Turing test

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

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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: 291 words: 81,703

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

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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: 696 words: 143,736

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

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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.


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

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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

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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: 210 words: 62,771

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

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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: 294 words: 81,292

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

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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: 481 words: 125,946

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

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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: 261 words: 10,785

The Lights in the Tunnel by Martin Ford

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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.

CONTENTS Introduction 1 Chapter 1: The Tunnel The Mass Market Visualizing the Mass Market Automation Comes to the Tunnel A Reality Check Summarizing 7 10 11 17 21 24 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 27 28 39 41 43 47 54 57 58 63 67 74 75 80 81 85 86 87 90 93 95 Copyrighted Material – Paperback/Kindle available @ Amazon THE LIGHTS IN THE TUNNEL / vi A More Ambitious View of Future Technological Progress: The Singularity A War on Technology 100 103 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 107 107 110 113 115 117 124 127 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 156 158 159 162 168 169 170 128 131 131 135 138 140 142 144 146 149 152 Copyrighted Material – Paperback/Kindle available @ Amazon Contents / vii 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 172 179 180 183 185 189 192 Chapter 5: The Green Light Attacking Poverty Fundamental Economic Constraints Removing the Constraints The Evolution toward Consumption The Green Light 194 196 201 202 204 207 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 209 About / Contacting the Author 246 Notes 247 Copyrighted Material – Paperback/Kindle available @ Amazon 210 223 224 227 237 239 241 INTRODUCTION Like most people, I have been giving a lot of thought to the economic situation as the most serious crisis since the Great Depression has continued to unfold. Since I develop software and run a high tech business, I also spend a great deal of time thinking about computer technology, and so I began to focus on how economics and technology intertwine.

Remote-controlled drone aircraft and bomb-diffusing ground robots are already making crucial contributions to the war effort in Iraq and Afghanistan. The Defense Advanced Research Projects Agency (DARPA)—the birthplace of the original computer network that led to the Internet—now considers military robotics to be one of its top research priorities.29 In the coming decades, we can anticipate far more advanced robots playing an increasingPlease see “Machine Intelligence and the Turing Test” in the Appendix for more on artificial intelligence. * Copyrighted Material – Paperback/Kindle available @ Amazon THE LIGHTS IN THE TUNNEL / 86 ly autonomous role in warfare in the air, on the ground and at sea. All of this makes for a rather harsh contrast between the foresight shown by the military as compared with civilian economists and analysts. Consider the uneven terrain and the highly unpredictable and dynamic situations that would be faced by battlefield robots.


pages: 224 words: 64,156

You Are Not a Gadget by Jaron Lanier

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1960s counterculture, accounting loophole / creative accounting, additive manufacturing, Albert Einstein, call centre, cloud computing, crowdsourcing, death of newspapers, 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: 551 words: 174,280

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

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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, Gödel, Escher, Bach, illegal immigration, invention of movable type, Isaac Newton, Islamic Golden Age, Jacquard loom, Jacquard loom, John Conway, John von Neumann, Joseph-Marie Jacquard, Loebner Prize, Louis Pasteur, pattern recognition, Richard Feynman, Richard Feynman, Search for Extraterrestrial Intelligence, Stephen Hawking, supervolcano, technological singularity, The Coming Technological Singularity, the scientific method, Thomas Malthus, Thorstein Veblen, Turing test, Vernor Vinge, Whole Earth Review, William of Occam

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.


pages: 405 words: 117,219

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

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


pages: 238 words: 46

When Things Start to Think by Neil A. Gershenfeld

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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, Jacquard loom, John von Neumann, 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.


pages: 237 words: 64,411

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

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Affordable Care Act / Obamacare, Amazon Web Services, asset allocation, autonomous vehicles, bank run, bitcoin, Brian Krebs, 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, invention of agriculture, Jaron Lanier, Jeff Bezos, job automation, 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, 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.


pages: 322 words: 88,197

Wonderland: How Play Made the Modern World by Steven Johnson

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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, Fellow of the Royal Society, game design, global village, Hedy Lamarr / George Antheil, HyperCard, invention of air conditioning, invention of the printing press, invention of the telegraph, Islamic Golden Age, Jacquard loom, Jacquard loom, Jacques de Vaucanson, James Watt: steam engine, Jane Jacobs, John von Neumann, joint-stock company, Joseph-Marie Jacquard, Landlord's Game, lone genius, megacity, Minecraft, Murano, Venice glass, music of the spheres, Necker cube, New Urbanism, Oculus Rift, On the Economy of Machinery and Manufactures, pattern recognition, 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, 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!?


pages: 189 words: 57,632

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

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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, mutually assured destruction, new economy, optical character recognition, patent troll, pattern recognition, Ponzi scheme, post scarcity, QWERTY keyboard, Ray Kurzweil, RFID, Sand Hill Road, Skype, slashdot, social software, speech recognition, Steve Jobs, 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.


pages: 219 words: 63,495

50 Future Ideas You Really Need to Know by Richard Watson

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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, Elon Musk, energy security, failed state, future of work, Geoffrey West, Santa Fe Institute, germ theory of disease, happiness index / gross national happiness, hive mind, hydrogen economy, Internet of things, Jaron Lanier, life extension, 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.”


pages: 118 words: 35,663

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

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

, 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: 48 words: 12,437

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

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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!”


pages: 479 words: 144,453

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

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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, European colonialism, experimental subject, falling living standards, Flash crash, Frank Levy and Richard Murnane: The New Division of Labor, glass ceiling, global village, invention of writing, invisible hand, Isaac Newton, job automation, Kevin Kelly, means of production, Mikhail Gorbachev, Minecraft, Moneyball by Michael Lewis explains big data, mutually assured destruction, new economy, pattern recognition, Peter Thiel, placebo effect, Ray Kurzweil, self-driving car, Silicon Valley, Silicon Valley ideology, stem cell, Steven Pinker, telemarketer, too big to fail, trade route, Turing machine, Turing test, ultimatum game, Watson beat the top human players on Jeopardy!

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?’


pages: 561 words: 167,631

2312 by Kim Stanley Robinson

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agricultural Revolution, double helix, full employment, hive mind, if you see hoof prints, think horses—not zebras, Kuiper Belt, late capitalism, mutually assured destruction, offshore financial centre, 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.”


pages: 394 words: 108,215

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

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Any sufficiently advanced technology is indistinguishable from magic, Apple II, back-to-the-land, Bill Duvall, Bill Gates: Altair 8800, Buckminster Fuller, California gold rush, card file, computer age, computer vision, conceptual framework, cuban missile crisis, Douglas Engelbart, Dynabook, El Camino Real, general-purpose programming language, Golden Gate Park, Hacker Ethic, hypertext link, informal economy, information retrieval, invention of the printing press, Jeff Rulifson, 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, 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

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Air France Flight 447, Airbnb, AltaVista, Amazon Mechanical Turk, augmented reality, autonomous vehicles, Bernie Sanders, book scanning, Brewster Kahle, Buckminster Fuller, Burning Man, Captain Sullenberger Hudson, centralized clearinghouse, cloud computing, cognitive bias, collaborative consumption, computer age, corporate governance, crowdsourcing, Danny Hillis, deskilling, Donald Trump, 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, job automation, Kevin Kelly, low skilled workers, Mark Zuckerberg, Marshall McLuhan, means of production, Menlo Park, mental accounting, natural language processing, Network effects, new economy, Nicholas Carr, 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?


“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.


Pandora's Brain by Calum Chace

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3D printing, AI winter, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, brain emulation, Extropian, friendly AI, hive mind, 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: 291 words: 77,596

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

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airport security, Albert Einstein, book scanning, cloud computing, conceptual framework, full text search, information retrieval, invention of writing, inventory management, Isaac Newton, 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.


pages: 238 words: 77,730

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

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23andMe, AI winter, Albert Einstein, artificial general intelligence, business process, call centre, clean water, 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: 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

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Amazon Mechanical Turk, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, business process, call centre, combinatorial explosion, corporate governance, 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, job automation, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Khan Academy, Kickstarter, knowledge worker, labour mobility, Loebner Prize, low skilled workers, minimum wage unemployment, patent troll, pattern recognition, 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!, winner-take-all economy

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: 742 words: 137,937

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

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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, computer age, computer vision, conceptual framework, corporate governance, crowdsourcing, Daniel Kahneman / Amos Tversky, death of newspapers, disintermediation, Douglas Hofstadter, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, Frank Levy and Richard Murnane: The New Division of Labor, 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, lump of labour, Marshall McLuhan, Narrative Science, natural language processing, Network effects, optical character recognition, personalized medicine, pre–internet, Ray Kurzweil, Richard Feynman, Richard Feynman, Second Machine Age, self-driving car, semantic web, Skype, social web, speech recognition, spinning jenny, strong AI, supply-chain management, telepresence, 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!, 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: 236 words: 50,763

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

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Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Andrew Wiles, Claude Shannon: information theory, cloud computing, complexity theory, Erdős number, four colour theorem, Gerolamo Cardano, Isaac Newton, John von Neumann, linear programming, new economy, NP-complete, Occam's razor, P = NP, Paul Erdős, Richard Feynman, Richard Feynman, 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

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Affordable Care Act / Obamacare, Albert Einstein, Arthur Eddington, Brownian motion, conceptual framework, en.wikipedia.org, failed state, Henri Poincaré, Isaac Newton, Jacques de Vaucanson, 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: 349 words: 95,972

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

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affirmative action, Air France Flight 447, Airbnb, airport security, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, 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, Internet of things, Jane Jacobs, Jeff Bezos, Loebner Prize, Louis Pasteur, 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

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.


pages: 502 words: 107,657

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

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Albert Einstein, algorithmic trading, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, backtesting, Black Swan, book scanning, bounce rate, business intelligence, business process, call centre, computer age, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data is the new oil, en.wikipedia.org, Erik Brynjolfsson, experimental subject, Google Glasses, happiness index / gross national happiness, job satisfaction, Johann Wolfgang von Goethe, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, 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, 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, Turing test, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra

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.


Powers and Prospects by Noam Chomsky

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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, Monroe Doctrine, 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: 542 words: 161,731

Alone Together by Sherry Turkle

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Albert Einstein, Columbine, global village, Hacker Ethic, helicopter parent, Howard Rheingold, industrial robot, information retrieval, Jacques de Vaucanson, Jaron Lanier, Kevin Kelly, Loebner Prize, Marshall McLuhan, meta analysis, meta-analysis, Nicholas Carr, Norbert Wiener, Ralph Waldo Emerson, Rodney Brooks, Skype, 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

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.


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

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Apple II, augmented reality, Bill Duvall, conceptual framework, 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, Menlo Park, Mother of all demos, new economy, Norbert Wiener, packet switching, QWERTY keyboard, Ralph Waldo Emerson, RAND corporation, RFC: Request For Comment, 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: 259 words: 73,193

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

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4chan, Albert Einstein, AltaVista, Andrew Keen, augmented reality, Burning Man, 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, Loebner Prize, Marshall McLuhan, McMansion, 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

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AltaVista, Amazon Web Services, cloud computing, en.wikipedia.org, Firefox, 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: 797 words: 227,399

Robotics Revolution and Conflict in the 21st Century by P. W. Singer

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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, clean water, Craig Reynolds: boids flock, cuban missile crisis, 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, 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, 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: 549 words: 116,200

With a Little Help by Cory Doctorow

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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, margin call, 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: 496 words: 70,263

Erlang Programming by Francesco Cesarini

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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: 394 words: 118,929

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

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A Pattern Language, Berlin Wall, c2.com, call centre, collaborative editing, conceptual framework, continuous integration, Douglas Engelbart, Douglas Hofstadter, Dynabook, en.wikipedia.org, Firefox, Ford paid five dollars a day, Francis Fukuyama: the end of history, Grace Hopper, Gödel, Escher, Bach, Howard Rheingold, index card, Internet Archive, inventory management, Jaron Lanier, John von Neumann, knowledge worker, life extension, Loma Prieta earthquake, Menlo Park, Merlin Mann, new economy, Nicholas Carr, Norbert Wiener, pattern recognition, Paul Graham, Potemkin village, RAND corporation, Ray Kurzweil, Richard Stallman, Ronald Reagan, semantic web, side project, Silicon Valley, Singularitarianism, slashdot, software studies, 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: 488 words: 148,340

Aurora by Kim Stanley Robinson

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back-to-the-land, cognitive bias, cognitive dissonance, dark matter, epigenetics, gravity well, mandelbrot fractal, microbiome, 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: 413 words: 119,587

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

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A Declaration of the Independence of Cyberspace, AI winter, airport security, Apple II, artificial general intelligence, augmented reality, autonomous vehicles, 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 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, 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 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, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, Mother of all demos, natural language processing, new economy, Norbert Wiener, PageRank, pattern recognition, pre–internet, RAND corporation, Ray Kurzweil, Richard Stallman, Robert Gordon, Rodney Brooks, Sand Hill Road, Second Machine Age, self-driving car, semantic web, shareholder value, side project, Silicon Valley, Silicon Valley startup, Singularitarianism, skunkworks, Skype, social software, speech recognition, stealth mode startup, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, strong AI, superintelligent machines, technological singularity, Ted Nelson, telemarketer, telepresence, telepresence robot, Tenerife airport disaster, The Coming Technological Singularity, the medium is the message, Thorstein Veblen, Turing test, Vannevar Bush, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, William Shockley: the traitorous eight

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: 510 words: 120,048

Who Owns the Future? by Jaron Lanier

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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, computer age, crowdsourcing, David Brooks, David Graeber, delayed gratification, digital Maoism, en.wikipedia.org, 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, invisible hand, Jacquard loom, Jaron Lanier, Jeff Bezos, job automation, Kevin Kelly, Khan Academy, Kickstarter, Kodak vs Instagram, life extension, Long Term Capital Management, Mark Zuckerberg, meta analysis, meta-analysis, moral hazard, mutually assured destruction, Network effects, new economy, Norbert Wiener, obamacare, packet switching, Peter Thiel, place-making, Plutocrats, plutocrats, Ponzi scheme, post-oil, pre–internet, race to the bottom, Ray Kurzweil, rent-seeking, reversible computing, Richard Feynman, Richard Feynman, Ronald Reagan, 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

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: 532 words: 140,406

The Turing Option by Harry Harrison, Marvin Minsky

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

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: 467 words: 116,094

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

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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, placebo effect, 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: 377 words: 97,144

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

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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, pattern recognition, Peter Thiel, phenotype, placebo effect, prisoner's dilemma, profit maximization, Ray Kurzweil, recommendation engine, reversible computing, Richard Feynman, 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, 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.


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

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Andrew Keen, computer age, corporate governance, deskilling, 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, RAND corporation, RFC: Request For Comment, Silicon Valley, Steve Jobs, The Structural Transformation of the Public Sphere, Turing test, 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: 350 words: 96,803

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

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Albert Einstein, 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, stem cell, Steven Pinker, The Bell Curve by Richard Herrnstein and Charles Murray, Turing test

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.


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

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Antoine Gombaud: Chevalier de Méré, availability heuristic, backtesting, Benoit Mandelbrot, Black Swan, complexity theory, corporate governance, currency peg, Daniel Kahneman / Amos Tversky, discounted cash flows, diversified portfolio, endowment effect, equity premium, global village, hindsight bias, Long Term Capital Management, loss aversion, mandelbrot fractal, mental accounting, meta analysis, meta-analysis, quantitative trading / quantitative finance, QWERTY keyboard, random walk, Richard Feynman, Richard Feynman, road to serfdom, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, Steven Pinker, stochastic process, 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?


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

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Airbnb, barriers to entry, book scanning, Buckminster Fuller, clean water, corporate social responsibility, crowdsourcing, global village, Google Earth, Indoor air pollution, jimmy wales, 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: 144 words: 43,356

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

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3D printing, Ada Lovelace, AI winter, Airbnb, artificial general intelligence, augmented reality, barriers to entry, 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, 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, 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, theory of mind, Turing machine, Turing test, universal basic income, Vernor Vinge, wage slave, Wall-E

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.


pages: 903 words: 235,753

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

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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, 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, Edward Snowden, Elon Musk, en.wikipedia.org, Eratosthenes, 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, intermodal, Internet of things, invisible hand, Jacob Appelbaum, Jaron Lanier, Jony Ive, Julian Assange, Khan Academy, linked data, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, Masdar, McMansion, means of production, megacity, megastructure, Menlo Park, Minecraft, Monroe Doctrine, Network effects, new economy, offshore financial centre, oil shale / tar sands, packet switching, PageRank, pattern recognition, peak oil, performance metric, personalized medicine, Peter Thiel, phenotype, place-making, planetary scale, RAND corporation, recommendation engine, reserve currency, RFID, 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, universal basic income, urban planning, Vernor Vinge, Washington Consensus, web application, 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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Darwin's Dangerous Idea: Evolution and the Meanings of Life by Daniel C. Dennett

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Albert Einstein, Alfred Russel Wallace, anthropic principle, 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, Richard Feynman, Rodney Brooks, Schrödinger's Cat, 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: 371 words: 78,103

Webbots, Spiders, and Screen Scrapers by Michael Schrenk

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Amazon Web Services, corporate governance, fault tolerance, Firefox, new economy, pre–internet, SpamAssassin, 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.


pages: 239 words: 56,531

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

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Albert Einstein, Andrew Keen, Apple II, Berlin Wall, British Empire, Brownian motion, Buckminster Fuller, Burning Man, butterfly effect, computer age, crowdsourcing, cuban missile crisis, Dissolution of the Soviet Union, don't be evil, Douglas Engelbart, Dynabook, East Village, Edward Lorenz: Chaos theory, Fall of the Berlin Wall, Francis Fukuyama: the end of history, Frank Gehry, Grace Hopper, gravity well, Guggenheim Bilbao, Honoré de Balzac, Howard Rheingold, invention of movable type, Isaac Newton, Jacquard loom, Jacquard loom, Jane Jacobs, Jeff Bezos, John von Neumann, Mark Zuckerberg, Marshall McLuhan, Mercator projection, Mother of all demos, mutually assured destruction, Network effects, new economy, Norbert Wiener, PageRank, pattern recognition, planetary scale, Plutocrats, plutocrats, Post-materialism, post-materialism, Potemkin village, RFID, Richard Feynman, Richard Feynman, Richard Stallman, 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: 250 words: 73,574

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

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Ada Lovelace, AltaVista, Claude Shannon: information theory, fault tolerance, information retrieval, Menlo Park, PageRank, pattern recognition, Richard Feynman, 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.


pages: 218 words: 63,471

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

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Albert Einstein, Andy Kessler, automated trading system, bank run, Big bang: deregulation of the City of London, Bretton Woods, British Empire, buttonwood tree, Claude Shannon: information theory, Corn Laws, Edward Lloyd's coffeehouse, fiat currency, 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, Jacquard loom, James Hargreaves, James Watt: steam engine, John von Neumann, joint-stock company, joint-stock limited liability company, Joseph-Marie Jacquard, Maui Hawaii, Menlo Park, Metcalfe's law, packet switching, price mechanism, probability theory / Blaise Pascal / Pierre de Fermat, profit motive, railway mania, RAND corporation, 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, transatlantic slave trade, tulip mania, Turing machine, Turing test, 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: 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

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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, John Nash: game theory, John von Neumann, linear programming, meta analysis, meta-analysis, Nate Silver, p-value, placebo effect, prediction markets, RAND corporation, recommendation engine, Renaissance Technologies, Richard Feynman, Richard Feynman, Richard Feynman: Challenger O-ring, Ronald Reagan, speech recognition, statistical model, stochastic process, 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.


pages: 396 words: 117,149

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

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3D printing, Albert Einstein, Amazon Mechanical Turk, Arthur Eddington, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, 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 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, P = NP, PageRank, pattern recognition, phenotype, planetary scale, pre–internet, random walk, Ray Kurzweil, recommendation engine, Richard Feynman, Richard Feynman, Second Machine Age, self-driving car, Silicon Valley, speech recognition, statistical model, Stephen Hawking, Steven Levy, Steven Pinker, superintelligent machines, the scientific method, The Signal and the Noise by Nate Silver, theory of mind, transaction costs, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, white flight

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: 504 words: 89,238

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

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bioinformatics, business intelligence, conceptual framework, elephant in my pajamas, en.wikipedia.org, finite state, Firefox, 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: 661 words: 187,613

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

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Albert Einstein, cloud computing, David Attenborough, double helix, Drosophila, elephant in my pajamas, finite state, illegal immigration, Loebner Prize, Maui Hawaii, meta analysis, meta-analysis, natural language processing, out of africa, P = NP, phenotype, rolodex, Ronald Reagan, Saturday Night Live, speech recognition, Steven Pinker, theory of mind, transatlantic slave trade, Turing machine, Turing test, 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.


pages: 855 words: 178,507

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

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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, 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, Jacquard loom, Jaron Lanier, jimmy wales, John von Neumann, Joseph-Marie Jacquard, Louis Daguerre, Marshall McLuhan, Menlo Park, microbiome, Milgram experiment, Network effects, New Journalism, Norbert Wiener, On the Economy of Machinery and Manufactures, PageRank, pattern recognition, phenotype, pre–internet, Ralph Waldo Emerson, RAND corporation, reversible computing, Richard Feynman, Richard Feynman, 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: 368 words: 96,825

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

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3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, cloud computing, crowdsourcing, Daniel Kahneman / Amos Tversky, dematerialisation, deskilling, Elon Musk, en.wikipedia.org, Exxon Valdez, fear of failure, Firefox, Galaxy Zoo, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, industrial robot, Internet of things, Jeff Bezos, John Harrison: Longitude, Jono Bacon, Just-in-time delivery, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, loss aversion, Louis Pasteur, Mahatma Gandhi, 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, 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, technoutopianism, telepresence, telepresence robot, Turing test, urban renewal, web application, X Prize, Y Combinator

,” 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

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Albert Einstein, bank run, banking crisis, battle of ideas, Black Swan, call centre, carbon footprint, cashless society, citizen journalism, 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 supply chain, global village, hive mind, industrial robot, invention of the telegraph, Jaron Lanier, Jeff Bezos, knowledge economy, linked data, low skilled workers, M-Pesa, 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 Sainsbury’s 215 Salt 187 sanctuary tourism 273 satellite tracking 166–7 Saudi Arabia 2, 69 Schwartz, Barry 186 science 13, 16, 40–62, 300 interruption 53 trends 40–42 scramble suits 57 scrapbooking 25, 108, 225 Sears Roebuck 137 seasonality 178, 193–4 second-hand goods 224 Second Life 133, 207–8 securitization 124, 140 security 16, 31, 151 security robots 209 self-driving cars 165 self-medication 242 self-publishing 103, 113–14 self-reliance 35, 75 self-repairing roads 57 322 self-replicating machines 23, 44 Selfridges 214 sensor motes 15, 50, 196 sensory internet 56 Sharia-based investment 125 Shop24 209 shopping 202–27 habitual 212 laser 212 malls 211–5 purposeful 212 slow 213 social 207 Shopping 2.0 224 short-wave scalpels 57 silicon photonics 56 simplicity 169–70, 179, 186, 202, 218, 224, 226, 272 Singapore 241 single-person households 19–20, 202–3, 208–9, 221, 244, 298, 304 skills shortage 293, 302 sky shields 57 sleep 159–60, 188, 228, 231, 246–7, 265 sleep debt 96, 266 sleep hotels 266 sleep surrogates 57 slow food 178, 193 slow shopping 213 slow travel 273 smart devices 26–7, 28, 32, 35, 44, 50, 56, 57, 164, 206, 207 smart dust 3, 15, 50, 196 smartisans 20 Smartmart 209 snakebots 55 social networks 97, 107, 110, 120, 133, 217, 261 social shopping 207 society 13, 15–16, 17–37 trends 15–16 Sodexho 193 solar energy 74 Sony 114, 121 South Africa 84, 149, 242 South America 82, 270 South Korea 2, 103, 128–9 space ladders 56 space mirrors 47 space tourism 271, 273 FUTURE FILES space tugs 57 speed 164, 202, 209, 245, 296–7 spirituality 16, 22, 282, 298, 306 spot knowledge 47 spray-on surgical gloves 57 St James’s Ethics Centre 282 stagflation 139 starch-based plastics 64 stealth retail 215 stealth taxation 86 Sterling, Bruce 55 storytelling 203 Strayer, David 161 street signs 162–3 stress 32, 96, 235, 243, 245–6, 258–9, 265, 257–9, 275, 277, 283–5 stress-control clothing 57 stupidity 151, 302 Stylehive 207 Sudan 73 suicide tourism 236 Super Suppers 185 supermarkets 135–6, 184–6, 188, 191–2, 194, 202–3, 212, 215, 218–19, 224, 229 surgery 2, 31 anti-ageing 2, 237 enhancement 249 Surowiecki, James 45 surveillance 35, 41 sustainability 4, 37, 74, 181, 193–5, 203, 281, 288, 298–9 Sweden 84 swine flu 38, 251, 272 Switzerland 168, 210, 215 synthetic biology 56 Taco Bell 184 Tactical Numerical Deterministic Model 49 tagging, location 86, 88 Taiwan 81 talent, war for 275, 279, 293; see also labor shortages Target 216 Tasmania 267 Tata Motors 174, 176 taxation 85–6, 92, 93 carbon 76, 172 conscientious objection 86 Index fat 190 flat 85–6 green 76 indirect 86 stealth 86 Tchibo 217 technology 3, 14–16, 18, 22, 26, 28, 32, 37, 40–62, 74–5, 82–3, 96, 119, 132, 147–8, 154, 157, 160, 162, 165–7, 178, 182, 195–8, 208, 221, 229, 237, 242–3, 249, 256, 261, 265–6, 268, 275–6, 280, 283–4, 292, 296–7, 300 refuseniks 30, 51, 97 trends 40–42 telemedicine 228, 238, 242 telepathy 29 teleportation 56 television 21, 96, 108, 117, 119 terrorism 67, 91, 108, 150, 262–3, 267, 272, 295–6, 301 Tesco 105, 135–6, 185, 206, 215, 219, 223 Thailand 247, 290 therapeutic robots 41, 54 thermal imaging 232 things that won’t change 10, 303–6 third spaces 224 ThisNext 207 thrift 224 Tik Tok Easy Shop 209 time scarcity 30, 96, 102, 178, 184–6, 218, 255 time shifting 96, 110, 116 time stamps 50 timeline, extinction 9 timeline, innovation 8 timelines 7 tired all the time 246 tobacco industry 251 tolerance 120 too much choice (TMC) 29, 202, 218–19 too much information (TMI) 29, 51, 53, 202, 229; see also information overload tourism 254–74 cultural 273 ethical 259 food 273 323 local 273 medical 2, 229, 247 sanctuary 273 space 271, 273 suicide 238 tribal 262 Tourism Concern 259 tourist quotas 254, 271 Toyota 48–9, 157 toys, mind-control 38 traceability 195 trading down 224 transparency 3, 15, 143, 152, 276, 282, 299 transport 15, 154–77, 298 public 155, 161 trends 154–6 transumerism 223 travel 2, 3, 11, 148, 254–74 economy 272 luxury 272 slow 273 trends 254–6 trend maps 6–7 trends 1, 5–7, 10, 13 financial services 123–5 food 178–80 healthcare 228–9 media 96–8 politics 63–5 retail 202–3 science and technology 40–42 society 15–16 transport 154–6 travel 254–6 work 275–7 tribal tourism 262 tribalism 15–16, 63, 127–8, 183, 192, 220, 260 trust 82, 133, 137, 139, 143, 192, 203, 276, 282–3 tunnels 171 Turing test 45 Turing, Alan 44 Turkey 2, 200, 247 Twitter 60, 120 two-way identity verification 132 UAE 2 UFOs 58 324 UK 19–20, 72, 76, 84, 86, 90–91, 100, 102–3, 105, 128–9, 132, 137, 139–42, 147–9, 150, 163, 167–8, 170–71, 175, 185, 195–6, 199, 200, 206, 210, 214–16, 238, 259, 267–8, 278–9, 284, 288 uncertainty 16, 30, 34, 52, 172, 199, 246, 263, 300, 303 unemployment 151 Unilever 195 University of Chicago 245–6 urban rental companies 176 urbanization 11, 18–19, 78, 84, 155, 233 Uruguay 200 US 1, 11, 19–21, 23, 55–6, 63, 67, 69, 72, 75, 77, 80–83, 86, 88–90, 92, 104–5, 106, 121, 129–33, 135, 139–42, 144, 147, 149, 150, 151, 162, 167, 169–71, 174, 185, 190–3, 195, 205–6, 209, 211, 213, 216, 218, 220, 222–3, 237–8, 240–8, 250, 260, 262, 267–8, 275, 279–80, 282–4, 287, 291 user-generated content (UGC) 46, 97, 104, 289 utility 224 values 36, 152 vending machines 209 Venezuela 69, 73 verbal signatures 132 VeriChip 126 video on demand 96 Vietnam 2, 290 Vino 100 113 Virgin Atlantic 261 virtual adultery 33 banks 134 economy 130–31 protests 65 reality 70 sex 32 stores 206–8 vacations 32, 261 worlds 157, 213, 255, 261, 270, 305 Vocation Vacations 259–60 Vodafone 137 voice recognition 41 voice-based internet search 56 voicelifts 2, 237 FUTURE FILES Volkswagen 175 voluntourism 259 Volvo 164 voting 3, 68, 90–91 Walgreens 244 Wal-Mart 105, 136–7, 215, 219–20, 223, 244, 282 war 68–9, 72 war for talent 275, 279; see also labor shortages war forecasting 49 water 69–70, 74, 77–9, 199 wearable computers 55 weather 64 weather insurance 264 Web 2.0 93, 224 Weinberg, Peter 125 wellbeing 2, 183, 188, 199 white flight 20 Wikipedia 46, 60, 104 wild swimming 273 Wilson, Edward O. 74 wind energy 74 wine producers 200 wisdom of idiots 47 Wizard 145 work 275–94 trends 275–94 work/life balance 64, 71, 260, 277, 289, 293 worldphone 19 xenophobia 16, 63 YouTube 46, 103, 107, 112 Zara 216–17 Zipcar 167 Zopa 124, 134


New Horizons in the Study of Language and Mind by Noam Chomsky

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dark matter, Isaac Newton, Jacques de Vaucanson, theory of mind, Turing test

That move has been made far too easily, 114 New horizons in the study of language and mind leading to extensive and it seems pointless debate over such alleged questions as whether machines can think: for example, as to “how one might empirically defend the claim that a given (strange) object plays chess” (Haugeland 1979), or determine whether some artifact or algorithm can translate Chinese, or reach for an object, or commit murder, or believe that it will rain. Many of these debates trace back to the classic paper by Alan Turing in which he proposed the Turing test for machine intelligence, but they fail to take note of his observation that “The original question, ‘Can machines think?,’ I believe to be too meaningless to deserve discussion” (Turing 1950: 442): it is not a question of fact, but a matter of decision as to whether to adopt a certain metaphorical usage, as when we say (in English) that airplanes fly but comets do not – and as for space shuttles, choices differ.

Minneapolis, MN, University of Minnesota Press. 214 Index Index abduction 80 ability, distinguished from knowledge 50–2, 97–8 abstract see concrete–abstract dimension access: to consciousness 93–8, 141, 147 – in principle 96–8, 141, 143 acoustic phonetics 174 acquisition 6–8, 181; and concept formation 61–6; “initial state” as a device for 4–5; innateness and selectivity x–xi, 121–2; labelling of innate concepts 61–2, 65; and lexical access 121–2; and sensory deficit 121–2; see also child language acquisition; Language Acquisition Device (LAD) adjacency 11, 121 agency, and objects 21–2 agreement 14 algorithms 113, 147, 159 Almog, Joseph 42 analytic–synthetic distinction xiv, 46–7, 61–5 anaphora 39, 140 animal, man and 3 animate–inanimate dimension 126 anthropological linguistics 6 anthropology 136 anti-foundationalism 76–7 arbitrariness, Saussurean 27, 120 argument-structure 11 Aristotle 187, 204n articulatory phonetics 174 articulatory–perceptual systems 28, 120, 123–6, 180 214 artifacts, capacities of 114 Artificial Intelligence 200n assertability conditions 109 assignment of derived constituent structure 199n association 92, 93 Atlas, Jay 151 “atomic” units 10 atomism, physical 111 auditory cortex 158 Austin, John 45, 132 authority: deference to 155; firstperson 142–3 autosegmental 40 Baker, Lynne Rudder 153–4 Baldwin, T.R. 79–80, 81, 144 Barinaga, Marcia 158 Bedeutung (Frege) 130, 131–2 Beekman, Isaac 110 behavior, causation of 72, 95 behaviorism 46–60, 80, 92, 93, 101, 103 belief systems: and the language faculty xiv, 63–4, 129; lexicon and 32; and the terms of language 21–2, 137, 148–9 beliefs: absence of term in other languages than English 119; attribution of 91, 119, 135, 146–7, 153–4, 200n; convictions about the nature, as a posteriori or a priori 89; different about the same subject 149, 192–3; false 33, 43; fixation 63–4; individuation of 165, see also I-beliefs justification Index as interest-relative 196n; and meaning 137; and properties of expressions 178–9; relation to the world 47, 135, 197n; similarity of 43–4, 152; social role of 197n; that correspond literally to animistic and intentional terminology 135–6 Berthelot, M. 111 “best theory” 112, 136, 142, 145, 173 “bifurcation thesis” xiv Bilgrami, Akeel 137, 150, 154–5, 190 binding theory 10, 11, 31, 39, 50; use of principle (C) 93, 99 biology vii, 1–2, 3–6, 139; of language 1–2, 3–5, 34; meaninglessness of intuitive categories for 161–3; and study of the mind 5–6 Black, Joseph 166, 184 blindsight 95–7 body: Cartesian theory of viii, 103; limitations of naturalistic theory of the 28, 143; as mental and physical 113, 167; theory of the 84, 86, 87, 199n body–mind problem see mind–body problem Bohr, Niels 43, 111, 151, 152 Boltzmann, Ludwig 110 boundary conditions 7–8 Boyle, Robert 108 Bradley, David 163, 203n brain: auditory, visual and tactual inputs 121–2; biochemical laws of 16; configurations relevant to meaning 19–20, 24–40; and consciousness 86, 145; electrical activity of the viii, 116–17, 140; homogenity of structure not found 184; language faculty xii, 73, 77–8 – computational theories 116–17; localization of analytic mechanisms of 121–2; properties of 27; shared initial state 5, 33–4, 73 – mental and organical structure of the 215 167–8; and mind 76; neural structure as natural realization of rule systems 54–5; provides mechanisms of thought 113–15, 183; scans 171; as solving problems and adapted to normal situations 159, 161; study at various levels 6, 24, 103; as thermoregulator 195n; things mental as emergent properties of 1–2; in a vat 158–9 brain sciences x, 19–20, 116 Brentano, Franz 22 Brock, William 110–11 Bromberger, Sylvain 82, 203n Burge, Tyler 72, 159, 171, 184, 192–3, 195n, 202n, 204n; on eliminativism 88, 92, 138; on naturalism 87, 109, 144 c-command 11, 40 C–R theories 24, 25–7, 40, 45, 104–5; as a form of syntax 34, 40; see also I-language Carnap, Rudolf 186, 187 Cartesianism 80, 83–4, 85, 132, 145, 167; collapse of 103, 108–9 case systems, language differences in 11–12 Case theory 10 categories 138–9 causality viii, 47, 72, 95, 137 “causative” properties 179 cellular theories 116 chain condition 10 Chastain, Charles 115 child language acquisition x–xi, 6–7, 101, 186; assigning labels to concepts 61–2, 65; compared with foreign adult’s 49; and the computational system 120; early exposure and language development 201n; and the LAD 92–3; limited exposure to semantic aspects in ambiguous circumstances 120, 185; rate of 120; of a specific language 53, 54 216 Index children: attribute beliefs to others before development of language 119; blind and language acquisition 121–2; innateness of the property of discrete infinity 3–4; intuitive understanding of concepts 62; phonetic data available to 185; usage differs from adult usage 191–2 Churchland, Patricia 107, 115 Churchland, Paul 64, 107, 115, 183, 184 clicks, displacement to phrase boundaries 25, 55, 58, 140, 201n cognition: internally generated modes to which experience conforms 182–3; knowledge of language and x, 73, 134 cognitive deficits, with intact language faculty 121, 146 cognitive development: and language growth 62; uniformity not found 184 cognitive reach 107 cognitive revolution (1950s) vi, 5–6 cognitive science 23, 33, 112, 116, 165; status of 165–6 cognitive state 55, 81, 82–3, 154 cognitive system 117–19, 125; and complex relational words 128–9; phonetic aspects of 118; and semantic representation 174; state changes that reflect experience 118–19; use of resources 129, 135 “cognoscitive powers”, innate 181–3 “coherent–abstraction test” (Almog) 42 common language approach 29–32, 33, 37, 99–100; see also “public language” common sense xvi, 80, 135, 138, 146, 163–4; and naturalistic inquiry 20–4, 37–45 communication 30, 78, 130, 154, 164, 202n community norms 40, 49, 71, 72, 142, 148, 155 competence: assumptions about drawn only from behavior 57–8; as a generative procedure 60; grammatical 26; pragmatic 26; see also I-language “competing hypothesis” 183–4, 185 complexity xii, 7, 13, 124, 169 computational approach to language xiii, 6, 10, 103–4, 116–17, 124, 159 computational procedure: “austerity” of 120–1; maps array of lexical choices into phonetic and logical form 125, 170; registers adjacency, but no “counters” 121 computational systems: complexity of 123–4; (generative) 78–9; with largely invariant principles 120, 169; properties 107, 120–1, 123, 145 computational–representational systems see C–R theories concepts: construction of artificial 51–2; as determining reference of a word 187; innate labelled in language acquisition 61–6; link with sound 120; locational 62; Putnam on short theories and formation of 66; use in understanding ordinary life 90 conceptual–intentional systems 9, 10, 28, 61–6, 124–6, 180 concrete–abstract dimension 126, 180–1 concreteness 168–9, 176 conditions, philosophically necessary 146–7 “Connection Principle” (CP), Searle’s 96–8 connectionism 103–4, 116 consciousness xiv, 83, 108, 145; “access in principle” to 96–8, 141, 142, 143; access to 93–8, 141, 147, 169; nature of 115, 143, 145; potential 86–7, 91–2, 93, 97; potential for, and blindsight 96–8; relation to neural structures 144–5; Searle’s “radical thesis” 86–7 Index constitution/constituency 189–90, 191 content: of fixed reference in natural language 42; locality of (Bilgrami) 150, 190; phonetic 151; as a technical notion 137, 153; wide and narrow 165, 170; see also perceptual content coordinate structure constraint, Quine on 55–6, 198n Cordemoy, Géraud de 114 covert movement 14–15 creativity, of language use 16–18, 145 cultural studies 157–8 D (domain) 39–40 Darwin, Charles, Origin of Species 163 Davidson, Donald 46, 61, 136; “A Nice Derangement of Epitaphs” 56, 67–70; “anomalism of the mental” 88–9; “interpreter” example 29–30, 56, 67–70, 102; “no such thing as language” 136, 202n Davies, Martin 23, 195n deep and surface structure x, xvi, 10, 28, 203n deference, patterns of 171 Dennett, Daniel 79, 91, 107–8, 144, 200n denotation, use of term 130 denotational theories of interpretation 131, 136, 177–9, 192 Descartes, René ix, xiii, 3, 17, 108, 112, 114, 133, 182 description xi, 145 descriptive adequacy 7–8, 120, 122, 165, 185 descriptive linguistics 54, 122, 184 descriptive semantics 47, 61 design of language 9–13 designer’s intent 125, 136–7, 180 deviance viii, 78–9; and computational theories of the language faculty of the brain 116–17; distinctive brain responses 217 to language 24; from community norms 98–9, 142 Dewey, John 47 dialect: as a nonlinguistic notion 31; prestige 156 dictionary, compared to complexity of human lexical recording 120, 185 Diderot, Denis 110 Dijksterhuis, E.J. 108 discourse representation 129 discrete infinity 3–4, 184 displacement property: explained 12–14; and legibility conditions 13–15 dissociations 117, 184 distal properties, correlation of internal processes with 162 distributional properties 179 division of linguistic labor 71, 187–8 du Marsais 196n dualism vii, xiv, 75–105, 117, 140, 142, 163; varieties of 98–105; see also Cartesianism; metaphysical dualism; methodological dualism Dummett, Michael xiii, 46, 56, 57, 102, 143; on LAD 94; on language as a social practice 48–9, 50; on naturalistic inquiry as psychological not philosophical 140–1 Dupoux, E. 118 economy conditions 123–4 Edelman, Gerald 103–4, 116 Egan, Frances 162 electrophysiological responses, to syntactic versus semantic violations 116–17 eliminativism see materialism, eliminative embedding, multiple 124 “emergent laws” 145 empirical inquiry 46–74, 76, 92–3 empty category 15, 181 English: importance of Japanese for the study of xv, 53–4, 58, 102; left-headed 93 218 Index entailment relation 34, 174 entities, beliefs about 135 environment: influence on initial state of language faculty 78, 162, 166, 189–90; role in specification of reference 41 epistemic boundedness, Dennett on 107–8 epistemic naturalism 79, 80–1 epistemology: evolutionary 80; naturalized (Quine) 46–7, 80, 81 Epstein, Samuel 11 error, problems of 142, 143 ethnoscience xv, 90–1, 135, 155, 160, 164, 165, 172–3 event-related potentials (ERPs) viii, 24–6, 38 evidence: intuitive categories as 162; legitimacy of wide use of x, 53–8, 60, 102, 139–40; linguistic 55, 57, 58, 139–40, 201n; psychological 55, 57, 58, 201n; role of initial state in determining what counts as 197n; useful about reference 171–2 evolution: of brain’s administration of linguistic categories 183; and innate concepts argument 65–6; and questions for empirical inquiry 73–4; theory of 139, 163 experience: effect on state changes of the cognitive system 118–19; and “initial state” 4–5, 7–8; sets boundary conditions 7–8 experts: deference to 155–6; role in determining reference of terms 41–2, 71, 72, 190–2, 196n explanation, and description xi explanatory adequacy 7–8, 45 explanatory models 19, 45, 183–4 explanatory theory 103, 106, 110, 115, 166; and intuitive judgements 171–2 expression, ways of thinking and means of 15–16 expressions: class generated by Ilanguage 78–9, 169; computational procedures that access the lexicon to form 170, 173–4, 180; internally-determined properties of 34–6; as a pair <PHON, SEM> 173, 175; relation with external world 129–30; structural problems for interpretation 124; universal and language-specific properties 35 extension 148 extensional equivalence (Quine) 132 externalist approaches xiii, 38–40, 43, 148–63, 190; and Twin-Earth thought experiments 148–50, 155 fact, truths of and truths of meaning 62–4 faculty of language vii, x–xi, xiii, 77–8, 168–73; assumes states that interact with other systems 168; “austerity” of 120–1; common to the species 70, 168; components of 117; evolution of 2, 3–5; as a function that maps evidence into I-language 73; innate structure and effect of external environment 60, 168; intact but cognitive deficits 121, 146; intrinsic properties of 121, 127; as natural object 119; perfection of 9–15; relations with mind/brain systems xii, 73, 77–8; specific structures and principles of 183–4; triggering of the analytic mechanisms 121–2; see also initial state; state L fallibility 191 features 10, 120, 179; attraction of 13–15; legibility conditions and 11–12; not interpreted at either phonetic or semantic interface 12 field linguist 46–60 first-person authority 142–3 “fitting”, and “guiding” (Quine) 94–5 Flaubert, Gustave 90 Fodor, Jerry 107, 117, 139, 184; “First Law of the Non-Existence of Cognitive Science” 165; “language of thought” 19 Index folk psychology 23, 28, 89, 154–5, 196–7n folk science xv, 84, 91, 127, 135, 137, 164, 172–3; and cultural conditions 119 folk semantics 172, 188 forces, immaterial 108–9, 144, 167 formal languages 12, 57, 199n, 202n free will ix, 108 Frege, Gottlob 30, 36, 80, 85, 130, 187, 188; “common public language” 30, 33, 131–2 Friedman, Michael 112 front-wh-phrase 56, 198n Galileo Galilei xiii, 4 “garden-path sentences” 124 generalizations, psychological 165–6, 168–9 generative faculty of human understanding 16–18 generative grammar vi, 132, 174; computational operations 13; explained 5–7; goals of study of mechanisms in everyday life 17; and grammaticality 63; and principles-and-parameters approach 122 generative phonology 44, 151 generative procedure; isolating a 29–32, 69; the right 132 genes, and “initial state” 4–5 Gestalt 182 Gibson, Roger 198n Goodman, Nelson 181 government 11 grammar: and descriptive adequacy 7, 120, 185; uses of term 5, 201n grammars: “innate skeletal” (Quine) 199n; as specific internalized rule systems 57–61 grammaticality, Quine on 63, 199n gravity, Newton’s 108–9, 166 “guiding”, and “fitting” (Quine) 94–5 Haas, W. 199n Halle, Morris 203n 219 Harris, James 64 Heisenberg, Werner 167 Herbert, Edward, Baron of Cherbury 80, 85 Higginbotham, James 73 Hobbes, Thomas, on names 182 holism 46, 48, see also meaning holism homonymy 181 Huarte, Juan 17 human being: concept of xv, 3, 20, 139; and language speaking 20–4 human faculty of language see faculty of language Humboldt, Wilhelm von 6, 73 Hume, David 4, 64, 80, 85, 133, 170; on fictitious ascribed identity 16, 182–3; on Newton 110, 167; “science of human nature” 141, 164, 173 Huygens, Christiaan 82, 108 hypotheses, Newton’s refusal of 109 I-beliefs xiii, 32–3, 193; changes in 193; expressed in I-language 72 I-conceptual system 193 I-language vii, ix, xi–xii, xiii, 123; as generative procedure 70–3, 78, 119–21, 203n; C-R theory of 26, 32, 38, 40–2, 78; and construction of semantic and phonetic representations 174; followed by principles-and-parameters approach 123; has computational procedure and a lexicon 120–1; as instantiation of the initial state 123; internal and individual and intensional 5, 70–3, 118–19, 132, 169; language-like accretions 42–3; and language-world relations 188–9; mastery and internal representation of a specific 73; normativity aspects of 99; and performance systems 27–32, 34–6; as a product of the language faculty 27, 42–3; relation to external events 174–5; restricted variety of 27, 33, 44–5; specifies 220 Index form and meaning and accounts for properties of complex expressions 26–7; use of term 131, 201n I-linguistics 171; and common-sense notions of language 169, 170, 173, 192–3; and use of properties which might include I-sound and I-meaning 187 I-meaning 170, 173, 175, 179 I-sound 170, 173, 175, 179 idealization 49–50, 100, 123, 197n ideas: history of xiv; as not things but ways of knowing 182; people have about meaning and sound 173; theory of 182 identity, ascription of fictitious (Hume) 16, 182–3 idiolect, communication between time slices of an 30 immunology, selective theory xi, 65 impairment, selective 117 indeterminacy, empirical 57–8, 198n indeterminacy of translation (Quine) 132, 140, 147, 198n indexicals 42, 181 “individual sense” 70, 72 individualist approach vii, 32, 162, 164;, see also internalist approach individuation: and nameable things 126–7; and referential use of language 180, 182–3 infants: with performance systems specialized for language 118; reification of bodies in 92–3 inference 121, 180; as interestrelative 196n inflection: as special property of human language 12; variations in richness 120 inflectional features, role in computation 10 inflectional systems: basically the same 120; language differences in 11–12 initial state x–xi, 4–5, 77–8, 123; and attained state 95; common to the species 4–5, 50, 53–4, 119; determines the computational system of language 27; as a fixed biologically-determined function that maps evidence 53–4; genetically determined 27, 53–4, 118; incorporates general principles of language structure 60; incorporates principles of referential dependence 50; integrated conceptual scheme 62; with parameters fixed 123; plus course of experience 4–5, 7–8; and postulated identity of all languages 122; richness of the 35–6; as shared structure 30, 33–4, 50; as Universal Grammar (UG) 73, 81, 101; see also I-language innate component, identifying the 172 innate endowment: and environmental factors 166; and impoverished input 121–2; role in understanding the world 90–1 innate semantic representations, theory of (TISR), Putnam’s critique of 184–9 innate structure of the organism, theory of and the mapping M 60–1 innateness, of knowledge of language x–xi, xiii, xv, 2, 3–4, 126 “innateness hypothesis”, Putnam on Chomsky’s 65, 66–7, 100–1, 187–9 “innatism” see “innateness hypothesis” inner states, ideas about 164–6, 168–9 input–output systems, of the language faculty 117–18 instinct 91 institutional role 180 intelligence 6, 122, 182; accessibility to human ix, 91; and language use 147; mechanisms of general 185; scope and limits of 107; see also Artificial Intelligence Index intelligibility, in scientific discourse 151–2 intention 62, 91, 125, 137, 180; referential 130–1; see also conceptual–intentional systems “intentional laws” 166 intentional terminology 113–15 intentionality: Brentano on 22; naturalistic inquiry and 45, 132 interests 125, 128, 137 interface: between language faculty and other systems of the mind 123; legibility conditions at the 10–12; levels 10, 28, 39, 173–5; location of the 174; phonetic and semantic representations at the 10–12, 160, 173–4; properties 124–6; weakest assumptions about relations 10, 128–9 interface condition, requires erasure of uninterpretable features 14–15 internal processes, correlation with distal properties 162 internal relational structure 22 internalism vii, xiv, xv, 15, 125; critique of 162; defined 134; form of syntax 129 internalism–externalism issues 148–63 internalist approach 33–4, 38–45, 134–63, 164–94; legitimacy of inquiries that go beyond 156; and other domains of psychology 158–9; to differing beliefs 193; to language-world relations 15–16 internalist linguistic theory (T) 142–3, 146 internalist semantics 34, 38–9, 45 interpretation, language and xiii–xiv, 46–74 interpretations, assignment of 160–1 “interpreter”, Davidson on the 29, 56, 67–70, 102 intuitions 44, 70, 84, 119, 130, 135, 138, 161, 197n intuitions: limits of xiv–xv; as subject of linguistic study 171–2; and technical terms 148–9 221 intuitive categories, meaninglessness for science 161–3 intuitive judgements: about statements 40–2; as data to be studied as evidence 171–2; different 64; forced with ordinary expectations withdrawn 172 invented forms 181 invented system, designed to violate principles of language 121 Jacob, François 139 Jacob, Margaret 108, 110 Jakobson, Roman 140 James, Henry 47, 90 Japanese: anaphora in 140; evidence from about referential dependence 53–4, 58, 102; importance for study of English xv, 53–4, 58, 102; right-headed 93 Jerne, Niels Kaj 65 Jespersen, Otto 73 K, as constant knowledge of language 51 K-ability 51 Kant, Immanuel 112, 182; method of transcendental argument 165 Kayne, Richard 123, 131 Kekulé von Stradonitz, August 111 Kenny, Anthony 50, 197n knowing-how 51–2 knowledge: distinguished from ability 50–2, 97–8; nature of 170; nature of tacit xiii knowledge of language vii, ix, xiv, 50–2; and cognition x, 73; defined 73; in English usage 170; as the internal representation of generative procedure in the brain 50–2; as learned ability 50; partial 48–9, 99–100, 146; uniform among languages 126; see also innateness Kripke, Saul 37, 141–2; Naming and Necessity 41 Kripke’s puzzle 191 222 Index La Mettrie, J.O. de 84, 113, 167 labels, assigning to concepts 61–2, 65 Lange, Friedrich 167 language: as a biological object vii; as a community property 99–100; elementary properties 6; as the finite means for infinite use (Humboldt) 6, 73; as a generative procedure assigning structural descriptions 50–2; internalist perspective on 134–63; and interpretation 46–74; as a natural object xiv, 106–33; naturalism and dualism in the study of 75–105; in naturalistic inquiry 77–9; no useful general sense in which to characterize 48–9; as a notion of structure that guides the speaker in forming free expressions 73; notions of in ethnoscience 90–1; as a portable interpreting machine 29, 68, 202n; as a process of generation 73; as property of organized matter 115; as a social fact 197n; specific properties of human 16; study of 3–18; terms for something like 119; use of term 106, 130–1 – in different speech communities 157–8 – views on the concept of 73 Language Acquisition Device (LAD) 81, 86, 92–3; as a physical not psychological mechanism 93–4 language change, the study of 6 language faculty see faculty of language language speaking, and human being 20–4 language use see use of language language-external systems 175, 179 language–thought relations 135–6 language–world relations: at the phonetic interface 175; internalist approach 15–16, 129–30; truth of 188–9 languages: apparent variability of 122; as cultural artifacts 157; diversity of 7; head-first or head-last xi; no such things as (Davidson) 136; in part unusable 124, 161 Lavoisier, Antoine 110 learnability of languages xiv, 124 learning: as acquiring rules that map LI into some other system of mind 176; “by forgetting” 118; generalized mechanisms 66, 101; incremental 30; selective process 65 left–right orientation 93 legibility conditions xii, 9–11; and the displacement property 13–15; impose three-way division among features 11–12 legitimacy, questions of 183–94 Leibniz, Gottfried Wilhelm 82, 108 Leonardo da Vinci 163 levels of analysis (Marr) 118, 159 Lewis, David 57 Lewis, G.N. 111, 112 Lewontin, Richard 161, 195n lexical items 10, 175–83; acquired on a single exposure 120, 185; attribution of semantic structure to 61–2; constituted by properties approach 120, 170, 179; different approaches to study of 36, 175–83; dissociation of either sound or meaning 175, 176–7; may be decomposed and reconstructed in the course of computation of SEM 175; relational approach to 179–83 “lexical semantics” 174 lexical structure 181; generative factors of (Moravcsik) 182–3, 204n lexicon: defined 10; mental 32; and properties of computation 123, 170; subject to a complex degree of conscious choice 170–1; things selected and individuated by properties of 137 LF see Logical Form Index lingua mentis, representations generated by I-language map into 185–9 linguistic, use of term 106, 134 linguistics: explanatory insight for vii; and science-forming faculty (SFF) 101; scientific status of xiv, 112; subject matter of 1–2, 139–40 linguosemantics 165 Llinás, Rodolfo 128 Locke, John 1, 167, 182–3 locomotion 147 locust–cricket example (Baker) 153–4 Logical Form (LF) xi–xii, 124–5, 129–30; instructions at the interface 128–9; origins of 28 “m-events” (events mentalistically described) 89–90 McGinn, Colin 145, 201n machine: ability to think debate 44–5, 114, 147; man and 3, 17, 84, 132 machine intelligence 114 malapropisms 70–3 mapping, and neural interaction 116 marked options 125 Marr, David 23, 118, 158–9, 161, 195n, 202n material: and abstract factors, simultaneity in meaning 16; or physical 91–2, 143 materialism 109–10, 144, 167; eliminative 26, 85, 87, 88, 90, 91, 92–3, 104, 117, 138, 144; and its critics 85–93; Nagel on 87–8 matter: altered concept of 113, 133; dark 85; thought and action as properties of organized 84, 86 meaning: analogies with sound 15–16, 175–9; and beliefs 137; disagreements about study of 15–16; “in the head” or externally determined 148–51; inquiry into meaning of 2, 173; internal conditions on 36; relevance of mental/brain configurations to 223 19–20, 24–38; as semantic features of an expression 125; and sound xi–xii, 9–10; theory of, and internalism–externalism debate 147–63; truths of and truths of fact 62–4 meaning holism xiv, 61, 66–7, 152, 186–7, 195n mechanical philosophy 83–4, 86, 104, 108, 110, 144, 163, 167 mechanics, laws of 82 mechanisms 17–18, 56 Mehler, J. 118 mental: all phenomena potentially conscious 86–7; “anomalism of the” (Davidson) 88–9; bridge laws relating to physical 89–90; characterized as access to consciousness 93–8; location within the physical 103; as the neurophysical at a higher level 104; phenomena described in terms of the physical 109; and physical 113; and physical reality 166–8; replacement by physical 138; to define in neurological terms 103; use of term xiv, 75–6, 106, 134 mental construct vii mental event tokens, and physical event tokens 89 mental properties: approaches to 147; and nervous system 167 mental representations: internalist study of 125; specifications of 165; see also C–R theories mental states, attribution of 91, 160–1, 169 Mentalese 176–8, 185–9 Merge operation 13 messages, decoding 185–9 metaphorical use of terms 114, 131, 159, 161 metaphysical, extracting from definitions 75–7 metaphysical dualism 108, 112, 163 metaphysical naturalism 79, 81–2, 85, 144 224 Index metaphysics vi, 112 methodological dualism 76, 77, 93, 112, 135, 140–1, 163 methodological naturalism 76, 77–8, 79, 81, 91, 135, 143 Mill, John Stuart 187 mind: architecture of the 14, 121, 135, 174;Cartesian theory of 83–4; as a computational state of the brain 128; as consciousness 86–7; as “Cryptographer” 185–9; explanatory theories of in study of language 77; history of the philosophy of 109; as mental aspects of the world 134; naturalism and dualism in the study of 75–105; naturalistic inquiry into 103; reflection on the nature of the 165; as res cogitans 83; study of in biological terms 6; theory of (TM), scientific status of 85–6; unraveling the anatomy of the 173, 183; use of term 75–6, 106, 130–1 mind–body problem vi, vii–viii, xiv, 84, 86–91, 88–9; as how consciousness relates to neural structures 144–5; lacks concept of matter or body or the physical 110, 199n; Nagel on 86–8; no intelligible 103, 112, 138; as a unification problem 108–9 mind/brain interaction 1–2, 9–11 mind/brain systems: integration of states of language faculty with 173–5; internalist study of 164–5 Minimalist Program x, xi, xv, 9–15 misperception 159–60 misuse of language, notion of 49, 70–3, 200n “MIT mentalism”, Putnam’s critque of 184–9 models: computer 105, 116, 157; constructing to learn 114 modifications, nonadaptive 163 modularity: of mental architecture 121; use of term 117–18 Moravcsik, Julius 128, 182–3, 204n motion: inherent in matter 167; studies using tachistoscopic presentations 159 motivation 162 motor systems 17–18 Move operation 13 movement xii, 13, 14–15 multilinguality 169 mutations 96–7 mysteries ix, 83, 107, 133 Nagel, Thomas 86–8, 90, 95–6; Language Acquisition Device (LAD) 92–4;on mind–body problem 86–8, 109, 115; on naturalistic theory of language 143 names: have no meaning 24, 42, 173, 181; Hobbes on 182 naming, as a kind of world-making 21, 127, 181 national languages, as codifications of usages 100 natural kinds xv, 19, 20–2, 89, 105, 137, 204n natural language: apparent imperfections of xii, 9–15, 123–4; properties of terms of 126–7; sometimes unparseable 108; and use of technical terms 130–2 natural object 117, 119; language as a 106–33 natural sciences vii, 135; defining 81–5, 92; as “first philosophy” 112; and knowledge of language 51; and notions of belief and desire 146; and psychic continuity of human beings 139; Quine’s definition 144; standard methods of 52–6 natural selection: replaced God 110; unselected functions in 163 “natural-language semantics” 174, 175 naturalism vii, xiii, xiv, xv, 109; Baldwin on 79–80; in the study of language and mind xiv, 75–105; use of term 76–7, 135; varieties of 79–85; see also epistemic Index naturalism; metaphysical naturalism; methodological naturalism naturalistic approach 1–2, 103, 106; compared with an internalist approach 134, 156 naturalistic inquiry; and commonsense perspectives 37–45, 85; defined 115, 134; detailed 117–33; divergence from natural language 23–4; and intentionality 45, 132; language in 77–9; as “Markovian” 196n; nature of 76–9, 82–5; as psychological not philosophical 140; scope of 19–24, 28–9, 90, 97; symbolic systems of 153 “naturalistic thesis”, Quine on 92–3, 144 nature, belief as unknowable 110 negation 124 nervous system 103–4, 116; and mental properties 167 neural net theories 103–4, 107 neural structures, relation of consciousness to 144–5 neurophysiology 25–6, 103, 104, 116 Newton, Isaac viii, 80, 83–4, 86, 93, 141, 163, 167; anti-materialism 1, 82, 84, 108–10, 144, 199n; on gravitation 108–9, 166 norms 49, 72, 148, 157, 171–2; violation of 98 numbers 121 object constancy 94, 97, 135 objectivity premise 159 objects: and agency 21–2; discontinuous 127; nameable 136–7; problems posed by artifacts compared with natural 105 observation, of linguistic aligned with non-linguistic behavior 46, 52 “observational adequacy” 198n “occult qualities” 83–4 ontology 184 optimality conditions xii, 10–11, 123, 125 ordinary English usage, Pateman’s description 169 225 ordinary language: accounts of mental and physical events 89–90; philosophy 46, 203n; use and terminology 141–2, 169 organic unity, and personal identity 182 organism: analogy 4, 17–18, 59–60; constraints on computing a cognitive function 162–3; dedicated to the use and interpretation of language 168; internal states of an 134; “solving problems” 159, 161 organization, “from within” 182 “p-events” (events physicalistically described) 89–90 “p-predicament” (Bromberger) 82 parameters see Principles and Parameters approach “parser” 69–70, 200n parsing 107–8, 124 Pateman, T. 169, 197n Pauling, Linus 106, 111 Peirce, Charles Sanders 80, 83 perception 2, 124–6; and the computational system 120, 180; as a dream modulated by sensory input 128; empiricial theories of 161–2; language-related differences in 118; veridical 23; see also articulatory–perceptual systems perceptual content 23, 196n perceptual organization, reduction to 183–4, 185 perfectness of language xii, xvi, 9–15, 123–4 performance: competence and ix; and computation theories 124 performance systems 45, 117, 118; fallibility of xiv, 124; and I-language 27–32, 143; I-languages embedded in 34–6; internal representations accessed by 160; specialized for language 118; use of expressions generated by I-language 124–6; see also articulatory–perceptual systems; conceptual–intentional systems 226 Index perspectives 40, 88, 150, 151–6, 180; conflicting for words 126; linguistic agent’s 137; range of 36–7; see also point of view PF see Phonetic Form philosophical explanation 142, 147; science and 140–1 philosophy vi, 46–74; causality and core problems of 145; naturalization of 144; and science 81–2, 87, 94, 140–1 philosophy of language xiii, 16–17, 46, 61 relations between expressions and things 129–30 PHON(E) 173, 175, 177, 180, 203n phonetic aspects, abundance of variety 185 phonetic features 12, 15–16, 44, 125; accessed by articulatory– perceptual systems 123, 180 Phonetic Form (PF) xi–xii, 28, 124–5, 129 phonetic level 11, 173 phonetic realization, different of inflectional systems 11 phonetic relations 179 phonetic representations 9, 10, 174, 185–9 phonetic value 129, 177 phonetics 174 phonological features 170, 192 phonological levels, in terms of intention 203n phonological units 43–4, 151–2 phonology 43–4, 147 phrase boundaries: and perceptual displacement of clicks 25, 55, 58, 140, 201n; and referential dependence in Japanese 53–4, 58 phrase-structure rules 10, 13, 53–4, 58 physical: anomalism of the 138; mechanical concept of 167; and mental reality 166–8 physicalism 117, 144 physics xv, 82, 84, 87–8, 112; development to permit of unification 166–7 Platonism 80 “Plato’s problem” 61 Poincaré, Jules 110 point of view 40, 164, 182; nameable objects and 136–7; and status of things 126–8; see also perspectives Popkin, Richard 57, 76–7 Port Royal Grammar 4 power and status issues 156 pragmatic competence, limited, and language faculty 146 pragmatics 132 pragmatism 46–7 Priestley, Joseph 84, 112–13, 115, 116, 167 priming effects 140 “primitive theory” 90 principles 138–9, 184, 192; fixed and innate 122; and underlying structures 168–9 Principles and Parameters approach x, xi, 11; explained 8–9, 121–3; see also Minimalist Program “prior theory” 67–70 problems, ix, 83, 107, 115 production 2 projection principle 10 pronominalization, “backwards” 196n pronouns 181; anaphoric properties of 39; dependency of reference 126 proper names, no logical (Strawson) 24, 181 properties: partial account of language 184; of sensation or perception and thought 113 propositional attitudes, attribution of 192–3 Proust, Marcel 90 psycholinguistic experiment 171 psychological evidence 139–40 psychological generalizations 165–6, 168–9 “psychological hypotheses” 140–1 Index psychological mechanisms 117–18 psychology: internalist 143; invented technical term 153; and software problems 105 psychology vi, vii, 1, 80, 136, 138, 154, 160, 181, 202n psychosemantics 165 “public language” 30, 32–3, 37, 38, 40, 127, 131–2, 136, 148, 155–8, 187–8 purposes 136–7 Pustejovsky, James 128 Putnam, Hilary xiii, 19, 41, 152, 156–7; on alleged facts 136; on Bohr 43; Chomsky’s critique of 19–45; critique of “MIT mentalism” 184–9; division of linguistic labor 71, 187–8; on impossibility of explanatory models for human beings 19–20; on intentionality 45; on languages and meanings as cultural realities 157–8; rejection of the “innateness hypothesis” 65, 66–7; “The Meaning of Meaning” 41–2; Twin-Earth thought experiment 40–1, 148–9, 155; on water 127–8 quantifiers 11, 124 quantum theory 111 Quine, Willard xiii, 46, 57, 61, 101, 141; coordinate structure constraint 55–6; displacement of clicks study 55, 58, 140; distinction between “fitting” and “guiding” 94–5; epistemology naturalized 46–7, 80, 81; on extensional equivalence 132; on grammaticality 63; indeterminacy of translation 132, 140, 147; “naturalistic thesis” 92–3, 144; no fact of the matter 58, 59; radical translation paradigm 52–5, 101–2; “revision can strike anywhere” 66–7, 188 R (“refer”) relation 38–40; and Rlike relation 41–2 227 rational inquiry, idealization to selected domains 49–50 reduction viii, xiv, 82, 87, 106, 144–5 reference 2, 148; as an invented technical notion 148–50, 152–3; causal theory of 41; choices about fixing of 67; cross-cultural similarities 171; fixation of 42, 44, 128; notions of independent 137; in philosophy of language 16–17; problem of relation 37–42; the “proto-science” of 171; in the sciences 152; semantics and 130–2; as a social phenomenon relying on experts 188; social-cooperation plus contribution of the environment theory of specification of 41–2; specification of 41–2; technical notion of 202n; transparence of relation 39–40; as a triadic relation 149–50; two aspects of the study of 171; use of term 36, 130, 188; usefulness of concept 38–45, 181 referential dependence 47, 50, 126, 180–1, 196n referential properties, debate on 24–5 referential use of language 180–1 reflection: evaluation by 166; operations of the mind which precede 170 regulative principle 46, 52 Reid, Thomas 80, 182, 196n, 203n reification 92–3, 94, 201n relatives 181 representations: “informational” with intentional content 195n; as postulated mental entities xiii, 159–60; two levels of phonetic and logical xi–xii, 173 rhyme, relations of 174 Richards, Theodore 111 rigidity principle 94 Romaine, Suzanne 156 Rorty, Richard 46–7, 52, 61, 63 Royal Society 110 228 Index rule following 48–9, 98–9; in terms of community norms 31, 142 rule system: attribution of a specific internalized 57–61; problem of finding general properties of a 7–8 rules: and behavior 94–5; and conditions of accessibility to consciousness xv, 99, 184; status of linguistic xiv, 98–9, 123; unconscious 184, 204n Russell, Bertrand 187 sameness 40–2; and referential dependence 126 Sapir, Edward 140 Sapir–Whorf hypothesis 136 Saussure, Ferdinand de 27, 120 Schweber, Silvan 145 science: boundary of self-justifying 112; and categories of intuition 162–3; history of xiv, 43, 109–12; origins of modern 83–5, 109; and philosophy 81–2, 87, 94; unification vi, viii–ix, x, xiv, 111, 145, 166, 168; unification goal 82, 106–7, 112; unification problem 79, 84, 85, 91, 103–4, 108, 116 science fiction, and theories about the world 152 “science of human nature” (Hume) 164, 165, 166, 169, 173, 183, 190 science-forming faculty (SFF) ix, 22, 33, 34, 82–3, 121, 133; and common-sense belief 43; and the linguist 101; property of constructing Fregean systems 131 sciences: “hard” 139; language of ordinary life and language of the 186 scientific discourse, intelligibility in 151–2 scientific inquiry see naturalistic inquiry scientific revolution 6, 110 scientism 153 SDs see structural descriptions Searle, John 94, 95, 113, 115, 141, 184, 203n, 204n; “Connection Principle” (CP) 96–8; “radical thesis” on consciousness 86–7 second-language learners xii segments, postulated 43 semantic connections 47, 61–5, 67, 137, 179 semantic features 12, 15–16, 125, 170, 173, 182–3, 192 semantic interpretation: approaches to 15–16; process of 14–15; and syntax in the technical sense 174 semantic level 11, 173 semantic properties 104, 137; innate and universal 185 semantic relations 179 semantic representations 9, 10, 170, 185–9; and relations of FL with cognitive system 174–5 semantic resources, gap between and thoughts expressed 135 semantic values 129–30, 178, 204n semantics: event 24–6; referential vii, 132, 174 SEM(E) 173, 175, 180 “sense”, of fixed reference in natural language 42 sensorimotor system 9, 10; inactivation of 14–15; as languagespecific in part 174; use of information made available by I-language 174–5, 180 sensory deficit, and language faculty 121–2 Shaftesbury, Anthony Ashley Cooper, 3rd Earl of 182 shared language/meanings thesis 29–32, 100, 148, 156–8 sign language of the deaf 121–2 signs 78, 182 similarity relation 40–2, 43–4, 152 “simplification” 56, 198n simulation, machine 114 Smith, Barry 142 Smith, Neil vi–xvi, 121 Soames, Scott 132 Index social co-operation, in specification of reference 41–2 social practice 32, 49, 50, 72; and different languages 48–9 sociolinguistics 156, 200n sociology of language 169 sound: analogies with meaning 15–16, 175–9; inquiry into meaning of 173; location by the auditory cortex 158; and meaning xi–xii, 9–10, 11, 168, 170; as phonetic features of an expression 125; the study of systems 6 space–time continuity, of things 127 species property 2, 3 speech acts 78 Spelke, Elizabeth 195n standard languages, partially invented 157–8 state L 78–9, 119, 170–1 Stich, Stephen 103, 149, 171, 196n stimulus, poverty of 56, 65, 126, 171 “strange worlds” scenario 172 Strawson, Peter 24, 181 structural descriptions (SDs), generation of 26, 27, 39–40, 199n structural linguistics, mentalistic approach to 5–6, 122 structural phonology 43–4, 151–2 structural representation 39 structure: degree of shared 152; and explanatory adequacy 7–8 structure dependence 121, 184 substances, special mental design for 127–8 “superlanguage” 189 switch settings, for particular languages xi, 8, 13 symbolic objects, properties and arrangements of 174 symbolic systems 12, 131 syntactic relations 63 syntax xii, xv, 132; “autonomy of ” thesis 203n; internalist form of 129; R–D relations as 39–40; and structure dependence 121; use of term 174 229 T-sentences, theory of 204n technical terms 40, 65, 148–9; invention of 188; with no counterpart in ordinary language 130–2; and truth or falsity 130–1; variation in translation of 188 temporal order, no parametric variation in 123 terminology: animistic and intentional 135–6; and ordinary language use 130–2, 141–2, 171 terms: forensic 182; languages lacking certain 135 theories: concepts arise from 66; “passing” 29, 30, 67–70, 202n; “short” 66, 200n theory, and explanatory adequacy xi–xii, 7–8 “Theory–Theory” 103 things: changes in 192; defining 136–7; in some kind of mental model 129; space–time continuity of 127; status of nameable 21, 127; in the world 129 thinking: Locke on faculty of 1, 167; ways of 15–16 thought: and action as properties of organized matter 84, 86; are contents externally determined 153–4; gap between semantic resources and expressed 135; individuation of 165; “language of ” (Fodor) 19; as a property of the nervous system/brain 113, 115, 116; relation to things in the world 149–50 thought experiments 153–4; which strip away background beliefs 172 TISR see innate semantic representations, theory of traditional grammar 13, 122, 123, 174 trajectory 94 transcendental argument, Kant’s method 165 230 Index transformational rules x, 12–13 translation: indeterminacy of xiii, 132, 140, 147; radical (Quine) 52–5, 101–2, 198n; rational reconstruction of practice 148 truth theories 130, 156 Turing, Alan 44–5, 114, 148 Turing test 114 Twin-Earth thought experiments xv, 40–1, 148–9, 155, 160–1, 172, 189–90 Ullman, Shimon 159 understanding 203n; by people, not parts of people 113–14; generative faculty of human 16–18; limits of human 156; of meaning without relevant experience 128; quest for theoretical 19, 77, 115, 134 unification problem see science uninterpretable features xii, 12–15 Universal Grammar (UG) 98–9, 103; and child’s intuitive understanding of concepts 62; theory of the initial state as 73, 81, 101 unmarked options 125 usage: change in and language change 32, 44–5; “correct” 157;, see also misuse of language and distinction of knowledge of language from ability 51 use, regular of objects 136–7, 180 use of language: alleged social factors in 32;creativity of 16–18, 145; explaining xiii, 19–45; and intelligence 147; and interpretation of meaning 15–16; and linguistic states 2; restrictions at PF or LF levels 35; similarities among species, not found 184 variables 42 variation among languages: and left– right orientation 93; as limited to certain options in the lexicon 79, 120, 123; and properties of inflectional systems 11–12 variation in language, as instructions by computational system to articulation and perception 120 Vaucanson, Jacques de 114 visual perception, Marr’s theory of 158–9, 161 visual system xiv, 17–18, 118–19, 147; and C–R theories 28–9 visualizability 167 Weinreich, Max 31 well-formedness category 78 will 109, 127 Wittgenstein, Ludwig 44–5, 46, 51–2, 98, 127, 132, 203n, Ludwig, later 51–2 words: can change meaning and still be the same 175; offer conflicting perspectives 126; as phonetic (or orthographic) units 175; relations with things in the world vii, 148–51; rich innate contribution to construction of semantic properties 179 world: external and internal set of reference frames 128–9; features of the real 148; how language engages the 164, 180; “material” 84–5; ways of looking at the 181; as the world of ideas 182 Wright, Crispin 143 X-bar theory 10 Yamada, Jeni 146 Yolton, John 113, 182, 203n zeugma 181


The Dream Machine: J.C.R. Licklider and the Revolution That Made Computing Personal by M. Mitchell Waldrop

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Ada Lovelace, air freight, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, anti-communist, Apple II, battle of ideas, Berlin Wall, Bill Duvall, Bill Gates: Altair 8800, Byte Shop, Claude Shannon: information theory, computer age, conceptual framework, cuban missile crisis, double helix, Douglas Engelbart, Dynabook, experimental subject, fault tolerance, Frederick Winslow Taylor, friendly fire, From Mathematics to the Technologies of Life and Death, Haight Ashbury, Howard Rheingold, information retrieval, invisible hand, Isaac Newton, James Watt: steam engine, Jeff Rulifson, John von Neumann, Menlo Park, New Journalism, Norbert Wiener, packet switching, pink-collar, popular electronics, RAND corporation, RFC: Request For Comment, Silicon Valley, Steve Crocker, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Ted Nelson, Turing machine, Turing test, Vannevar Bush, Von Neumann architecture, Wiener process

Turing's answer to the first question was not too surprising: by "machine" he meant "a digital computer." This in itself was no real restriction, he argued, since a digital computer was universal and could simulate any other machine-includ- ing, presumably, the human mind. His answer to the second question, however, was vintage Turing: idiosyncratic, startling, and yet utterly logical. "Thinking," he declared, could be defined via his now-famous Turing test, a kind of party-game THE FREEDOM TO MAKE MISTAKES 123 affair in which a human and a computer are hidden from view and answer queries posed by an interrogator who is trying to determine which is which. Thus Q Please write me a sonnet on the subject of the Forth Bridge. A: Count me out on this one. I never could write poetry. Q Add 34957 to 70764. A: (Pause about 30 seconds and then give as answer) 1 05621

A: (After a pause of 15 seconds) R-R8 mate. IS and so on. Turing argued that if the interrogator could not tell, no matter how many questions he or she asked, then one had to admit that the machine was re- ally thinking. After all, he noted, at that point the interrogator would have pre- cisely as much evidence for the computer's thinking ability as for the human's. Viewed in retrospect, Turing's test for machine intelligence has to rank as one of the most provocative assertions in all of modern science. To this day, people are still talking about it, writing commentaries on it, and voicing outraged objec- tions to it (most of which he anticipated in his original paper, by the way).t Of course, like so much of Turing's work, the 1950 paper wasn't widely read at the time, and it had essentially no impact on the artificial-intelligence research that was just beginning in the United States.

This group included essentially all the cyberneticists, ranging from McCulloch and Pitts, with their neural-network models, to Gray Walter in England with his "turtles," little wheeled devices that used photoelectric cells and feedback circuits to seek out light sources with creepily lifelike determination. For that matter, it included J. C. R. Licklider and THE TALE OF THE FIG TREE AND THE WASP 161 his analog circuit models of the brain. Intelligent behavior resided in the hard- ware, went the cybernetic line. In fact, says McCarthy, the first person to make a reasonably explicit case for the software approach was Alan Turing, in the 1950 paper that introduced his Turing test. And even there, says McCarthy, it was not very prominent; the first time he read that paper, the software idea didn't even sink in. But it was sinking in now, and bringing with it a new resolve to launch yet an- other frontal assault on machine intelligence. "I had this idea that if only we could avoid all these distractions and devote some time to it," says McCarthy, "if we could just get everyone who was interested in the subject together, then we might make some real progress."


pages: 798 words: 240,182

The Transhumanist Reader by Max More, Natasha Vita-More

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23andMe, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, augmented reality, Bill Joy: nanobots, bioinformatics, brain emulation, Buckminster Fuller, cellular automata, clean water, cloud computing, cognitive bias, cognitive dissonance, combinatorial explosion, conceptual framework, Conway's Game of Life, cosmological principle, data acquisition, discovery of DNA, Drosophila, en.wikipedia.org, experimental subject, Extropian, fault tolerance, Flynn Effect, Francis Fukuyama: the end of history, Frank Gehry, friendly AI, game design, germ theory of disease, hypertext link, impulse control, index fund, John von Neumann, joint-stock company, Kevin Kelly, Law of Accelerating Returns, life extension, Louis Pasteur, Menlo Park, meta analysis, meta-analysis, moral hazard, Network effects, Norbert Wiener, P = NP, pattern recognition, phenotype, positional goods, prediction markets, presumed consent, Ray Kurzweil, reversible computing, RFID, Richard Feynman, Ronald Reagan, silicon-based life, Singularitarianism, stem cell, stochastic process, superintelligent machines, supply-chain management, supply-chain management software, technological singularity, Ted Nelson, telepresence, telepresence robot, telerobotics, the built environment, The Coming Technological Singularity, the scientific method, The Wisdom of Crowds, transaction costs, Turing machine, Turing test, Upton Sinclair, Vernor Vinge, Von Neumann architecture, Whole Earth Review, women in the workforce

Taking this as a hypothesis to be tested, how would one know whether the hypothesis was confirmed? Panels of experts could interview the cyber-conscious being to determine its sentience as ­compared to a flesh human – these type of interviews, when conducted in blinded fashion as to the forms of each interviewee, are called Turing Tests in honor of the mathematician who first suggested them in the 1940s, Alan Turing (1950: 442). The prospect of being the first to pass such Turing Tests is motivating many computer science teams (Christian 2011: 16). They are doing their utmost to build into their software the full range of human feelings, including ­feelings of angst and dread. Hence, the unstoppable human motivation to invent something as amazing as a cyber-conscious mind will result in the creation of countless partially successful efforts that would be unethical if accomplished in flesh.

In fact, I usually don’t even notice the loss. A model of your brain that described the behavior of every synapse and nerve impulse, and did a reasonably accurate job at that level, would seem to capture everything that is essential to being “you.” Yet how can we tell? How will we judge the “accuracy” of our computational model? How can we say what is “significant” and what is “insignificant”? We might adopt a variation of the Turing test: if an external tester can’t tell the difference, then there is no difference. But is the opinion of an external tester enough? How about your opinion? If you “feel” a difference, wouldn’t this mean that the model was a “mere copy” and not really you? Well, we could ask: “Hi! We’ve uploaded your brain into an Intel Pentadecium, how are you feeling?” “Absolutely top notch!” “Do you think you’re not you?”


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Mining the Social Web: Finding Needles in the Social Haystack by Matthew A. Russell

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Climategate, cloud computing, crowdsourcing, en.wikipedia.org, fault tolerance, Firefox, full text search, Georg Cantor, Google Earth, information retrieval, Mark Zuckerberg, natural language processing, NP-complete, profit motive, Saturday Night Live, semantic web, Silicon Valley, slashdot, social graph, social web, statistical model, Steve Jobs, supply-chain management, text mining, traveling salesman, Turing test, web application

Look no further than a sentence containing a homograph[51] such as “fish” or “bear” as a case in point; either one could be a noun or a verb. NLP is inherently complex and difficult to do even reasonably well, and completely nailing it for a large set of commonly spoken languages may very well be the problem of the century. After all, a complete mastery of NLP is practically synonymous with acing the Turing Test, and to the most careful observer, a computer program that achieves this demonstrates an uncanny amount of human-like intelligence. Whereas structured or semi-structured sources are essentially collections of records with some presupposed meaning given to each field that can immediately be analyzed, there are more subtle considerations to be handled with natural language data for even the seemingly simplest of tasks.

tokenization, couchdb-lucene: Full-Text Indexing and More, Data Hacking with NLTK, Before You Go Off and Try to Build a Search Engine…, A Typical NLP Pipeline with NLTK, Sentence Detection in Blogs with NLTK, Visualizing Wall Data As a (Rotating) Tag Cloud definition and example of, A Typical NLP Pipeline with NLTK Facebook Wall data for tag cloud visualization, Visualizing Wall Data As a (Rotating) Tag Cloud mapper that tokenizes documents, couchdb-lucene: Full-Text Indexing and More NLTK tokenizers, Sentence Detection in Blogs with NLTK using split method, Data Hacking with NLTK, Before You Go Off and Try to Build a Search Engine… TreebankWord Tokenizer, Sentence Detection in Blogs with NLTK trends, Twitter search, Tinkering with Twitter’s API TrigramAssociationMeasures class, Common Similarity Metrics for Clustering triples, Entity-Centric Analysis: A Deeper Understanding of the Data, Man Cannot Live on Facts Alone true negatives (TN), Quality of Analytics true positives (TP), Quality of Analytics Turing Test, Syntax and Semantics tutorials, Installing Python Development Tools, Visualizing Mail “Events” with SIMILE Timeline, k-means clustering Getting Started with Timeline, Visualizing Mail “Events” with SIMILE Timeline official Python tutorial, Installing Python Development Tools Tutorial on Clustering Algorithms, k-means clustering tweets, Collecting and Manipulating Twitter Data, What are people talking about right now?


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

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

As Abraham Verghese nicely put it in his 2014 Stanford medical school commencement address, “You can heal even when you cannot cure by that simple act of being at the bedside—your presence.”35 To that end, it’s hard to improve upon the words of the sixteenth century physician Paracelsus (his full name was Philippus Aureolus Theophrastus Bombastus von Hohenheim!), “This is my vow: to love the sick, each and all of them, more than if my own body were at stake.”35 There will never be algorithms, supercomputers, avatars, or robots to pull that off. The Turing test for medicine won’t be passed, and Kurzweil’s “singularity” will remain a plurality. A New Wisdom of the Body While some people connect “wisdom of the body” with the notion that infants can innately self-select their diet for proper nutrition36 or that food cravings during pregnancy are for critically needed nutrients,37 the term goes back to Walter Cannon’s The Wisdom of the Body book published in 1932.38 Cannon, an eminent Harvard physiologist and medical researcher, developed the concept of homeostasis—that our body tightly regulates itself, with steady-state levels of blood glucose, electrolytes, pH, body temperature, and many other components.

Schmid, “Why Clinicians Are Natural Bayesians,” British Medical Journal 330 (2005): 1080–1083. 71. R. N. Chitty, “Why Clinicians Are Natural Bayesians: Is There a Bayesian Doctor in the House?,” British Medical Journal 330 (2005): 1390. 72. D. Hernandez, “Artificial Intelligence Is Now Telling Doctors How to Treat You,” Wired, June 2, 2014, http://www.wired.com/2014/06/ai-healthcare/. 73. R. M. French, “Dusting Off the Turing Test,” Science 336 (2012): 164–165. 74. G. Poste, “Bring on the Biomarkers,” Nature 469 (2011): 156–157. 75. A. B. Jensen et al., “Temporal Disease Trajectories Condensed from Population-Wide Registry Data Covering 6.2 Million Patients,” Nature Communications, June 24, 2014, http://www.readbyqxmd.com/read/24959948/temporal-disease-trajectories-condensed-from-population-wide-registry-data-covering-6-2-million-patients. 76.


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The Age of Em: Work, Love and Life When Robots Rule the Earth by Robin Hanson

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8-hour work day, artificial general intelligence, augmented reality, Berlin Wall, bitcoin, blockchain, brain emulation, business process, Clayton Christensen, cloud computing, correlation does not imply causation, demographic transition, Erik Brynjolfsson, ethereum blockchain, experimental subject, fault tolerance, financial intermediation, Flynn Effect, hindsight bias, job automation, job satisfaction, Just-in-time delivery, lone genius, Machinery of Freedom by David Friedman, market design, meta analysis, meta-analysis, Nash equilibrium, new economy, prediction markets, rent control, rent-seeking, reversible computing, risk tolerance, Silicon Valley, smart contracts, statistical model, stem cell, Thomas Malthus, trade route, Turing test, Vernor Vinge

We’ve seen similar booms of excitement and anxiety regarding rapid automation progress every few decades for centuries, and we are seeing another such boom today (Mokyr et al. 2015). Since the 1950s, a few people have gone out of their way to publish forecasts on the duration of time it would take AI developers to achieve human level abilities. (Our focus here is on AI that does human jobs well, not on passing a “Turing test.”) While the earliest forecasts tended to have shorter durations, soon the median forecasted duration became roughly constant at about 30 years. Obviously, the first 30 years of such forecasts were quite wrong. However, researchers who don’t go out of their way to publish predictions, but are instead asked for forecasts in a survey, tend to give durations roughly 10 years longer than researchers who do make public predictions (Armstrong and Sotala 2012; Grace 2014).

After all, an em may sometimes be tempted to substitute a cheap automated “bot” program that mimics him or her during some interactions with other ems, so that he or she can attend to other things. Ems may prefer not to be given such a bot to interact with, in part because it might suggest their low status. As a result, during interactions ems may try to act in complex and subtle ways that bots could not effectively mimic, continually running their own bots that try to mimic themselves and their associates to detect fakes. That is, ems might always feel they are part of a Turing test. Such habits could raise the costs of interacting for distrustful ems, and raise the gains from trust. Information about whether one is interacting with a bot might be obtained directly via direct brain access, or perhaps indirectly by requiring that a high price be paid to place what appears to be a full em in a particular role. Ems also typically want to see the voice tone, facial expressions, and gaze directions of themselves and their current interaction partners, and to know if they are seeing or showing direct and unfiltered versions of these tones and expressions.


pages: 548 words: 147,919

How Everything Became War and the Military Became Everything: Tales From the Pentagon by Rosa Brooks

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airport security, Albert Einstein, Berlin Wall, big-box store, clean water, cognitive dissonance, Edward Snowden, facts on the ground, failed state, illegal immigration, Internet Archive, Mark Zuckerberg, pattern recognition, Peace of Westphalia, personalized medicine, RAND corporation, Silicon Valley, South China Sea, Turing test, unemployed young men, Wall-E, War on Poverty, WikiLeaks

Many of the alarmist scenarios involving autonomous weapons systems make the opposite assumption, however, envisioning intelligent, autonomous robots that decide to override the code that created them, and turn upon us all. But when the robots go rogue—when lust for blood, money, or power begins to guide their actions—they’ll have ceased to be robots in any meaningful sense. They’ll finally be able to pass the Turing Test. For all intents and purposes, they will have become humans—and it’s humans we’ve had reason to fear, all along. Nonlethal Weapons If we fought a war with weapons that did no permanent physical harm to our enemies, would they still be weapons, and would it still be a war? The advent of cyberwar forces us to ask this question, but similar questions also arise when we consider advances in “nonlethal weapons.”

., Guantánamo rulings of, 58–59, 60–61, 410 surveillance, 355, 364 new rules needed for, 355 post-9/11 increase in, 303–4, 414–15 SWAT teams, 298–99 Syria, 12, 157, 226, 227, 229, 280, 344, 349 bombing in, 291 chemical weapons in, 248, 283, 314–15 civil war in, 248 U.S. drone strikes in, 107 U.S. military intervention in, 251 Syrian civil war, 261 Taliban, 29, 33, 55, 56, 59, 60, 74, 75, 98, 99, 100, 121, 232, 277, 278, 279, 293, 329, 331 Tanzania, bombing of U.S. embassy in, 83, 223 targeted killings, 27, 103, 108, 115–16, 118, 119, 122–23, 124, 134, 196–97, 266, 273, 274, 276, 284, 286, 343, 363, 383, 409 new rules needed for, 354–55 secrecy surrounding, 355, 364 see also drone strikes technological change, history of, 264 10th Special Forces Group, 17 terrorists, terrorism, 12, 41, 295, 339 drone strikes on, see drone strikes unconventional tactics of, 120–21 Terry, James, 148 Thirty Years War, 229, 261 Thomas Aquinas, Saint, 185 Thonden, Yodon, 235 3–2 “Stryker” Brigade Combat Team, 7th Infantry Division, 147 Through the Looking-Glass (Carroll), 287 Tilly, Charles, 217–18, 230 Tokyo, firebombing of, 138, 190, 365 Tokyo tribunals, 192, 193, 215 Too Fat to Fight, 321 Tora Bora, Battle of (2001), 119 torture, 193 Bush administration’s definition of, 58, 199–200, 201–3, 204 legal prohibition on, 200–201 Obama’s banning of, 34 U.S. use of, 33, 58, 60–61, 199–200, 320–21, 322, 363, 410 “Tragedy of the American Military, The” (Fallows), 15 Training and Doctrine Command (TRADOC), 150 Tripoli, 48, 49 Truman, Harry, 329 Tueller, Matthew, 154–55 Tunis, 48 Turing Test, 139 Turkey, 26 Turse, Nick, 147–48 Twitter, 349 Uganda, 27, 84, 85 author in, 235–36, 237–38, 241 Lord’s Resistance Army in, 176–81, 235–40, 241, 242 Ukraine, 280 high-tech warfare vs. low-tech in, 333 uncertainty, geopolitical: as increased by U.S. counterterrorism actions and legal arguments, 284–89 interconnectedness and, 261–67 rule of law as undermined by, 283 Uniform Code of Military Justice, 197–98, 202 Union Army, 185, 187 United Kingdom, 248 United Nations, 190, 232–33, 262, 365, 366 Dutch peacekeeping troops of, 215, 396 politics and, 192 Responsibility to Protect doctrine and, 247 United Nations Charter, 35, 190, 191–92, 231–32, 233, 250, 251, 290, 339, 342–44, 366 military intervention and, 194–95, 234–35, 243–44, 246, 248–49, 252, 286, 343–44 United Nations General Assembly, 247, 394, 407 United Nations Security Council, 194–95, 215 military intervention and, 234–35, 243–44, 246, 248–49, 252, 286 paralysis of, 291 veto powers in, 289 United States, 234 Barbary pirates and, 47–49 China’s relations with, 349 core values of, 63–64, 100, 101, 203, 295, 353–54 detention and interrogation policies of, 33, 54–61, 276, 284, 355, 363, 410 geopolitical power of, 266–67 hubris of, 97 idealism of, see idealism, American “imminent threat” as defined by, 286–87 increasingly unpredictable behavior of, 284 military of, see military, U.S.


Toast by Stross, Charles

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anthropic principle, Buckminster Fuller, cosmological principle, dark matter, double helix, Ernest Rutherford, Extropian, Francis Fukuyama: the end of history, glass ceiling, gravity well, Khyber Pass, Mars Rover, Mikhail Gorbachev, NP-complete, oil shale / tar sands, peak oil, performance metric, phenotype, Plutocrats, plutocrats, Ronald Reagan, Silicon Valley, slashdot, speech recognition, strong AI, traveling salesman, Turing test, urban renewal, Vernor Vinge, Whole Earth Review, Y2K

“More of the same all round!” At the next table a person with make-up and long hair who's wearing a dress -- Manfred doesn't want to speculate about the gender of these crazy mixed-up Euros -- is reminiscing about wiring the fleshpots of Tehran for cybersex. Two collegiate-looking dudes are arguing intensely in German: the translation stream in his glasses tell him they're arguing over whether the Turing Test is a Jim Crow law that violates European corpus juris standards on human rights. The beer arrives and Bob slides the wrong one across to Manfred: “here, try this. You'll like it.” “Okay.” It's some kind of smoked doppelbock, chock-full of yummy superoxides: just inhaling over it makes Manfred feel like there's a fire alarm in his nose screaming danger, Will Robinson! Cancer! Cancer!. “Yeah, right.


pages: 267 words: 82,580

The Dark Net by Jamie Bartlett

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3D printing, 4chan, bitcoin, blockchain, brain emulation, carbon footprint, crowdsourcing, cryptocurrency, deindustrialization, Edward Snowden, Filter Bubble, Francis Fukuyama: the end of history, global village, Google Chrome, Howard Rheingold, Internet of things, invention of writing, Johann Wolfgang von Goethe, Julian Assange, Kuwabatake Sanjuro: assassination market, life extension, litecoin, Mark Zuckerberg, Marshall McLuhan, moral hazard, Occupy movement, pre–internet, Ray Kurzweil, Satoshi Nakamoto, Skype, slashdot, technological singularity, technoutopianism, Ted Kaczynski, The Coming Technological Singularity, Turing test, Vernor Vinge, WikiLeaks, Zimmermann PGP

Silk Road 2.0 also offers the widest variety of products from the largest number of vendors: 13,000 listings, compared to the second largest, Agora Market, which has 7,400. Positive endorsements, a wide range of products, excellent security. I need no more persuading. Vendors and Products Signing up to Silk Road 2.0 is extremely simple. Username. Password. Complete the CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), and you’re in. ‘Welcome Back!’ reads the landing page. The forums were right – I am immediately overwhelmed by choice. There are around 870 vendors to choose from, selling more drugs than I’d ever thought possible. Under ecstasy alone, I find listed: 4-emc, 4-mec, 5-apb, 5-it, 6-apb, butylone, mda, mdai, mdma, methylone, mpa, pentedrone, pills. But the choice is not limited to drugs.


pages: 247 words: 71,698

Avogadro Corp by William Hertling

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Any sufficiently advanced technology is indistinguishable from magic, cloud computing, crowdsourcing, Hacker Ethic, hive mind, invisible hand, natural language processing, Netflix Prize, private military company, Ray Kurzweil, recommendation engine, Richard Stallman, technological singularity, Turing test, web application

I suppose that I, like him, assumed that there would be a more intentional, deliberate action that would spawn an A.I.” He paused, and then continued, smiling a bit. “Gentlemen, you may indeed have put the entire company at risk. But let me first, very briefly, congratulate you on creating the first successful, self-directed, goal oriented, artificial intelligence that can apparently pass a Turing test by successfully masquerading as a human. If not for the fact that the company, and perhaps the entire world, is at risk, I’d suggest a toast would be in order.” Sean looked around to see where his parents had sat, and then continued. “But since we are facing some serious challenges, let me go say goodbye to my parents, and then we can figure out our next step.” “Thank you Sean. Thank you so much,” David said.


pages: 304 words: 82,395

Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer-Schonberger, Kenneth Cukier

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23andMe, Affordable Care Act / Obamacare, airport security, AltaVista, barriers to entry, Berlin Wall, big data - Walmart - Pop Tarts, Black Swan, book scanning, business intelligence, business process, call centre, cloud computing, computer age, correlation does not imply causation, dark matter, double entry bookkeeping, Eratosthenes, Erik Brynjolfsson, game design, IBM and the Holocaust, index card, informal economy, Internet of things, invention of the printing press, Jeff Bezos, Louis Pasteur, Mark Zuckerberg, Menlo Park, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, obamacare, optical character recognition, PageRank, performance metric, Peter Thiel, Post-materialism, post-materialism, random walk, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, smart grid, smart meter, social graph, speech recognition, Steve Jobs, Steven Levy, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, Turing test, Watson beat the top human players on Jeopardy!

So he looked for something that is easy for people to do but hard for machines. He came up with the idea of presenting squiggly, hard-to-read letters during the sign-up process. People would be able to decipher them and type in the correct text in a few seconds, but computers would be stumped. Yahoo implemented his method and reduced its scourge of spambots overnight. Von Ahn called his creation Captcha (for Completely Automated Public Turing Test to Tell Computers and Humans Apart). Five years later, millions of Captchas were being typed each day. Captcha brought von Ahn considerable fame and a job teaching computer science at Carnegie Mellon University after he earned his PhD. It was also a factor in his receiving, at 27, one of the MacArthur Foundation’s prestigious “genius” awards of half a million dollars. But when he realized that he was responsible for millions of people wasting lots of time each day typing in annoying, squiggly letters—vast amounts of information that was simply discarded afterwards—he didn’t feel so smart.


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The Logician and the Engineer: How George Boole and Claude Shannon Created the Information Age by Paul J. Nahin

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Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Any sufficiently advanced technology is indistinguishable from magic, Claude Shannon: information theory, conceptual framework, Fellow of the Royal Society, finite state, four colour theorem, Georg Cantor, Grace Hopper, Isaac Newton, John von Neumann, knapsack problem, New Journalism, reversible computing, Richard Feynman, Richard Feynman, Schrödinger's Cat, Steve Jobs, Steve Wozniak, thinkpad, Turing machine, Turing test, V2 rocket

For example, what do you get if you multiply all the rational fractions by all the rational fractions? Why, nothing more or less than just all the rational fractions back again!6 NOTES AND REFERENCES 1. The reference to Turing is almost certainly due to Shannon having read Turing’s famous paper “Computing Machinery and Intelligence,” Mind, October 1950, pp. 433–460. It was in this paper that Turing put forth what was to become famous in computer science as the Turing test, an experimental procedure to unemotionally decide if a machine possessed artificial intelligence. For Turing’s comparison of ideas to neutrons, see in particular, p. 454. 2. MIT electrical engineering professor Marvin Minsky refers to this issue in his beautiful book Computation: Finite and Infinite Machines, Prentice Hall, 1967, p. 128. Despite what he writes as the “staggering inefficiency” of a Turing machine, Minsky goes on to say, “It is possible to execute the most elaborate possible computational procedures with Turing machines whose fixed structures [that is, the finite-state machine and the read/write head] contain only dozens of parts [this excludes the arbitrarily long tape if we count each writable/erasable square as a distinct ‘part’].


Cartesian Linguistics by Noam Chomsky

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job satisfaction, speech recognition, Steven Pinker, theory of mind, Turing test

., 15, 23, 56, 117 (n.40), 123 (n.59), 128 (n.80), 133 (n.100), 138 (n.114) 154 Index of Subjects abstraction, 138 (n.114); see also generalized learning procedures acquisition of concepts, 9–10, 20, 25–27, 30–32, 35, 38, 43 (n.15) of language, 10, 25–32, 35, 38, 40, 41 (n.2), 43 (n.15), 45, 55, 63, 94–103, 104, 112 (n.25), 119 (n.48) 124 (n.63), 133 (n.94), 135 (n.105) 136 (n.110), 138 (n.114) adequacy, see descriptive adequacy, explanatory adequacy analogy, 37–38, 58, 90, 104, 111 (n.21) 112 (n.22), 122 (n.53), 124 (n.61) animal, 13, 37, 51–59, 62, 64, 67, 70, 102, 107 (n.8), 107 (n.9), 110 (n.11), 110 (n.12), 110 (n.13), 110 (n.14), 111 (n.21), 112 (n.23), 116 (n.38), 117 (n.40), 120 (n.51) communication, 52, 55–57, 62, 70, 108 (n.8), 110 (n.14), 111 (n.21) 112 (n.23), 116 (n.38), 117 (n.40) appropriateness of language use, 11, 13–15, 19, 20–24, 42 (n.10), 52–54, 107 (n.8); see also creative aspect of language use art, 9, 32, 42 (n.9), 46, 61, 99, 114 (n.33), 114 (n.34) artistic creativity, see creativity association, 33, 55, 65, 85, 98, 138 (n.114); see also generalized learning procedures automaton, 13, 51–59, 62, 67, 107 (n.8), 108 (n.9), 110 (n.9), 111 (n.14), 111 (n.16), 120 (n.51), 124 (n.62) 140 (n.117) base rules, see rules behaviorism, 17, 23, 33–34, 40 (n.1), 110 (n.11), 133 (n.94); see also conditioning biological limits of human intelligence, see limits of human intelligence biological system, language as, 11–12, 18, 21–22, 24, 27–30, 35–39 biology, 22, 32, 66, 118 (n.46), 136 (n.110) boundlessness of language, 12–15, 18, 20, 37–38, 41 (n.4), 43 (n.10), 52, 59, 61–64, 107 (n.8), 112 (n.22); see also creative aspect of language use Cartesian linguistics, see linguistics case marking, 132 (n.88) system, 82 central processor hypothesis, 20, 24, 37 character of language (Humboldt), 68 common notions (Herbert of Cherbury), 32, 94–96 common sense, 12, 16, 18–19, 22–23 43 (n.18) comparative grammar, see grammar competence (opposed to performance), 19, 25–26, 45, 69, 105 (n.2), 108 (n.8) computer, 42 (n.4), 41 (n.10), 140 155 Cartesian Linguistics (n.123); see also automaton concept, 9–12, 18–40, 42 (n.14), 43 (n.15), 63, 68, 70, 72, 95, 101, 103, 116 (n.38), 126 (n.70); see also innate idea/concept/ machinery conceptual-intentional system, 21–22, 30, 36, 129 (n.80) conditioning, 10, 32–34, 38, 55, 58, 69, 97, 98, 104, 110 (n.11), 138 (n.114); see also general learning procedures, behaviorism, empiricism, standard social science model core grammar, see grammar creative aspect of language use, 8–25, 31–40, 42 (n.9), 43 (n.18), 51–72, 87, 90, 102, 104, 108 (n.9), 111 (n.18), 113 (n.29), 113 (n.30), 117 (n.44), 118 (n.45), 124 (n.61), 134 (n.100), 139 (n.115) creativity artistic, 12, 61, 65, 114 (n.34) ordinary, see creative aspect of language use scientific, 11, 44 (n.18) critical period hypothesis for language acquisition, 98 diversity of human language, 25–29, 119 (n.48), 125 (n.63) education, 9, 11, 18, 27, 39–40, 67, 139 (n.115) empiricism, 7, 9–11, 17, 31–39, 40, 41 (n.6), 86, 98, 101, 107 (n.7) 109 (n.9), 137 (n.110), 138 (n.114) empiricist linguistics, see linguistics, empiricism English, 29, 122 (n.53) evolution, 22, 42 (n.12), 107 (n.7) explanatory adequacy, 26–27, 42 (n.11) 135 (n.105) explanatory grammar, see grammar expression (sound-meaning pair), 12, 17–21, 24, 37 externalism, 10, 33 faculty, 9, 16, 17, 20–24, 32, 43 (n.14), 43 (n.15), 59–61, 95, 99, 100, 109 (n.9), 120 (n.51), 124 (n.62), 140 (n.117) folk science, 16. see also common sense form of language (Humboldt), 17, 62–69, 94, 117 (n.39), 117 (n.42) 117 (n.44), 118 (n.45) free will, 9, 15, 16, 18, 22, 23, 36, 66, 108 (n.9) free word order, 129 (n.82), 132 (n.88) freedom, social and political, 8, 11, 22, 23–24, 33, 39, 59, 66, 67, 120 (n.51) French, 29, 74, 78, 79, 82, 85, 86, 91 122 (n.53), 122 (n.54), 130 (n.83) 134 (n.101) functionalism, 23, 34, 39 deep structure, 29, 73–87, 88–93, 125 (n.67), 127 (n.70), 127 (n.73), 129 (n.80), 132 (n.93) derivation, 30, 83, 129 (n.80); see also transformational grammar Descartes’ problem, see creative aspect of language use descriptive adequacy, 27, 90, 42 (n.11) descriptive grammar, see grammar descriptivism, see linguistics general grammar, see grammar 156 Index of Subjects general linguistics, see linguistics generalization, 58, 69, 98, 104, 138 (n.114); see also generalized learning procedures generalized learning procedures, 10, 32–39, 98. see also conditioning, empiricism, standard social science model, behaviorism generative grammar, see grammar generative principles, 20, 63–66; see also rules German, 17, 33, 66, 116 (n.37), 126 (n.69) Germany, 66 government, see state grammar comparative, 122 (n.53) core, 105 (n.2) descriptive, 88, 91 explanatory, 91, 134 (n.103) general, 69, 88–89, 94, 105 (n.3), 114 (n.30), 122 (n.53), 123 (n.59), 125 (n.67), 130 (n.83), 133 (n.95), 134 (n.103), 138 (n.112) generative, 49, 68–70, 102, 105 (n.2), 106 (n.4), 117 (n.39), 135 (n.106) particular, 68, 88–89, 94, 133 (n.95), 134 (n.101) philosophical, 17, 89–92, 105 (n.3), 130 (n.83), 132 (n.94) 133 (n.95) Port-Royal, 17, 29, 33, 72–83, 86, 90, 91, 105 (n.3), 114 (n.30), 125 (n.67), 126 (n.69), 126 (n.70), 127 (n.72), 128 (n.75), 131 (n.85), 133 (n.94), 133 (n.95), 135 (n.106) speculative, 134 (n.101) transformational generative, 29, 77–87, 94, 127 (n.73), 128 (n.75), 135 (n.106) universal, 17, 19, 27–31, 72, 92, 104, 105 (n.3), 132 (n.88), 134 (n.100), 134 (n.103) grammatical transformation, 74–87 habit, language as, 10, 33, 58, 69, 104, 111 (n.21), 123 (n.56), 138 (n.114); see also conditioning and behaviorism Hebrew, 17 head-first language, 29 head-last language, 29 history of linguistics, see linguistics human nature, 11, 34, 40, 59, 66, 67, 102, 103, 120 (n.51) idea (Descartes), 126 (n.70) I-language, 19, 20, 25, 27 inflectional devices, 82 innate idea/concept/mental machinery, 9–12, 17–18, 24–39, 41 (n.5), 41–42 (n.9), 43–44 (n.14), 43 (n.18), 45, 59, 65, 94–101, 104, 113 (n.29), 138 (n.114), 135 (n.105), 138 (n.114), 139 (n.115) instinct, 10, 34, 36, 53, 58, 59, 60, 63, 94, 95, 96, 113 (n.29), 121 (n.51) intension, see semantics interface level, 19–20, 21–22, 30, 37, 129 (n.80); see also LF, SEM, phonetic form, phonetic interpretation internalism, 10, 33–34, 130 (n.83) 157 Cartesian Linguistics Japanese, 29 judgment, 32, 72–79, 100, 101 language faculty, 20–22, 59, 42 (n.14) 43 (n.15), 59; see also universal grammar language game (Wittgenstein), 117 (n.40) language, function of, 15, 52, 58, 62, 64, 72. langue (Saussure), 131 (n.85), 132 (n.89) Latin, 74, 78, 79, 81, 85, 121 (n.53), 130 (n.83), 131 (n.88) lexical item, 17, 19, 20, 22, 25, 26, 27, 43 (n.15), 90, 131 (n.85); see also concept lexicon, 63, 68 LF, 129 (n.80), 130 (n.83); see also logical form, SEM, semantic interface liberalism, 39 libertarianism, 39–40 limits of human intelligence, 8, 16, 20, 23, 25, 35, 37, 111 (n.18) linguistic universals, 27, 28, 29, 92, 94, 136 (n.108); see also innate idea, concept, machinery linguistics Cartesian, 49, 50, 62, 64, 69, 70, 72, 73, 87, 88, 90, 93, 94, 96, 97, 103, 104, 105 (n.3), 115 (n.36), 129 (n.80), 134 (n.101) descriptivism in, 90, 134 (n.101) 135 (n.105) empiricist, 41 (n.6) 106 (n.3); see also empiricism general, 62 history of, 49, 105 (n.1) minimalism in, 129 (n.80) modern, 45, 49, 57, 67–68, 90, 92, 98, 132 (n.92), 132 (n.93) pre-modern/traditional, 130 (n.83), 133 (n.94) prescriptivism in, 133 (n.96) principles and parameters in, 29, 136 (n.108); see also parameters standard theory of, 17, 29 structuralism in, 67, 68, 90, 106 (n.3) taxonomic, 106 (n.3) logical form, 82, 129 (n.80); see also LF, SEM machine, see automaton meaning, see semantics mechanism and mechanical explanation, 8, 12–13, 15–16, 51–55, 65, 106 (n.3), 106 (n.5), 107 (n.8) 120 (n.51), 124 (n.62) mechanical form (Romantics), 65, 118 (n.45) methodological dualism, 32 mind-body distinction, 15–16, 28, 73 problem, 16, 28 Miskito, 29 modularity of the mind, 19–24, 35, 36 37, 110 (n.11) morality, 39, 101, 137 (n.111) morpheme, 92, 127 (n.73) morphogenesis, 42 (n.12), 107 (n.7) mystery, 11, 25, 124 (n. 61), see also limits of human intelligence, creative aspect of language use nativism, 10, 24–34, 94–101, 104; see also innate idea/concept/mental machinery natural history, 93 natural philosophy, see philosophy 158 Index of Subjects natural rights, 66, 120 (n.51) neoliberalism, 39 organic form (Romantics), 65, 68, 117 (n.44), 118 (n.45), 118 (n.46) other minds, problem of, 53–54, 106 (n.5) output level, see interface level parameters, 20, 27, 29–30, 136 (n.108). see also principles and parameters parole (Saussure), 131 (n.85), 132 (n.89) particular grammar, see grammar perception, 22, 31–32, 46, 63, 66, 72, 84, 85, 86, 93, 98–103, 104, 105 (n.2), 105 (n.3), 116 (n.38), 141 (n.123) performance, opposed to competence, 15, 21, 24, 55, 69, 104, 105 (n.2) perspectives (provided by language faculty), 15, 21, 22, 37 philosophical grammar, see grammar philosophy, 33, 68, 93, 41 (n.6), 106 (n.3), 106 (n.4), 118 (n.46), 123 (n.59), 128 (n.80), 133 (n.95), 139 (n.114), 139 (n.115), 140 (n.120) of language, 62, 115 (n.35) of mind, 31, 46, 98, 105 (n.3) phoneme, see phonetics phonetic form/interpretation, see phonetics and surface structure phonetics, 92, 136 (n.108) phonetic form, 88 phonetic interpretation, 29, 73, 77 phonology, 28, 46, 43 (n.15), 121 (n.52), 141 (n.124) phonological system, 68 phrase structure, 80, 127 (n.73); see also deep structure physics, 51 physiology, 51 plasticity of the mind, 32–36, 40, 113 (n.29), 115 (n.34) Platonism, 33, 96, 124 (n.61), 136 (n.110), 140 (n.120) Plato’s problem, see poverty of stimulus facts poetry, 46, 61, 68, 41 (n.9), 114 (n.33) 114 (n.34), 139 (n.115) politics, see state Port-Royal grammar, see grammar postmodernism, 32 poverty of stimulus facts, 24–28, 30–31, 35, 38, 39, 43 (n.16), 98, 135 (n.105) premodern linguistics, see linguistics prescriptivism, see linguistics principles and parameters, see linguistics prototype, 119 (n.47) psychological reality, 117 (n.41) psychology, 45, 46, 51, 53, 96, 106 (n.3), 106 (n.4), 111 (n.19) psychological explanation, 111 (n.19) rationalism, 9, 10, 15, 30–33, 36–40, 41 (n.6), 41 (n.7), 50, 94, 97–99, 105 (n.3), 137 (n.110), 140 (n.119) rationality, 59, 60 recursion, 80 reference, see semantics reminiscence theory (of Plato), 26–27, 137 (n.111) representation, 21, 63, 80, 85, 103, 42 (n.14), 127 (n.73), 129 (n.80), 130 (n.83) romanticism, 9–11, 17–18, 24, 27, 30– 33, 38–40, 41 (n.7), 50, 60–71, 97, 101, 105 (n.3), 115 (n.35) 124 159 Cartesian Linguistics (n.61), 125 (n.63), 140 (n.120) rules base, 80 transformational, 75, 78–80 schemata, 103 science, 91, 133 (n.100), 134 (n.103) of behavior, 8, 16, 20, 22–23, 24, 25, 35 of language, 11–12, 15–18, 20–21, 26, 38–39, 134 (n.100); see also grammar and linguistics of mind, 11, 16–17, 21, 24, 25–26, 42 (n.11) scientific creativity, see creativity second language acquisition, 124 (n.63), 138 (n.114) semantics intension, 127 (n.70) meaning, 20, 43 (n.15), 75, 77, 79, 84, 86, 90, 91, 127 (n.70), 130 (n.83), 131 (n.85) reference, 75, 91 SEM, 129 (n.80) semantic content, 75, 88, 92, 103 129 (n.80) semantic interface, 21, 22, 30, 129 (n.80) semantic interpretation, 29, 73 ,77 127 (n.70), 129 (n.80) semantic representation, 130 (n.83) signification, 123 (n.57) sign language, 21, 25, 46 simplicity, 82, 42 (n.11) species specificity of traits, 19, 32, 45, 51, 52, 116 (n.38), 120 (n.51) speech organs, 52, 53, 56, 58, 62, 84 standard social science model, 34–39 standard theory of linguistics, see linguistics state (political), 8, 11, 17, 39–40, 66, 67, 121 (n.51), 139 (n.115) stimulus freedom, 13–15, 18–19, 23–24, 41 (n.5), 42 (n.10), 52, 58–61, 107 (n.8), 113 (n.29), 113 (n.30); see also creative aspect of language use structuralism, see linguistics surface structure, 73–80, 84–87, 88–92, 127 (n.72), 129 (n.80), 131 (n.86), 132 (n.92), 132 (n.93), 133 (n.94). see also phonetic form, phonetic interpretation syntactic structure, see deep structure syntactic theory, see syntax syntax, 28, 46, 68, 69, 72, 80–85, 93, 132 (n.93), 141 (n.124) taxonomic linguistics, see linguistics thematic assignment, 129 (n.80) theory of language, 17–18, 49, 80, 43 (n.15), 119 (n.48), 125 (n.67), 133 (n.100); see also grammar, linguistics, science of language theory of mind, 50, 94, 104; see also science of mind training, language acquisition as, see conditioning, standard social science model, general learning procedures transformational rules, see rules transformational generative grammar, see grammar translation, 125 (n.63) trigger/triggering 10, 12, 25, 27–29, 31, 33, 38, 96. Turing test, 13–14 ; see also computer. universal grammar (UG), see grammar Urform (Goethe), 66. 160 Index of Subjects use of language, 10, 15, 18, 45, 94, 132 (n.89), 133 (n.94), 133 (n.100) vision, theory of, 18, 19–20, 24, 30, 33, 35, 42 (n.14) will, see free will. 161


On Nature and Language by Noam Chomsky

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Alfred Russel Wallace, anti-communist, Berlin Wall, Bretton Woods, complexity theory, dark matter, Fall of the Berlin Wall, Murray Gell-Mann, Steven Pinker, theory of mind, Turing test

The most striking example for the Cartesians was the normal use of language: humans can express their thoughts in novel and limitless ways that are constrained by bodily state but not determined by it, appropriate to situations but not caused by them, and that evoke in others thoughts that they could have expressed in similar ways – what we may call “the creative aspect of language use.” It is worth bearing in mind that these conclusions are correct, as far as we know. 66 Language and the brain In these terms, Cartesian scientists developed experimental procedures to determine whether some other creature has a mind like ours – elaborate versions of what has been revived as the Turing test in the past half century, though without some crucial fallacies that have attended this revival, disregarding Turing’s explicit warnings, an interesting topic that I will put aside.6 In the same terms, Descartes could formulate a relatively clear mind–body problem: having established two principles of nature, the mechanical and mental principles, we can ask how they interact, a major problem for seventeenth-century science.


pages: 685 words: 203,949

The Organized Mind: Thinking Straight in the Age of Information Overload by Daniel J. Levitin

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airport security, Albert Einstein, Amazon Mechanical Turk, Anton Chekhov, big-box store, business process, call centre, Claude Shannon: information theory, cloud computing, cognitive bias, complexity theory, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, delayed gratification, Donald Trump, en.wikipedia.org, epigenetics, Eratosthenes, Exxon Valdez, framing effect, friendly fire, fundamental attribution error, Golden Gate Park, Google Glasses, haute cuisine, impulse control, index card, indoor plumbing, information retrieval, invention of writing, iterative process, jimmy wales, job satisfaction, Kickstarter, life extension, meta analysis, meta-analysis, more computing power than Apollo, Network effects, new economy, Nicholas Carr, optical character recognition, pattern recognition, phenotype, placebo effect, pre–internet, profit motive, randomized controlled trial, Skype, Snapchat, statistical model, Steve Jobs, supply-chain management, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Turing test, ultimatum game

One of the most common applications of crowdsourcing is hidden behind the scenes: reCAPTCHAs. These are the distorted words that are often displayed on websites. Their purpose is to prevent computers, or “bots,” from gaining access to secure websites, because such problems are difficult to solve for computers and usually not too difficult for humans. (CAPTCHA is an acronym for Completely Automated Public Turing test to tell Computers and Humans Apart. reCAPTCHAs are so-named for recycling—because they recycle human processing power.) reCAPTCHAs act as sentries against automated programs that attempt to infiltrate websites to steal e-mail addresses and passwords, or just to exploit weaknesses (for example, computer programs that might buy large numbers of concert tickets and then attempt to sell them at inflated prices).

., 339 Church, Alexander Hamilton, 270 Cialdini, Robert, 429n153 Cicero, 340 circadian rhythms, 193–94 Citizendium, 472n335 Claiborne, Liz, 274 “clearing the mind,” 68 cloud storage, 322–23 cognitive blind spots, 11–12 cognitive efficiency, 56–57, 110 cognitive flexibility, 307 cognitive illusions and gambling, 226 illusory correlation, 253–56 and multitasking, 96, 306, 319 and procrastination, 200 and social relations, 144–49, 152 and time perception, 162 and visual illusions, 21, 21–22 cognitive overload. See information overload cognitive processing, 183–95 cognitive science, 22–32, 228 collaborative filtering, 117 color perception, 30–31, 162 command structures, 272–76 Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA), 118 complexity, 120–35, 209, 220–32, 315 concentration, 41, 293–94. See also attention conformity, 157–59 consumer decision-making, 20, 310, 311 Consumer Reports, 20, 116 contact files, 122–23 contingencies, 231, 232, 286, 319–26 controlled experimentation, 345–47, 348 Cook, Perry R., 323, 324–25 cooperative behavior, 135–36 coronary bypass surgery, 259 corporate structures, 271–76, 464n283 correlation, 60, 347–51, 348 cost-benefits analysis, 5, 212–13 Coulter, Ann, 340 covariation, 347–48, 348 creativity and aging, 217–18 and attention, 38 and central executive function, 202, 210, 375–76 and flow state, 203–8, 209 and focus, 171 and organizational systems, 304 and serendipity, 376, 378, 380–81 and time management, 170–71, 202–15 critical thinking, 336, 341, 343, 352, 478n352 crowdsourcing, 114–17, 133, 333 Csikszentmihalyi, Mihaly, 203, 206, 400n7 Cuban, Mark, 292 Cuban Missile Crisis, 155, 366 Curie, Marie, 283 Dali, Salvador, 375 Darley, John, 157–58, 159 data compression, 311–12, 314 data losses, 321–26 Dawkins, Richard, 26–27 daydreaming mode and attention, 38–39 and creativity, 202, 217, 375–76, 380 and free association, 364–65 and online dating, 132 and organizational systems, 304 and reading fiction, 367 and social relations, 152 and time organization, 169, 170 decisions, 73, 98, 100, 132, 218, 220–32, 276–83, 310–11, 423n132 Deepwater Horizon oil rig disaster, 134 defining features, 65–66 delayed gratification, 166, 197 De Morgan, Augustus, 377 Dennett, Daniel, 45 denominator neglect, 255–56 Descartes, René, 14–15 designated places, 83, 83–86, 88 De Waal, Frans, 282–83 Dewey Decimal System, 296, 378 dietary supplements, 253–54, 255, 258, 260 diffusion of responsibility, 157–59 digital storage, 91–106 diphtheria, 250 disciplined initiative, 286 disk failures, 321 dispositional explanations, 145–46 distraction, 198, 209–10 distributed processing, xxi, 303 division of labor, 269 divorce rates, 133, 261–62 document organization, 293–306, 413n95.


pages: 743 words: 201,651

Free Speech: Ten Principles for a Connected World by Timothy Garton Ash

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A Declaration of the Independence of Cyberspace, Affordable Care Act / Obamacare, Andrew Keen, Apple II, Ayatollah Khomeini, battle of ideas, Berlin Wall, bitcoin, British Empire, Cass Sunstein, Chelsea Manning, citizen journalism, Clapham omnibus, colonial rule, crowdsourcing, David Attenborough, don't be evil, Edward Snowden, Etonian, European colonialism, eurozone crisis, failed state, Fall of the Berlin Wall, Ferguson, Missouri, Filter Bubble, financial independence, Firefox, Galaxy Zoo, global village, index card, Internet Archive, invention of movable type, invention of writing, Jaron Lanier, jimmy wales, Julian Assange, Mark Zuckerberg, Marshall McLuhan, megacity, mutually assured destruction, national security letter, Netflix Prize, Nicholas Carr, obamacare, Peace of Westphalia, Peter Thiel, pre–internet, profit motive, RAND corporation, Ray Kurzweil, Ronald Reagan, semantic web, Silicon Valley, Simon Singh, Snapchat, social graph, Stephen Hawking, Steve Jobs, Steve Wozniak, The Death and Life of Great American Cities, The Wisdom of Crowds, Turing test, We are Anonymous. We are Legion, WikiLeaks, World Values Survey, Yom Kippur War

Already in the 1960s, the computer scientist Joseph Weizenbaum developed a computer programme named Eliza, after Eliza Doolittle in George Bernard Shaw’s Pygmalion—better known as Julie Andrews in ‘My Fair Lady’.31 Eliza was capable of having rudimentary conversations with people, of a vacuously sympathetic kind (‘I am sorry to hear you are depressed’). More recently, a chatbot called Eugene Goostman was alleged by its developers to have passed the Turing test—can you tell if you are talking to a human or machine?—although that claim was soon disputed.32 Many Chinese have apparently found comfort in talking to a purportedly female Microsoft chatbot called Xiaoice.33 Leading scientists have argued that artificial intelligence may be upon us sooner than we think and that we should address the issue seriously.34 Since, however, there is still a little while to go until that singular moment, this book is concerned only with human speech—not that attributed to other animals or to machines.

, Free Speech Debate, http://freespeechdebate.com/en/2012/12/free-but-not-able/. The minimal definition of literacy was established in 1958. Note that the UN estimate includes some 775 million adults and 122 million illiterate youth 29. Shteyngart 2010 30. Kurzweil 2005 31. Weizenbaum 1984 and Carr 2010, 201–8 32. Ian Sample and Alex Hern, ‘Scientists Dispute Whether Computer ‘Eugene Goostman’ Passed Turing Test’, The Guardian, 9 June 2014, http://perma.cc/9YMC-LJW7 33. John Markoff et al., ‘For Sympathetic Ear, More Chinese Turn to Smartphone Program’, New York Times, 31 July 2015, http://www.nytimes.com/2015/08/04/science/for-sympathetic-ear-more-chinese-turn-to-smartphone-program.html 34. see Future of Life Institute, ‘Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter’, http://perma.cc/ZD2A-DP7E, and Martin Rees, ‘Cheer Up, the Post-Human Era Is Dawning’, Financial Times, 10 July 2015, http://www.ft.com/cms/s/0/4fe10870-20c2-11e5-ab0f-6bb9974f25d0.html#axzz3qv6zRoSp 35. this is essentially also the conclusion of Wu 2013 36. on the often neglected subject of touch, see Linden 2015 37.


pages: 791 words: 85,159

Social Life of Information by John Seely Brown, Paul Duguid

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AltaVista, business process, Claude Shannon: information theory, computer age, cross-subsidies, disintermediation, double entry bookkeeping, Frank Gehry, frictionless, frictionless market, future of work, George Gilder, global village, Howard Rheingold, informal economy, information retrieval, invisible hand, Isaac Newton, Just-in-time delivery, Kevin Kelly, knowledge economy, knowledge worker, loose coupling, Marshall McLuhan, medical malpractice, moral hazard, Network effects, new economy, Productivity paradox, rolodex, Ronald Coase, shareholder value, Silicon Valley, Steve Jobs, Superbowl ad, Ted Nelson, telepresence, the medium is the message, The Nature of the Firm, The Wealth of Nations by Adam Smith, Thomas Malthus, transaction costs, Turing test, Vannevar Bush, Y2K

The spread of the telephone was more dramatic, growing from 14 miles in 1845 in the United Page 259 States to 670,000 in 1886, but the infrastructure of the telephone was far easier to build than railway lines. The spread of the radio was more impressive yet. 44. See Campbell-Kelly and Aspray, 1996. 45. Wellman (1988) provides one of the few worthwhile studies of the effects of information technologies on social communities and networks. Chapter 2: Agents and Angels 1. Distinguishing a computer from a human is the essence of the famous Turing test, developed by mathematician Alan Turing (1963). He argued that if you couldn't tell the difference then you could say the machine was intelligent. Shallow Red is not quite there yet. Indeed, the continuation of the exchange suggests that Shallow Red is still rather shallow (though pleasantly honest): What are VSRs? The botmaster has not provided me with a definition of ''VSRs. " Thank you for asking.


pages: 311 words: 94,732

The Rapture of the Nerds by Cory Doctorow, Charles Stross

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3D printing, Ayatollah Khomeini, butterfly effect, cognitive dissonance, combinatorial explosion, complexity theory, Credit Default Swap, dematerialisation, Drosophila, epigenetics, Extropian, gravity well, greed is good, haute couture, hive mind, margin call, phenotype, Plutocrats, plutocrats, rent-seeking, Richard Feynman, Richard Feynman, telepresence, Turing machine, Turing test, union organizing

Whether or not sim-Huw is really Huw, whether or not uploading is a kind of death, whether or not posthumanity is immortal or just kidding itself, the single, inviolable fact remains: Human simspace is no more tasteful than the architectural train wreck that the Galactic Authority has erected. The people who live in it have all the aesthetic sense of a senile jackdaw. Huw is prepared to accept—for the sake of argument, mind—that uploading leaves your soul intact, but she is never going give one nanometer on the question of whether uploading leaves your taste intact. If the Turing test measured an AI’s capacity to conduct itself with a sense of real style, all of simspace would be revealed for a machine-sham. Give humanity a truly unlimited field, and it would fill it with Happy Meal toys and holographic, sport-star, collectible trading card game art. There’s a whole gang of dirtside refuseniks who make this their primary objection to transcendence. They’re severe Bauhaus cosplayers, so immaculately and plainly turned out that they look more like illustrations than humans.


pages: 398 words: 108,889

The Paypal Wars: Battles With Ebay, the Media, the Mafia, and the Rest of Planet Earth by Eric M. Jackson

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bank run, business process, call centre, disintermediation, Elon Musk, index fund, Internet Archive, iterative process, Joseph Schumpeter, market design, Menlo Park, moral hazard, Network effects, new economy, offshore financial centre, Peter Thiel, Sand Hill Road, shareholder value, Silicon Valley, Silicon Valley startup, telemarketer, The Chicago School, Turing test

The duo placed an image on the sign-up page that contained a random sequence of black letters on top of a yellow background crisscrossed by thin black lines. The person opening the account was asked to read the image and type the letters into a nearby text box. While the human eye could easily interpret the random string of letters contained in the image, the slight distortion caused by the background prevented even the most sophisticated computer from doing the same. Max would later refer to it as a “reverse Turing test,” a way to discern a human being opening an account from a computer. Using an automated script to churn out hundreds of fraudulent PayPal accounts linked to stolen credit cards was now effectively impossible. The Gausebeck-Levchin test, as this addition to the sign-up process became known around the office, proved successful in combating fraud without slowing down sign-ups. And not just for PayPal.


pages: 323 words: 95,939

Present Shock: When Everything Happens Now by Douglas Rushkoff

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algorithmic trading, Andrew Keen, bank run, Benoit Mandelbrot, big-box store, Black Swan, British Empire, Buckminster Fuller, cashless society, citizen journalism, clockwork universe, cognitive dissonance, Credit Default Swap, crowdsourcing, Danny Hillis, disintermediation, Donald Trump, double helix, East Village, Elliott wave, European colonialism, Extropian, facts on the ground, Flash crash, game design, global supply chain, global village, Howard Rheingold, hypertext link, Inbox Zero, invention of agriculture, invention of hypertext, invisible hand, iterative process, John Nash: game theory, Kevin Kelly, laissez-faire capitalism, Law of Accelerating Returns, loss aversion, mandelbrot fractal, Marshall McLuhan, Merlin Mann, Milgram experiment, mutually assured destruction, Network effects, New Urbanism, Nicholas Carr, Norbert Wiener, Occupy movement, passive investing, pattern recognition, peak oil, price mechanism, prisoner's dilemma, Ralph Nelson Elliott, RAND corporation, Ray Kurzweil, recommendation engine, Silicon Valley, Skype, social graph, South Sea Bubble, Steve Jobs, Steve Wozniak, Steven Pinker, Stewart Brand, supply-chain management, the medium is the message, The Wisdom of Crowds, theory of mind, Turing test, upwardly mobile, Whole Earth Catalog, WikiLeaks, Y2K

The antithesis of the Law of Diminishing Returns, the Law of Accelerating Returns holds that technology will overtake humanity and nature, no matter what. In his numerous books, talks, and television appearances, Kurzweil remains unswerving in his conviction that humanity was just a temporary step in technology’s inevitable development. It’s not all bad. According to Kurzweil, by 2029 artificial intelligences will pass the Turing test and be able to fool us into thinking they are real people. By the 2030s, virtual-reality simulations will be “as real and compelling as ‘real’ reality, and we’ll be doing it from within the nervous system. So the nanobots in your brain—which will get to your brain through the bloodstream, noninvasively and without surgery—will shut down the signals coming from your real senses and replace them with senses that your brain will be receiving from the virtual environment.”2 Just be sure to read the fine print in the iTunes agreement before clicking “I agree” and hope that the terms don’t change while you’re in there.


pages: 366 words: 94,209

Throwing Rocks at the Google Bus: How Growth Became the Enemy of Prosperity by Douglas Rushkoff

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3D printing, Airbnb, algorithmic trading, Amazon Mechanical Turk, Andrew Keen, bank run, banking crisis, barriers to entry, bitcoin, blockchain, Burning Man, business process, buy low sell high, California gold rush, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, centralized clearinghouse, citizen journalism, clean water, cloud computing, collaborative economy, collective bargaining, colonial exploitation, Community Supported Agriculture, corporate personhood, crowdsourcing, cryptocurrency, disintermediation, diversified portfolio, Elon Musk, Erik Brynjolfsson, ethereum blockchain, fiat currency, Firefox, Flash crash, full employment, future of work, gig economy, Gini coefficient, global supply chain, global village, Google bus, Howard Rheingold, IBM and the Holocaust, impulse control, income inequality, index fund, iterative process, Jaron Lanier, Jeff Bezos, jimmy wales, job automation, Joseph Schumpeter, Kickstarter, loss aversion, Lyft, Mark Zuckerberg, market bubble, market fundamentalism, Marshall McLuhan, means of production, medical bankruptcy, minimum viable product, Naomi Klein, Network effects, new economy, Norbert Wiener, Oculus Rift, passive investing, payday loans, peer-to-peer lending, Peter Thiel, post-industrial society, profit motive, quantitative easing, race to the bottom, recommendation engine, reserve currency, RFID, Richard Stallman, ride hailing / ride sharing, Ronald Reagan, Satoshi Nakamoto, Second Machine Age, shareholder value, sharing economy, Silicon Valley, Snapchat, social graph, software patent, Steve Jobs, TaskRabbit, trade route, transportation-network company, Turing test, Uber and Lyft, Uber for X, unpaid internship, Y Combinator, young professional, Zipcar

Other companies have opted to become what are known as “flexible purpose” corporations, which allows them to emphasize pretty much any priority over profits—it doesn’t even have to be explicitly beneficial to society at large.74 Flexible purpose corporations also enjoy looser reporting standards than do benefit corporations.75 Vicarious, a tech startup based in the Bay Area, is the sort of business for which the flex corp structure works well. Vicarious operates in the field of artificial intelligence and deep learning; its most celebrated project to date is an attempt to crack CAPTCHAs (those annoying tests of whether a user is human) using AI. Vicarious claims to have succeeded, and its first Turing test demonstrations appear to back up its claim.76 How would such a technology be deployed or monetized? Vicarious doesn’t need to worry about that just yet. As a flexible purpose corporation, Vicarious can work with the long-term, big picture, experimental approach required to innovate in a still-emerging field such as AI. Although investors including Mark Zuckerberg and Peter Thiel have invested $56 million in the company, the flexible purpose structure prevents them from exerting the sort of pressure to get to market that venture capitalists typically put on their investments.


pages: 330 words: 91,805

Peers Inc: How People and Platforms Are Inventing the Collaborative Economy and Reinventing Capitalism by Robin Chase

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3D printing, Airbnb, Amazon Web Services, Andy Kessler, banking crisis, barriers to entry, bitcoin, blockchain, Burning Man, business climate, call centre, car-free, cloud computing, collaborative consumption, collaborative economy, collective bargaining, congestion charging, crowdsourcing, cryptocurrency, decarbonisation, don't be evil, Elon Musk, en.wikipedia.org, ethereum blockchain, Ferguson, Missouri, Firefox, frictionless, Gini coefficient, hive mind, income inequality, index fund, informal economy, Internet of things, Jane Jacobs, Jeff Bezos, jimmy wales, job satisfaction, Kickstarter, Lean Startup, Lyft, means of production, megacity, Minecraft, minimum viable product, Network effects, new economy, Oculus Rift, openstreetmap, optical character recognition, pattern recognition, peer-to-peer lending, Richard Stallman, ride hailing / ride sharing, Ronald Coase, Ronald Reagan, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, self-driving car, shareholder value, sharing economy, Silicon Valley, six sigma, Skype, smart cities, smart grid, Snapchat, sovereign wealth fund, Steve Crocker, Steve Jobs, Steven Levy, TaskRabbit, The Death and Life of Great American Cities, The Nature of the Firm, transaction costs, Turing test, Uber and Lyft, Zipcar

The solution that they popularized is one that we all know well: those annoying little boxes of warped and scrambled numbers and letters that appear on our computer screen, requiring us to transcribe them before we can do certain things—send an email, make a comment, or sign up for something. That little test is one way of proving your humanness. In 2000, von Ahn’s team coined the term CAPTCHA (for “completely automated public Turing test to tell computers and humans apart”) for this tool, and soon the tool was being widely used. Von Ahn would tell you that by 2005, “approximately 200 million CAPTCHAs [were] typed every day around the world.” He could have rested on his laurels with that remarkable adoption of his innovation. But, being an engineer, von Ahn made some additional calculations. “It takes about 10 seconds to type a CAPTCHA,” von Ahn said.


pages: 303 words: 67,891

Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the Agi Workshop 2006 by Ben Goertzel, Pei Wang

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AI winter, artificial general intelligence, bioinformatics, brain emulation, combinatorial explosion, complexity theory, computer vision, conceptual framework, correlation coefficient, epigenetics, friendly AI, information retrieval, Isaac Newton, John Conway, Loebner Prize, Menlo Park, natural language processing, Occam's razor, p-value, pattern recognition, performance metric, Ray Kurzweil, Rodney Brooks, semantic web, statistical model, strong AI, theory of mind, traveling salesman, Turing machine, Turing test, Von Neumann architecture, Y2K

However, due to the inevitable difference in experience, the system cannot always be able to use a natural language as a native speaker. Even so, its proficiency in that language should be sufficient for many practical purposes. Being able to use any natural language is not a necessary condition for being intelligent. Since the aim of NARS is not to accurately duplicate human behaviors so as to pass the Turing Test [5], natural language processing is optional for the system. 3.3 Education NARS processes tasks using available knowledge, though the system is not designed with a ready-made knowledge base as a necessary part. Instead, all the knowledge, in principle, should come from the system’s experience. In other words, NARS as designed is like a baby that has great potential, but little instinct. P. Wang / From NARS to a Thinking Machine 85 For the system to serve any practical purpose, extensive education, or training, is needed, which means to build a proper internal knowledge base (or call it belief network, long-term memory, etc.) by feeding the system with certain (initial) experience.


pages: 489 words: 148,885

Accelerando by Stross, Charles

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call centre, carbon-based life, cellular automata, cognitive dissonance, Conway's Game of Life, dark matter, dumpster diving, Extropian, finite state, Flynn Effect, glass ceiling, gravity well, John von Neumann, knapsack problem, Kuiper Belt, Magellanic Cloud, mandelbrot fractal, market bubble, means of production, packet switching, performance metric, phenotype, planetary scale, Pluto: dwarf planet, reversible computing, Richard Stallman, SETI@home, Silicon Valley, Singularitarianism, slashdot, South China Sea, stem cell, technological singularity, telepresence, The Chicago School, theory of mind, Turing complete, Turing machine, Turing test, upwardly mobile, Vernor Vinge, Von Neumann architecture, web of trust, Y2K

"More of the same all round!" At the next table, a person with makeup and long hair who's wearing a dress – Manfred doesn't want to speculate about the gender of these crazy mixed-up Euros – is reminiscing about wiring the fleshpots of Tehran for cybersex. Two collegiate-looking dudes are arguing intensely in German: The translation stream in his glasses tell him they're arguing over whether the Turing Test is a Jim Crow law that violates European corpus juris standards on human rights. The beer arrives, and Bob slides the wrong one across to Manfred: "Here, try this. You'll like it." "Okay." It's some kind of smoked doppelbock, chock-full of yummy superoxides: Just inhaling over it makes Manfred feel like there's a fire alarm in his nose screaming danger, Will Robinson! Cancer! Cancer!. "Yeah, right.


pages: 570 words: 115,722

The Tangled Web: A Guide to Securing Modern Web Applications by Michal Zalewski

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barriers to entry, business process, defense in depth, easy for humans, difficult for computers, fault tolerance, finite state, Firefox, Google Chrome, information retrieval, RFC: Request For Comment, semantic web, Steve Jobs, telemarketer, Turing test, Vannevar Bush, web application, WebRTC, WebSocket

Case in point: Collin Jackson and several other researchers proposed a simple scheme that involved presenting a faux CAPTCHA[58] consisting of seven-segment, LCD-like digits.[215] Rather than being an actual, working challenge, the number the user would see depended on the :visited-based styling applied to superimposed links (see Figure 11-5); by typing that number back onto the page, the user would unwittingly tell the author of the site what exact styling had been applied and, therefore, what sites appeared in the victim’s browsing history. Figure 11-5. A fake seven-segment display can be used to read back link styling when the displayed number is entered into the browser in an attempt to solve a CAPTCHA. The user will see 5, 6, 9, or 8, depending on prior browsing history. * * * [58] CAPTCHA (sometimes expanded as Completely Automated Public Turing test to tell Computers and Humans Apart) is a term for a security challenge that is believed to be difficult to solve using computer algorithms but that should be easy for a human being. It is usually implemented by showing an image of several randomly selected, heavily distorted characters and asking the user to type them back. CAPTCHA may be used to discourage the automation of certain tasks, such as opening new accounts or sending significant volumes of email.


pages: 566 words: 122,184

Code: The Hidden Language of Computer Hardware and Software by Charles Petzold

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Bill Gates: Altair 8800, Claude Shannon: information theory, computer age, Douglas Engelbart, Dynabook, Eratosthenes, Grace Hopper, invention of the telegraph, Isaac Newton, Jacquard loom, Jacquard loom, James Watt: steam engine, John von Neumann, Joseph-Marie Jacquard, Louis Daguerre, millennium bug, Norbert Wiener, optical character recognition, popular electronics, Richard Feynman, Richard Feynman, Richard Stallman, Silicon Valley, Steve Jobs, Turing machine, Turing test, Vannevar Bush, Von Neumann architecture

Turing (1912–1954), who is most famous these days for writing two influential papers. The first, published in 1937, pioneered the concept of "computability," which is an analysis of what computers can and can't do. He conceived of an abstract model of a computer that's now known as the Turing Machine. The second famous paper Turing wrote was on the subject of artificial intelligence. He introduced a test for machine intelligence that's now known as the Turing Test. At the Moore School of Electrical Engineering (University of Pennsylvania), J. Presper Eckert (1919–1995) and John Mauchly (1907–1980) designed the ENIAC (Electronic Numerical Integrator and Computer). It used 18,000 vacuum tubes and was completed in late 1945. In sheer tonnage (about 30), the ENIAC was the largest computer that was ever (and probably will ever be) made. By 1977, you could buy a faster computer at Radio Shack.


pages: 527 words: 147,690

Terms of Service: Social Media and the Price of Constant Connection by Jacob Silverman

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23andMe, 4chan, A Declaration of the Independence of Cyberspace, Airbnb, airport security, Amazon Mechanical Turk, augmented reality, Brian Krebs, California gold rush, call centre, cloud computing, cognitive dissonance, correlation does not imply causation, Credit Default Swap, crowdsourcing, don't be evil, Edward Snowden, feminist movement, Filter Bubble, Firefox, Flash crash, game design, global village, Google Chrome, Google Glasses, hive mind, income inequality, informal economy, information retrieval, Internet of things, Jaron Lanier, jimmy wales, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, late capitalism, license plate recognition, life extension, Lyft, Mark Zuckerberg, Mars Rover, Marshall McLuhan, meta analysis, meta-analysis, Minecraft, move fast and break things, national security letter, Network effects, new economy, Nicholas Carr, Occupy movement, optical character recognition, payday loans, Peter Thiel, postindustrial economy, prediction markets, pre–internet, price discrimination, price stability, profit motive, quantitative hedge fund, race to the bottom, Ray Kurzweil, recommendation engine, rent control, RFID, ride hailing / ride sharing, self-driving car, sentiment analysis, shareholder value, sharing economy, Silicon Valley, Silicon Valley ideology, Snapchat, social graph, social web, sorting algorithm, Steve Ballmer, Steve Jobs, Steven Levy, TaskRabbit, technoutopianism, telemarketer, transportation-network company, Turing test, Uber and Lyft, Uber for X, universal basic income, unpaid internship, women in the workforce, Y Combinator, Zipcar

On many customer service lines, we already use our voices to navigate menus, and some telemarketing operations have advanced this practice, using robots to give a sales pitch before transferring the customer to a human sales associate. In recent years, apps that mimic your Twitter or Facebook posts, often in vaguely accurate but also amusingly bizarre ways, have become an Internet phenomenon. It’s the Turing test as entertainment. Soon, one might choose a Google bot that promises verisimilitude or one of these more ham-fisted creations that would entertain you and your friends with a funhouse-mirror version of your online persona. In the eyes of the platform owner, the difference is likely to be immaterial: ads are still being shown, data will be created. Given the currencies of digital life—data, attention, ad impressions, likes—bots may prove the more reliable moneymakers.


pages: 515 words: 126,820

Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World by Don Tapscott, Alex Tapscott

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Airbnb, altcoin, asset-backed security, autonomous vehicles, barriers to entry, bitcoin, blockchain, Bretton Woods, business process, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, clean water, cloud computing, cognitive dissonance, corporate governance, corporate social responsibility, Credit Default Swap, crowdsourcing, cryptocurrency, disintermediation, distributed ledger, Donald Trump, double entry bookkeeping, Edward Snowden, Elon Musk, Erik Brynjolfsson, ethereum blockchain, failed state, fiat currency, financial innovation, Firefox, first square of the chessboard, first square of the chessboard / second half of the chessboard, future of work, Galaxy Zoo, George Gilder, glass ceiling, Google bus, Hernando de Soto, income inequality, informal economy, interest rate swap, Internet of things, Jeff Bezos, jimmy wales, Kickstarter, knowledge worker, Kodak vs Instagram, Lean Startup, litecoin, Lyft, M-Pesa, Mark Zuckerberg, Marshall McLuhan, means of production, microcredit, mobile money, Network effects, new economy, Oculus Rift, pattern recognition, peer-to-peer lending, performance metric, Peter Thiel, planetary scale, Ponzi scheme, prediction markets, price mechanism, Productivity paradox, quantitative easing, ransomware, Ray Kurzweil, renewable energy credits, rent-seeking, ride hailing / ride sharing, Ronald Coase, Ronald Reagan, Satoshi Nakamoto, Second Machine Age, seigniorage, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, smart grid, social graph, social software, Stephen Hawking, Steve Jobs, Steve Wozniak, Stewart Brand, supply-chain management, TaskRabbit, The Fortune at the Bottom of the Pyramid, The Nature of the Firm, The Wisdom of Crowds, transaction costs, Turing complete, Turing test, Uber and Lyft, unbanked and underbanked, underbanked, unorthodox policies, X Prize, Y2K, Zipcar

Interview with Robin Chase, September 2, 2015. 36. https://news.ycombinator.com/item?id=9437095. 37. This scenario was originally explained by Don Tapscott in “The Transparent Burger,” Wired, March 2004; http://archive.wired.com/wired/archive/12.03/start.html?pg=2%3ftw=wn_tophead_7. 38. Interview with Yochai Benkler, August 26, 2015. 39. Called “the wiki workplace” in Wikinomics. 40. CAPTCHA stands for “Completely Automated Public Turing Test to Tell Computers and Humans Apart.” 41. Interview with Joe Lubin, July 13, 2015. 42. Ibid. Chapter 6: The Ledger of Things: Animating the Physical World 1. Not their real names. This story is based on discussions with individuals familiar with the situation. 2. Primavera De Filippi, “It’s Time to Take Mesh Networks Seriously (and Not Just for the Reasons You Think),” Wired, January 2, 2014. 3.


pages: 764 words: 188,807

The Prefect by Alastair Reynolds

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gravity well, Turing test

Valery was assigned to the Laboratory for Cognitive Studies, a department within SIAM. Her function was to evaluate the creative potential of these new minds, with the goal of creating a generation of gamma-level intelligences with the ability to solve problems by intuitive breakthrough, rather than step-by-step analysis. In essence, they wanted to create gamma-levels that were not only capable of passing the standard Turing tests, but which had the potential for intuitive thinking." Dreyfus touched a finger to his upper lip. "Valery tried to coax these machines into making art. To one degree or another, she usually got something out of them. But it was more like children daubing paint with their fingers than true creative expression. Valery began to despair of finding anything with an artistic impulse. Then she was introduced to a new machine."


pages: 1,201 words: 233,519

Coders at Work by Peter Seibel

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Ada Lovelace, bioinformatics, cloud computing, Conway's Game of Life, domain-specific language, fault tolerance, Fermat's Last Theorem, Firefox, George Gilder, glass ceiling, HyperCard, information retrieval, loose coupling, Menlo Park, Metcalfe's law, premature optimization, publish or perish, random walk, revision control, Richard Stallman, rolodex, Saturday Night Live, side project, slashdot, speech recognition, the scientific method, Therac-25, Turing complete, Turing machine, Turing test, type inference, Valgrind, web application

People would leave me comments: “It would be better if you did this” or, “I tried this, and it didn't work.” That actually helped spread Weizenbaum's idea beyond its boundaries. It was written, at first, in the PDP-1 Lisp. But they were building a Lisp on the PDP-6 at that point—or maybe the PDP-10. But it was the Lisp that had spread across the ARPANET. So Doctor went along with it, it turns out. I got a little glimmer of fame because Danny Bobrow wrote up “A Turing Test Passed”. That was one of the first times I actually got some notice for my stupid hacking: I had left Doctor up. And one of the execs at BBN came into the PDP-1 computer room and thought that Danny Bobrow was dialed into that and thought he was talking to Danny. For us folk that had played with ELIZA, we all recognized the responses and we didn't think about how humanlike they were. But for somebody who wasn't real familiar with ELIZA, it seemed perfectly reasonable.


pages: 571 words: 162,958

Rewired: The Post-Cyberpunk Anthology by James Patrick Kelly, John Kessel

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back-to-the-land, Columbine, dark matter, Extropian, Firefox, gravity well, haute couture, Internet Archive, pattern recognition, phenotype, post-industrial society, price stability, Silicon Valley, slashdot, Stephen Hawking, technological singularity, telepresence, the scientific method, Turing test, urban renewal, Vernor Vinge, wage slave, Y2K, zero day

“More of the same all round!” At the next table a person with make-up and long hair who’s wearing a dress — Manfred doesn’t want to speculate about the gender of these crazy mixed-up Euros — is reminiscing about wiring the fleshpots of Tehran for cybersex. Two collegiate-looking dudes are arguing intensely in German: the translation stream in his glasses tell him they’re arguing over whether the Turing Test is a Jim Crow law that violates European corpus juris standards on human rights. The beer arrives and Bob slides the wrong one across to Manfred: “here, try this. You’ll like it.” “Okay.” It’s some kind of smoked doppelbock, chock-full of yummy super-oxides: just inhaling over it makes Manfred feel like there’s a fire alarm in his nose screaming danger, Will Robinson! Cancer! Cancer! “Yeah, right.


pages: 741 words: 179,454

Extreme Money: Masters of the Universe and the Cult of Risk by Satyajit Das

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affirmative action, Albert Einstein, algorithmic trading, Andy Kessler, Asian financial crisis, asset allocation, asset-backed security, bank run, banking crisis, banks create money, Basel III, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Black Swan, Bonfire of the Vanities, bonus culture, Bretton Woods, BRICs, British Empire, capital asset pricing model, Carmen Reinhart, carried interest, Celtic Tiger, clean water, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, debt deflation, Deng Xiaoping, deskilling, discrete time, diversification, diversified portfolio, Doomsday Clock, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, eurozone crisis, Fall of the Berlin Wall, financial independence, financial innovation, fixed income, full employment, global reserve currency, Goldman Sachs: Vampire Squid, Gordon Gekko, greed is good, happiness index / gross national happiness, haute cuisine, high net worth, Hyman Minsky, index fund, interest rate swap, invention of the wheel, invisible hand, Isaac Newton, job automation, Johann Wolfgang von Goethe, joint-stock company, Joseph Schumpeter, Kenneth Rogoff, Kevin Kelly, labour market flexibility, laissez-faire capitalism, load shedding, locking in a profit, Long Term Capital Management, Louis Bachelier, margin call, market bubble, market fundamentalism, Marshall McLuhan, Martin Wolf, merger arbitrage, Mikhail Gorbachev, Milgram experiment, Mont Pelerin Society, moral hazard, mortgage debt, mortgage tax deduction, mutually assured destruction, Naomi Klein, Network effects, new economy, Nick Leeson, Nixon shock, Northern Rock, nuclear winter, oil shock, Own Your Own Home, pets.com, Plutocrats, plutocrats, Ponzi scheme, price anchoring, price stability, profit maximization, quantitative easing, quantitative trading / quantitative finance, Ralph Nader, RAND corporation, random walk, Ray Kurzweil, regulatory arbitrage, rent control, rent-seeking, reserve currency, Richard Feynman, Richard Feynman, Richard Thaler, risk-adjusted returns, risk/return, road to serfdom, Robert Shiller, Robert Shiller, Rod Stewart played at Stephen Schwarzman birthday party, rolodex, Ronald Reagan, Ronald Reagan: Tear down this wall, savings glut, shareholder value, Sharpe ratio, short selling, Silicon Valley, six sigma, Slavoj Žižek, South Sea Bubble, special economic zone, statistical model, Stephen Hawking, Steve Jobs, The Chicago School, The Great Moderation, the market place, the medium is the message, The Myth of the Rational Market, The Nature of the Firm, The Predators' Ball, The Wealth of Nations by Adam Smith, Thorstein Veblen, too big to fail, trickle-down economics, Turing test, Upton Sinclair, value at risk, Yogi Berra, zero-coupon bond

In 2008 Warren Buffett, the CEO of Berkshire Hathaway, bet $1 million with Protégé Partners, a New York fund of hedge funds, that even a rigorously selected portfolio of hedge funds would not beat the return on the market over 10 years. Buffett argued that large fees mean that hedge funds have to earn substantially greater returns than the S&P 500 index to match let alone beat its performance.32 The Buffet bet paralleled a $20,000 wager between Lotus founder Mitchell Kapor and futurist Ray Kurzweil that by 2029 no computer or machine intelligence will pass the Turing Test, where a computer successfully impersonates a human being. In 2010, Stanley Druckenmiller, who had been one of the traders at Soros’ Quantum Fund that broke the pound, announced that he was closing his fund Duquesne Capital Management. Druckenmiller confessed that increased volatility following the global financial crisis made it difficult to make money. It suggested a difficult outlook for Minsky machines.33 Make Money Not War Known as “Woodstock for capitalists,” Berkshire Hathaway’s annual shareholder meeting in the Qwest Center in Omaha, Nebraska, is characterized by folksy, comic wisdom.


pages: 607 words: 185,228

Antarctica by Kim Stanley Robinson

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Exxon Valdez, Fermat's Last Theorem, gravity well, hiring and firing, late capitalism, Occam's razor, Turing test, Zeno's paradox

It would make the work go a lot faster. He looked to the side as he told X about this, almost as if embarrassed, although otherwise he showed no sign of any emotion at all; on the contrary he exhibited what X had come to think of as the pure beaker style, consisting of a Spocklike objectivity and deadened affect so severe that it was an open question whether he would have been able to pass a Turing test. So: writing down numbers. "Fine, " X said. It had to beat picking nails off the floor. And at first it did. Forbes wandered away from the other beakers, and X followed, and they got right to work. But it was a windy day, the katabatic wind falling off the polar ice cap and whistling down the dry valleys, making all outdoor work miserable indeed, especially if you were just sitting on the ground writing figures in a notebook.


pages: 1,263 words: 371,402

The Year's Best Science Fiction: Twenty-Sixth Annual Collection by Gardner Dozois

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augmented reality, clean water, computer age, cosmological constant, David Attenborough, Deng Xiaoping, double helix, financial independence, game design, gravity well, jitney, John Harrison: Longitude, Kuiper Belt, Mahatma Gandhi, Paul Graham, Richard Feynman, Richard Feynman, Richard Feynman: Challenger O-ring, Search for Extraterrestrial Intelligence, Skype, stem cell, theory of mind, Turing machine, Turing test, urban renewal, Wall-E

Its task was far from trivial, and there was no guarantee that its translations were perfect, but Daniel did not believe it could hallucinate an entire language and fabricate these rich, detailed conversations out of thin air. He flicked between statistical summaries, technical overviews of linguistic structure, and snippets from the millions of conversations the software had logged. Food, weather, sex, death. As human dialogue the translations would have seemed utterly banal, but in context they were riveting. These were not chatterbots blindly following Markov chains, designed to impress the judges in a Turing test. The Phites were discussing matters by which they genuinely lived and died. When Daniel brought up a page of conversational topics in alphabetical order, his eyes were caught by the single entry under the letter G. Grief. He tapped the link, and spent a few minutes reading through samples, illustrating the appearance of the concept following the death of a child, a parent, a friend. He kneaded his eyelids.


pages: 1,280 words: 384,105

The Mammoth Book of the Best of Best New SF by Gardner Dozois

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back-to-the-land, Buckminster Fuller, Burning Man, call centre, Columbine, congestion charging, dark matter, Doomsday Book, double helix, Extropian, gravity well, Mason jar, offshore financial centre, out of africa, pattern recognition, phenotype, Silicon Valley, slashdot, Stephen Hawking, telepresence, Turing machine, Turing test, Winter of Discontent, Y2K

“More of the same all round!” At the next table a person with make-up and long hair who’s wearing a dress – Manfred doesn’t want to speculate about the gender of these crazy mixed-up Euros – is reminiscing about wiring the fleshpots of Tehran for cybersex. Two collegiate-looking dudes are arguing intensely in German: the translation stream in his glasses tell him they’re arguing over whether the Turing Test is a Jim Crow law that violates European corpus juris standards on human rights. The beer arrives and Bob slides the wrong one across to Manfred: “here, try this. You’ll like it.” “Okay.” It’s some kind of smoked doppelbock, chock-full of yummy superoxides: just inhaling over it makes Manfred feel like there’s a fire alarm in his nose screaming danger, Will Robinson! Cancer! Cancer! “Yeah, right.