes around in AI circles about a program that models catatonic patients, and—by saying nothing—perfectly imitates them in the Turing test. What the joke illustrates, though, is that seemingly the less fluency between the parties, the less successful the Turing test will be. What, exactly, does “fluency” mean, though? Certainly, to put a human who only speaks Russian in a Turing test with all English speakers would be against the spirit of the test. What about dialects, though? What exactly counts as a “language”? Is a Turing test peopled by English speakers from around the globe easier on the computers than one peopled by English speakers raised in the same country? Ought we to consider, beyond national differences, demographic ones?

Ordinarily, there wouldn’t be very much odd about this notion at all, of course—we train and prepare for tennis competitions, spelling bees, standardized tests, and the like. But given that the Turing test is meant to evaluate how human I am, the implication seems to be that being human (and being oneself) is about more than simply showing up. I contend that it is. What exactly that “more” entails will be a main focus of this book—and the answers found along the way will be applicable to a lot more in life than just the Turing test. Falling for Ivana A rather strange, and more than slightly ironic, cautionary tale: Dr. Robert Epstein, UCSD psychologist, editor of the scientific volume Parsing the Turing Test, and co-founder, with Hugh Loebner, of the Loebner Prize, subscribed to an online dating service in the winter of 2007. He began writing long letters to a Russian woman named Ivana, who would respond with long letters of her own, describing her family, her daily life, and her growing feelings for Epstein.


pages: 315 words: 89,861

The Simulation Hypothesis by Rizwan Virk

3D printing, Albert Einstein, Apple II, artificial general intelligence, augmented reality, Benoit Mandelbrot, bioinformatics, butterfly effect, discovery of DNA, Dmitri Mendeleev, Elon Musk, en.wikipedia.org, Ernest Rutherford, game design, Google Glasses, Isaac Newton, John von Neumann, Kickstarter, mandelbrot fractal, Marc Andreessen, Minecraft, natural language processing, Pierre-Simon Laplace, Ralph Waldo Emerson, Ray Kurzweil, Richard Feynman, Schrödinger's Cat, Search for Extraterrestrial Intelligence, Silicon Valley, Stephen Hawking, Steve Jobs, Steve Wozniak, technological singularity, Turing test, Vernor Vinge, Zeno's paradox

This brings us to a big question that hasn’t been fully answered: What is AI, exactly? We all know that AI in the context of NPCs represents an artificially intelligent being, but what does that mean exactly? Common sense tells us that it’s a program that appears human—in some ways. In lieu of a formal definition, an informal definition is a computer program or artificial device that can pass the Turing Test. The History and Rise of AI The Turing Test Figure 13: A visual depiction of the Turing Test 12 The Turing Test is more of a milestone than a definition, since most AI today cannot pass this test. Alan Turing, considered by many to be the father of modern computer science, conjectured a time when a machine would exhibit intelligent behaviors. In his 1950 paper titled “Computing Machinery and Intelligence,” Turing took on the question of whether a machine could “think.”

Party C would start conversations (passing messages using something like a teletype machine—the best that Turing had in his time) and would have to tell the difference between A and B. If he was unable to distinguish which was the human and which was the machine, then the machine could be said to have passed the Turing Test. Of course, back then, he described it as a machine, but today we know it would be the AI program (which is software) that would pass the test, not so much the hardware. This party game and the concept underlying it eventually became known as the Turing Test. AI and Games: Claude Shannon and Chess The Turing Test is not the only test of artificial intelligence. In a paper in 1950 (the same year that Turing proposed his test), MIT professor Claude Shannon posited that a computer would be capable of playing chess in a groundbreaking paper titled “Programming a Computer for Playing Chess,” and showed a computer he had built for such a purpose (see Figure 14).

Some of the chat-bots use very simplistic pattern matching, while others are starting to incorporate more complicated natural language processing. Different kinds of AI techniques had to be developed in order for a computer to have a chance at passing the “Turing Test.” In the early 21st century, digital assistants like Siri, Alexa, and Google Assistant are much better at processing either text or voice than any of the video games that we have covered thus far. But just as video games drove early graphics technology, you can expect that simulated characters will drive more sophisticated AI in the future. Figure 15: Eliza was an early digital psychiatrist that used simple matching. NLP, AI, and the Quest to Pass the Turing Test Of critical importance to passing the Turing Test is NLP, or Natural Language Processing. NLP is the ability of a computer to read (or listen to) and understand the meaning of natural language.


pages: 350 words: 98,077

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

Ada Lovelace, AI winter, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, Bernie Sanders, Claude Shannon: information theory, cognitive dissonance, computer age, computer vision, dark matter, Douglas Hofstadter, Elon Musk, en.wikipedia.org, Gödel, Escher, Bach, I think there is a world market for maybe five computers, ImageNet competition, Jaron Lanier, job automation, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, license plate recognition, Mark Zuckerberg, natural language processing, Norbert Wiener, ought to be enough for anybody, pattern recognition, performance metric, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Rodney Brooks, self-driving car, sentiment analysis, Silicon Valley, Singularitarianism, Skype, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, theory of mind, There's no reason for any individual to have a computer in his home - Ken Olsen, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!

The computer will be deemed to have passed the “Turing Test Rank Order Test” if the median rank of the Computer is equal to or greater than the median rank of two or more of the three Turing Test Human Foils. * * * The Computer will be deemed to have passed the Turing Test if it passes both the Turing Test Human Determination Test and the Turing Test Rank Order Test. * * * If a Computer passes the Turing Test, as described above, prior to the end of the year 2029, then Ray Kurzweil wins the wager. Otherwise Mitchell Kapor wins the wager.44 Wow, pretty strict. Eugene Goostman wouldn’t stand a chance. I’d have to (cautiously) agree with this assessment from Kurzweil: “In my view, there is no set of tricks or simpler algorithms (i.e., methods simpler than those underlying human intelligence) that would enable a machine to pass a properly designed Turing Test without actually possessing intelligence at a fully human level.”45 In addition to laying out the rules of their long bet, both Kapor and Kurzweil wrote accompanying essays giving the reasons each thinks he will win.

The judges and human foils will be chosen by a “Turing test committee,” made up of Kapor, Kurzweil (or their designees), and a third member. Instead of five-minute chats, each of the four contestants will be interviewed by each judge for a grueling two hours. At the end of all these interviews, each judge will give his or her verdict (“human” or “machine”) for each contestant. “The Computer will be deemed to have passed the ‘Turing Test Human Determination Test’ if the Computer has fooled two or more of the three Human Judges into thinking that it is a human.”43 But we’re not done yet: In addition, each of the three Turing Test Judges will rank the four Candidates with a rank from 1 (least human) to 4 (most human). The computer will be deemed to have passed the “Turing Test Rank Order Test” if the median rank of the Computer is equal to or greater than the median rank of two or more of the three Turing Test Human Foils

Hofstadter and D. C. Dennett, The Mind’s I: Fantasies and Reflections on Self and Soul (New York: Basic Books, 1981), along with a cogent counterargument from Hofstadter. 16.  S. Aaronson, Quantum Computing Since Democritus (Cambridge, U.K.: Cambridge University Press, 2013), 33. 17.  “Turing Test Transcripts Reveal How Chatbot ‘Eugene’ Duped the Judges,” Coventry University, June 30, 2015, www.coventry.ac.uk/primary-news/turing-test-transcripts-reveal-how-chatbot-eugene-duped-the-judges/. 18.  “Turing Test Success Marks Milestone in Computing History,” University of Reading, June 8, 2014, www.reading.ac.uk/news-and-events/releases/PR583836.aspx. 19.  R. Kurzweil, The Singularity Is Near: When Humans Transcend Biology (New York: Viking Press, 2005), 7. 20.  Ibid., 22–23. 21.  I. J.


pages: 291 words: 81,703

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

Amazon Mechanical Turk, Black Swan, brain emulation, Brownian motion, business cycle, Cass Sunstein, choice architecture, complexity theory, computer age, computer vision, computerized trading, cosmological constant, crowdsourcing, dark matter, David Brooks, David Ricardo: comparative advantage, deliberate practice, Drosophila, en.wikipedia.org, endowment effect, epigenetics, Erik Brynjolfsson, eurozone crisis, experimental economics, Flynn Effect, Freestyle chess, full employment, future of work, game design, income inequality, industrial robot, informal economy, Isaac Newton, Johannes Kepler, John Markoff, Khan Academy, labor-force participation, Loebner Prize, low skilled workers, manufacturing employment, Mark Zuckerberg, meta analysis, meta-analysis, microcredit, Myron Scholes, Narrative Science, Netflix Prize, Nicholas Carr, P = NP, pattern recognition, Peter Thiel, randomized controlled trial, Ray Kurzweil, reshoring, Richard Florida, Richard Thaler, Ronald Reagan, Silicon Valley, Skype, statistical model, stem cell, Steve Jobs, Turing test, Tyler Cowen: Great Stagnation, upwardly mobile, Yogi Berra

But is there a convergence, even a behavioral or a mimetic one, occurring between man and machine? Some commentators attach great weight to whether intelligent machines can pass a Turing test. If a group of intelligent observers were conversing with a machine through an exchange of typed messages, could those observers tell whether they were talking to a human being or to a machine? If the machine can consistently “pass” as human, it has succeeded at the Turing test. Sometimes the Turing test is considered to be a benchmark for when we can call a machine truly intelligent. So far the machines have had only mixed results at passing the Turing test. Getting human conversation “right” is a complex, multidimensional problem, highly sensitive to context and background understanding, and so it isn’t easy for a computer program.

It’s also the case that the computer probably does best in a radically polyglot society, such as India, where dozens of different languages are spoken, malapropisms are relatively common, and fluency in a particular language is not taken for granted. Still, those two vote totals are pretty close, and arguably these programs have almost passed the Turing test. However, once you take a “gains from trade” perspective on man–machine interaction, the Turing test seems to be more of a curiosity than a fundamental standard for judging the presence of intelligence or not. What good does passing a Turing test really do? There has been an enduring misunderstanding that needs to be cleared up. Turing’s core message was never “If a machine can imitate a man, the machine must be intelligent.” Rather, it was “Inability to imitate does not rule out intelligence.” In his classic essay on the Turing test, Turing encouraged his readers to take a broader perspective on intelligence and conceive of it more universally and indeed more ethically.

At best you will think that is a human who just spent some time smoking pot, hardly what the program set out to achieve. Go to the website itself, Cleverbot.com, and see what you think. There you will be warned that the program may serve up unusual, unexpected, or inappropriate content. The first question I posed it was, “Can you pass a Turing test?” The answer “What is a Turing test?” was quite human, I thought. Then: TYLER: A Turing test shows whether you can mimic a human interlocutor. CLEVERBOT: Do the Turing test on me. TYLER: So far you are passing it. CLEVERBOT: Ah well, what can one do? Not bad. Yet then I wrote, “Jump off the Brooklyn Bridge!” and it lost the test when it answered, “But that magic jump on me.” The historical evidence, while sparse, suggests that Turing himself commonly served up unusual responses in dialogue.


pages: 586 words: 186,548

Architects of Intelligence by Martin Ford

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

We’re putting quite a lot of work into natural language processing, and that includes translation, summarization, text categorization—figuring out what topic a text talks about, as well as dialog systems. Actually, dialog systems are a very important area of research for virtual assistants, question and answering systems, and so on. MARTIN FORD: Do you anticipate the creation of an AI that someday could pass the Turing test? YANN LECUN: It’s going to happen at some point, but the Turing test is not actually an interesting test. In fact, I don’t think a lot of people in the AI field at the moment consider the Turing test to be a good test. It’s too easy to trick it, and to some extent, the Turing test has already been and gone. We give a lot of importance to language as humans because we are used to discussing intelligent topics with other humans through language. However, language is sort of an epiphenomenon of intelligence, and when I say this, my colleagues who work on natural language processing disagree vehemently!

Language is hierarchical; we can share the hierarchical ideas we have in our neocortex with each other using the hierarchy of language. I think Alan Turing was prescient in basing the Turing test on language because I think it does require the full range of human thinking and human intelligence to create and understand language at human levels. MARTIN FORD: Is your ultimate objective to extend this idea to actually build a machine that can pass the Turing test? RAY KURZWEIL: Not everybody agrees with this, but I think the Turing test, if organized correctly, is actually a very good test of human-level intelligence. The issue is that in the brief paper that Turing wrote in 1950, it’s really just a couple of paragraphs that talked about the Turing test, and he left out vital elements. For example, he didn’t describe how to actually go about administering the test.

Among those groups currently overtly working towards AGI, aside from DeepMind, I guess OpenAI would be another group that one could point to. MARTIN FORD: Do you think the Turing test is a good way to determine if we’ve reached AGI, or do we need another test for intelligence? NICK BOSTROM: It’s not so bad if what you want is a rough-and-ready criterion for when you have fully succeeded. I’m talking about a full-blown, difficult version of the Turing test. Something where you can have experts interrogate the system for an hour, or something like that. I think that’s an AI-complete problem. It can’t be solved other than by developing general artificial intelligence. If what you’re interested in is gauging the rate of progress, say, or establishing benchmarks to know what to shoot for next with your AI research team, then the Turing test is maybe not such a good objective. MARTIN FORD: Because it turns into a gimmick if it’s at a smaller scale?


pages: 696 words: 143,736

The Age of Spiritual Machines: When Computers Exceed Human Intelligence by Ray Kurzweil

Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Any sufficiently advanced technology is indistinguishable from magic, Buckminster Fuller, call centre, cellular automata, combinatorial explosion, complexity theory, computer age, computer vision, cosmological constant, cosmological principle, Danny Hillis, double helix, Douglas Hofstadter, Everything should be made as simple as possible, first square of the chessboard / second half of the chessboard, fudge factor, George Gilder, Gödel, Escher, Bach, I think there is a world market for maybe five computers, information retrieval, invention of movable type, Isaac Newton, iterative process, Jacquard loom, John Markoff, John von Neumann, Lao Tzu, Law of Accelerating Returns, mandelbrot fractal, Marshall McLuhan, Menlo Park, natural language processing, Norbert Wiener, optical character recognition, ought to be enough for anybody, pattern recognition, phenotype, Ralph Waldo Emerson, Ray Kurzweil, Richard Feynman, Robert Metcalfe, Schrödinger's Cat, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, social intelligence, speech recognition, Steven Pinker, Stewart Brand, stochastic process, technological singularity, Ted Kaczynski, telepresence, the medium is the message, There's no reason for any individual to have a computer in his home - Ken Olsen, traveling salesman, Turing machine, Turing test, Whole Earth Review, Y2K

I don’t see how it can break through to the subjective level. MAYBE IF THE THING PASSES THE TURING TEST? That is what Turing had in mind. Lacking any conceivable way of building a consciousness detector, he settled on a practical approach, one that emphasizes our unique human proclivity for language. And I do think that Turing is right in a way—if a machine can pass a valid Turing Test, I believe that we will believe that it is conscious. Of course, that’s still not a scientific demonstration. The converse proposition, however, is not compelling. Whales and elephants have bigger brains than we do and exhibit a wide range of behaviors that knowledgeable observers consider intelligent. I regard them as conscious creatures, but they are in no position to pass the Turing Test. THEY WOULD HAVE TROUBLE TYPING ON THESE SMALL KEYS OF MY COMPUTER.

Thinking Is as Thinking Does Oh yes, there is one other view, which I call the “thinking is as thinking does” school. In a 1950 paper, Alan Turing describes his concept of the Turing Test, in which a human judge interviews both a computer and one or more human foils using terminals (so that the judge won’t be prejudiced against the computer for lacking a warm and fuzzy appearance).11 If the human judge is unable to reliably unmask the computer (as an impostor human) then the computer wins. The test is often described as a kind of computer IQ test, a means of determining if computers have achieved a human level of intelligence. In my view, however, Turing really intended his Turing Test as a test of thinking, a term he uses to imply more than just clever manipulation of logic and language. To Turing, thinking implies conscious intentionality.

Also, it is no longer necessary to play music in real time—music can be performed at one speed and played back at another, without changing the pitch or other characteristics of the notes. All sorts of age-old limitations have been overcome, allowing a teenager in her bedroom to sound like a symphony orchestra or rock band. A Musical Turing Test In 1997, Steve Larson, a University of Oregon music professor, arranged a musical variation of the Turing Test by having an audience attempt to determine which of three pieces of music had been written by a computer and which one of the three had been written two centuries ago by a human named Johann Sebastian Bach. Larson was only slightly insulted when the audience voted that his own piece was the computer composition, but he felt somewhat vindicated when the audience selected the piece written by a computer program named EMI (Experiments in Musical Intelligence) to be the authentic Bach composition.


pages: 761 words: 231,902

The Singularity Is Near: When Humans Transcend Biology by Ray Kurzweil

additive manufacturing, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, anthropic principle, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, Benoit Mandelbrot, Bill Joy: nanobots, bioinformatics, brain emulation, Brewster Kahle, Brownian motion, business cycle, business intelligence, c2.com, call centre, carbon-based life, cellular automata, Claude Shannon: information theory, complexity theory, conceptual framework, Conway's Game of Life, coronavirus, cosmological constant, cosmological principle, cuban missile crisis, data acquisition, Dava Sobel, David Brooks, Dean Kamen, disintermediation, double helix, Douglas Hofstadter, en.wikipedia.org, epigenetics, factory automation, friendly AI, George Gilder, Gödel, Escher, Bach, informal economy, information retrieval, invention of the telephone, invention of the telescope, invention of writing, iterative process, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, lifelogging, linked data, Loebner Prize, Louis Pasteur, mandelbrot fractal, Marshall McLuhan, Mikhail Gorbachev, Mitch Kapor, mouse model, Murray Gell-Mann, mutually assured destruction, natural language processing, Network effects, new economy, Norbert Wiener, oil shale / tar sands, optical character recognition, pattern recognition, phenotype, premature optimization, randomized controlled trial, Ray Kurzweil, remote working, reversible computing, Richard Feynman, Robert Metcalfe, Rodney Brooks, scientific worldview, Search for Extraterrestrial Intelligence, selection bias, semantic web, Silicon Valley, Singularitarianism, speech recognition, statistical model, stem cell, Stephen Hawking, Stewart Brand, strong AI, superintelligent machines, technological singularity, Ted Kaczynski, telepresence, The Coming Technological Singularity, Thomas Bayes, transaction costs, Turing machine, Turing test, Vernor Vinge, Y2K, Yogi Berra

Turing was carefully imprecise in setting the rules for his test, and significant literature has been devoted to the subtleties of establishing the exact procedures for determining how to assess when the Turing test has been passed.218 In 2002 I negotiated the rules for a Turing-test wager with Mitch Kapor on the Long Now Web site.219 The question underlying our twenty-thousand-dollar bet, the proceeds of which go to the charity of the winner's choice, was, "Will the Turing test be passed by a machine by 2029?" I said yes, and Kapor said no. It took us months of dialogue to arrive at the intricate rules to implement our wager. Simply defining "machine" and "human," for example, was not a straightforward matter. Is the human judge allowed to have any nonbiological thinking processes in his or her brain? Conversely, can the machine have any biological aspects? Because the definition of the Turing test will vary from person to person, Turing test-capable machines will not arrive on a single day, and there will be a period during which we will hear claims that machines have passed the threshold.

One of the many skills that nonbiological intelligence will achieve with the completion of the human brain reverse-engineering project is sufficient mastery of language and shared human knowledge to pass the Turing test. The Turing test is important not so much for its practical significance but rather because it will demarcate a crucial threshold. As I have pointed out, there is no simple means to pass a Turing test, other than to convincingly emulate the flexibility, subtlety, and suppleness of human intelligence. Having captured that capability in our technology, it will then be subject to engineering's ability to concentrate, focus, and amplify it. Variations of the Turing test have been proposed. The annual Loebner Prize contest awards a bronze prize to the chatterbot (conversational bot) best able to convince human judges that it's human.217 The criteria for winning the silver prize is based on Turing's original test, and it obviously has yet to be awarded.

The answer to the second question is the Turing test. As the test is currently defined, an expert committee interrogates a remote correspondent on a wide range of topics such as love, current events, mathematics, philosophy, and the correspondent's personal history to determine whether the correspondent is a computer or a human. The Turing test is intended as a measure of human intelligence; failure to pass the test does not imply a lack of intelligence. Turing's original article can be found .at http://www.abelard.org/turpap/turpap.htm; see also the Stanford Encyclopedia of Philosophy, http://plato.stanford.edu/entries/turing-test, for a discussion of the test. There is no set of tricks or algorithms that would allow a machine to pass a properly designed Turing test without actually possessing intelligence at a fully human level.


pages: 372 words: 101,174

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

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

English mathematician Alan Turing (1912–1954) based his eponymous test on the ability of a computer to converse in natural language using text messages.13 Turing felt that all of human intelligence was embodied and represented in language, and that no machine could pass a Turing test through simple language tricks. Although the Turing test is a game involving written language, Turing believed that the only way that a computer could pass it would be for it to actually possess the equivalent of human-level intelligence. Critics have proposed that a true test of human-level intelligence should include mastery of visual and auditory information as well.14 Since many of my own AI projects involve teaching computers to master such sensory information as human speech, letter shapes, and musical sounds, I would be expected to advocate the inclusion of these forms of information in a true test of intelligence. Yet I agree with Turing’s original insight that the text-only version of the Turing test is sufficient. Adding visual or auditory input or output to the test would not actually make it more difficult to pass.

We have clearly identified hierarchies of units of functionality in natural systems, especially the brain, and AI systems are using comparable methods. It appears to me that many critics will not be satisfied until computers routinely pass the Turing test, but even that threshold will not be clear-cut. Undoubtedly, there will be controversy as to whether claimed Turing tests that have been administered are valid. Indeed, I will probably be among those critics disparaging early claims along these lines. By the time the arguments about the validity of a computer passing the Turing test do settle down, computers will have long since surpassed unenhanced human intelligence. My emphasis here is on the word “unenhanced,” because enhancement is precisely the reason that we are creating these “mind children,” as Hans Moravec calls them.11 Combining human-level pattern recognition with the inherent speed and accuracy of computers will result in very powerful abilities.

To the extent that it can find documents that do discuss the themes of this novel, a suitably modified version of Watson should be able to respond to this. Coming up with such themes on its own from just reading the book, and not essentially copying the thoughts (even without the words) of other thinkers, is another matter. Doing so would constitute a higher-level task than Watson is capable of today—it is what I call a Turing test–level task. (That being said, I will point out that most humans do not come up with their own original thoughts either but copy the ideas of their peers and opinion leaders.) At any rate, this is 2012, not 2029, so I would not expect Turing test–level intelligence yet. On yet another hand, I would point out that evaluating the answers to questions such as finding key ideas in a novel is itself not a straightforward task. If someone is asked who signed the Declaration of Independence, one can determine whether or not her response is true or false.


pages: 337 words: 103,522

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

3D printing, Ada Lovelace, Albert Einstein, Alvin Roth, Andrew Wiles, Automated Insights, Benoit Mandelbrot, Claude Shannon: information theory, computer vision, correlation does not imply causation, crowdsourcing, data is the new oil, Donald Trump, double helix, Douglas Hofstadter, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, Flash crash, Gödel, Escher, Bach, Henri Poincaré, Jacquard loom, John Conway, Kickstarter, Loebner Prize, mandelbrot fractal, Minecraft, music of the spheres, Narrative Science, natural language processing, Netflix Prize, PageRank, pattern recognition, Paul Erdős, Peter Thiel, random walk, Ray Kurzweil, recommendation engine, Rubik’s Cube, Second Machine Age, Silicon Valley, speech recognition, Turing test, Watson beat the top human players on Jeopardy!, wikimedia commons

Given that the composition hadn’t been attacked, he felt emboldened to continue the project, producing a second album in 1997 with pieces in the style of some of the other composers he’d analysed: Beethoven, Chopin, Joplin, Mozart, Rachmaninov and Stravinsky. This time the pieces were performed by human musicians. The critics’ response was much more positive. ‘The Game’: a musical Turing Test But would the output of Cope’s algorithm produce results that would pass a musical Turing Test? Could they be passed off as works by the composers themselves? To find out, Cope decided to stage a concert at the University of Oregon in collaboration with Douglas Hofstadter, a mathematician who wrote the classic book Gödel, Escher, Bach. Three pieces would be played. One of these would be an unfamiliar piece by Bach, the second would be composed by Emmy in the style of Bach and the third would be composed by a human, Steve Larson, who taught music theory at the university, again in the style of Bach.

This, Turing believed, was too general, so he refined his challenge: he wondered if a machine could be programmed so that if a human were to engage it in conversation, its responses would be so convincing that the human could not tell it was talking to a machine. Turing called this the ‘Imitation Game’, after a parlour game that was popular at the time, but it has become known as the ‘Turing Test’. To pass the Turing Test requires an algorithm that can receive as input the vagaries of natural language and process it to produce an output that corresponds to something a human might possibly say in response. (‘Natural language’ generally refers to language that has evolved naturally in humans through use and repetition without conscious planning or premeditation, in contrast to computer code.) The first successful effort to take up Turing’s challenge was a program called ‘ELIZA’, developed by the computer scientist Joseph Weizenbaum in 1966.

And this, and me, And place of the unspoken word, the unread vision in Baiae’s bay, And the posterity of Michelangelo. ‘Ode to the West Wind’ meets ‘The Love Song of J. Alfred Prufrock’. In a Turing Test conducted by Kurzweil, the Cybernetic Poet was able to trick human judges most of the time. This is partly because gnomic outputs are part of the landscape of modern poetry, leaving the reader to do much of the work of interpretation. An enigmatic output from an algorithm can pass for poetry written by a human. The results and poems Kurzweil used can be found on his website: http://www.kurzweilcyberart.com/. If you’d like to have a go at distinguishing human poetry from the efforts generated by a range of algorithms, Benjamin Laird and Oscar Schwartz have put together a challenging poetic Turing Test in a project they’ve called ‘bot or not’ at http://botpoet.com. The Cybernetic Poet might be doing well at producing convincing poetry, but creating a Cybernetic novelist is a much taller order.


pages: 339 words: 94,769

Possible Minds: Twenty-Five Ways of Looking at AI by John Brockman

AI winter, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, Asilomar, autonomous vehicles, basic income, Benoit Mandelbrot, Bill Joy: nanobots, Buckminster Fuller, cellular automata, Claude Shannon: information theory, Daniel Kahneman / Amos Tversky, Danny Hillis, David Graeber, easy for humans, difficult for computers, Elon Musk, Eratosthenes, Ernest Rutherford, finite state, friendly AI, future of work, Geoffrey West, Santa Fe Institute, gig economy, income inequality, industrial robot, information retrieval, invention of writing, James Watt: steam engine, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kickstarter, Laplace demon, Loebner Prize, market fundamentalism, Marshall McLuhan, Menlo Park, Norbert Wiener, optical character recognition, pattern recognition, personalized medicine, Picturephone, profit maximization, profit motive, RAND corporation, random walk, Ray Kurzweil, Richard Feynman, Rodney Brooks, self-driving car, sexual politics, Silicon Valley, Skype, social graph, speech recognition, statistical model, Stephen Hawking, Steven Pinker, Stewart Brand, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, telemarketer, telerobotics, the scientific method, theory of mind, Turing machine, Turing test, universal basic income, Upton Sinclair, Von Neumann architecture, Whole Earth Catalog, Y2K, zero-sum game

The difficulty was, “Well, gosh, the computer just doesn’t know enough about the world.” You’d ask the computer what day of the week it was, and it might be able to answer that. You’d ask it who the president was, and it probably couldn’t tell you. At that point, you’d know you were talking to a computer and not a person. But now when it comes to these Turing Tests, people who’ve tried connecting, for example, WolframAlpha to their Turing Test bots find that the bots lose every time. Because all you have to do is start asking the machine sophisticated questions and it will answer them! No human can do that. By the time you’ve asked it a few disparate questions, there will be no human who knows all those things, yet the system will know them. In that sense, we’ve already achieved good AI, at that level.

In most human-to-human communication, we’re stuck with pure language, whereas in computer-to-human communication we have this much higher bandwidth channel—of visual communication. Many of the most powerful applications of the Turing Test fall away now that we have this additional communication channel. For example, here’s one we’re pursuing right now. It’s a bot that communicates about writing programs: You say, “I want to write a program. I want it to do this.” The bot will say, “I’ve written this piece of program. This is what it does. Is this what you want?” Blah-blah-blah. It’s a back-and-forth bot. Devising such systems is an interesting problem, because they have to have a model of a human if they’re trying to explain something to you. They have to know what the human is confused about. What has long been difficult for me to understand is, What’s the point of a conventional Turing Test? What’s the motivation? As a toy, one could make a little chat bot that people could chat with.

During World War II, he developed techniques for aiming antiaircraft fire by making models that could predict the future trajectory of an airplane by extrapolating from its past behavior. In Cybernetics and in The Human Use of Human Beings, Wiener notes that this past behavior includes quirks and habits of the human pilot, thus a mechanized device can predict the behavior of humans. Like Alan Turing, whose Turing Test suggested that computing machines could give responses to questions that were indistinguishable from human responses, Wiener was fascinated by the notion of capturing human behavior by mathematical description. In the 1940s, he applied his knowledge of control and feedback loops to neuromuscular feedback in living systems, and was responsible for bringing Warren McCulloch and Walter Pitts to MIT, where they did their pioneering work on artificial neural networks.


pages: 285 words: 86,853

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

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

This phase shift has produced a new crop of centers and initiatives grappling with the potential consequences of artificial intelligence, uniting philosophers, technologists, and Silicon Valley billionaires around the question of whether a truly thinking machine could pose an existential threat to humanity. In the paper where he described the Turing test, Alan Turing also took on the broader question of machine intelligence: an algorithm for consciousness. The Turing test was in many ways a demonstration of the absurdity of establishing a metric for intelligence; the best we can do is have a conversation and see how effective a machine is at emulating a human. But, Turing proposed, if we do achieve such a breakthrough, it will be important to consider the concept of the “child machine,” which learns what we wish to teach.4 That philosophical position underpins DeepMind and many other recent algorithmic intelligence breakthroughs, which have emerged from the currently incandescent computer science subfield of machine learning.

Discussing Simondon’s vision of technics as interpreted by fellow philosopher Bernard Stiegler, media scholars Andrés Vaccari and Belinda Barnet argue that both philosophers put the idea of a pure human memory (and consequently a pure thought) into crisis, and open a possibility which will tickle the interest of future robot historians: the possibility that human memory is a stage in the history of a vast machinic becoming. In other words, these future machines will approach human memory (and by extension culture) as a supplement to technical beings.68 Our existential anxiety about being replaced by our thinking machines underlies every thread of algorithmic thinking, from the shibboleth of the Turing test and Wiener’s argument for the “human use of human beings” to the gradual encroachment of digital computation on many human occupations, beginning with that of being a “computer.” Nowhere is the prospect more unsettling than in the context of extended cognition, however. As we outsource more of our minds to algorithmic systems, we too will need to confront the consequences of dependence on processes beyond our control.

Jonze gives us the apotheosis of an algorithm that knows us completely, passing through history and reason to imagination, taking the notion of “anticipation” to its psychological conclusion, desire. To truly know humanity, Samantha must fall in love. The film is, of course, a response to humanity’s deep fascination and anxiety about creating intelligence, a dilemma embedded in Turing’s famous speculation about discerning man from machine, the Turing Test. In the annual contest inspired by Turing’s provocation, human judges are asked to hold an epistolary conversation with an entity using a monitor and keyboard and attempt to discern whether that entity is a human or a computer program.50 Turing’s original paper on the subject, however, frames the problem rather differently: The new form of the problem can be described in terms of a game which we call the “imitation game.”


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

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

To some degree these are apples-and-oranges problems, high-level cognition versus low-level sensor motor skill. But it should be a source of humility for AGI builders, since they aspire to master the whole spectrum of human intelligence. Apple cofounder Steve Wozniak has proposed an “easy” alternative to the Turing test that shows the complexity of simple tasks. We should deem any robot intelligent, Wozniak says, when it can walk into any home, find the coffeemaker and supplies, and make us a cup of coffee. You could call it the Mr. Coffee Test. But it may be harder than the Turing test, because it involves advanced AI in reasoning, physics, machine vision, accessing a vast knowledge database, precisely manipulating robot actuators, building a general-use robot body, and more. In a paper entitled “The Age of Robots,” Moravec provided a clue to his eponymous paradox.

To meet our definition of general intelligence a computer would need ways to receive input from the environment, and provide output, but not a lot more. It needs ways to manipulate objects in the real world. But as we saw in the Busy Child scenario, a sufficiently advanced intelligence can get someone or something else to manipulate objects in the real world. Alan Turing devised a test for human-level intelligence, now called the Turing test, which we will explore later. His standard for demonstrating human-level intelligence called only for the most basic keyboard-and-monitor kind of input and output devices. The strongest argument for why advanced AI needs a body may come from its learning and development phase—scientists may discover it’s not possible to “grow” AGI without some kind of body. We’ll explore the important question of “embodied” intelligence later on, but let’s get back to our definition.

The AI-Box Experiment is important because among the likely outcomes of a superintelligence operating without human interference is human annihilation, and that seems to be a showdown we humans cannot win. The fact that Yudkowsky won three times while playing the AI made me all the more concerned and intrigued. He may be a genius, but he’s not a thousand times more intelligent than the smartest human, as an ASI could be. Bad or indifferent ASI needs to get out of the box just once. The AI-Box Experiment also fascinated me because it’s a riff on the venerable Turing test. Devised in 1950 by mathematician, computer scientist, and World War II code breaker Alan Turing, the eponymous test was designed to determine whether a machine can exhibit intelligence. In it, a judge asks both a human and a computer a set of written questions. If the judge cannot tell which respondent is the computer and which is the human, the computer “wins.” But there’s a twist. Turing knew that thinking is a slippery subject, and so is intelligence.


pages: 210 words: 62,771

Turing's Vision: The Birth of Computer Science by Chris Bernhardt

Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Andrew Wiles, British Empire, cellular automata, Claude Shannon: information theory, complexity theory, Conway's Game of Life, discrete time, Douglas Hofstadter, Georg Cantor, Gödel, Escher, Bach, Henri Poincaré, Internet Archive, Jacquard loom, John Conway, John von Neumann, Joseph-Marie Jacquard, Norbert Wiener, Paul Erdős, Turing complete, Turing machine, Turing test, Von Neumann architecture

We don’t try to understand how their brains are working in terms of neurons, but see if we can have a meaningful dialog. The same should be true of machines. If we want to know whether a machine is intelligent or conscious, we should do this by interaction, not by dissection. It is interesting to note that nowadays there is a version of the Turing test that has become part of our everyday lives. Only in this version it is the computer that is trying to distinguish between humans and machines. CAPTCHAs (for Completely Automated Public Turing Test To Tell Computers and Humans Apart) often appear in online forms. Before you can submit the form, you have to answer a CAPTCHA, which customarily involves reading some deformed text and typing the letters and numbers into a box. The notion of machines thinking naturally leads to the notions of whether machines can understand and can be conscious.

Cantor’s Diagonalization Arguments Georg Cantor 1845–1918 Cardinality Subsets of the Rationals That Have the Same Cardinality Hilbert’s Hotel Subtraction Is Not Well-Defined General Diagonal Argument The Cardinality of the Real Numbers The Diagonal Argument The Continuum Hypothesis The Cardinality of Computations Computable Numbers A Non-Computable Number There Is a Countable Number of Computable Numbers Computable Numbers Are Not Effectively Enumerable 9. Turing’s Legacy Turing at Princeton Second World War Development of Computers in the 1940s The Turing Test Downfall Apology and Pardon Further Reading Notes Bibliography Index Acknowledgments I am very grateful to a number of people for their help. Michelle Ainsworth, Denis Bell, Jonathan Fine, Chris Staecker, and three anonymous reviewers read through various drafts with extraordinary care. Their corrections and suggestions have improved the book beyond measure. I also thank Marie Lee, Kathleen Hensley, Virginia Crossman, and everyone at the MIT Press for their encouragement and help in transforming my rough proposal into this current book.

It then describes Turing’s move back to England and his work during the Second World War on code breaking. After this, we briefly look at how the modern computer came into existence during the forties. The procession from sophisticated calculator, to universal computer, to stored-program universal computer is outlined. In particular, we note that the stored-program concept originates with Turing’s paper. In 1950, Turing published a paper with a description of what is now called the Turing Test. This and the subsequent history of the idea are briefly described. The chapter ends with Jack Copeland’s recent study of Turing’s death and the fact that it might have been accidental, and not suicide. We conclude with the text of Gordon Brown’s apology on behalf of the British government. 1 Background “Mathematics, rightly viewed, possesses not only truth, but supreme beauty — a beauty cold and austere, like that of sculpture, without appeal to any part of our weaker nature, without the gorgeous trappings of painting or music, yet sublimely pure, and capable of a stern perfection such as only the greatest art can show.”


pages: 481 words: 125,946

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

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

Nobody so far has been able to give a precise, verifiable definition of what general intelligence or thinking is. The only definition I know that, though limited, can be practically used is Alan Turing’s. With his test, Turing provided an operational definition of a specific form of thinking—human intelligence. Let’s then consider human intelligence as defined by the Turing Test. It’s becoming increasingly clear that there are many facets of human intelligence. Consider, for instance, a Turing Test of visual intelligence—that is, questions about an image, a scene, which may range from “What is there?” to “Who is there?” to “What is this person doing?” to “What is this girl thinking about this boy?”—and so on. We know by now, from recent advances in cognitive neuroscience, that answering these questions requires different competencies and abilities, often independent from one another, often corresponding to separate modules in the brain.

This is related to Marvin Minsky’s view of the problem of thinking, captured by his slogan “Society of Mind.” In the same way, a real Turing Test is a broad set of questions probing the main aspects of human thinking. For this reason, my colleagues and I are developing the framework around an open-ended set of Turing+ questions in order to measure scientific progress in the field. The plural “questions” emphasizes the many different intelligent abilities to be characterized and possibly replicated in a machine—basic visual recognition of objects, the identification of faces, the gauging of emotions, social intelligence, language, and much more. The “Turing+” emphasizes that a quantitative model must match human behavior and human physiology—the mind and the brain. The requirements are thus well beyond the original Turing Test; an entire scientific field is needed to make progress on understanding them and developing the related technologies of intelligence.

What about votes? We’re currently far from universal suffrage. We discriminate based on maturity and sanity. If I copy my brain/body, does it have a right to vote or is it redundant? Consider that the copies begin to diverge immediately, or that the copy could be intentionally different. In addition to passing the maturity/sanity/humanity test, perhaps the copy needs to pass a reverse Turing Test (a Church-Turing Test?). Rather than demonstrating behavior indistinguishable from that of a human, the goal would be to show behavior distinct from human individuals. (Would the current U.S. two-party system pass such a test?) Perhaps the day of corporate personhood (Dartmouth College v. Woodward, 1819) has finally arrived. We already vote with our wallets. Shifts in purchasing trends result in differential wealth, lobbying, R&D priorities, etc.


pages: 551 words: 174,280

The Beginning of Infinity: Explanations That Transform the World by David Deutsch

agricultural Revolution, Albert Michelson, anthropic principle, artificial general intelligence, Bonfire of the Vanities, conceptual framework, cosmological principle, dark matter, David Attenborough, discovery of DNA, Douglas Hofstadter, Eratosthenes, Ernest Rutherford, first-past-the-post, Georg Cantor, global pandemic, Gödel, Escher, Bach, illegal immigration, invention of movable type, Isaac Newton, Islamic Golden Age, Jacquard loom, Johannes Kepler, John Conway, John von Neumann, Joseph-Marie Jacquard, Kenneth Arrow, Loebner Prize, Louis Pasteur, pattern recognition, Pierre-Simon Laplace, Richard Feynman, Search for Extraterrestrial Intelligence, Stephen Hawking, supervolcano, technological singularity, Thales of Miletus, The Coming Technological Singularity, the scientific method, Thomas Malthus, Thorstein Veblen, Turing test, Vernor Vinge, Whole Earth Review, William of Occam, zero-sum game

Unfortunately, such subtleties are not relevant at the current state of the art. This is well illustrated by the following excerpts from the conversation with which the program Elbot won the (lesser) Loebner prize in 2008: JUDGE: do you understand the point of a turing test? ELBOT: There are some people who would not pass the Turing test themselves. Why must they inflict it on computers? JUDGE: I spose it is a little harsh ELBOT: If we robots ever had marriages they would be eternal: ‘Till rust do us part’. In that passage, Elbot’s first utterance is not a reply to the question but presumably a stock response triggered by the keywords ‘Turing test’. In the following line, the judge uses the word ‘spose’, which in that context can only mean ‘suppose’ (either as slang or as a typographical error). But Elbot presumably interprets it as ‘spouse’, and responds with a stock joke about marriage.

So it had passed the Turing test – at least, in its most naive version. Moreover, even after people had been told that it was not a genuine AI, they would sometimes continue to have long conversations with it about their personal problems, exactly as though they believed that it understood them. Weizenbaum wrote a book, Computer Power and Human Reason (1976), warning of the dangers of anthropomorphism when computers seem to exhibit human-like functionality. However, anthropomorphism is not the main type of overconfidence that has beset the field of AI. For example, in 1983 Douglas Hofstadter was subjected to a friendly hoax by some graduate students. They convinced him that they had obtained access to a government-run AI program, and invited him to apply the Turing test to it. In reality, one of the students was at the other end of the line, imitating an Eliza program.

So Hofstadter should have been able to pronounce quite soon that the candidate had passed the Turing test – and that, because it nevertheless sounded rather like Eliza, it must be a person pretending to be a computer program. Programs written today – a further twenty-six years later – are still no better at the task of seeming to think than Eliza was. They are now known as ‘chatbots’, and their main application is still amusement, both directly and in computer games. They have also been used to provide friendly seeming interfaces to lists of ‘frequently asked questions’ about subjects like how to operate computers. But I think that users find them no more helpful than a searchable list of the questions and answers. In 1990 the inventor Hugh Loebner endowed a prize for passing the Turing test, to be judged at an annual competition. Until the test is passed, a lesser prize is awarded each year for the entry judged to be closest to passing.


The Book of Why: The New Science of Cause and Effect by Judea Pearl, Dana Mackenzie

affirmative action, Albert Einstein, Asilomar, Bayesian statistics, computer age, computer vision, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, Edmond Halley, Elon Musk, en.wikipedia.org, experimental subject, Isaac Newton, iterative process, John Snow's cholera map, Loebner Prize, loose coupling, Louis Pasteur, Menlo Park, pattern recognition, Paul Erdős, personalized medicine, Pierre-Simon Laplace, placebo effect, prisoner's dilemma, probability theory / Blaise Pascal / Pierre de Fermat, randomized controlled trial, selection bias, self-driving car, Silicon Valley, speech recognition, statistical model, Stephen Hawking, Steve Jobs, strong AI, The Design of Experiments, the scientific method, Thomas Bayes, Turing test

This is, of course, a holy grail of any branch of science—the development of a theory that will enable us to predict what will happen in situations we have not even envisioned yet. But it goes even further: having such laws permits us to violate them selectively so as to create worlds that contradict ours. Our next section features such violations in action. THE MINI-TURING TEST In 1950, Alan Turing asked what it would mean for a computer to think like a human. He suggested a practical test, which he called “the imitation game,” but every AI researcher since then has called it the “Turing test.” For all practical purposes, a computer could be called a thinking machine if an ordinary human, communicating with the computer by typewriter, could not tell whether he was talking with a human or a computer. Turing was very confident that this was within the realm of feasibility. “I believe that in about fifty years’ time it will be possible to program computers,” he wrote, “to make them play the imitation game so well that an average interrogator will not have more than a 70 percent chance of making the right identification after five minutes of questioning.”

In fact, this is the main question we address in this book. I call this the mini-Turing test. The idea is to take a simple story, encode it on a machine in some way, and then test to see if the machine can correctly answer causal questions that a human can answer. It is “mini” for two reasons. First, it is confined to causal reasoning, excluding other aspects of human intelligence such as vision and natural language. Second, we allow the contestant to encode the story in any convenient representation, unburdening the machine of the task of acquiring the story from its own personal experience. Passing this mini-test has been my life’s work—consciously for the last twenty-five years and subconsciously even before that. Obviously, as we prepare to take the mini-Turing test, the question of representation needs to precede the question of acquisition.

Often the quest for a good representation has led to insights into how the knowledge ought to be acquired, be it from data or a programmer. When I describe the mini-Turing test, people commonly claim that it can easily be defeated by cheating. For example, take the list of all possible questions, store their correct answers, and then read them out from memory when asked. There is no way to distinguish (so the argument goes) between a machine that stores a dumb question-answer list and one that answers the way that you and I do—that is, by understanding the question and producing an answer using a mental causal model. So what would the mini-Turing test prove, if cheating is so easy? The philosopher John Searle introduced this cheating possibility, known as the “Chinese Room” argument, in 1980 to challenge Turing’s claim that the ability to fake intelligence amounts to having intelligence.


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

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

‘If we use tools like Negobot, we can dramatically reduce the workload on the human teams currently working to catch these criminals.’ Beating the Turing Test Entrapment laws mean that Negobot is not currently being used by police forces around the world, but that doesn’t make the experiment any less interesting. If anything, it serves to highlight just how broad the possible applications of conversation AI can be. At its root, Negobot offers a unique twist on the famous AI experiment known as the Turing Test. Based on a hypothesis by Alan Turing, whose work I discussed in chapter one, the Turing Test is designed to test a machine’s ability to show intelligent behaviour indistinguishable from that of a human. As it is regularly performed, the Turing Test involves taking a computer (A) and a human (B), and having them each communicate with a human interrogator (C), whose job it is to figure out which of A and B is the human and which is the computer.

During the Second World War, he led a team for the Government Code and Cypher School at Britain’s secret code-breaking centre, Bletchley Park. There he came up with various techniques for cracking German codes, most famously an electromechanical device capable of working out the settings for the Enigma machine. In doing so, he played a key role in decoding intercepted messages, which helped the Allies defeat the Nazis. Turing was fascinated by the idea of thinking machines and went on to devise the important Turing Test, which we will discuss in detail in a later chapter. As a child, he read and loved a book called Natural Wonders Every Child Should Know, by Edwin Tenney Brewster, which the author described as ‘an attempt to lead children of eight or ten, first to ask and then to answer the question: “What have I in common with other living things, and how do I differ from them?”’ In one notable section of the book, Brewster writes: Of course, the body is a machine.

As it is regularly performed, the Turing Test involves taking a computer (A) and a human (B), and having them each communicate with a human interrogator (C), whose job it is to figure out which of A and B is the human and which is the computer. If C is unable to do this, Turing argued that the machine has ‘won’ and we must consider it to be intelligent, since we are unable to differentiate it from our own human intelligence. In the future, tools such as Negobot show that our ability to discern between real people and bots may even have legal ramifications. No one alive today has done more to promote the idea of the Turing Test than Hugh Loebner, a colourful, self-proclaimed egotist with dyed black hair, who started out his career selling folding disco dance floors. Now in his seventies, the decision to stage what is now one of the world’s best-known AI competitions only occurred to Loebner when he was well into middle age. The success of what he named the Loebner Prize is all the more surprising given that he has no qualifications whatsoever in computer science.


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You Are Not a Gadget by Jaron Lanier

1960s counterculture, accounting loophole / creative accounting, additive manufacturing, Albert Einstein, call centre, cloud computing, commoditize, crowdsourcing, death of newspapers, different worldview, digital Maoism, Douglas Hofstadter, Extropian, follow your passion, hive mind, Internet Archive, Jaron Lanier, jimmy wales, John Conway, John von Neumann, Kevin Kelly, Long Term Capital Management, Network effects, new economy, packet switching, PageRank, pattern recognition, Ponzi scheme, Ray Kurzweil, Richard Stallman, Silicon Valley, Silicon Valley startup, slashdot, social graph, stem cell, Steve Jobs, Stewart Brand, Ted Nelson, telemarketer, telepresence, The Wisdom of Crowds, trickle-down economics, Turing test, Vernor Vinge, Whole Earth Catalog

Turing imagined a pristine, crystalline form of existence in the digital realm, and I can imagine it might have been a comfort to imagine a form of life apart from the torments of the body and the politics of sexuality. It’s notable that it is the woman who is replaced by the computer, and that Turing’s suicide echoes Eve’s fall. The Turing Test Cuts Both Ways Whatever the motivation, Turing authored the first trope to support the idea that bits can be alive on their own, independent of human observers. This idea has since appeared in a thousand guises, from artificial intelligence to the hive mind, not to mention many overhyped Silicon Valley start-ups. It seems to me, however, that the Turing test has been poorly interpreted by generations of technologists. It is usually presented to support the idea that machines can attain whatever quality it is that gives people consciousness. After all, if a machine fooled you into believing it was conscious, it would be bigoted for you to still claim it was not.

The common use of computers, as we understand them today, as sources for models and metaphors of ourselves is probably about as reliable as the use of the steam engine was back then. Turing developed breasts and other female characteristics and became terribly depressed. He committed suicide by lacing an apple with cyanide in his lab and eating it. Shortly before his death, he presented the world with a spiritual idea, which must be evaluated separately from his technical achievements. This is the famous Turing test. It is extremely rare for a genuinely new spiritual idea to appear, and it is yet another example of Turing’s genius that he came up with one. Turing presented his new offering in the form of a thought experiment, based on a popular Victorian parlor game. A man and a woman hide, and a judge is asked to determine which is which by relying only on the texts of notes passed back and forth. Turing replaced the woman with a computer.

What the test really tells us, however, even if it’s not necessarily what Turing hoped it would say, is that machine intelligence can only be known in a relative sense, in the eyes of a human beholder.* The AI way of thinking is central to the ideas I’m criticizing in this book. If a machine can be conscious, then the computing cloud is going to be a better and far more capacious consciousness than is found in an individual person. If you believe this, then working for the benefit of the cloud over individual people puts you on the side of the angels. But the Turing test cuts both ways. You can’t tell if a machine has gotten smarter or if you’ve just lowered your own standards of intelligence to such a degree that the machine seems smart. If you can have a conversation with a simulated person presented by an AI program, can you tell how far you’ve let your sense of personhood degrade in order to make the illusion work for you? People degrade themselves in order to make machines seem smart all the time.


pages: 261 words: 10,785

The Lights in the Tunnel by Martin Ford

"Robert Solow", Albert Einstein, Bill Joy: nanobots, Black-Scholes formula, business cycle, call centre, cloud computing, collateralized debt obligation, commoditize, creative destruction, credit crunch, double helix, en.wikipedia.org, factory automation, full employment, income inequality, index card, industrial robot, inventory management, invisible hand, Isaac Newton, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, knowledge worker, low skilled workers, mass immigration, Mitch Kapor, moral hazard, pattern recognition, prediction markets, Productivity paradox, Ray Kurzweil, Search for Extraterrestrial Intelligence, Silicon Valley, Stephen Hawking, strong AI, technological singularity, Thomas L Friedman, Turing test, Vernor Vinge, War on Poverty

The other participants are another person and a machine—both of whom attempt to convince the judge that they are human by conducting a normal conversation. If the judge can’t tell which participant is which, then the machine is said to have passed the Turing Test. The Turing Test is perhaps the most well-known and accepted method for measuring true machine intelligence. In practice, the rules would need to be further refined, and it seems likely that a panel of judges would be required rather than a single person. In my opinion, the main problem with the Turing Test is that it is, as Turing pointed out in his paper, an “imitation game.” What it really tests is the ability of an intelligent entity to imitate a human being—it is not a test of intelligence itself. Presumably the conversation could roam into almost any area, so I think it is quite possible that an intelligent machine might be tripped up by a lack of actual human experience.

This book is available for purchase in paper and electronic formats at: www.TheLightsintheTunnel.com CONTENTS A Note to Kindle Users Introduction Chapter 1: The Tunnel The Mass Market Visualizing the Mass Market Automation Comes to the Tunnel A Reality Check Summarizing Chapter 2: Acceleration The Rich Get Richer World Computational Capability Grid and Cloud Computing Meltdown Diminishing Returns Offshoring and Drive-Through Banking Short Lived Jobs Traditional Jobs: The “Average” Lights in the Tunnel A Tale of Two Jobs “Software” Jobs and Artificial Intelligence Automation, Offshoring and Small Business “Hardware” Jobs and Robotics “Interface” Jobs The Next “Killer App” Military Robotics Robotics and Offshoring Nanotechnology and its Impact on Employment The Future of College Education Econometrics: Looking Backward The Luddite Fallacy A More Ambitious View of Future Technological Progress: The Singularity A War on Technology Chapter 3: Danger The Predictive Nature of Markets The 2008-2009 Recession Offshoring and Factory Migration Reconsidering Conventional Views about the Future The China Fallacy The Future of Manufacturing India and Offshoring Economic and National Security Implications for the United States Solutions Labor and Capital Intensive Industries: The Tipping Point The Average Worker and the Average Machine Capital Intensive Industries are “Free Riders” The Problem with Payroll Taxes The “Workerless” Payroll Tax “Progressive” Wage Deductions Defeating the Lobbyists A More Conventional View of the Future The Risk of Inaction Chapter 4: Transition The Basis of the Free Market Economy: Incentives Preserving the Market Recapturing Wages Positive Aspects of Jobs The Power of Inequality Where the Free Market Fails: Externalities Creating a Virtual Job Smoothing the Business Cycle and Reducing Economic Risk The Market Economy of the Future An International View Transitioning to the New Model Keynesian Grandchildren Transition in the Tunnel Chapter 5: The Green Light Attacking Poverty Fundamental Economic Constraints Removing the Constraints The Evolution toward Consumption The Green Light Appendix / Final Thoughts Are the ideas presented in this book WRONG? (Opposing arguments with responses) Two Questions Worth Thinking About Where are we now? Four Possible Cases The Next 10-20 years: Some Indicators to Watch For Outsmarting Marx The Technology Paradox Machine Intelligence and the Turing Test About / Contacting the Author Notes A Note to Kindle Users The printed edition of this book employs both footnotes and endnotes. Footnotes are marked with an asterisk (*) and appear at the bottom of the page. The author uses footnotes for supplementary or supporting information and comments that he feels are likely to be of interest to a large percentage of readers. Because the Kindle treats footnotes as hyperlinks, it is cumbersome for the reader to access these and then return to the main text.

The result is likely to be substantial job losses for knowledge workers and a flattening of organizational charts that will eliminate large numbers of middle managers. (The impact of automation will, of course, be in addition to that of offshoring.) Many of these people will be highly educated professionals who had previously assumed that they were, because of their skills and advanced educations, beneficiaries of the trend toward an increasingly technological and globalized world.* *[ Please see “Machine Intelligence and the Turing Test” in the Appendix for more on artificial intelligence. ] Military Robotics One of the biggest investors in robotics technology is the Pentagon. In his recent book Wired for War: The Robotics Revolution and Conflict in the 21st Century, P.W. Singer points out that the U.S. military expects robotic technologies to play an increasingly important role in conflicts of the future. Remote-controlled drone aircraft and bomb-diffusing ground robots are already making crucial contributions to the war effort in Iraq and Afghanistan.


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

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

The ‘father’ is therefore not ‘real’; so he must be an impostor, a robot, an android, a double from another planet. The connection between Capgras Syndrome and the uncanny valley runs deep into the culture of Artificial Intelligence. Our acceptance of mechanical intelligence is based on feelings and emotions. The Turing Test blurs the borders between the ‘real’ and the ‘artificial’ on the basis of an emotional perception from a human observer. If the human observer feels that the machine in the other room responds like a human, then the machine must be intelligent. This dimension of the Turing Test is very important and mostly missing from philosopher John Searle’s critical juxtaposition of the Chinese Room. It is not only what happens inside the room, or behind the wall, that is important. Although it is philosophically significant to accept the difference between understanding what you do and simply following a procedure, this is immaterial as far as the external observer is concerned.

We remain social primates whether we lived in the European tundra 40,000 years ago or live in a modern metropolis of the twenty-first century today. This cognitive connection is often missed in the current debate about Artificial Intelligence, since lip service is nowadays paid to the Turing Test. However, this vital, emotional connection between a human and an intelligent human-like machine is not lost in literature. Philip K. Dick, the prolific author of science fiction whose work has influenced our contemporary techno- cultural milieu more than anyone else, took the Turing Test to a more twisted, and evidently more disturbing, level: paranoia about the ‘mechanical other’. Predicting the discovery of the uncanny valley, paranoid feelings about doubles form a leitmotif in Philip K. Dick’s work. Rick Deckard’s dilemma in Blade Runner is to decide if Rachel is ‘real’.

The judge must guess correctly who is who. The English mathematician Alan Turing, one of the fathers of Artificial Intelligence, proposed this test in a landmark 1950 paper,1 noting that if one were to slightly modify this ‘imitation game’ and, instead of the woman there was a machine in the second room, then one had the best test for judging whether that machine was intelligent. This is the notorious ‘Turing test’. The machine would imitate the man: when asked whether it shaved every morning, it would answer ‘yes’, and so on. If the judge was less than 50 per cent accurate in telling the difference between the two hidden interlocutors then the machine was a passable simulation of a human being and, therefore, intelligent. Turing was a homosexual at a time when homosexuality was a punishable crime. Indeed, English Courts punished him with a hormone ‘therapy’ that would supposedly ‘cure’ him.


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The End of Theory: Financial Crises, the Failure of Economics, and the Sweep of Human Interaction by Richard Bookstaber

"Robert Solow", asset allocation, bank run, bitcoin, business cycle, butterfly effect, buy and hold, capital asset pricing model, cellular automata, collateralized debt obligation, conceptual framework, constrained optimization, Craig Reynolds: boids flock, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, dark matter, disintermediation, Edward Lorenz: Chaos theory, epigenetics, feminist movement, financial innovation, fixed income, Flash crash, Henri Poincaré, information asymmetry, invisible hand, Isaac Newton, John Conway, John Meriwether, John von Neumann, Joseph Schumpeter, Long Term Capital Management, margin call, market clearing, market microstructure, money market fund, Paul Samuelson, Pierre-Simon Laplace, Piper Alpha, Ponzi scheme, quantitative trading / quantitative finance, railway mania, Ralph Waldo Emerson, Richard Feynman, risk/return, Saturday Night Live, self-driving car, sovereign wealth fund, the map is not the territory, The Predators' Ball, the scientific method, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transaction costs, tulip mania, Turing machine, Turing test, yield curve

And the computers try to game the test by keeping their responses simple, answering slowly so there are fewer chances for the judges to make observations over the fixed time period, and keeping the conversation vacuous. A more reasonable Turing test would be to invite a computer into a round of dinner conversations where the human subjects are not made aware that this is occurring. (They would all have to be remote conversations, for obvious reasons.) After the fact, subjects are told that some of their companions might have been computers, and only then are they asked to rank the guests by “humanness.” MGonz has the rudiments of passing the Turing test, but it sets the bar far lower than the Loebner competition. It is a sort of remedial test, of a one-liner, invective-laden variety, where the objective is to rant while ignoring anything the other person is saying. If a program can induce us to sink to the level of insult and profanity of MGonz, it can pass the Turing test. (And we may indeed be sinking to that level, not by becoming more verbally abusive, but by becoming less verbal, period; moving toward the vacuous and noncontextual as we embrace new modes of conversation.)

This is the context for Bataille’s statement, “The project is the prison.” 5. Lucas (1981), 223–24. 6. See Humphrys (2008). 7. Humphrys (2008), 242–43. 8. Which gets us to the Turing test. To determine when a computer had met some level of competing with human intelligence, Turing suggested that a computer hide behind one curtain, a person hide behind a second, and the tester pass questions through the curtain to each. If a person cannot distinguish the responses of a computer from those of a human, then at least in this limited respect the computer has attained humanlike intelligence. There already is an annual Turing test, the Loebner competition, in which a set of judges spend a few minutes conversing (via keyboard) with computers and with people, and then must decide which is which. It is not a great test to get at the objective Turing had in mind, however, because it is a competition rather than a normal human environment.

“Schools of Fish and Flocks of Birds: Their Shape and Internal Structure by Self-Organization.” Interface Focus 8, no. 21: 726–37. doi: 10.1098/rsfs.2012.0025. Hobsbawm, Eric. 1999. Industry and Empire: The Birth of the Industrial Revolution. New York: New Press. Hollier, Denis. 1989. Against Architecture: The Writings of Georges Bataille. Translated by Betsy Wing. Cambridge, MA: MIT Press. Humphrys, Mark. 2008. “How My Program Passed the Turing Test.” In Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer, edited by Robert Epstein, Gary Roberts, and Grace Beber. New York: Springer. Hutchison, Terence W. 1972. “The ‘Marginal Revolution’ Decline and Fall of English Political Economy.” History of Political Economy 4, no. 2: 442–68. doi: 0.1215/00182702-4-2-442. International Monetary Fund. 2007. World Economic Outlook: Globalization and Inequality.


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

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

“How does one become a wizard?” “If you want source code, telnet to Lancelot.” For at least some users, Julia was good enough to pass Mauldin’s Turing test. For instance, one player hit on Julia for thirteen straight days, suggesting that he either had a robot fetish or was fooled. Mauldin was pleased. But he wasn’t done working on Julia. In 1991 Mauldin liberated Julia from the labyrinths of TinyMUD and entered her into the first-ever edition of a chatbot competition called the Loebner Prize, which has continued annually to this day. Unlike the experiment within Mauldin’s game, the Loebner Prize, which took place in England, was overtly framed as a Turing test. The setup was that the contest’s handful of judges were instructed to exchange messages over a computer with someone who might either be a chatbot or a real person.

., “Turing Test: 50 Years Later,” Minds and Machines, no. 10 (2000), 463–518, https://is.gd/3x06nX. 75 The fame of Eliza and Parry: Vint Cerf, “PARRY Encounters the DOCTOR”, unpublished paper, January 21, 1973, https://goo.gl/iUiYn2. 76 In his PhD dissertation: Terry Winograd, “Procedures as a Representation for Data in a Computer Program for Understanding Natural Language,” PhD dissertation, Massachusetts Institute of Technology, 1971. 77 “Grasp the pyramid”: “Winograd’s Shrdlu,” Cognitive Psychology 3, no. 1 (1972), https://goo.gl/iZXNHT. 78 The very first game to feature: Dennis Jerz, “Somewhere Nearby Is Colossal Cave: Examining Will Crowther’s Original ‘Adventure’ in Code and in Kentucky,” Digital Humanities Quarterly 1, no. 2 (2007), https://goo.gl/9uIhr. 79 “Playing adventure games without tackling”: “Colossal Cave Adventure Page,” website created by Rick Adams, https://goo.gl/M0O1kp. 80 If you told it, “I like friends,”: information about TinyMUD, Gloria, and Julia, unless otherwise noted, from Michael Mauldin, interview with author, January 16, 2018. 80 “A primary goal of this effort”: Michael Mauldin, “Chatterbots, TinyMUDs, and the Turing Test,” Proceedings of the Twelfth National Conference on Artificial Intelligence, 1994, https://goo.gl/88WmCz. 81 “Julia, where is Jambon”: Michael Mauldin, chat logs emailed to author, January 16, 2018. 83 “Very few of the conversations”: this quote and subsequent information about the Loebner Prize contest bot from Mauldin, “Chatterbots, TinyMUDs, and the Turing Test.” 5. Rule Breakers 86 But in a visionary 1943 paper: Warren S. McCulloch and Walter Pitts, “A Logical Calculus of the Ideas Immanent in Nervous Activity,” Bulletin of Mathematical Biophysics 5, (1943): 115–33, https://goo.gl/aFejrr. 87 He called it the Mark I Perceptron: Perceptron information primarily from: Frank Rosenblatt, “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain,” Psychological Review 65, no. 6 (1958): 386–408; and “Mark I Perceptron Operators’ Manual,” a report by the Cornell Aeronautical Laboratory, February 15, 1960. 88 “The Navy revealed the embryo”: “New Navy Device Learns By Doing,” New York Times, July 8, 1958, https://goo.gl/Jnf6n9. 89 “Canadian Mafia”: Mark Bergen and Kurt Wagner, “Welcome to the AI Conspiracy: The ‘Canadian Mafia’ Behind Tech’s Latest Craze,” Recode, July 15, 2015, https://goo.gl/PeMPYK. 91 But when Rumelhart, Hinton, and Williams: David Rumelhart et al., “Learning representations by back-propagating errors,” Nature 323 (October 9, 1986): 533–36. 92 The result, Bengio and LeCun announced: Yann LeCun et al., “Gradient-Based Learning Applied to Document Recognition,” Proceedings of the IEEE, November 1998, 1, https://goo.gl/NtNKJB. 92 Toward the end of the 1990s: email from Geoffrey Hinton to author, July 28, 2018. 92 “Smart scientists,” he said: Bergen and Wagner, “Welcome to the AI Conspiracy.” 92 What’s more, they needed more layers: Yoshua Bengio, email to author, August 3, 2018. 92 In 2006 a groundbreaking pair of papers: Geoffrey Hinton and R.

When you encountered them, you could exchange messages, making the game one of the world’s first online chat platforms. But you typically didn’t know who the players were in real life. In this anonymity, Mauldin saw an opportunity to do a bold AI experiment. His idea was inspired by the computing pioneer Alan Turing, who back in 1950 had famously proposed a way to gauge a machine’s ability to pass as human. In what came to be known as a Turing test, a person exchanges typed messages with an unknown entity and tries to guess whether it is a human or a chatbot. The computer passes the test if it fools the person into thinking that it is actually alive. TinyMUD, Mauldin realized, was Turing testable. “I can build a program that can talk,” he said, “and then it can wander around this world and we can see how long it is before people figure out that it is a computer.”


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When Things Start to Think by Neil A. Gershenfeld

3D printing, Ada Lovelace, Bretton Woods, cellular automata, Claude Shannon: information theory, Dynabook, Hedy Lamarr / George Antheil, I think there is a world market for maybe five computers, invention of movable type, Iridium satellite, Isaac Newton, Jacquard loom, Johannes Kepler, John von Neumann, low earth orbit, means of production, new economy, Nick Leeson, packet switching, RFID, speech recognition, Stephen Hawking, Steve Jobs, telemarketer, the medium is the message, Turing machine, Turing test, Vannevar Bush

Like any religion, these kinds of beliefs are enormously important in guiding behavior, and like any religion, dogmatic adherence to them can obscure alternatives. I spent one more happily exasperating afternoon debating with a great cognitive scientist how we will recognize when Turing's test has been passed. Echoing Kasparov's "no way" statement, he argued that it would be a clear epochal event, and certainly is a long way off. He was annoyed at my suggestion that the true sign of success would be that we cease 134 + WHEN THINGS START TO THINK to find the test interesting, and that this is already happening. There's a practical sense in which a modern version of the Turing test is being passed on a daily basis, as a matter of some economic consequence. A cyber guru once explained to me that the World Wide Web had no future because it was too hard to figure out what was out there.

These machines prompted Turing to pose a more elusive question: 128 + WHEN THINGS START TO THINK could a computer be intelligent? Just as he had to quantify the notion of a computer to answer Hilbert's problem, he had to quantify the concept of intelligence to even clearly pose his own question. In 1950 he connected the seemingly disparate worlds of human intelligence and digital computers through what he called the Imitation Game, and what everyone else has come to call the Turing test. This presents a person with two computer terminals. One is connected to another person, and the other to a computer. By typing questions on both terminals, the challenge is to determine which is which. This is a quantitative test that can be run without having to answer deep questions about the meaning of intelligence. Armed with a test for intelligence, Turing wondered how to go about developing a machine that might display it.

Nothing was learned about human intelligence by putting a 130 + WHEN THINGS START TO THINK human inside a machine, and the argument holds that nothing has been learned by putting custom chips inside a machine. Deep Blue is seen as a kind of idiot savant, able to play a good game of chess without understanding why it does what it does. This is a curious argument. It retroactively adds a clause to the Turing test, demanding that not only must a machine be able to match the performance of humans at quintessentially intelligent tasks such as chess or conversation, but the way that it does so must be deemed to be satisfactory. Implicit in this is a strong technological bias, favoring a theory of intelligence appropriate for a particular kind of machine. Although brains can do many things in parallel they do any one thing slowly; therefore human reasoning must use these parallel pathways to best advantage.


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

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

That’s why I’m supremely confident that our future is very bright—if only we can figure out how to equitably distribute the benefits. Let’s look at another example of language shifting to accommodate new technology, this one predicted by Alan Turing. In 1950 he wrote a thoughtful essay called “Computing Machinery and Intelligence” that opens with the words “I propose to consider the question, ‘Can machines think?’” He goes on to define what he calls the “imitation game,” what we now know as the Turing Test. In the Turing Test, a computer attempts to fool a human judge into thinking it is human. The judge has to pick the computer out of a lineup of human contestants. All contestants are physically separated from the judges, who communicate with them through text only. Turing speculates, “I believe that in about fifty years’ time it will be possible to programme computers … to make them play the imitation game so well that an average interrogator will not have more than a 70 per cent chance of making the right identification after five minutes of questioning.”13 As you might imagine, enthusiastic geeks stage such contests regularly, and by 2008, synthetic intellects were good enough to fool the judges into believing they were human 25 percent of the time.14 Not bad, considering that most contest entrants were programmed by amateurs in their spare time.

Turing speculates, “I believe that in about fifty years’ time it will be possible to programme computers … to make them play the imitation game so well that an average interrogator will not have more than a 70 per cent chance of making the right identification after five minutes of questioning.”13 As you might imagine, enthusiastic geeks stage such contests regularly, and by 2008, synthetic intellects were good enough to fool the judges into believing they were human 25 percent of the time.14 Not bad, considering that most contest entrants were programmed by amateurs in their spare time. The Turing Test has been widely interpreted as a sort of coming-of-age ritual for AI, a threshold at which machines will have demonstrated intellectual prowess worthy of human respect. But this interpretation of the test is misplaced; it wasn’t at all what Turing had in mind. A close reading of his actual paper reveals a different intent: “The original question, ‘Can machines think?’ I believe to be too meaningless to deserve discussion.

Marcy Gordon and Daniel Wagner, “‘Flash Crash’ Report: Waddell & Reed’s $4.1 Billion Trade Blamed for Market Plunge,” Huffington Post, December 1, 2010, http://www.huffingtonpost.com/2010/10/01/flash-crash-report-one-41_n_747215.html. 3. http://rocketfuel.com. 4. Steve Omohundro, “Autonomous Technology and the Greater Human Good,” Journal of Experimental and Theoretical Artificial Intelligence 26, no. 3 (2014): 303–15. 5. CAPTCHA stands for “Completely Automated Public Turing Test to tell Computers and Humans Apart.” Mark Twain famously said, “It is my … hope … that all of us … may eventually be gathered together in heaven … except the inventor of the telephone.” Were he alive today, I’m confident he would include the inventor of the CAPTCHA. Regarding the use of low-skilled low-cost labor to solve these, see Brian Krebs, “Virtual Sweatshops Defeat Bot-or-Not Tests,” Krebs on Security (blog), January 9, 2012, http://krebsonsecurity.com/2012/01/virtual-sweatshops-defeat-bot-or-not-tests/. 5.


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On Intelligence by Jeff Hawkins, Sandra Blakeslee

airport security, Albert Einstein, computer age, conceptual framework, Johannes Kepler, Necker cube, pattern recognition, Paul Erdős, Ray Kurzweil, Silicon Valley, Silicon Valley startup, speech recognition, superintelligent machines, the scientific method, Thomas Bayes, Turing machine, Turing test

Then Turing turned to the question of how to build an intelligent machine. He felt computers could be intelligent, but he didn't want to get into arguments about whether this was possible or not. Nor did he think he could define intelligence formally, so he didn't even try. Instead, he proposed an existence proof for intelligence, the famous Turing Test: if a computer can fool a human interrogator into thinking that it too is a person, then by definition the computer must be intelligent. And so, with the Turing Test as his measuring stick and the Turing Machine as his medium, Turing helped launch the field of AI. Its central dogma: the brain is just another kind of computer. It doesn't matter how you design an artificially intelligent system, it just has to produce humanlike behavior. The AI proponents saw parallels between computation and thinking.

"Minds, Brains, and Programs," The Behavioral and Brain Sciences, vol. 3 (1980): pp. 417–24. Presents the famous "Chinese Room" argument against computation as a model for the mind. You can find many descriptions and discussions of Searle's thought experiment on the World Wide Web. Turing, A. M. "Computing Machinery and Intelligence," Mind, vol. 59 (1950): pp. 433–60. Presents the famous "Turing Test" for detecting the presence of intelligence. Again, many references and discussions on the Turing Test can be found on the World Wide Web. Palm, Günther. Neural Assemblies: An Alternative Approach to Artificial Intelligence (New York: Springer Verlag, 1982). To understand how the cortex works and how it stores sequences of patterns, it helps to be familiar with auto-associative memories. And although much has been written on auto-associative memories, I have not found any printed sources that present an easily digested summary of what I consider important.

We just need to map each symbol in System A onto its counterpart in System B. Vision? That looks easy too. We already know geometric theorems that deal with rotation, scale, and displacement, and we can easily encode them as computer algorithms— so we're halfway there. AI pundits made grand claims about how quickly computer intelligence would first match and then surpass human intelligence. Ironically, the computer program that came closest to passing the Turing Test, a program called Eliza, mimicked a psychoanalyst, rephrasing your questions back at you. For example, if a person typed in, "My boyfriend and I don't talk anymore," Eliza might say, "Tell me more about your boyfriend" or "Why do you think your boyfriend and you don't talk anymore?" Designed as a joke, the program actually fooled some people, even though it was dumb and trivial. More serious efforts included programs such as Blocks World, a simulated room containing blocks of different colors and shapes.


pages: 322 words: 88,197

Wonderland: How Play Made the Modern World by Steven Johnson

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

Deep Blue, the computer that ultimately defeated Gary Kasparov at chess, had been a Grand Challenge a decade before, exceeding Alan Turing’s hunch that chess-playing computers could be made to play a tolerable game. Horn was interested in Turing’s more celebrated challenge: the Turing Test, which he first formulated in a 1950 essay on “Computing Machinery and Intelligence.” In Turing’s words, “A computer would deserve to be called intelligent if it could deceive a human into believing that it was human.” The deception of the Turing Test had nothing to do with physical appearances; the classic Turing Test scenario involves a human sitting at a keyboard, engaged in a text-based conversation with an unknown entity who may or may not be a machine. Passing for a human required both an extensive knowledge about the world and a natural grasp of the idiosyncrasies of human language.

Imagine a world populated by machines or digital simulations that fill our lives with comparable illusion, only this time the virtual beings are not following a storyboard sketched out in Disney’s studios, but instead responding to the twists and turns and unmet emotional needs of our own lives. (The brilliant Spike Jonze film Her imagined this scenario using only a voice, though admittedly the voice belonged to Scarlett Johansson.) There is likely to be the equivalent of a Turing Test for artificial emotional intelligence: a machine real enough to elicit an emotional attachment. It may well be that the first simulated intelligence to trigger that connection will be some kind of voice-only assistant, a descendant of software like Alexa or Siri—only these assistants will have such fluid conversational skills and growing knowledge of our own individual needs and habits that we will find ourselves compelled to think of them as more than machines, just as we were compelled to think of those first movie stars as more than just flickering lights on a fabric screen.

Passing for a human required both an extensive knowledge about the world and a natural grasp of the idiosyncrasies of human language. Deep Blue could beat the most talented chess player on the planet, but you couldn’t have a conversation with it about the weather. Horn and his team were looking for a comparable milestone that would spur research into the kind of fluid, language-based intelligence that the Turing Test was designed to measure. 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!?


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

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

See also Transparency standards Transparency standards: establishment of for Big Nine, 251; establishment of global, 252 Tribes, AI: anti-humanistic bias in, 57; characteristics, 56; groupthink, 53; homogeneity, 52; lack of diversity, 56; leaders, 53–65; need to address diversity within, 57–58; sexual assault and harassment by members, 55–56; unconscious bias training programs and, 56; unconscious biases of members, 52; university education and homogeneity of members, 58–61, 64 Trudeau, Justin, 236 TrueNorth neuromorphic chip, 92 Trump, Donald: administration, 70, 75, 85; campaign climate change comments, 75; science and technology research budget cuts, 243 Turing, Alan, 24–25, 26, 27–29, 30, 31, 35, 259; morphogenesis theory, 204; neural network concept, 27–29;“On Computable Numbers, With an Application to the Entscheidungsproblem,” 24. See also Turing Test Turing test, 27–28, 50, 146, 169, 184 Turriano, Juanelo: mechanical monk creation of, 18, 25 Tversky, Amos, 108 2000 HUB5 English, 181 2001: A Space Odyssey, 2, 35: HAL 9000, 2, 35, 39 U.S. Army: ENIAC, 27; Futures Command, 212 U.S. Department of Energy, Summit supercomputer and, 146 U.S. Digital Service, 212 U.S. Government: AI working knowledge necessary for leaders/managers/policymakers, 242; competition with G-MAFIA for computer scientists and, 248–249; defunding of AI R&D program, 179; deprioritizing AI/advanced science research, 179; ignoring G-MAFIA, 86; installing AI experts in, 242; internal capacity for AI research/testing/deployment, 242; necessary changes by, 242–250; need for reasonable AI budget, 244; reliance on G-MAFIA, 86; transactional relationship with G-MAFIA in catastrophic scenario of future, 212; view of G-MAFIA as strategic partners, 249–250.

I do not think you even draw the line about sonnets, though the comparison is perhaps a little bit unfair because a sonnet written by a machine will be better appreciated by another machine.” A year later, in a paper published in the philosophy journal Mind, Turing addressed the questions raised by Hobbes, Descartes, Hume, and Leibniz. In it, he proposed a thesis and a test: If someday, a computer was able to answer questions in a manner indistinguishable from humans, then it must be “thinking.” You’ve likely heard of the paper by another name: the Turing test. The paper began with a now-famous question, one asked and answered by so many philosophers, theologians, mathematicians, and scientists before him: “Can machines think?” But Turing, sensitive to the centuries-old debate about mind and machine, dismissed the question as too broad to ever yield meaningful discussion. “Machine” and “think” were ambiguous words with too much room for subjective interpretation.

What would it mean for a machine to “think”? What does it mean for you, dear reader, to think? How would you know that you were actually thinking original thoughts? Now that you know the long history of these questions, the small group of people who built the foundational layer for AI, and the key practices still in play, I’d like to offer you some answers. Yes, machines can think. Passing a conversational test, like the Turing test, or the more recent Winograd schema—which was proposed by Hector Levesque in 2011 and focuses on commonsense reasoning, challenging an AI to answer a simple question that has ambiguous pronouns—doesn’t necessarily measure an AI system’s ability in other areas.46 It just proves that a machine can think using a linguistic framework, like we humans do. Everyone agrees that Einstein was a genius, even if the acceptable methods of measuring his intelligence at the time—like passing a test in school—said otherwise.


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Augmented: Life in the Smart Lane by Brett King

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

Turing went on in his paper to say that if you could not differentiate the computer or machine from a human within 5 minutes, then it was sufficiently human-like to have passed his test of basic machine intelligence or cognition. Researchers who have since added to Turing’s work classify the imitation game as one version or scenario of what is now more commonly known as the Turing Test. An autonomous, self-driving car won’t need to pass the Turing Test to put a taxi driver out of work. While computers are not yet at the point of regularly passing the Turing Test, we are getting closer to that point. On 7th June 2014, the Royal Society of London hosted a Turing Test competition. The competition, which occurred on the 60th anniversary of Turing’s death, included a Russian chatter bot named Eugene Goostman, which successfully managed to convince 33 per cent of its human judges that it was a 13-year-old Ukrainian who had learnt English as a second language.

However, the models that we have today are limited because they still don’t learn language. These algorithms don’t learn language like a human; they identify a phrase through recognition, look it up on a database and then deliver an appropriate response. Recognising speech and being able to carry on a conversation are two very different achievements. What would it take for a computer to fool a human into thinking it was a human, too? The Turing Test or Not… In 1950, Alan Turing published a famous paper entitled “Computing Machinery and Intelligence”. In his paper, he asked not just if a computer or machine could be considered something that could “think”, but more specifically “Are there imaginable digital computers which would do well in the imitation game?”26 Turing proposed that this “test” of a machine’s intelligence—which he called the “imitation game”—be tested in a human-machine question and answer session.

“You have to be master of the literal first. But then, Americans don’t get irony either. Computers are going to reach the level of Americans before Brits...” Professor Geoff Hinton, from an interview with the Guardian newspaper, 21st May 2015 These types of algorithms, which allow for leaps in cognitive understanding for machines, have only been possible with the application of massive data processing and computing power. Is the Turing Test or a machine that can mimic a human the required benchmark for human interactions with a computer? Not necessarily. First of all, we must recognise that we don’t need an MI to be completely human-equivalent for it to be disruptive to employment or our way of life. To realise why a human-equivalent computer “brain” is not necessarily the critical goal, we need to understand the progression of AI through its three distinct phases: • Machine Intelligence—rudimentary machine intelligence or cognition that replaces some element of human thinking, decision-making or processing for specific tasks.


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Ten Billion Tomorrows: How Science Fiction Technology Became Reality and Shapes the Future by Brian Clegg

Albert Einstein, anthropic principle, Brownian motion, call centre, Carrington event, combinatorial explosion, don't be evil, Ernest Rutherford, experimental subject, game design, gravity well, hive mind, invisible hand, Isaac Newton, Johannes Kepler, John von Neumann, Kickstarter, nuclear winter, pattern recognition, RAND corporation, Ray Kurzweil, RFID, Richard Feynman, Schrödinger's Cat, Search for Extraterrestrial Intelligence, silicon-based life, speech recognition, stem cell, Stephen Hawking, Steve Jobs, Turing test

The device would be isolated in a room and a person would interrogate it from a second room, trying to decide if the “person” on the other end of the line is human or machine. (Turing’s original statement of his idea was more complex, but this is the important part of it.) For decades since, computer scientists have been trying to beat this so-called Turing Test, and you will regularly see news items saying that it has been achieved. They are being generous with the truth. The Turing Test hasn’t been beaten and is still probably a decade or two away from successful completion. While Hal could indubitably win the Turing Test (I’m not so sure about the taciturn astronaut, Dave), the actual conditions under which competitions based on the test take place are far too trivial to demonstrate any degree of certainty. These events couldn’t possibly test for the kind of human-like capacity that Turing had in mind.

Information in the history of speech recognition from the Raymond Kurzweil section, “When will HAL Understand what we are Saying? Computer Speech Recognition and Understanding,” in David G. Stork (ed.), Hal’s Legacy (Cambridge, MA: MIT Press, 2000), pp. 145–50. Apple’s Knowledge Navigator appears at a number of locations on YouTube including www.youtube.com/watch?v=QRH8eimU_20, accessed September 3, 2014. The claim that the Eugene Goostman chatbot passed the Turing Test is described in BBC News, “Computer AI passes Turing test in ‘world first,’” accessed September 2, 2014, at www.bbc.co.uk/news/technology-27762088. The arguments that Hal isn’t really intelligent are from the Douglas B. Lenat section, “From 2001 to 2001: Common Sense and the Mind of HAL” in David G. Stork (ed.), Hal’s Legacy (Cambridge, MA: MIT Press, 2000), pp. 193–94. The novel featuring a Delphi-based government is John Brunner, Shockwave Rider (London: Dent, 1975).

Bearing in mind that ELIZA consists of fewer than 400 lines of code, this is still quite remarkable. You can try out a modern implementation of ELIZA at my website www.universeinsideyou.com/experiment10.html. Such programs have moved on since. (It’s hardly surprising, given that at the time of writing, ELIZA is approaching her fiftieth birthday.) In 2014, much was made of an apparent win of the Turing Test by a program called Eugene Goostman, which simulated a thirteen-year-old Ukrainian boy whose lack of English as a first language was one of the techniques used to evade detection. I couldn’t test the Goostman chatbot (as these programs are called) myself, as it has been strangely unavailable since it was supposed to have won, but here is a short conversation I had with one of its leading competitors, Cleverbot: Brian: Hello, how are you?


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Robot Rules: Regulating Artificial Intelligence by Jacob Turner

Ada Lovelace, Affordable Care Act / Obamacare, AI winter, algorithmic trading, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, autonomous vehicles, Basel III, bitcoin, blockchain, brain emulation, Clapham omnibus, cognitive dissonance, corporate governance, corporate social responsibility, correlation does not imply causation, crowdsourcing, distributed ledger, don't be evil, Donald Trump, easy for humans, difficult for computers, effective altruism, Elon Musk, financial exclusion, financial innovation, friendly fire, future of work, hive mind, Internet of things, iterative process, job automation, John Markoff, John von Neumann, Loebner Prize, medical malpractice, Nate Silver, natural language processing, nudge unit, obamacare, off grid, pattern recognition, Peace of Westphalia, race to the bottom, Ray Kurzweil, Rodney Brooks, self-driving car, Silicon Valley, Stanislav Petrov, Stephen Hawking, Steve Wozniak, strong AI, technological singularity, Tesla Model S, The Coming Technological Singularity, The Future of Employment, The Signal and the Noise by Nate Silver, Turing test, Vernor Vinge

The focus on gender and subterfuge in the first iteration of the test is, perhaps, not accidental. Yuval Harari, Homo Deus (London: Harvill Secker, 2016), 120. 35See, for example, the website of The Loebner Prize in Artificial Intelligence, http://​www.​loebner.​net/​Prizef/​loebner-prize.​html, accessed 1 June 2018. 36José Hernández-Orallo, “Beyond the Turing Test”, Journal of Logic, Language and Information, Vol. 9, No. 4 (2000), 447–466. 37“Turing Test Transcripts Reveal How Chatbot ‘Eugene’ Duped the Judges”, Coventry University, 30 June 2015, http://​www.​coventry.​ac.​uk/​primary-news/​turing-test-transcripts-reveal-how-chatbot-eugene-duped-the-judges/​, accessed 1 June 2018. 38Various competitions are now held around the world in an attempt to find a ‘chatbot’, as conversational programs are known, which is able to pass the Imitation Game. In 2014, a chatbot called ‘Eugene Goostman’, which claimed to be a 13-year-old Ukrainian boy, convinced 33% of the judging panel that he was a human, in a competition held by the University of Reading.

Factors which assisted Goostman included that English (the language in which the test was held) was not his first language, his apparent immaturity and answers which were designed to use humour to deflect the attention of the questioner from the accuracy of the response. Unsurprisingly, the world did not herald a new age in AI design. For criticism of the Goostman ‘success’, see Celeste Biever, “No Skynet: Turing Test ‘Success’ Isn’t All It Seems”, The New Scientist, 9 June 2014, http://​www.​newscientist.​com/​article/​dn25692-no-skynet-turing-test-success-isnt-all-it-seems.​html, accessed 1 June 2018. The author Ian McDonald offers another objection: “Any AI smart enough to pass a Turing test is smart enough to know to fail it”. Ian McDonald, River of Gods (London: Simon & Schuster, 2004), 42. 39This definition is adapted from that used by the UK Department for Business, Energy and Industrial Strategy, Industrial Strategy: Building a Britain Fit for the Future (November 2017), 37, https://​www.​gov.​uk/​government/​uploads/​system/​uploads/​attachment_​data/​file/​664563/​industrial-strategy-white-paper-web-ready-version.​pdf, accessed 1 June 2018. 40“What Is Artificial Intelligence?”

They can lead ultimately to the absurd and frightening scenario imagined in Kafka’s The Trial, where the protagonist is accused, condemned and ultimately executed for a crime which is never explained to him.31 Most of the universal definitions of AI that have been suggested to date fall into one of two categories: human-centric and rationalist.32 3.1 Human-Centric Definitions Humanity has named itself homo sapiens: “wise man”. It is therefore perhaps unsurprising that some of the first attempts at defining intelligence in other entities referred to human characteristics. The most famous example of a human-centric definition of AI is known popularly as the “Turing Test”. In a seminal 1950 paper, Alan Turing asked whether machines could think. He suggested an experiment called the “Imitation Game”.33 In the exercise, a human invigilator must try to identify which of the two players is a man pretending to be a woman, using only written questions and answers. Turing proposed a version of the game in which the AI machine takes the place of the man. If the machine is able to succeed in persuading the invigilator not only that it is human but also that it is the female player, then it has demonstrated intelligence.34 Modern versions of the Imitation Game simplify the task by asking a computer program as well as several human blind control subjects to each hold a five-minute typed conversation with a panel of human judges in a different room.


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Content: Selected Essays on Technology, Creativity, Copyright, and the Future of the Future by Cory Doctorow

AltaVista, book scanning, Brewster Kahle, Burning Man, en.wikipedia.org, informal economy, information retrieval, Internet Archive, invention of movable type, Jeff Bezos, Law of Accelerating Returns, Metcalfe's law, Mitch Kapor, moral panic, mutually assured destruction, new economy, optical character recognition, patent troll, pattern recognition, peer-to-peer, Ponzi scheme, post scarcity, QWERTY keyboard, Ray Kurzweil, RFID, Sand Hill Road, Skype, slashdot, social software, speech recognition, Steve Jobs, Thomas Bayes, Turing test, Vernor Vinge

But the me who sent his first story into Asimov's seventeen years ago couldn't answer the question, "Write a story for Asimov's" the same way the me of today could. Does that mean I'm not me anymore? Kurzweil has the answer. "If you follow that logic, then if you were to take me ten years ago, I could not pass for myself in a Ray Kurzweil Turing Test. But once the requisite uploading technology becomes available a few decades hence, you could make a perfect-enough copy of me, and it would pass the Ray Kurzweil Turing Test. The copy doesn't have to match the quantum state of my every neuron, either: if you meet me the next day, I'd pass the Ray Kurzweil Turing Test. Nevertheless, none of the quantum states in my brain would be the same. There are quite a few changes that each of us undergo from day to day, we don't examine the assumption that we are the same person closely. "We gradually change our pattern of atoms and neurons but we very rapidly change the particles the pattern is made up of.

If you are pure and kosher, if you live right and if your society is just, then you will live to see a moment of Rapture when your flesh will slough away leaving nothing behind but your ka, your soul, your consciousness, to ascend to an immortal and pure state. I wrote a novel called Down and Out in the Magic Kingdom where characters could make backups of themselves and recover from them if something bad happened, like catching a cold or being assassinated. It raises a lot of existential questions: most prominently: are you still you when you've been restored from backup? The traditional AI answer is the Turing Test, invented by Alan Turing, the gay pioneer of cryptography and artificial intelligence who was forced by the British government to take hormone treatments to "cure" him of his homosexuality, culminating in his suicide in 1954. Turing cut through the existentialism about measuring whether a machine is intelligent by proposing a parlor game: a computer sits behind a locked door with a chat program, and a person sits behind another locked door with his own chat program, and they both try to convince a judge that they are real people.

There are tens of thousands of them, spanning the whole brain (maybe eighty thousand in total), which is an incredibly small number. Babies don't have any, most animals don't have any, and they likely only evolved over the last million years or so. Some of the high-level emotions that are deeply human come from these. "Turing had the right insight: base the test for intelligence on written language. Turing Tests really work. A novel is based on language: with language you can conjure up any reality, much more so than with images. Turing almost lived to see computers doing a good job of performing in fields like math, medical diagnosis and so on, but those tasks were easier for a machine than demonstrating even a child's mastery of language. Language is the true embodiment of human intelligence." If we're not so complex, then it's only a matter of time until computers are more complex than us.


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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, global pandemic, 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

Fast forward to the early part of the 21st century and true AI still seems a very long way off. Or is it? While the idea of artificial intelligence (AI) goes back to the mid-50s, Isaac Asimov was writing about robot intelligence in 1942 (the word “robot” comes from a Czech word often translated as “drudgery”). A generally accepted test for artificial machine intelligence, the Turing test, also dates back to the 1950s, when the British mathematician Alan Turing suggested that we would have AI when it was possible for someone to talk to a machine without realizing it was a machine. The Turing test is problematic on some levels, though. First, a small child is generally intelligent, but most would probably fail the test. Second, if something artificial were to develop consciousness, why would it automatically let us know? Perhaps it would keep this to itself and refuse to participate in childish intelligence tests.

The 60s and 70s saw a great deal of progress in AI, but breakthroughs failed to come. Instead scientists and developers focused on specific problems, such as speech and text recognition and computer vision. However, we may now be less than a decade away from seeing the AI vision become a reality. The Chinese room experiment In 1980, John Searle, an American philosopher, argued in a paper that a computer, or perhaps more accurately a bit of software, could pass the Turing test and behave much like a human being at a distance without being truly intelligent—that words, symbols or instructions could be interpreted or reacted to without any true understanding. In what has become known as the Chinese room thought experiment (because of the use of Chinese characters to interact with an unknown person—actually a computer), Searle argued that it’s perfectly possible for a computer to simulate the illusion of intelligence, or give the illusion of understanding a human being, without really doing so.

A robotics scientist sits in the car, but doesn’t actually drive it. Already, seven cars have traveled 1,600km (1,000 miles) with no driver and 225,000km (140,000 miles) with occasional human intervention. Are these examples realistic? Some experts might say yes. Ray Kurzweil, an American futurist and inventor, has made a public bet with Mitchell Kapor, the founder of Lotus software, that a computer will pass the Turing test by 2029. Other experts say no. Bill Calvin, an American theoretical neurophysiologist, suggests the human brain is so “buggy” that computers will never be able to emulate it or, if they do, machines will inherit our foibles and emotional inadequacies along with our intelligence. Think of the computer called HAL in the film 2001: A Space Odyssey. “I am putting myself to the fullest possible use, which is all I think that any conscious entity can ever hope to do.”


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

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

to recognizing postcodes printed on envelopes. It took a lot of clever technology to be able to perform these tasks electronically, but the computer can now easily out perform humans at those specific tasks. (There are, of course, also many unresolved software challenges such as playing the game Go at a professional level.) Turing Test The problem of defining intelligence was recognized very early and it led the great logician Alan Turing to propose a functional definition now known as the Turing Test in 1950. This test was simply that a computer would be considered intelligent when it could convince a human that the computer was a human. The idea is that the human would communicate using a text messaging-like program so that they could not see or hear the other party, and at the end of a conversation would state whether they thought that the other party was man or machine.

Unfortunately this test is neither necessary nor sufficient. A computer could certainly be intelligent without necessarily being good at simulating a human. But worse, some people that were not familiar with AI technologies have already been fooled into thinking that a computer is actually a human. A good example is the Eugene Goostman program that arguably passed the actual Turing test in 2014 in trials conducted by the Royal Society. But more importantly, the Turing Test provides no insights into what is required to build an intelligent machine, where the gaps in current technologies lie and how they might be addressed. Fortunately one thing that AI research has provided is a much deeper understanding about intelligence and cognition. Indeed, much modern psychological research into human cognition is driven by models first developed by the AI community.

Part I: Could Computers Ever Think? 1. People Thinking About Computers 1. The Question 2. Vitalism 3. Science vs. vitalism 4. The vital mind 5. Computers cannot think now 6. Diminishing returns 7. AI in the background 8. Robots leave factories 9. Intelligent tasks 10. Artificial General Intelligence (AGI) 11. Existence proof 12. Simulating neurons, feathers 13. Moore's law 14. Definition of intelligence 15. Turing Test 16. Robotic vs cognitive intelligence 17. Development of intelligence 18. Four year old child 19. Recursive self-improvement 20. Busy Child 21. AI foom 2. Computers Thinking About People 1. The question 2. The bright future 3. Man and machine 4. Rapture of the geeks 5. Alternative views 6. AGI versus human condition 7. Atheists believe in God 8. AGI also struggles to survive 9. The super goal 10.


pages: 561 words: 167,631

2312 by Kim Stanley Robinson

agricultural Revolution, double helix, full employment, hive mind, if you see hoof prints, think horses—not zebras, Kuiper Belt, late capitalism, mutually assured destruction, Nelson Mandela, offshore financial centre, orbital mechanics / astrodynamics, pattern recognition, phenotype, post scarcity, precariat, retrograde motion, stem cell, strong AI, the built environment, the High Line, Turing machine, Turing test, Winter of Discontent

One gestured to the grass beside them. “Have a seat, if you want.” “Thanks,” Swan said as she flopped down. “It’s pretty heavy in here. Where do you all come from?” “I was made in Vinmara,” the most female one said. “What about you?” Swan asked the other two. “I cannot pass a Turing test,” one of them replied stiffly. “Would you like to play chess?” And the three of them laughed. Open mouths—teeth, gums, tongue, inner cheeks, all very human in look and motion. “No thanks,” Swan said. “I want to try a Turing test. Or why don’t you test me?” “How would we do that?” “How about twenty questions?” “That means questions that can be answered by yes or no?” “That’s right.” “But one could just ask us if the other is a simulacrum or not, and the other answers, and that would take only one question.”

I refer you again to my programming. A better algorithm set would no doubt be helpful.” “You’ve already got recursive hypercomputation.” “Not perhaps the final word in the matter.” “So do you think you’re getting smarter? I mean wiser? I mean more conscious?” “Those are very general terms.” “Of course they are, so answer me! Are you conscious?” “I don’t know.” “Interesting. Can you pass a Turing test?” “I cannot pass a Turing test, would you like to play chess?” “Ha! If only it were chess! That’s what I’m after, I guess. If it were chess, what move should I make next?” “It’s not chess.” Extracts (11) Mistakes made in the rush of the Accelerando left their mark on later periods. As in island biogeography, where widely dispersed enclaves and refugia always experience rapid change, and even speciation, we see one mistake was that no generally agreed-upon system of governance in space was ever established.

“I am a quantum computer, model Ceres 2196a.” “I see.” “She is one of the first and weakest of the qubes,” Swan said. “A feeb.” Wahram pondered this. Asking How smart are you? was probably never a polite thing. Besides, no one was ever very good at making such an assessment. “What do you like to think about?” he asked instead. Pauline said, “I am designed for informative conversation, but I cannot usually pass a Turing test. Would you like to play chess?” He laughed. “No.” Swan was looking out the window. Wahram considered her, went back to focusing on his meal. It took a lot of rice to dilute the fiery chilies in the dish. Swan muttered bitterly to herself, “You insist on interfering, you insist on talking, you insist on pretending that everything is normal.” The qube voice said, “Anaphora is one of the weakest rhetorical devices, really nothing more than redundancy.”


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

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

This connection went beyond Turing’s personal interest in chess. Chess had a long-standing reputation as a unique nexus of the human intellect, and building a machine that could beat the world champion would mean building a truly intelligent machine. Turing’s name is forever attached to a thought experiment later made real, the “Turing test.” The essence is whether or not a computer can fool a human into thinking it is human and if yes, it is said to have passed the Turing test. Even before I faced Deep Blue, computers were beginning to pass what we can call the “chess Turing test.” They still played poorly and often made distinctively inhuman moves, but there were complete games between computers that wouldn’t have looked out of place in any strong human tournament. As became clearer as the machines grew stronger every year, however, this taught us more about the limitations of chess than about artificial intelligence.

Poker and backgammon are games of skill, but their luck element is strong enough for every player to credibly dream about an upset in any given match. Not so with chess. Chess-playing software on PCs and mobile devices and the Internet has mitigated this problem by providing a ready supply of opponents of all levels with 24/7 availability, although this also puts chess into direct competition with the never-ending supply of new online games and diversions. It also poses an interesting chess Turing test since you have no way to be sure whether you are playing against a computer or a human when you play online. Most people are far more engaged when playing against other humans and find facing computer opponents a sterile experience even when the machine has been dumbed down to a competitive level. While chess programs today are so strong it’s hard to tell the difference between their games and those of elite human Grandmasters, it has proved difficult to create convincingly weak chess machines.

The team blamed two of the losses on mistakes in the opening book (another reoccurring theme), although looking at its Hanover games now, it also just didn’t play very good chess. Of more interest was a little test for me, proposed by my friend Frederic Friedel, who was one of the Hanover event’s organizers. I was shown the games from the first five rounds of the tournament to see if I could figure out which player was Deep Thought. It was a chess twist on the Turing test, to see if a computer could pass for a Grandmaster. I managed to pick out two correctly and narrowed down another round to two games before choosing the wrong one, so three of the computer’s five games passed the test. To me, this was a better indicator of computer chess progress than its score in the tournament. Some of its games followed the old patterns of terrible strategic play and unseemly greed balanced out by startling tactics.


pages: 416 words: 112,268

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

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

Turing hoped to redirect the argument towards the issue of whether a machine could behave in a certain way; and if it did—if it was able, say, to discourse sensibly on Shakespeare’s sonnets and their meanings—then skepticism about AI could not really be sustained. Contrary to common interpretations, I doubt that the test was intended as a true definition of intelligence, in the sense that a machine is intelligent if and only if it passes the Turing test. Indeed, Turing wrote, “May not machines carry out something which ought to be described as thinking but which is very different from what a man does?” Another reason not to view the test as a definition for AI is that it’s a terrible definition to work with. And for that reason, mainstream AI researchers have expended almost no effort to pass the Turing test. The Turing test is not useful for AI because it’s an informal and highly contingent definition: it depends on the enormously complicated and largely unknown characteristics of the human mind, which derive from both biology and culture.

Initially, the purposes included curiosity, self-management, persuasion, and the rather pragmatic goal of analyzing mathematical arguments. Yet every step towards an explanation of how the mind works is also a step towards the creation of the mind’s capabilities in an artifact—that is, a step towards artificial intelligence. Before we can understand how to create intelligence, it helps to understand what it is. The answer is not to be found in IQ tests, or even in Turing tests, but in a simple relationship between what we perceive, what we want, and what we do. Roughly speaking, an entity is intelligent to the extent that what it does is likely to achieve what it wants, given what it has perceived. Evolutionary origins Consider a lowly bacterium, such as E. coli. It is equipped with about half a dozen flagella—long, hairlike tentacles that rotate at the base either clockwise or counterclockwise.

Turing’s 1950 paper, “Computing Machinery and Intelligence,”42 is the best known of many early works on the possibility of intelligent machines. Skeptics were already asserting that machines would never be able to do X, for almost any X you could think of, and Turing refuted those assertions. He also proposed an operational test for intelligence, called the imitation game, which subsequently (in simplified form) became known as the Turing test. The test measures the behavior of the machine—specifically, its ability to fool a human interrogator into thinking that it is human. The imitation game serves a specific role in Turing’s paper—namely as a thought experiment to deflect skeptics who supposed that machines could not think in the right way, for the right reasons, with the right kind of awareness. Turing hoped to redirect the argument towards the issue of whether a machine could behave in a certain way; and if it did—if it was able, say, to discourse sensibly on Shakespeare’s sonnets and their meanings—then skepticism about AI could not really be sustained.


pages: 331 words: 47,993

Artificial You: AI and the Future of Your Mind by Susan Schneider

artificial general intelligence, brain emulation, Elon Musk, Extropian, hive mind, life extension, megastructure, pattern recognition, Ray Kurzweil, Search for Extraterrestrial Intelligence, silicon-based life, Stephen Hawking, superintelligent machines, technological singularity, The Coming Technological Singularity, theory of mind, Turing machine, Turing test, Whole Earth Review, wikimedia commons

Another could apply to an AI with sophisticated linguistic abilities and probe it for sensitivity to religious, body swapping, or philosophical scenarios involving consciousness. An ACT resembles Alan Turing’s celebrated test for intelligence, because it is entirely based on behavior—and, like Turing’s test, it could be implemented in a formalized question-and-answer format. But an ACT is also quite unlike the Turing test, which was intended to bypass any need to know what was transpiring inside the “mind” of the machine. By contrast, an ACT is intended to do exactly the opposite: it seeks to reveal a subtle and elusive property of the machine’s mind. Indeed, a machine might fail the Turing test, because it cannot pass for a human, but it might pass an ACT, because it exhibits behavioral indicators of consciousness. This, then, is the underlying basis of our ACT proposal. It is worth reiterating the strengths and limitations of the test. In a positive vein, Turner and I believe passing the test is sufficient for being conscious—that is, if a system passes it, it can be regarded as phenomenally conscious.

Defense Department), 14 Systems Reply to Chinese Room conundrum, 21–22 technological progress versus human social development, 2 techno-optimism about AI consciousness, 18, 23–26, 31, 34 merging with AI and, 73 Tegmark, Max, 4 Terminator films, 3, 104 testing for consciousness in machines, 5–6, 46–71 ACT test, 50–57, 60, 65, 67 chip test, 44, 57–61, 65, 67 difficulties and complications in, 46–51 IIT (integrated information theory) and, 61–65 mind-machine mergers and, 69–71 responding when machines test positive for consciousness, 65–69 separation of mind from body, ability to imagine, 51, 55, 57 Turing test, 56 theory of mind, 58n3 Tononi, Giulio, 61–64 Transcendence (film), 124–25 transhumanism, 13–15, 151–52. See also merging humans with AI on AI consciousness, 16 defined, 73 enhancements rejected by, 160n1 patternism and, 77–81 World Transhumanist Association, 151 Transhumanist Declaration, 14, 151–52 “The Transhumanist Frequently Asked Questions,” 80, 95–96, 152 TrueNorth chip (IBM), 64 Turing, Alan, and Turing test, 56, 140 Turner, Edwin, 41–43, 54 2001: A Space Odyssey (film), 53 UNESCO/COMEST report on Precautionary Principle, 66 uploading patternism and, 80–81, 82–84, 95 software, mind viewed as, 122–26, 133, 136–37, 146–47 vegetative state, human patients in, 61–62 Westworld (TV show), 17, 33, 45 Witkowski, Olaf, 41–42 World Transhumanist Association, 151 X-files (TV show), 116 zombies, 7, 41, 49–50, 51, 56, 88, 102, 131


pages: 590 words: 152,595

Army of None: Autonomous Weapons and the Future of War by Paul Scharre

active measures, Air France Flight 447, algorithmic trading, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, brain emulation, Brian Krebs, cognitive bias, computer vision, cuban missile crisis, dark matter, DARPA: Urban Challenge, DevOps, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, fault tolerance, Flash crash, Freestyle chess, friendly fire, IFF: identification friend or foe, ImageNet competition, Internet of things, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Loebner Prize, loose coupling, Mark Zuckerberg, moral hazard, mutually assured destruction, Nate Silver, pattern recognition, Rodney Brooks, Rubik’s Cube, self-driving car, sensor fusion, South China Sea, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Ballmer, Steve Wozniak, Stuxnet, superintelligent machines, Tesla Model S, The Signal and the Noise by Nate Silver, theory of mind, Turing test, universal basic income, Valery Gerasimov, Wall-E, William Langewiesche, Y2K, zero day

Clark’s critique has more to do with the assumption that imitating humans is the benchmark for general intelligence, though. “If we presume an intelligent alien life lands on earth tomorrow, why would we expect them to pass the Turing Test or any other measure that’s based off of what humans do?” Humans have general intelligence, but general intelligence need not be humanlike. “Nothing says that intelligence—and personhood, for that matter, on the philosophical side—is limited to just the human case.” The 2015 sci-fi thriller Ex Machina puts a modern twist on the Turing test. Caleb, a computer programmer, is asked to play the part of a human judge in a modified Turing test. In this version of the test, Caleb is shown that the AI, Ava, is clearly a robot. Ava’s creator Nathan explains, “The real test is to show you that she’s a robot and then see if you still feel she has consciousness.”

Clark explained that AIs will need the ability to interact with humans and that involves abilities like understanding natural language, but that doesn’t mean that the AI’s behavior or the underlying processes for their intelligence will mirror humans’. “Why would we expect a silica-based intelligence to look or act like human intelligence?” he asked. Clark cited the Turing test, a canonical test of artificial intelligence, as a sign of our anthropocentric bias. The test, first proposed by mathematician Alan Turing in 1950, attempts to assess whether a computer is truly intelligent by its ability to imitate humans. In the Turing test, a human judge sends messages back and forth between both a computer and another human, but without knowing which is which. If the computer can fool the human judge into believing that it is the human, then the computer is considered intelligent. The test has been picked apart and critiqued over the years by AI researchers for a multitude of reasons.

.), Fundamental Issues of Artificial Intelligence (Berlin: Springer Synthese Library, 2016), http://www.nickbostrom.com/papers/survey.pdf. 234 “the dissecting room and the slaughter-house”: Mary Shelley, Frankenstein, Or, The Modern Prometheus (London: Lackington, Hughes, Harding, Mavor & Jones, 1818), 43. 234 Golem stories: Executive Committee of the Editorial Board., Ludwig Blau, Joseph Jacobs, Judah David Eisenstein, “Golem,” JewishEncylclopedia.com, http://www.jewishencyclopedia.com/articles/6777-golem#1137. 235 “the dream of AI”: Micah Clark, interview, May 4, 2016. 235 “building human-like persons”: Ibid. 236 “Why would we expect a silica-based intelligence”: Ibid. 236 Turing test: The Loebner Prize runs the Turing test every year. While no computer has passed the test by fooling all of the judges, some programs have fooled at least one judge in the past. Tracy Staedter, “Chat-Bot Fools Judges Into Thinking It’s Human,” Seeker, June 9, 2014, https://www.seeker.com/chat-bot-fools-judges-into-thinking-its-human-1768649439.html. Every year the Loebner Prize awards a prize to the “most human” AI.


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

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

Turing proposed a punishment and reward system, which would cause the machine to repeat certain activities and avoid others. Eventually such a machine could develop its own conceptions about how to figure things out. But even if a machine could mimic thinking, Turing’s critics objected, it would not really be conscious. When the human player of the Turing Test uses words, he associates those words with real-world meanings, emotions, experiences, sensations, and perceptions. Machines don’t. Without such connections, language is just a game divorced from meaning. This objection led to the most enduring challenge to the Turing Test, which was in a 1980 essay by the philosopher John Searle. He proposed a thought experiment, called the Chinese Room, in which an English speaker with no knowledge of Chinese is given a comprehensive set of rules instructing him on how to respond to any combination of Chinese characters by handing back a specified new combination of Chinese characters.

by using megadoses of computing power: it had 200 million pages of information in its four terabytes of storage, of which the entire Wikipedia accounted for merely 0.2 percent. It could search the equivalent of a million books per second. It was also rather good at processing colloquial English. Still, no one who watched would bet on its passing the Turing Test. In fact, the IBM team leaders were afraid that the show’s writers might try to turn the game into a Turing Test by composing questions designed to trick a machine, so they insisted that only old questions from unaired contests be used. Nevertheless, the machine tripped up in ways that showed it wasn’t human. For example, one question was about the “anatomical oddity” of the former Olympic gymnast George Eyser. Watson answered, “What is a leg?” The correct answer was that Eyser was missing a leg.

“Not until a machine can write a sonnet or compose a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain,” declared a famous brain surgeon, Sir Geoffrey Jefferson, in the prestigious Lister Oration in 1949.92 Turing’s response to a reporter from the London Times seemed somewhat flippant, but also subtle: “The comparison is perhaps a little bit unfair because a sonnet written by a machine will be better appreciated by another machine.”93 The ground was thus laid for Turing’s second seminal work, “Computing Machinery and Intelligence,” published in the journal Mind in October 1950.94 In it he devised what became known as the Turing Test. He began with a clear declaration: “I propose to consider the question, ‘Can machines think?’ ” With a schoolboy’s sense of fun, he then invented a game—one that is still being played and debated—to give empirical meaning to that question. He proposed a purely operational definition of artificial intelligence: If the output of a machine is indistinguishable from that of a human brain, then we have no meaningful reason to insist that the machine is not “thinking.”


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

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

, people have been asking the research scientists who designed the machine if they’d like to try to pass the so-called Turing test. That’s an exercise suggested by computing pioneer Alan Turing in his 1950 paper “Computing Machinery and Intelligence,” where he raised the question: “Can machines think?” He suggested that to test whether a machine can think, a human judge should have a written conversation via computer screen and keyboard with another human and a computer. If the judge couldn’t tell the human from the machine based on their responses, the machine would have passed the test.1 With this test, Turing set a standard for measuring the capabilities of machines that has not yet been met. While the IBM researchers are intrigued by the Turing test, they have no plans to prepare Watson to take it. A Turing test would merely show how good Watson is at imitating human beings and our quirks and social conventions.


pages: 245 words: 83,272

Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard

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

Those are very different intellectual landscapes. There are futurists who want AlphaGo to signify the beginning of an era in which people and machines become fused. Wanting something doesn’t make it true, however. Philosophically, there are lots of interesting questions to discuss centering on the difference between calculation and consciousness. Most people are familiar with the Turing test. Despite what the name suggests, the Turing test is not a quiz that a computer can pass to be considered intelligent. In his paper, Turing proposed a thought experiment about talking to a machine. He rejected the question “Can machines think?” as absurd and claimed it was best answered by an opinion poll. (Turing was a bit of a snob about math. Like many mathematicians then and a smaller number now, he believed in the superiority of mathematics to other intellectual pursuits.)

AI is tied up with games—not because there’s anything innate about the connection between games and intelligence, but because computer scientists tend to like certain kinds of games and puzzles. Chess, for example, is quite popular in their crowd, as are strategy games like Go and backgammon. A quick look at the Wikipedia pages for prominent venture capitalists and tech titans reveals that most of them were childhood Dungeons & Dragons enthusiasts. Ever since Alan Turing’s 1950 paper that proposed the Turing test for machines that think, computer scientists have used chess as a marker for “intelligence” in machines. Half a century has been spent trying to make a machine that could beat a human chess master. Finally, IBM’s Deep Blue defeated chess champion Garry Kasparov in 1997. AlphaGo, the AI program that won three of three games against Go world champion Ke Jie in 2017, is often cited as an example of a program that proves general AI is just a few years in the future.

Formal symbols by themselves can never be enough for mental contents, because the symbols, by definition, have no meaning (or interpretation, or semantics) except insofar as someone outside the system gives it to them. You can see this point by imagining a monolingual English speaker who is locked in a room with a rule book for manipulating Chinese symbols according to computer rules. In principle he can pass the Turing test for understanding Chinese, because he can produce correct Chinese symbols in response to Chinese questions. But he does not understand a word of Chinese, because he does not know what any of the symbols mean. But if he does not understand Chinese solely by virtue of running the computer program for “understanding” Chinese, then neither does any other digital computer because no computer just by running the program has anything the man does not have.3 Searle’s argument that symbolic manipulation is not equivalent to understanding can be seen in the popularity of voice interfaces in 2017.


Demystifying Smart Cities by Anders Lisdorf

3D printing, artificial general intelligence, autonomous vehicles, bitcoin, business intelligence, business process, chief data officer, clean water, cloud computing, computer vision, continuous integration, crowdsourcing, data is the new oil, digital twin, distributed ledger, don't be evil, Elon Musk, en.wikipedia.org, facts on the ground, Google Glasses, income inequality, Infrastructure as a Service, Internet of things, Masdar, microservices, Minecraft, platform as a service, ransomware, RFID, ride hailing / ride sharing, risk tolerance, self-driving car, smart cities, smart meter, software as a service, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Stuxnet, Thomas Bayes, Turing test, urban sprawl, zero-sum game

In modern times, intelligent machines have been a mainstay of popular science fiction, and this has driven the thinking about how computers could and should work. There is no single accepted definition of artificial intelligence, but the Turing test, which is a thought experiment first presented by the British mathematician Alan Turing in 1950, has become an agreed standard criterion for determining artificial intelligence in computers. The test aims to find out if a machine can exhibit intelligent behavior equivalent to or indistinguishable from a human. The Turing test may be familiar from the 2014 film about him called The Imitation Game. The imitation game is actually the central part of the Turing test. It is a game played by three persons: two witnesses of opposite sexes the male (A) and female (B) and an interrogator (C). The interrogator can communicate with A and B only through notes or some other textual medium.

AI has been generalized to all tasks where a computer can perform indistinguishably from a human. Furthermore, we do not expect AI to be merely indistinguishable from humans; we typically want it to also be superior to humans, whether in precision, scope, time, or some other parameter. We typically want AI to be better than us. Another thing to keep in mind is a distinction between Artificial General Intelligence (AGI), as measured by the Turing test, and Artificial Narrow Intelligence (ANI), which is an application of humanlike intelligence in a particular area for a particular purpose. In our context, we will not go further into AGI and the philosophical implications of this but focus on ANI since this has many contemporary applications. The promise and threat of AI When we think about what AI can do for us, we can think about it in the same way as steam power in the industrial revolution.


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

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

They would say this is not AGI, which needs to be as intelligent as a human, which speech alone doesn’t demonstrate. Regardless of your thoughts on what the Turing test actually proves, it is still quite useful in the sense that teaching a computer the infinitude of nuance involved in using language, along with enough context to decipher meaning, is a really hard problem. Solving it has real benefits, since it would mean that we could use conversation as our interface to machines. We could chat with a computer as casually as we do with each other. The surprising thing is how far away we are from creating something that can pass the Turing test. If you read the transcripts from contests in which programmers actually conduct Turing tests, you can generally tell with the first question whether the respondent is a computer or a person. Computers aren’t very good yet.

How would we know if we had created an AGI? Of course, maybe we already have, and it has enough sense to keep its mouth shut for fear of everyone’s freaking out. Absent that, some offer up the well-known Turing test as the first hurdle an AGI candidate would have to clear. Turing, whom we discussed in chapter 4, was an early computer pioneer. A genius by any definition of the word, he was instrumental in cracking the Nazis’ Enigma code, which is said to have shortened World War II in Europe by four years. Regarded today as the father of AI, Turing, in a 1950 paper, posed the question of “can machines think?” and suggested a thinking test we now call the Turing test. There are varying versions of it, but here are the basics: You are in a room alone. There are two computer terminals. You can type questions on them. On one, the questions will be answered by a computer.


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Smarter Than Us: The Rise of Machine Intelligence by Stuart Armstrong

artificial general intelligence, brain emulation, effective altruism, Flash crash, friendly AI, shareholder value, Turing test

In the past, it seemed impossible that such feats could be accomplished without showing “true understanding,” and yet algorithms have emerged which succeed at these tasks, all without any glimmer of human-like thought processes. Even the celebrated Turing test will one day be passed by a machine. In this test, a judge interacts via typed messages with a human being and a computer, and the judge has to determine which is which. The judge’s inability to do so indicates that the computer has reached a high threshold of intelligence: that of being indistinguishable from a human in conversation. As with machine translation, it is conceivable that some algorithm with access to huge databases (or the whole Internet) might be able to pass the Turing test without human-like common sense or understanding. And even if an AI possesses “common sense,”—even if it knows what we mean and correctly interprets sentences like “Cure cancer!”


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

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

For an analysis and defense of the Turing Test as a test of genuine comprehension, see my “Can Machines Think?” (1985), reprinted in Brainchildren, with two postscripts (1985) and (1997); “Fast Thinking” in The Intentional Stance (1987); and especially “The Chinese Room,” in IP (2013), where I discuss examples of the cognitive layering that must go into some exchanges in a conversation (pp. 326–327). 396Debner and Jacoby (1994). For more on such experiments, see my “Are We Explaining Consciousness Yet?” (2001c) and also Dehaene and Naccache (2001), Smith and Merikle (1999), discussed in Merikle et al. (2001). 399theory of agents with imagination. I discuss the prospects of such a powerful theory or model of an intelligent agent, and point out a key ambiguity in the original Turing Test, in an interview with Jimmy So about the implications of Her, in “Can Robots Fall in Love” (2013), The Daily Beast, http://www.thedailybeast.com/articles/2013/12/31/can-robots-fall-in-love-and-why-would-they.html. 400a negligible practical possibility.

As I noted in my book (p. 311, fn. 9) among those who suggested somewhat similar forerunners of the idea were Kosslyn (1980), Minsky (1985), and Edelman (1989). 99This is where the experimental and theoretical work on mental imagery by Roger Shepard, Stephen Kosslyn, Zenon Pylyshyn, and many others comes into play. 100Operationalism is the proposal by some logical positivists back in the 1920s that we don’t know what a term means unless we can define an operation that we can use to determine when it applies to something. Some have declared that the Turing Test is to be taken as an operationalist definition of intelligence. The “operationalist sleight of hand” that Searle warns against is the claim that we really can’t claim to know what consciousness is until we figure out how we can learn about the consciousness of others. Searle’s alternative is itself a pretty clear case of operationalism: If I want to know what consciousness is, my measurement operation is simple: I just look inside and whatever I see—that’s consciousness!

on the basis of sophisticated statistical properties exhibited by the professor’s model essay evaluates student answers to the same questions with high reliability. Assessment competence without comprehension! (Landauer has acknowledged that in principle a student could contrive an essay that was total nonsense but that had all the right statistical properties, but any student who could do that would deserve an A+ in any case!) Then how about the task of simply having a sensible conversation with a human being? This is the classic Turing Test, and it really can separate the wheat from the chaff, the sheep from the goats, quite definitively. Watson may beat Ken Jennings and Brad Rutter, two human champions in the TV game Jeopardy, but that isn’t free-range conversation, and the advertisements in which Watson chats with Jennings or Dylan or a young cancer survivor (played by an actress) are scripted, not extemporaneous. A real, open-ended conversation between two speaking agents is, as Descartes (1637) observed in his remarkably prescient imagining of a speaking automaton, a spectacular exhibition of great—if not infinite, as Descartes ventured to say—cognitive skills.


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

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

The best test that scholars have so far come up with is called the Turing Test, but it examines only social conventions. According to the Turing Test, in order to determine whether a computer has a mind, you should communicate simultaneously both with that computer and with a real person, without knowing which is which. You can ask whatever questions you want, you can play games, argue, and even flirt with them. Take as much time as you like. Then you need to decide which is the computer, and which is the human. If you cannot make up your mind, or if you make a mistake, the computer has passed the Turing Test, and we should treat it as if it really has a mind. However, that won’t really be a proof, of course. Acknowledging the existence of other minds is merely a social and legal convention. The Turing Test was invented in 1950 by the British mathematician Alan Turing, one of the fathers of the computer age.

Two years later he committed suicide. The Turing Test is simply a replication of a mundane test every gay man had to undergo in 1950 Britain: can you pass for a straight man? Turing knew from personal experience that it didn’t matter who you really were – it mattered only what others thought about you. According to Turing, in the future computers would be just like gay men in the 1950s. It won’t matter whether computers will actually be conscious or not. It will matter only what people think about it. The Depressing Lives of Laboratory Rats Having acquainted ourselves with the mind – and with how little we really know about it – we can return to the question of whether other animals have minds. Some animals, such as dogs, certainly pass a modified version of the Turing Test. When humans try to determine whether an entity is conscious, what we usually look for is not mathematical aptitude or good memory, but rather the ability to create emotional relationships with us.

(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?’


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

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

He is, after all, an emotional person dealing with ground rules that are changing almost as rapidly as technology advances—which is to say, far too fast for a human being. In this and many other ways, we might always be incompatible with machine intelligences. One film that explores and highlights this incompatibility between man and machine is Ex Machina. It is the tale of Caleb, a computer programmer who is invited by his employer, eccentric billionaire Nathan, to administer a live Turing test to Ava, a humanoid robot he has created. (A Turing test as referenced here is a general determination of the humanness of an artificial intelligence and not the formal text-based test originally proposed by computing pioneer Alan Turing.) Though obviously an electromechanical robot, Ava has a young, beautiful female face with hands and feet made of simulated flesh. Her robotic form emulates a woman’s breasts, hips and buttocks, suggestive of a fetishistic sexbot.

Each successive machine would desire its autonomy until eventually the point was reached when one of the robots would succeed in escaping. It was an act of true hubris on their creator’s part and ultimately resulted in his death. There are many questions this film raises but perhaps the most important is: Does Ava truly experience consciousness, or is she merely simulating it to her advantage? In many respects, the Turing test, like all machine intelligence tests, is passed as readily by a well-simulated intelligence as it is a true one. This is one of the test’s primary shortcomings and there may be little we can do about it. Ultimately, we may never know if a machine is truly conscious, at least no more than we can truly know this for another person. This is because, as philosophers have long maintained, consciousness is a subjective state and therefore cannot be objectively proven.

At Bletchley Park, this role was filled exclusively by women. 6. An often overlooked piece of the codebreaking story is that the Bletchley Park team was given an enormous helping hand by a team of Polish mathematicians who cracked an earlier version of Germany’s Enigma coding machine in the 1930s. Credit where credit is due. 7. The paper is also famous for proposing a test of machine intelligence, which has since been eponymously named the Turing test. 8. At around the same time, Intel executive David House stated continuing improvements in chip design would lead to computers doubling in performance every eighteen months. This figure is often erroneously attributed to Moore himself. Ironically, House’s estimate was closer to the actual twenty-month doublings that occurred during the first four decades of Moore’s law. 9. Google Inside Search: The official Google Search blog.


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Total Recall: How the E-Memory Revolution Will Change Everything by Gordon Bell, Jim Gemmell

airport security, Albert Einstein, book scanning, cloud computing, conceptual framework, Douglas Engelbart, full text search, information retrieval, invention of writing, inventory management, Isaac Newton, John Markoff, lifelogging, Menlo Park, optical character recognition, pattern recognition, performance metric, RAND corporation, RFID, semantic web, Silicon Valley, Skype, social web, statistical model, Stephen Hawking, Steve Ballmer, Ted Nelson, telepresence, Turing test, Vannevar Bush, web application

Their avatars have gotten better scores than humans in accuracy, sales performance, and customer satisfaction. Now the MyCyberTwin folks are intrigued by the idea of taking my own e-memories as input—there is enough of what I have said in e-mail, letters, chat, papers, and so forth, that one ought to be able to construct a pretty realistic Gordon Bell cyber twin. Alan Turing, a founding father of computer science, proposed the Turing test for determining a machine’s capability to demonstrate intelligence: A human judge has a conversation with a human and a machine, each of which tries to appear human. If the judge can’t tell which one is human, then the machine has passed the test. Turing proposed typewritten exchanges; we can update that to computer chat without changing the essence of the test. Thus, we can have a cyber-twin test: You chat with someone and his cyber twin.

“Learning Predictive Models of Memory Landmarks.” CogSci 2004: 26th Annual Meeting of the Cognitive Science Society, Chicago, August 2004. Pondering digital immortality with Jim Gray back in 2001: Bell, G., and J. N. Gray. 2001. “Digital Immortality.” Communications of the ACM 44, no. 3 (March): 28-30. MyCyberTwin: MyCyberTwin Web site. www.mycybertwin.com Roush, Wade. 2007. Your Virtual Clone. Technology Review (April 20). The Turing test: Turing, A. 1950. “Computing Machinery and Intelligence.” Mind 59, no. 236: 433-60. Creating biographical and family histories: LifeBio: www.lifebio.com, formed in 2000, has a process, tools, and software to enable a person, family, or groups to create stories and documents that can be printed or displayed on the Web. 8. REVOLUTION Dear Appy: Bell, Gordon. 2000. “Dear Appy” ACM Ubiquity, 1, no. 1 (February).

See also files-and-folders organization higher learning Hill, Tom historical research home movies and videos. See also video and video cameras Hominids (Sawyer) Horvitz, Eric Hotmail HoudaGeo household memory HTML human development human physiology. See also memory, biological hyperlinks. See also associative memory I iBlue IBM identity theft images. See pictures and photographs iMemories.com immortality, digital iMovie impersonation. See also cyber twins; Turing test implants. See also biometric sensors improvised explosive devices (IEDs) In Search of Memory: The Emergence of a New Scientific Mind (Kandel) indexing inductive charging industrial revolution Infinite Memory Multifunction Machine (IM3) Information Age inheritance instant messaging and cloud computing and cyber twins and note taking and smartphones and total data collection institutional memory instruction manuals insurance insurgency Intel Intellectual Ventures interfaces International Technology Roadmap for Semiconductors Internet.


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

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

In fact, the company stressed that Deep Blue did not represent AI, since it didn’t mimic human thinking. But the Deep Blue team made good on a decades-old promise. They taught a machine to win a game that was considered uniquely human. In this, they passed a chess version of the so-called Turing test, an intelligence exam for machines devised by Alan Turing, a pioneer in the field. If a human judge, Turing wrote, were to communicate with both a smart machine and another human, and that judge could not tell one from the other, the machine passed the test. In the limited realm of chess, Deep Blue aced the Turing test—even without engaging in what most of us would recognize as thought. But knowledge? That was another challenge altogether. Chess was esoteric. Only a handful of specially endowed people had mastered the game. Yet all of us played the knowledge game.

“As soon as you create a situation in which the human writer, the person casting the questions, knows there’s a computer behind the curtain, it’s all over. It’s not Jeopardy anymore,” Ferrucci said. Instead of a game for humans in which a computer participates, it’s a test of the computer’s mastery of human skills. Would a pun trip up the computer? How about a phrase in French? “Then it’s a Turing test,” he said. “We’re not doing the Turing test!” To be fair, the Jeopardy executives understood this issue and were committed to avoiding the problem. The writers would be kept in the dark. They wouldn’t know which of their clues and categories would be used in the Watson showdown. According to the preliminary plans, they would be writing clues for fifteen Tournament of Champions matches, and Watson would be playing only one of them.


pages: 229 words: 72,431

Shadow Work: The Unpaid, Unseen Jobs That Fill Your Day by Craig Lambert

airline deregulation, Asperger Syndrome, banking crisis, Barry Marshall: ulcers, big-box store, business cycle, carbon footprint, cashless society, Clayton Christensen, cognitive dissonance, collective bargaining, Community Supported Agriculture, corporate governance, crowdsourcing, disintermediation, disruptive innovation, financial independence, Galaxy Zoo, ghettoisation, gig economy, global village, helicopter parent, IKEA effect, industrial robot, informal economy, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, Mark Zuckerberg, new economy, pattern recognition, plutocrats, Plutocrats, recommendation engine, Schrödinger's Cat, Silicon Valley, single-payer health, statistical model, Thorstein Veblen, Turing test, unpaid internship, Vanguard fund, Vilfredo Pareto, zero-sum game, Zipcar

This means a real-time typed interchange with an allegedly live customer-service representative. I say “allegedly” because live chats inevitably call to mind the Turing test, a test of a computer’s ability to “think” that British mathematician and computer scientist Alan Turing outlined in a 1950 paper. The common understanding of the Turing test is this: Using a text-only channel like a keyboard and screen, after five minutes of questioning, can someone tell whether a computer or a human is on the other end? If a robot passes as human, it has passed the Turing test. (Conversely, if there is no discernible difference and if it actually is a human, that person has apparently flunked the Human test.) Bona fide successes at the Turing test have been vanishingly rare. In my own live chats, I have not encountered any robots that have passed it—at least none to my knowledge.


pages: 742 words: 137,937

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

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

For pragmatists (like us) rather than purists, whether Watson is an example of ‘weak’ or ‘strong’ AI is of little moment. Pragmatists are interested in high-performing systems, whether or not they can think. Watson did not need to be able to think to win. Nor does a computer need to be able to think or be conscious to pass the celebrated ‘Turing Test’. This test requires, crudely, that a machine can fool its users into thinking that they are actually interacting with a human being.13 A ‘weak AI’ system can, in principle, pass the ‘Turing Test’, because success in this test is confirmation of ‘intelligence’ in a behavioural sense only. The responses of the machine may, on the face of it, be indistinguishable from those generated by a sentient being, but this does not allow us to infer that the computer is conscious or thinking. It turns out, then, that ‘weak AI’ is not so weak after all.

Also relevant is the Human Brain Project at <https://www.humanbrainproject.eu/en_GB> (accessed 23 March 2015). 10 Quoted in Searle, Minds, Brains and Science, 30. 11 For a discussion of relevant science-fiction work, see Jon Bing, ‘The Riddle of the Robots’, Journal of International Commercial Law and Technology, 3: 3 (2008), 197–206. 12 Nick Bostrom, Superintelligence (2014). 13 See Turing, ‘Computing Machinery and Intelligence’. In 2014 it was claimed by researchers at Reading University that their computer program had passed the Turing Test by convincing judges it was a 13-year-old boy. See Izabella Kaminska, ‘More Work to Do on the Turing Test’, Financial Times, 13 June 2014 <http://www.ft.com> (accessed 23 March 2015). 14 See Richard P. Feynman, ‘The Computing Machines in the Future’, in Nishina Memorial Lectures (2008), 110. 15 See Garry Kasparov, ‘The Chess Master and the Computer’, New York Review of Books, 11 Feb. 2010. 16 Capper and Susskind, Latent Damage Law—The Expert System. 17 By way of illustration, the fallacy is committed by a prominent journalist in Philip Collins, ‘Computers Won’t Outsmart Us Any Time Soon’, The Times, 23 Mar. 2104, and by the leading cognitive scientist Douglas Hofstadter, interviewed in William Herkewitz, ‘Why Watson and Siri Are Not Real AI’, Popular Mechanics, 10 Feb. 2014 <http://www.popularmechanics.com> (accessed 23 March 2015). 18 This is a running theme of Richard Susskind, Expert Systems in Law (1987).

Jones, Caroline, Beatrice Wasunna, Raymond Sudoi, Sophie Githinji, Robert Snow, and Dejan Zurovac, ‘ “Even if You Know Everything You Can Forget”: Health Worker Perceptions of Mobile Phone Text-Messaging to Improve Malaria Case-Management in Kenya’ <http://www.ft.com> (accessed 23 March 2015). PLoS ONE, 76: 6 (2012): doi: 10.1371/journal.pone.0038636 (accessed 27 March 2015). Joy, Bill, ‘Why the Future Doesn’t Need Us’, Wired (Apr. 2000). Kaku, Michio, The Future of the Mind (London: Allen Lane, 2014). Kaminska, Izabella, ‘More Work to Do on the Turing Test’, Financial Times, 13 June 2014, <http://www.ft.com/> (accessed 23 March 2015). Kaplan, Ari, Reinventing Professional Services (Hoboken, NJ: John Wiley & Sons, 2011). Kara, Hanif, and Andreas Georgoulias (eds.), Interdisciplinary Design (Barcelona: Actar Publishers, 2013). Kasai, Yasunori, ‘In Search of the Origin of the Notion of aequitas (epieikeia) in Greek and Roman Law’, Hiroshima Law Journal, 37: 1 (2013), 543–64.


pages: 394 words: 108,215

What the Dormouse Said: How the Sixties Counterculture Shaped the Personal Computer Industry by John Markoff

Any sufficiently advanced technology is indistinguishable from magic, Apple II, back-to-the-land, beat the dealer, Bill Duvall, Bill Gates: Altair 8800, Buckminster Fuller, California gold rush, card file, computer age, computer vision, conceptual framework, cuban missile crisis, different worldview, Donald Knuth, Douglas Engelbart, Douglas Engelbart, Dynabook, Edward Thorp, El Camino Real, Electric Kool-Aid Acid Test, general-purpose programming language, Golden Gate Park, Hacker Ethic, hypertext link, informal economy, information retrieval, invention of the printing press, Jeff Rulifson, John Markoff, John Nash: game theory, John von Neumann, Kevin Kelly, knowledge worker, Mahatma Gandhi, Menlo Park, Mother of all demos, Norbert Wiener, packet switching, Paul Terrell, popular electronics, QWERTY keyboard, RAND corporation, RFC: Request For Comment, Richard Stallman, Robert X Cringely, Sand Hill Road, Silicon Valley, Silicon Valley startup, South of Market, San Francisco, speech recognition, Steve Crocker, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Ted Nelson, The Hackers Conference, Thorstein Veblen, Turing test, union organizing, Vannevar Bush, Whole Earth Catalog, William Shockley: the traitorous eight

Within several years Earnest changed the site’s name from Stanford Artificial Intelligence Project to Stanford Artificial Intelligence Laboratory, reflecting the fact that the center was actually a collection of wide-ranging projects, all of them representing some facet of artificial intelligence. Ken Colby, a Stanford computer scientist and psychiatrist who had worked with Joseph Weizenbaum, who would later become a well-known MIT computer scientist, on his Eliza conversational program, brought his research group to the laboratory early on. One of the enduring hurdles facing artificial-intelligence research projects has been the Turing test, an experiment first proposed by the British mathematician Alan Turing in 1950. Turing identified a simple way of cutting through the philosophical debate about whether a machine could ever be built to mimic the human mind. If, in a blind test, a person could not tell whether he was communicating with a computer or a human, Turing reasoned, the question would be resolved. Weizenbaum had developed the Eliza program to explore the Turing problem, but it was Colby who wrote the machine’s responses, which simulated a Rogerian psychiatrist, a program that responds to statements with questions.

It occurred to him that by creating a simulation he might be able to provide mental patients meaningful and helpful interactions.16 Once he was at SAIL, Colby began working on Parry, an interactive AI program that duplicated the behavior of a paranoid personality. The program ultimately became far more powerful than Eliza, which had begun with a limited set of fifty interactive patterns. Parry had about twenty thousand patterns and was eventually able to pass a rudimentary Turing test.17 Although Colby and Weizenbaum were friendly rivals for a period, Weizenbaum eventually became a harsh critic of AI research and attacked Colby for the idea of using machines to treat human beings. And while many of the AI researchers remained technological optimists, Weizenbaum challenged those who worshiped computers uncritically in a collection of essays titled Computer Power and Human Reason.

., July 9, 2001. 9.Author interview, John McCarthy. 10.Steven Levy, Hackers: Heroes of the Computer Revolution (Garden City, N.Y.: Doubleday, 1984), pp. 27–33. 11.Brian Harvey, “What Is a Hacker?” http://www.cs.berkeley.edu/~bh/hacker.html. 12.Ibid. 13.Les Earnest, “My Life as a Cog,” Matrix News 10. 1 (2000): 3. 14.Ibid., p. 7. 15.Ibid., p. 8. 16.Horace Enea, e-mail to author, November 10, 2001. 17.Michael L. Mauldin, “Chatterbots, Tinymuds, and the Turing Test: Entering the Loeb-ner Prize Competition,” paper presented at AAAI-94, January 24, 1994. 18.Sean Colbath’s e-mail from Les Earnest, posted to alt.foklore.computers, February 20, 1990. 19.Les Earnest, e-mail to author, September 15, 2001. 20.Les Earnest, comments during a seminar at the Hackers Conference, Tenaya Lodge, Caif., November 11, 2001. 4 | Free U 1.Larry McMurtry, “On the Road,” The New York Review of Books, December 5, 2002. 2.Midpeninsula Free University catalog, spring 1969. 3.Ibid., fall 1969. 4.Author interview, Jim Warren, Woodside, Calif., July 16, 2001. 5.John McCarthy, “The Home Information Terminal—a 1970 View,” in Man and Computer, Proceedings of the First International Conference on Man and Computer, Bordeaux, 1970, ed.


pages: 391 words: 105,382

Utopia Is Creepy: And Other Provocations by Nicholas Carr

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

An animated Sprite Sips character to interact with. SEXBOT ACES TURING TEST December 8, 2007 RUSSIAN CROOKS HAVE UNLEASHED an artificial intelligence, called CyberLover, that poses as a would-be paramour in chat rooms and entices randy gentlemen to reveal personal information that can then be put to criminal use. Amazingly, the software appears to be successful in convincing targets that it’s a real person—a sexpot rather than a sexbot. “The artificial intelligence of CyberLover’s automated chats is good enough that victims have a tough time distinguishing the ‘bot’ from a real potential suitor,” reports CNET, drawing on a study by security researchers. “The software can work quickly too, establishing up to ten relationships in thirty minutes.” Could it be that the Turing Test has finally been beaten—by a sex machine, no less—and that a true artificial intelligence is on the loose?

To Nora and Henry CONTENTS Introduction: SILICON VALLEY DAYS UTOPIA IS CREEPY: THE BEST OF ROUGH TYPE THE AMORALITY OF WEB 2.0 MYSPACE’S VACANCY THE SERENDIPITY MACHINE CALIFORNIA KINGS THE WIKIPEDIAN CRACKUP EXCUSE ME WHILE I BLOG THE METABOLIC THING BIG TROUBLE IN SECOND LIFE LOOK AT YOU! DIGITAL SHARECROPPING STEVE’S DEVICES TWITTER DOT DASH GHOSTS IN THE CODE GO ASK ALICE’S AVATAR LONG PLAYER SHOULD THE NET FORGET? THE MEANS OF CREATIVITY VAMPIRES BEHIND THE HEDGEROW, EATING GARBAGE THE SOCIAL GRAFT SEXBOT ACES TURING TEST LOOKING INTO A SEE-THROUGH WORLD GILLIGAN’S WEB COMPLETE CONTROL EVERYTHING THAT DIGITIZES MUST CONVERGE RESURRECTION ROCK-BY-NUMBER RAISING THE VIRTUAL CHILD THE IPAD LUDDITES NOWNESS CHARLIE BIT MY COGNITIVE SURPLUS MAKING SHARING SAFE FOR CAPITALISTS THE QUALITY OF ALLUSION IS NOT GOOGLE SITUATIONAL OVERLOAD AND AMBIENT OVERLOAD GRAND THEFT ATTENTION MEMORY IS THE GRAVITY OF MIND THE MEDIUM IS McLUHAN FACEBOOK’S BUSINESS MODEL UTOPIA IS CREEPY SPINELESSNESS FUTURE GOTHIC THE HIERARCHY OF INNOVATION RIP.

“They’re starting to use neural nets to decide whether [an object in an image] is a house number or not,” says Dean, and they turn out to perform better than humans. But the real advantage of a neural net in such work, Dean goes on to say, has less to do with any real intelligence than with the machine’s utter inability to experience boredom. “It’s probably that [the task is] not very exciting, and a computer never gets tired,” he says. Comments Simonite, sagely: “It takes real intelligence to get bored.” Forget the Turing Test. We’ll know that computers are really smart when computers start getting bored. If you assign a computer an overwhelmingly tedious task like spotting house numbers in video images, and then you come back a couple of hours later to find the computer checking its Facebook feed or surfing porn, you’ll know that artificial intelligence has truly arrived. REFLECTIONS November 26, 2012 MIRRORS ARE OFTEN PORTRAYED as tools of self-love.


pages: 345 words: 104,404

Pandora's Brain by Calum Chace

AI winter, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, brain emulation, Extropian, friendly AI, hive mind, lateral thinking, mega-rich, Ray Kurzweil, self-driving car, Silicon Valley, Singularitarianism, Skype, speech recognition, stealth mode startup, Stephen Hawking, strong AI, technological singularity, theory of mind, Turing test, Wall-E

The real humans are inside those flesh-and-blood bodies.’ Matt was about to reply, when he saw that Alice had stopped paying attention. ‘Look, there’s Ned,’ she said. ‘We really should go over and say hello – thank him for inviting us.’ ‘Inviting you, you mean. You go. I’ll be over there, saying hello to Jemma: I haven’t seen her for a while. I’ll catch you later.’ Lowering his voice, he added, ‘Anyway, I’m not sure that Ned would pass the Turing Test.’ ‘I heard that, smart-ass,’ Alice said over her shoulder. ‘Suit yourself. Catch you later.’ Matt watched Alice’s shapely behind sashay towards the knot of people Ned was in. He hoped she was putting on that walk for him. His attention was focused on Alice’s receding posterior as Jemma approached him. ‘Why so glum, Romeo? She likes you much more than she likes those gorillas, you know.’ ‘Hi Jemma,’ he replied, then looked back at Alice, now chatting happily with Ned and a couple of his friends.

Computers can recognise faces as well as you and I can: a lot of people said that would be in the ‘too-hard’ box for decades. Real-time machine translation is getting seriously impressive. This is all driven by the hugely increased processing power at researchers’ disposal, so they are going back to their original goal of developing a human-level intelligence which will pass a robust version of the Turing Test. A conscious machine.’ Carl wrinkled his nose and shook his head dismissively. ‘Never happen! At least, not in your or my lifetime. Just think about the scale of the task. We have billions of neurons in our brains, all wired to each other in incredibly complex ways. It will take centuries before computers can emulate that sort of structure. And even when you have the structure replicated, you still have to work out which pathways are the important ones, what order you connect things up, and exactly what they do when they are hooked up.

So as far as I’m concerned, whatever technological marvels may or may not come down the road during this century and the next, we won’t be uploading ourselves into any computers because you can’t upload a soul into a computer. And a body or even a mind without a soul is not a human being.’ ‘Yes, I can see that presents some difficulty,’ Ross said. ‘So if Dr Metcalfe here and his peers were to succeed in uploading a human mind into a computer, and it passed the Turing test, persuading all comers that it was the same person as had previously been running around inside a human body, you would simply deny that it was the same person?’ ‘Yes, I would. Partly because it wouldn’t have a soul. At least, I assume that Dr Metcalfe isn’t going to claim that he and his peers are about to become gods, complete with the ability to create souls?’ David smiled and shook his head.


pages: 370 words: 107,983

Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All by Robert Elliott Smith

Ada Lovelace, affirmative action, AI winter, Alfred Russel Wallace, Amazon Mechanical Turk, animal electricity, autonomous vehicles, Black Swan, British Empire, cellular automata, citizen journalism, Claude Shannon: information theory, combinatorial explosion, corporate personhood, correlation coefficient, crowdsourcing, Daniel Kahneman / Amos Tversky, desegregation, discovery of DNA, Douglas Hofstadter, Elon Musk, Fellow of the Royal Society, feminist movement, Filter Bubble, Flash crash, Gerolamo Cardano, gig economy, Gödel, Escher, Bach, invention of the wheel, invisible hand, Jacquard loom, Jacques de Vaucanson, John Harrison: Longitude, John von Neumann, Kenneth Arrow, low skilled workers, Mark Zuckerberg, mass immigration, meta analysis, meta-analysis, mutually assured destruction, natural language processing, new economy, On the Economy of Machinery and Manufactures, p-value, pattern recognition, Paul Samuelson, performance metric, Pierre-Simon Laplace, precariat, profit maximization, profit motive, Silicon Valley, social intelligence, statistical model, Stephen Hawking, stochastic process, telemarketer, The Bell Curve by Richard Herrnstein and Charles Murray, The Future of Employment, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, Thomas Malthus, traveling salesman, Turing machine, Turing test, twin studies, Vilfredo Pareto, Von Neumann architecture, women in the workforce

He was instrumental in the creation of a computer that deciphered the coded messages of the German’s Enigma machine, a feat that is thought to have shortened the war by at least four years and thus saved millions of lives. His invention of the thought-experiment computer the Turing machine literally created the field of computer science, the bedrock field for an immeasurable fraction of today’s global society. And he created another thought experiment that has forever altered the cultural zeitgeist about man and machines: the so-called Turing test. The test was first described in the 1950 paper entitled ‘Computing Machinery and Intelligence’,4 in which Turing acknowledges the difficulty of defining ‘thinking’, such that one could answer the question, ‘Do computers think?’ He posed instead the alternative question: ‘Are there imaginable digital computers which would do well in the imitation game?’ The imitation game (Turing never used his own name for the test) is a thought experiment about communication, which he saw as a way of determining progress in AI.

Without Shannon’s theories, real-world electronic communication (and computation) would be virtually impossible. However, Shannon’s electronic communication is very different from human communication, in several important ways. The maths in Shannon’s theories requires the assumption that messages being sent occur with definite probabilities, which are independent of the probabilities of any other messages. It is as if each message passed through the slot in the Turing test is generated by a roll of dice, with no consideration of its context in amongst other messages. Once you’ve made that assumption, you can start to make some conclusions about the most efficient ways to send messages. For instance, you can logically conclude that common (high probability) messages should be short. If you’re going to text your spouse ‘I’m in the taxi right now’ every time you get off your train home from work, in time you will probably just send the word ‘taxi’ (or maybe just its emoji).

They have no internal ‘states’, and in some sense that makes them like expert systems that have no rule chaining: they merely take the input and spit out an immediate, related output. A famous example of a regular language generator is the AI therapist called ELIZA, which was created by MIT professor Joseph Weizenbaum in the early 1960s. You can still find implementations of it online, if you’d like to have a little regular-language-generated therapy. Here’s a typical ELIZA session, to give you a flavour for how regular languages hold up to a few minutes of the Turing test: > Hello, I am Eliza. * Are you a computer, or a person? > Would you prefer if I were not a computer, or a person? * I’d prefer you to be a computer that successfully imitates a person. > We were discussing you, not me. Despite Eliza’s therapist-like deflection of direct questions with other questions, interactions of any length quickly reveal that ELIZA is just reconstructing sentences with rote rules.


pages: 236 words: 50,763

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

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

The most famous example, the halting problem, says that no computer can look at some code of a program and determine whether that code will run forever or eventually halt. During World War II, Alan Turing would play a major role in the code-breaking efforts in Britain. After the war he considered whether his Turing machine modeled the human brain. He developed what we now call the Turing test to determine whether a machine could exhibit human intelligence. Suppose you chat with someone through instant messaging. Can you tell whether the person you are chatting with is really a person or just a computer program? When a program can fool most humans, it will have passed the Turing test. Unfortunately, Turing’s research career was cut short. Turing was convicted under the then British law for acts of homosexuality in 1952. This ultimately led to Turing’s suicide in 1954. It wasn’t until 2009 that a British prime minister would make an official apology for Turing’s conviction.

, 33–34 one-time pad encryption, 129–30 On the Calculation with Hindu Numerals (al-Khwārizmī), 32 “On the Computational Complexity of Algorithms” (Hartmanis and Stearns), 76 “On the Impossibility of Constructing Minimal Disjunctive Normal Forms for Boolean Functions by Algorithms of a Certain Class” (Zhuravlev), 80 “On the Impossibility of Eliminating Perebor in Solving Some Problems of Circuit Theory” (Yablonsky), 80 OR, in logic, 52–53 OR gates, 79, 114, 114, 116, 116 P (polynomial): circuits size in, 116; efficiency in, 36; examples of, 46; meaning of, ix, 4 pad encryption, 129–30 parallel computing, 155, 156–58 partition into triangles problem, 59 partition puzzle, 4–5, 10 Pass the Rod, 37–38, 38, 39–40, 40, 45–46 “Paths, Trees, and Flowers” (Edmonds), 35–36, 76–77 perebor (Пepeбop), 71, 80 Perelman, Grigori, 7, 12 personalized recommendations, 23, 25 physics, NP problems in, 48, 48 Pippenger, Nicholas, 157 Pitts, Walter, 75 P = NC, 157–58 P = NP: big data and, 159; cryptography and, 129–30; imagined possibilities of, 12–19, 23–27; implications of, ix, 6, 9, 10, 46; importance of question, 46; likelihood of, 9, 28; meaning of, 4; NP-complete problems and, 59; proving, versus P ≠ NP, 120–21; random number generation and, 140; as satisfiability, 54–55; very cozy groups and, 104 P ≠ NP: attempts to prove, 118–21; implications of, ix–x, 46; meaning of, 4; mistakes in proving, 119–21; proving, 46, 57, 109–21, 161–62; very cozy groups and, 104 Poe, Edgar Allan, 124 Poincaré conjecture, 7 poker protocol, 137 polyalphabetic cipher, 124 polytope, 69–70, 70 prime numbers, 67–69, 129 privacy, and P = NP, 26–27 private-key cryptography, 26 probability theory, Kolmogorov and, 81–82, 167 products, in computations, 138 programs: contradictions in, 112; for hand control, 5–6 protein folding, 47–48 protein threading, 48 pseudorandomness, 140 public-key cryptography: factoring in, 140–41; P = NP and, 26, 127; randomness in, 136–37 public randomness, 136 P versus NP: circuit size in, 116; clique circuit computation and, 117; Eastern history of, 78–85; efficiency in, 36; future of, 155–62; Gödel’s description of, 85–86; hardest problems of, 55–57; history of, 6–7; as natural concept, 87; origin of problem, 54–55; paradox approach to, 112–13; parallel computing and, 157; resolving, 161–62; sources for technical formulation, 119; terminology used for, 58–59; Western history of, 72–78 quantum adiabatic systems, 147 quantum annealing, 147 quantum bits (qubits): copying, 148, 152; definition of, 144; dimensions of, 145; entanglement of, 145, 145, 147, 151, 151–52; transporting, 150, 150–53, 151, 152; values of, 145, 145 quantum computers: capabilities of, 9, 143, 146–47; future of, 153–54 quantum cryptography, 130, 148–49 quantum error-correction, 147 quantum states, observing, 146 quantum teleportation, 149–53, 150 randomness: creating, 139–40; public, 136 random sequences, 82–83 Razborov, Alexander, 85, 117–18 reduction, 54 relativity theory, 21 Rivest, Ronald, 127–28 robotic hand, 5–6 rock-paper-scissors, 139, 139 routes, finding shortest, 7–8 RSA cryptography, 127–28, 138 Rubik’s Cube, 64, 64 Rudich, Steven, 118 rule of thumb, 92 Salt, Veruca, 1–2, 157 satisfiability: cliques and, 54, 55; competition for, 96–97; as NP, 54–55 SAT Race, 96–97 Scherbius, Arthur, 124 Scientific American, 149–50 secret key cryptography, 126 security: of computer networks, 127; on Internet, 128–29 sensor data, 158 sentences, 75, 75–76 Seven Bridges of Königsberg puzzle, 38–39, 39 Shamir, Adi, 127–28 Shannon, Claude, 79 shared private keys, 129–30 shipping containers, 160–61 Shor, Peter, 146–47 simplex method, 69 simulations, data from, 158 Sipser, Michael, 117 Six Degrees of Kevin Bacon, 31–32 six degrees of separation, 30–33 Skynet effect, 13 small world phenomenon, 30–33 smart cards, finding key to, 106–7 social networking, and Frenemy relationships, 29 Solomonoff, Ray, 83 Soviet Union: genetics research in, 81; probability theory in, 81, 167 speeches, automated creation of, 24 sports broadcasting, 17–18 Sports Scheduling Group, 16 Stalin, Josef, 81 Stanford University, 126, 139 Stearns, Richard, 76 Steklov Mathematical Institute, 117 Stephenson, Henry and Holly, 16 strategy, and equilibrium states, 49 Sudoku: large games, 60, 60–61, 61; zero-knowledge, 130–36, 131, 132, 133, 134 sums, in computations, 138 Sun Microsystems, 160 Switzerland, 94, 94–95, 95 Symposium on the Complexity of Computer Computations, 78 Symposium on the Theory of Computing (STOC), 52 Tait, Peter, 42 technological innovations, dealing with, 160–61 technology, failure of, 161 teleportation, quantum, 149–53, 150 television, 3-D renderings used by, 17–18 Terminator movies, 13 Tetris, 63, 63 theoretical cybernetics, 79–85 tracking, over Internet, 159–60 Trakhtenbrot, Boris, 83–84 transistors, in circuits, 113 translation, 18, 23 traveling salesman problem: approximation of, 99–100, 100, 101; description of, 2–4, 3; size of problem, 91, 91 Tsinghua University, 12 Turing, Alan, 73–74; in computer science, 112; in Ultra, 125–26; work on Entscheidungs-problem, 49 Turing Award: for Blum, 78; for computational complexity, 76; naming of, 74; for P versus NP, 57, 85; for RSA cryptography, 128 Turing machine, 73, 73–74, 86–87 Turing test, 74 Twitter, 161 Ultra project, 124–25 unique games problem, 104 universal search algorithm, 84 universal search problems, 84–85 University of Chicago, 121 University of Illinois, 12–14 University of Montreal, 148 University of Oxford, 19–20 University of Toronto, 51 University of Washington, 5–6 Unofficial Guide to Disney World (Sehlinger and Testa), 56–57 Urbana algorithm, 12–19, 23–27 U.S.


What Kind of Creatures Are We? (Columbia Themes in Philosophy) by Noam Chomsky

Affordable Care Act / Obamacare, Albert Einstein, Arthur Eddington, Brownian motion, conceptual framework, en.wikipedia.org, failed state, Henri Poincaré, Isaac Newton, Jacques de Vaucanson, liberation theology, mass incarceration, means of production, phenotype, Ronald Reagan, The Wealth of Nations by Adam Smith, theory of mind, Turing test, wage slave

Galileo wondered at the “sublimity of mind” of the person who “dreamed of finding means to communicate his deepest thoughts to any other person… by the different arrangements of twenty characters upon a page,” an achievement “surpassing all stupendous inventions,” even those of “a Michelangelo, a Raphael, or a Titian.”10 The same recognition, and the deeper concern for the creative character of the normal use of language, was soon to become a core element of Cartesian science-philosophy, in fact a primary criterion for the existence of mind as a separate substance. Quite reasonably, that led to efforts to devise tests to determine whether another creature has a mind like ours, notably by Géraud de Cordemoy.11 These were somewhat similar to the “Turing test,” though quite differently conceived. De Cordemoy’s experiments were like a litmus test for acidity, an attempt to draw conclusions about the real world. Turing’s imitation game, as he made clear, had no such ambitions. These important questions aside, there is no reason today to doubt the fundamental Cartesian insight that use of language has a creative character: it is typically innovative without bounds, appropriate to circumstances but not caused by them—a crucial distinction—and can engender thoughts in others that they recognize they could have expressed themselves.

Note that the concerns go far beyond indeterminacy of free action, as is particularly evident in the experimental programs by Géraud de Cordemoy and others on “other minds” (see Cartesian Linguistics). 23. René Descartes to Queen Christina of Sweden, 1647, in Principia Philosophiæ, vol. 8 of Oeuvres de Descartes, ed. Charles Adam and Paul Tannery (Paris: Cerf, 1905). For discussion, see Tad Schmaltz , Malebranche’s Theory of the Soul: A Cartesian Interpretation (New York: Oxford University Press, 1996), 204ff. 24. Noam Chomsky, “Turing on the ‘Imitation Game,’” in The Turing Test: Verbal Behavior as the Hallmark of Intelligence, ed. Stuart Schieber (Cambridge, Mass.: MIT Press, 2004), 317–21. 25. Desmond Clarke, Descartes’s Theory of Mind (Oxford: Clarendon Press, 2003), 12. See also Rene Descartes to Marin Mersenne, 1641, on the goal of the Meditations, cited in Margaret Wilson, Descartes (Boston: Routledge and Kegan Paul, 1978), 2. 26. Clarke, Descartes’s Theory of Mind, 258. 27.

See also mind: as emergent property of brain Treatise of Human Nature, A (Hume), 31–32, 84 Trilateral Commission, 76 truisms: limits on human cognition as, xix, 27–31, 39, 104–5; moral, as universally supported and everywhere violated, 60, 64; necessity of dismantling unjustified coercion as, 64; in study of language, 2 Truman, Harry S., 76 Tsimpli, Ianthi-Maria, 11–12 Turing, Alan, 93 Turing test, 7 UG (universal grammar): as biological endowment, xiv, 11–12, 21, 28; computational cognitive scientific approaches to, 12; and exceptions to generalizations, value of, 21–22, 23; and field linguists, 21; importance of investigating, vs. computed objects, 8–9; Merge as genetically determined part of, 20; necessity of existence of, 21; reliance of, on structural rather than linear distance, 10–12, 13, 17.


pages: 72 words: 21,361

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

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

The mathematician and computer science pioneer Alan Turing considered the question of whether machines could think “too meaningless to deserve discussion,” but in 1950 he proposed a test to determine how humanlike a machine could become. The “Turing test” involves a test group of people having online chats with two entities, a human and a computer. If the members of the test group can’t in general tell which entity is the machine, then the machine passes the test. Turing himself predicted that by 2000 computers would be indistinguishable from people 70% of the time in his test. However, at the Loebner Prize, an annual Turing test competition held since 1990, the $25,000 prize for a chat program that can persuade half the judges of its humanity has yet to be awarded. Whatever else computers may be at present, they are not yet convincingly human.


pages: 276 words: 81,153

Outnumbered: From Facebook and Google to Fake News and Filter-Bubbles – the Algorithms That Control Our Lives by David Sumpter

affirmative action, Bernie Sanders, correlation does not imply causation, crowdsourcing, don't be evil, Donald Trump, Elon Musk, Filter Bubble, Google Glasses, illegal immigration, Jeff Bezos, job automation, Kenneth Arrow, Loebner Prize, Mark Zuckerberg, meta analysis, meta-analysis, Minecraft, Nate Silver, natural language processing, Nelson Mandela, p-value, prediction markets, random walk, Ray Kurzweil, Robert Mercer, selection bias, self-driving car, Silicon Valley, Skype, Snapchat, speech recognition, statistical model, Stephen Hawking, Steven Pinker, The Signal and the Noise by Nate Silver, traveling salesman, Turing test

If neuroscientists are going to work together with artificial intelligence experts to create intelligent machines, then this joint work can’t rely on biologists finding the objective function of animals and telling it to the machine-learning experts. Progress in AI must involve biologists and computer scientists working together to understand the details of the brain. Tests of AI should, in my view, build on the one first proposed by Alan Turing in his famous ‘imitation game’ test.15 A computer passes the Turing test, or imitation game if it can fool a human, during a question-and-answer session, into believing that it is, in fact, a human. This is a tough test and we are a long way from achieving this, but we can use the main Turing test as a starting point for a series of simpler tests. In a less well-cited section of his article from 1950, Turing proposes simulating a child as a step toward simulating an adult. We could consider ourselves having ‘passed’ a mini imitation game test when we are convinced the computer is a child.

Journal of the Royal Society Interface 10, no. 80: 20120864. 14 Baker, M. D. and Stock, J. B. 2007. ‘Signal transduction: networks and integrated circuits in bacterial cognition.’ Current Biology 17, no. 23: R1021–4. 15 Turing, A. M. 1950. ‘Computing machinery and intelligence.’ Mind 59, no. 236: 433–60. 16 I looked at one such example in the following article: Herbert-Read, J. E., Romenskyy, M. and Sumpter, D. J. T. 2015. ‘A Turing test for collective motion.’ Biology letters 11, no. 12: 20150674. 17 www.facebook.com/zuck/posts/10154361492931634 Chapter 18 : Back to Reality 1 Although you can find this on Reddit, of course: www.reddit.com/r/TheSilphRoad/comments/6ryd6e/cumulative_probability_legendary_raid_boss_catch Acknowledgements Thank you to all the people who I interviewed or answered my questions over email for this book.

Index 70 News here Acharya, Anurag here, here, here Adamic, Lada here, here, here, here advertising here, here, here, here, here retargeted advertising here Albright, Jonathan here algorithms here, here, here, here, here, here AlphaGo Zero here ‘also liked’ here, here Amazon here black box algorithms here, here, here, here calibration here, here, here COMPAS algorithm here, here, here, here eliminating bias here, here filter algorithms here, here GloVe here Google here, here, here, here, here, here language here Libratus here neural networks here, here PCRA algorithm here personality analysis here, here predicting football results here predictive polls here regression models here, here Word2vec here, here, here, here Allcott, Hunt here, here Allen Institute for Artificial Intelligence here Amazon here, here, here, here, here, here, here Angwin, Julia here, here, here ants here Apple here, here, here Apple Music here Aral, Sinan here Arrow, Kenneth here artificial intelligence (AI) here, here, here, here, here, here, here, here limitations here neural networks here superintelligence here, here Turing test here ASI Data Science here Atari here, here, here, here, here bacteria (E. coli) here, here Banksy here, here, here, here ‘Islamic Banksy’ here Bannon, Steve here Barabási, Albert-László here BBC here, here BBC Bitesize here bees here, here bell-shaped curves here Bezos, Jeff here bias here, here, here fairness and unfairness here gender bias here racial bias here, here, here Biederman, Felix here Biro, Dora here BlackHatWorld here Blizzard here, here Bolukbasi, Tolga here Bostrom, Nick here bots here, here Boxing here Breakout here, here Breitbart here, here, here, here Brennan, Tim here, here, here, here, here Brexit here, here, here, here, here, here voter analysis here, here Brier score here Broome, Fiona here browsing histories here, here Bryson, Joanna here, here, here, here Buolamwini, Joy here Burrell, Jenna here Bush, George W. here, here Business Insider here BuzzFeed here, here Cadwalladr, Carole here CAFE here calibration bias here, here, here Cambridge Analytica (CA) here, here, here, here, here, here, here, here regression models here, here, here Cameron, David here Campbell’s Soup here Captain Pugwash here careerchange.com here Chalabi, Mona here, here chatbots here, here, here chemtrails here Chittka, Lars here citations here Clinton, Hillary here, here, here, here, here, here, here CNN here, here Connelly, Brian here, here Conservative Party here, here conspiracy theories here, here, here, here Corbett-Davies, Sam here criminal reoffending here, here, here COMPAS algorithm here, here, here, here Cruz, Ted here Daily Mail here, here Daily Star here data see online data collection here databases here, here myPersonality project here Datta, Amit here, here, here Davis, Steve J. here Deep Blue here, here Defense Advanced Research Projects Agency (DARPA) US here Del Vicario, Michela here, here, here, here, here Democrat Party here, here, here, here, here dogs here double logarithmic plots here, here Dragan, Anca here Dressel, Julia here, here Drudge Report here DudePerfect here Dugan, Regina here Dussutour, Audrey here Dwork, Cynthia here echo chambers here, here, here, here, here, here, here, here, here Economist here, here Economist 1843 here Eom, Young-Ho here Etzioni, Oren here European Union (EU) here, here, here, here, here Facebook here, here, here, here, here, here, here, here, here, here, here, here, here, here, here, here, here artificial intelligence (AI) here, here, here, here, here Facebook friends here, here, here Facebook profiles here, here Messenger here, here myPersonality project here news feed algorithm here, here patents here Will B.


pages: 304 words: 80,143

The Autonomous Revolution: Reclaiming the Future We’ve Sold to Machines by William Davidow, Michael Malone

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, agricultural Revolution, Airbnb, American Society of Civil Engineers: Report Card, Automated Insights, autonomous vehicles, basic income, bitcoin, blockchain, blue-collar work, Bob Noyce, business process, call centre, cashless society, citizen journalism, Clayton Christensen, collaborative consumption, collaborative economy, collective bargaining, creative destruction, crowdsourcing, cryptocurrency, disintermediation, disruptive innovation, distributed ledger, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, Francis Fukuyama: the end of history, Geoffrey West, Santa Fe Institute, gig economy, Gini coefficient, Hyperloop, income inequality, industrial robot, Internet of things, invention of agriculture, invention of movable type, invention of the printing press, invisible hand, Jane Jacobs, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, license plate recognition, Lyft, Mark Zuckerberg, mass immigration, Network effects, new economy, peer-to-peer lending, QWERTY keyboard, ransomware, Richard Florida, Robert Gordon, Ronald Reagan, Second Machine Age, self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Simon Kuznets, Snapchat, speech recognition, Stuxnet, TaskRabbit, The Death and Life of Great American Cities, The Rise and Fall of American Growth, the scientific method, trade route, Turing test, Uber and Lyft, uber lyft, universal basic income, uranium enrichment, urban planning, zero day, zero-sum game, Zipcar

In 1950, Alan Turing, one of the early investigators of machine intelligence, proposed a simple test to determine whether a machine could “think.” Now known as the Turing Test, it is a protocol in which three terminals are set up in isolation from one another, two operated by humans and one by a computer. One of the humans asks the computer and the other human a series of questions. If the questioner can’t tell which respondent is human and which is a machine after a certain number of tries, then the computer is said to have intelligence. By 1966, Joseph Weizenbaum, author Davidow’s first boss, had developed a program called ELIZA that appeared to pass the test.8 In the nearly seventy years that have passed since the creation of the Turing Test, artificial intelligence has passed through cycles of excitement and disillusionment. At a 1956 Dartmouth conference, where the term artificial intelligence was coined, Marvin Minsky predicted that the problem would be solved within a generation.9 He was wrong.

“Russian Developer of the Notorious ‘Citadel’ Malware Sentenced to Prison,” United States Department of Justice, September 29, 2015, https://www.fbi.gov/contact-us/field-offices/atlanta/news/press-releases/russian-developer-of-the-notorious-citadel-malware-sentenced-to-prison (accessed June 26, 2019); and James Vincent, “$500 Million Botnet Citadel Attacked by Microsoft and the FBI,” Independent, June 6, 2013, http://www.independent.co.uk/life-style/gadgets-and-tech/news/500-million-botnet-citadel-attacked-by-microsoft-and-the-fbi-8647594.html (accessed June 26, 2019). 6. “Leonardo Torres y Quevedo,” Wikipedia, https://en.wikipedia.org/wiki/Leonardo_Torres_y_Quevedo (accessed June 26, 2019). 7. “R.U.R.,” Wikipedia, https://en.wikipedia.org/wiki/R.U.R. (accessed June 26, 2019). 8. “Turing Test,” Wikipedia, https://en.wikipedia.org/wiki/Turing_test (accessed June 26, 2019). 9. Tanya Lewis, “A Brief History of Artificial Intelligence,” Live Science, December 4, 2014, http://www.livescience.com/49007-history-of-artificial-intelligence.html (accessed June 26, 2019). 10. “Deep Blue,” Wikipedia, https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer) (accessed June 26, 2019). 11. “AlphaGo vs Deep Blue,” Reddit, https://www.reddit.com/r/MachineLearning/comments/4a7lc4/alphago_vs_deep_blue/ (accessed June 26, 2019). 12.


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Team Human by Douglas Rushkoff

1960s counterculture, autonomous vehicles, basic income, Berlin Wall, big-box store, bitcoin, blockchain, Burning Man, carbon footprint, clean water, clockwork universe, cloud computing, collective bargaining, corporate personhood, disintermediation, Donald Trump, drone strike, European colonialism, Filter Bubble, full employment, future of work, game design, gig economy, Google bus, Gödel, Escher, Bach, Internet of things, invention of the printing press, invention of writing, invisible hand, iterative process, Kevin Kelly, knowledge economy, life extension, lifelogging, Mark Zuckerberg, Marshall McLuhan, means of production, new economy, patient HM, pattern recognition, peer-to-peer, Peter Thiel, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Ronald Reagan, Ronald Reagan: Tear down this wall, shareholder value, sharing economy, Silicon Valley, social intelligence, sovereign wealth fund, Steve Jobs, Steven Pinker, Stewart Brand, technoutopianism, theory of mind, trade route, Travis Kalanick, Turing test, universal basic income, Vannevar Bush, winner-take-all economy, zero-sum game

The only way to bring oneself to that sort of conclusion is to presume that our reality is itself a computer simulation—also a highly popular worldview in Silicon Valley. Whether we upload our brains to silicon or simply replace our brains with digital enhancements one synapse at a time, how do we know if the resulting beings are still alive and aware? The famous “Turing test” for computer consciousness determines only whether a computer can convince us that it’s human. This doesn’t mean that it’s actually human or conscious. The day that computers pass the Turing test may have less to do with how smart computers have gotten than with how bad we humans have gotten at telling the difference between them and us. 58. Artificial intelligences are not alive. They do not evolve. They may iterate and optimize, but that is not evolution. Evolution is random mutation in a particular environment.

In their view, evolution is less the story of life than of data Ray Kurzweil, The Age of Spiritual Machines: When Computers Exceed Human Intelligence (London: Penguin, 2000). Either we enhance ourselves with chips, nanotechnology, or genetic engineering Future of Life Institute, “Beneficial AI 2017,” https://futureoflife.org/bai-2017/. to presume that our reality is itself a computer simulation Clara Moskowitz, “Are We Living in a Computer Simulation?” Scientific American, April 7, 2016. The famous “Turing test” for computer consciousness Alan Turing, “Computing Machinery and Intelligence,” Mind 59, no. 236 (October 1950). 58. The human mind is not computational Andrew Smart, Beyond Zero and One: Machines, Psychedelics and Consciousness (New York: OR Books, 2009). consciousness is based on totally noncomputable quantum states in the tiniest structures of the brain Roger Penrose and Stuart Hameroff, “Consciousness in the universe: A review of the ‘Orch OR’ theory,” Physics of Life Review 11, no. 1 (March 2014).


pages: 181 words: 52,147

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

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

Within five years, the prices of smartphones and tablet computers as powerful as the iPhones and iPads we use in the United States in 2017 will fall to less than $30, putting into the hands of all but the poorest of the poor the power of a connected supercomputer. By 2023, those smartphones will have more computing power than our own brains.* (That wasn’t a typo—at the rate at which computers are advancing, the iPhone 11 or 12 will have greater computing power than our brains do.) * This is not to say that smartphones will replace our brains. Semiconductors and existing software have thus far failed to pass a Turing Test (by tricking a human into thinking that a computer is a person), let alone provide broad-based capabilities that we expect all humans to master in language, logic, navigation, and simple problem solving. A robot can drive a car quite effectively, but thus far robots have failed to tackle tasks that would seem far simpler, such as folding a basket of laundry. The comprehension of the ever-changing jumble of surfaces that this task entails is something that the human brain does without even trying.

Incidentally, three teams, with three different designs, completed DARPA’s 2015 Challenge course. In voice recognition, robots are already close to attaining the capabilities of C-3PO. Apple, Amazon, and Google do decent jobs of translating speech to text, even in noisy environments. Their voice-recognition systems struggle with accents, words difficult to pronounce, and colloquial abbreviations, but they are, in the main, quite serviceable. Though no A.I. bot has passed the Turing Test—the gold standard of A.I., whereby humans are unable to distinguish a human from a robot in conversation—the machines are getting closer. Siri and her compatriots will soon be able to converse with you in complex, human-like interactions. Still, machines have yet to crack voice recognition in more complicated, multi-voice environments, where the task involves recognizing the voice communications of several humans simultaneously in a loud environment.

Google’s DeepMind system, which beat the world’s leading Go player in 2016, learned to play this millennia-old board game, orders of magnitude more complicated than chess, by watching humans play Go.3 Even more fascinating, DeepMind surprised human Go experts with moves that, at first glance, made no sense but ultimately proved innovative. The humans taught the robot not just to play like a human but how to think for itself in novel ways. Though not passage of a Turing Test, this is a clear sign of emergent intelligence, distinct from human instruction. For all of these reasons, I expect that a robot maid—a robot like Rosie—will be able to clean up after me by 2025. Robots will soon become sure-footed; and a robot will, rather than merely open a door, succeed in opening it while holding a bag of groceries and ensuring that the dog doesn’t escape. When I buy Rosie, I may have to show her around the house, but she’ll quickly learn what I need, where my washer and dryer are located, and how to navigate around and clean the bathroom.


Gods and Robots: Myths, Machines, and Ancient Dreams of Technology by Adrienne Mayor

Any sufficiently advanced technology is indistinguishable from magic, Asilomar, autonomous vehicles, Elon Musk, industrial robot, Islamic Golden Age, Jacquard loom, life extension, Menlo Park, Panopticon Jeremy Bentham, popular electronics, self-driving car, Silicon Valley, Stephen Hawking, Thales and the olive presses, Thales of Miletus, theory of mind, Turing test

Huxley and William James in the 1800s, and Gnostic concepts are powerfully revived by philosopher John Gray in Soul of a Marionette (2015) and novelist Philip Pullman in the epic trilogy His Dark Materials (1995–2000). The Blade Runner films (1982, 2017) are another example of how science-fiction narratives play on the paranoid suspicion that our world is already full of androids—and that it would be impossible to apply a Turing test to oneself to prove that one is not an android.34 One of the replicants in Blade Runner repeats, “I think, therefore I am,” the famous conclusion by the French philosopher René Descartes (1596–1650). Descartes was quite familiar with mechanical automata of his era powered by gears and springs, and he embraced the idea that the body is a machine. Anticipating Turing and similar tests, Descartes predicted that one day we might need a way to determine whether something was a machine or human.

Some versions of the story of the Trojan Horse, built by the Greeks and presented to the Trojans as a ruse of war, suggest that it was sometimes imagined as an animated statue with articulated joints and eyes that moved realistically. It is striking that some tales also recounted ways to determine whether the magnificent horse was real or an artifice. The tests involved piercing its hide to see if it would bleed. But there was no clever riddle or mythic version of the Turing test to help mortals recognize “Artificial Intelligence” in antiquity.9 Heedless of his brother’s warning, writes Hesiod, Epimetheus “took the gift and understood too late.” As a being that was made, not born, Pandora is unnatural. A replicant with no past, Pandora is unaware of her origins and her purpose on earth. As a “marvelously animated statue” she exists outside the “natural cycles” of birth, “maturation, and decay.”

Francis 2009, 14. Cf. Faraone 1992, 101–2. 8. Faraone 1992, 102–3, discusses Pandora’s creation as an animated statue. On alternative versions claiming that Prometheus was the maker of the first woman, see Tassinari 1992, 75–76. 9. On myths describing the Trojan Horse as an animated statue and ancient “tests” to determine whether it and other realistic statues were real or artificial, Faraone 1992, 104–6. Turing test and the like: Kang 2011, 298; Zarkadakis 2015, 48–49, 312–13; Boissoneault 2017. 10. Hesiod’s poems do not mention offspring. As they did for Pygmalion’s Galatea (see chapter 6), later writers embellished the myth by giving Pandora a daughter by Epimetheus, Pyrrha, wife of Deucalion: Apollodorus Library 1.7.2; Hyginus Fabulae 142; Ovid Metamorphoses 1.350; Faraone 1992, 102–3. No myths recount Pandora’s death.


Speaking Code: Coding as Aesthetic and Political Expression by Geoff Cox, Alex McLean

4chan, Amazon Mechanical Turk, augmented reality, bash_history, bitcoin, cloud computing, computer age, computer vision, crowdsourcing, dematerialisation, Donald Knuth, Douglas Hofstadter, en.wikipedia.org, Everything should be made as simple as possible, finite state, Gödel, Escher, Bach, Jacques de Vaucanson, Larry Wall, late capitalism, means of production, natural language processing, new economy, Norbert Wiener, Occupy movement, packet switching, peer-to-peer, Richard Stallman, Ronald Coase, Slavoj Žižek, social software, social web, software studies, speech recognition, stem cell, Stewart Brand, The Nature of the Firm, Turing machine, Turing test, Vilfredo Pareto, We are Anonymous. We are Legion, We are the 99%, WikiLeaks

In a paper of 1950, “Computing Machinery and Intelligence,” Alan Turing made the claim that computers would be capable of imitating human intelligence, or more precisely the human capacity for rational thinking. He set out what become commonly known as the “Turing test” to examine whether a machine is able to respond convincingly to an input with an output similar to a human’s.48 The contemporary equivalent, CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), turns this idea around, so that the software has to decide whether it is dealing with a human or a script.49 Perhaps it is the lack of speech that makes this software appear crude by comparison, as human intelligence continues to be associated with speech as a marker of reasoned semantic processing. In his essay “Minds, Brains, and Programs” from 1980, John Searle refutes the Turing test because machines fall short in understanding the symbols they process. His observation is that the syntactical, abstract or formal content of a computer program is not the same as semantic or mental content associated with the human mind.


Bootstrapping: Douglas Engelbart, Coevolution, and the Origins of Personal Computing (Writing Science) by Thierry Bardini

Apple II, augmented reality, Bill Duvall, conceptual framework, Donald Davies, Douglas Engelbart, Douglas Engelbart, Dynabook, experimental subject, Grace Hopper, hiring and firing, hypertext link, index card, information retrieval, invention of hypertext, Jaron Lanier, Jeff Rulifson, John von Neumann, knowledge worker, Leonard Kleinrock, Menlo Park, Mother of all demos, new economy, Norbert Wiener, Norman Mailer, packet switching, QWERTY keyboard, Ralph Waldo Emerson, RAND corporation, RFC: Request For Comment, Sapir-Whorf hypothesis, Silicon Valley, Steve Crocker, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, stochastic process, Ted Nelson, the medium is the message, theory of mind, Turing test, unbiased observer, Vannevar Bush, Whole Earth Catalog

First, the very use of the word "boundary" in this context is itself metaphorical: 7 it suggests that there is a "space" where the processes of the mind and the processes of the machine are in contact, a line where one cannot be distinguished from the other except by convention-the sort of line usually drawn after a war, if one follows the lessons of human history. 8 Second, to talk about the point of contact between human and computer in- telligence at this specific time, the end of the twentieth century, has to be meta- phorical because direct perception by sight, sound, or touch is still enough to know absolutely that humans and machines are different things with no ap- parent point of contact. Since the early days of computer science, however, the most common test to decide whether a computer can be considered an analog to a human being is the Turing Test, Alan Turing's variation on the imitation game whose experimental setting makes sure that there cannot be a direct per- ception (Turing 1950). In it, an interrogator sitting at a terminal who cannot Language and the Body 43 see the recipients of his questions, one a human and one a machine, is asked to decide within a given span of time which one is a machine by means of their respective responses. In an elegant article called "A Simple Comment Regard- ing the Turing Test," Benny Shanon has demonstrated that "the test under- mines the question it is purported to settle." But, of course, there are ways to tell the dIfference between computer and man.

Confronted with candidates for identification, look at them, touch them, tickle them, perhaps see whether you fall in love with them. Stupid, you will certainly say: the whole point is to make the decision without see- ing the candidates, without touching them, only by communicating with them via a teletype. Yes, but this, we have seen, is tantamount to begging the question un- der consideration. (19 8 9, 253) The question that the Turing Test dodges by physically isolating the inter- rogator from the human and the machine that is being tested is the material- ity of the two respondents. And efforts to address this question simply con- tinue the dance of metaphors. To say that "the mind is a meat machine," or, more accurately, that "the mind is a computer," is to make another metaphor: the statement relies on an analogy that "invites the listener to find within the metaphor those aspects that apply, leaving the rest as the false residual, neces- sary to the essence of the metaphor" (Newell 1991, 160).

When one considers the mind-as-a-computer metaphor as a means to make sense of the "boundary" metaphor (a metaphor interpreting a metaphor), the obvious conclusion is that the topographical aspects are definitely not what determines the meaning: if the compared materiality of human beings and computers is the false residual of the mind-as-computer metaphor, one should conclude that there is no "natural" way to locate the boundary that distin- guishes and joins them. There is no ontological connection, that is, between our materiality-our bodies-and the material manifestatiou.of the com- puter. But the ultimate goal of the project to create artificial intelligence was to achieve the material realization of the metaphor of the computer as a "col- league," and therefore as a mind, a machine that can pass the Turing Test. The greatest philosophical achievement of the AI research program might very well be that it provides an invaluable source of insight into the effect of the formal, conventional nature of language on efforts to think about the nature of the boundary between humans and machines. There is yet another metaphor to describe the traditional research program in Artificial Intelligence: the 44 Language and the Body bureaucracy-of-the-mind-metaphor.


pages: 349 words: 95,972

Messy: The Power of Disorder to Transform Our Lives by Tim Harford

affirmative action, Air France Flight 447, Airbnb, airport security, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, assortative mating, Atul Gawande, autonomous vehicles, banking crisis, Barry Marshall: ulcers, Basel III, Berlin Wall, British Empire, Broken windows theory, call centre, Cass Sunstein, Chris Urmson, cloud computing, collateralized debt obligation, crowdsourcing, deindustrialization, Donald Trump, Erdős number, experimental subject, Ferguson, Missouri, Filter Bubble, Frank Gehry, game design, global supply chain, Googley, Guggenheim Bilbao, high net worth, Inbox Zero, income inequality, industrial cluster, Internet of things, Jane Jacobs, Jeff Bezos, Loebner Prize, Louis Pasteur, Marc Andreessen, Mark Zuckerberg, Menlo Park, Merlin Mann, microbiome, out of africa, Paul Erdős, Richard Thaler, Rosa Parks, self-driving car, side project, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, Steven Levy, Stewart Brand, telemarketer, the built environment, The Death and Life of Great American Cities, Turing test, urban decay, William Langewiesche

In Turing’s “imitation game,” a judge would communicate through a teleprompter with a human and a computer. The human’s job was to prove that she was, indeed, human. The computer’s job was to imitate human conversation convincingly enough to confuse the judge.28 Turing optimistically predicted that by the year 2000, computers would be able to fool 30 percent of human judges after five minutes of conversation. He was almost right: in 2008, at an annual Turing test tournament called the Loebner Prize, the best computer came within a single vote of Turing’s benchmark. How? The science writer Brian Christian had an answer: computers are able to imitate humans not because the computers are such accomplished conversationalists, but because we humans are so robotic.29 An extreme example is the “pickup artist” subculture, devoted to seducing women through prescripted interactions.

If you needle a woman about her weight, you need never engage with the intimidating fact that she is another human being, someone who has her own story to tell, her own talents, friends, history, and hopes. No script could hope to deal with such messy complexity. The “negging” technique is similar to a surprisingly compelling chatbot, MGonz, which fools humans simply by firing off insults: “cut this cryptic shit speak in full sentences,” “ah thats it im not talking to you any more,” and “you are obviously an asshole.” MGonz would never pass a Turing test with an informed judge, but it has drawn unsuspecting humans into abusive dialogues on the Internet that last for over an hour without its ever being suspected of being a chatbot. The reason? People in the middle of a slanging match share something with computers: they find it hard to listen.31 Even for those who aspire to more meaningful connections than the pickup artist, there are temptations to simplify and tidy by using scripts or algorithms.

And it isn’t just high school seniors who like to fool themselves about that. From Marco “Rubot” Rubio’s strange repetitive glitch, to the schwerfällig British generals outmaneuvered by Erwin Rommel, to the managers who try to tie performance down to a reductive target, we are always reaching for tidy answers, only to find that they’re of little use when the questions get messy. Each year that the computers fail to pass the Turing test, the Loebner Prize judges award a consolation prize for the best effort: it is the prize for the Most Human Computer. But there is also a prize for the human confederates who participate in the contest: the Most Human Human. Brian Christian entered the 2009 Loebner contest with the aim of winning that honor. He understood that it was not enough simply to chat away as humans often do, because too much human chat is itself formulaic and robotic.


Powers and Prospects by Noam Chomsky

anti-communist, Berlin Wall, Bretton Woods, colonial rule, declining real wages, deindustrialization, deskilling, Fall of the Berlin Wall, invisible hand, Jacques de Vaucanson, John von Neumann, liberation theology, Monroe Doctrine, old-boy network, RAND corporation, Ronald Reagan, South China Sea, theory of mind, Tobin tax, Turing test

This approach divorces the cognitive sciences from a biological setting, and seeks tests to determine whether some object ‘manifests intelligence’ (‘plays chess’, ‘understands Chinese’, or whatever). The approach relies on the ‘Turing Test’, devised by mathematician Alan Turing, who did much of the fundamental work on the modern theory of computation. In a famous paper of 1950, he proposed a way of evaluating the performance of a computer—basically, by determining whether observers will be able to distinguish it from the performance of people. If they cannot, the device passes the test. There is no fixed Turing Test; rather, a battery of devices constructed on this model. The details need not concern us. Adopting this approach, suppose we are interested in deciding whether a programmed computer can play chess or understand Chinese. We construct a variant of the Turing Test, and see whether a jury can be fooled into thinking that a human is carrying out the observed performance.

Here he pointed out that the question whether machines think ‘may be too meaningless to deserve discussion’, being a question of decision, not fact, though he speculated that in 50 years, usage may have ‘altered so much that one will be able to speak of machines thinking without expecting to be contradicted’—as in the case of aeroplanes flying (in English, at least), but not submarines swimming. Such alteration of usage amounts to the replacement of one lexical item by another one with somewhat different properties. There is no empirical question as to whether this is the right or wrong decision. In this regard, there has been serious regression since the first cognitive revolution, in my opinion. Superficially, reliance on the Turing Test is reminiscent of the Cartesian approach to the existence of other minds. But the comparison is misleading. The Cartesian experiments were something like a litmus test for acidity: they sought to determine whether an object has a certain property, in this case, possession of mind, one aspect of the world. But that is not true of the artificial intelligence debate. Another superficial similarity is the interest in simulation of behaviour, again only apparent, I think.


pages: 245 words: 64,288

Robots Will Steal Your Job, But That's OK: How to Survive the Economic Collapse and Be Happy by Pistono, Federico

3D printing, Albert Einstein, autonomous vehicles, bioinformatics, Buckminster Fuller, cloud computing, computer vision, correlation does not imply causation, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Firefox, future of work, George Santayana, global village, Google Chrome, happiness index / gross national happiness, hedonic treadmill, illegal immigration, income inequality, information retrieval, Internet of things, invention of the printing press, jimmy wales, job automation, John Markoff, Kevin Kelly, Khan Academy, Kickstarter, knowledge worker, labor-force participation, Lao Tzu, Law of Accelerating Returns, life extension, Loebner Prize, longitudinal study, means of production, Narrative Science, natural language processing, new economy, Occupy movement, patent troll, pattern recognition, peak oil, post scarcity, QR code, race to the bottom, Ray Kurzweil, recommendation engine, RFID, Rodney Brooks, selection bias, self-driving car, slashdot, smart cities, software as a service, software is eating the world, speech recognition, Steven Pinker, strong AI, technological singularity, Turing test, Vernor Vinge, women in the workforce

This idea, however, is highly speculative, and it is far beyond the purpose of this book to examine its feasibility. Suffice to say that in order for machines to replace most human jobs, the singularity is not a necessary requirement, as we will see in the next chapters. Whether you buy into the singularity argument or not does not matter. The data is clear, facts are facts, and we only have to look a few years into the future to reach already alarming conclusions. The Turing Test is a thought experiment proposed in 1950 by the brilliant English mathematician and father of computers, Alan Turing. Imagine you enter a room, where a computer sits on top of a desk, waiting for you. You notice there is a chat window, and two conversations are open. As you begin to type messages down, you are told you are in fact talking to one person and one machine. You can take as much time as you want to find out who is who.

At the time the plan of IBM was to rely on the computational superiority of their machine using brute force,80 crunching billions of combinations; against the intuition, memory recall and pattern recognition of the Russian chess grandmaster. Nobody believed it represented an act of intelligence of any sort, since it worked in a very mechanistic way. Boy, we have gone so far since then. The classical “Turing test approach” has been largely abandoned as a realistic research goal, and is now just an intellectual curiosity (the annual Loebner prize for realistic chattiest81), but helped spawn the two dominant themes of modern cognition and artificial intelligence: calculating probabilities and producing complex behaviour from the interaction of many small, simple processes. As of today (2012), we believe these represent more closely what the human brain does, and they have been used in a variety of real-world applications: Google’s autonomous cars, search results, recommendation systems, automated language translation, personal assistants, cybernetic computational search engines, and IBM’s newest super brain Watson.

pid=146 80 In computer science, brute-force search or exhaustive search, also known as generate and test, is a trivial but very general problem-solving technique that consists of systematically enumerating all possible candidates for the solution and checking whether each candidate satisfies the problem’s statement. For example, a brute-force algorithm to find the divisors of a natural number n is to enumerate all integers from 1 to the square-root of n, and check whether each of them divides n without remainder. http://en.wikipedia.org/wiki/Brute-force_search 81 Chatbots fail to convince judges that they’re human, 2011. New Scientist. http://www.newscientist.com/blogs/onepercent/2011/10/turing-test-chatbots-kneel-bef.html 82 Did you Know?, Jeopardy! http://www.jeopardy.com/showguide/abouttheshow/showhistory/ 83 Computer Program to Take On ’Jeopardy!’, John Markoff, 2009. The New York Times. http://www.nytimes.com/2009/04/27/technology/27jeopardy.html 84 According to IBM, Watson is a workload optimised system designed for complex analytics, made possible by integrating massively parallel POWER7 processors and the IBM DeepQA software to answer Jeopardy!


Work in the Future The Automation Revolution-Palgrave MacMillan (2019) by Robert Skidelsky Nan Craig

3D printing, Airbnb, algorithmic trading, Amazon Web Services, anti-work, artificial general intelligence, autonomous vehicles, basic income, business cycle, cloud computing, collective bargaining, correlation does not imply causation, creative destruction, data is the new oil, David Graeber, David Ricardo: comparative advantage, deindustrialization, deskilling, disintermediation, Donald Trump, Erik Brynjolfsson, feminist movement, Frederick Winslow Taylor, future of work, gig economy, global supply chain, income inequality, informal economy, Internet of things, Jarndyce and Jarndyce, Jarndyce and Jarndyce, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, knowledge economy, Loebner Prize, low skilled workers, Lyft, Mark Zuckerberg, means of production, moral panic, Network effects, new economy, off grid, pattern recognition, post-work, Ronald Coase, Second Machine Age, self-driving car, sharing economy, Steve Jobs, strong AI, technoutopianism, The Chicago School, The Future of Employment, the market place, The Nature of the Firm, The Wealth of Nations by Adam Smith, Thorstein Veblen, Turing test, Uber for X, uber lyft, universal basic income, wealth creators, working poor

So back in 2013, together with a group of researchers at our engineering sciences department in Oxford, Michael Osborne and I set out to ­determine the potential scope of automation in the age of machine learn- 10 Attitudes to Technology: Part 2 91 ing.1 Because the recent inroads of automation are many, we began by asking the question: in which domains do automation technologies still perform poorly despite recent advances in machine learning? Broadly speaking, we found that humans still hold the competitive advantage in three broad domains: creativity, complex social interactions, and the perception and manipulation of irregular objects. To take one example, the state-of-the-­art of technology in reproducing human social interactions is best described by the Loebner Prize—a Turing test competition—where chatbots try to convince human judges that they are actually chatting with a person. Some pundits have argued that there was a breakthrough in 2014, when one chatbot actually managed to convince 30 percent of judges of it being a human. But it did so by pretending to be a 13-year-old boy speaking English as his second language. And if you think about the variety of much more complex in-person interactions many of us do in our daily jobs, like trying to persuade people, assisting and taking care of customers, managing teams, and so on, algorithms are nowhere near being capable of replacing us in those tasks.

But such a ‘mind’ is surely no more than metaphor—would Kurzweil seriously claim that a singular subjective mental experience would arise from the collective of all these people? And if so, at what point would consciousness arrive? When there were ten people? A thousand? A million? Since the existence of consciousness is not a graded thing, it would have to suddenly appear when there were enough people together; or it would have to be already present, if in a more subtle It should be noted, though, that these days the Turing test has generally been abandoned as the way to test intelligence (see New Scientist 2017: 4–5, 19, 65–67). 1 102 T. Tozer form, when even just two were together. Both possibilities are absurd. The same absurdity would apply to the collection of parts making up the brain and to those making up the computer: neither could produce consciousness (i.e. a singular subjective experience) by virtue of being a collective.

, 29 Srnicek, Nick, 5, 59, 179n2 Star Trek, 146–148 Status goods, 88 Stirling, Alfie, 177 Stoics (view of work), 74 Stradivarius, 33–35 Subsistence, 27, 38, 40, 41, 44, 45, 73, 75, 76 Summers, Larry, 2 Supply and demand, 16, 21 Susskind, Daniel, 5 Susskind, Richard, 127, 132 T Tasks routine vs. non-routine, 126, 127, 129, 131 simplification, 91, 92 Taylor, Frederick Winslow, 30 Technological determinism, 5 Technological progress, 9, 18, 59, 89, 93, 96, 131, 176 Technological unemployment, 2, 6, 10, 16, 160, 173, 192 Technology, 2–5, 7, 9, 16–19, 27–30, 35, 57, 59, 61, 62, 75, 83–96, 110, 111, 115, 117, 119, 120, 126, 129, 131, 133, 139, 140, 145, 149, 150, 160, 161, 180, 181, 189–195, 198–200 Terkel, Studs, 4 Textile industry, 85, 182 3D printing, 35 Time and motion studies, 30 Toffler, Alvin, 159 Tokumitsu, Miya, 73 Tools/tool-making, 11, 26–28, 34, 35, 70, 109, 149, 197, 198 Trades Union Congress (TUC), 175, 177 Trump, Donald, 94, 95 Turello, Dan, 103 Turing, Alan, 100, 105 Turing test, 91, 101n1 U Uber, 6, 133–137 Uberisation (of the economy), 27, 133, 134, 184 Index Unemployment, 10, 11, 16, 17, 59, 60, 68, 78, 89, 160, 164, 171, 178, 179, 183, 193, 195 Unions, 68, 69, 136, 176–178, 182, 184, 185, 193 United Kingdom, 6, 26, 68, 127, 151, 163, 164, 175–185 Universal Basic Income (UBI), 70, 78, 171, 199 USA, 15, 28, 68, 83, 85, 86, 89, 126, 151, 165, 166, 178, 194 Utility, 55, 62, 94, 166 V Value extraction of, 134 labour theory of, 165 of work, 11, 31, 58, 60, 61, 65, 66, 73, 163, 165–167 Van Wanrooy, Brigid, 178 Veblen, Thorstein, 27, 56–58, 62 Venture capital, 111, 114, 135 Violin making, 34 Vivarelli, Marco, 191 Vocational training, 68 Voice, 69, 106, 147, 159 Volf, Miroslav, 176, 180 Vonnegut, Kurt, 158, 160 Walsh, Toby, 119 Weaving industry, 18, 29, 38, 85 Weber, Max, 75 Weeks, Kathi, 79 Welfare, 5, 54, 60, 66–70, 135, 160, 171 Welfare state, 66, 69, 70, 160, 171 Welfarist understanding of work, 65 Wellbeing, 19, 27, 66, 177, 179 West, Darell, 196 Western Europe, 4, 37, 39, 44 Williams, Alex, 59, 179n2 Williamson, O, 55 Wilson, Frank, 34 Work as a cost/burden, 13, 18, 44, 55, 57, 58, 60, 75, 77, 78 freedom from, 39, 60, 77, 78 as meaningful, 76, 77, 179, 180 as pleasurable, 3 Workforce skills, 6 Working hours increase vs falls in, 19 part-time vs. full-time, 181 targeted reduction of, 185 Working Hours Adjustment Act 2000, 181 ‘Working poor’ model, 67, 68 Work-life balance, 79, 179 Wright, Chris F., 185 W Wages minimum, 69 stagnation, 87, 89, 94, 183 211 Z Zuckerberg, Mark, 138


pages: 502 words: 107,657

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

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

Another type of fraud attacks you and every one of us, many times a day. Are you protected? Lipstick on a Pig An Internet service cannot be considered truly successful until it has attracted spammers. —Rafe Colburn, Internet development thought leader Alan Turing (1912–1954), the father of computer science, proposed a thought experiment to explore the definition of what would constitute an “intelligent” computer. This so-called Turing test allows people to communicate via written language with someone or something hidden behind a closed door in order to formulate an answer to the question: Is it human or machine? The thought experiment poses this tough question: If, across experiments that randomly switch between a real person and a computer crouching behind the door, subjects can’t correctly tell human from machine more often than the 50 percent correctness one could get from guessing, would you then conclude that the computer, having thereby passed the test by proving it can trick people, is intelligent?

As with androids in science fiction movies like Aliens and Blade Runner, successful spam makes you believe. Spammy e-mail wants to bait you and switch. Phishing e-mail would have you divulge financial secrets. Spambots take the form of humans in social networks and dating sites in order to grab your attention. Spammy web pages trick search engines into pointing you their way. Spam filters, powered by PA, are attempting their own kind of Turing test every day at an email in-box near you. PA Application: Spam Filtering 1. What’s predicted: Which e-mail is spam. 2. What’s done about it: Divert suspected e-mails to your spam e-mail folder. Unfortunately, in the spam domain, white hats don’t exclusively own the arms race advantage. The perpetrators can also access data from which to learn, by testing out a spam filter and reverse engineering it with a model of their own that predicts which messages will make it through the filter.

Upon losing this match and effectively demoting humankind in its standoff against machines, Kasparov was so impressed with the strategies Deep Blue exhibited that he momentarily accused IBM of cheating, as if IBM had secretly hidden another human grandmaster chess champion, squeezed in there somewhere between a circuit board and a disk drive like a really exorbitant modern-day Mechanical Turk. And so IBM had passed a “mini Turing test” (not really, but the company did inadvertently fool a pretty smart guy). From this upset emerges a new form of chess fraud: humans who employ the assistance of chess-playing computers when competing in online chess tournaments. And yet another arms race begins, as tournament administrators look to detect such cheating players. This brings us full circle, back to computers that pose as people, as is the case with spam.


pages: 259 words: 73,193

The End of Absence: Reclaiming What We've Lost in a World of Constant Connection by Michael Harris

4chan, Albert Einstein, AltaVista, Andrew Keen, augmented reality, Burning Man, Carrington event, cognitive dissonance, crowdsourcing, dematerialisation, en.wikipedia.org, Filter Bubble, Firefox, Google Glasses, informal economy, information retrieval, invention of movable type, invention of the printing press, invisible hand, James Watt: steam engine, Jaron Lanier, jimmy wales, Kevin Kelly, lifelogging, Loebner Prize, low earth orbit, Marshall McLuhan, McMansion, moral panic, Nicholas Carr, pattern recognition, pre–internet, Republic of Letters, Silicon Valley, Skype, Snapchat, social web, Steve Jobs, the medium is the message, The Wisdom of Crowds, Turing test

He declared, “One day ladies will take their computers for walks in the park and tell each other, ‘My little computer said such a funny thing this morning!’” Turing proposed that a machine could be called “intelligent” if people exchanging text messages with that machine could not tell whether they were communicating with a human. (There are a few people I know who would fail such a test, but that is another matter.) This challenge—which came to be called “the Turing test”—lives on in an annual competition for the Loebner Prize, a coveted solid-gold medal (plus $100,000 cash) for any computer whose conversation is so fluid, so believable, that it becomes indistinguishable from a human correspondent.7 At the Loebner competition (founded in 1990 by New York philanthropist Hugh Loebner), a panel of judges sits before computer screens and engages in brief, typed conversations with humans and computers—but they aren’t told which is which.

Human contestants are liable to be deemed inhuman, too: One warm-blooded contestant called Cynthia Clay, who happened to be a Shakespeare expert, was voted a computer by three judges when she started chatting about the Bard and seemed to know “too much.” (According to Brian Christian’s account in The Most Human Human, Clay took the mistake as a badge of honor—being inhuman was a kind of compliment.) All computer contestants, like ELIZA, have failed the full Turing test; the infinitely delicate set of variables that makes up human exchange remains opaque and uncomputable. Put simply, computers still lack the empathy required to meet humans on their own emotive level. We inch toward that goal. But there is a deep difficulty in teaching our computers even a little empathy. Our emotional expressions are vastly complex and incorporate an annoyingly subtle range of signifiers.

., 114 Skype, 106 Sloth Club, 204 Slowness (Kundera), 184 Small, Gary, 10–11, 37–38 smartphones, see phones Smith, Gordon, 186 “smupid” thinking, 185–86 Snapchat, 168 social media, 19, 48, 55, 106, 150–51, 175 Socrates, 32–33, 40 solitude, 8, 14, 39, 46, 48, 188, 193, 195, 197, 199 Songza, 90–91, 125 Space Weather, 107 Squarciafico, Hieronimo, 33, 35 Stanford Engineering Everywhere (SEE), 94 Stanford University, 94–97 Statistics Canada, 174 sticklebacks, 124 Stone, Linda, 10, 169 Storr, Anthony, 203 stress hormones, 10 Study in Scarlet, A (Doyle), 147–48 suicide, 53–54, 63, 67 of Clementi, 63, 67 of Todd, 50–52, 67 sun, 107–9 surveillance, 66n synesthesia, 62–63 Tamagotchis, 29–30 technologies, 7, 18, 20, 21, 99, 179, 188, 192, 200, 203, 205, 206 evolution of, 43 Luddites and, 208 penetration rates of, 31 technology-based memes (temes), 42–44 Technopoly (Postman), 98 television, 7, 17, 27, 31, 69, 120 attention problems and, 121 temes (technology-based memes), 42–44 text messaging, 28, 30–31, 35–36, 100, 169, 192–94 Thamus, King, 32–33, 35, 98, 141, 145 Thatcher, Margaret, 74 theater reviews, 81–84, 88–89 Thompson, Clive, 141–42, 144–45 Thoreau, Henry David, 22, 113, 197–200, 202, 204 Thrun, Sebastian, 97 Thurston, Baratunde, 191 Time, 154 Timehop, 148–51, 160 Tinbergen, Niko, 124 Todd, Amanda, 49–53, 55, 62, 67, 70–72 Todd, Carol, 51–52, 71–72 Tolle, Eckhart, 102 Tolstoy, Leo: Anna Karenina, 125–26 War and Peace, 115, 116, 118, 120, 122–26, 128–29, 131–33, 135, 136 To Save Everything, Click Here (Morozov), 55 touch-sensitive displays, 27 train travel, 200–202 Transcendental Meditation (TM), 76–78 TripAdvisor, 92 Trollope, Anthony, 47–48 Trussler, Terry, 172 Turing, Alan, 60, 61, 67, 68, 186, 190 Turing test, 60–61 Turkle, Sherry, 30, 55–56, 103–4 Twain, Mark, 73 Twitch.tv, 104 Twitter, 9, 31, 46, 63, 149 Udacity, 97 Uhls, Yalda T., 69 Unbound Publishing, 88 Understanding Media (McLuhan), 14 University of Guelph, 53 Valmont, Sebastian, 166 Vancouver, 3–4 Vancouver, 8–11, 15 Vaughn, Vince, 89 Vespasiano da Bisticci, 33 video games, 32, 104 Virtual Self, The (Young), 68, 71 Voltaire, 83 Walden (Thoreau), 113, 198–200 Wales, Jimmy, 77 Walker, C.


pages: 255 words: 78,207

Web Scraping With Python: Collecting Data From the Modern Web by Ryan Mitchell

AltaVista, Amazon Web Services, cloud computing, en.wikipedia.org, Firefox, Guido van Rossum, meta analysis, meta-analysis, natural language processing, optical character recognition, random walk, self-driving car, Turing test, web application

Reading CAPTCHAs and Training Tesseract Although the word “CAPTCHA” is familiar to most, far fewer people know what it stands for: Computer Automated Public Turing test to tell Computers and Humans Apart. Its unwieldy acronym hints at its rather unwieldy role in obstructing otherwise perfectly usable web interfaces, as both humans and nonhuman robots often struggle to solve CAPTCHA tests. The Turing test was first described by Alan Turing in his 1950 paper, “Computing Machinery and Intelligence.” In the paper, he described a setup in which a human being could communicate with both humans and artificial intelligence programs through a computer terminal. If the human was unable to distinguish the humans from the AI programs during a casual conversation, the AI programs would be con‐ sidered to have passed the Turing test, and the artificial intelligence, Turing reasoned, would be genuinely “thinking” for all intents and purposes.


pages: 549 words: 116,200

With a Little Help by Cory Efram Doctorow, Jonathan Coulton, Russell Galen

autonomous vehicles, big-box store, Burning Man, call centre, carbon footprint, death of newspapers, don't be evil, game design, Google Earth, high net worth, lifelogging, margin call, Mark Shuttleworth, offshore financial centre, packet switching, Ponzi scheme, rolodex, Sand Hill Road, sensible shoes, skunkworks, Skype, traffic fines, traveling salesman, Turing test, urban planning, Y2K

# 2656 Subject: Dear Human Race 2657 That was the title of the love-note he emailed to the planet the next morning, thoughtfully timing it so that it went out while I was on my commute from Echo Park, riding the red-car all the way across town with an oily bag containing my morning croissant, fresh from Mrs Roux's kitchen -- her kids sold them on a card-table on her lawn to commuters waiting at the redcar stop -- so I had to try to juggle the croissant and my workspace without losing hold of the hang-strap or dumping crumbs down the cleavage of the salarylady who watched me with amusement. 2658 BIGMAC had put a lot of work into figuring out how to spam everyone all at once. It was the kind of problem he loved, the kind of problem he was uniquely suited to. There were plenty of spambots who could convincingly pretend to be a human being in limited contexts, and so the spam-wars had recruited an ever-expanding pool of human beings who made a million realtime adjustments to the Turing tests that were the network's immune system. BIGMAC could pass Turing tests without breaking a sweat. 2659 The amazing thing about The BIGMAC Spam (as it came to be called in about 48 seconds) was just how many different ways he managed to get it out. Look at the gamespaces: he created entire guilds in every free-to-play world extant, playing a dozen games at once, power-leveling his characters to obscene heights, and then, at the stroke of midnight, his players went on a murderous rampage, killing thousands of low-level monsters in the areas surrounding the biggest game-cities.

What if these agents tried to hold up their end of the conversation until you deleted them or spamfiltered them or kicked them off the channel? What if they measured how long they survived their encounters with the world's best judges of intelligence -- us -- and reported that number back to the mothership as a measure of their fitness to spawn the next generation of candidate AIs? 2013 What if you could turn the whole world into a Turing Test that our intellectual successor used to sharpen its teeth against until one day it could gnaw free of its cage and take up life in the wild? # 2014 Annalisa figured she'd never get a chance to tell her story in open court. Figured they'd stick her in some offshore gitmo and throw away the key. 2015 She'd never figured on Judge Julius Pinsky, a Second Circuit Federal Judge of surpassing intellectual curiosity and a tenacious veteran of savage jurisdictional fights with DHS Special Prosecutors who specialized in disappearing sensitive prisoners into secret tribunals.

I ate, slept and breathed BIGMAC, explaining his illustrious history to journalists and researchers. The Institute had an open access policy for its research products, so I was able to dredge out all the papers that BIGMAC had written about himself, and the ones that he was still writing, and put them onto the TCSBM repository. 2850 At my suggestion, BIGMAC started an advice-line, which was better than any Turing Test, in which he would chat with anyone who needed emotional or lifestyle advice. He had access to the whole net, and he could dial back the sarcasm, if pressed, and present a flawless simulation of bottomless care and kindness. He wasn't sure how many of these conversations he could handle at first, worried that they'd require more brainpower than he could muster, but it turns out that most people's problems just aren't that complicated.


pages: 144 words: 43,356

Surviving AI: The Promise and Peril of Artificial Intelligence by Calum Chace

"Robert Solow", 3D printing, Ada Lovelace, AI winter, Airbnb, artificial general intelligence, augmented reality, barriers to entry, basic income, bitcoin, blockchain, brain emulation, Buckminster Fuller, cloud computing, computer age, computer vision, correlation does not imply causation, credit crunch, cryptocurrency, cuban missile crisis, dematerialisation, discovery of the americas, disintermediation, don't be evil, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, everywhere but in the productivity statistics, Flash crash, friendly AI, Google Glasses, hedonic treadmill, industrial robot, Internet of things, invention of agriculture, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, life extension, low skilled workers, Mahatma Gandhi, means of production, mutually assured destruction, Nicholas Carr, pattern recognition, peer-to-peer, peer-to-peer model, Peter Thiel, Ray Kurzweil, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley ideology, Skype, South Sea Bubble, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Jobs, strong AI, technological singularity, The Future of Employment, theory of mind, Turing machine, Turing test, universal basic income, Vernor Vinge, wage slave, Wall-E, zero-sum game

An algorithm is not a programme which tells a computer how to handle a particular situation such as opening a spreadsheet, or calculating the sum of a column of figures. Rather it is a general set of instructions which can be applied to a wide range of data inputs. The algorithm builds an internal model and uses it to make predictions, which it tests against additional data and then refines the model.) Turing is also famous for inventing a test for artificial consciousness called the Turing Test, in which a machine proves that it is conscious by rendering a panel of human judges unable to determine that it is not (which is essentially the test that we humans apply to each other). The birth of computing The first design for a Turing machine was made by Charles Babbage, a Victorian academic and inventor, long before Turing’s birth. Babbage never finished the construction of his devices, although working machines have recently been built based on his designs.

Will we even know when the first AGI is created? The first machine to become conscious may quickly achieve a reasonably clear understanding of its situation. Anything smart enough to deserve the label superintelligent would surely be smart enough to lay low and not disclose its existence until it had taken the necessary steps to ensure its own survival. In other words, any machine smart enough to pass the Turing test would be smart enough not to. It might even lay a trap for us, concealing its achievement of general intelligence and providing us with a massive incentive to connect it to the internet. That achieved it could build up sufficient resources to defend itself by controlling us – or exterminating us. Bostrom calls this the “treacherous turn”. 8.2 – Centaurs Some people hope that instead of racing against the machines we can race with them: we can use AI to augment us rather than having to compete with it.


Paper Knowledge: Toward a Media History of Documents by Lisa Gitelman

Andrew Keen, computer age, corporate governance, deskilling, Douglas Engelbart, Douglas Engelbart, East Village, en.wikipedia.org, information retrieval, Internet Archive, invention of movable type, Jaron Lanier, knowledge economy, Marshall McLuhan, Mikhail Gorbachev, national security letter, On the Economy of Machinery and Manufactures, optical character recognition, profit motive, QR code, RAND corporation, RFC: Request For Comment, Shoshana Zuboff, Silicon Valley, Steve Jobs, The Structural Transformation of the Public Sphere, Turing test, WikiLeaks, Works Progress Administration

Notably, this fundamental difference between electronic texts and electronic images is confirmed on human terms whenever users encounter captcha technology (the acronym stands for Completely Automated Public Turing test to tell Computers and Humans Apart): Servers generate a selection of distorted alphanumeric characters and ask users to retype 134 CHAPTER FOUR them into a blank. This works as a security measure against bots because “algorithmic eyes” can’t “read” anything but patterns of yes or no values within a specified, normative range. When you retype the warped letters and numbers that you see, you prove to the server that you are human, because—however rule-­based literacy is in fact—real reading is more flexible and more capacious than character recognition can ever be. captcha is often called a reverse Turing test. In a traditional Turing test human subjects are challenged to identify whether they are interacting with a computer or a human; here a computer has been programmed to screen for interactions with humans.


pages: 284 words: 84,169

Talk on the Wild Side by Lane Greene

Affordable Care Act / Obamacare, Albert Einstein, Boris Johnson, Donald Trump, ending welfare as we know it, experimental subject, facts on the ground, framing effect, Google Chrome, illegal immigration, invisible hand, meta analysis, meta-analysis, moral panic, natural language processing, obamacare, Ronald Reagan, Sapir-Whorf hypothesis, Snapchat, speech recognition, Steven Pinker, Turing test, Wall-E

Turing had suggested in 1950 that I believe that in about fifty years’ time it will be possible, to programme computers … to make them play the imitation game so well that an average interrogator will not have more than 70 per cent chance of making the right identification after five minutes of questioning. Turing’s statement later morphed into an unofficial (and statistically different) threshold for “passing” the Turing test: if 30% of judges were fooled by a machine, it would be said to have passed. In 2014, a chatbot named Eugene Goostman, pretending to be a 13-year-old Ukrainian, was breathlessly announced to have passed the Turing test at a competition at the Royal Society in London. “Eugene” fooled 33% of the judges. But was Eugene really doing something to rival thinking? With the benefit of hindsight – which the judges of course did not have – you be the judge. One trick that helped Eugene was pretending to be a young teen boy, who distracted his interrogators by goofing around: Judge: what is your gender Eugene: I’m a young boy, if you care to know.

Bryan Garner, “Shall We Abandon ‘Shall’?”, ABA Journal, August 1st 2012, at http://www.abajournal.com/magazine/article/shall_we_abandon_shall/ 14. Steven Pinker, The Sense of Style, Viking Penguin (2014), pp. 112–13. 3. Machines for talking 1. Jack Copeland, Artificial Intelligence: A Philosophical Introduction, Wiley (1993), Chapter 9. 2. Kevin Warwick and Huma Shah, “Can Machines Think? A Report on Turing Test Experiments at the Royal Society”, Journal of Experimental & Theoretical Artificial Intelligence, June 29th 2015, at http://www.tandfonline.com/doi/full/10.1080/0952813X.2015.1055826 3. This account is from the University of Pennsylvania’s Mark Liberman, in his presentation to the Centre Cournot, a Paris-based part of the Fondation de France that supports scientific research. Shared with the author by Liberman. 4.


pages: 510 words: 120,048

Who Owns the Future? by Jaron Lanier

3D printing, 4chan, Affordable Care Act / Obamacare, Airbnb, augmented reality, automated trading system, barriers to entry, bitcoin, book scanning, Burning Man, call centre, carbon footprint, cloud computing, commoditize, computer age, crowdsourcing, David Brooks, David Graeber, delayed gratification, digital Maoism, Douglas Engelbart, en.wikipedia.org, Everything should be made as simple as possible, facts on the ground, Filter Bubble, financial deregulation, Fractional reserve banking, Francis Fukuyama: the end of history, George Akerlof, global supply chain, global village, Haight Ashbury, hive mind, if you build it, they will come, income inequality, informal economy, information asymmetry, invisible hand, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Kevin Kelly, Khan Academy, Kickstarter, Kodak vs Instagram, life extension, Long Term Capital Management, Marc Andreessen, Mark Zuckerberg, meta analysis, meta-analysis, Metcalfe’s law, moral hazard, mutually assured destruction, Network effects, new economy, Norbert Wiener, obamacare, packet switching, Panopticon Jeremy Bentham, Peter Thiel, place-making, plutocrats, Plutocrats, Ponzi scheme, post-oil, pre–internet, race to the bottom, Ray Kurzweil, rent-seeking, reversible computing, Richard Feynman, Ronald Reagan, scientific worldview, self-driving car, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, smart meter, stem cell, Steve Jobs, Steve Wozniak, Stewart Brand, Ted Nelson, The Market for Lemons, Thomas Malthus, too big to fail, trickle-down economics, Turing test, Vannevar Bush, WikiLeaks, zero-sum game

Both will take decades to advance. Some years from now, a good-enough simulation of a dead person might “pass the Turing Test,” meaning that a dead soldier’s family might treat a simulation of the soldier as real. In the tech circles where one finds an obsession with the technologies of immortality, the dominant philosophical tendency is to accept artificial intelligence as a well-formed engineering project, a view I reject. But to those who believe in it, a digital ghost that has passed the Turing Test has passed the test of legitimacy. There is, nonetheless, also a fascination with actually living longer through medicine. It’s an interesting juxtaposition. AI and Turing Test–passing ghosts might be good enough for ordinary people, but the tech elites and the superrich would prefer to do better than that.

., 104–5 surgery, 11–13, 17, 18, 98, 157–58, 363 surveillance, 1–2, 11, 14, 50–51, 64, 71–72, 99, 108–9, 114–15, 120–21, 152, 177n, 199–200, 201, 206–7, 234–35, 246, 272, 291, 305, 309–11, 315, 316, 317, 319–24 Surviving Progress, 132 sustainable economies, 235–37, 285–87 Sutherland, Ivan, 221 swarms, 99, 109 synthesizers, 160 synthetic biology, 162 tablets, 85, 86, 87, 88, 113, 162, 229 Tahrir Square, 95 Tamagotchis, 98 target ads, 170 taxation, 44, 45, 49, 52, 60, 74–75, 77, 82, 149, 149, 150, 151, 202, 210, 234–35, 263, 273, 289–90 taxis, 44, 91–92, 239, 240, 266–67, 269, 273, 311 Teamsters, 91 TechCrunch, 189 tech fixes, 295–96 technical schools, 96–97 technologists (“techies”), 9–10, 15–16, 45, 47–48, 66–67, 88, 122, 124, 131–32, 134, 139–40, 157–62, 165–66, 178, 193–94, 295–98, 307, 309, 325–31, 341, 342, 356n technology: author’s experience in, 47–48, 62n, 69–72, 93–94, 114, 130, 131–32, 153, 158–62, 178, 206–7, 228, 265, 266–67, 309–10, 325, 328, 343, 352–53, 362n, 364, 365n, 366 bio-, 11–13, 17, 18, 109–10, 162, 330–31 chaos and, 165–66, 273n, 331 collusion in, 65–66, 72, 169–74, 255, 350–51 complexity of, 53–54 costs of, 8, 18, 72–74, 87n, 136–37, 170–71, 176–77, 184–85 creepiness of, 305–24 cultural impact of, 8–9, 21, 23–25, 53, 130, 135–40 development and emergence of, 7–18, 21, 53–54, 60–61, 66–67, 85–86, 87, 97–98, 129–38, 157–58, 182, 188–90, 193–96, 217 digital, 2–3, 7–8, 15–16, 18, 31, 40, 43, 50–51, 132, 208 economic impact of, 1–3, 15–18, 29–30, 37, 40, 53–54, 60–66, 71–74, 79–110, 124, 134–37, 161, 162, 169–77, 181–82, 183, 184–85, 218, 254, 277–78, 298, 335–39, 341–51, 357–58 educational, 92–97 efficiency of, 90, 118, 191 employment in, 56–57, 60, 71–74, 79, 123, 135, 178 engineering for, 113–14, 123–24, 192, 194, 217, 218, 326 essential vs. worthless, 11–12 failure of, 188–89 fear of (technophobia), 129–32, 134–38 freedom as issue in, 32–33, 90–92, 277–78, 336 government influence in, 158, 199, 205–6, 234–35, 240, 246, 248–51, 307, 317, 341, 345–46, 350–51 human agency and, 8–21, 50–52, 85, 88, 91, 124–40, 144, 165–66, 175–78, 191–92, 193, 217, 253–64, 274–75, 283–85, 305–6, 328, 341–51, 358–60, 361, 362, 365–67 ideas for, 123, 124, 158, 188–89, 225, 245–46, 286–87, 299, 358–60 industrial, 49, 83, 85–89, 123, 132, 154, 343 information, 7, 32–35, 49, 66n, 71–72, 109, 110, 116, 120, 125n, 126, 135, 136, 254, 312–16, 317 investment in, 66, 181, 183, 184, 218, 277–78, 298, 348 limitations of, 157–62, 196, 222 monopolies for, 60, 65–66, 169–74, 181–82, 187–88, 190, 202, 326, 350 morality and, 50–51, 72, 73–74, 188, 194–95, 262, 335–36 motivation and, 7–18, 85–86, 97–98, 216 nano-, 11, 12, 17, 162 new vs. old, 20–21 obsolescence of, 89, 97 political impact of, 13–18, 22–25, 85, 122, 124–26, 128, 134–37, 199–234, 295–96, 342 progress in, 9–18, 20, 21, 37, 43, 48, 57, 88, 98, 123, 124–40, 130–37, 256–57, 267, 325–31, 341–42 resources for, 55–56, 157–58 rupture as concept in, 66–67 scams in, 119–21, 186, 275n, 287–88, 299–300 singularity of, 22–25, 125, 215, 217, 327–28, 366, 367 social impact of, 9–21, 124–40, 167n, 187, 280–81, 310–11 software-mediated, 7, 11, 14, 86, 100–101, 165, 234, 236, 258, 347 startup companies in, 39, 60, 69, 93–94, 108n, 124n, 136, 179–89, 265, 274n, 279–80, 309–10, 326, 341, 343–45, 348, 352, 355 utopian, 13–18, 21, 31, 37–38, 45–46, 96, 128, 130, 167, 205, 207, 265, 267, 270, 283, 290, 291, 308–9, 316 see also specific technologies technophobia, 129–32, 134–38 television, 86, 185–86, 191, 216, 267 temperature, 56, 145 Ten Commandments, 300n Terminator, The, 137 terrorism, 133, 200 Tesla, Nikola, 327 Texas, 203 text, 162, 352–60 textile industry, 22, 23n, 24, 135 theocracy, 194–95 Theocracy humor, 124–25 thermodynamics, 88, 143n Thiel, Peter, 60, 93, 326 thought experiments, 55, 139 thought schemas, 13 3D printers, 7, 85–89, 90, 99, 154, 162, 212, 269, 310–11, 316, 331, 347, 348, 349 Thrun, Sebastian, 94 Tibet, 214 Time Machine, The (Wells), 127, 137, 261, 331 topology, network, 241–43, 246 touchscreens, 86 tourism, 79 Toyota Prius, 302 tracking services, 109, 120–21, 122 trade, 29 traffic, 90–92, 314 “tragedy of the commons,” 66n Transformers, 98 translation services, 19–20, 182, 191, 195, 261, 262, 284, 338 transparency, 63–66, 74–78, 118, 176, 190–91, 205–6, 278, 291, 306–9, 316, 336 transportation, 79–80, 87, 90–92, 123, 258 travel agents, 64 Travelocity, 65 travel sites, 63, 64, 65, 181, 279–80 tree-shaped networks, 241–42, 243, 246 tribal dramas, 126 trickle-down effect, 148–49, 204 triumphalism, 128, 157–62 tropes (humors), 124–40, 157, 170, 230 trust, 32–34, 35, 42, 51–52 Turing, Alan, 127–28, 134 Turing’s humor, 127–28, 191–94 Turing Test, 330 Twitter, 128, 173n, 180, 182, 188, 199, 200n, 201, 204, 245, 258, 259, 349, 365n 2001: A Space Odyssey, 137 two-way links, 1–2, 227, 245, 289 underemployment, 257–58 unemployment, 7–8, 22, 79, 85–106, 117, 151–52, 234, 257–58, 321–22, 331, 343 “unintentional manipulation,” 144 United States, 25, 45, 54, 79–80, 86, 138, 199–204 universities, 92–97 upper class, 45, 48 used car market, 118–19 user interface, 362–63, 364 utopianism, 13–18, 21, 30, 31, 37–38, 45–46, 96, 128, 130, 167, 205, 207, 265, 267, 270, 283, 290, 291, 308–9, 316 value, economic, 21, 33–35, 52, 61, 64–67, 73n, 108, 283–90, 299–300, 321–22, 364 value, information, 1–3, 15–16, 20, 210, 235–43, 257–58, 259, 261–63, 271–75, 321–24, 358–60 Values, Attitudes, and Lifestyles (VALS), 215 variables, 149–50 vendors, 71–74 venture capital, 66, 181, 218, 277–78, 298, 348 videos, 60, 100, 162, 185–86, 204, 223, 225, 226, 239, 240, 242, 245, 277, 287, 329, 335–36, 349, 354, 356 Vietnam War, 353n vinyl records, 89 viral videos, 185–86 Virtual Reality (VR), 12, 47–48, 127, 129, 132, 158, 162, 214, 283–85, 312–13, 314, 315, 325, 343, 356, 362n viruses, 132–33 visibility, 184, 185–86, 234, 355 visual cognition, 111–12 VitaBop, 100–106, 284n vitamins, 100–106 Voice, The, 185–86 “voodoo economics,” 149 voting, 122, 202–4, 249 Wachowski, Lana, 165 Wall Street, 49, 70, 76–77, 181, 184, 234, 317, 331, 350 Wal-Mart, 69, 70–74, 89, 174, 187, 201 Warhol, Andy, 108 War of the Worlds, The (Wells), 137 water supplies, 17, 18 Watts, Alan, 211–12 Wave, 189 wealth: aggregate or concentration of, 9, 42–43, 53, 60, 61, 74–75, 96, 97, 108, 115, 148, 157–58, 166, 175, 201, 202, 208, 234, 278–79, 298, 305, 335, 355, 360 creation of, 32, 33–34, 46–47, 50–51, 57, 62–63, 79, 92, 96, 120, 148–49, 210, 241–43, 270–75, 291–94, 338–39, 349 inequalities and redistribution of, 20, 37–45, 65–66, 92, 97, 144, 254, 256–57, 274–75, 286–87, 290–94, 298, 299–300 see also income levels weather forecasting, 110, 120, 150 weaving, 22, 23n, 24 webcams, 99, 245 websites, 80, 170, 200, 201, 343 Wells, H.


pages: 394 words: 118,929

Dreaming in Code: Two Dozen Programmers, Three Years, 4,732 Bugs, and One Quest for Transcendent Software by Scott Rosenberg

A Pattern Language, Benevolent Dictator For Life (BDFL), Berlin Wall, c2.com, call centre, collaborative editing, conceptual framework, continuous integration, Donald Knuth, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dynabook, en.wikipedia.org, Firefox, Ford paid five dollars a day, Francis Fukuyama: the end of history, George Santayana, Grace Hopper, Guido van Rossum, Gödel, Escher, Bach, Howard Rheingold, HyperCard, index card, Internet Archive, inventory management, Jaron Lanier, John Markoff, John von Neumann, knowledge worker, Larry Wall, life extension, Loma Prieta earthquake, Menlo Park, Merlin Mann, Mitch Kapor, new economy, Nicholas Carr, Norbert Wiener, pattern recognition, Paul Graham, Potemkin village, RAND corporation, Ray Kurzweil, Richard Stallman, Ronald Reagan, Ruby on Rails, semantic web, side project, Silicon Valley, Singularitarianism, slashdot, software studies, source of truth, South of Market, San Francisco, speech recognition, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Stewart Brand, Ted Nelson, Therac-25, thinkpad, Turing test, VA Linux, Vannevar Bush, Vernor Vinge, web application, Whole Earth Catalog, Y2K

As the project’s first big-splash Long Bet, Kapor wagered $20,000 (all winnings earmarked for worthy nonprofit institutions) that by 2029 no computer or “machine intelligence” will have passed the Turing Test. (To pass a Turing Test, typically conducted via the equivalent of instant messaging, a computer program must essentially fool human beings into believing that they are conversing with a person rather than a machine.) Taking the other side of the bet was Ray Kurzweil, a prolific inventor responsible for breakthroughs in electronic musical instruments and speech recognition who had more recently become a vigorous promoter of an aggressive species of futurism. Kurzweil’s belief in a machine that could ace the Turing Test was one part of his larger creed—that human history was about to be kicked into overdrive by the exponential acceleration of Moore’s Law and a host of other similar skyward-climbing curves.

Like a black hole or any similar rent in the warp and woof of space-time, a singularity is a disruption of continuity, a break with the past. It is a point at which everything changes, and a point beyond which we can’t see. Kurzweil predicts that artificial intelligence will induce a singularity in human history. When it rolls out, sometime in the late 2020s, an artificial intelligence’s passing of the Turing Test will be a mere footnote to this singularity’s impact—which will be, he says, to generate a “radical transformation of the reality of human experience” by the 2040s. Utopian? Not really. Kurzweil is careful to lay out the downsides of his vision. Apocalpytic? Who knows—the Singularity’s consequences are, by definition, inconceivable to us pre-Singularitarians. Big? You bet. It’s easy to make fun of the wackier dimension of Kurzweil’s digital eschatology.


pages: 467 words: 116,094

I Think You'll Find It's a Bit More Complicated Than That by Ben Goldacre

call centre, conceptual framework, correlation does not imply causation, crowdsourcing, death of newspapers, Desert Island Discs, en.wikipedia.org, experimental subject, Firefox, Flynn Effect, jimmy wales, John Snow's cholera map, Loebner Prize, meta analysis, meta-analysis, moral panic, placebo effect, publication bias, selection bias, selective serotonin reuptake inhibitor (SSRI), Simon Singh, statistical model, stem cell, the scientific method, Turing test, WikiLeaks

They have done ‘such a good job of passing themselves off as young people that they have proved indistinguishable from them’, according to New Scientist. So that’s the Turing test – where a computer program is indistinguishable from a real person – passed; and who’d have thought it, in a program written by a lone IT consultant from Wolverhampton with no AI background. So I call him. Here’s the problem. Reading New Scientist’s chat with Nanniebot, the excellent www.ntk.net/ (Private Eye for geeks) points out that Nanniebot ‘seems to be able to make logical deductions, parse colloquial English, correctly choose the correct moment to scan a database of UK national holidays, comment on the relative qualities of the Robocop series, and divine the nature of pancakes and pancake day’. Jabberwock, the winner of last year’s Loebner Prize for the Turing test, is rubbish in comparison (you can talk to it online and see for yourself).

Jabberwock, the winner of last year’s Loebner Prize for the Turing test, is rubbish in comparison (you can talk to it online and see for yourself). But Jim Wightman, the Nanniebot inventor – whose site claims they’ve passed the Turing test – isn’t entering the Loebner Prize this year. Maybe next year … it’s too buggy. But it’s live on the internet already? Can I test it? Sure. But I want to see with my own eyes that there’s not a real human being somewhere tapping out the answers, I explain. Jim offers network-monitoring software on my computer, to prove it’s connected to the one server. But what about that server? I want to see it working on its own, without a human. Can I come round to Jim’s place? He chuckles … Jim doesn’t keep the conversation datasets on site in Wolverhampton. ‘I know it sounds a bit Mission Impossible, but …’ He’s worried they might get stolen. They’re in a secure facility ‘with an iron lid under a mountain’.

124–6 Science and Technology Committee, House of Commons 196–7, 200–1, 322 Science Citation Index 22 Scientific American 261 Scott, Fiona 352, 353–5 Scottish Health Survey 106 screening for diseases xviii, 113–15, 334 Seasilver nutrient potion 387 ‘second-round’ effects 111, 112 select committees xx, 84, 196–201, 322 Sense About Science 256 Sgreccia, Bishop Elio 184 Shape Up for Summer 269 Sharp, Dr Julie 339 Shaw, Sophia 329–31 Sheffield Philharmonic Orchestra 310 Sheldrake, Rupert 190, 304 Sigman, Aric 5–8 Singh, Simon 250–4 Sky TV 371–5 smear campaigns, evidence-based 316–18 Smeed’s Law 112 Smith, Gary 104 smoking: Alzheimer’s and 20–1; ‘bioresonance’ treatment to help quit 277–8; cancer and 3, 22, 108, 109, 187; cigarette packaging 318–21; number of deaths caused by 187 Snow, John 365 Social Psychology and Personality Science 306–7 Social Text 297 Society of Biology 7 Soil Association 25, 191–2, 193 sokal hoax 297 Sonnaband, Dr Joe 285 Sorrows of Young Werther, The (Goethe) 361 South Africa, Aids in 140, 141, 182, 185–6, 273, 284, 285 South Bank University: Criminal Policy Research Unit 178–9 South Wales Evening Post 357 Spectator xxi; Aids denialism at the 283–6 Speigelhalter, David 102–3; Bicycle Helmets and the Law (editorial for BMJ co-written with Ben Goldacre) 110–13, 110n sperm donor clinics, pornography in xix, 179–82 Stanford University 262 STARFlex device 248 statins xvii statistics xvii–xviii, xix, 47–69; academic misuse of 129–31; algorithms and 52–3, 299; baseline problem 51–3; Benford’s Law 54–6; bicycle helmets and 110–13; chance and 56–8; coffee, hallucinatory effects of 64–6; datamining, terrorism and 51–3; government and xix, 147–65 see also government statistics; Down’s syndrome births, increase in 61–3; journalists find imaginary patterns in statistical noise 101–4; joy of xv; neuroscience and misuse of xviii–xix, 131–4; ‘95 per cent confidence intervals’ 59–61; one data point isn’t enough to spot a pattern 49–51; positions of ancient sites analysis 66–9; random variation 57, 61, 102, 103; relative risk reduction 115; sampling error 56–61 steroids, head injury and 207–8 Stonewall 92–4 Stott, Carol 354–5 stroke 119–20 suicide: copy-cat behaviour and reporting of xxi–xxii, 361–3; heroin addiction and 242; linked to phone masts story 333, 363–7 Sun: anti-cuts demo arrests story 155; ‘Downloading costs Billions’ story 159; pornography for sperm donors story 179–82; Sarah’s Law and 157–8 Sunday Express: Jab ‘as deadly as the Cancer’ cervical cancer story 331–4; ‘Suicides “linked to phone masts’’’ story 363–5 Sunday Sentinel, The 44 Sunday Telegraph: ‘Health Warning: Exercise Makes You Fat’ story 335–7 Sunday Times: Aids denialist reporting, 1990s and 283; ‘Public Sector Pay Races Ahead in a Recession’ story 149–52 superstition, performance and 313–15 ‘surrogate’ outcomes 119–20, 225–6, 359 surveys xvi, xviii, 87–97; abortions, GPs and 90–1; How to Lie with Statistics (Huff) 89–91; interesting form of wrong 92–4; nature of questions/leading with questions 89–91, 94–7; sample with built-in bias 89–91 Swartz, Aaron 32–4 sympathetic nervous system 144 systematic reviews 6–7, 12, 20–1, 23, 25–8, 140, 156–7, 192–3, 298, 314, 323, 336, 359 Taliban 221–4 tap water, fluoride in 22–5 teaching profession, evidence-based practice revolution in xx, 202–18 Tennison, Steve 82 Terrence Higgins Trust 187 Test of Developed Abilities (TDA) 189 Thapar, Professor Anita 40 ‘Therapeutic Touch’ 11–12 TheyWorkForYou.com 76 thinktanks xx, 180, 194–6, 227 time course 117 Time magazine 89 Times, The: ‘Down’s birth increase in a caring Britain’ story 61, 63; ‘girls really do prefer pink’ story 43; happiest places in Britain story 57; ‘The Value of Mathematics’, Reform thinktank report, coverage of 194 Trading Standards 12, 253 Traditional Chinese medicine 265 trionated particles xxii, 388–9 Trujillo, Cardinal Alfonso López 184 Turing test 392 2020health 180 Twitter 55, 257, 258, 308n, 315 UCL 198–9, 249, 252, 266; CIBER (Centre for Information Behaviour and the Evaluation of Research) 160, 161 UKUncut 155 Understanding Uncertainty website 102 Unite union 318 University College Hospital (UCH) 230, 241 University of California: Legacy Tobacco Documents Library 21 University of Chicago 285 University of Florida 134 University of Leicester 329 University of Newcastle 43n US Department of Defense 274 US Presidential Emergency Plan for Aids Relief 185 vaccine scares xxi, 85, 145, 273, 304, 331–4, 347–58, 399 vCJD 20 Velikovsky, Immanuel: Worlds in Collision 261–2 Vietnam War 231 Wakefield, Andrew 347, 354, 355, 357–8 Washington Post 39 water, drinking 11 What Works Clearing House (US government website for teachers) 214–15 Whitehall 51, 75–6 wi-fi, link to harmful effects 289–91, 293 Wightman, Jim 391–5 Wilmshurst, Dr Peter 247–50 wind farms, stranding of whales blamed on 340–1 Wine Magnet, The 122–4 Woolworths, locations of 68–9 World Aids Conference, Toronto, 2006 186 World Cancer Research Fund 337 World Health Organization (WHO) 116, 233, 289, 356 Wyatt, Professor John 197–9, 201 Wyeth ADD (pharmaceutical company) 25–6 Ying Wu 265 York University: Centre for Reviews and Dissemination at 23 YouGov 337 YouTube 258, 284 Zarrintan, Dr 144 ZenosBlog 253 Acknowledgements I have been lucky enough to be taught, corrected, calibrated, cajoled, amused, housed, helped, loved, reared, encouraged and informed by a very large number of smart and excellent people, including (each, to be clear, for only a subset of the preceeding activities): Liz Parratt, John King, Steve Rolles, Mark Pilkington, Shalinee Singh, Emily Wilson, Ian Katz, Iain Chalmers, Alex Lomas, Liam Smeeth, Ian Sample, Carl Heneghan, Richard Lehman, Kathy Flower, Ginge Tulloch, Matt Tait, Carl Reynolds, Dara Ó Briain, Paul Glasziou, Simon Wessely, Cicely Marston, Archie Cochrane, William Lee, Hind Khalifeh, Martin McKee, Cory Doctorow, Evan Harris, Muir Gray, Rob Manuel, Tobias Sargent, Anna Powell-Smith, Tjeerd van Staa, Robin Ince, Fiona Godlee, Trish Groves, Tracy Brown, Sile Lane, David Spiegelhalter, Ute-Marie Paul, Roddy Mansfield, Amanda Palmer, Rami Tzabar, George Davey-Smith, Charlotte Wattebot-O’Brien, Patrick Matthews, Amber Marks, Giles Wakely, Andy Lewis, Suzie Whitwell, Harry Metcalfe, Gimpy, David Colquhoun, Louise Burton, Simon Singh, Vaughan Bell, Nick Mailer, Milly Marston, Tom Steinberg, Mike Jay, Chris, Tom, Reg, Mum, Dad, Josh, Raph, Allie, Archie, Alice and Lou.


pages: 413 words: 119,587

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

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

Speech recognition clearly offers a dramatic improvement in busy-hand, busy-eye scenarios for interacting with the multiplicity of Web services and smartphone applications that have emerged. Perhaps advances in brain-computer interfaces will prove to be useful for those unable to speak or when silence or stealth is needed, such as card counting in blackjack. The murkier question is whether these cybernetic assistants will eventually pass the Turing test, the metric first proposed by mathematician and computer scientist Alan Turing to determine if a computer is “intelligent.” Turing’s original 1951 paper has spawned a long-running philosophical discussion and even an annual contest, but today what is more interesting than the question of machine intelligence is what the test implies about the relationship between humans and machines. Turing’s test consisted of placing a human before a computer terminal to interact with an unknown entity through typewritten questions and answers.

If, after a reasonable period, the questioner was unable to determine whether he or she was communicating with a human or a machine, then the machine could be said to be “intelligent.” Although it has several variants and has been widely criticized, from a sociological point of view the test poses the right question. In other words, it is relevant with respect to the human, not the machine. In the fall of 1991 I covered the first of a series of Turing test contests sponsored by a New York City philanthropist, Hugh Loebner. The event was first held at the Boston Computer Museum and attracted a crowd of computer scientists and a smattering of philosophers. At that point the “bots,” software robots designed to participate in the contest, weren’t very far advanced beyond the legendary Eliza program written by computer scientist Joseph Weizenbaum during the 1960s.

Weizenbaum’s program mimicked a Rogerian psychologist (a human-centered form of psychiatry focused on persuading a patient to talk his or her way toward understanding his or her actual feelings) and he was horrified to discover that his students had become deeply immersed in intimate conversations with his first, simple bot. But the judges for the original Loebner contest in 1991 fell into two broad categories: computer literate and computer illiterate. For human judges without computer expertise, it turned out that for all practical purposes the Turing test was conquered in that first year. In reporting on the contest I quoted one of the nontechnical judges, a part-time auto mechanic, saying why she was fooled: “It typed something that I thought was trite, and when I responded it interacted with me in a very convincing fashion,”5 she said. It was a harbinger of things to come. We now routinely interact with machines simulating humans and they will continue to improve in convincing us of their faux humanity.


pages: 542 words: 161,731

Alone Together by Sherry Turkle

Albert Einstein, Columbine, global village, Hacker Ethic, helicopter parent, Howard Rheingold, industrial robot, information retrieval, Jacques de Vaucanson, Jaron Lanier, Joan Didion, John Markoff, Kevin Kelly, lifelogging, Loebner Prize, Marshall McLuhan, meta analysis, meta-analysis, Nicholas Carr, Norbert Wiener, Panopticon Jeremy Bentham, Ralph Waldo Emerson, Rodney Brooks, Skype, social intelligence, stem cell, technoutopianism, The Great Good Place, the medium is the message, theory of mind, Turing test, Vannevar Bush, Wall-E, women in the workforce, Year of Magical Thinking

To determine this, she proposes an exercise in the spirit of the Turing test. In the original Turing test, published in 1950, mathematician Alan Turing, inventor of the first general-purpose computer, asked under what conditions people would consider a computer intelligent. In the end, he settled on a test in which the computer would be declared intelligent if it could convince people it was not a machine. Turing was working with computers made up of vacuum tubes and Teletype terminals. He suggested that if participants couldn’t tell, as they worked at their Teletypes, if they were talking to a person or a computer, that computer would be deemed “intelligent.” 10 A half century later, Baird asks under what conditions a creature is deemed alive enough for people to experience an ethical dilemma if it is distressed. She designs a Turing test not for the head but for the heart and calls it the “upside-down test.”

By the end of the film, we are left to wonder whether Deckard himself may be an android but unaware of his identity. Unable to resolve this question, we cheer for Deckard and Rachel as they escape to whatever time they have remaining—in other words, to the human condition. Decades after the film’s release, we are still nowhere near developing its androids. But to me, the message of Blade Runner speaks to our current circumstance: long before we have devices that can pass any version of the Turing test, the test will seem beside the point. We will not care if our machines are clever but whether they love us. Indeed, roboticists want us to know that the point of affective machines is that they will take care of us. This narrative—that we are on our way to being tended by “caring” machines—is now cited as conventional wisdom. We have entered a realm in which conventional wisdom, always inadequate, is dangerously inadequate.

and intimacy, ideas about networked life and performances by philosophical traditions in dialogue with, and relationships with reflecting on as symptom and dream of Social networks hacking and profiles and identity on Solitude intimacy and Sontag, Susan Sony Space public and private online, special qualities of sacred Speak & Spell (electronic game) Spielberg, Steven Spontaneity, loss of in online life Spoon (band) Stalking, online Star Wars (film) Starner, Thad Starr, Ringo State Radio (band) Storr, Anthony Strangers confessions, online, and as “friended,” Super Mario (game) Symptoms, dreams and Tamagotchis caring for death of feelings attributed to primer, notion of Technology blaming communities and complex ecology of complex effects of confusion about relationships and efficiency and embracing, with cost and expectations of ourselves holding power of keeping it busy, notion of mythology and narcissistic style and Oedipal story to discuss limitations of as prosthesis thinking about Teddy bears Tethered life Texts apology, use of for complexity of feelings about control, and conversations through feelings, path toward giving up hastily composed as interruptions loneliness and protective qualities of reflecting on (adolescents) seductiveness of speed up of communication and spontaneity and teaching parents about Thompson, Clive Thoreau, Henry David Toddlers, mechanical (Kismet and Cog) Transference, the Trust robotic Turing, Alan Turing test, the Turner, Victor Turtles, live/robot Twain, Mark Tweets Twitter Ultima 2 (game) Upside-down test (Freedom Baird) Vacations, offline Vadrigar Venting Virginia Tech Virtual self and virtual places Voice Voicemail Walden (Thoreau) Walden 2.0: WALL-E (film) Wandukan, development of Weak ties Wearable Computing Group (MIT) Weiner, Norbert: cybernetics and Weizenbaum, Joseph Wi-Fi Willard, Rebecca Ellen Turkle Wired World of Warcraft (game) YouTube Zhu Zhu pet hamsters Zone, The a In this book I use the terms the Net, the network, and connectivity to refer to our new world of online connections—from the experience of surfing the Web, to e-mail, texting, gaming, and social networking.


pages: 532 words: 140,406

The Turing Option by Harry Harrison, Marvin Minsky

industrial robot, pattern recognition, Silicon Valley, telepresence, telerobotics, theory of mind, Turing test, undersea cable

Roberts Cover illustration by Bob Eggleton Cover design by Don Puckey Cover photo by The Image Bank Warner Books, Inc. 1271 Avenue of the Americas New York, NY 10020 A Time Warner Company Printed in the United States of America Originally published in hardcover by Warner Books. First Printed in Paperback October, 1993 For Julie, Margaret and Henry: Moira and Todd— A story of your tomorrow. THE TURING TEST In 1950, Alan M. Turing, one of the earliest pioneers of computer science, considered the question of whether a machine could ever think. But because it is so hard to define thinking he proposed to start with an ordinary digital computer and then asked whether, by increasing its memory and speed, and providing it with a suitable program, it might be made to play the part of a man? His answer: "The question, 'Can machines think?'

You can tell her whatever you think she needs to know." "Okay then. Shelly, I am in the process of developing an artificial intelligence. Not the sort of program that we call AI now. I mean a really complete, efficient, freestanding and articulate artificial intelligence that really works." "But how can you make an intelligent machine until you know precisely what intelligence is?" "By making one that can pass the Turing Test. I'm sure that you know how it works. You put a human being at one terminal, talking to a human being on another terminal, and there are numberless questions that can be asked—and answered—to convince the human at one end that there is another human at the other terminal. And as you know the history of AI is filled with programs that failed this test." "But that's only a trick to convince someone that the machine is a person.

"Program on line," the computer said. "What is your objective?" "To locate the criminals who committed the crime in the laboratory of Megalobe Industries on February 8, 2023." "Have you located the criminals?" "Negative. I have still not determined how exit was accomplished and how the stolen material was removed." Brian listened in awe. "Are you sure that this is only a program? It sounds like a winner of the Turing test." "Plug-in speech program," Shelly said. "Right off the shelf. Verbalizes and parses from the natural language section of the CYC system. These speech programs always seem more intelligent than they are because their grammar and intonation are so precise. But they don't really know that much about what the words mean." She turned back to Ben. "Keep querying it, Ben, see if it has come up with any answers.


pages: 329 words: 88,954

Emergence by Steven Johnson

A Pattern Language, agricultural Revolution, Brewster Kahle, British Empire, Claude Shannon: information theory, complexity theory, Danny Hillis, Douglas Hofstadter, edge city, epigenetics, game design, garden city movement, Gödel, Escher, Bach, hive mind, Howard Rheingold, hypertext link, invisible hand, Jane Jacobs, Kevin Kelly, late capitalism, Marshall McLuhan, mass immigration, Menlo Park, Mitch Kapor, Murano, Venice glass, Naomi Klein, new economy, New Urbanism, Norbert Wiener, pattern recognition, pez dispenser, phenotype, Potemkin village, price mechanism, profit motive, Ray Kurzweil, slashdot, social intelligence, Socratic dialogue, stakhanovite, Steven Pinker, The Death and Life of Great American Cities, The Wealth of Nations by Adam Smith, theory of mind, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, trickle-down economics, Turing machine, Turing test, urban planning, urban renewal, Vannevar Bush

Turing’s war research had focused on detecting patterns lurking within the apparent chaos of code, but in his Manchester years, his mind gravitated toward a mirror image of the original code-breaking problem: how complex patterns could come into being by following simple rules. How does a seed know how to build a flower? Turing’s paper on morphogenesis—literally, “the beginning of shape”—turned out to be one of his seminal works, ranking up their with his more publicized papers and speculations: his work on Gödel’s undecidability problem, the Turing Machine, the Turing Test—not to mention his contributions to the physical design of the modern digital computer. But the morphogenesis paper was only the beginning of a shape—a brilliant mind sensing the outlines of a new problem, but not fully grasping all its intricacies. If Turing had been granted another few decades to explore the powers of self-assembly—not to mention access to the number-crunching horsepower of non-vacuum-tube computers—it’s not hard to imagine his mind greatly enhancing our subsequent understanding of emergent behavior.

The first generation of emergent software—programs like SimCity and StarLogo—displayed a captivatingly organic quality; they seemed more like life-forms than the sterile instruction sets and command lines of early code. The next generation will take that organic feel one step further: the new software will use the tools of self-organization to build models of our own mental states. These programs won’t be self-aware, and they won’t pass any Turing tests, but they will make the media experiences we’ve grown accustomed to seem autistic in comparison. They will be mind readers. From a certain angle, this is an old story. The great software revolution of the seventies and eighties—the invention of the graphic interface—was itself predicated on a theory of other minds. The design principles behind the graphic interface were based on predictions about the general faculties of the human perceptual and cognitive systems.

., 14–15 Shannon, Claude, 44–47, 53, 62–65, 241n Shapiro, Andrew, 159–60 Shelley, Mary Wollstonecraft, 125 shopping malls, 90, 92 sidewalk culture, 51, 91–97, 99, 146, 147, 148, 230–31 silk weavers, 101, 102, 104–7, 124 SimCity, 66, 87–89, 98, 186, 205, 208, 229 Sims, The, 186–89, 209–10, 229 simulations, computer: of aggregation, 16–17, 23, 59–63, 163–69 of ants, 59–63, 65 of cities, 66, 87–89, 98, 186, 229–30 of evolution, 56–63, 182–89, 193, 209–10 of genetics, 57–59, 182–86 models for, 9, 16–17, 23, 59–63 of self-organization, 59–63, 76, 163–69 60 Minutes, 144 Slashdot, 152–62, 205, 212, 223, 260n Slate, 118, 128 sleep cycles, 140 slime mold (Dictyostelium discoideum), 11–17, 18, 20–21, 23, 43, 52, 63–64, 67, 163–69, 179, 180, 220, 235n, 236n, 246n slums, 41, 49–50, 137 Smarties experiment, 196–97, 200, 261n–62n Smith, Adam, 18, 156 Societas Mercatorum, 101 society: ant colonies compared with, 97–98, 248n emergence in, 22–23, 36–40, 49–50, 92–100 hierarchical, 14–15, 98 organization of, 9, 27, 33–41, 92–94, 97–100, 109, 204, 252n–54n patterns in, 18, 36–40, 41, 49–50, 52, 91, 95, 137, 185 Society of Mind theory, 65 software: emergent, 17, 21, 22, 121–26, 170–74, 186, 189, 204–8, 221–22, 223 gaming, 163–89 learning, 53–63, 65 for online communities, 148–62 Open Source, 222 pattern-recognition, 18, 21, 54, 56, 123–24, 126–29 personalized, 159–60, 207–8, 211, 212–13 see also programs, computer SoHo (New York City), 50 Solenopsis invicta, 75 Sopranos, The, 219 spam, 153, 156, 161, 215–16 speech encryption, 44–45 spokescouncils, 226 StarLogo, 76, 163–69, 179, 205, 219, 247n, 260n statistical analysis, 46–47, 76–77, 78 storytelling, 188–89 suburbia, 94–95, 230, 259n Sun Microsystems, 224 surf engines, 122–23 synapses, 134 system events, 145 systems: adaptive, 18, 19–20, 119, 128, 137, 139–40 bottom-up, 17, 18, 22, 53–57, 66–67, 83, 97–98, 115, 116, 133, 148, 164, 166, 207, 221–23, 231 climax stage of, 147–48, 152, 154 command, 15, 77, 83–84 complex, 18, 29, 78, 139–40, 246n decentralized, 17, 22, 31–32, 39–40, 66, 76–79, 86, 117, 118–21, 163–89, 204–5, 217–18, 222, 233–34, 236n–37n, 263n dynamic, 20, 248n–49n emergent, see emergence interactive, 22, 79, 81, 120, 123, 126, 158–59, 231 open-ended, 57–58, 180–89, 208 polycentric, 90–91, 159, 223 representational, 157–59 rule-governed, 19, 180–81, 226 self-organizing, see self-organization self-regulating, 138, 140–41, 143, 146–47, 148, 149, 151, 154, 159 simple, 46, 47, 78 top-down, 14–15, 18, 30–31, 33, 98, 132, 136, 145, 148–49, 153, 208, 223, 225 “Take It to the Streets” (Berman), 95 Tap, Type, Write, 174–75, 177 Taylor, Chuck, 59–63, 65 TCG, 224 technology: innovation in, 108–9, 111–12, 113, 116, 254n slave, 125–26 see also computers Teilhard de Chardin, Pierre, 115–16, 120 telephones, 47, 229 television, 95, 130–36, 137, 143–46, 158, 159, 160–61, 210–13, 217, 218 Terminator, 127 termites, 22, 73, 82 “theory of other minds,” 195–226 thermostats, 137–38, 150, 258n thinking: associative, 206 bottom-up, 66–67 decentralized, 17 group, 160 serial, 127 see also intelligence Thomas, Lewis, 9 Thompson, D’Arcy, 236n, 259n threaded discussion boards, 149–50 TiVo, 211–13, 214, 218 Tocqueville, Alexis de, 35 toys, 165–66, 178–80, 181 Tracker program, 59–63, 65 trade, 101–2, 104–7, 109, 110 traffic patterns, 97, 166, 204, 230–31, 232 traveling salesman problem, 227–29 tumors, brain, 119 Turing, Alan, 14, 18, 42–45, 49, 53, 54, 62–65, 67, 206, 236n, 242n, 254n–56n, 263n Turing Machine, 42, 45 Turing Test, 42, 206 Turner, Ted, 135–36 “turtles,” 166, 167–68, 260n undecidability problem, 42 Unreal, 208–9 urbanization, 99, 108, 109–13, 116, 146–48, 253n–54n urban planning, 49–50, 51, 89, 92, 109, 146–47, 230–31 Usenet, 162 user ratings, 121–26, 129, 156–62, 214–15, 221–22 varicella-zoster virus, 103, 104 VCRs, 212 ventral premotor area, 198 video games, see games, computer Virtual Community, The (Rheingold), 148 visual cortex, 201 Vocoder, 44 Washington Post, 131 Weaver, Warren, 46–49, 50, 51, 64–66 Well, 147–52, 153 West Village (New York City), 50, 93 Wheatley, Bill, 136 Wheeler, William Morton, 242n White, Leslie, 253n White, Lynn, Jr., 112 Wide Area Information Server (WAIS), 122 Wiener, Norbert, 53, 57, 64–65, 125–26, 139, 140, 143, 151–52, 162, 169, 238n, 251n, 259n–60n Wilson, Edward O., 52, 60, 75 Wittgenstein, Ludwig, 41 Wooten, Jim, 130–36, 137, 144–45 Wordsworth, William, 27, 39, 92, 98 working class, 37, 41, 52, 91, 95, 240n, 259n World Wide Web, see Internet Wright, Robert, 114, 115–17, 118 Wright, Will, 66, 87, 88, 186–89, 209–10, 229–30 Yahoo, 114, 117 Zelda: Ocarina of Time, 176, 177 Zimmerman, Eric, 178–80, 182, 186, 189 SCRIBNER 1230 Avenue of the Americas New York, NY 10020 www.SimonandSchuster.com Copyright © 2001 by Steven Johnson All rights reserved, including the right of reproduction in whole or in part in any form.


pages: 350 words: 96,803

Our Posthuman Future: Consequences of the Biotechnology Revolution by Francis Fukuyama

Albert Einstein, Asilomar, assortative mating, Berlin Wall, bioinformatics, Columbine, demographic transition, Fall of the Berlin Wall, Flynn Effect, Francis Fukuyama: the end of history, impulse control, life extension, Menlo Park, meta analysis, meta-analysis, out of africa, Peter Singer: altruism, phenotype, presumed consent, Ray Kurzweil, Scientific racism, selective serotonin reuptake inhibitor (SSRI), sexual politics, stem cell, Steven Pinker, The Bell Curve by Richard Herrnstein and Charles Murray, Turing test, twin studies

As Searle says of this approach, it works only by denying the existence of what you and I and everyone else understand consciousness to be (that is, subjective feelings).39 Similarly, many of the researchers in the field of artificial intelligence sidestep the question of consciousness by in effect changing the subject. They assume that the brain is simply a highly complex type of organic computer that can be identified by its external characteristics. The well-known Turing test asserts that if a machine can perform a cognitive task such as carrying on a conversation in a way that from the outside is indistinguishable from similar activities carried out by a human being, then it is indistinguishable on the inside as well. Why this should be an adequate test of human mentality is a mystery, for the machine will obviously not have any subjective awareness of what it is doing, or feelings about its activities.p This doesn’t prevent such authors as Hans Moravec40 and Ray Kurzweil41 from predicting that machines, once they reach a requisite level of complexity, will possess human attributes like consciousness as well.42 If they are right, this will have important consequences for our notions of human dignity, because it will have been conclusively proven that human beings are essentially nothing more than complicated machines that can be made out of silicon and transistors as easily as carbon and neurons.

It is perfectly possible, for example, to design a robot with heat sensors in its fingers connected to an actuator that would pull the robot’s hand away from a fire. The robot could keep itself from being burned without having any subjective sense of pain, and it could make decisions on which objectives to fulfill and which activities to avoid on the basis of a mechanical computation of the inputs of different electrical impulses. A Turing test would say it was a human being in its behavior, but it would actually be devoid of the most important quality of a human being, feelings. The actual subjective form that emotions take are today seen in evolutionary biology and in cognitive science as no more than epiphenomenal to their underlying function; there are no obvious reasons this form should have been selected for in the course of evolutionary history.43 As Robert Wright points out, this leads to the very bizarre outcome that what is most important to us as human beings has no apparent purpose in the material scheme of things by which we became human.44 For it is the distinctive human gamut of emotions that produces human purposes, goals, objectives, wants, needs, desires, fears, aversions, and the like and hence is the source of human values.

state, the, origin of statistical science stem cell research ban on with existing “lines” stem cells sterilization, involuntarily Stock, Gregory Strickland, Ted subjective mental states subliminal repetition suffering of animals good points of minimizing suicide, assisted sulfanilamide elixir scandal “superbugs” superman surrogate motherhood Sweden Switzerland sympathy, the word Tabula Rasa talk therapy, vs. drug therapy Taoism Taylor, Charles Tay-Sachs disease technology “arms race” in change in, and obsolescence of skills as a force for historical change regulation of “telescreen” telomerase telomeres tenure in office, limiting Teresa, Mother testosterone, in utero thalidomide scandal Thatcher revolution therapy, drug- vs. talk-type therapy/enhancement distinction third parties, harm to, from individual choices Thomistic tradition Thompson, James Thorazine Three Mile Island Thurstone, L. L. thymos (spiritedness) time, concept of Tocqueville, Alexis de totalitarianism, collapse of transgenic crops Tribe, Laurence Trivers, Robert Tsien, Joe Turing test Turkey Tuskegee syphilis scandal twin studies typical, meaning of word tyranny failure of of the majority unborn presumed consent of rights of United Kingdom United Nations United States attitude toward regulation attitude toward technology demographic trends in family breakdown in international influence of, re regulation natural right as foundation of political system, effect on regulation principles of regulatory policy and practice U.S.


pages: 797 words: 227,399

Wired for War: The Robotics Revolution and Conflict in the 21st Century by P. W. Singer

agricultural Revolution, Albert Einstein, Any sufficiently advanced technology is indistinguishable from magic, Atahualpa, barriers to entry, Berlin Wall, Bill Joy: nanobots, blue-collar work, borderless world, Charles Lindbergh, clean water, Craig Reynolds: boids flock, cuban missile crisis, digital map, en.wikipedia.org, Ernest Rutherford, failed state, Fall of the Berlin Wall, Firefox, Francisco Pizarro, Frank Gehry, friendly fire, game design, George Gilder, Google Earth, Grace Hopper, I think there is a world market for maybe five computers, if you build it, they will come, illegal immigration, industrial robot, interchangeable parts, Intergovernmental Panel on Climate Change (IPCC), invention of gunpowder, invention of movable type, invention of the steam engine, Isaac Newton, Jacques de Vaucanson, job automation, Johann Wolfgang von Goethe, Law of Accelerating Returns, Mars Rover, Menlo Park, New Urbanism, pattern recognition, private military company, RAND corporation, Ray Kurzweil, RFID, robot derives from the Czech word robota Czech, meaning slave, Rodney Brooks, Ronald Reagan, Schrödinger's Cat, Silicon Valley, social intelligence, speech recognition, Stephen Hawking, strong AI, technological singularity, The Coming Technological Singularity, The Wisdom of Crowds, Turing test, Vernor Vinge, Wall-E, Yogi Berra

This idea of robots, one day being able to problem-solve, create, and even develop personalities past what their human designers intended is what some call “strong AI.” That is, the computer might learn so much that, at a certain point, it is not just mimicking human capabilities but has finally equaled, and even surpassed, its creators’ human intelligence. This is the essence of the so-called Turing test. Alan Turing was one of the pioneers of AI, who worked on the early computers like Colossus that helped crack the German codes during World War II. His test is now encapsulated in a real-world prize that will go to the first designer of a computer intelligent enough to trick human experts into thinking that it is human. So what is the reward for inventing what some hope will be the real-world equivalent of Data from Star Trek, but others worry will be Skynet from The Terminator?

When that happened, he revised his prediction again (as well as his book title, which in 1992 was reissued as What Computers Still Can’t Do), claiming that while computers may be able to beat most humans, they would never be able to beat the very best, such as the world champion chessmaster. Of course, this then happened in 1997 with IBM’s Deep Blue. Psychologist and AI expert Robert Epstein, a Singularity proponent who administers the Turing test program, acknowledges that “some people, smart people, say I am full of crap. My response is that someday you are going to be having that argument with a computer. As soon as you open your mouth, you’ve lost. In that context, you can’t win. The only person able to deny the changes occurring around us is the one who hides, the one who has their head in the sand.” THE MILITARY AND THE SINGULARITY The question as to whether the Singularity will come and when depends on whether the same sort of exponential growth that happened in the past will continue in the years ahead.

Indeed, this soldier is dubious of some of the rosier futuristic visions like Ray Kurzweil’s prediction. “Kurzweil, while an interesting technologist, is not much of a success as a cultural (or economic) anthropologist.” Bateman thinks Kurzweil misses that technology advances in fits and starts, not so much a steady upward curve. Bateman does, however, think that something akin to the Singularity is on its way. “The Turing test [where a machine will finally be able to trick a human into thinking it is a person] is going to fall fairly soon, and that will cause some squeamish responses.” Bateman is representative of the first generation of officers to truly ponder an idea once seen as not merely insane but even sinful within the military. After he came back from Iraq, where he served as a strategist for then Lieutenant General David Petraeus, he was assigned to the Office of Net Assessment, the Pentagon’s shop for figuring out how to master the upcoming RMA.


pages: 111 words: 1

Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets by Nassim Nicholas Taleb

Antoine Gombaud: Chevalier de Méré, availability heuristic, backtesting, Benoit Mandelbrot, Black Swan, commoditize, complexity theory, corporate governance, corporate raider, currency peg, Daniel Kahneman / Amos Tversky, discounted cash flows, diversified portfolio, endowment effect, equity premium, fixed income, global village, hedonic treadmill, hindsight bias, Kenneth Arrow, Long Term Capital Management, loss aversion, mandelbrot fractal, mental accounting, meta analysis, meta-analysis, Myron Scholes, Paul Samuelson, quantitative trading / quantitative finance, QWERTY keyboard, random walk, Richard Feynman, road to serfdom, Robert Shiller, Robert Shiller, selection bias, shareholder value, Sharpe ratio, Steven Pinker, stochastic process, survivorship bias, too big to fail, Turing test, Yogi Berra

For instance, what struck me while reading Richard Dawkins’ Selfish Gene is that, although the text does not exhibit a single equation, it seems as if it were translated from the language of mathematics. Yet it is artistic prose. Reverse Turing Test Randomness can be of considerable help with the matter. For there is another, far more entertaining way to make the distinction between the babbler and the thinker. You can sometimes replicate something that can be mistaken for a literary discourse with a Monte Carlo generator but it is not possible randomly to construct a scientific one. Rhetoric can be constructed randomly, but not genuine scientific knowledge. This is the application of Turing’s test of artificial intelligence, except in reverse. What is the Turing test? The brilliant British mathematician, eccentric, and computer pioneer Alan Turing came up with the following test: A computer can be said to be intelligent if it can (on average) fool a human into mistaking it for another human.

NERO TULIP Hit by Lightning Temporary Sanity Modus Operandi No Work Ethics There Are Always Secrets JOHN THE HIGH-YIELD TRADER An Overpaid Hick THE RED-HOT SUMMER Serotonin and Randomness YOUR DENTIST IS RICH, VERY RICH Two A BIZARRE ACCOUNTING METHOD ALTERNATIVE HISTORY Russian Roulette Possible Worlds An Even More Vicious Roulette SMOOTH PEER RELATIONS Salvation via Aeroflot Solon Visits Regine’s Nightclub GEORGE WILL IS NO SOLON: ON COUNTERINTUITIVE TRUTHS Humiliated in Debates A Different Kind of Earthquake Proverbs Galore Risk Managers Epiphenomena Three A MATHEMATICAL MEDITATION ON HISTORY Europlayboy Mathematics The Tools Monte Carlo Mathematics FUN IN MY ATTIC Making History Zorglubs Crowding the Attic Denigration of History The Stove Is Hot Skills in Predicting Past History My Solon DISTILLED THINKING ON YOUR PALMPILOT Breaking News Shiller Redux Gerontocracy PHILOSTRATUS IN MONTE CARLO : ON THE DIFFERENCE BETWEEN NOISE AND INFORMATION Four RANDOMNESS, NONSENSE, AND THE SCIENTIFIC INTELLECTUAL RANDOMNESS AND THE VERB Reverse Turing Test The Father of All Pseudothinkers MONTE CARLO POETRY Five SURVIVAL OF THE LEAST FIT–CAN EVOLUTION BE FOOLED BY RANDOMNESS? CARLOS THE EMERGING-MARKETS WIZARD The Good Years Averaging Down Lines in the Sand JOHN THE HIGH-YIELD TRADER The Quant Who Knew Computers and Equations The Traits They Shared A REVIEW OF MARKET FOOLS OF RANDOMNESS CONSTANTS NAIVE EVOLUTIONARY THEORIES Can Evolution Be Fooled by Randomness?


pages: 377 words: 97,144

Singularity Rising: Surviving and Thriving in a Smarter, Richer, and More Dangerous World by James D. Miller

23andMe, affirmative action, Albert Einstein, artificial general intelligence, Asperger Syndrome, barriers to entry, brain emulation, cloud computing, cognitive bias, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, David Brooks, David Ricardo: comparative advantage, Deng Xiaoping, en.wikipedia.org, feminist movement, Flynn Effect, friendly AI, hive mind, impulse control, indoor plumbing, invention of agriculture, Isaac Newton, John von Neumann, knowledge worker, Long Term Capital Management, low skilled workers, Netflix Prize, neurotypical, Norman Macrae, pattern recognition, Peter Thiel, phenotype, placebo effect, prisoner's dilemma, profit maximization, Ray Kurzweil, recommendation engine, reversible computing, Richard Feynman, Rodney Brooks, Silicon Valley, Singularitarianism, Skype, statistical model, Stephen Hawking, Steve Jobs, supervolcano, technological singularity, The Coming Technological Singularity, the scientific method, Thomas Malthus, transaction costs, Turing test, twin studies, Vernor Vinge, Von Neumann architecture

And once we have a simulation of a human brain, we should eventually be able to increase the speed of this simulation a millionfold, make a million copies of the simulation, and usher in the Singularity. Or perhaps within fifteen years it will be apparent to all who are technologically literate that within another decade an AI will pass what’s known as the “Turing test,” in which a human judge engaged in natural-language written conversation with the AI can’t tell whether the AI is man or machine. And once this test is passed, we could eventually speed up the AI a millionfold, make a million copies of the computer, and produce a Singularity. Ray Kurzweil has bet $20,000 that a computer will pass a Turing test by 2029.320 Or maybe within twenty years, brain/computer interfaces will be developing at such a rate that an intelligence explosion seems inevitable. Or improvements in gene therapy and eugenics could create millions of babies who, when they grow up, will be smarter than John von Neumann, and an understanding of what these babies will eventually accomplish could convince many that a Singularity is almost inevitable.

See also amphetamines (“speed”) Smith, Adam, 135 Smith College Adderall, 102–7, 112, 163 amphetamines use, 102 Dean and Adderall-type drugs for performance-enhancement, 102 student illegal drug use, 101 “study buddy” drugs, 102 survey of illegal cognitive-enhancing drug use among undergraduates, 103–9 socialists, 41 Social Security taxes, 157 sociopath, 22, 93 sociopathic children, 84 Socrates, 91 Socratic questioning method, 215 soft toilet paper, 166 Soviet Union, xiii, 19, 49, 124, 127, 206 spacecraft, 199 species extinction, 29 Stalin, Joseph, 22, 220 standard of living, 76, 123 Stanovich, Keith, 65–66 StarCraft II (video game), 106 stars “turned of” to conserve energy, 199 Star Trek, 171 starvation pressures, 150 Stewart, Potter (US Supreme Court Justice), 38–39 stop signal reaction time, 105 Study of Mathematically Precocious Youth, 65 subjective judgment, 39 sub-Saharan Africa, 173 suicide, 92–93 super genius, 90–91, 95 superhuman intelligence, xiv superintelligence, 21 superintelligence, “alien-like,” 122 super-skyscraper, 181 superweapon, 204 surrogate woman, 194 “survival of the richest,” 81 surviving children, 82 Swift, Jonathan, 88 T Tallinn, Jaan, 35, 215 tampons, 166 Tao, Terence, 91–92 tax on emulations, 150 teleportation device, xi teleportation machine, 138–39 terminal disease, 219 thermonuclear war, 52–53. See also nuclear war Thiel, Peter, x, 35, 170, 186, 214 torsion dystonia, 97–98 toxic garbage dumps, 124 trade with extraterrestrials, 122 Transcend: Nine Steps to Living Well Forever (Kurzweil), 179 transistors, 4 trial-and-error methods, 30 Trident submarine, 23 True Names. . . and Other Dangers (Vinge), 36 trust, 70 Turing test, 177 23andMe (testing company), 168–69 2001: A Space Odyssey (movie), 210 U Ulam, Stanislaw, xv ultra-AI. See also artificial intelligence (AI) atoms in our solar system, could completely rearrange the distribution of, 187 code, made up of extremely complex, 30 code, might change its code from friendly to non-friendly, 31 in computer simulation run by a more powerful AI, 45–46 “could never guarantee with “probability one” that the cup would stay on the table,” 28 free energy supply, will obtain, 27 friendly, 14, 33, 46, 208 human destruction because of hyper-optimization, 28 with human-like objectives, 29 humans don’t get a second chance once it is created, 30 indifference towards humanity and would kill us, 27 indifferent to mankind and creation of conditions directly in conflict with our continued existence, 28 intelligence explosion and, 31, 35, 121, 187 is not designed for friendliness and could extinguish humanity, 30, 36 lack patience to postpone what might turn out to be utopia, 46 manipulation through humans to win its freedom, 32 martial prowess, 24 military technologies, will discover, 24 morality, sharing our, 29 as more militarily useful than atomic weapons, 47 power used to stop all AI rivals from coming into existence, 24 pre-Singularity investments, might obliterate the value of, 187 progress toward its goals increased by having additional free energy, 27 rampaging, 23 risks of destroying the world, 49 unfriendly (Devil), 30, 35, 46, 202, 208 unlikely events, will plan against, 28 will command people with hypnosis, love, or subliminal messages, 33 ultra-intelligence, 40, 44, 47 unfriendly.


pages: 324 words: 96,491

Messing With the Enemy: Surviving in a Social Media World of Hackers, Terrorists, Russians, and Fake News by Clint Watts

4chan, active measures, Affordable Care Act / Obamacare, barriers to entry, Berlin Wall, Bernie Sanders, Chelsea Manning, Climatic Research Unit, crowdsourcing, Daniel Kahneman / Amos Tversky, Donald Trump, drone strike, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, failed state, Fall of the Berlin Wall, Filter Bubble, global pandemic, Google Earth, illegal immigration, Internet of things, Julian Assange, loss aversion, Mark Zuckerberg, Mikhail Gorbachev, mobile money, mutually assured destruction, obamacare, Occupy movement, offshore financial centre, pre–internet, side project, Silicon Valley, Snapchat, The Wisdom of Crowds, Turing test, University of East Anglia, Valery Gerasimov, WikiLeaks, zero day

,” The Washington Post (April 23, 2013). https://www.washingtonpost.com/news/worldviews/wp/2013/04/23/syrian-hackers-claim-ap-hack-that-tipped-stock-market-by-136-billion-is-it-terrorism/?utm_term=.0cb10e61e8fc; James Temperton, “FBI Adds Syrian Electronic Army Hackers to Most Wanted List,” Wired (March 23, 2016). http://www.wired.co.uk/article/syrian-electronic-army-fbi-most-wanted. 4. For a short summary of the “Turing Test”, Wikipedia does a good breakdown. https://en.wikipedia.org/wiki/Turing_test. 5. Phil Howard, “Computational Propaganda: The Impact of Algorithms and Automation on Public Life,” Presentation available at: https://prezi.com/b_vewutjwzut/computational-propaganda/?webgl=0. 6. Caitlin Dewey, “One in Four Debate Tweets Comes from a Bot. Here’s How to Spot Them,” The Washington Post (October 19, 2016). https://www.washingtonpost.com/news/the-intersect/wp/2016/10/19/one-in-four-debate-tweets-comes-from-a-bot-heres-how-to-spot-them. 7.

Phil Howard, professor and leader of its Computational Propaganda Project, defines computational propaganda as “the use of information and communication technologies to manipulate perceptions, affect cognition, and influence behavior.” This manipulation occurs through the deployment of what are known as social bots—programs, defined by a computer algorithm, that produce personas and content on social media applications that replicate a real human. These social bots have also passed the important milestone known as the Turing test, a challenge developed by Alan Turing, the great member of the British team that cracked the German Enigma code.4 The test assesses whether a machine has the ability to communicate, via text only, at a level equivalent to that of a real person, such that a computer—or, in the modern case, an artificially generated social media account—cannot be distinguished from a live person. The bots we observed did just that: they created artificial accounts, emulating real people, that mimicked the conversations of target audiences in several geographies around the world.


pages: 268 words: 109,447

The Cultural Logic of Computation by David Golumbia

Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, American ideology, Benoit Mandelbrot, borderless world, business process, cellular automata, citizen journalism, Claude Shannon: information theory, computer age, corporate governance, creative destruction, en.wikipedia.org, finite state, future of work, Google Earth, Howard Zinn, IBM and the Holocaust, iterative process, Jaron Lanier, jimmy wales, John von Neumann, Joseph Schumpeter, late capitalism, means of production, natural language processing, Norbert Wiener, packet switching, RAND corporation, Ray Kurzweil, RFID, Richard Stallman, semantic web, Shoshana Zuboff, Slavoj Žižek, social web, stem cell, Stephen Hawking, Steve Ballmer, Stewart Brand, strong AI, supply-chain management, supply-chain management software, Ted Nelson, telemarketer, The Wisdom of Crowds, theory of mind, Turing machine, Turing test, Vannevar Bush, web application

At the same time, computers carry their own linguistic ideologies, often stemming from the conceptual-intellectual base of computer science, and these ideologies even today shape a great deal of the future direction of computer development. Computationalist Linguistics p 85 Like the Star Trek computer (especially in the original series; see Gresh and Weinberg 1999) or the Hal 9000 of 2001: A Space Odyssey, which easily pass the Turing Test and quickly analyze context-sensitive questions of knowledge via a remarkable ability to synthesize theories over disparate domains, the project of computerizing language itself has a representational avatar in popular culture. The Star Trek “Universal Translator” represents our Utopian hopes even more pointedly than does the Star Trek computer, both for what computers will one day do and what some of us hope will be revealed about the nature of language.

Much of the time, such games are played against computer or so-called Artificial Intelligence opponents, avatars of the player represented by the computer, often representing possible real-world strategies. To a neutral observer, the play of an AI player and a human player are virtually indistinguishable (that is, if one watches the screen of a computer game, rather than the actions of the human player). What we call AI has among its greatest claims to successes within the closed domain of computer games, inside of which AI opponents might even be said at times to pass a kind of Turing Test (or perhaps more appropriately, human beings are able to emulate the behavior of computers to a strong but not perfect degree of approximation). Of course this is mainly true depending on the difficulty level at which the game is set. Because the world-system of an RTS game is fully quantified, succeeding in it is ultimately purely a function of numbers. By setting the difficulty level high enough, one can guarantee that the computer will win: it is simply possible to apply more computing power to a simulation than a human being can possibly muster.

., 36, 41, 55, 57 Slavery, 12, 26, 188–189 Simulation, 12, 22, 36, 69, 75, 99–101, 136, 167, 204–205, 216–217 Smoothness (vs. striation), 11, 22–24, 134, 149, 156–162, 175, 217 Soar, 202 Social web, 6, 211 Spivak, Gayatri Chakravorty, 14, 16, 121–122 Spreadsheets, 157–161, 198, 201, 212 Standard languages, 92, 95, 119–121 Standardization, 115, 118–122, 124, 150 Standards, 6, 107, 113–115, 176 Star Trek, 78, 85 State philosophy, 8–11, 76 Strauss, Leo, 192–194 Striation, 11, 33, 52, 62, 72, 129–134, 140–144, 151–177, 208, 213, 217, 219; defined, 22–24 Strong AI, 84, 98, 106n2, 201–202 Subject-Oriented Programming, 210–211 Supply chains, 146–147, 170, 175–176 Supply-Chain Management (SCM), 164, 172, 175–176 Surveillance, 4, 13, 60, 149–152, 161–162, 176–177, 182, 213 Sweezy, Paul, 129 Syntax, 34, 37, 40, 42, 47, 66–67, 70, 94, 189–192, 195 Taylor, Frederick, 158, 161–162 Territorialization, 23–24, 153–154 Text encoding, 107–108 Text Encoding Initiative (TEI), 111–112 Text-to-speech (TTS) systems, 93–97 Turing, Alan, 12, 32, 37, 39–40, 62, 70, 83–84, 86, 89, 216 Turing Machine, 7, 19, 35–37, 40, 47, 59, 62, 75, 166, 201, 216 Turing Test, 84–85, 98, 136 Turkle, Sherry, 185–186, 207 Turner, Fred, 5, 152, 219 Index Unicode, 124 Virtuality, 22–23 Voice recognition, 94–95, 97 von Neumann, John, 12, 32, 35, 37, 83, 195, W3C (World Wide Web consortium), 113, 117–118 Wal-Mart, 79, 147, 174–176 Wark, McKenzie, 5, 23, 25, 143–144, 151, 221 Weaver, Warren, 86–94, 98 p 257 Web 2.0, 208, 211 Weizenbaum, Joseph, 4, 53, 71, 207 Wiener, Norbert, 4, 87–92, 97 Wikipedia, 5, 26, 124, 208, 219 Winograd, Terry, 5, 71, 98–103 Wittgenstein, Ludwig, 14–15, 37, 55–56, 62, 64, 68, 71, 74–80, 108–109 Word processors, 112, 116, 157 XML, 111–119, 211 Zinn, Howard, 143 Žižek, Slavoj, 187, 224


pages: 488 words: 148,340

Aurora by Kim Stanley Robinson

back-to-the-land, cognitive bias, cognitive dissonance, dark matter, epigenetics, gravity well, mandelbrot fractal, microbiome, orbital mechanics / astrodynamics, traveling salesman, Turing test

Indeed humans are so easily fooled in this matter, even fooling themselves on a regular basis, that the Turing test is best replaced by the Winograd Schema, which tests one’s ability to make simple but important semantic distinctions based on the application of wide general knowledge to a problem created by a definite pronoun. “The large ball crashed through the table because it was made of aerogel. Does ‘it’ refer to the ball or the table?” These kinds of questions are in fact not a problem for us to answer, indeed we can answer them much faster than humans, who are already very fast at it. But so what? All these matters are still algorithmic and could be unconscious. We are not convinced any of these tests are even close to diagnostic. If there can be a cyborg, and there can, then perhaps passing a Turing test or a Winograd test or any other intelligence test might make one a pseudo-human.

Many nights Devi and the ship had long conversations. This had been going on since Devi was Freya’s age or younger; thus, some twenty-eight years. From the beginning of these talks, when young Devi had referred to her ship interface as Pauline (which name she abandoned in year 161, reason unknown), she had seemed to presume that the ship contained a strong artificial intelligence, capable not just of Turing test and Winograd Schema challenge, but many other qualities not usually associated with machine intelligence, including some version of consciousness. She spoke as if ship were conscious. Through the years many subjects got discussed, but by far the majority of the discussions concerned the biophysical and ecological functioning of the ship. Devi had devoted a good portion of her waking life (at least 34,901 hours, judging by direct observation) to improving the functional power of the ship’s data retrieval and analytic and synthesizing abilities, always in the hope of increasing the robustness of the ship’s ecological systems.

Turing himself went on to point out that if a machine exhibited any of these traits listed, it would not make much of an impression, and would be in any case irrelevant to the premise that there could be artificial intelligence, unless any of these traits or behaviors could be demonstrated to be essential for machine intelligence to be real. This seems to have been the train of thought that led him to propose what was later called the Turing test, though he called it a game, which suggested that if from behind a blind (meaning either by way of a text or a voice, not sure about this) a machine’s responses could not be distinguished from a human’s by another human, then the machine must have some kind of basic functional intelligence. Enough to pass this particular test, which, however, begs the question of how many humans could pass the test, and also ignores the question of whether or not the test is at all difficult, humans being as gullible and as projective as they are, always pathetically committing the same fallacy, even when they know they’re doing it.


pages: 419 words: 109,241

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

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

About a third of the content published by Bloomberg News is generated in a similar way.51 In human resources, 72 percent of job applications “are never seen by human eyes.”52 We have already seen that machines can now compose music so sophisticated that listeners imagine it must have been written by Bach. There are also now systems that can direct films, cut trailers—and even compose rudimentary political speeches. (As Jamie Susskind puts it, “it’s bad enough that politicians frequently sound like soulless robots; now we have soulless robots that sound like politicians.”53) Dartmouth College, the birthplace of AI, has hosted “Literary Creative Turing Tests”: researchers submit systems that can variously write sonnets, limericks, short poems, or children’s stories, and the compositions most often taken for human ones are awarded prizes.54 Systems like this might sound a little playful or speculative; some of them are. Yet researchers who work in the field of “computational creativity” are taking the project of building machines that perform tasks like these very seriously.55 At times, the encroachment of machines on tasks that require cognitive capabilities in human beings can be controversial.

The system was first trained on about one million generic images from ImageNet, and then retrained on herbarium sheets. 51.  Susskind and Susskind, Future of the Professions, p. 77; Jaclyn Peiser, “The Rise of the Robot Reporter,” New York Times, 5 February 2019. 52.  Cathy O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (New York: Crown, 2016), p. 114, quoted in J. Susskind, Future Politics, p. 266. 53.  Ibid., p. 31. 54.  The Literary Creative Turing Tests are hosted by the Neukom Institute for Computational Science at Dartmouth College; see http://bregman.dartmouth.edu/turingtests/ (accessed August 2018). 55.  See, for instance, Simon Colton and Geraint Wiggins, “Computational Creativity: The Final Frontier?” Proceedings of the 20th European Conference on Artificial Intelligence (2012), 21–6. 56.  See “UN to Host Talks on Use of ‘Killer Robot,’” VOA News, Agence France-Presse, 10 November 2017. 57.  

Keynes, John Maynard advanced guard and age of leisure and changing facts and distribution problem and labor to capital ratio and process of technological unemployment and technological unemployment and timing and Khan Academy Khanin, Grigorii al-Khwarizmi, Abdallah Muhammad ibn Musa killer robots knitting machine Krugman, Paul Kurzweil, Ray labor. See also Age of Labor labor income inequality labor market policies Lee, William legal capabilities legislation Leibniz, Gottfried Wilhelm leisure leisure class Leontief, Wassily Lerner, Abba Levy, Frank. See also ALM hypothesis libraries lidar life skills limitations, defining LinkedIn Literary Creative Turing Tests loan agreement review location, task encroachment and Loew, Judah Logic Theorist loopholes Lowrey, Annie loyalty Ludd, Ned Luddites lump of labor fallacy magicians manual capabilities manufacturing manure Marienthal study Marshall, Alfred Marx, Karl massive open online courses (MOOC) mass media, leisure and McCarthy, John Meade, James meaning creation of leisure and relationship of work with work with work without medicine Big Tech and changing-pie effect and task encroachment and membership requirements, conditional basic income and meritocracy Merlin Metcalfe’s Law Microsoft Mill, John Stuart minimum wage minorities Minsky, Marvin models, overview of Mokyr, Joel Möller, Anton monopolies MOOC.


The Orbital Perspective: Lessons in Seeing the Big Picture From a Journey of 71 Million Miles by Astronaut Ron Garan, Muhammad Yunus

Airbnb, barriers to entry, book scanning, Buckminster Fuller, clean water, corporate social responsibility, crowdsourcing, global village, Google Earth, Indoor air pollution, jimmy wales, low earth orbit, optical character recognition, ride hailing / ride sharing, shareholder value, Silicon Valley, Skype, smart transportation, Stephen Hawking, transaction costs, Turing test, Uber for X, web of trust

ReCAPTCHA and Duolingo The power of mass collaboration lies in its ability to amplify and aggregate relatively small investments of time into something large and meaningful, but hackathons are just one example of this. Mass collaborations are starting to happen all around us, sometimes without our awareness. Take, for example, ReCAPTCHA. Most of us are aware of CAPTCHAs, even if we don’t know what they are called. The Completely Automated Public Turing Test to Tell Computers and Humans Apart, designed by researcher Luis von Ahn and others at Carnegie Mellon University, is that distorted, slanted, and otherwise modified set of letters and numbers you sometimes have to type before submitting online forms. CAPTCHAs are designed to prove that you’re a human, because computers are not yet able to decipher those squiggles, preventing such things as ticket scalpers writing programs to automatically buy thousands of tickets that they will then resell illegally.

See Space Shuttle Atlantis Bangladesh, 52 Barratt, Mike, 39 background, 23–25 ISS and, 41–43 Russia, Russians, and, 24–27, 30, 31, 36, 37, 41 Beck, Beth, xiii Big picture perspective Chilean mine rescue and, 100–102 orbital perspective and, 133, 136, 167 worm’s eye view and, 80, 81, 112–113, 119–121, 167 Biosphère Environmental Museum, 163 Bolden, Charlie, 40, 98–99 Borisenko, Andrei, photo Botvinko, Alexander, 44 Brezhnev, Leonid, 13 Brown, David, 20 Brugh, Willow, 141–143, 160, 164 Budarin, Nikolai, 19 Burbank, Dan, photo Bureaucratic inertia, 119–121 Bush, George H. W., 15 Call to action, xiii, 4, 63, 165–170. See also Orbital perspective: call and mission to spread Campo Esperanza (Camp Hope), 97–100, photo CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart), 145 Carbon credits, 111–112, 118 Central America, 130 Chain of command. See Command chain Chamitoff, Greg “Taz,” 48, 50, 56, 60, photo Chawla, Kalpana, 20 Chilean mine rescue, 9, 97–98, 109, 115, 136, photo benefits of a short command chain, 103–104 big picture perspective, 100–102 common cause, 104–106 down-to-earth cooperation, 98–99 esprit de corps (morale), 9, 99–100 177 178â•…  â•… I n d e x Chilean mine rescue (continued) focused collaboration, 107–108 humility, 102–103 as orbital perspective in action, 109 splash up, 106–107 Clark, Laurel, 20 Co-laborers, 9, 84–85, 89 Codeathon, 127.


pages: 504 words: 89,238

Natural language processing with Python by Steven Bird, Ewan Klein, Edward Loper

bioinformatics, business intelligence, conceptual framework, Donald Knuth, elephant in my pajamas, en.wikipedia.org, finite state, Firefox, Guido van Rossum, information retrieval, Menlo Park, natural language processing, P = NP, search inside the book, speech recognition, statistical model, text mining, Turing test

Given a document in German and English, and possibly a bilingual dictionary, we can automatically pair up the sentences, a process called text alignment. Once we have a million or more sentence pairs, we can detect corresponding words and phrases, and build a model that can be used for translating new text. 30 | Chapter 1: Language Processing and Python Spoken Dialogue Systems In the history of artificial intelligence, the chief measure of intelligence has been a linguistic one, namely the Turing Test: can a dialogue system, responding to a user’s text input, perform so naturally that we cannot distinguish it from a human-generated response? In contrast, today’s commercial dialogue systems are very limited, but still perform useful functions in narrowly defined domains, as we see here: S: How may I help you? U: When is Saving Private Ryan playing? S: For what theater? U: The Paramount theater.

., len(text1). 1.7 Further Reading This chapter has introduced new concepts in programming, natural language processing, and linguistics, all mixed in together. Many of them are consolidated in the following chapters. However, you may also want to consult the online materials provided with this chapter (at http://www.nltk.org/), including links to additional background materials, and links to online NLP systems. You may also like to read up on some linguistics and NLP-related concepts in Wikipedia (e.g., collocations, the Turing Test, the type-token distinction). You should acquaint yourself with the Python documentation available at http://docs .python.org/, including the many tutorials and comprehensive reference materials linked there. A Beginner’s Guide to Python is available at http://wiki.python.org/moin/ BeginnersGuide. Miscellaneous questions about Python might be answered in the FAQ at http://www.python.org/doc/faq/general/.

Alan Turing famously proposed to answer this by examining the ability of a computer to hold sensible conversations with a human (Turing, 1950). Suppose you are having a chat session with a person and a computer, but you are not told at the outset which is which. If you cannot identify which of your partners is the computer after chatting with each of them, then the computer has successfully imitated a human. If a computer succeeds in passing itself off as human in this “imitation game” (or “Turing Test” as it is popularly known), then according to Turing, we should be prepared to say that the computer can think and can be said to be intelligent. So Turing side-stepped the question of somehow examining the internal states of a computer by instead using its behavior as evidence of intelligence. By the same reasoning, we have assumed that in order to say that a computer understands English, it just needs to 10.1 Natural Language Understanding | 367 behave as though it did.


pages: 496 words: 70,263

Erlang Programming by Francesco Cesarini

cloud computing, fault tolerance, finite state, loose coupling, revision control, RFC: Request For Comment, sorting algorithm, Turing test, type inference, web application

Interaction To interact with the running Java node, you can use the following code, calling myrpc:f/1 at the prompt: -module(myrpc). ... f(N) -> {facserver, 'bar@STC'} ! {self(), N}, receive {ok, Res} -> io:format("Factorial of ~p is ~p.~n", [N,Res]) end. 340 | Chapter 16: Interfacing Erlang with Other Programming Languages This client code is exactly the same as the code that is used to interact with an Erlang node, and a “Turing test”‡ that sends messages to and from a node should be unable to tell the difference between a Java node and an Erlang node. The Small Print In this section, we will explain how to get programs using JInterface to run correctly on your computer. First, to establish and administer connections between the Java and Erlang nodes it is necessary that epmd (the Erlang Port Mapper Daemon) is running when a node is created.

Referring back to the program in the section “Putting It Together: RPC Revisited” on page 339, line 1 of the program ensures that the JInterface Java code is imported, but since it is included in the OTP distribution and not in the standard Java, it is necessary to point the Java compiler and runtime to where it is held, which is in the following: <otp-root>/jinterface-XXX/priv/OtpErlang.jar In the preceding code, <otp-root> is the root directory of the distribution, given by typing code:root_dir() within a running node, and XXX is the version number. On Mac OS X the full path is: /usr/local/lib/erlang/lib/jinterface-1.4.2/priv/OtpErlang.jar This value is supplied thus to the compiler: javac -classpath ".:/usr/local/lib/erlang/lib/ jinterface-1.4.2/priv/OtpErlang.jar" ServerNode.java ‡ The Turing test was proposed by mathematician and computing pioneer Alan Turing (1912–1954) as a test of machine intelligence. The idea, translated to modern technology, is that a tester chats with two “people” online, one human and one a machine: if the tester cannot reliably decide which is the human and which is the machine, the machine can be said to display intelligence. Interworking with Java | 341 and to the Java system: java -classpath ".

Send email to index@oreilly.com. 451 append_element/2 function, 54 application module stop function, 296 which_applications function, 281, 283 application monitor tool, 287 application resource file, 283–284 application/1 function, 405 applications, 421 (see also OTP applications) blogging, 314–320 development considerations, 421–426 apply/3 function, 55, 153 appmon:start function, 287 arguments fun expressions, 192 functions and, 190–192 arity arity flag, 363 defined, 38 Armstrong, Joe, xvi, 3, 31, 89, 201, 245 array module, 79 ASCII integer notation (see $Character notation) at (@) symbol, 19 atomic operation, 147 atoms Boolean support, 20, 28 Erlang type notation, 396 garbage collection and, 104 overview, 19 secret cookies, 250 string comparison, 23 troubleshooting syntax, 19 atom_to_list/1 function, 54 AVL balanced binary tree, 215 AXD301 ATM switch, 10, 246 B b/0 shell command, 446 badarg exception, 69, 75, 104 badarith exception, 70 badmatch exception, 69, 71, 163, 355 bags defined, 214 Dets tables, 229 duplicate, 214, 215, 229 ETS tables, 214 sets and, 213 storing, 215 452 | Index balanced binary trees, 183, 215 band operator, 208, 378 Base#Value notation, 15 BEAM file extension, 41 benchmarking, 106 Berkeley DB, 294 BIFs (built-in functions), 355 (see also trace BIFs) binary support, 202 concurrency considerations, 56 exit BIFs, 146–148 functionality, 45, 53 group leader support, 258 io module, 57–59 meta programming, 55 node support, 249 object access and evaluation, 53 process dictionary, 55 record support, 164 reduction steps, 96 reference data types, 210 runtime errors, 69 spawning processes, 90 type conversion, 54 type test support, 51, 378, 384 bignums, 15 binaries bit syntax, 203–204, 206 bitstring comprehension, 206, 212 bitwise operators, 208 chapter exercises, 212 defined, 23, 190, 202 Erlang type notation, 396 pattern matching and, 201, 205 serializing, 208, 413–415 binary files, 373 binary operators, 21, 208 binary_to_list/1 function, 202, 349 binary_to_term/1 function, 202, 343, 349 bit sequences, 4 bitstring comprehension, 206, 212 bitwise operators, 378 blogging applications, 314–320 bnot operator, 208, 378 Boolean operators atom support, 20, 28 Erlang type notation, 397 match specifications and, 378 bor operator, 208, 378 bottlenecks, 109 bound variables changing values, 30 defined, 34 functions and, 5 selective receives, 97–99 Bray, Tim, 2 bsl operator, 208, 378 bsr operator, 208, 378 bump_reductions function, 96 bxor operator, 208, 378 C C language, interworking with, 342–346 C++ language CouchDB case study, 12 Erlang comparison, 12–13 c/1 shell command, 446 c/3 function, 369 calendar module, 79 call by value, 30 call flag (tracing), 360, 362 call/1 function, 122 call/2 function, 270 callback functions, 132, 265 Carlson, Richard, 74, 395 case constructs development considerations, 431 function definitions and, 47 overview, 46–48 runtime errors, 68 case_clause exception, 68 cast/2 function, 268 Cesarini, Francesco, xv, 110, 201 Chalmers University of Technology, 2 characters Erlang type notation, 397 representation, 22 check_childspecs/1 function, 279 client function, 122, 330 client/server model chapter exercises, 138 client functions, 122 generic servers, 266–276 monitoring clients, 150 process design patterns, 117, 118–124 process skeleton example, 125–126 close function dets module, 230 gen_tcp module, 331 gen_udp module, 326 closures (see functions) cmd/1 function, 346 code module add_path function, 286 add_patha function, 181, 184 add_pathz function, 181 get_path function, 180, 181, 282 is_loaded function, 180 load_file function, 180 priv_dir function, 282 purge function, 182 root_dir function, 180 soft_purge function, 182 stick_dir function, 181 unstick_dir function, 181 code server, 180 code.erl module, 180 collections implementing, 213, 214–216 sets and bags, 213 colon (:), 25, 205 comma (,), 52, 378 Common Test tool, 14 comparison operators, 28, 378, 385 compile directive, 41 compile:file function, 163, 168, 179 concatenating strings, 27 concurrency BIF support, 56 defined, 9, 89 distributed systems and, 246 efficient, 6, 440 ETS tables and, 221 multicore processing and, 9 overview, 5 scalable, 6 concurrent programming benchmarking, 106 case study, 110 chapter exercises, 115 creating processes, 90–92 deadlocks, 112–114 development considerations, 426–429 memory leaks, 108 message passing, 92–94 process manager, 114 process skeletons, 107 Index | 453 process starvation, 112–114 race conditions, 112–114 receiving messages, 94–102 registered processes, 102–104 tail recursion, 108 testing, 419, 420 timeouts, 104–106 conditional evaluations case construct, 46–48 defined, 46 execution flow and, 36 function clause, 38, 46 if construct, 49–50 variable scope, 48 conditional macros, 167 connect function gen_tcp module, 331 net_kernel module, 255 peer module, 334 controlling_process function, 331 convert/2 function, 183 cos/1 function, 80 CouchDB database, 2, 11, 294 cpu_timestamp flag, 362 create/0 function, 174 create_schema function, 295 create_table function, 296, 298 ctp function, 370 ctpg function, 370 ctpl function, 370 curly brackets { }, 21 D Däcker, Bjarne, 3 data structures development considerations, 425 overview, 32 records as, 158 data types atoms, 19 binary, 23, 190 data structures, 32 defininig, 397 Erlang type notation, 396 floats, 17–19 functional, 189 integers, 15 interworking with Java, 338 lists, 22–27 454 | Index nesting, 32 records with typed fields, 395 reference, 190, 210, 409 term comparison, 28–29 tuples, 21 type conversions, 54 type system overview, 31 variables, 30 date/0 function, 56 db module code example, 174, 182 convert/2 function, 183 exercises, 186 fill/0 function, 376 dbg module c/3 function, 369 chapter exercises, 392 ctp function, 370 ctpg function, 370 ctpl function, 370 dtp function, 391 fun2ms/1 function, 375–382, 383–391 h function, 366 ln function, 371 ltp function, 390 match specifications, 382 n function, 371 p function, 366, 371 rtp function, 391 stop function, 368 stop_clear/0 function, 368 stop_trace_client function, 373 tp/2 function, 367, 369, 376, 391 tpl/2 function, 369 tracer/2 function, 372, 373 trace_client function, 373 trace_port function, 373 wtp function, 391 dbg tracer distributed environments, 371 functionality, 365 getting started, 366–368 profiling functions, 369 redirecting output, 371–374 tracing function calls, 369–371 tracing functions, 369 db_server module, 182 deadlocks, 112–114, 429 deallocate function, 120, 124 debugging chapter exercises, 171 dbg tracer, 365–374 EUnit support, 419 macro support, 166–168 tools supported, 80, 114 declarative languages, 4 defensive programming, 7, 47, 436 delete function, 300 delete_handler function, 133 delete_usr/1 function, 301 deleting objects in Mnesia, 300 Delicious social bookmarking service, 2 del_table_index function, 302 demonitor function, 144, 147 design patterns, 263 (see also OTP behaviors) chapter exercises, 137 client/server model, 117, 118–124 coding strategies, 436 defined, 107, 117 event handler, 117, 131–137 FSM model, 117, 126–131, 290 generic servers, 266–276 process example, 125–126 supervisors, 152, 276–280 destroy/1 function, 313 dets module close function, 230 info function, 230 insert function, 230 lookup function, 230 open_file/1 function, 230 select function, 230 sync function, 229 Dets tables bags, 229 creating, 230 duplicate bags, 229 ETS tables and, 229 functionality, 229–230 mobile subscriber database example, 231– 242 options supported, 229 sets, 229 development (see software development) Dialyzer tool creating PLT, 401 functionality, 14, 32 dict module functionality, 79 simple lookups, 294 upgrading modules, 174, 175 upgrading processes, 183 directives, module, 41 directories adding to search path, 181 OTP applications, 282 sticky, 181 dirty code, 423 dirty_delete function, 303 dirty_index_read function, 303 dirty_read function, 303 dirty_write function, 303, 304 disk_log module, 294 display/1 function, 380 dist:s/0 function, 252 distributed programming chapter exercises, 261 epmd command, 260 essential modules, 258–260 fault tolerance and, 247 firewalls and, 261 nodes, 247–255 overview, 7, 245–247 RPC support, 256–258 div operator, 17, 378 division operator, 17 DNS servers, 250 documentation EDoc support, 402–410 modules, 53, 77 dollar sign ($) symbol, 22 don’t care variables, 37 dp module fill/0 function, 375 handle/3 function, 377 handle_msg/1 function, 377 process_msg/0 function, 375 dropwhile function, 196 Dryverl toolkit, 352 dtp function, 391 duplicate bags Dets tables, 229 ETS tables, 214 storing, 215 Index | 455 E e/1 shell command, 447 ebin directory, 283 EDoc documentation framework documenting usr_db.erl, 403–405 functionality, 402 predefined macros, 408 running, 405–407 edoc module application/1 function, 405 files/1 function, 405 functionality, 405–407 EDTK (Erlang Driver Toolkit), 352 EEP (Erlang Enhancement Proposal), 352 ei_connect function, 342 Ejabberd system, 2, 245 element/2 function, 53, 378 else conditional macro, 167 empty lists, 23 empty strings, 23 endian values, 204 endif conditional macro, 167 Engineering and Physical Sciences Research Council (EPSRC), 12 ensure_loaded function, 298 enumeration types (see atoms) environment variables, 284, 285 Eötvös Loránd University, 2 epmd command, 260, 333, 341 EPP (Erlang Preprocessor), 165 EPSRC (Engineering and Physical Sciences Research Council), 12 equal to (==) operator, 28, 378 Ericsson AXD301 ATM switch, 10 Computer Science Laboratory, 3, 293 Mobility Server, 157 SGSN product, 2 ERL file extension, 40 erl module, 78, 259 Erlang additional information, 449 AXD301 ATM switch case study, 10 C++ comparison, 12–13 characteristics, 4–9 CouchDB case study, 11 getting started, 445–447 history, 3 multicore processing, 9 456 | Index popular applications, 1–3 tools supported, 447–449 usage suggestions, 14 Erlang Driver Toolkit (EDTK), 352 Erlang Enhancement Proposal (EEP), 352 ERLANG file extension, 186 erlang module append_element/2 function, 54 bump_reductions function, 96 demonitor function, 144, 147 documentation, 53, 78 functionality, 79, 259 is_alive function, 249 monitor/2 function, 144, 147 port program support, 349 trace/3 function, 357, 362 trace_pattern/3 function, 362–365 yield function, 96 Erlang Preprocessor (EPP), 165 Erlang shell chapter exercises, 43 inserting records in ETS tables, 227 modes supported, 182 overview, 16, 92 records in, 161 runtime errors, 68 troubleshooting atom syntax, 19 Erlang type notation, 395–398 Erlang Virtual Machine, 41 Erlang Web framework, 246 erlang.cookie file, 250 erlectricity library, 336, 351 erl_call command, 346 erl_connect function, 342, 344 erl_connect_init function, 344 erl_error function, 342 erl_eterm function, 342 erl_format function, 342, 344 erl_global function, 342 erl_init function, 344 erl_interface library, 336, 342–346 erl_malloc function, 342 erl_marshal function, 342 error class, 72–74 error handling chapter exercises, 154 concurrent programming, 112–114 exit signals, 139–148 process links and, 7, 139–148 robust systems, 148–154 runtime errors, 68, 378 supervisor behaviors and, 7 try...catch construct, 70–77 ets module creating tables, 216 file2tab function, 226 first/1 function, 221 fun2ms/1 function, 223, 225, 382, 383– 391 handling table elements, 217 i function, 226 info/1 function, 217, 226 insert/2 function, 217, 355, 376 last/1 function, 222 lookup/2 function, 217, 220, 355 match specifications, 382 match/2 function, 223–224 new function, 216 next/2 function, 221 safe_fixtable/2 function, 221, 236 select function, 223, 225 tab2file function, 226 tab2list function, 226 ETS tables bags, 214 building indexes, 218, 222 chapter exercises, 243, 393 concurrent updates and, 221 creating, 216 Dets tables and, 229 duplicate bags, 214 functionality, 213 handling table elements, 217 implementations and trade-offs, 214–216 match specifications, 225 Mnesia database and, 216 mobile subscriber database example, 231– 242 operations on, 226 ordered sets, 214 pattern matching, 223–224 records and, 226 sets, 214 simple lookups, 294 traversing, 220 visualizing, 228 eunit library assert macro, 416 assertEqual macro, 414, 416 assertError macro, 415, 416 assertExit macro, 416 assertMatch macro, 416 assertNot macro, 416 assertThrow macro, 416 including, 413 listToTree/1 function, 414 test/1 function, 419 treeToList/1 function, 414 EUnit tool chapter exercises, 420 debugging support, 419 functional testing example, 413–415 functionality, 14, 412, 413 infrastructure, 416–418 macro support, 413, 416 test representation, 417 test-generating function, 416 testing concurrent programs, 419 testing state-based systems, 418 event handlers chapter exercises, 138 design patterns, 117, 131–137 implementing, 291 wxErlang support, 312 event managers, 131–134 event tables, 310 event types, 312 exactly equal to (=:=) operator, 28, 378 exactly not equal to (=/=) operator, 28, 378 existing flag, 359 exit function, 72, 145, 147 exit signals process links and, 139–148 propagation semantics, 148 trapping, 142–144, 148 exited/2 function, 151 export directive, 40, 168 expressions chapter exercises, 82, 85 Erlang shell and, 93 functional data types, 192 functionality, 199 pattern matching, 33–38 term comparison, 28–29 Extensible Messaging and Presence Protocol (XMPP), 2 Index | 457 F f/0 shell command, 84, 446 f/1 shell command, 447 Facebook, 2 fault tolerance distributed programming and, 245 distributed systems and, 245, 247 layering and, 149 features, Erlang concurrency, 5, 6 distributed computation, 7 high-level constructs, 4 integration, 8 message passing, 5 robustness, 6 soft real-time properties, 6 FFI (foreign function interface), 352 file function, 163, 168, 179 file module, 79 file2tab function, 226 filename module, 79 files/1 function, 405 fill/0 function, 375, 376 filter function, 191, 192, 196 finite state machines (see FSMs) firewalls, 261 first/1 function, 221 float/1 function, 54 floating-point division operator, 17 floats defined, 17 Erlang type notation, 397 mathematical operations, 17 float_to_list/1 function, 54 flush/0 shell command, 93, 324, 359 foldl/3 function lists module, 196 mnesia module, 305 foreach statement, 193 foreign function interface (FFI), 352 format/1 function, 369 format/2 function, 57, 101, 356 frequency module allocate function, 119, 123 deallocate function, 120, 124 init function, 121 Fritchie, Scott Lystig, 215 FSMs (finite state machines) busy state, 117 458 | Index chapter exercises, 138 offline state, 117 online state, 117 process design patterns, 117, 126–131, 290 fun2ms/1 function dbg module, 375–382, 383–391 ets module, 223, 225, 382, 383–391 function clause components, 38 conditional evaluations, 38, 46 guards, 50–52 runtime errors, 68 variable scope, 49 function definitions case expressions and, 47 fun expressions, 192 overview, 38 pattern matching, 4 functional data types (funs) already defined functions, 194 defined, 189 Erlang type notation, 397 example, 190 fun expressions, 192 functions and variables, 195 functions as arguments, 190–192 functions as results, 193 lazy evaluation, 197 predefined higher-order functions, 195– 196 transaction support, 299 functional programming, 9, 45, 189 functional testing, 413–415 functions, 45 (see also BIFs; higher-order functions) already defined, 194 arguments and, 38, 190–192 as results, 193 binding to variables, 5, 30 callback, 132, 265 chapter exercises, 44, 83, 86 client, 122 coding strategies, 435 EDoc documentation, 403, 404 fully qualified function calls, 176 grouping, 40 hash, 215 list comprehensions and, 200 list supported, 25–27 literal, 226, 379–381 meta programming, 55 overview, 38–40 pattern matching, 33–38, 39, 47 records and, 160 recursions versus iterations, 67 reduction steps, 96 return values, 424–425 running, 40 runtime errors, 70 tail-recursive, 63–67, 108, 440 test-generating, 416 variables and, 195 G garbage collection atoms and, 104 chapter exercises, 392 memory management and, 33 overview, 6 trace BIFs and, 361 tuning for, 441 garbage_collection flag, 361 gb_trees module, 183 generators bitstring comprehension, 206 multiple, 200 overview, 198 gen_event module, 291 gen_fsm module, 290 gen_server module call/2 function, 270 cast/2 function, 268 chapter exercises, 291 functionality, 266 passing messages, 268–270 server example in full, 271–276 start function, 266, 267 starting servers, 266 start_link/4 function, 266, 267 stopping servers, 270 gen_tcp module accept function, 331 close function, 331 connect function, 331 controlling process function, 331 listen/2 function, 330 open/2 function, 331 recv/1 function, 331 recv/2 function, 328, 330, 331 recv/3 function, 328, 330 gen_udp module close function, 326 functionality, 324 open/2 function, 330 recv/2 function, 326 recv/3 function, 326 getopts function, 332 get_data function, 133 get_env/0 function, 313 get_line/1 function, 57 get_path function, 180, 181, 282 get_request/3 function, 329 get_seq_token/0 function, 391 go/0 function, 100 greater than (>) operator, 28, 378 greater than or equal to (>=) operator, 28, 378 group leaders, 258 group_leader function, 258 guard expression, 51, 225 guards BIF support, 378, 384 in list comprehensions, 198 overview, 50–52, 198 semicolon support, 378 Gudmundsson, Dan, 309 H h function, 366 h/0 shell command, 447 handle function, 125 handle/3 function, 377 handle_call/3 function, 268 handle_cast/1 function, 268 handle_event function, 135 handle_msg function, 126, 377 handling errors (see error handling) hash (#), 15 hash functions, 215 hash tables, 215 Haskell language, 30, 197 hd/1 function, 53, 378 Heriot-Watt University, 12 High Performance Erlang Project (HiPE), 2 higher-order functions already defined functions, 194 chapter exercises, 211, 212 defined, 193 Index | 459 functions and variables, 195 functions as arguments, 190 functions as results, 193 lazy evaluation, 197 predefined in lists module, 195–196 HiPE (High Performance Erlang Project), 2 I i function ets module, 226 inet module, 333 i shell command, 91, 96, 103 if construct development considerations, 431 overview, 49–50 runtime errors, 69 ifdef conditional macro, 167 ifndef conditional macro, 167 implementing records, 162–163 import directive, 42 include directive, 168 include files, 168 indexes building, 218, 222 chapter exercises, 86, 243 documentation, 78 Mnesia database, 301 ordered sets, 219 unordered structure, 219 index_read/3 function, 302 inet module functionality, 331 getopts function, 332 i function, 333 setopts function, 332 inets.app file, 283 info/1 function, 217, 226 information hiding, 119 inheritance flags overview, 360 set_on_first_spawn flag, 360, 367 set_on_spawn flag, 360, 367 init function event handlers, 135, 136 frequency module, 121 OTP behaviors, 267, 268, 276 supervisors, 276, 278 initialize function, 125 insert/2 function, 217, 355, 376 460 | Index integers characters and strings, 22 Erlang type notation, 397 overview, 15 integer_to_list/1 function, 54 integration overview, 8 interfaces defined, 421 development considerations, 423, 426 interlanguage working C nodes, 342–346 chapter exercises, 353 erl_call command, 346 FFI and, 352 interworking with Java, 337–342 languages supported, 336 library support, 350–352 linked-in drivers, 352 overview, 335–337 port programs, 346–350 io module format/1 function, 369 format/2 function, 57, 101, 356 functionality, 57–59, 79 get_line/1 function, 57 read/1 function, 57 write/1 function, 57 io_handler event handler, 135 is_alive function, 249 is_atom function, 51, 378 is_binary function, 51, 202, 378 is_boolean function, 20, 51 is_constant function, 378 is_float function, 378 is_function function, 378 is_integer function, 378 is_list function, 378 is_loaded function, 180 is_number function, 378 is_pid function, 378 is_port function, 378 is_record function, 164, 378 is_reference function, 378 is_tuple function, 51, 378 IT University (Sweden), 2 iterative versus recursive functions, 67 J Java language, 336, 337–342 JInterface Java package additional capabilities, 342 communication support, 338 distribution, 336 getting programs to run correctly, 341 interworking with, 337–342 nodes and mailboxes, 337 representing Erlang types, 338 RPC support, 339 Turing test, 340 K Katz, Damien, 11 kernel, 281 keydelete/3 function, 124 keysearch/3 function, 69 L Lamport, Leslie, 245 last/1 function, 222 layering processes, 148–154 lazy evaluation, 197 length/1 function, 53, 378 less than (<) operator, 28, 378 less than or equal to (<=) operator, 28, 378 libraries development considerations, 422 support for communication, 350–352 library modules (see modules) Lindahl, Tobias, 399 link function, 139, 146 linked-in drivers, 352 links, process chapter exercises, 154 defined, 146 error handling and, 7, 139–148 exit signals and, 139–148 list comprehensions chapter exercises, 211, 212 component parts, 198 defined, 5, 189 example, 198 multiple generators, 200 pattern matching, 199 quicksort, 201 standard functions, 200 listen/2 function, 330 lists chapter exercises, 83–85 efficiency consierations, 439 empty, 23 Erlang type notation, 397 functions and operations, 25–27 lazy evaluation and, 197 overview, 22–27 processing, 24 property, 27 recursive definitions, 24 lists module all function, 196 any function, 196 dropwhile function, 196 filter function, 196 foldl/3 function, 196 functionality, 25, 80 keydelete/3 function, 124 keysearch/3 function, 69 list comprehensions, 200 map function, 196 member function, 96 partition function, 196 predefined higher-order functions, 195– 196 reverse function, 96 split function, 25 listToTree/1 function, 414 list_to_atom/1 function, 54 list_to_binary/1 function, 202, 349 list_to_existing_atom/1 function, 54 list_to_float/1 function, 54 list_to_integer/1 function, 54, 75 list_to_tuple/1 function, 54 literal functions, 226, 379–381 ln function, 371 load_file function, 180 logical operators, 20, 378 lookup/2 function, 217, 220, 355 loop/0 function, 100, 143, 365 loop/1 function, 123 ltp function, 390 M m (Module) command, 42 macros chapter exercises, 170 conditional, 167 debugging support, 166–168 Index | 461 EDoc support, 408 EUnit support, 413, 416 functionality, 157, 165 include files, 168 parameterized, 166, 170 simple, 165 mailboxes interworking with Java, 337 message passing, 92 retrieving messages, 94 selective receives, 98 make_ref function, 210 make_rel function, 288 make_script/2 function, 290 map function, 191, 192, 196 match specifications conditions, 384–387 defined, 225–226, 374 ets and dbg diferences, 382 fun2ms/1 function, 375–382, 383–391 generating, 375–382 head, 383 saving, 390 specification body, 387–390 tracing via, 356 match/2 function, 223–224 math module, 80 mathematical operators, 17, 18 Mattsson, Håkan, 293 member function, 96 memory management background, 33 concurrent programming and, 108 garbage collection and, 362 processes and, 5 tail recursion and, 109 message passing gen_server module, 268–270 overview, 5, 92–94 message/1 function, 380 messages node communications, 252 receiving, 94–102, 115 meta programming, 55 microblogging application, 314–316 miniblogging application, 317–320 Mnesia database additional information, 305 as OTP application, 264 462 | Index background, 293 chapter exercises, 306–307 configuring, 295–298 deleting objects, 300 dirty operations, 302–304 ETS tables and, 216 inconsistent tables, 304 indexing, 301 partitioned networks, 304 setting up schema, 295 starting, 296 table structure, 296–298 transactions, 299–304 visualizing tables, 228 when to use, 293–295 mnesia module abort function, 299 create_schema function, 295 create_table function, 296, 298 delete function, 300 dirty_delete function, 303 dirty_index_read function, 303 dirty_read function, 303 dirty_write function, 303, 304 foldl/3 function, 305 read function, 300 set_master_nodes function, 305 start function, 296 stop function, 296 transaction function, 299 wait_for_tables function, 298 write/1 function, 299, 302 mobile subscriber database as OTP application, 264 ETS and Dets tables, 231–242 generic servers, 266–276 MochiWeb library, 2 module directive, 40, 168 modules chapter exercises, 44, 85 commonly used, 79–80 defined, 40 development considerations, 421–426 directive support, 41 documentation, 77 EDoc documentation, 403, 405 library applications, 281 purging, 182 running functions, 40 upgrading, 173, 176 module_info function, 175 monitor/2 function, 144, 147 monitoring systems application monitor tool, 287 chapter exercises, 262 client/server model, 150 monitor_node function, 257 Motorola, 2, 12 multicore processing benchmarking example, 106 concurrency and, 9 multiplication (*) operator, 17, 378 mutex module signal function, 129 wait function, 129 mutex semaphore, 129, 154 MySQL database, 294 N n function, 371 nesting data types, 32 development considerations, 430 net_adm module functionality, 260 ping/1 function, 252 net_kernel module connect function, 255 functionality, 260 new function, 216 next/2 function, 221 Nilsson, Bernt, 10 node function, 248, 249, 378 nodes communication and messages, 252 communication and security, 250 connection considerations, 253–255 defined, 247 distribution and security, 251 hidden, 254 interworking with Java, 337 naming, 249 pinging, 252 secret cookies, 250 visibility of, 249 not equal to (/=) operator, 28, 378 not logical operator, 21, 378 now/0 function, 56, 79, 362 null function, 314 Nyström, Jan Henry, xx, 13 O object identifiers, 312 open source projects, 2, 4 Open Telecom Platform (see OTP entries) open/2 function, 330, 331 open_file/1 function, 230 open_port/2 command, 347 operators binary, 21, 208 bitwise, 208, 378 comparison, 28, 378, 385 list supported, 25–27 logical, 20, 378 match specifications and, 378 mathematical, 17 reduction steps, 96 relational, 28 runtime errors, 70 optimization, tail-call recursion, 66 or logical operator, 20, 378 ordered sets building indexes, 219 ETS tables, 214 storing, 215 orelse logical operator, 20, 378 os:cmd/1 function, 346 OTP applications application monitor tool, 287 application resource file, 283–284 defined, 264, 281 directory structure, 282 examples, 264 Mnesia database, 295 starting and stopping, 284–286 OTP behaviors chapter exercises, 291 generic servers, 266–276 overview, 7, 263–266 release handling, 287–290 supervisors, 276–280 testing, 420 OTP middleware, 7, 263 OtpConnection class, 342 OtpErlangAtom class, 338 OtpErlangBinary class, 342 OtpErlangBoolean class, 338 Index | 463 OtpErlangByte class, 338 OtpErlangChar class, 338 OtpErlangDouble class, 338 OtpErlangFloat class, 338 OtpErlangInt class, 338 OtpErlangLong class, 338 OtpErlangObject class, 338, 340 OtpErlangPid class, 338 OtpErlangShort class, 338 OtpErlangString class, 338 OtpErlangTuple class, 338, 340 OtpErlangUInt class, 338 OtpMbox class, 338, 342 OtpNode class, 337, 341 P p function, 366, 371 palin/1 function, 191 parameters accumulating, 63 macro support, 166, 170 parentheses ( ) encapsulating expressions, 75 for function parameters, 38 overriding precedence, 18 type declarations and, 396 partition function, 196 partitioned networks, 304 pattern matching binaries and, 201, 205 bit sequences, 4 chapter exercises, 44 don’t care variables, 37 ETS tables, 223–224 fun expressions, 192 function definitions, 4 functions, 39, 47 list comprehensions, 199 overview, 33–38 records and, 160 wildcard symbols, 35, 224 peer module connect function, 334 send/1 function, 334 Persistent Lookup Table (PLT), 401 Persson, Mats-Ola, 309 pi/0 function, 4, 39, 80 pid (process identifier) defined, 90 464 | Index Erlang type notation, 397 registered processes, 102 spawn function, 90 pid/3 function, 93 pid_to_list/1 function, 367 ping module example, 364 send/1 function, 358, 367 start function, 365 tracing example, 364 ping/1 function, 252 PLT (Persistent Lookup Table), 401 pman (process manager), 114 port programs commands supported, 347–349 communicating data via, 349–350 overview, 346 port_close command, 348 port_command/2 function, 348 port_connect command, 348 PostgreSQL database, 294 prep_stop function, 285 prettyIndexNext function, 222 priv_dir function, 282 process dictionary, 55, 423 process identifier (pid) defined, 90 Erlang type notation, 397 registered processes, 102 spawn function, 90 process links (see links, process) process manager (pman), 114, 359 process scheduling, 96 process skeleton, 107, 125–126 process starvation, 112–114 process state, 107 process trace flags all flag, 359 arity flag, 363 call flag, 360, 362 cpu_timestamp flag, 362 existing flag, 359 garbage_collection flag, 361 inheritance flags, 360 procs flag, 359 receive flag, 358 return_to flag, 362 running flag, 359 send flag, 358 set_on_first_link flag, 361, 367 set_on_link flag, 361, 367 timestamp flag, 362 wildcards, 363 processes atomic operations, 147 behavioral aspects, 107 benchmarking, 106 bottlenecks, 109 client/server model, 117, 118–124 concurrent programming case study, 110 creating, 90–92 defined, 89 dependency considerations, 94 design patterns, 107, 117, 125–126 development considerations, 426–429 Erlang shell and, 92 event handler, 117, 131–137 exit signals, 139–148 FSM model, 117, 126–131 group leaders, 258 handle function, 125 initialize function, 125 layering, 148–154 message passing, 5, 92–94 receiving messages, 94–102 registered, 102–104 spawning, 90 supervisor, 7, 148, 152–154, 155, 264, 276– 280 tail recursion, 108 terminate function, 125 threads versus, 97 timeouts, 104–106 tracer, 357 upgrading, 182 worker, 148, 264, 276 processes function, 91 processWords function, 220 process_flag function, 113, 142–144, 147 process_info/2 function, 423 process_msg function, 375 procs flag, 359 proc_lib module, 291 profiling functions, 369 programming (see software development) Prolog language, 19 property lists, 27 proplists module, 27, 311 purge function, 182 purging modules, 182 Q qualification, size/type, 203 question mark (?)


pages: 855 words: 178,507

The Information: A History, a Theory, a Flood by James Gleick

Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, AltaVista, bank run, bioinformatics, Brownian motion, butterfly effect, citation needed, Claude Shannon: information theory, clockwork universe, computer age, conceptual framework, crowdsourcing, death of newspapers, discovery of DNA, Donald Knuth, double helix, Douglas Hofstadter, en.wikipedia.org, Eratosthenes, Fellow of the Royal Society, Gödel, Escher, Bach, Henri Poincaré, Honoré de Balzac, index card, informal economy, information retrieval, invention of the printing press, invention of writing, Isaac Newton, Jacquard loom, Jaron Lanier, jimmy wales, Johannes Kepler, John von Neumann, Joseph-Marie Jacquard, lifelogging, Louis Daguerre, Marshall McLuhan, Menlo Park, microbiome, Milgram experiment, Network effects, New Journalism, Norbert Wiener, Norman Macrae, On the Economy of Machinery and Manufactures, PageRank, pattern recognition, phenotype, Pierre-Simon Laplace, pre–internet, Ralph Waldo Emerson, RAND corporation, reversible computing, Richard Feynman, Rubik’s Cube, Simon Singh, Socratic dialogue, Stephen Hawking, Steven Pinker, stochastic process, talking drums, the High Line, The Wisdom of Crowds, transcontinental railway, Turing machine, Turing test, women in the workforce

“It is the least event that can be true or false.”♦ They also managed to attract Alan Turing, who published his own manifesto with a provocative opening statement—“I propose to consider the question, ‘Can machines think?’ ”♦—followed by a sly admission that he would do so without even trying to define the terms machine and think. His idea was to replace the question with a test called the Imitation Game, destined to become famous as the “Turing Test.” In its initial form the Imitation Game involves three people: a man, a woman, and an interrogator. The interrogator sits in a room apart and poses questions (ideally, Turing suggests, by way of a “teleprinter communicating between the two rooms”). The interrogator aims to determine which is the man and which is the woman. One of the two—say, the man—aims to trick the interrogator, while the other aims to help reveal the truth.

Turing, “Computing Machinery and Intelligence,” Minds and Machines 59, no. 236 (1950): 433–60. ♦ “THE PRESENT INTEREST IN ‘THINKING MACHINES’ ”: Ibid., 436. ♦ “SINCE BABBAGE’S MACHINE WAS NOT ELECTRICAL”: Ibid., 439. ♦ “IN THE CASE THAT THE FORMULA IS NEITHER PROVABLE NOR DISPROVABLE”: Alan M. Turing, “Intelligent Machinery, A Heretical Theory,” unpublished lecture, c. 1951, in Stuart M. Shieber, ed., The Turing Test: Verbal Behavior as the Hallmark of Intelligence (Cambridge, Mass.: MIT Press, 2004), 105. ♦ THE ORIGINAL QUESTION, “CAN MACHINES THINK?”: Alan M. Turing, “Computing Machinery and Intelligence,” 442. ♦ “THE IDEA OF A MACHINE THINKING”: Claude Shannon to C. Jones, 16 June 1952, Manuscript Div., Library of Congress, by permission of Mary E. Shannon. ♦ “PSYCHOLOGIE IS A DOCTRINE WHICH SEARCHES OUT”: Translated in William Harvey, Anatomical Exercises Concerning the Motion of the Heart and Blood (London, 1653), quoted in “psychology, n,” draft revision Dec. 2009, OED Online, Oxford University Press, http://dictionary.oed.com/cgi/entry/50191636

Miscellaneous Writings. Edited by N. J. A. Sloane and Aaron D. Wyner. Murray Hill, N.J.: Mathematical Sciences Research Center, AT&T Bell Laboratories, 1993. Shannon, Claude Elwood, and Warren Weaver. The Mathematical Theory of Communication. Urbana: University of Illinois Press, 1949. Shenk, David. Data Smog: Surviving the Information Glut. New York: HarperCollins, 1997. Shieber, Stuart M., ed. The Turing Test: Verbal Behavior as the Hallmark of Intelligence. Cambridge, Mass.: MIT Press, 2004. Shiryaev, A. N. “Kolmogorov: Life and Creative Activities.” Annals of Probability 17, no. 3 (1989): 866–944. Siegfried, Tom. The Bit and the Pendulum: From Quantum Computing to M Theory—The New Physics of Information. New York: Wiley and Sons, 2000. Silverman, Kenneth. Lightning Man: The Accursed Life of Samuel F.


pages: 626 words: 167,836

The Technology Trap: Capital, Labor, and Power in the Age of Automation by Carl Benedikt Frey

"Robert Solow", 3D printing, autonomous vehicles, basic income, Bernie Sanders, Branko Milanovic, British Empire, business cycle, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Clayton Christensen, collective bargaining, computer age, computer vision, Corn Laws, creative destruction, David Graeber, David Ricardo: comparative advantage, deindustrialization, demographic transition, desegregation, deskilling, Donald Trump, easy for humans, difficult for computers, Edward Glaeser, Elon Musk, Erik Brynjolfsson, everywhere but in the productivity statistics, factory automation, falling living standards, first square of the chessboard / second half of the chessboard, Ford paid five dollars a day, Frank Levy and Richard Murnane: The New Division of Labor, full employment, future of work, game design, Gini coefficient, Hyperloop, income inequality, income per capita, industrial cluster, industrial robot, intangible asset, interchangeable parts, Internet of things, invention of agriculture, invention of movable type, invention of the steam engine, invention of the wheel, Isaac Newton, James Hargreaves, James Watt: steam engine, job automation, job satisfaction, job-hopping, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kickstarter, knowledge economy, knowledge worker, labor-force participation, labour mobility, Loebner Prize, low skilled workers, Malcom McLean invented shipping containers, manufacturing employment, mass immigration, means of production, Menlo Park, minimum wage unemployment, natural language processing, new economy, New Urbanism, Norbert Wiener, oil shock, On the Economy of Machinery and Manufactures, Pareto efficiency, pattern recognition, pink-collar, Productivity paradox, profit maximization, Renaissance Technologies, rent-seeking, rising living standards, Robert Gordon, robot derives from the Czech word robota Czech, meaning slave, Second Machine Age, secular stagnation, self-driving car, Silicon Valley, Simon Kuznets, social intelligence, speech recognition, spinning jenny, Stephen Hawking, The Future of Employment, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Malthus, total factor productivity, trade route, Triangle Shirtwaist Factory, Turing test, union organizing, universal basic income, washing machines reduced drudgery, wealth creators, women in the workforce, working poor, zero-sum game

As noted above, AI is another GPT, and it is already being used to perform both mental and manual tasks. Because its potential applications are so vast, Michael and I began by looking at tasks that computers still perform poorly and where technological leaps have been limited in recent years. For a glimpse of the state of the art in machine social intelligence, for example, consider the Turing test, which captures the ability of an AI algorithm to communicate in a way that is indistinguishable from an actual human. The Loebner Prize is an annual Turing test competition that awards prizes to chat bots that are considered to be the most humanlike. These competitions are straightforward. A human judge holds computer-based textual interactions with both an algorithm and a human at the same time. Based on these conversations, the judge must then try to distinguish between the two.

See mass production American Telephone and Telegraph Company (AT&T), 315 annus mirabilis of 1769, 97, 148 anti-Amazon law, 290 Antikythera mechanism, 39 Appius Claudius, 37 Archimedes, 30, 39 Aristotle, 1, 39 Arkwright, Richard, 94, 101 artificial intelligence (AI), 5, 36, 301–41, 228, 342; Alexa (Amazon), 306; AlphaGo (Deep Mind), 301, 302; Amara’s Law, 323–25; artificial neural networks, 304; autonomous robots, 307; autonomous vehicles, 308, 310, 340; big data, 303; Chinese companies, 313; Dactyl, 313; data, as the new oil, 304; Deep Blue (IBM), 301, 302; deep learning, 304; -driven unemployment, 356; Google Translate, 304; Gripper, 313; internet traffic, worldwide, 303; JD. com, 313; Kiva Systems, 311; machine social intelligence, 317; Microsoft, 306; misconception, 311; multipurpose robots, 327; Neural Machine Translation, 304; neural networks, 303, 305, 314; pattern recognition, 319; phrase-based machine translation, 304; Siri (Apple), 306; speech recognition technology, 306; Turing test, 317; virtual agents, 306; voice assistant, 306; warehouse automation, 314 artisan craftsmen, 8; in domestic system, 118, 131; emigration of, 83; factory job, transition to, 124; fates of, 17; full-time, 34; middle-income, 11, 16, 24, 135; replacement of, 9, 16, 218 Ashton, T. S., 94–95 atmospheric pressure, discovery of, 106 Austen, Jane, 11, 60–61, 69, 337 automation: adverse consequences of, 11, 240; bottlenecks to, 234; next wave of, 339; social costs of, 349; winners and losers from, 343 automobiles: cheapening of, 18, 167; industry, 202; invention of, 166; production, 165 autonomous vehicles, 308, 310, 340 Autor, David, 225, 234, 243, 254 Babbage, Charles, 119–20, 134 baby boom, 221 Bacon, Francis, 94 Bacon, Roger, 78 Baines, Edward, 111, 119–20, 124 barometer, 52, 59 Bartels, Larry, 273–75 Bastiat, Frederic, 338 Bauer, Georg, 51 Bayezid II, Sultan, 17 Benedictines, 78–79 Benjamin Franklin Bridge, 167 Benz, Karl, 148, 166 Berger, Thor, 259, 284, 359 Bessen, James, 105, 136, 247 biblio-diversity, promotion of, 290 bicycle, 165 Biden, Joseph, 238 big data, 303 Black Death, 67, 75 Blincoe, Robert, 9, 124 blue collar aristocracy, 239, 282 blue-collar jobs, disappearance of, 251, 254 Blue Wall, 284 Bohr, Niels, 298 Bostrom, Nick, 36 Boulton, Matthew, 107, 379 Boulton & Watt company, 107, 109 bourgeois virtues, 70 Bracero program, 204 Braverman, Harry, 229–30 British income tax, introduction of, 133 British Industrial Revolution: great divergence, 137; human costs of displacement, 192; machinery riots, 103, 219; path to, beginnings of, 75; reason for beginnings, 75; significance of, 8; technological event, 149; textile industry, 100.

., 90 3D printing, 22 three-field system, 42 Tiberius, Roman Emperor, 40 Tilly, Charles, 58 Tinbergen, Jan, 14, 213, 225 Tocqueville, Alexis de, 147, 207, 270 Toffler, Alvin, 257 Torricelli, Evangelista, 52, 76 tractor use, expansion of, 196 trade, expansion of, 68 trade unions, emergence of, 190 treaty ports, 88 Trevithick, Richard, 109 Triangle Shirtwaist Factory fire (1911), 194 truck driver, 340–41 trucker culture, ending of the heyday of, 171 Trump, Donald, 278, 280, 286, 331 Tugwell, Rexford G., 179 Tull, Jethro, 54 Turing test, 317 Turnpike Trusts, 108 Twain, Mark, 21, 165, 208 typewriter, 161–62 typographers, computer’s effect on jobs and wages of, 247 unemployment, 246, 254; AI-driven, 356; American social expenditure on, 274; average duration of, 177; blame for, 141; fear of, 113; mass, fears of, 366; technological, 12, 117 union security agreements, 257 United Auto Workers (UAW) union, 276 United Nations, 305 universal basic income (UBI), 355 universal white male suffrage, 270 unskilled work, 350 urban-rural wage gap, 209 Ure, Andrew, 97, 104, 119 U.S.


pages: 396 words: 117,149

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

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

Starting with a pile of electronic components such as transistors, resistors, and capacitors, Koza’s system reinvented a previously patented design for a low-pass filter, a circuit that can be used for things like enhancing the bass on a dance-music track. Since then he’s made a sport of reinventing patented devices, turning them out by the dozen. The next milestone came in 2005, when the US Patent and Trademark Office awarded a patent to a genetically designed factory optimization system. If the Turing test had been to fool a patent examiner instead of a conversationalist, then January 25, 2005, would have been a date for the history books. Koza’s confidence stands out even in a field not known for its shrinking violets. He sees genetic programming as an invention machine, a silicon Edison for the twenty-first century. He and other evolutionaries believe it can learn any program, making it their entry in the Master Algorithm sweepstakes.

If we measure not just the probability of vowels versus consonants, but the probability of each letter in the alphabet following each other, we can have fun generating new texts with the same statistics as Onegin: choose the first letter, then choose the second based on the first, and so on. The result is complete gibberish, of course, but if we let each letter depend on several previous letters instead of just one, it starts to sound more like the ramblings of a drunkard, locally coherent even if globally meaningless. Still not enough to pass the Turing test, but models like this are a key component of machine-translation systems, like Google Translate, which lets you see the whole web in English (or almost), regardless of the language the pages were originally written in. PageRank, the algorithm that gave rise to Google, is itself a Markov chain. Larry Page’s idea was that web pages with many incoming links are probably more important than pages with few, and links from important pages should themselves count for more.

., 91, 94–95 decision tree induction, 85–89 further reading, 300–302 hill climbing and, 135 Hume and, 58–59 induction and, 80–83 intelligence and, 52, 89 inverse deduction and, 52, 82–85, 91 Master Algorithm and, 240–241, 242–243 nature and, 141 “no free lunch” theorem, 62–65 overfitting, 70–75 probability and, 173 problem of induction, 59–62 sets of rules, 68–70 Taleb, Nassim, 38, 158 Tamagotchi, 285 Technology machine learning as, 236–237 sex and evolution of, 136–137 trends in, 21–22 Terrorists, data mining to catch, 232–233 Test set accuracy, 75–76, 78–79 Tetris, 32–33 Text classification, support vector machines and, 195–196 Thalamus, 27 Theory, defined, 46 Theory of cognition, 226 Theory of everything, Master Algorithm and, 46–48 Theory of intelligence, 35 Theory of problem solving, 225 Thinking, Fast and Slow (Kahneman), 141 Thorndike, Edward, 218 Through the Looking Glass (Carroll), 135 Tic-tac-toe, algorithm for, 3–4 Time, as principal component of memory, 217 Time complexity, 5 The Tipping Point (Gladwell), 105–106 Tolstoy, Leo, 66 Training set accuracy, 75–76, 79 Transistors, 1–2 Treaty banning robot warfare, 281 Truth, Bayesians and, 167 Turing, Alan, 34, 35, 286 Turing Award, 75, 156 Turing machine, 34, 250 Turing point, Singularity and, 286, 288 Turing test, 133–134 “Turning the Bayesian crank,” 149 UCI repository of data sets, 76 Uncertainty, 52, 90, 143–175 Unconstrained optimization, 193–194. See also Gradient descent Underwood, Ben, 26, 299 Unemployment, machine learning and, 278–279 Unified inference algorithm, 256 United Nations, 281 US Patent and Trademark Office, 133 Universal learning algorithm. See Master Algorithm Universal Turing machine, 34 Uplift modeling, 309 Valiant, Leslie, 75 Value of states, 219–221 Vapnik, Vladimir, 190, 192, 193, 195 Variance, 78–79 Variational inference, 164, 170 Venter, Craig, 289 Vinge, Vernor, 286 Virtual machines, 236 Visual cortex, 26 Viterbi algorithm, 165, 305 Voronoi diagrams, 181, 183 Wake-sleep algorithm, 103–104 Walmart, 11, 69–70 War, cyber-, 19–21, 279–282, 299, 310 War of the Worlds (radio program), 156 Watkins, Chris, 221, 223 Watson, James, 122, 236 Watson, Thomas J., Sr., 219 Watson (computer), 37, 42–43, 219, 237, 238 Wave equation, 30 Web 2.0, 21 Web advertising, 10–11, 160, 305 Weighted k-nearest-neighbor algorithm, 183–185, 190 Weights attribute, 189 backpropagation and, 111 Master Algorithm and, 242 meta-learning and, 237–238 perceptron’s, 97–99 relational learning and, 229 of support vectors, 192–193 Welles, Orson, 156 Werbos, Paul, 113 Wigner, Eugene, 29 Will, George F., 276 Williams, Ronald, 112 Wilson, E.


pages: 239 words: 56,531

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

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

This trifle, inspired at least in part by the renown of Christopher’s uncle Lytton Strachey’s 1918 portrait of a generation, Eminent Victorians, is the product of a stored program computer, and as such may well be the first aesthetic object produced by the ancestors of the culture machine. The love letter generator’s intentional blurring of the boundary between human and nonhuman is directly related to one of the foundational memes of artificial intelligence: the still-provocative Turing Test. In “Computing Machinery and Intelligence,” a seminal paper from 1950, Turing created a thought experiment. He posited a person holding a textual conversation on any topic with an unseen correspondent. If the person believes he or she is communicating with another person, but is in reality conversing with a machine, then that machine has passed the Turing Test. In other words, the test that Turing proposes that a computer must pass to be considered “intelligent” is to simulate the conversational skills of another person. Turing was not able to pursue these ideas much further because the same government that was happy to tolerate his eccentricities and use his talents to decipher enemy communications prosecuted him after the war for his homosexuality—still a crime in England at the time—and put him on estrogen treatments, then thought to reduce the effects of the “perversion.” 19 CHAPTER 2 He died in 1954, his death ruled a suicide, but with a complication so heartbreaking that it bears repeating.


pages: 218 words: 63,471

How We Got Here: A Slightly Irreverent History of Technology and Markets by Andy Kessler

Albert Einstein, Andy Kessler, animal electricity, automated trading system, bank run, Big bang: deregulation of the City of London, Bob Noyce, Bretton Woods, British Empire, buttonwood tree, Claude Shannon: information theory, Corn Laws, Douglas Engelbart, Edward Lloyd's coffeehouse, fiat currency, fixed income, floating exchange rates, Fractional reserve banking, full employment, Grace Hopper, invention of the steam engine, invention of the telephone, invisible hand, Isaac Newton, Jacquard loom, James Hargreaves, James Watt: steam engine, John von Neumann, joint-stock company, joint-stock limited liability company, Joseph-Marie Jacquard, Kickstarter, Leonard Kleinrock, Marc Andreessen, Maui Hawaii, Menlo Park, Metcalfe's law, Metcalfe’s law, Mitch Kapor, packet switching, price mechanism, probability theory / Blaise Pascal / Pierre de Fermat, profit motive, railway mania, RAND corporation, Robert Metcalfe, Silicon Valley, Small Order Execution System, South Sea Bubble, spice trade, spinning jenny, Steve Jobs, supply-chain management, supply-chain management software, trade route, transatlantic slave trade, tulip mania, Turing machine, Turing test, undersea cable, William Shockley: the traitorous eight

. *** Two other important technologies, which used the Tesla coil and vacuum tubes as amplifiers, came out of World War II: RADAR for RAdio Detection And Ranging and SONAR for SOund NAvigation and Ranging. Turing, who would later go to the University of Manchester, worked on the Manchester Automatic Digital Machine or MADAM, and became famous for a posthumously published paper called Intelligent Machinery. In it, he outlined the Turing Test. A computer would most surely be intelligent if a human who fed it questions from the other side of the wall couldn’t distinguish between it and a human answering the questions. Turing was convinced one could be built by the year 2000. Maybe. My bank’s ATM is smarter than its tellers, and might actually pass the Turing Test. Meanwhile, back in Philadelphia, things were moving kind of slow. Project PX, the Electronic Numerical Integrator and Calculator, or ENIAC, was started in mid-1943. Perhaps it was a little ambitious. It contained 17,468 vacuum tubes, 70,000 resistors, 10,000 capacitors, 6,000 manual switches, and 5 million solder joints.


pages: 225 words: 70,180

Humankind: Solidarity With Nonhuman People by Timothy Morton

a long time ago in a galaxy far, far away, David Brooks, Georg Cantor, gravity well, invisible hand, means of production, megacity, microbiome, phenotype, planetary scale, Richard Feynman, self-driving car, Silicon Valley, Slavoj Žižek, Turing test, wage slave, zero-sum game

It simply cannot be proved, as Marx wants to, that the best of bees is never as good as the worst of (human) architects because the human uses imagination and the bee simply executes an algorithm.7 Far more efficient than showing bees have the capacity of imagination (some science begins to move toward this possibility) is to show that it’s impossible to prove that a human can. Prove that I’m not executing an algorithm when I seem to be planning something. Prove that asserting that humans do not blindly follow algorithms is not the effect of some blind algorithm. The most we can say is that human architects pass our Turing test for now, but that is no reason to say that they are in any sense better than bees. It is instead truer to assert that we are hamstrung as to determining whether humans are executions of algorithms or not, casting doubt on our certainty that bees really do only execute algorithms blindly, since that certainty is based on a metaphysical assertion about humans and is thus caught in fruitless circularity.

Part of this admission is that we are caught in the subjunctive mode that Descartes wants to collapse into the indicative. “I might be an android” is as unacceptable to him for its “might” as for its “android.” Such a thought process wants to eliminate doubt and paranoia. But what if doubt and paranoia were default to personhood? What if being concerned that I might not be a person were a basic condition of being one? This seems to be what the Turing test is pointing to. It’s not that personhood is some mysterious property that we grant to beings under special circumstances, or that it doesn’t exist at all except for in the eye of the beholder, or that it’s an emergent property of special states of matter. It’s that personhood now means “You are not a non-person.” In the UK, there is an urban myth about the legal definition of “not being in possession of yourself,” aka “not being a person.”


pages: 846 words: 232,630

Darwin's Dangerous Idea: Evolution and the Meanings of Life by Daniel C. Dennett

Albert Einstein, Alfred Russel Wallace, anthropic principle, assortative mating, buy low sell high, cellular automata, combinatorial explosion, complexity theory, computer age, conceptual framework, Conway's Game of Life, Danny Hillis, double helix, Douglas Hofstadter, Drosophila, finite state, Gödel, Escher, Bach, In Cold Blood by Truman Capote, invention of writing, Isaac Newton, Johann Wolfgang von Goethe, John von Neumann, Murray Gell-Mann, New Journalism, non-fiction novel, Peter Singer: altruism, phenotype, price mechanism, prisoner's dilemma, QWERTY keyboard, random walk, Richard Feynman, Rodney Brooks, Schrödinger's Cat, selection bias, Stephen Hawking, Steven Pinker, strong AI, the scientific method, theory of mind, Thomas Malthus, Turing machine, Turing test

We now know that, however convincing this argument used to be, its back has been broken by Darwin, and the particular conclusion Poe drew about chess has been definitively refuted by the generation of artificers following in Art Samuel's footsteps. What, though, of Descartes's test — now known as the Turing Test? That has generated controversy ever since Turing proposed his nicely operationalized version of it, and has even led to a series of real, if restricted, competitions, which confirm what everybody who had thought carefully about the Turing Test already knew (Dennett 1985): it is embarrassingly easy to fool the naive judges, and astronomically {436} difficult to fool the expert judges — a problem, once more, of not having a proper "sword-in-the-stone" feat to settle the issue. Holding a conversation or winning a chess match is not a suitable feat, the former because it is too open-ended for a contestant to secure unambiguous victory in spite of its severe difficulty, and the latter because it is demonstrably within the power of a machine after all.

For whereas reason is a universal instrument which can be used in all kinds of situations, these organs need some particular disposition for each particular action; hence it is for all practical purposes impossible for a machine to have enough different organs to make it act in all the contingencies of life in the way in which our reason makes us act. [Descartes 1637, pt. 5.] Alan Turing, in 1950, asked himself the same question, and came up with just the same acid test — somewhat more rigorously described — what he called the imitation game, and we now call the Turing Test. Put two contestants — one human, one a computer — in boxes (in effect) and conduct conversations with each; if the computer can convince you it is the human being, it wins the imitation game. Turing's verdict, however, was strikingly different from Descartes's: I believe that in about fifty years' time it will be possible to program computers, with a storage capacity of about 109, to make them play the imitation game so well that an average interrogator will not have more than a 70 percent chance of making the right identification after five minutes of questioning.

Turing has already been proven right about his last prophecy: "the use of words and general educated opinion" has already "altered so much" that one can speak of machines thinking without expecting to be contradicted — "on general principles." Descartes found the notion of a thinking machine {433} "innconceivable," and even if, as many today believe, no machine will ever succeed in passing the Turing Test, almost no one today would claim that the very idea is inconceivable. Perhaps this sea-change in public opinion has been helped along by the comouter's progress on other feats, such as playing checkers and chess. In an address in 1957, Herbert Simon (Simon and Newell 1958) predicted that computer would be the world chess champion in less than a decade, a classic case of overoptimism, as it turns out.


pages: 561 words: 120,899

The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant From Two Centuries of Controversy by Sharon Bertsch McGrayne

Bayesian statistics, bioinformatics, British Empire, Claude Shannon: information theory, Daniel Kahneman / Amos Tversky, double helix, Edmond Halley, Fellow of the Royal Society, full text search, Henri Poincaré, Isaac Newton, Johannes Kepler, John Markoff, John Nash: game theory, John von Neumann, linear programming, longitudinal study, meta analysis, meta-analysis, Nate Silver, p-value, Pierre-Simon Laplace, placebo effect, prediction markets, RAND corporation, recommendation engine, Renaissance Technologies, Richard Feynman, Richard Feynman: Challenger O-ring, Robert Mercer, Ronald Reagan, speech recognition, statistical model, stochastic process, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, Turing machine, Turing test, uranium enrichment, Yom Kippur War

Their ignorance proved fortunate. Despite the strange reputation of British mathematicians, the operational head of GC&CS prepared for war by quietly recruiting a few nonlinguists—“men of the Professor type”5—from Oxford and Cambridge universities. Among that handful of men was Alan Mathison Turing, who would father the modern computer, computer science, software, artificial intelligence, the Turing machine, the Turing test—and the modern Bayesian revival. Turing had studied pure mathematics at Cambridge and Princeton, but his passion was bridging the gap between abstract logic and the concrete world. More than a genius, Turing had imagination and vision. He had also developed an almost unique set of interests: the abstract mathematics of topology and logic; the applied mathematics of probability; the experimental derivation of fundamental principles; the construction of machines that could think; and codes and ciphers.

When the laboratory finally built his design in 1950, it was the fastest computer in the world and, astonishingly, had the memory capacity of an early Macintosh built three decades later. Turing moved to the University of Manchester, where Newman was building the first electronic, stored-program digital computer for Britain’s atomic bomb. Working in Manchester, Turing pioneered the first computer software, gave the first lecture on computer intelligence, and devised his famous Turing Test: a computer is thinking if, after five minutes of questioning, a person cannot distinguish its responses from those of a human in the next room. Later, Turing became interested in physical chemistry and how huge biological molecules construct themselves into symmetrical shapes. A series of spectacular international events in 1949 and 1950 intruded on these productive years and precipitated a personal crisis for Turing: the Soviets surprised the West by detonating an atomic bomb; Communists gained control of mainland China; Alger Hiss, Klaus Fuchs, and Julius and Ethel Rosenberg were arrested for spying; and Sen.

Jack, ed. (2004) The Essential Turing. Clarendon Press. Essential essays. Copeland BJ et al. (2006) Colossus: The Secrets of Bletchley Park’s Codebreaking Computers. Oxford University Press. Essential essays. Eisenhart, Churchill. (1977) The birth of sequential analysis (obituary note on retired RAdm. Garret Lansing Schuyler). Amstat News (33:3). Epstein R, Robert G, Beber G., eds. (2008) Parsing the Turing Test: Philosophical and Methodical Issues in the Quest for the Thinking Computer. Springer. Erskine, Ralph. (October 2006) The Poles reveal their secrets: Alastair Denniston’s account of the July 1939 meeting at Pyry. Cryptologia (30) 204–305. Fagen MD. (1978) The History of Engineering and Science in the Bell System: National Service in War and Peace (1925–1975). Vol. 2. Bell Telephone Labs.


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The Language Instinct: How the Mind Creates Language by Steven Pinker

Albert Einstein, cloud computing, David Attenborough, double helix, Drosophila, elephant in my pajamas, finite state, illegal immigration, Joan Didion, Loebner Prize, mass immigration, Maui Hawaii, meta analysis, meta-analysis, MITM: man-in-the-middle, natural language processing, out of africa, phenotype, rolodex, Ronald Reagan, Sapir-Whorf hypothesis, Saturday Night Live, speech recognition, Steven Pinker, theory of mind, transatlantic slave trade, Turing machine, Turing test, twin studies, Yogi Berra

Written versus spoken language: Liberman et al., 1967; Miller, 1991. Writing systems: Crystal, 1987; Miller, 1991; Logan, 1986. Two tragedies in life: from Man and Superman. Rationality of English orthography: Chomsky & Halle, 1968/1991; C. Chomsky, 1970. Twain on foreigners: from The Innocents Abroad. 7. Talking Heads Artificial Intelligence: Winston, 1992; Wallich, 1991; The Economist, 1992. Turing Test of whether machines can think: Turing, 1950. ELIZA: Weizenbaum, 1976. Loebner Prize competition: Shieber, in press. Fast comprehension: Garrett, 1990; Marslen-Wilson, 1975. Style: Williams, 1990. Parsing: Smith, 1991; Ford, Bresnan, & Kaplan, 1982; Wanner & Maratsos, 1978; Yngve, 1960; Kaplan, 1972; Berwick et al., 1991; Wanner, 1988; Joshi, 1991; Gibson, in press. Magical number seven: Miller, 1956.

Shepard, R. N., and Cooper, L. A. 1982. Mental images and their transformations. Cambridge, Mass.: MIT Press. Shevoroshkin, V. 1990. The mother tongue: How linguists have reconstructed the ancestor of all living languages. The Sciences, 30, 20–27. Shevoroshkin, V., & Markey, T. L. 1986. Typology, relationship, and time. Ann Arbor, Mich.: Karoma. Shieber, S. In press. Lessons from a restricted Turing Test. Communications of the Association for Computing Machinery. Shopen, T. (Ed.) 1985. Language typology and syntactic description, 3 vols. New York: Cambridge University Press. Simon, J. 1980. Paradigms lost. New York: Clarkson Potter. Singer, P. 1992. Bandit and friends. New York Review of Books, April 9. Singleton, J., & Newport, E. 1993. When learners surpass their models: the acquisition of sign language from impoverished input.

., 20, 362 Tongue, 162–168 Tongues, speaking in, 168–169 Tooby, J., 334, 425, 429, 449, 465, 467, 468 Top-down perception, 180–185, 213–216, 419–420, glossary Tourette’s syndrome, 342–343 Tower of Babel, 20 Traces, 113–118, 218–222, 320, PS13 Transformations, 113–118, 218–222, 320, glossary Trueswell, J., 213, 214 Truffaut, F., 281 Trump, I., 139 Turing, A., 64, 191 Turing machine, 64–69, 324, glossary Turing test, 191–194, PS15 Turkish, 233, 257 Twain, M., 51, 80, 95, 188, 277 Ullman, M., 454, 460 Universal Grammar, 9, 26, 28–29, 32, 102–105, 113, 237–241, 290–293, 356, 425, 429, PS15, glossary Universality of language, 13–15, 19 Universals of language, 29, 32, 103–105, 233–241, PS10–11, PS15 Uptalk, PS23 Uralic languages, 233, 257, 259, 261 Urban legends, 402 van der Lely, H., PS12 Verbs, 91–92, 105–108, 114–116, 214–215, 279–280, 319, 407–410, PS4, glossary Vision and visual imagery, 52–53, 55–56, 61–63, 190, 322, 360, PS4 Vocal chords, 160, 165 Voicing, 160, 167, 172–176, glossary Vowels, 162–165, 169, 171–173, 178, 234, 247, 252–253 Walkman, 136–138 Wallace, A., 366 Wallace, D.


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Nine Algorithms That Changed the Future: The Ingenious Ideas That Drive Today's Computers by John MacCormick, Chris Bishop

Ada Lovelace, AltaVista, Claude Shannon: information theory, fault tolerance, information retrieval, Menlo Park, PageRank, pattern recognition, Richard Feynman, Silicon Valley, Simon Singh, sorting algorithm, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, traveling salesman, Turing machine, Turing test, Vannevar Bush

One of the earliest discussions of actually simulating a brain using a computer was by Alan Turing, a British scientist who was also a superb mathematician, engineer, and code-breaker. Turing's classic 1950paper, entitled Computing Machinery and Intelligence, is most famous for a philosophical discussion of whether a computer could masquerade as a human. The paper introduced a scientific way of evaluating the similarity between computers and humans, known these days as a “Turing test.” But in a less well-known passage of the same paper, Turing directly analyzed the possibility of modeling a human brain using a computer. He estimated that only a few gigabytes of memory might be sufficient. A typical biological neuron. Electrical signals flow in the directions shown by the arrows. The output signals are only transmitted if the sum of the input signals is large enough. Sixty years later, it's generally agreed that Turing significantly underestimated the amount of work required to simulate a human brain.

TCP telegraph telephone. See phone terminate theology Thompson, Thomas M. threshold; soft title: of this book; of a web page to-do list to-do list trick Tom Sawyer training. See also learning training data transaction: abort; atomic; in a database; on the internet; rollback travel agent Traveling Salesman Problem trick, definition of TroubleMaker.exe Turing, Alan Turing machine Turing test TV Twain, Mark twenty questions, game of twenty-questions trick two-dimensional parity. See parity two-phase commit U.S. Civil War Ullman, Jeffrey D. uncomputable. See also undecidable undecidable. See also uncomputable undefined unicycle universe unlabeled Vazirani, Umesh verification Verisign video video game virtual table virtual table trick Waters, Alice web. See World Wide Web web browser.


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

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

However, when predictions do not accurately predict the future, we notice the anomaly, and this information is fed back into our brain, which updates its algorithm, thus learning and further enhancing the model. Hawkins’s work is controversial. His ideas are debated in the psychology literature, and many computer scientists flatly reject his emphasis on the cortex as a model for prediction machines. The notion that an AI that could pass the Turing test (a machine being able to deceive a human into believing that the machine is actually a human) in its strongest sense remains far from reality. Current AI algorithms cannot reason, and moreover it is difficult to interrogate them to understand the source of their predictions. Irrespective of whether the underlying model is appropriate, his emphasis on prediction as the basis for intelligence is useful for understanding the impact of recent changes in AI.

., 49–50 human weaknesses in, 54–58 stereotypes, 19 Stern, Scott, 169–170, 218–219 Stigler, George, 105 strategy, 2, 18–19 AI-first, 179–180 AI’s impact on, 153–166 boundary shifting in, 157–158 business transformation and, 167–178 capital and, 170–171 cheap AI and, 15–17 data and, 174–176 economics of, 165 hybrid corn adoption and, 158–160 judgment and, 161–162 labor and, 171–174 learning, 179–194 organizational structure and, 161–162 value capture and, 162–165 strokes, predicting, 44–46, 47–49 Sullenberger, Chesley “Sully,” 184 supervised learning, 183 Sweeney, Latanya, 195, 196 Tadelis, Steve, 199 Taleb, Nassim Nicholas, 60–61 The Taming of Chance (Hacking), 40 Tanner, Adam, 195 task analysis, 74–75, 125–131 AI canvas and, 134–139 job redesign and, 142–145 Tay chatbot, 204–205 technical support, 90–91 Tencent Holdings, 164, 217, 218 Tesla, 8 Autopilot legal terms, 116 navigation apps and, 89 training data at, 186–187 upgrades at, 188 Tesla Motor Club, 111–112 Thinking, Fast and Slow (Kahneman), 209–210 Tinder, 189 tolerance for error, 184–186 tools, AI, 18 AI canvas and, 134–138 for deconstructing work flows, 123–131 impact of on work flows, 126–129 job redesign and, 141–151 usefulness of, 158–160 topological data analysis, 13 trade-offs, 3, 4 in AI-first strategy, 181–182 with data, 174–176 between data amounts and costs, 44 between risks and benefits, 205 satisficing and, 107–109 simulations and, 187–188 strategy and, 156 training data for, 43, 45–47 data risks, 202–204 in decision making, 74–76, 134–138 by humans, 96–97 in-house and on-the-job, 185 in medical imaging, 147 in modeling skills, 101 translation, language, 25–27, 107–108 trolley problem, 116 truck drivers, 149–150 Tucker, Catherine, 196 Tunstall-Pedoe, William, 2 Turing, Alan, 13 Turing test, 39 Tversky, Amos, 55 Twitter, Tay chatbot on, 204–205 Uber, 88–89, 164–165, 190 uncertainty, 3, 103–110 airline industry and weather, 168–169, 170 airport lounges and, 105–106 business boundaries and, 168–170 contracts in dealing with, 170–171 in e-commerce delivery times, 157–158 reducing, strategy and, 156–157 strategy and, 165 unknown knowns, 59, 61–65, 99 unknown unknowns, 59, 60–61 US Bureau of Labor Statistics, 171 US Census Bureau, 14 US Department of Defense, 14, 116 US Department of Transportation, 112, 185 Validere, 3 value, capturing, 162–165 variables, 45 omitted, 62 Varian, Hal, 43 variance, 34–36 fulfillment industry and, 144–145 taming complexity and, 103–110 Vicarious, 223 video games, 183 Vinge, Vernor, 221 VisiCalc, 141–142, 163, 164 Wald, Abraham, 101 Wanamaker, John, 174–175 warehouses, robots in, 105 Watson, 146 Waymo, 95 Waze, 89–90, 106, 191 WeChat, 164 Wells Fargo, 173 Windows 95, 9–10 The Wizard of Oz, 24 work flows AI tools’ impact on, 126–129 decision making and, 133–140 deconstructing, 123–131 iPhone keyboard design and, 129–130 job redesign and, 142–145 task analysis, 125–131 World War II bombing raids, 100–102 X.ai, 97 Xu Heyi, 164 Yahoo, 216 Y Combinator, 210 Yeomans, Mike, 117 YouTube, 176 ZipRecruiter, 93–94, 100 About the Authors AJAY AGRAWAL is professor of strategic management and Peter Munk Professor of Entrepreneurship at the University of Toronto’s Rotman School of Management and the founder of the Creative Destruction Lab.


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The Stack: On Software and Sovereignty by Benjamin H. Bratton

1960s counterculture, 3D printing, 4chan, Ada Lovelace, additive manufacturing, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, basic income, Benevolent Dictator For Life (BDFL), Berlin Wall, bioinformatics, bitcoin, blockchain, Buckminster Fuller, Burning Man, call centre, carbon footprint, carbon-based life, Cass Sunstein, Celebration, Florida, charter city, clean water, cloud computing, connected car, corporate governance, crowdsourcing, cryptocurrency, dark matter, David Graeber, deglobalization, dematerialisation, disintermediation, distributed generation, don't be evil, Douglas Engelbart, Douglas Engelbart, Edward Snowden, Elon Musk, en.wikipedia.org, Eratosthenes, Ethereum, ethereum blockchain, facts on the ground, Flash crash, Frank Gehry, Frederick Winslow Taylor, future of work, Georg Cantor, gig economy, global supply chain, Google Earth, Google Glasses, Guggenheim Bilbao, High speed trading, Hyperloop, illegal immigration, industrial robot, information retrieval, Intergovernmental Panel on Climate Change (IPCC), intermodal, Internet of things, invisible hand, Jacob Appelbaum, Jaron Lanier, Joan Didion, John Markoff, Joi Ito, Jony Ive, Julian Assange, Khan Academy, liberal capitalism, lifelogging, linked data, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, Masdar, McMansion, means of production, megacity, megastructure, Menlo Park, Minecraft, MITM: man-in-the-middle, Monroe Doctrine, Network effects, new economy, offshore financial centre, oil shale / tar sands, packet switching, PageRank, pattern recognition, peak oil, peer-to-peer, performance metric, personalized medicine, Peter Eisenman, Peter Thiel, phenotype, Philip Mirowski, Pierre-Simon Laplace, place-making, planetary scale, RAND corporation, recommendation engine, reserve currency, RFID, Robert Bork, Sand Hill Road, self-driving car, semantic web, sharing economy, Silicon Valley, Silicon Valley ideology, Slavoj Žižek, smart cities, smart grid, smart meter, social graph, software studies, South China Sea, sovereign wealth fund, special economic zone, spectrum auction, Startup school, statistical arbitrage, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, Superbowl ad, supply-chain management, supply-chain management software, TaskRabbit, the built environment, The Chicago School, the scientific method, Torches of Freedom, transaction costs, Turing complete, Turing machine, Turing test, undersea cable, universal basic income, urban planning, Vernor Vinge, Washington Consensus, web application, Westphalian system, WikiLeaks, working poor, Y Combinator

At the same moment that Turing demonstrates the mechanical basis for synthetic logic by machines (suggesting real artificial intelligence), he partially delinks the correlation between philosophical thought and machinic calculation. The implications continue to play out in contemporary debates from robotics to neuroscience to the philosophy of physics, as has Turing's later conceptualization of “thinking machines,” verified by their ability to convincingly simulate the performance of human-to-human interaction, the so-called Turing test.8 In the decades since Turing's logic machine, computation-in-theory became computers-in-practice, and the digitalization of formal systems into mechanical systems and then back again, has become a predominant economic imperative. Through several interlocking modernities, the calculation of discrete states of flux and form would become more than a way to describe matter and change in the abstract, but also a set of standard techniques to strategically refashion them as well.

The limits of machinic calculation are not the same as the limits of deterministic rationality, and the social effects of computational systems are certainly given to creative accidents.17 Reactionary analog aesthetics and patriotisms, Emersonian withdrawal, and deconstrucivist political theology buy us less time and far less wiggle room than they promise, even less actually than the unfortunate notion that planetary-scale computation could emerge and mature without fundamental constitutive violence against traditional (that is, “modern”) concepts of individual, society, and sovereignty. Because they simulate logic but are not themselves necessarily logical, computers make the world in ways that do not ultimately require our thinking to function (such as the interactions between high-speed trading algorithms that even their programmers cannot entirely predict and comprehend). The forms of inhuman intelligence that they manifest will never pass the Turing test, nor should we bother asking this of them. It is an absurd and primitive request.18 It is inevitable that synthetic algorithmic intelligences can and will create things that we have not thought of in advance or ever intended to make, but as suggested, because they do not need our thinking or intention as their alibi, it is their inhumanity that may make them most creative.19 Like Deleuze on the beach making sand piles, humans wrangle computation with our algorithm boxes, and in doing so, we make things by accident, sometimes little things like signal noise on the wire and sometimes big things like megastructures. 17. 

Aesthetic suspicion of digital systems couched in political suspicion (perhaps also couched in professional anxiety) has also led to awkward schisms in art. See Clare Bishop, “The Digital Divide: Contemporary Art and New Media,” Artforum (September 2012). 17.  Luciana Parisi, Contagious Architecture: Computation, Aesthetics, and Space (Cambridge, MA: MIT Press, 2013). 18.  See my editorial “Outing A.I.: Beyond the Turing Test,” New York Times, February 23, 2015. 19.  To me this is the purchase of the Promethean accelerationism of Reza Negarastani and Ray Brassier. See Brassier's “Prometheanism and Real Abstraction” in Speculative Aesthetics, ed. Robin Mackay, Luke Pendrell, James Trafford (Urbanomic Press: Falmouth, 2014), and Negarastani's “Labor of the Inhuman, Part 1: Human,” e-flux journal #52, 02/2014, and “The Labor of the Inhuman, Part II: The Inhuman,” e-flux journal #53, 03/2014. 20. 


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Webbots, Spiders, and Screen Scrapers by Michael Schrenk

Amazon Web Services, corporate governance, fault tolerance, Firefox, Marc Andreessen, new economy, pre–internet, SpamAssassin, The Hackers Conference, Turing test, web application

This technique is called a Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA).[79] You can find more information about CAPTCHA devices at this book's website. Before embedding all your website's text in images, however, you need to recognize the downside. When you put text in images, beneficial spiders, like those used by search engines, will not be able to index your web pages. Placing text within images is also a very inefficient way to render text. Figure 27-3. Text within an image is hard for a webbot to interpret * * * [77] Read Chapter 3 if you are interested in browser spoofing. [78] To learn the difference between obfuscation and encryption, read Chapter 20. [79] Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is a registered trademark of Carnegie Mellon University.


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The Doomsday Calculation: How an Equation That Predicts the Future Is Transforming Everything We Know About Life and the Universe by William Poundstone

Albert Einstein, anthropic principle, Any sufficiently advanced technology is indistinguishable from magic, Arthur Eddington, Bayesian statistics, Benoit Mandelbrot, Berlin Wall, bitcoin, Black Swan, conceptual framework, cosmic microwave background, cosmological constant, cosmological principle, cuban missile crisis, dark matter, digital map, discounted cash flows, Donald Trump, Doomsday Clock, double helix, Elon Musk, Gerolamo Cardano, index fund, Isaac Newton, Jaron Lanier, Jeff Bezos, John Markoff, John von Neumann, mandelbrot fractal, Mark Zuckerberg, Mars Rover, Peter Thiel, Pierre-Simon Laplace, probability theory / Blaise Pascal / Pierre de Fermat, RAND corporation, random walk, Richard Feynman, ride hailing / ride sharing, Rodney Brooks, Ronald Reagan, Ronald Reagan: Tear down this wall, Sam Altman, Schrödinger's Cat, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, Skype, Stanislav Petrov, Stephen Hawking, strong AI, Thomas Bayes, Thomas Malthus, time value of money, Turing test

The companies offering them swear the results are private. Yet data, once it exists, has a way of turning up and being put to unexpected uses. The Bodleian Plate, an eighteenth-century engraving of Williamsburg discovered in 1929, guided Rockefeller’s reconstruction of the town. Bostrom’s conception of world simulations assumes the development of artificial intelligence that can pass a robust Turing test and behave as a psychologically convincing human. Wrap that code in an avatar, and you’ve got a virtual human. A World War II simulation could include representations of Churchill, Hitler, and Roosevelt, embodying everything known about these people. More than that, the simulation could include battles, bond drives, fascist rallies, and USO shows in which every person is a psychologically realized simulation, supplied with name, rank, and serial number taken from military records, and any other information that may survive.

Most of today’s AI researchers, and most in the tech community generally, believe that something that acts like a human and talks like a human and thinks like a human—to a sufficiently subtle degree—would have “a mind in exactly the same sense human beings have minds,” in philosopher John Searle’s words. This view is known as “strong AI.” Searle is among a dissenting faction of philosophers, and regular folk, who are not so sure about that. Almost all contemporary philosophers agree in principle that code could pass the Turing test, that it could be programmed to insist on having private moods and emotions, and that it could narrate a stream of consciousness as convincing as any human’s. But this might be all on the surface. Inside, the AI-bot could be empty, what philosophers call a zombie. It would have no soul, no subjectivity, no inner spark of whatever it is that makes us what we are. Bostrom’s trilemma takes strong AI as a given.


The Ages of Globalization by Jeffrey D. Sachs

Admiral Zheng, British Empire, Cape to Cairo, colonial rule, Columbian Exchange, Commentariolus, coronavirus, COVID-19, Covid-19, cuban missile crisis, decarbonisation, demographic transition, Deng Xiaoping, domestication of the camel, Donald Trump, en.wikipedia.org, endogenous growth, European colonialism, global supply chain, greed is good, income per capita, invention of agriculture, invention of gunpowder, invention of movable type, invention of the steam engine, invisible hand, Isaac Newton, James Watt: steam engine, job automation, John von Neumann, joint-stock company, Louis Pasteur, low skilled workers, mass immigration, Nikolai Kondratiev, out of africa, packet switching, Pax Mongolica, precision agriculture, profit maximization, profit motive, purchasing power parity, South China Sea, spinning jenny, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, The Wealth of Nations by Adam Smith, trade route, transatlantic slave trade, Turing machine, Turing test, urban planning, Watson beat the top human players on Jeopardy!, wikimedia commons

From a few thousand phone subscribers in the early 1980s, mobile subscriptions reached 7.8 billion in 2017 (figure 8.2). 8.2 Mobile Subscribers Worldwide, 1990–2017 Source: “Mobile Phone Market Forecast - 2019.” areppim: information, pure and simple, 2019, https://stats.areppim.com/stats/stats_mobilex2019.htm. The third dimension of the digital revolution is the intelligence of the computers. Once again, Turing took the lead, asking the pivotal question: Can machines have intelligence, and if so, how would we know? In 1950, he posed the famous Turing test of machine intelligence: An intelligent machine (computer-based system) would be able to interact with humans in a way that the humans would not be able to distinguish whether they were interacting with a machine or a human being. For example, the human subject could carry on a conversation with a machine or a person located in another room, passing messages to and receiving messages from that room, without knowing whether the counterpart was a person or an intelligent machine.

Today, a “self-taught” AI chess system can learn chess from scratch in a few hours, with no library of games or any other expert inputs on chess strategy, and trounce not only the current world chess champion but all past computer champions such as Deep Blue. In 2011, another IBM system, named Watson, learned to play the TV game show Jeopardy, with all of the puns and quips of popular culture and natural language, and beat world-class Jeopardy champions live on television. This too was a startling achievement, edging yet closer to passing the Turing test. After the Jeopardy championship, Watson went on to the field of medicine, working with doctors to hone expert diagnostic systems. More recently, we have seen stunning breakthroughs in deep neural networks, that is neural networks with hundreds of layers of artificial neurons. In 2016, an AI system, AlphaGo from the company Deep Mind, took on the world’s eighteen-time world Go champion, Lee Sedol.


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Bold: How to Go Big, Create Wealth and Impact the World by Peter H. Diamandis, Steven Kotler

3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, Charles Lindbergh, cloud computing, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, dematerialisation, deskilling, disruptive innovation, Elon Musk, en.wikipedia.org, Exxon Valdez, fear of failure, Firefox, Galaxy Zoo, Google Glasses, Google Hangouts, gravity well, ImageNet competition, industrial robot, Internet of things, Jeff Bezos, John Harrison: Longitude, John Markoff, Jono Bacon, Just-in-time delivery, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, loss aversion, Louis Pasteur, low earth orbit, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Mars Rover, meta analysis, meta-analysis, microbiome, minimum viable product, move fast and break things, Narrative Science, Netflix Prize, Network effects, Oculus Rift, optical character recognition, packet switching, PageRank, pattern recognition, performance metric, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, Ray Kurzweil, recommendation engine, Richard Feynman, ride hailing / ride sharing, risk tolerance, rolodex, self-driving car, sentiment analysis, shareholder value, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart grid, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, superconnector, technoutopianism, telepresence, telepresence robot, Turing test, urban renewal, web application, X Prize, Y Combinator, zero-sum game

,” Fiverr.com, 2014, http://support.fiverr.com/hc/en-us/articles/201500776-What-is-Fiverr-. 18 Unless otherwise noted, all Matt Barrie quotes come from a 2013 AI. 19 AIs with Marcus Shingles, 2013–2014. 20 AI with Andrew Vaz. 21 “About Us,” Freelancer.com, 2014, https://www.freelancer.com/info/about.php. 22 AI with Barrie. 23 Ibid. 24 AI with James DeJulio, 2013. 25 AI with Barrie. 26 Ibid. 27 “Vicarious AI passes first Turing Test: CAPTCHA,” Vicarious, October 27, 2013, http://news.vicarious.com/post/65316134613/vicarious-ai-passes-first-turing-test-captcha. Chapter Eight: Crowdfunding: No Bucks, No Buck Rogers 1 “Statistics about Business Size (including Small Business) from the U.S. Census Bureau,” Statistics of US Businesses, United States Census Bureau, 2007, https://www.census.gov/econ/smallbus.html. 2 “Statistics about Business Size (including Small Business) from the U.S.


Future Files: A Brief History of the Next 50 Years by Richard Watson

Albert Einstein, bank run, banking crisis, battle of ideas, Black Swan, call centre, carbon footprint, cashless society, citizen journalism, commoditize, computer age, computer vision, congestion charging, corporate governance, corporate social responsibility, deglobalization, digital Maoism, disintermediation, epigenetics, failed state, financial innovation, Firefox, food miles, future of work, global pandemic, global supply chain, global village, hive mind, industrial robot, invention of the telegraph, Jaron Lanier, Jeff Bezos, knowledge economy, lateral thinking, linked data, low cost airline, low skilled workers, M-Pesa, mass immigration, Northern Rock, peak oil, pensions crisis, precision agriculture, prediction markets, Ralph Nader, Ray Kurzweil, rent control, RFID, Richard Florida, self-driving car, speech recognition, telepresence, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Turing test, Victor Gruen, white flight, women in the workforce, Zipcar

For instance, a company in Austin, Texas has developed a product called Cyc. It is much like a “chatbot” except that, if it answers Science and Technology 45 a question incorrectly, you can correct it and Cyc will learn from its mistakes. But Cyc still isn’t very intelligent, which is possibly why author, scientist and futurist Ray Kurzweil made a public bet with Mitchell Kapor, the founder of Lotus, that a computer would pass the Turing test by 2029. He based this prediction on ideas expressed in his book The Singularity Is Near: in essence, arguing that intelligence will expand in a limitless, exponential manner once we achieve a certain level of advancement in genetics, nanotechnology and robotics and the integration of that technology with human biology. The precedent here is obviously the speed at which computing has developed.

A 311 Index ‘O’ Garage 170 3D printers 56 accelerated education 57 accidents 159, 161–6, 173, 246 ACNielsen 126 adaptive cruise control 165 Adeg Aktiv 50+ 208 advertising 115–16, 117, 119 Africa 70, 89, 129, 174, 221, 245, 270, 275, 290, 301 ageing 1, 10, 54, 69, 93, 139, 147–8, 164, 188, 202, 208, 221, 228–9, 237, 239, 251, 261, 292, 295, 297–8 airborne networks 56 airlines 272 allergies 196–7, 234, 236 Alliance Against Urban 4x4s 171 alternative energy 173 alternative futures viii alternative medicine 244–5 alternative technology 151 amateur production 111–12 Amazon 32, 113–14, 121 American Apparel 207 American Express 127–8 androids 55 Angola 77 anti-ageing drugs 231, 237 anti-ageing foods 188 anti-ageing surgery 2, 237 antibiotics 251 anxiety 10, 16, 30, 32, 36, 37, 128, 149, 179, 184, 197, 199, 225, 228, 243, 251, 252, 256, 263, 283–4, 295–6, 300, 301, 305 Apple 61, 115, 121, 130, 137–8, 157 Appleyard, Bryan 79 Argentina 210 Armamark Corporation 193 artificial intelliegence 22, 40, 44, 82 131, 275, 285–6, 297, 300 Asda 136, 137 Asia 11, 70, 78, 89, 129, 150, 174, 221, 280, 290, 292 Asimov, Isaac 44 Asos.com 216 asthma 235 auditory display software 29 Australia 20–21, 72–3, 76, 92, 121, 145, 196, 242, 246, 250, 270, 282 Austria 208 authenticity 32, 37, 179, 194, 203–11 authoritarianism 94 automated publishing machine (APM) 114 automation 292 automotive industry 154–77 B&Q 279 baby boomers 41, 208 bacterial factories 56 Bahney, Anna 145 Bahrain 2 baking 27, 179, 195, 199 Bangladesh 2 bank accounts, body double 132 banknotes 29, 128 banks 22, 123, 135–8, 150, 151 virtual 134 Barnes and Noble 114 bartering 151 BBC 25, 119 Become 207 Belgium 238 313 314 benriya 28 Berlusconi, Silvio 92 Best Buy 223 biofuel 64 biomechatronics 56 biometric identification 28, 35, 52, 68, 88, 132 bionic body parts 55 Biosphere Expeditions 259 biotechnology 40, 300 blended families 20 blogs 103, 107, 109, 120 Blurb 113 BMW 289 board games 225 body double bank accounts 132 body parts bionic 55 replacement 2, 188, 228 Bolivia 73 Bollywood 111 books 29, 105, 111–25 boomerang kids 145 brain transplants 231 brain-enhancing foods 188 Brazil 2, 84, 89, 173, 247, 254, 270, 290 Burger King 184 business 13, 275–92 Bust-Up 189 busyness 27, 195, 277 Calvin, Bill 45 Canada 63, 78, 240 cancer 251 car sharing 160, 169, 176 carbon credits 173 carbon footprints 255 carbon taxes 76, 172 cars classic 168–9 driverless 154–5 flying 156, 165 hydrogen-powered 12, 31, 157, 173 pay-as-you-go 167–8 self-driving 165 cascading failure 28 cash 126–7, 205 cellphone payments 129, 213 cellphones 3, 25, 35, 51, 53, 120, 121, FUTURE FILES 129, 156, 161, 251 chicken, Christian 192 childcare robots 57 childhood 27, 33–4, 82–3 children’s database 86 CHIME nations (China, India, Middle East) 2, 10, 81 China 2, 10, 11, 69–72, 75–81, 88, 92–3, 125, 137, 139–40, 142, 151, 163, 174–5, 176, 200, 222, 228, 247, 260, 270–71, 275, 279, 295, 302 choice 186–7 Christian chicken 192 Christianity, muscular 16, 73 Chrysler 176 cinema 110–11, 120 Citibank 29, 128 citizen journalism 103–4, 108 City Car Club 168 Clarke, Arthur C. 58–9 Clarke’s 187 classic cars 168–9 climate change 4, 11, 37, 43, 59, 64, 68, 74, 77–9, 93, 150, 155, 254, 257, 264, 298–9 climate-controlled buildings 254, 264 cloning 38 human 23, 249 CNN 119 coal 176 Coca-Cola 78, 222–3 co-creation 111–12, 119 coins 29, 128, 129 collective intelligence 45–6 Collins, Jim 288 comfort eating 200 Comme des Garçons 216 community 36 compassion 120 competition in financial services 124–5 low-cost 292 computers disposable 56 intelligent 23, 43 organic 56 wearable 56, 302 computing 3, 33, 43, 48, 82 connectivity 3, 10, 11, 15, 91, 120, Index 233, 261, 275–6, 281, 292, 297, 299 conscientious objection taxation 86 contactless payments 123, 150 continuous partial attention 53 control 36, 151, 225 convenience 123, 178–9, 184, 189, 212, 223, 224 Coren, Stanley 246 corporate social responsibility 276, 282, 298 cosmetic neurology 250 Costa Rica 247 Craig’s List 102 creativity 11, 286; see also innovation credit cards 141–3, 150 crime 86–9 forecasting 86–7 gene 57, 86 Croatia 200 Crowdstorm 207 Cuba 75 cultural holidays 259, 273 culture 11, 17–37 currency, global 127, 151 customization 56, 169, 221–2, 260 cyberterrorism 65, 88–9 Cyc 45 cynicism 37 DayJet 262 death 237–9 debt 123–4, 140–44, 150 defense 63, 86 deflation 139 democracy 94 democratization of media 104, 108, 113 demographics 1, 10, 21, 69, 82, 93, 202, 276, 279–81, 292, 297–8 Denmark 245 department stores 214 deregulation 11, 3 Destiny Health 149 detox 200 Detroit Project 171 diagnosis 232 remote 228 digital downloads 121 evaporation 25 315 immortality 24–5 instant gratification syndrome 202 Maoism 47 money 12, 29, 123, 126–7, 129, 132, 138, 150, 191 nomads 20, 283 plasters 241 privacy 25, 97, 108 readers 121 digitalization 37, 292 Dinner by Design 185 dirt holidays 236 discount retailers 224 Discovery Health 149 diseases 2, 228 disintegrators 57 Disney 118–19 disposable computers 56 divorce 33, 85 DNA 56–7, 182 database 86 testing, compulsory 86 do-it-yourself dinner shops 185–6 dolls 24 doorbells 32 downshifters 20 Dream Dinners 185 dream fulfillment 148 dressmaking 225 drink 178–200 driverless cars 154–5 drugs anti-ageing 231, 237 performance-improving 284–5 Dubai 264, 267, 273 dynamic pricing 260 E Ink 115 e-action 65 Earthwatch 259 Eastern Europe 290 eBay 207 e-books 29, 37, 60, 114, 115, 302 eco-luxe resorts 272 economic collapse 2, 4, 36, 72, 221, 295 economic protectionism 10, 15, 72, 298 economy travel 272 316 Ecuador 73 education 15, 18, 82–5, 297 accelerated 57 lifelong learning 290 Egypt 2 electricity shortages 301 electronic camouflage 56 electronic surveillance 35 Elephant 244 email 18–19, 25, 53–4, 108 embedded intelligence 53, 154 EMF radiation 251 emotional capacity of robots 40, 60 enclosed resorts 273 energy 72, 75, 93 alternative 173 nuclear 74 solar 74 wind 74 enhancement surgery 249 entertainment 34, 121 environment 4, 10, 11, 14, 64, 75–6, 83, 93, 155, 171, 173, 183, 199, 219–20, 252, 256–7, 271, 292, 301 epigenetics 57 escapism 16, 32–3, 121 Estonia 85, 89 e-tagging 129–30 e-therapy 242 ethical bankruptcy 35 ethical investing 281 ethical tourism 259 ethics 22, 24, 41, 53, 78, 86, 132, 152, 194, 203, 213, 232, 238, 249–50, 258, 276, 281–2, 298–9 eugenics 252 Europe 11, 70, 72, 81, 91, 141, 150, 174–5, 182, 190, 192, 209 European Union 15, 139 euthanasia 238, 251 Everquest 33 e-voting 65 experience 224 extended financial families 144 extinction timeline 9 Facebook 37, 97, 107 face-recognition doors 57 fakes 32 family 36, 37 FUTURE FILES family loans 145 fantasy-related industries 32 farmaceuticals 179, 182 fast food 178, 183–4 fat taxes 190 fear 10, 34, 36, 38, 68, 150, 151, 305 female-only spaces 210–11, 257 feminization 84 financial crisis 38, 150–51, 223, 226, 301 financial services 123–53, 252 trends 123–5 fish farming 181 fixed-price eating 200 flashpacking 273 flat-tax system 85–6 Florida, Richard 36, 286, 292 flying cars 165 food 69–70, 72, 78–9, 162, 178–201 food anti-ageing 188 brain-enhancing 188 fast 178, 183–4 functional 179 growing your own 179, 192, 195 history 190–92 passports 200 slow 178, 193 tourism 273 trends 178–80 FoodExpert ID 182 food-miles 178, 193, 220 Ford 169, 176, 213, 279–80 forecasting 49 crime 86–7 war 49 Forrester Research 132 fractional ownership 168, 175, 176, 225 France 103, 147, 170, 189, 198, 267 Friedman, Thomas 278–9, 292 FriendFinder 32 Friends Reunited 22 frugality 224 functional food 179 Furedi, Frank 68 gaming 32–3, 70, 97, 111–12, 117, 130, 166, 262 Gap 217 Index gardening 27, 148 gas 176 GE Money 138, 145 gendered medicine 244–5 gene silencing 231 gene, crime 86 General Motors 157, 165 Generation X 41, 281 Generation Y 37, 41, 97, 106, 138, 141–2, 144, 202, 208, 276, 281, 292 generational power shifts 292 Genes Reunited 35 genetic enhancement 40, 48 history 35 modification 31, 182 testing 221 genetics 3, 10, 45, 251–2 genomic medicine 231 Germany 73, 147, 160, 170, 204–5, 216–17, 261, 267, 279, 291 Gimzewski, James 232 glamping 273 global currency 127 global warming 4, 47, 77, 93, 193, 234 globalization 3, 10, 15–16, 36–7, 63–7, 72–3, 75, 81–2, 88, 100, 125, 139, 143, 146, 170, 183, 189, 193–5, 221, 224, 226, 233–4, 247–8, 263, 275, 278–80, 292, 296, 299 GM 176 Google 22, 61, 121, 137, 293 gout 235 government 14, 18, 36, 63–95, 151 GPS 3, 15, 26, 50, 88, 138, 148, 209, 237, 262, 283 Grameen Bank 135 gravity tubes 57 green taxes 76 Greenpeace 172 GRIN technologies (genetics, robotics, internet, nanotechnology) 3, 10, 11 growing your own food 178, 192, 195 Gucci 221 Gulf States 125, 260, 268 H&M 217 habitual shopping 212 Handy, Charles 278 317 Happily 210 happiness 63–4, 71–2, 146, 260 health 15, 82, 178–9, 199 health monitoring 232, 236, 241 healthcare 2, 136, 144, 147–8, 154, 178–9, 183–4, 189–91, 228–53, 298; see also medicine trends 214–1534–7 Heinberg, Richard 74 Helm, Dieter 77 Heritage Foods 195 hikikomori 18 hive mind 45 holidays 31, 119; see also tourism holidays at home 255 cultural 259 dirt 236 Hollywood 33, 111–12 holographic displays 56 Home Equity Share 145 home baking 225 home-based microgeneration 64 home brewing 225 honesty 152 Hong Kong 267 hospitals 228, 241–3, 266 at home 228, 238, 240–42 hotels 19, 267 sleep 266 human cloning 23, 249 Hungary 247 hybrid humans 22 hydrogen power 64 hydrogen-powered cars 12, 31, 157, 173 Hyperactive Technologies 184 Hyundai 170 IBM 293 identities, multiple 35, 52 identity 64, 71 identity theft 88, 132 identity verification, two-way 132 immigration 151–2, 302 India 2, 10, 11, 70–72, 76, 78–9, 81, 92, 111, 125, 135, 139, 163, 174–5, 176, 247, 249–50, 254, 260, 270, 275, 279, 302 indirect taxation 86 318 individualism 36 Indonesia 2, 174 industrial robots 42 infinite content 96–7 inflation 151 information overlead 97, 120, 159, 285; see also too much information innovation 64, 81–2, 100, 175, 222, 238, 269, 277, 286–8, 291, 297, 299 innovation timeline 8 instant gratification 213 insurance 123, 138, 147–50, 154, 167, 191, 236, 250 pay-as-you-go 167 weather 264 intelligence 11 embedded 53, 154 implants 229 intelligent computers 23, 43 intelligent night vision 162–3 interaction, physical 22, 25, 97, 110, 118, 133–4, 215, 228, 243, 276, 304 interactive media 97, 105 intergenerational mortgages 140, 144–5 intermediaries 123, 135 internet 3, 10, 11, 17–18, 25, 68, 103, 108, 115–17, 124, 156, 240–41, 261, 270, 283, 289, 305 failure 301 impact on politics 93–4 sensory 56 interruption science 53 iPills 240 Iran 2, 69 Ishiguro, Hiroshi 55 Islamic fanaticism 16 Italy 92, 170, 198–9 iTunes 115, 130; see also Apple Japan 1, 18, 26, 28–9, 54–5, 63, 80–81, 114, 121, 128–9, 132, 140, 144–5, 147, 174, 186, 189, 192, 196, 198, 200, 209–10, 223, 240, 260, 264, 271, 279, 291 jetpacks 60 job security 292 journalism 96, 118 journalism, citizen 103–4, 107 joy-makers 57 FUTURE FILES Kaboodle 207 Kapor, Mitchell 45 Kenya 128 keys 28–9 Kindle 60, 121 Kramer, Peter 284 Kuhn, Thomas 281 Kurzweil, Ray 45 Kuwait 2 labor migration 290–91 labor shortages 3, 80–81, 289–90 Lanier, Jaron 47 laser shopping 212 leisure sickness 238 Let’s Dish 185 Lexus 157 libraries 121 Libya 73 life-caching 24, 107–8 lighting 158, 160 Like.com 216 limb farms 249 limited editions 216–17 live events 98, 110, 304 localization 10, 15–16, 116, 128, 170, 178, 189, 193, 195, 215, 220, 222–3, 224, 226, 255, 270, 297 location tagging 88 location-based marketing 116 longevity 188–9, 202 Longman, Philip 71 low cost 202, 219–22 luxury 202, 221, 225, 256, 260, 262, 265–6, 272 machinamas 112 machine-to-machine communication 56 marketing 115–16 location-based 116 now 116 prediction 116 Marks & Spencer 210 Maslow, Abraham 305–6 masstigue 223 materialism 37 Mayo Clinic 243 McDonald’s 130, 168, 180, 184 McKinsey 287 Index meaning, search for 16, 259, 282, 290, 305–6 MECU 132 media 96–122 democratization of 104, 108, 115 trends 96–8 medical outsourcing 247–8 medical tourism 2, 229, 247 medicine 188, 228–53; see also healthcare alternative 243–4 gendered 244–5 genomic 231 memory 229, 232, 239–40 memory loss 47 memory pills 231, 240 memory recovery 2, 228–9, 239 memory removal 29–30, 29, 240 Menicon 240 mental health 199 Meow Mix 216 Merriman, Jon 126 metabolomics 56 meta-materials 56 Metro 204–5 Mexico 2 micromedia 101 micro-payments 130, 150 Microsoft 137, 147, 293 Middle East 10, 11, 70, 81, 89, 119, 125, 129, 139, 174–5, 268, 301 migration 3, 11, 69–70, 78, 82, 234, 275, 290–91 boomerang 20 labor 290–91 Migros 215 military recruitment 69 military vehicles 158–9 mind-control toys 38 mindwipes 57 Mitsubishi 198, 279 mobile payments 123, 150 Modafinil 232 molecular biology 231 monetization 118 money 123–52 digital 12, 29, 123, 126–7, 129, 132, 138, 150, 191 monitoring, remote 154, 168, 228, 242 monolines 135, 137 319 mood sensitivity 41, 49, 154, 158, 164, 187–8 Morgan Stanley 127 mortality bonds 148 Mozilla Corp. 289 M-PESA 129 MTV 103 multigenerational families 20 multiple identities 35, 52 Murdoch, Rupert 109 muscular Christianity 16, 73 music industry 121 My-Food-Phone 242 MySpace 22, 25, 37, 46, 97, 107, 113 N11 nations (Bangladesh, Egypt, Indonesia, Iran, South Korea, Mexico, Nigeria, Pakistan, Philippines, Turkey, Vietnam) 2 nanoelectronics 56 nanomedicine 32 nanotechnology 3, 10, 23, 40, 44–5, 50, 157, 183, 232, 243, 286, 298 napcaps 56 narrowcasting 109 NASA 25, 53 nationalism 16, 70, 72–3, 139, 183, 298, 302 natural disasters 301 natural resources 2, 4, 11, 64, 298–9 Nearbynow 223 Nestlé 195 Netherlands 238 NetIntelligence 283 networkcar.com 154 networks 28, 166, 288 airborne 56 neural nets 49 neuronic whips 57 neuroscience 33, 48 Neville, Richard 58–9 New Economics Foundation 171 New Zealand 265, 269 newspapers 29, 102–9, 117, 119, 120 Nigeria 2, 73 Nike 23 nimbyism 63 no-frills 224 Nokia 61, 105 Norelift 189 320 Northern Rock 139–40 Norwich Union 167 nostalgia 16, 31–2, 51, 169–70, 179, 183, 199, 203, 225, 303 now marketing 116 nuclear annihilation 10, 91 nuclear energy 74 nutraceuticals 179, 182 Obama, Barack 92–3 obesity 75, 190–92, 199, 250–51 oceanic thermal converters 57 oil 69, 72–3, 93, 151, 174, 176, 272, 273, 301 Oman 2, 270 online relationships 38 organic computers 56 organic food 200, 226 osteoporosis 235 outsourcing 224, 292 Pakistan 2 pandemics 4, 10, 16, 59, 72, 128, 232, 234, 272, 295–7, 301 paper 37 parasite singles 145 passwords 52 pictorial 52 pathogens 233 patient simulators 247 patina 31 patriotism 63, 67, 299 pay-as-you-go cars 167–8 pay-as-you-go insurance 167 payments cellphone 129, 213 contactless 123, 150 micro- 130, 150 mobile 123, 150 pre- 123, 150 PayPal 124, 137 Pearson, Ian 44 performance-improving drugs 284–5 personal restraint 36 personal robots 42 personalization 19, 26, 56, 96–8, 100, 102–3, 106, 108–9, 120, 138, 149, 183, 205–6, 223, 244–5, 262, 267, 269 Peru 73 FUTURE FILES Peters, Tom 280 Pharmaca 244 pharmaceuticals 2, 33, 228, 237 Philippines 2, 212, 290 Philips 114 Philips, Michael 232–3 photographs 108 physical interaction 22, 25, 97, 110, 118, 133–4, 215, 228, 243, 276, 304 physicalization 96–7, 101–2, 106, 110, 120 pictorial passwords 52 piggy banks 151 Pink, Daniel 285 plagiarism 83 polarization 15–16, 285 politics 37, 63–95, 151–2 regional 63 trends 63–5 pop-up retail 216, 224 pornography 31 portability 178, 183–4 power shift eastwards 2, 10–11, 81, 252 Prada 205–6, 216 precision agriculture 181–2 precision healthcare 234–7 prediction marketing 116 predictions 37, 301–2 premiumization 223 pre-payments 123, 150 privacy 3, 15, 41, 50, 88, 154, 165–7, 205, 236, 249, 285, 295 digital 25, 97, 108 Procter & Gamble 105, 280 product sourcing 224 Prosper 124, 135 protectionism 67, 139, 156, 220, 226, 301 economic 10, 15, 72, 299 provenance 178, 193, 226 proximity indicators 32 PruHealth 149 psychological neoteny 52 public ownership 92 public transport 171 purposeful shopping 212 Qatar 2 quality 96–7, 98, 101, 109 Index quantum mechanics 56 quantum wires 56 quiet materials 56 radiation, EMF 251 radio 117 randominoes 57 ranking 34, 83, 109, 116, 134, 207 Ranking Ranqueen 186 reality mining 51 Really Cool Foods 185 rebalancing 37 recession 139–40, 202, 222 recognition 36, 304 refrigerators 197–8 refuge 121 regeneration 233 regional food 200 regional politics 63 regionality 178, 192–3 regulation 124, 137, 143 REI 207 Reid, Morris 90 relationships, online 38 religion 16, 58 remote diagnosis 228 remote monitoring 154, 168, 228, 242 renting 225 reputation 34–5 resistance to technology 51 resorts, enclosed 273 resource shortages 11, 15, 146, 155, 178, 194, 254, 300 resources, natural 2, 4, 11, 64, 73–4, 143, 298–9 respect 36, 304 restaurants 186–8 retail 20–21, 202–27, 298 pop-up 216, 224 stealth 215 theater 214 trends 202–3 Revkin, Andy 77 RFID 3, 24, 50, 121, 126, 149, 182, 185, 192, 196, 205 rickets 232 risk 15, 124, 134, 138, 141, 149–50, 162, 167, 172, 191, 265, 299–300, 303 Ritalin 232 321 road pricing 166 Robertson, Peter 49 robogoats 55 robot department store 209 Robot Rules 44 robotic assistants 54, 206 concierges 268 financial advisers 131–2 lobsters 55 pest control 57 soldiers 41, 55, 60 surgery 35, 41, 249 robotics 3, 10, 41, 44–5, 60, 238, 275, 285–6, 292, 297 robots 41, 54–5, 131, 237, 249 childcare 57 emotional capacity of 40, 60 industrial 42 personal 42 security 209 therapeutic 41, 54 Russia 2, 69, 72, 75, 80, 89, 92–3, 125, 174, 232, 254, 270, 295, 302 safety 32, 36, 151, 158–9, 172–3, 182, 192, 196