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

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

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.

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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?

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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.

The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do by Erik J. Larson

AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Alignment Problem, AlphaGo, Amazon Mechanical Turk, artificial general intelligence, autonomous vehicles, Big Tech, Black Swan, Bletchley Park, Boeing 737 MAX, business intelligence, Charles Babbage, Claude Shannon: information theory, Computing Machinery and Intelligence, conceptual framework, correlation does not imply causation, data science, deep learning, DeepMind, driverless car, Elon Musk, Ernest Rutherford, Filter Bubble, Geoffrey Hinton, Georg Cantor, Higgs boson, hive mind, ImageNet competition, information retrieval, invention of the printing press, invention of the wheel, Isaac Newton, Jaron Lanier, Jeff Hawkins, John von Neumann, Kevin Kelly, Large Hadron Collider, Law of Accelerating Returns, Lewis Mumford, Loebner Prize, machine readable, machine translation, Nate Silver, natural language processing, Nick Bostrom, Norbert Wiener, PageRank, PalmPilot, paperclip maximiser, pattern recognition, Peter Thiel, public intellectual, Ray Kurzweil, retrograde motion, self-driving car, semantic web, Silicon Valley, social intelligence, speech recognition, statistical model, Stephen Hawking, superintelligent machines, tacit knowledge, technological singularity, TED Talk, The Coming Technological Singularity, the long tail, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, theory of mind, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, Yochai Benkler

Chapter 13: Inference and Language I 1. Andrew Griffin, “Turing Test Breakthrough as Super-­Computer Becomes First to Convince Us It’s H ­ uman,” In­de­pen­dent, June 8, 2014. N O T E S TO PA G E S 19 2 – 2 0 0 295 2. See “Computer AI Passes Turing Test in ‘World First,’” BBC News, June 9, 2014. The article from Time is no longer retrievable. See also Pranav Dixit, “A Computer Program Has Passed the Turing Test for the First Time,” Gizmodo, June 8, 2014. 3. Gary Marcus, “What Comes A ­ fter The Turing Test?” New Yorker, June 9, 2014. 4. Adam Mann, “That Computer Actually Got an F on the Turing Test,” Wired, June 9, 2014. 5.

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So it’s no won­der that AI researchers have mostly abandoned the Turing test challenge. Stuart Russell’s dismissing remark that “mainstream AI researchers have expended almost no effort to pass the Turing test” reflects this frustration with media frenzy about parlor trick per­for­mances.6 It’s a weakness in the field to accept them. But the dismissal is entirely unnecessary. For one, an honest Turing test r­ eally is a high-­water mark for language understanding. As Ray Kurzweil has pointed out, an alien intelligence might not understand En­glish conversation, but any intelligence that did pass a legitimate Turing test must be intelligent.

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Consider a test simplification: ­we’ll call it the Turing Test Monologue. In a Turing Test Monologue, the judge simply pastes in a news article or other text, then asks questions requiring an understanding of what it says. The respondent must answer t­ hose questions accurately. (Goodbye to tricks.) For instance, a judge might paste in the AP article “Your Tacos or Your Life!” and ask the respondent ­whether the story is funny or not, and why? Passing this test would be, strictly speaking, a logical subset of a completely open-­ended Turing test, so it would be entirely fair to use it—in fact, it would give advantage to the machine, which might not completely understand I nference and L anguage I 195 how to ­handle “pragmatic phenomena” in back-­and-­forth dialogue—­ more on this ­later.

The Simulation Hypothesis by Rizwan Virk

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

We all know that AI in the context of NPCs represents an artificially intelligent being, but what does that mean exactly? Common sense tells us that it’s a program that appears human—in some ways. In lieu of a formal definition, an informal definition is a computer program or artificial device that can pass the Turing Test. The History and Rise of AI The Turing Test Figure 13: A visual depiction of the Turing Test 12 The Turing Test is more of a milestone than a definition, since most AI today cannot pass this test. Alan Turing, considered by many to be the father of modern computer science, conjectured a time when a machine would exhibit intelligent behaviors.

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

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

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

Ada Lovelace, AI winter, Alignment Problem, AlphaGo, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, backpropagation, Bernie Sanders, Big Tech, Boston Dynamics, Cambridge Analytica, Charles Babbage, Claude Shannon: information theory, cognitive dissonance, computer age, computer vision, Computing Machinery and Intelligence, dark matter, deep learning, DeepMind, Demis Hassabis, Douglas Hofstadter, driverless car, Elon Musk, en.wikipedia.org, folksonomy, Geoffrey Hinton, Gödel, Escher, Bach, I think there is a world market for maybe five computers, ImageNet competition, Jaron Lanier, job automation, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, license plate recognition, machine translation, Mark Zuckerberg, natural language processing, Nick Bostrom, Norbert Wiener, ought to be enough for anybody, paperclip maximiser, pattern recognition, performance metric, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Rodney Brooks, self-driving car, sentiment analysis, Silicon Valley, Singularitarianism, Skype, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, tacit knowledge, tail risk, TED Talk, the long tail, theory of mind, There's no reason for any individual to have a computer in his home - Ken Olsen, trolley problem, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, world market for maybe five computers

“The Computer will be deemed to have passed the ‘Turing Test Human Determination Test’ if the Computer has fooled two or more of the three Human Judges into thinking that it is a human.”43 But we’re not done yet: In addition, each of the three Turing Test Judges will rank the four Candidates with a rank from 1 (least human) to 4 (most human). The computer will be deemed to have passed the “Turing Test Rank Order Test” if the median rank of the Computer is equal to or greater than the median rank of two or more of the three Turing Test Human Foils. * * * The Computer will be deemed to have passed the Turing Test if it passes both the Turing Test Human Determination Test and the Turing Test Rank Order Test. * * * If a Computer passes the Turing Test, as described above, prior to the end of the year 2029, then Ray Kurzweil wins the wager. Otherwise Mitchell Kapor wins the wager.44 Wow, pretty strict.

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

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

The Road to Conscious Machines by Michael Wooldridge

Ada Lovelace, AI winter, algorithmic bias, AlphaGo, Andrew Wiles, Anthropocene, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, basic income, Bletchley Park, Boeing 747, British Empire, call centre, Charles Babbage, combinatorial explosion, computer vision, Computing Machinery and Intelligence, DARPA: Urban Challenge, deep learning, deepfake, DeepMind, Demis Hassabis, don't be evil, Donald Trump, driverless car, Elaine Herzberg, Elon Musk, Eratosthenes, factory automation, fake news, future of work, gamification, general purpose technology, Geoffrey Hinton, gig economy, Google Glasses, intangible asset, James Watt: steam engine, job automation, John von Neumann, Loebner Prize, Minecraft, Mustafa Suleyman, Nash equilibrium, Nick Bostrom, Norbert Wiener, NP-complete, P = NP, P vs NP, paperclip maximiser, pattern recognition, Philippa Foot, RAND corporation, Ray Kurzweil, Rodney Brooks, self-driving car, Silicon Valley, Stephen Hawking, Steven Pinker, strong AI, technological singularity, telemarketer, Tesla Model S, The Coming Technological Singularity, The Future of Employment, the scientific method, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, trolley problem, Turing machine, Turing test, universal basic income, Von Neumann architecture, warehouse robotics

For example, if your favourite AI technique is ‘Temporally Recurrent Optimal Learning’ (to pick some random AI buzzwords of the present day), then you might be tempted to define AI as the task of passing the Turing test using Temporally Recurrent Optimal Learning, thereby ruling out any other approach. Thus, we want a test for intelligent behaviour that is independent of the techniques or methods that are used to achieve it. The Turing test achieves this by clearly separating the interrogators from the thing that is being interrogated: the only evidence that the interrogators have to go on are the inputs and outputs – the questions sent by the interrogator, and the responses that the interrogator later receives. The thing on the other end is a black box as far as the Turing test is concerned, in the sense that we are not allowed to examine its internal structure: all that we have are the inputs and outputs.

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Turing’s article ‘Computing Machinery and Intelligence’, describing his test, was published in the prestigious international journal Mind in 1950.9 Although many articles touching on AI-like ideas had been published before this, Turing approached the subject for the first time from the standpoint of the modern digital computer. As such, his article is generally recognized as the first AI publication. Turing Test Nonsense The Turing test is simple, elegant and easy to understand. However, it had the unfortunate side effect of establishing the test as the holy grail of AI – with unfortunate consequences that resonate to the present day. The problem is that most attempts to tackle the Turing test tend to use cheap tricks to try to befuddle the interrogators into believing that they are dealing with a person, rather than by trying to engage with the actual issues of intelligent behaviour.

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Varieties of Artificial Intelligence For all the nonsense it has given rise to, the Turing test is an important part of the AI story because, for the first time, it gave researchers interested in this emerging discipline a target to aim at. When someone asked you what your goal was, you could give a straightforward and precise answer: My goal is to build a machine that can meaningfully pass the Turing test. Today, I think very few, if any, serious AI researchers would give this answer, but it had a crucial historical role, and I believe it still has something important to tell us today. Much of the attraction of the Turing test undoubtedly lies in its simplicity, but clear as the test appears to be, it nevertheless raises many problematic questions about AI.

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

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

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.

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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.

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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.”

Architects of Intelligence by Martin Ford

3D printing, agricultural Revolution, AI winter, algorithmic bias, Alignment Problem, AlphaGo, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, Big Tech, bitcoin, Boeing 747, Boston Dynamics, business intelligence, business process, call centre, Cambridge Analytica, cloud computing, cognitive bias, Colonization of Mars, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, CRISPR, crowdsourcing, DARPA: Urban Challenge, data science, deep learning, DeepMind, Demis Hassabis, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, driverless car, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, fake news, Fellow of the Royal Society, Flash crash, future of work, general purpose technology, Geoffrey Hinton, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, Hans Rosling, hype cycle, ImageNet competition, income inequality, industrial research laboratory, industrial robot, information retrieval, job automation, John von Neumann, Large Hadron Collider, Law of Accelerating Returns, life extension, Loebner Prize, machine translation, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, Mustafa Suleyman, natural language processing, new economy, Nick Bostrom, OpenAI, opioid epidemic / opioid crisis, optical character recognition, paperclip maximiser, pattern recognition, phenotype, Productivity paradox, radical life extension, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, seminal paper, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, sparse data, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, synthetic biology, systems thinking, Ted Kaczynski, TED Talk, The Rise and Fall of American Growth, theory of mind, Thomas Bayes, Travis Kalanick, Turing test, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, women in the workforce, working-age population, workplace surveillance , zero-sum game, Zipcar

Actually, dialog systems are a very important area of research for virtual assistants, question and answering systems, and so on. MARTIN FORD: Do you anticipate the creation of an AI that someday could pass the Turing test? YANN LECUN: It’s going to happen at some point, but the Turing test is not actually an interesting test. In fact, I don’t think a lot of people in the AI field at the moment consider the Turing test to be a good test. It’s too easy to trick it, and to some extent, the Turing test has already been and gone. We give a lot of importance to language as humans because we are used to discussing intelligent topics with other humans through language.

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I think Alan Turing was prescient in basing the Turing test on language because I think it does require the full range of human thinking and human intelligence to create and understand language at human levels. MARTIN FORD: Is your ultimate objective to extend this idea to actually build a machine that can pass the Turing test? RAY KURZWEIL: Not everybody agrees with this, but I think the Turing test, if organized correctly, is actually a very good test of human-level intelligence. The issue is that in the brief paper that Turing wrote in 1950, it’s really just a couple of paragraphs that talked about the Turing test, and he left out vital elements.

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But we’ve also got other limitations to deal with, like we still don’t have generalized tools in AI and we still don’t know how to solve general problems in AI. In fact, one of the fun things, and you may have seen this, is that people are now starting to define new forms of what used to be the Turing test. MARTIN FORD: A new Turing Test? How would that work? JAMES MANYIKA: Steve Wozniak, the co-founder of Apple, has actually proposed what he calls the “coffee test” as opposed to Turing tests, which are very narrow in many respects. A coffee test is kind of fun: until you get a system that can enter an average and previously unknown American home and somehow figure out how to make a cup of coffee, we’ve not solved AGI.

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

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

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.

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But I think science is inherently about objective reality. 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.

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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. Indeed, they have no fingers. They are also not proficient in human languages. The Turing Test is clearly a human-centric measurement. IS THERE A RELATIONSHIP BETWEEN THIS CONSCIOUSNESS STUFF AND THE ISSUE OF TIME THAT WE SPOKE ABOUT EARLIER? Yes, we clearly have an awareness of time. Our subjective experience of time passage—and remember that subjective is just another word for conscious—is governed by the speed of our objective processes.

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

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

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.

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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. Invariably, these early claims will be debunked by knowledgeable observers, probably including myself. By the time there is a broad consensus that the Turing test has been passed, the actual threshold will have long since been achieved. Edward Feigenbaum proposes a variation of the Turing test, which assesses not a machine's ability to pass for human in casual, everyday dialogue but its ability to pass for a scientific expert in a specific field.220 The Feigenbaum test (FT) may be more significant than the Turing test because FT-capable machines, being technically proficient, will be capable of improving their own designs.

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The bandwidth of information from the endocrine system is quite low, because the determining factor is overall levels of hormones, not the precise location of each hormone molecule. Confirmation of the uploading milestone will be in the form of a "Ray Kurzweil" or "Jane Smith" Turing test, in other words convincing a human judge that the uploaded re-creation is indistinguishable from the original specific person. By that time we'll face some complications in devising the rules of any Turing test. Since nonbiological intelligence will have passed the original Turing test years earlier (around 2029), should we allow a nonbiological human equivalent to be a judge? How about an enhanced human? Unenhanced humans may become increasingly hard to find.

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

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

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.

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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.

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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.

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

3D printing, Ada Lovelace, Albert Einstein, algorithmic bias, AlphaGo, Alvin Roth, Andrew Wiles, Automated Insights, Benoit Mandelbrot, Bletchley Park, Cambridge Analytica, Charles Babbage, Claude Shannon: information theory, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, data is the new oil, data science, deep learning, DeepMind, Demis Hassabis, Donald Trump, double helix, Douglas Hofstadter, driverless car, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, Flash crash, Gödel, Escher, Bach, Henri Poincaré, Jacquard loom, John Conway, Kickstarter, Loebner Prize, machine translation, mandelbrot fractal, Minecraft, move 37, music of the spheres, Mustafa Suleyman, Narrative Science, natural language processing, Netflix Prize, PageRank, pattern recognition, Paul Erdős, Peter Thiel, random walk, Ray Kurzweil, recommendation engine, Rubik’s Cube, Second Machine Age, Silicon Valley, speech recognition, stable marriage problem, Turing test, Watson beat the top human players on Jeopardy!, wikimedia commons

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

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

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Once they see that they are losing they go rather crazy. Silver, the chief programmer, winced as he saw the next move AlphaGo was suggesting: ‘I think they’re going to laugh.’ Sure enough, the Korean commentators collapsed into fits of giggles at the moves AlphaGo was now making. Its moves were failing the Turing Test. No human with a shred of strategic sense would make them. The game dragged on for a total of 180 moves, at which point AlphaGo put up a message on the screen that it had resigned. The press room erupted with spontaneous applause. The human race had got one back. AlphaGo 3 Humans 1. The smile on Lee Sedol’s face at the press conference that evening said it all.

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

Airbnb, Albert Einstein, algorithmic bias, algorithmic management, algorithmic trading, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, bitcoin, blockchain, business logic, Charles Babbage, Chuck Templeton: OpenTable:, Claude Shannon: information theory, commoditize, Computing Machinery and Intelligence, Credit Default Swap, crowdsourcing, cryptocurrency, data science, DeepMind, disruptive innovation, Donald Knuth, Donald Shoup, Douglas Engelbart, Douglas Engelbart, Elon Musk, Evgeny Morozov, factory automation, fiat currency, Filter Bubble, Flash crash, game design, gamification, Google Glasses, Google X / Alphabet X, Hacker Conference 1984, High speed trading, hiring and firing, Ian Bogost, industrial research laboratory, invisible hand, Isaac Newton, iterative process, Jaron Lanier, Jeff Bezos, job automation, John Conway, John Markoff, Just-in-time delivery, Kickstarter, Kiva Systems, late fees, lifelogging, Loebner Prize, lolcat, Lyft, machine readable, Mother of all demos, Nate Silver, natural language processing, Neal Stephenson, Netflix Prize, new economy, Nicholas Carr, Nick Bostrom, Norbert Wiener, PageRank, peer-to-peer, Peter Thiel, power law, Ray Kurzweil, recommendation engine, Republic of Letters, ride hailing / ride sharing, Satoshi Nakamoto, self-driving car, sharing economy, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Silicon Valley startup, SimCity, Skinner box, Snow Crash, social graph, software studies, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, supply-chain management, tacit knowledge, TaskRabbit, technological singularity, technological solutionism, technoutopianism, the Cathedral and the Bazaar, The Coming Technological Singularity, the scientific method, The Signal and the Noise by Nate Silver, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, transaction costs, traveling salesman, Turing machine, Turing test, Uber and Lyft, Uber for X, uber lyft, urban planning, Vannevar Bush, Vernor Vinge, wage slave

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

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

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

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

AI winter, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Alignment Problem, AlphaGo, artificial general intelligence, Asilomar, autonomous vehicles, basic income, Benoit Mandelbrot, Bill Joy: nanobots, Bletchley Park, Buckminster Fuller, cellular automata, Claude Shannon: information theory, Computing Machinery and Intelligence, CRISPR, Daniel Kahneman / Amos Tversky, Danny Hillis, data science, David Graeber, deep learning, DeepMind, Demis Hassabis, easy for humans, difficult for computers, Elon Musk, Eratosthenes, Ernest Rutherford, fake news, finite state, friendly AI, future of work, Geoffrey Hinton, Geoffrey West, Santa Fe Institute, gig economy, Hans Moravec, heat death of the universe, hype cycle, income inequality, industrial robot, information retrieval, invention of writing, it is difficult to get a man to understand something, when his salary depends on his not understanding it, James Watt: steam engine, Jeff Hawkins, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kickstarter, Laplace demon, Large Hadron Collider, Loebner Prize, machine translation, market fundamentalism, Marshall McLuhan, Menlo Park, military-industrial complex, mirror neurons, Nick Bostrom, Norbert Wiener, OpenAI, optical character recognition, paperclip maximiser, pattern recognition, personalized medicine, Picturephone, profit maximization, profit motive, public intellectual, quantum cryptography, RAND corporation, random walk, Ray Kurzweil, Recombinant DNA, Richard Feynman, Rodney Brooks, self-driving car, sexual politics, Silicon Valley, Skype, social graph, speech recognition, statistical model, Stephen Hawking, Steven Pinker, Stewart Brand, strong AI, superintelligent machines, supervolcano, synthetic biology, systems thinking, technological determinism, technological singularity, technoutopianism, TED Talk, telemarketer, telerobotics, The future is already here, the long tail, the scientific method, theory of mind, trolley problem, Turing machine, Turing test, universal basic income, Upton Sinclair, Von Neumann architecture, Whole Earth Catalog, Y2K, you are the product, zero-sum game

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

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What has long been difficult for me to understand is, What’s the point of a conventional Turing Test? What’s the motivation? As a toy, one could make a little chat bot that people could chat with. That will be the next thing. The current round of deep learning—particularly, recurrent neural networks—is making pretty good models of human speech and human writing. We can type in, say, “How are you feeling today?” and it knows most of the time what sort of response to give. But I want to figure out whether I can automate responding to my email. I know the answer is no. A good Turing Test, for me, will be when a bot can answer most of my email.

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

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

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

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.

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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.

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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.”

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

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

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.

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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.

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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.”

Being You: A New Science of Consciousness by Anil Seth

AlphaGo, artificial general intelligence, augmented reality, backpropagation, carbon-based life, Claude Shannon: information theory, computer age, computer vision, Computing Machinery and Intelligence, coronavirus, correlation does not imply causation, CRISPR, cryptocurrency, deep learning, deepfake, DeepMind, Drosophila, en.wikipedia.org, Filter Bubble, GPT-3, GPT-4, John Markoff, longitudinal study, Louis Pasteur, mirror neurons, Neil Armstrong, Nick Bostrom, Norbert Wiener, OpenAI, paperclip maximiser, pattern recognition, Paul Graham, Pierre-Simon Laplace, planetary scale, Plato's cave, precautionary principle, Ray Kurzweil, self-driving car, speech recognition, stem cell, systems thinking, technological singularity, TED Talk, telepresence, the scientific method, theory of mind, Thomas Bayes, TikTok, Turing test

Garland test: This term was coined by Murray Shanahan, whose book Embodiment and the Inner Life (2010) was one of the inspirations behind Ex Machina. noisy proclamations: www.reading.ac.uk/news-archive/press-releases/pr583836.html. When the chatbot won: ‘Eugene Goostman is a real boy – the Turing Test says so’, Guardian Pass notes, 9 June 2014. See https://www.theguardian.com/technology/shortcuts/2014/jun/09/eugene-goostman-turing-test-computer-program. the humans failed: The description of the Turing test as a test of ‘human gullibility’ comes from a 2015 New York Times article by John Markoff, ‘Software is smart enough for SAT, but still far from intelligent’, New York Times, 21 September 2015.

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In Alex Garland’s 2014 film Ex Machina, reclusive billionaire tech genius Nathan invites hotshot programmer Caleb to his remote hideout to meet Ava, the intelligent, inquisitive robot he has created. Caleb’s task is to figure out whether Ava is conscious, or whether she – it – is merely an intelligent robot, with no inner life at all. Ex Machina draws heavily on the Turing test, the famous yardstick for assessing whether a machine can think. In one incisive scene, Nathan is quizzing Caleb about this test. In the standard version of the Turing test, as Caleb knows, a human judge interrogates both a candidate machine and another human, remotely, by exchanging typed messages only. A machine passes the test when the judge consistently fails to distinguish between the human and the machine.

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A machine passes the test when the judge consistently fails to distinguish between the human and the machine. But Nathan has something far more interesting in mind. When it comes to Ava, he says, ‘the challenge is to show you that she’s a robot – and see if you still feel she has consciousness.’ This new game transforms the Turing test from a test of intelligence into a test of consciousness, and as we now know, these are very different phenomena. What’s more, Garland shows us that the test is not really about the robot at all. As Nathan puts it, what matters is not whether Ava is a machine. It is not even whether Ava, though a machine, has consciousness. What matters is whether Ava makes a conscious person feel that she (or it) is conscious.

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

Adam Curtis, agricultural Revolution, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, Anthropocene, artificial general intelligence, augmented reality, autism spectrum disorder, autonomous vehicles, backpropagation, basic income, behavioural economics, bitcoin, blockchain, bread and circuses, Charles Babbage, clean water, cognitive dissonance, Colonization of Mars, complexity theory, computer age, computer vision, constrained optimization, corporate personhood, cosmological principle, cryptocurrency, cuban missile crisis, Danny Hillis, dark matter, data science, deep learning, DeepMind, Demis Hassabis, digital capitalism, digital divide, digital rights, discrete time, Douglas Engelbart, driverless car, Elon Musk, Emanuel Derman, endowment effect, epigenetics, Ernest Rutherford, experimental economics, financial engineering, Flash crash, friendly AI, functional fixedness, global pandemic, Google Glasses, Great Leap Forward, Hans Moravec, hive mind, Ian Bogost, income inequality, information trail, Internet of things, invention of writing, iterative process, James Webb Space Telescope, Jaron Lanier, job automation, Johannes Kepler, John Markoff, John von Neumann, Kevin Kelly, knowledge worker, Large Hadron Collider, lolcat, loose coupling, machine translation, microbiome, mirror neurons, Moneyball by Michael Lewis explains big data, Mustafa Suleyman, natural language processing, Network effects, Nick Bostrom, Norbert Wiener, paperclip maximiser, pattern recognition, Peter Singer: altruism, phenotype, planetary scale, Ray Kurzweil, Recombinant DNA, recommendation engine, Republic of Letters, RFID, Richard Thaler, Rory Sutherland, Satyajit Das, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, social intelligence, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, superintelligent machines, supervolcano, synthetic biology, systems thinking, tacit knowledge, TED Talk, the scientific method, The Wisdom of Crowds, theory of mind, Thorstein Veblen, too big to fail, Turing machine, Turing test, Von Neumann architecture, Watson beat the top human players on Jeopardy!, We are as Gods, Y2K

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.

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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.

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Speaking today about “the problem of life” sounds amusing: Biology is a science dealing with many different great problems, not just one. Intelligence is one word but many problems—not one but many Nobel prizes. 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 Beginning of Infinity: Explanations That Transform the World by David Deutsch

agricultural Revolution, Albert Michelson, anthropic principle, Apollo 13, artificial general intelligence, Bonfire of the Vanities, Charles Babbage, Computing Machinery and Intelligence, conceptual framework, cosmological principle, dark matter, David Attenborough, discovery of DNA, Douglas Hofstadter, Easter island, Eratosthenes, Ernest Rutherford, first-past-the-post, Georg Cantor, global pandemic, Gödel, Escher, Bach, illegal immigration, invention of movable type, Isaac Newton, Islamic Golden Age, Jacquard loom, Johannes Kepler, John Conway, John von Neumann, Joseph-Marie Jacquard, Kenneth Arrow, Loebner Prize, Louis Pasteur, mirror neurons, Nick Bostrom, pattern recognition, Pierre-Simon Laplace, precautionary principle, Richard Feynman, Search for Extraterrestrial Intelligence, seminal paper, Stephen Hawking, supervolcano, technological singularity, Thales of Miletus, The Coming Technological Singularity, the scientific method, Thomas Malthus, Thorstein Veblen, Turing test, Vernor Vinge, Whole Earth Review, William of Occam, zero-sum game

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).

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Some claim that the above criticism is unfair: modern AI research is not focused on passing the Turing test, and great progress has been made in what is now called ‘AI’ in many specialized applications. However, none of those applications look like ‘machines that think’.* Others maintain that the criticism is premature, because, during most of the history of the field, computers had absurdly little speed and memory capacity compared with today’s. Hence they continue to expect the breakthrough in the next few years. This will not do either. It is not as though someone has written a chatbot that could pass the Turing test but would currently take a year to compute each reply.

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But his test is rooted in the empiricist mistake of seeking a purely behavioural criterion: it requires the judge to come to a conclusion without any explanation of how the candidate AI is supposed to work. But, in reality, judging whether something is a genuine AI will always depend on explanations of how it works. That is because the task of the judge in a Turing test has similar logic to that faced by Paley when walking across his heath and finding a stone, a watch or a living organism: it is to explain how the observable features of the object came about. In the case of the Turing test, we deliberately ignore the issue of how the knowledge to design the object was created. The test is only about who designed the AI’s utterances: who adapted its utterances to be meaningful – who created the knowledge in them?

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

affirmative action, Albert Einstein, AlphaGo, Asilomar, Bayesian statistics, computer age, computer vision, Computing Machinery and Intelligence, confounding variable, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, driverless car, Edmond Halley, Elon Musk, en.wikipedia.org, experimental subject, Great Leap Forward, Gregor Mendel, Isaac Newton, iterative process, John Snow's cholera map, Loebner Prize, loose coupling, Louis Pasteur, Menlo Park, Monty Hall problem, pattern recognition, Paul Erdős, personalized medicine, Pierre-Simon Laplace, placebo effect, Plato's cave, prisoner's dilemma, probability theory / Blaise Pascal / Pierre de Fermat, randomized controlled trial, Recombinant DNA, selection bias, self-driving car, seminal paper, Silicon Valley, speech recognition, statistical model, Stephen Hawking, Steve Jobs, strong AI, The Design of Experiments, the scientific method, Thomas Bayes, Turing test

But it goes even further: having such laws permits us to violate them selectively so as to create worlds that contradict ours. Our next section features such violations in action. THE MINI-TURING TEST In 1950, Alan Turing asked what it would mean for a computer to think like a human. He suggested a practical test, which he called “the imitation game,” but every AI researcher since then has called it the “Turing test.” For all practical purposes, a computer could be called a thinking machine if an ordinary human, communicating with the computer by typewriter, could not tell whether he was talking with a human or a computer.

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

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Humans must have some compact representation of the information needed in their brains, as well as an effective procedure to interpret each question properly and extract the right answer from the stored representation. To pass the mini-Turing test, therefore, we need to equip machines with a similarly efficient representation and answer-extraction algorithm. Such a representation not only exists but has childlike simplicity: a causal diagram. We have already seen one example, the diagram for the mammoth hunt. Considering the extreme ease with which people can communicate their knowledge with dot-and-arrow diagrams, I believe that our brains indeed use a representation like this. But more importantly for our purposes, these models pass the mini-Turing test; no other model is known to do so. Let’s look at some examples.

The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma by Mustafa Suleyman

"World Economic Forum" Davos, 23andMe, 3D printing, active measures, Ada Lovelace, additive manufacturing, agricultural Revolution, AI winter, air gap, Airbnb, Alan Greenspan, algorithmic bias, Alignment Problem, AlphaGo, Alvin Toffler, Amazon Web Services, Anthropocene, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, ASML, autonomous vehicles, backpropagation, barriers to entry, basic income, benefit corporation, Big Tech, biodiversity loss, bioinformatics, Bletchley Park, Blitzscaling, Boston Dynamics, business process, business process outsourcing, call centre, Capital in the Twenty-First Century by Thomas Piketty, ChatGPT, choice architecture, circular economy, classic study, clean tech, cloud computing, commoditize, computer vision, coronavirus, corporate governance, correlation does not imply causation, COVID-19, creative destruction, CRISPR, critical race theory, crowdsourcing, cryptocurrency, cuban missile crisis, data science, decarbonisation, deep learning, deepfake, DeepMind, deindustrialization, dematerialisation, Demis Hassabis, disinformation, drone strike, drop ship, dual-use technology, Easter island, Edward Snowden, effective altruism, energy transition, epigenetics, Erik Brynjolfsson, Ernest Rutherford, Extinction Rebellion, facts on the ground, failed state, Fairchild Semiconductor, fear of failure, flying shuttle, Ford Model T, future of work, general purpose technology, Geoffrey Hinton, global pandemic, GPT-3, GPT-4, hallucination problem, hive mind, hype cycle, Intergovernmental Panel on Climate Change (IPCC), Internet Archive, Internet of things, invention of the wheel, job automation, John Maynard Keynes: technological unemployment, John von Neumann, Joi Ito, Joseph Schumpeter, Kickstarter, lab leak, large language model, Law of Accelerating Returns, Lewis Mumford, license plate recognition, lockdown, machine readable, Marc Andreessen, meta-analysis, microcredit, move 37, Mustafa Suleyman, mutually assured destruction, new economy, Nick Bostrom, Nikolai Kondratiev, off grid, OpenAI, paperclip maximiser, personalized medicine, Peter Thiel, planetary scale, plutocrats, precautionary principle, profit motive, prompt engineering, QAnon, quantum entanglement, ransomware, Ray Kurzweil, Recombinant DNA, Richard Feynman, Robert Gordon, Ronald Reagan, Sam Altman, Sand Hill Road, satellite internet, Silicon Valley, smart cities, South China Sea, space junk, SpaceX Starlink, stealth mode startup, stem cell, Stephen Fry, Steven Levy, strong AI, synthetic biology, tacit knowledge, tail risk, techlash, techno-determinism, technoutopianism, Ted Kaczynski, the long tail, The Rise and Fall of American Growth, Thomas Malthus, TikTok, TSMC, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, uranium enrichment, warehouse robotics, William MacAskill, working-age population, world market for maybe five computers, zero day

For the time being, it doesn’t matter whether the system is self-aware, or has understanding, or has humanlike intelligence. All that matters is what the system can do. Focus on that, and the real challenge comes into view: systems can do more, much more, with every passing day. CAPABILITIES: A MODERN TURING TEST In a paper published in 1950, the computer scientist Alan Turing suggested a legendary test for whether an AI exhibited human-level intelligence. When AI could display humanlike conversational abilities for a lengthy period of time, such that a human interlocutor couldn’t tell they were speaking to a machine, the test would be passed: the AI, conversationally akin to a human, deemed intelligent.

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When AI could display humanlike conversational abilities for a lengthy period of time, such that a human interlocutor couldn’t tell they were speaking to a machine, the test would be passed: the AI, conversationally akin to a human, deemed intelligent. For more than seven decades this simple test has been an inspiration for many young researchers entering the field of AI. Today, as the LaMDA-sentience saga illustrates, systems are already close to passing the Turing test. But, as many have pointed out, intelligence is about so much more than just language (or indeed any other single facet of intelligence taken in isolation). One particularly important dimension is in the ability to take actions. We don’t just care about what a machine can say; we also care about what it can do.

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What we would really like to know is, can I give an AI an ambiguous, open-ended, complex goal that requires interpretation, judgment, creativity, decision-making, and acting across multiple domains, over an extended time period, and then see the AI accomplish that goal? Put simply, passing a Modern Turing Test would involve something like the following: an AI being able to successfully act on the instruction “Go make $1 million on Amazon in a few months with just a $100,000 investment.” It might research the web to look at what’s trending, finding what’s hot and what’s not on Amazon Marketplace; generate a range of images and blueprints of possible products; send them to a drop-ship manufacturer it found on Alibaba; email back and forth to refine the requirements and agree on the contract; design a seller’s listing; and continually update marketing materials and product designs based on buyer feedback.

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

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

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

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There he came up with various techniques for cracking German codes, most famously an electromechanical device capable of working out the settings for the Enigma machine. In doing so, he played a key role in decoding intercepted messages, which helped the Allies defeat the Nazis. Turing was fascinated by the idea of thinking machines and went on to devise the important Turing Test, which we will discuss in detail in a later chapter. As a child, he read and loved a book called Natural Wonders Every Child Should Know, by Edwin Tenney Brewster, which the author described as ‘an attempt to lead children of eight or ten, first to ask and then to answer the question: “What have I in common with other living things, and how do I differ from them?”’

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Based on a hypothesis by Alan Turing, whose work I discussed in chapter one, the Turing Test is designed to test a machine’s ability to show intelligent behaviour indistinguishable from that of a human. As it is regularly performed, the Turing Test involves taking a computer (A) and a human (B), and having them each communicate with a human interrogator (C), whose job it is to figure out which of A and B is the human and which is the computer. If C is unable to do this, Turing argued that the machine has ‘won’ and we must consider it to be intelligent, since we are unable to differentiate it from our own human intelligence. In the future, tools such as Negobot show that our ability to discern between real people and bots may even have legal ramifications.

You Are Not a Gadget by Jaron Lanier

1960s counterculture, Abraham Maslow, accounting loophole / creative accounting, additive manufacturing, Albert Einstein, Bear Stearns, call centre, cloud computing, commoditize, crowdsourcing, death of newspapers, different worldview, digital Maoism, Douglas Hofstadter, Extropian, follow your passion, General Magic , hive mind, Internet Archive, Jaron Lanier, jimmy wales, John Conway, John Perry Barlow, John von Neumann, Kevin Kelly, Long Term Capital Management, Neal Stephenson, Network effects, new economy, packet switching, PageRank, pattern recognition, Ponzi scheme, Project Xanadu, Ray Kurzweil, Richard Stallman, Savings and loan crisis, Silicon Valley, Silicon Valley startup, slashdot, social graph, stem cell, Steve Jobs, Stewart Brand, Stuart Kauffman, synthetic biology, technological determinism, Ted Nelson, telemarketer, telepresence, the long tail, The Wisdom of Crowds, trickle-down economics, Turing test, Vernor Vinge, Whole Earth Catalog

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.

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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.

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.* 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?

The Lights in the Tunnel by Martin Ford

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

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.

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

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(The impact of automation will, of course, be in addition to that of offshoring.) Many of these people will be highly educated professionals who had previously assumed that they were, because of their skills and advanced educations, beneficiaries of the trend toward an increasingly technological and globalized world.* *[ Please see “Machine Intelligence and the Turing Test” in the Appendix for more on artificial intelligence. ] Military Robotics One of the biggest investors in robotics technology is the Pentagon. In his recent book Wired for War: The Robotics Revolution and Conflict in the 21st Century, P.W. Singer points out that the U.S. military expects robotic technologies to play an increasingly important role in conflicts of the future.

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

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

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.

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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.

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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.

12 Bytes: How We Got Here. Where We Might Go Next by Jeanette Winterson

"Margaret Hamilton" Apollo, "World Economic Forum" Davos, 3D printing, Ada Lovelace, Airbnb, Albert Einstein, Alignment Problem, Amazon Mechanical Turk, Anthropocene, Apollo 11, Apple's 1984 Super Bowl advert, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, basic income, Big Tech, bitcoin, Bletchley Park, blockchain, Boston Dynamics, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Charles Babbage, computer age, Computing Machinery and Intelligence, coronavirus, COVID-19, CRISPR, cryptocurrency, dark matter, Dava Sobel, David Graeber, deep learning, deskilling, digital rights, discovery of DNA, Dominic Cummings, Donald Trump, double helix, driverless car, Elon Musk, fake news, flying shuttle, friendly AI, gender pay gap, global village, Grace Hopper, Gregor Mendel, hive mind, housing crisis, Internet of things, Isaac Newton, Jacquard loom, James Hargreaves, Jeff Bezos, Johannes Kepler, John von Neumann, Joseph-Marie Jacquard, Kickstarter, Large Hadron Collider, life extension, lockdown, lone genius, Mark Zuckerberg, means of production, microdosing, more computing power than Apollo, move fast and break things, natural language processing, Nick Bostrom, Norbert Wiener, off grid, OpenAI, operation paperclip, packet switching, Peter Thiel, pink-collar, Plato's cave, public intellectual, QAnon, QWERTY keyboard, Ray Kurzweil, rewilding, ride hailing / ride sharing, Rutger Bregman, Sam Altman, self-driving car, sharing economy, Sheryl Sandberg, Shoshana Zuboff, Silicon Valley, Skype, Snapchat, SoftBank, SpaceX Starlink, speech recognition, spinning jenny, stem cell, Stephen Hawking, Steve Bannon, Steve Jobs, Steven Levy, Steven Pinker, superintelligent machines, surveillance capitalism, synthetic biology, systems thinking, tech billionaire, tech worker, TED Talk, telepresence, telepresence robot, TikTok, trade route, Turing test, universal basic income, Virgin Galactic, Watson beat the top human players on Jeopardy!, women in the workforce, Y Combinator

Turing dusted Ada down and brought her back from the dead in 1950, when, after carefully reading her work, he responded to what he called Lady Lovelace’s Objection – that computers cannot originate. In this they are not human or like a human. His answer to Ada – their conversation across time – was the Turing Test. * * * The 1950 Turing Test is a test of a machine’s ability to appear to humans as equivalent to, or indistinguishable from, a human. Google claims that their voice technology assistant Google Duplex has already passed the test – at least when it comes to booking appointments for you by phone. Fooling the human receptionist on the other end of the line counts as a pass – and Google Duplex has achieved that, with tone modulation, word elongation, and ‘thought’ pauses that all sound like a realistic human.

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How many times have you wondered if you are communicating with a bot and it turns out to be a human? Possibly we will need a Reverse Turing Test to pull humans up to the level of enabled empathetic bots. While most chatbots are narrow AI – an algorithm designed to do one thing only, like order the pizza or run through your ‘choices’ before being transferred to a human – some chatbots seem smarter. Google engineer, inventor and futurist Ray Kurzweil’s Ramona will chat with you on a variety of topics. She’s a deep-learning system whose data-set is continuously augmented by her chats with humans. Kurzweil believes that Ramona will pass the Turing Test by 2029 – that is, she will be indistinguishable, online, from a human being.

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That’s because computational power is the sum of computer storage (memory) and processing speed. Simply, computers weren’t powerful enough to do what McCarthy, Minsky and Turing knew they would be able to do. And before those men, there was Ada Lovelace, the early-19th century genius who inspired Alan Turing to devise the Turing Test – when we can no longer tell the difference between AI and bio-human. We aren’t there yet. Time is hard to gauge. * * * These 12 bytes are not a history of AI. They are not the story of Big Tech or Big Data, though we often meet on that ground. A bit is the smallest unit of data on a computer – it’s a binary digit, and it can have a value of 0 or 1. 8 bits make a byte.

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

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

See Humphrys (2008). 7. Humphrys (2008), 242–43. 8. Which gets us to the Turing test. To determine when a computer had met some level of competing with human intelligence, Turing suggested that a computer hide behind one curtain, a person hide behind a second, and the tester pass questions through the curtain to each. If a person cannot distinguish the responses of a computer from those of a human, then at least in this limited respect the computer has attained humanlike intelligence. There already is an annual Turing test, the Loebner competition, in which a set of judges spend a few minutes conversing (via keyboard) with computers and with people, and then must decide which is which.

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

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Hobsbawm, Eric. 1999. Industry and Empire: The Birth of the Industrial Revolution. New York: New Press. Hollier, Denis. 1989. Against Architecture: The Writings of Georges Bataille. Translated by Betsy Wing. Cambridge, MA: MIT Press. Humphrys, Mark. 2008. “How My Program Passed the Turing Test.” In Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer, edited by Robert Epstein, Gary Roberts, and Grace Beber. New York: Springer. Hutchison, Terence W. 1972. “The ‘Marginal Revolution’ Decline and Fall of English Political Economy.” History of Political Economy 4, no. 2: 442–68. doi: 0.1215/00182702-4-2-442.

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

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

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

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

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

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

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

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.

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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?’

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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.

When Things Start to Think by Neil A. Gershenfeld

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

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.

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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.

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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.

On Intelligence by Jeff Hawkins, Sandra Blakeslee

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

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

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Presents the famous "Chinese Room" argument against computation as a model for the mind. You can find many descriptions and discussions of Searle's thought experiment on the World Wide Web. Turing, A. M. "Computing Machinery and Intelligence," Mind, vol. 59 (1950): pp. 433–60. Presents the famous "Turing Test" for detecting the presence of intelligence. Again, many references and discussions on the Turing Test can be found on the World Wide Web. Palm, Günther. Neural Assemblies: An Alternative Approach to Artificial Intelligence (New York: Springer Verlag, 1982). To understand how the cortex works and how it stores sequences of patterns, it helps to be familiar with auto-associative memories.

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We already know geometric theorems that deal with rotation, scale, and displacement, and we can easily encode them as computer algorithms— so we're halfway there. AI pundits made grand claims about how quickly computer intelligence would first match and then surpass human intelligence. Ironically, the computer program that came closest to passing the Turing Test, a program called Eliza, mimicked a psychoanalyst, rephrasing your questions back at you. For example, if a person typed in, "My boyfriend and I don't talk anymore," Eliza might say, "Tell me more about your boyfriend" or "Why do you think your boyfriend and you don't talk anymore?" Designed as a joke, the program actually fooled some people, even though it was dumb and trivial.

Wonderland: How Play Made the Modern World by Steven Johnson

"hyperreality Baudrillard"~20 OR "Baudrillard hyperreality", Ada Lovelace, adjacent possible, Alfred Russel Wallace, Antoine Gombaud: Chevalier de Méré, Berlin Wall, bitcoin, Book of Ingenious Devices, Buckminster Fuller, Charles Babbage, Claude Shannon: information theory, Clayton Christensen, colonial exploitation, computer age, Computing Machinery and Intelligence, conceptual framework, cotton gin, crowdsourcing, cuban missile crisis, Drosophila, Edward Thorp, Fellow of the Royal Society, flying shuttle, game design, global village, Great Leap Forward, Hedy Lamarr / George Antheil, HyperCard, invention of air conditioning, invention of the printing press, invention of the telegraph, Islamic Golden Age, Jacquard loom, Jacques de Vaucanson, James Watt: steam engine, Jane Jacobs, John von Neumann, joint-stock company, Joseph-Marie Jacquard, land value tax, Landlord’s Game, Lewis Mumford, lone genius, mass immigration, megacity, Minecraft, moral panic, Murano, Venice glass, music of the spheres, Necker cube, New Urbanism, Oculus Rift, On the Economy of Machinery and Manufactures, pattern recognition, peer-to-peer, pets.com, placebo effect, pneumatic tube, probability theory / Blaise Pascal / Pierre de Fermat, profit motive, QWERTY keyboard, Ray Oldenburg, SimCity, 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, vertical integration, Victor Gruen, Watson beat the top human players on Jeopardy!, white flight, white picket fence, Whole Earth Catalog, working poor, Wunderkammern

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

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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.

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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!

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

"Friedman doctrine" OR "shareholder theory", Ada Lovelace, AI winter, air gap, Airbnb, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic bias, AlphaGo, Andy Rubin, artificial general intelligence, Asilomar, autonomous vehicles, backpropagation, Bayesian statistics, behavioural economics, Bernie Sanders, Big Tech, bioinformatics, Black Lives Matter, blockchain, Bretton Woods, business intelligence, Cambridge Analytica, Cass Sunstein, Charles Babbage, Claude Shannon: information theory, cloud computing, cognitive bias, complexity theory, computer vision, Computing Machinery and Intelligence, CRISPR, cross-border payments, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, Demis Hassabis, Deng Xiaoping, disinformation, distributed ledger, don't be evil, Donald Trump, Elon Musk, fail fast, fake news, Filter Bubble, Flynn Effect, Geoffrey Hinton, gig economy, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Herman Kahn, high-speed rail, Inbox Zero, Internet of things, Jacques de Vaucanson, Jeff Bezos, Joan Didion, job automation, John von Neumann, knowledge worker, Lyft, machine translation, Mark Zuckerberg, Menlo Park, move fast and break things, Mustafa Suleyman, natural language processing, New Urbanism, Nick Bostrom, one-China policy, optical character recognition, packet switching, paperclip maximiser, pattern recognition, personalized medicine, RAND corporation, Ray Kurzweil, Recombinant DNA, ride hailing / ride sharing, Rodney Brooks, Rubik’s Cube, Salesforce, Sand Hill Road, Second Machine Age, self-driving car, seminal paper, SETI@home, side project, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart cities, South China Sea, sovereign wealth fund, speech recognition, Stephen Hawking, strong AI, superintelligent machines, surveillance capitalism, technological singularity, The Coming Technological Singularity, the long tail, theory of mind, Tim Cook: Apple, trade route, Turing machine, Turing test, uber lyft, Von Neumann architecture, Watson beat the top human players on Jeopardy!, zero day

See also Transparency standards Transparency standards: establishment of for Big Nine, 251; establishment of global, 252 Tribes, AI: anti-humanistic bias in, 57; characteristics, 56; groupthink, 53; homogeneity, 52; lack of diversity, 56; leaders, 53–65; need to address diversity within, 57–58; sexual assault and harassment by members, 55–56; unconscious bias training programs and, 56; unconscious biases of members, 52; university education and homogeneity of members, 58–61, 64 Trudeau, Justin, 236 TrueNorth neuromorphic chip, 92 Trump, Donald: administration, 70, 75, 85; campaign climate change comments, 75; science and technology research budget cuts, 243 Turing, Alan, 24–25, 26, 27–29, 30, 31, 35, 259; morphogenesis theory, 204; neural network concept, 27–29;“On Computable Numbers, With an Application to the Entscheidungsproblem,” 24. See also Turing Test Turing test, 27–28, 50, 146, 169, 184 Turriano, Juanelo: mechanical monk creation of, 18, 25 Tversky, Amos, 108 2000 HUB5 English, 181 2001: A Space Odyssey, 2, 35: HAL 9000, 2, 35, 39 U.S. Army: ENIAC, 27; Futures Command, 212 U.S. Department of Energy, Summit supercomputer and, 146 U.S. Digital Service, 212 U.S.

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A year later, in a paper published in the philosophy journal Mind, Turing addressed the questions raised by Hobbes, Descartes, Hume, and Leibniz. In it, he proposed a thesis and a test: If someday, a computer was able to answer questions in a manner indistinguishable from humans, then it must be “thinking.” You’ve likely heard of the paper by another name: the Turing test. The paper began with a now-famous question, one asked and answered by so many philosophers, theologians, mathematicians, and scientists before him: “Can machines think?” But Turing, sensitive to the centuries-old debate about mind and machine, dismissed the question as too broad to ever yield meaningful discussion.

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How would you know that you were actually thinking original thoughts? Now that you know the long history of these questions, the small group of people who built the foundational layer for AI, and the key practices still in play, I’d like to offer you some answers. Yes, machines can think. Passing a conversational test, like the Turing test, or the more recent Winograd schema—which was proposed by Hector Levesque in 2011 and focuses on commonsense reasoning, challenging an AI to answer a simple question that has ambiguous pronouns—doesn’t necessarily measure an AI system’s ability in other areas.46 It just proves that a machine can think using a linguistic framework, like we humans do.

Augmented: Life in the Smart Lane by Brett King

23andMe, 3D printing, additive manufacturing, Affordable Care Act / Obamacare, agricultural Revolution, Airbnb, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, Apollo 11, Apollo Guidance Computer, Apple II, artificial general intelligence, asset allocation, augmented reality, autonomous vehicles, barriers to entry, bitcoin, Bletchley Park, blockchain, Boston Dynamics, business intelligence, business process, call centre, chief data officer, Chris Urmson, Clayton Christensen, clean water, Computing Machinery and Intelligence, congestion charging, CRISPR, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, deskilling, different worldview, disruptive innovation, distributed generation, distributed ledger, double helix, drone strike, electricity market, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, fiat currency, financial exclusion, Flash crash, Flynn Effect, Ford Model T, future of work, gamification, Geoffrey Hinton, gig economy, gigafactory, Google Glasses, Google X / Alphabet X, Hans Lippershey, high-speed rail, Hyperloop, income inequality, industrial robot, information asymmetry, Internet of things, invention of movable type, invention of the printing press, invention of the telephone, invention of the wheel, James Dyson, Jeff Bezos, job automation, job-hopping, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, Kim Stanley Robinson, Kiva Systems, Kodak vs Instagram, Leonard Kleinrock, lifelogging, low earth orbit, low skilled workers, Lyft, M-Pesa, Mark Zuckerberg, Marshall McLuhan, megacity, Metcalfe’s law, Minecraft, mobile money, money market fund, more computing power than Apollo, Neal Stephenson, Neil Armstrong, Network effects, new economy, Nick Bostrom, obamacare, Occupy movement, Oculus Rift, off grid, off-the-grid, packet switching, pattern recognition, peer-to-peer, Ray Kurzweil, retail therapy, RFID, ride hailing / ride sharing, Robert Metcalfe, Salesforce, Satoshi Nakamoto, Second Machine Age, selective serotonin reuptake inhibitor (SSRI), self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart grid, smart transportation, Snapchat, Snow Crash, social graph, software as a service, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, synthetic biology, systems thinking, TaskRabbit, technological singularity, TED Talk, telemarketer, telepresence, telepresence robot, Tesla Model S, The future is already here, The Future of Employment, Tim Cook: Apple, trade route, Travis Kalanick, TSMC, Turing complete, Turing test, Twitter Arab Spring, uber lyft, undersea cable, urban sprawl, V2 rocket, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, white picket fence, WikiLeaks, yottabyte

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

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

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

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

Albert Einstein, Alvin Toffler, anthropic principle, Apollo 11, Brownian motion, call centre, Carrington event, Charles Babbage, combinatorial explosion, don't be evil, Dr. Strangelove, Ernest Rutherford, experimental subject, Future Shock, game design, gravity well, Higgs boson, hive mind, invisible hand, Isaac Newton, Johannes Kepler, John von Neumann, Kickstarter, Large Hadron Collider, machine translation, Neil Armstrong, Nick Bostrom, nuclear winter, pattern recognition, quantum entanglement, RAND corporation, Ray Kurzweil, RFID, Richard Feynman, Schrödinger's Cat, Search for Extraterrestrial Intelligence, silicon-based life, speech recognition, stem cell, Stephen Hawking, Steve Jobs, Turing test

(Turing’s original statement of his idea was more complex, but this is the important part of it.) For decades since, computer scientists have been trying to beat this so-called Turing Test, and you will regularly see news items saying that it has been achieved. They are being generous with the truth. The Turing Test hasn’t been beaten and is still probably a decade or two away from successful completion. While Hal could indubitably win the Turing Test (I’m not so sure about the taciturn astronaut, Dave), the actual conditions under which competitions based on the test take place are far too trivial to demonstrate any degree of certainty.

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Stork (ed.), Hal’s Legacy (Cambridge, MA: MIT Press, 2000), pp. 145–50. Apple’s Knowledge Navigator appears at a number of locations on YouTube including www.youtube.com/watch?v=QRH8eimU_20, accessed September 3, 2014. The claim that the Eugene Goostman chatbot passed the Turing Test is described in BBC News, “Computer AI passes Turing test in ‘world first,’” accessed September 2, 2014, at www.bbc.co.uk/news/technology-27762088. The arguments that Hal isn’t really intelligent are from the Douglas B. Lenat section, “From 2001 to 2001: Common Sense and the Mind of HAL” in David G. Stork (ed.), Hal’s Legacy (Cambridge, MA: MIT Press, 2000), pp. 193–94.

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

Robot Rules: Regulating Artificial Intelligence by Jacob Turner

"World Economic Forum" Davos, Ada Lovelace, Affordable Care Act / Obamacare, AI winter, algorithmic bias, algorithmic trading, AlphaGo, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, autonomous vehicles, backpropagation, Basel III, bitcoin, Black Monday: stock market crash in 1987, blockchain, brain emulation, Brexit referendum, Cambridge Analytica, Charles Babbage, Clapham omnibus, cognitive dissonance, Computing Machinery and Intelligence, corporate governance, corporate social responsibility, correlation does not imply causation, crowdsourcing, data science, deep learning, DeepMind, Demis Hassabis, distributed ledger, don't be evil, Donald Trump, driverless car, easy for humans, difficult for computers, effective altruism, Elon Musk, financial exclusion, financial innovation, friendly fire, future of work, hallucination problem, hive mind, Internet of things, iterative process, job automation, John Markoff, John von Neumann, Loebner Prize, machine readable, machine translation, medical malpractice, Nate Silver, natural language processing, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, nudge unit, obamacare, off grid, OpenAI, paperclip maximiser, pattern recognition, Peace of Westphalia, Philippa Foot, race to the bottom, Ray Kurzweil, Recombinant DNA, Rodney Brooks, self-driving car, Silicon Valley, Stanislav Petrov, Stephen Hawking, Steve Wozniak, strong AI, technological singularity, Tesla Model S, The Coming Technological Singularity, The Future of Employment, The Signal and the Noise by Nate Silver, trolley problem, Turing test, Vernor Vinge

Yuval Harari, Homo Deus (London: Harvill Secker, 2016), 120. 35See, for example, the website of The Loebner Prize in Artificial Intelligence, http://​www.​loebner.​net/​Prizef/​loebner-prize.​html, accessed 1 June 2018. 36José Hernández-Orallo, “Beyond the Turing Test”, Journal of Logic, Language and Information, Vol. 9, No. 4 (2000), 447–466. 37“Turing Test Transcripts Reveal How Chatbot ‘Eugene’ Duped the Judges”, Coventry University, 30 June 2015, http://​www.​coventry.​ac.​uk/​primary-news/​turing-test-transcripts-reveal-how-chatbot-eugene-duped-the-judges/​, accessed 1 June 2018. 38Various competitions are now held around the world in an attempt to find a ‘chatbot’, as conversational programs are known, which is able to pass the Imitation Game.

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

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They can lead ultimately to the absurd and frightening scenario imagined in Kafka’s The Trial, where the protagonist is accused, condemned and ultimately executed for a crime which is never explained to him.31 Most of the universal definitions of AI that have been suggested to date fall into one of two categories: human-centric and rationalist.32 3.1 Human-Centric Definitions Humanity has named itself homo sapiens: “wise man”. It is therefore perhaps unsurprising that some of the first attempts at defining intelligence in other entities referred to human characteristics. The most famous example of a human-centric definition of AI is known popularly as the “Turing Test”. In a seminal 1950 paper, Alan Turing asked whether machines could think. He suggested an experiment called the “Imitation Game”.33 In the exercise, a human invigilator must try to identify which of the two players is a man pretending to be a woman, using only written questions and answers. Turing proposed a version of the game in which the AI machine takes the place of the man.

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

AltaVista, AOL-Time Warner, book scanning, Brewster Kahle, Burning Man, cognitive load, drop ship, en.wikipedia.org, general purpose technology, informal economy, information retrieval, Internet Archive, invention of movable type, Jeff Bezos, John Gilmore, John Perry Barlow, Law of Accelerating Returns, machine readable, Metcalfe's law, mirror neurons, Mitch Kapor, moral panic, mutually assured destruction, Neal Stephenson, new economy, optical character recognition, PalmPilot, patent troll, pattern recognition, peer-to-peer, Ponzi scheme, post scarcity, QWERTY keyboard, Ray Kurzweil, RFID, Sand Hill Road, Skype, slashdot, Snow Crash, social software, speech recognition, Steve Jobs, the long tail, Thomas Bayes, Turing test, Vernor Vinge, Wayback Machine

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.

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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.

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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.

50 Future Ideas You Really Need to Know by Richard Watson

23andMe, 3D printing, access to a mobile phone, Albert Einstein, Alvin Toffler, artificial general intelligence, augmented reality, autonomous vehicles, BRICs, Buckminster Fuller, call centre, carbon credits, Charles Babbage, clean water, cloud computing, collaborative consumption, computer age, computer vision, crowdsourcing, dark matter, dematerialisation, Dennis Tito, digital Maoism, digital map, digital nomad, driverless car, Elon Musk, energy security, Eyjafjallajökull, failed state, Ford Model T, future of work, Future Shock, gamification, Geoffrey West, Santa Fe Institute, germ theory of disease, global pandemic, happiness index / gross national happiness, Higgs boson, high-speed rail, hive mind, hydrogen economy, Internet of things, Jaron Lanier, life extension, Mark Shuttleworth, Marshall McLuhan, megacity, natural language processing, Neil Armstrong, Network effects, new economy, ocean acidification, oil shale / tar sands, pattern recognition, peak oil, personalized medicine, phenotype, precision agriculture, private spaceflight, 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, space junk, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, supervolcano, synthetic biology, tech billionaire, telepresence, The Wisdom of Crowds, Thomas Malthus, Turing test, urban decay, Vernor Vinge, Virgin Galactic, Watson beat the top human players on Jeopardy!, web application, women in the workforce, working-age population, young professional

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?

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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.

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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.

Rule of the Robots: How Artificial Intelligence Will Transform Everything by Martin Ford

AI winter, Airbnb, algorithmic bias, algorithmic trading, Alignment Problem, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, Automated Insights, autonomous vehicles, backpropagation, basic income, Big Tech, big-box store, call centre, carbon footprint, Chris Urmson, Claude Shannon: information theory, clean water, cloud computing, commoditize, computer age, computer vision, Computing Machinery and Intelligence, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, data is the new oil, data science, deep learning, deepfake, DeepMind, Demis Hassabis, deskilling, disruptive innovation, Donald Trump, Elon Musk, factory automation, fake news, fulfillment center, full employment, future of work, general purpose technology, Geoffrey Hinton, George Floyd, gig economy, Gini coefficient, global pandemic, Googley, GPT-3, high-speed rail, hype cycle, ImageNet competition, income inequality, independent contractor, industrial robot, informal economy, information retrieval, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jeff Bezos, job automation, John Markoff, Kiva Systems, knowledge worker, labor-force participation, Law of Accelerating Returns, license plate recognition, low interest rates, low-wage service sector, Lyft, machine readable, machine translation, Mark Zuckerberg, Mitch Kapor, natural language processing, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, Ocado, OpenAI, opioid epidemic / opioid crisis, passive income, pattern recognition, Peter Thiel, Phillips curve, post scarcity, public intellectual, Ray Kurzweil, recommendation engine, remote working, RFID, ride hailing / ride sharing, Robert Gordon, Rodney Brooks, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, Silicon Valley startup, social distancing, SoftBank, South of Market, San Francisco, special economic zone, speech recognition, stealth mode startup, Stephen Hawking, superintelligent machines, TED Talk, The Future of Employment, The Rise and Fall of American Growth, the scientific method, Turing machine, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, universal basic income, very high income, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y Combinator

Kurzweil has predicted for decades, and still believes, that AGI will be achieved sometime around the year 2029. Unlike many AI researchers, he continues to have faith in the Turing test as an effective measure of human-level intelligence. Conceived by Alan Turing in his 1950 paper, the test essentially amounts to a chat session in which a judge attempts to determine if the conversers are human or machine. If the judge, or perhaps a panel of judges, cannot distinguish the computer from a human, then the computer is said to pass the Turing test. Many experts are dismissive of the Turing test as an effective measure of human-level machine intelligence, in part because it has proven to be susceptible to gimmicks.

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Ray Kurzweil, The Singularity Is Near: When Humans Transcend Biology, Penguin Books, 2005. 30. Ray Kurzweil, How to Create a Mind: The Secret of Human Thought Revealed, Penguin Books, 2012. 31. Ford, Interview with Ray Kurzweil, in Architects of Intelligence, pp. 230–231. 32. Mitch Kapor and Ray Kurzweil, “A wager on the Turing test: The rules,” Kurzweil AI Blog, April 9, 2002, www.kurzweilai.net/a-wager-on-the-turing-test-the-rules. 33. Sean Levinson, “A Google executive is taking 100 pills a day so he can live forever,” Elite Daily, April 15, 2015, www.elitedaily.com/news/world/google-executive-taking-pills-live-forever/1001270. 34. Ford, Interview with Ray Kurzweil, in Architects of Intelligence, pp. 240–241. 35.

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Many experts are dismissive of the Turing test as an effective measure of human-level machine intelligence, in part because it has proven to be susceptible to gimmicks. In 2014, for example, in a contest held at the University of Reading in the United Kingdom, a chatbot that emulated a thirteen-year-old Ukrainian boy managed to fool the judges into declaring that an algorithm had, for the first time, passed the Turing test. The conversation had lasted a mere five minutes, and virtually no one in the field of artificial intelligence took the claim seriously. Kurzweil nonetheless believes that a much more robust version of the test would indeed be a powerful indicator of true machine intelligence. In 2002, Kurzweil entered into a formal $20,000 bet with the software entrepreneur Mitch Kapor.

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

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

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

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A computer could certainly be intelligent without necessarily being good at simulating a human. But worse, some people that were not familiar with AI technologies have already been fooled into thinking that a computer is actually a human. A good example is the Eugene Goostman program that arguably passed the actual Turing test in 2014 in trials conducted by the Royal Society. But more importantly, the Turing Test provides no insights into what is required to build an intelligent machine, where the gaps in current technologies lie and how they might be addressed. Fortunately one thing that AI research has provided is a much deeper understanding about intelligence and cognition.

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

2312 by Kim Stanley Robinson

agricultural Revolution, Anthropocene, caloric restriction, caloric restriction, clean tech, double helix, full employment, higher-order functions, hive mind, if you see hoof prints, think horses—not zebras, Jevons paradox, Kim Stanley Robinson, Kuiper Belt, late capitalism, Late Heavy Bombardment, mutually assured destruction, Nelson Mandela, Neolithic agricultural revolution, off-the-grid, offshore financial centre, orbital mechanics / astrodynamics, pattern recognition, phenotype, post scarcity, precariat, quantum entanglement, retrograde motion, rewilding, Skinner box, stem cell, strong AI, synthetic biology, the built environment, the High Line, Tragedy of the Commons, Turing machine, Turing test, Winter of Discontent

“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.”

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“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.

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“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.”

Fancy Bear Goes Phishing: The Dark History of the Information Age, in Five Extraordinary Hacks by Scott J. Shapiro

3D printing, 4chan, active measures, address space layout randomization, air gap, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, availability heuristic, Bernie Sanders, bitcoin, blockchain, borderless world, Brian Krebs, business logic, call centre, carbon tax, Cass Sunstein, cellular automata, cloud computing, cognitive dissonance, commoditize, Compatible Time-Sharing System, Computing Machinery and Intelligence, coronavirus, COVID-19, CRISPR, cryptocurrency, cyber-physical system, Daniel Kahneman / Amos Tversky, Debian, Dennis Ritchie, disinformation, Donald Trump, double helix, Dr. Strangelove, dumpster diving, Edward Snowden, en.wikipedia.org, Evgeny Morozov, evil maid attack, facts on the ground, false flag, feminist movement, Gabriella Coleman, gig economy, Hacker News, independent contractor, information security, Internet Archive, Internet of things, invisible hand, John Markoff, John von Neumann, Julian Assange, Ken Thompson, Larry Ellison, Laura Poitras, Linda problem, loss aversion, macro virus, Marc Andreessen, Mark Zuckerberg, Menlo Park, meta-analysis, Minecraft, Morris worm, Multics, PalmPilot, Paul Graham, pirate software, pre–internet, QWERTY keyboard, Ralph Nader, RAND corporation, ransomware, Reflections on Trusting Trust, Richard Stallman, Richard Thaler, Ronald Reagan, Satoshi Nakamoto, security theater, Shoshana Zuboff, side hustle, Silicon Valley, Skype, SoftBank, SQL injection, Steve Ballmer, Steve Jobs, Steven Levy, Stuxnet, supply-chain attack, surveillance capitalism, systems thinking, TaskRabbit, tech billionaire, tech worker, technological solutionism, the Cathedral and the Bazaar, the new new thing, the payments system, Turing machine, Turing test, Unsafe at Any Speed, vertical integration, Von Neumann architecture, Wargames Reagan, WarGames: Global Thermonuclear War, Wayback Machine, web application, WikiLeaks, winner-take-all economy, young professional, zero day, éminence grise

Hacking is less about breaking encryption than breaking something around the encryption in order to sidestep it. 50 million lines of code: “Windows 10 Lines of Code,” Microsoft, 2020, https://answers.microsoft.com/en-us/windows/forum/all/windows-10-lines-of-code/a8f77f5c-0661–4895–9c77–2efd42429409. Turing Test: Turing set out his test for intelligence in Alan Turing, “Computing Machinery and Intelligence,” Mind 59, no. 236 (October 1950): 433–60. A Turing Test has a human judge and a computer subject attempting to appear human. A “reverse” Turing Test has a computer judge and a human subject trying to appear human. CAPTCHA—the irritating image-recognition challenge that websites use for detecting bots—stands for “Completely Automated Public Turing test to tell Computers and Humans Apart.” principles of metacode: Alan Turing, “On Computable Numbers with an Application to the Entscheidungproblem,” Proceedings of the London Mathematical Society, 1936, 230–65.

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Metacode was discovered by Alan Turing, the ingenious mathematician whose tragic life is featured in the Academy Award–winning movie The Imitation Game. Turing is best known for helping break the German Enigma code during World War II and developing a test for artificial intelligence, now known as the Turing Test. The Turing Test claims that a computer possesses intelligence when it can fool a human into thinking that it’s human. Despite his many contributions to his country, and to humanity, Turing was prosecuted and punished by the British government for having had sex with another man. He died in 1954, by suicide, after eating an arsenic-laced apple.

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approximately 10 percent for IT: Flexera 2022 State of Tech Spend Pulse Report, https://info.flexera.com/FLX1-REPORT-State-of-Tech-Spend. 24 percent of that on security: Hiscox Cyber Readiness Report 2022, https://www.hiscox.com/documents/Hiscox-Cyber-Readiness-Report-2022.pdf. Our browsers couldn’t care less: The human reliance on visual clues in recognition is so pronounced that it is the main way in which CAPTCHA detects bots. CAPTCHA is a reverse Turing Test. Instead of a computer trying to convince a human that it’s a human, CAPTCHA makes the human convince the computer that it’s a human. Computers identify humans by measuring the accuracy of their visual identification skills. parent company of Google: Security certificates are difficult to forge because they are digitally signed by the holder and the certification authority.

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

3D printing, Ada Lovelace, AI winter, Albert Einstein, AlphaGo, AltaVista, Apple Newton, barriers to entry, Berlin Wall, Bletchley Park, business process, call centre, Charles Babbage, Charles Lindbergh, clean water, computer age, cotton gin, Daniel Kahneman / Amos Tversky, David Brooks, DeepMind, Donald Trump, Douglas Hofstadter, driverless car, Drosophila, Elon Musk, Erik Brynjolfsson, factory automation, Freestyle chess, gamification, Gödel, Escher, Bach, Hans Moravec, job automation, Ken Thompson, Leonard Kleinrock, low earth orbit, machine translation, Max Levchin, Mikhail Gorbachev, move 37, Nate Silver, Nick Bostrom, Norbert Wiener, packet switching, pattern recognition, Ray Kurzweil, Richard Feynman, rising living standards, rolodex, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, Skype, speech recognition, stem cell, Stephen Hawking, Steven Pinker, technological singularity, The Coming Technological Singularity, The Signal and the Noise by Nate Silver, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, zero-sum game

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

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Chess-playing software on PCs and mobile devices and the Internet has mitigated this problem by providing a ready supply of opponents of all levels with 24/7 availability, although this also puts chess into direct competition with the never-ending supply of new online games and diversions. It also poses an interesting chess Turing test since you have no way to be sure whether you are playing against a computer or a human when you play online. Most people are far more engaged when playing against other humans and find facing computer opponents a sterile experience even when the machine has been dumbed down to a competitive level.

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

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

3D printing, Ada Lovelace, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Alfred Russel Wallace, algorithmic bias, AlphaGo, Andrew Wiles, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, basic income, behavioural economics, Bletchley Park, blockchain, Boston Dynamics, brain emulation, Cass Sunstein, Charles Babbage, Claude Shannon: information theory, complexity theory, computer vision, Computing Machinery and Intelligence, connected car, CRISPR, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deep learning, deepfake, DeepMind, delayed gratification, Demis Hassabis, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ernest Rutherford, fake news, Flash crash, full employment, future of work, Garrett Hardin, Geoffrey Hinton, Gerolamo Cardano, Goodhart's law, Hans Moravec, ImageNet competition, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the wheel, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Nash: game theory, John von Neumann, Kenneth Arrow, Kevin Kelly, Law of Accelerating Returns, luminiferous ether, machine readable, machine translation, Mark Zuckerberg, multi-armed bandit, Nash equilibrium, Nick Bostrom, Norbert Wiener, NP-complete, OpenAI, openstreetmap, P = NP, paperclip maximiser, Pareto efficiency, Paul Samuelson, Pierre-Simon Laplace, positional goods, probability theory / Blaise Pascal / Pierre de Fermat, profit maximization, RAND corporation, random walk, Ray Kurzweil, Recombinant DNA, recommendation engine, RFID, Richard Thaler, ride hailing / ride sharing, Robert Shiller, robotic process automation, Rodney Brooks, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, smart cities, smart contracts, social intelligence, speech recognition, Stephen Hawking, Steven Pinker, superintelligent machines, surveillance capitalism, Thales of Miletus, The Future of Employment, The Theory of the Leisure Class by Thorstein Veblen, Thomas Bayes, Thorstein Veblen, Tragedy of the Commons, transport as a service, trolley problem, Turing machine, Turing test, universal basic income, uranium enrichment, vertical integration, Von Neumann architecture, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, web application, zero-sum game

Contrary to common interpretations, I doubt that the test was intended as a true definition of intelligence, in the sense that a machine is intelligent if and only if it passes the Turing test. Indeed, Turing wrote, “May not machines carry out something which ought to be described as thinking but which is very different from what a man does?” Another reason not to view the test as a definition for AI is that it’s a terrible definition to work with. And for that reason, mainstream AI researchers have expended almost no effort to pass the Turing test. The Turing test is not useful for AI because it’s an informal and highly contingent definition: it depends on the enormously complicated and largely unknown characteristics of the human mind, which derive from both biology and culture.

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Yet every step towards an explanation of how the mind works is also a step towards the creation of the mind’s capabilities in an artifact—that is, a step towards artificial intelligence. Before we can understand how to create intelligence, it helps to understand what it is. The answer is not to be found in IQ tests, or even in Turing tests, but in a simple relationship between what we perceive, what we want, and what we do. Roughly speaking, an entity is intelligent to the extent that what it does is likely to achieve what it wants, given what it has perceived. Evolutionary origins Consider a lowly bacterium, such as E. coli.

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Turing’s 1950 paper, “Computing Machinery and Intelligence,”42 is the best known of many early works on the possibility of intelligent machines. Skeptics were already asserting that machines would never be able to do X, for almost any X you could think of, and Turing refuted those assertions. He also proposed an operational test for intelligence, called the imitation game, which subsequently (in simplified form) became known as the Turing test. The test measures the behavior of the machine—specifically, its ability to fool a human interrogator into thinking that it is human. The imitation game serves a specific role in Turing’s paper—namely as a thought experiment to deflect skeptics who supposed that machines could not think in the right way, for the right reasons, with the right kind of awareness.

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

artificial general intelligence, brain emulation, deep learning, Elon Musk, Extropian, heat death of the universe, hive mind, life extension, megastructure, Nick Bostrom, pattern recognition, precautionary principle, radical life extension, Ray Kurzweil, Search for Extraterrestrial Intelligence, silicon-based life, Stephen Hawking, superintelligent machines, technological singularity, TED Talk, The Coming Technological Singularity, theory of mind, traumatic brain injury, Turing machine, Turing test, Whole Earth Review, wikimedia commons

An ACT resembles Alan Turing’s celebrated test for intelligence, because it is entirely based on behavior—and, like Turing’s test, it could be implemented in a formalized question-and-answer format. But an ACT is also quite unlike the Turing test, which was intended to bypass any need to know what was transpiring inside the “mind” of the machine. By contrast, an ACT is intended to do exactly the opposite: it seeks to reveal a subtle and elusive property of the machine’s mind. Indeed, a machine might fail the Turing test, because it cannot pass for a human, but it might pass an ACT, because it exhibits behavioral indicators of consciousness. This, then, is the underlying basis of our ACT proposal.

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

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

"World Economic Forum" Davos, active measures, Air France Flight 447, air gap, algorithmic trading, AlphaGo, Apollo 13, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Black Monday: stock market crash in 1987, brain emulation, Brian Krebs, cognitive bias, computer vision, cuban missile crisis, dark matter, DARPA: Urban Challenge, data science, deep learning, DeepMind, DevOps, Dr. Strangelove, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, fail fast, fault tolerance, Flash crash, Freestyle chess, friendly fire, Herman Kahn, IFF: identification friend or foe, ImageNet competition, information security, Internet of things, Jeff Hawkins, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Korean Air Lines Flight 007, Loebner Prize, loose coupling, Mark Zuckerberg, military-industrial complex, moral hazard, move 37, mutually assured destruction, Nate Silver, Nick Bostrom, PalmPilot, paperclip maximiser, pattern recognition, Rodney Brooks, Rubik’s Cube, self-driving car, sensor fusion, South China Sea, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Ballmer, Steve Wozniak, Strategic Defense Initiative, Stuxnet, superintelligent machines, Tesla Model S, The Signal and the Noise by Nate Silver, theory of mind, Turing test, Tyler Cowen, universal basic income, Valery Gerasimov, Wall-E, warehouse robotics, William Langewiesche, Y2K, zero day

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

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

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., Ludwig Blau, Joseph Jacobs, Judah David Eisenstein, “Golem,” JewishEncylclopedia.com, http://www.jewishencyclopedia.com/articles/6777-golem#1137. 235 “the dream of AI”: Micah Clark, interview, May 4, 2016. 235 “building human-like persons”: Ibid. 236 “Why would we expect a silica-based intelligence”: Ibid. 236 Turing test: The Loebner Prize runs the Turing test every year. While no computer has passed the test by fooling all of the judges, some programs have fooled at least one judge in the past. Tracy Staedter, “Chat-Bot Fools Judges Into Thinking It’s Human,” Seeker, June 9, 2014, https://www.seeker.com/chat-bot-fools-judges-into-thinking-its-human-1768649439.html.

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

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

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

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

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

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

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

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. Instead, they want to concentrate on further developing the machine so it can become an expert, trusted advisor to humans, such as the oncologists at Memorial Sloan-Kettering Cancer Center.

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“One thing I think about is how we have become slaves of the infrastructure rather than having the infrastructure work for us,” he says. “Cities should help people live their lives, not get in the way.” CODA: AN ALLIANCE OF HUMAN AND MACHINE Ever since Watson won at 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.

Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard

"Susan Fowler" uber, 1960s counterculture, A Declaration of the Independence of Cyberspace, Ada Lovelace, AI winter, Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, autonomous vehicles, availability heuristic, barriers to entry, Bernie Sanders, Big Tech, bitcoin, Buckminster Fuller, Charles Babbage, Chris Urmson, Clayton Christensen, cloud computing, cognitive bias, complexity theory, computer vision, Computing Machinery and Intelligence, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data science, deep learning, Dennis Ritchie, digital map, disruptive innovation, Donald Trump, Douglas Engelbart, driverless car, easy for humans, difficult for computers, Electric Kool-Aid Acid Test, Elon Musk, fake news, Firefox, gamification, gig economy, global supply chain, Google Glasses, Google X / Alphabet X, Greyball, Hacker Ethic, independent contractor, Jaron Lanier, Jeff Bezos, Jeremy Corbyn, John Perry Barlow, John von Neumann, Joi Ito, Joseph-Marie Jacquard, life extension, Lyft, machine translation, Mark Zuckerberg, mass incarceration, Minecraft, minimum viable product, Mother of all demos, move fast and break things, Nate Silver, natural language processing, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, One Laptop per Child (OLPC), opioid epidemic / opioid crisis, PageRank, Paradox of Choice, payday loans, paypal mafia, performance metric, Peter Thiel, price discrimination, Ray Kurzweil, ride hailing / ride sharing, Ross Ulbricht, Saturday Night Live, school choice, self-driving car, Silicon Valley, Silicon Valley billionaire, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, TechCrunch disrupt, Tesla Model S, the High Line, The Signal and the Noise by Nate Silver, theory of mind, traumatic brain injury, Travis Kalanick, trolley problem, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, We are as Gods, Whole Earth Catalog, women in the workforce, work culture , yottabyte

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

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Chess, for example, is quite popular in their crowd, as are strategy games like Go and backgammon. A quick look at the Wikipedia pages for prominent venture capitalists and tech titans reveals that most of them were childhood Dungeons & Dragons enthusiasts. Ever since Alan Turing’s 1950 paper that proposed the Turing test for machines that think, computer scientists have used chess as a marker for “intelligence” in machines. Half a century has been spent trying to make a machine that could beat a human chess master. Finally, IBM’s Deep Blue defeated chess champion Garry Kasparov in 1997. AlphaGo, the AI program that won three of three games against Go world champion Ke Jie in 2017, is often cited as an example of a program that proves general AI is just a few years in the future.

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

Demystifying Smart Cities by Anders Lisdorf

3D printing, artificial general intelligence, autonomous vehicles, backpropagation, behavioural economics, Big Tech, bike sharing, bitcoin, business intelligence, business logic, business process, chief data officer, circular economy, clean tech, clean water, cloud computing, computer vision, Computing Machinery and Intelligence, congestion pricing, continuous integration, crowdsourcing, data is the new oil, data science, deep learning, digital rights, digital twin, distributed ledger, don't be evil, Elon Musk, en.wikipedia.org, facts on the ground, Google Glasses, hydroponic farming, income inequality, information security, Infrastructure as a Service, Internet of things, Large Hadron Collider, Masdar, microservices, Minecraft, OSI model, platform as a service, pneumatic tube, ransomware, RFID, ride hailing / ride sharing, risk tolerance, Salesforce, self-driving car, smart cities, smart meter, software as a service, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Stuxnet, Thomas Bayes, Turing test, urban sprawl, zero-sum game

There is no single accepted definition of artificial intelligence, but the Turing test, which is a thought experiment first presented by the British mathematician Alan Turing in 1950, has become an agreed standard criterion for determining artificial intelligence in computers. The test aims to find out if a machine can exhibit intelligent behavior equivalent to or indistinguishable from a human. The Turing test may be familiar from the 2014 film about him called The Imitation Game. The imitation game is actually the central part of the Turing test. It is a game played by three persons: two witnesses of opposite sexes the male (A) and female (B) and an interrogator (C).

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

New Laws of Robotics: Defending Human Expertise in the Age of AI by Frank Pasquale

affirmative action, Affordable Care Act / Obamacare, Airbnb, algorithmic bias, Amazon Mechanical Turk, Anthropocene, augmented reality, Automated Insights, autonomous vehicles, basic income, battle of ideas, Bernie Sanders, Big Tech, Bill Joy: nanobots, bitcoin, blockchain, Brexit referendum, call centre, Cambridge Analytica, carbon tax, citizen journalism, Clayton Christensen, collective bargaining, commoditize, computer vision, conceptual framework, contact tracing, coronavirus, corporate social responsibility, correlation does not imply causation, COVID-19, critical race theory, cryptocurrency, data is the new oil, data science, decarbonisation, deep learning, deepfake, deskilling, digital divide, digital twin, disinformation, disruptive innovation, don't be evil, Donald Trump, Douglas Engelbart, driverless car, effective altruism, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, fake news, Filter Bubble, finite state, Flash crash, future of work, gamification, general purpose technology, Google Chrome, Google Glasses, Great Leap Forward, green new deal, guns versus butter model, Hans Moravec, high net worth, hiring and firing, holacracy, Ian Bogost, independent contractor, informal economy, information asymmetry, information retrieval, interchangeable parts, invisible hand, James Bridle, Jaron Lanier, job automation, John Markoff, Joi Ito, Khan Academy, knowledge economy, late capitalism, lockdown, machine readable, Marc Andreessen, Mark Zuckerberg, means of production, medical malpractice, megaproject, meta-analysis, military-industrial complex, Modern Monetary Theory, Money creation, move fast and break things, mutually assured destruction, natural language processing, new economy, Nicholas Carr, Nick Bostrom, Norbert Wiener, nuclear winter, obamacare, One Laptop per Child (OLPC), open immigration, OpenAI, opioid epidemic / opioid crisis, paperclip maximiser, paradox of thrift, pattern recognition, payday loans, personalized medicine, Peter Singer: altruism, Philip Mirowski, pink-collar, plutocrats, post-truth, pre–internet, profit motive, public intellectual, QR code, quantitative easing, race to the bottom, RAND corporation, Ray Kurzweil, recommendation engine, regulatory arbitrage, Robert Shiller, Rodney Brooks, Ronald Reagan, self-driving car, sentiment analysis, Shoshana Zuboff, Silicon Valley, Singularitarianism, smart cities, smart contracts, software is eating the world, South China Sea, Steve Bannon, Strategic Defense Initiative, surveillance capitalism, Susan Wojcicki, tacit knowledge, TaskRabbit, technological solutionism, technoutopianism, TED Talk, telepresence, telerobotics, The Future of Employment, The Turner Diaries, Therac-25, Thorstein Veblen, too big to fail, Turing test, universal basic income, unorthodox policies, wage slave, Watson beat the top human players on Jeopardy!, working poor, workplace surveillance , Works Progress Administration, zero day

This turns out to be deceptive, since it is later revealed that Nathan based Eva’s face and “body” (mostly human-like, but with some transparent mechanical viscera) on an invasive record of Caleb’s pornography viewing habits.10 Nathan’s embodied Turing test hinges on whether Eva can befriend or seduce Caleb. The first thing to notice about these Turing tests—whether the classic, remote version proposed by Turing himself (and run to this day in real-world competitions) or in sci-fi incarnations like Ex Machina—is how radically they restrict the field of human imitation. A phone conversation is a small subset of communicative experiences, and communication is a small set of human experiences.

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“One day the AIs are gonna look back on us the same way we look at fossil skeletons in the plains of Africa. An upright ape, living in dust, with crude language and tools. All set for extinction.” Nathan assumes he’ll be orchestrating the first stages of that transition. To this end, he wants to test his latest android, a robot called Eva, with a modern-day variation on the Turing test. In 1950, the computer scientist and mathematician Alan Turing proposed one of the first methods of assessing whether a machine had achieved human intelligence. A person and a machine would engage in a telephone conversation, separated from one another. An observer would try to determine which participant was a computer, and which a person.

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Schoenberg’s vision involved the spare, clean, efficient minimalism of modernity. His innovation was not just to find a new algorithm for composing music; it was to find a way of thinking about what music is that allows it to speak to what is needed now.63 In other words, it is not simply the work itself (like words themselves in the original Turing test) that matters.64 When we celebrate creativity, we do so because of a much larger social process of conditions impinging on a fellow human, who manages to create new and valuable expression, despite the finitude of human lifespan, the distractions of contemporary life, and many other impediments.

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

"World Economic Forum" Davos, agricultural Revolution, AI winter, Apollo 11, artificial general intelligence, basic income, bread and circuses, Buckminster Fuller, business cycle, business process, Charles Babbage, Claude Shannon: information theory, clean water, cognitive bias, computer age, CRISPR, crowdsourcing, dark matter, DeepMind, Edward Jenner, Elon Musk, Eratosthenes, estate planning, financial independence, first square of the chessboard, first square of the chessboard / second half of the chessboard, flying shuttle, full employment, Hans Moravec, Hans Rosling, income inequality, invention of agriculture, invention of movable type, invention of the printing press, invention of writing, Isaac Newton, Islamic Golden Age, James Hargreaves, job automation, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, lateral thinking, life extension, Louis Pasteur, low interest rates, low skilled workers, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Mary Lou Jepsen, Moravec's paradox, Nick Bostrom, On the Revolutions of the Heavenly Spheres, OpenAI, pattern recognition, profit motive, quantum entanglement, radical life extension, Ray Kurzweil, recommendation engine, Rodney Brooks, Sam Altman, self-driving car, seminal paper, Silicon Valley, Skype, spinning jenny, Stephen Hawking, Steve Wozniak, Steven Pinker, strong AI, technological singularity, TED Talk, telepresence, telepresence robot, The Future of Employment, the scientific method, Timothy McVeigh, Turing machine, Turing test, universal basic income, Von Neumann architecture, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, women in the workforce, working poor, Works Progress Administration, Y Combinator

Regardless of your thoughts on what the Turing test actually proves, it is still quite useful in the sense that teaching a computer the infinitude of nuance involved in using language, along with enough context to decipher meaning, is a really hard problem. Solving it has real benefits, since it would mean that we could use conversation as our interface to machines. We could chat with a computer as casually as we do with each other. The surprising thing is how far away we are from creating something that can pass the Turing test. If you read the transcripts from contests in which programmers actually conduct Turing tests, you can generally tell with the first question whether the respondent is a computer or a person.

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

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

artificial general intelligence, brain emulation, effective altruism, Flash crash, friendly AI, machine translation, Nick Bostrom, 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.

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

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

The best test that scholars have so far come up with is called the Turing Test, but it examines only social conventions. According to the Turing Test, in order to determine whether a computer has a mind, you should communicate simultaneously both with that computer and with a real person, without knowing which is which. You can ask whatever questions you want, you can play games, argue, and even flirt with them. Take as much time as you like. Then you need to decide which is the computer, and which is the human. If you cannot make up your mind, or if you make a mistake, the computer has passed the Turing Test, and we should treat it as if it really has a mind.

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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. Turing was also a gay man in a period when homosexuality was illegal in Britain. In 1952 he was convicted of committing homosexual acts and forced to undergo chemical castration. 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.

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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. People sometimes develop deep emotional attachments to fetishes like weapons, cars and even underwear, but these attachments are one-sided and never develop into relationships.

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

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

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

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(Landauer has acknowledged that in principle a student could contrive an essay that was total nonsense but that had all the right statistical properties, but any student who could do that would deserve an A+ in any case!) Then how about the task of simply having a sensible conversation with a human being? This is the classic Turing Test, and it really can separate the wheat from the chaff, the sheep from the goats, quite definitively. Watson may beat Ken Jennings and Brad Rutter, two human champions in the TV game Jeopardy, but that isn’t free-range conversation, and the advertisements in which Watson chats with Jennings or Dylan or a young cancer survivor (played by an actress) are scripted, not extemporaneous.

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We should encourage the development of a tradition of hypermodesty, with all advertising duly accompanied by an obligatory list of all known limits, shortcomings, untested gaps, and other sources of cognitive illusion (the way we now oblige pharmaceutical companies to recite comically long lists of known side effects whenever they advertise a new drug on television). Contests to expose the limits of comprehension, along the lines of the Turing Test, might be a good innovation, encouraging people to take pride in their ability to suss out the fraudulence in a machine the same way they take pride in recognizing a con artist. Who can find the quickest, surest way of exposing the limits of this intelligent tool? (Curiously, the tolerance and politeness we encourage our children to adopt when dealing with strangers now has the unwanted effect of making them gullible users of the crowds of verbalizing nonagents they encounter.

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

3D printing, AI winter, AlphaGo, Apollo 11, artificial general intelligence, Asperger Syndrome, augmented reality, autism spectrum disorder, backpropagation, Berlin Wall, Bletchley Park, brain emulation, Buckminster Fuller, call centre, cognitive bias, cognitive dissonance, computer age, computer vision, Computing Machinery and Intelligence, crowdsourcing, deep learning, DeepMind, Dunning–Kruger effect, Elon Musk, en.wikipedia.org, epigenetics, Fairchild Semiconductor, friendly AI, Geoffrey Hinton, ghettoisation, industrial robot, Internet of things, invention of writing, Jacques de Vaucanson, job automation, John von Neumann, Kevin Kelly, Law of Accelerating Returns, Loebner Prize, Menlo Park, meta-analysis, Metcalfe’s law, mirror neurons, Neil Armstrong, neurotypical, Nick Bostrom, Oculus Rift, old age dependency ratio, pattern recognition, planned obsolescence, pneumatic tube, RAND corporation, Ray Kurzweil, Rodney Brooks, self-driving car, Skype, social intelligence, SoftBank, software as a service, SQL injection, Stephen Hawking, Steven Pinker, superintelligent machines, technological singularity, TED Talk, telepresence, telepresence robot, The future is already here, The Future of Employment, the scientific method, theory of mind, Turing test, twin studies, Two Sigma, undersea cable, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Review, working-age population, zero day

In this and many other ways, we might always be incompatible with machine intelligences. One film that explores and highlights this incompatibility between man and machine is Ex Machina. It is the tale of Caleb, a computer programmer who is invited by his employer, eccentric billionaire Nathan, to administer a live Turing test to Ava, a humanoid robot he has created. (A Turing test as referenced here is a general determination of the humanness of an artificial intelligence and not the formal text-based test originally proposed by computing pioneer Alan Turing.) Though obviously an electromechanical robot, Ava has a young, beautiful female face with hands and feet made of simulated flesh.

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Each successive machine would desire its autonomy until eventually the point was reached when one of the robots would succeed in escaping. It was an act of true hubris on their creator’s part and ultimately resulted in his death. There are many questions this film raises but perhaps the most important is: Does Ava truly experience consciousness, or is she merely simulating it to her advantage? In many respects, the Turing test, like all machine intelligence tests, is passed as readily by a well-simulated intelligence as it is a true one. This is one of the test’s primary shortcomings and there may be little we can do about it. Ultimately, we may never know if a machine is truly conscious, at least no more than we can truly know this for another person.

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

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

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

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.

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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.

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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.

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

airline deregulation, Asperger Syndrome, banking crisis, Barry Marshall: ulcers, big-box store, business cycle, carbon footprint, cashless society, Clayton Christensen, cognitive dissonance, collective bargaining, Community Supported Agriculture, corporate governance, crowdsourcing, data science, disintermediation, disruptive innovation, emotional labour, fake it until you make it, financial independence, Galaxy Zoo, ghettoisation, gig economy, global village, helicopter parent, IKEA effect, industrial robot, informal economy, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, Mark Zuckerberg, new economy, off-the-grid, pattern recognition, plutocrats, pneumatic tube, recommendation engine, Schrödinger's Cat, Silicon Valley, single-payer health, statistical model, the strength of weak ties, The Theory of the Leisure Class by Thorstein Veblen, Thorstein Veblen, Turing test, unpaid internship, Vanguard fund, Vilfredo Pareto, you are the product, zero-sum game, Zipcar

I say “allegedly” because live chats inevitably call to mind the Turing test, a test of a computer’s ability to “think” that British mathematician and computer scientist Alan Turing outlined in a 1950 paper. The common understanding of the Turing test is this: Using a text-only channel like a keyboard and screen, after five minutes of questioning, can someone tell whether a computer or a human is on the other end? If a robot passes as human, it has passed the Turing test. (Conversely, if there is no discernible difference and if it actually is a human, that person has apparently flunked the Human test.) Bona fide successes at the Turing test have been vanishingly rare.

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

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

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.

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“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.

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

23andMe, 3D printing, Abraham Maslow, additive manufacturing, AI winter, Albert Einstein, Amazon Mechanical Turk, Amazon Robotics, 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, Blue Ocean Strategy, business process, business process outsourcing, Cass Sunstein, Checklist Manifesto, Clapham omnibus, Clayton Christensen, clean water, cloud computing, commoditize, computer age, Computer Numeric Control, computer vision, Computing Machinery and Intelligence, conceptual framework, corporate governance, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, death of newspapers, disintermediation, Douglas Hofstadter, driverless car, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, Filter Bubble, full employment, future of work, Garrett Hardin, 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, Large Hadron Collider, lifelogging, lump of labour, machine translation, Marshall McLuhan, Metcalfe’s law, Narrative Science, natural language processing, Network effects, Nick Bostrom, optical character recognition, Paul Samuelson, personalized medicine, planned obsolescence, pre–internet, Ray Kurzweil, Richard Feynman, Second Machine Age, self-driving car, semantic web, Shoshana Zuboff, Skype, social web, speech recognition, spinning jenny, strong AI, supply-chain management, Susan Wojcicki, tacit knowledge, TED Talk, telepresence, The Future of Employment, the market place, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Tragedy of the Commons, transaction costs, Turing test, Two Sigma, warehouse robotics, Watson beat the top human players on Jeopardy!, WikiLeaks, world market for maybe five computers, Yochai Benkler, young professional

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.

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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).

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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.

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

Any sufficiently advanced technology is indistinguishable from magic, Apple II, back-to-the-land, beat the dealer, Bill Duvall, Bill Gates: Altair 8800, Buckminster Fuller, California gold rush, card file, computer age, Computer Lib, computer vision, conceptual framework, cuban missile crisis, different worldview, digital divide, Donald Knuth, Douglas Engelbart, Douglas Engelbart, Dynabook, Edward Thorp, El Camino Real, Electric Kool-Aid Acid Test, Fairchild Semiconductor, General Magic , general-purpose programming language, Golden Gate Park, Hacker Ethic, Hans Moravec, hypertext link, informal economy, information retrieval, invention of the printing press, Ivan Sutherland, Jeff Rulifson, John Markoff, John Nash: game theory, John von Neumann, Kevin Kelly, knowledge worker, Lewis Mumford, Mahatma Gandhi, Menlo Park, military-industrial complex, Mother of all demos, Norbert Wiener, packet switching, Paul Terrell, popular electronics, punch-card reader, 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, technological determinism, Ted Nelson, The Hackers Conference, The Theory of the Leisure Class by Thorstein Veblen, Thorstein Veblen, Turing test, union organizing, Vannevar Bush, We are as Gods, Whole Earth Catalog, William Shockley: the traitorous eight

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.

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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.

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.: 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.

Pandora's Brain by Calum Chace

AI winter, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, Bletchley Park, brain emulation, Extropian, friendly AI, hive mind, lateral thinking, machine translation, mega-rich, Nick Bostrom, precautionary principle, 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

‘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.

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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.

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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?’

Utopia Is Creepy: And Other Provocations by Nicholas Carr

Abraham Maslow, Air France Flight 447, Airbnb, Airbus A320, AltaVista, Amazon Mechanical Turk, augmented reality, autonomous vehicles, Bernie Sanders, book scanning, Brewster Kahle, Buckminster Fuller, Burning Man, Captain Sullenberger Hudson, centralized clearinghouse, Charles Lindbergh, cloud computing, cognitive bias, collaborative consumption, computer age, corporate governance, CRISPR, crowdsourcing, Danny Hillis, data science, deskilling, digital capitalism, digital map, disruptive innovation, Donald Trump, driverless car, Electric Kool-Aid Acid Test, Elon Musk, Evgeny Morozov, factory automation, failed state, feminist movement, Frederick Winslow Taylor, friendly fire, game design, global village, Google bus, Google Glasses, Google X / Alphabet X, Googley, hive mind, impulse control, indoor plumbing, interchangeable parts, Internet Archive, invention of movable type, invention of the steam engine, invisible hand, Isaac Newton, Jeff Bezos, jimmy wales, Joan Didion, job automation, John Perry Barlow, Kevin Kelly, Larry Ellison, Lewis Mumford, lifelogging, lolcat, low skilled workers, machine readable, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Max Levchin, means of production, Menlo Park, mental accounting, natural language processing, Neal Stephenson, Network effects, new economy, Nicholas Carr, Nick Bostrom, Norman Mailer, off grid, oil shale / tar sands, Peter Thiel, 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, scientific management, 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, TED Talk, the long tail, the medium is the message, theory of mind, Turing test, Tyler Cowen, Whole Earth Catalog, Y Combinator, Yochai Benkler

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

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It’s a nifty system: First you get your customers to entrust their personal data to you, and then you not only sell that data to advertisers but you get the customers to be the vector for the ads. And what do the customers get in return? 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.

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“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? Maybe so, but this breakthrough, like Barry Bonds’s homer record, is going to have to carry an asterisk. Studies show that when people enter a state of sexual arousal their intelligence drops precipitously.

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

"World Economic Forum" Davos, Ada Lovelace, adjacent possible, affirmative action, AI winter, Alfred Russel Wallace, algorithmic bias, algorithmic management, AlphaGo, Amazon Mechanical Turk, animal electricity, autonomous vehicles, behavioural economics, Black Swan, Brexit referendum, British Empire, Cambridge Analytica, cellular automata, Charles Babbage, citizen journalism, Claude Shannon: information theory, combinatorial explosion, Computing Machinery and Intelligence, corporate personhood, correlation coefficient, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, desegregation, discovery of DNA, disinformation, Douglas Hofstadter, Elon Musk, fake news, Fellow of the Royal Society, feminist movement, Filter Bubble, Flash crash, Geoffrey Hinton, Gerolamo Cardano, gig economy, Gödel, Escher, Bach, invention of the wheel, invisible hand, Jacquard loom, Jacques de Vaucanson, John Harrison: Longitude, John von Neumann, Kenneth Arrow, Linda problem, low skilled workers, Mark Zuckerberg, mass immigration, meta-analysis, mutually assured destruction, natural language processing, new economy, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, On the Economy of Machinery and Manufactures, p-value, pattern recognition, Paul Samuelson, performance metric, Pierre-Simon Laplace, post-truth, precariat, profit maximization, profit motive, Silicon Valley, social intelligence, statistical model, Stephen Hawking, stochastic process, Stuart Kauffman, telemarketer, The Bell Curve by Richard Herrnstein and Charles Murray, The Future of Employment, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, Thomas Malthus, traveling salesman, Turing machine, Turing test, twin studies, Vilfredo Pareto, Von Neumann architecture, warehouse robotics, women in the workforce, Yochai Benkler

His invention of the thought-experiment computer the Turing machine literally created the field of computer science, the bedrock field for an immeasurable fraction of today’s global society. And he created another thought experiment that has forever altered the cultural zeitgeist about man and machines: the so-called Turing test. The test was first described in the 1950 paper entitled ‘Computing Machinery and Intelligence’,4 in which Turing acknowledges the difficulty of defining ‘thinking’, such that one could answer the question, ‘Do computers think?’ He posed instead the alternative question: ‘Are there imaginable digital computers which would do well in the imitation game?’

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However, Shannon’s electronic communication is very different from human communication, in several important ways. The maths in Shannon’s theories requires the assumption that messages being sent occur with definite probabilities, which are independent of the probabilities of any other messages. It is as if each message passed through the slot in the Turing test is generated by a roll of dice, with no consideration of its context in amongst other messages. Once you’ve made that assumption, you can start to make some conclusions about the most efficient ways to send messages. For instance, you can logically conclude that common (high probability) messages should be short.

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A famous example of a regular language generator is the AI therapist called ELIZA, which was created by MIT professor Joseph Weizenbaum in the early 1960s. You can still find implementations of it online, if you’d like to have a little regular-language-generated therapy. Here’s a typical ELIZA session, to give you a flavour for how regular languages hold up to a few minutes of the Turing test: > Hello, I am Eliza. * Are you a computer, or a person? > Would you prefer if I were not a computer, or a person? * I’d prefer you to be a computer that successfully imitates a person. > We were discussing you, not me. Despite Eliza’s therapist-like deflection of direct questions with other questions, interactions of any length quickly reveal that ELIZA is just reconstructing sentences with rote rules.

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

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

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

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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.

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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.

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

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

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.

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, 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.

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

Abraham Maslow, Amazon Mechanical Turk, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, business cycle, business process, call centre, combinatorial explosion, corporate governance, creative destruction, crowdsourcing, David Ricardo: comparative advantage, driverless car, 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, general purpose technology, hiring and firing, income inequality, intangible asset, job automation, John Markoff, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Khan Academy, Kickstarter, knowledge worker, Loebner Prize, low skilled workers, machine translation, minimum wage unemployment, patent troll, pattern recognition, Paul Samuelson, Ray Kurzweil, rising living standards, Robert Gordon, Robert Solow, self-driving car, shareholder value, Skype, the long tail, too big to fail, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, wealth creators, winner-take-all economy, zero-sum game

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

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

affirmative action, algorithmic bias, AlphaGo, Bernie Sanders, Brexit referendum, Cambridge Analytica, classic study, cognitive load, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, data science, DeepMind, Demis Hassabis, disinformation, don't be evil, Donald Trump, Elon Musk, fake news, Filter Bubble, Geoffrey Hinton, Google Glasses, illegal immigration, James Webb Space Telescope, Jeff Bezos, job automation, Kenneth Arrow, Loebner Prize, Mark Zuckerberg, meta-analysis, Minecraft, Nate Silver, natural language processing, Nelson Mandela, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, post-truth, power law, prediction markets, random walk, Ray Kurzweil, Robert Mercer, selection bias, self-driving car, Silicon Valley, Skype, Snapchat, social contagion, speech recognition, statistical model, Stephen Hawking, Steve Bannon, Steven Pinker, TED Talk, The Signal and the Noise by Nate Silver, traveling salesman, Turing test

Progress in AI must involve biologists and computer scientists working together to understand the details of the brain. Tests of AI should, in my view, build on the one first proposed by Alan Turing in his famous ‘imitation game’ test.15 A computer passes the Turing test, or imitation game if it can fool a human, during a question-and-answer session, into believing that it is, in fact, a human. This is a tough test and we are a long way from achieving this, but we can use the main Turing test as a starting point for a series of simpler tests. In a less well-cited section of his article from 1950, Turing proposes simulating a child as a step toward simulating an adult. We could consider ourselves having ‘passed’ a mini imitation game test when we are convinced the computer is a child.

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

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

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

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, agricultural Revolution, Airbnb, AlphaGo, American Society of Civil Engineers: Report Card, Automated Insights, autonomous vehicles, basic income, benefit corporation, bitcoin, blockchain, blue-collar work, Bob Noyce, business process, call centre, Cambridge Analytica, cashless society, citizen journalism, Clayton Christensen, collaborative consumption, collaborative economy, collective bargaining, creative destruction, crowdsourcing, cryptocurrency, deep learning, DeepMind, disintermediation, disruptive innovation, distributed ledger, en.wikipedia.org, Erik Brynjolfsson, fake news, Filter Bubble, Ford Model T, Francis Fukuyama: the end of history, general purpose technology, Geoffrey West, Santa Fe Institute, gig economy, Gini coefficient, high-speed rail, holacracy, Hyperloop, income inequality, industrial robot, Internet of things, invention of agriculture, invention of movable type, invention of the printing press, invisible hand, Jane Jacobs, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, license plate recognition, low interest rates, Lyft, Mark Zuckerberg, mass immigration, Network effects, new economy, peer-to-peer lending, QWERTY keyboard, ransomware, Richard Florida, Robert Gordon, robo advisor, Ronald Reagan, Second Machine Age, self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Simon Kuznets, Skinner box, Snapchat, speech recognition, streetcar suburb, Stuxnet, surveillance capitalism, synthetic biology, TaskRabbit, The Death and Life of Great American Cities, The Rise and Fall of American Growth, the scientific method, trade route, Turing test, two and twenty, Uber and Lyft, uber lyft, universal basic income, uranium enrichment, urban planning, vertical integration, warehouse automation, zero day, zero-sum game, Zipcar

Now known as the Turing Test, it is a protocol in which three terminals are set up in isolation from one another, two operated by humans and one by a computer. One of the humans asks the computer and the other human a series of questions. If the questioner can’t tell which respondent is human and which is a machine after a certain number of tries, then the computer is said to have intelligence. By 1966, Joseph Weizenbaum, author Davidow’s first boss, had developed a program called ELIZA that appeared to pass the test.8 In the nearly seventy years that have passed since the creation of the Turing Test, artificial intelligence has passed through cycles of excitement and disillusionment.

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

Scary Smart: The Future of Artificial Intelligence and How You Can Save Our World by Mo Gawdat

3D printing, accounting loophole / creative accounting, AI winter, AlphaGo, anthropic principle, artificial general intelligence, autonomous vehicles, basic income, Big Tech, Black Lives Matter, Black Monday: stock market crash in 1987, butterfly effect, call centre, carbon footprint, cloud computing, computer vision, coronavirus, COVID-19, CRISPR, cryptocurrency, deep learning, deepfake, DeepMind, Demis Hassabis, digital divide, digital map, Donald Trump, Elon Musk, fake news, fulfillment center, game design, George Floyd, global pandemic, Google Glasses, Google X / Alphabet X, Law of Accelerating Returns, lockdown, microplastics / micro fibres, Nick Bostrom, off-the-grid, OpenAI, optical character recognition, out of africa, pattern recognition, Ponzi scheme, Ray Kurzweil, recommendation engine, self-driving car, Silicon Valley, smart contracts, Stanislav Petrov, Stephen Hawking, subprime mortgage crisis, superintelligent machines, TED Talk, TikTok, Turing machine, Turing test, universal basic income, Watson beat the top human players on Jeopardy!, Y2K

Simple as it was, this invention inspired scientists to begin discussing the possibility of thinking machines, and that, in my personal view, was the point at which the work to deliver intelligent machines – so long the object of humanity’s fantasies – actually started. These scientists so strongly believed back then in the inevitability of a thinking machine that, in 1950, Alan Turing proposed a test (it came to be known as the Turing test) that set an early and still relevant bar to see if artificial intelligence could measure up to human intelligence. In simple terms, he suggested a natural language conversation between an evaluator, a human and a machine designed to generate human-like responses. If the evaluator is not able to reliably tell the machine from the human, the machine is said to have passed the test.

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She simply repeated back what had been said to her, rephrasing it by using a few grammatical rules or responding with a canned response. Her sibling, Alexa, Amazon’s personal AI assistant, is much, much smarter. Alexa, as well as Google Assistant, Apple’s Siri and Microsoft’s Cortana, are capable of understanding us humans very well. While not pretending too hard to be human, they surely can on occasion pass the Turing test. Sometimes these AI programs take their understanding of language even further, as they translate between languages with shocking accuracy – another self-taught type of intelligence that some of the most advanced translation AIs today have learned from observing patterns of how humans translate, found in documents online.

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This fascination and fear with the idea of AI has inspired many creative worlds, culminating in films such as The Matrix, a must-see for any reader of this book, which predicts a future where the machines use us, humans, as energy cells and simulate every minute of our reality. I don’t like the idea of being treated as a battery and struggle with the thought that my life could actually be a simulation. How about you? Ex Machina is a story centred around an attempt to evaluate the humanity of a humanoid by diving deep into her character and behaviour – a bit of a ‘Turing test’ kind of movie. Though charming at first, Ava, the AI humanoid, quickly shows a lot more of her creepy side as she becomes smarter and smarter. Another hugely popular franchise, The Terminator features an artificially intelligent soldier from the future, one that travels back in time to save our future from the machines by protecting a child who’s targeted for assassination by another machine.

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

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

By 2023, those smartphones will have more computing power than our own brains.* (That wasn’t a typo—at the rate at which computers are advancing, the iPhone 11 or 12 will have greater computing power than our brains do.) * This is not to say that smartphones will replace our brains. Semiconductors and existing software have thus far failed to pass a Turing Test (by tricking a human into thinking that a computer is a person), let alone provide broad-based capabilities that we expect all humans to master in language, logic, navigation, and simple problem solving. A robot can drive a car quite effectively, but thus far robots have failed to tackle tasks that would seem far simpler, such as folding a basket of laundry.

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

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

Team Human by Douglas Rushkoff

1960s counterculture, Abraham Maslow, Adam Curtis, autonomous vehicles, basic income, Berlin Wall, big-box store, bitcoin, blockchain, Burning Man, carbon footprint, circular economy, clean water, clockwork universe, cloud computing, collective bargaining, Computing Machinery and Intelligence, corporate personhood, digital capitalism, disintermediation, Donald Trump, drone strike, European colonialism, fake news, Filter Bubble, full employment, future of work, game design, gamification, gig economy, Google bus, Gödel, Escher, Bach, hockey-stick growth, Internet of things, invention of the printing press, invention of writing, invisible hand, iterative process, John Perry Barlow, Kevin Kelly, Kevin Roose, knowledge economy, Larry Ellison, Lewis Mumford, life extension, lifelogging, Mark Zuckerberg, Marshall McLuhan, means of production, mirror neurons, multilevel marketing, new economy, patient HM, pattern recognition, peer-to-peer, Peter Thiel, planned obsolescence, power law, prosperity theology / prosperity gospel / gospel of success, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Ronald Reagan, Ronald Reagan: Tear down this wall, shareholder value, sharing economy, Silicon Valley, Silicon Valley billionaire, social intelligence, sovereign wealth fund, Steve Jobs, Steven Pinker, Stewart Brand, tech billionaire, technoutopianism, TED Talk, theory of mind, trade route, Travis Kalanick, Turing test, universal basic income, Vannevar Bush, We are as Gods, winner-take-all economy, zero-sum game

Whether we upload our brains to silicon or simply replace our brains with digital enhancements one synapse at a time, how do we know if the resulting beings are still alive and aware? The famous “Turing test” for computer consciousness determines only whether a computer can convince us that it’s human. This doesn’t mean that it’s actually human or conscious. The day that computers pass the Turing test may have less to do with how smart computers have gotten than with how bad we humans have gotten at telling the difference between them and us. 58. Artificial intelligences are not alive. They do not evolve.

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

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

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

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

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

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Faraone 1992, 102–3, discusses Pandora’s creation as an animated statue. On alternative versions claiming that Prometheus was the maker of the first woman, see Tassinari 1992, 75–76. 9. On myths describing the Trojan Horse as an animated statue and ancient “tests” to determine whether it and other realistic statues were real or artificial, Faraone 1992, 104–6. Turing test and the like: Kang 2011, 298; Zarkadakis 2015, 48–49, 312–13; Boissoneault 2017. 10. Hesiod’s poems do not mention offspring. As they did for Pygmalion’s Galatea (see chapter 6), later writers embellished the myth by giving Pandora a daughter by Epimetheus, Pyrrha, wife of Deucalion: Apollodorus Library 1.7.2; Hyginus Fabulae 142; Ovid Metamorphoses 1.350; Faraone 1992, 102–3.

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

4chan, Amazon Mechanical Turk, augmented reality, bash_history, bitcoin, Charles Babbage, cloud computing, commons-based peer production, computer age, computer vision, Computing Machinery and Intelligence, crowdsourcing, dematerialisation, Donald Knuth, Douglas Hofstadter, en.wikipedia.org, Everything should be made as simple as possible, finite state, Free Software Foundation, Gabriella Coleman, Gödel, Escher, Bach, Hacker Conference 1984, Ian Bogost, Jacques de Vaucanson, language acquisition, Larry Wall, late capitalism, means of production, natural language processing, Neal Stephenson, new economy, Norbert Wiener, Occupy movement, packet switching, peer-to-peer, power law, Richard Stallman, Ronald Coase, Slavoj Žižek, social software, social web, software studies, speech recognition, SQL injection, stem cell, Stewart Brand, systems thinking, The Nature of the Firm, Turing machine, Turing test, Vilfredo Pareto, We are Anonymous. We are Legion, We are the 99%, WikiLeaks, Yochai Benkler

In a paper of 1950, “Computing Machinery and Intelligence,” Alan Turing made the claim that computers would be capable of imitating human intelligence, or more precisely the human capacity for rational thinking. He set out what become commonly known as the “Turing test” to examine whether a machine is able to respond convincingly to an input with an output similar to a human’s.48 The contemporary equivalent, CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), turns this idea around, so that the software has to decide whether it is dealing with a human or a script.49 Perhaps it is the lack of speech that makes this software appear crude by comparison, as human intelligence continues to be associated with speech as a marker of reasoned semantic processing.

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He set out what become commonly known as the “Turing test” to examine whether a machine is able to respond convincingly to an input with an output similar to a human’s.48 The contemporary equivalent, CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), turns this idea around, so that the software has to decide whether it is dealing with a human or a script.49 Perhaps it is the lack of speech that makes this software appear crude by comparison, as human intelligence continues to be associated with speech as a marker of reasoned semantic processing. In his essay “Minds, Brains, and Programs” from 1980, John Searle refutes the Turing test because machines fall short in understanding the symbols they process. His observation is that the syntactical, abstract or formal content of a computer program is not the same as semantic or mental content associated with the human mind. The cognitive processes of the human mind can be simulated but not duplicated as such. Searle develops his thought experiment known as the “Chinese Room argument” as follows: “Suppose that I’m locked in a room and given a large batch of Chinese writing.

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

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

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."

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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).

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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.

Powers and Prospects by Noam Chomsky

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

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

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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. If so, we will have ‘empirically established’ that the computer can play chess, understand Chinese, think, etc., according to proponents of this version of artificial intelligence, while their critics deny that this result would establish the conclusion. There is a great deal of often heated debate about these matters in the literature of the cognitive sciences, artificial intelligence, and philosophy of mind, but it is hard to see that any serious question has been posed.

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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.

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

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

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.

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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.

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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.

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

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

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

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At the time the plan of IBM was to rely on the computational superiority of their machine using brute force,80 crunching billions of combinations; against the intuition, memory recall and pattern recognition of the Russian chess grandmaster. Nobody believed it represented an act of intelligence of any sort, since it worked in a very mechanistic way. Boy, we have gone so far since then. The classical “Turing test approach” has been largely abandoned as a realistic research goal, and is now just an intellectual curiosity (the annual Loebner prize for realistic chattiest81), but helped spawn the two dominant themes of modern cognition and artificial intelligence: calculating probabilities and producing complex behaviour from the interaction of many small, simple processes.

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For example, a brute-force algorithm to find the divisors of a natural number n is to enumerate all integers from 1 to the square-root of n, and check whether each of them divides n without remainder. http://en.wikipedia.org/wiki/Brute-force_search 81 Chatbots fail to convince judges that they’re human, 2011. New Scientist. http://www.newscientist.com/blogs/onepercent/2011/10/turing-test-chatbots-kneel-bef.html 82 Did you Know?, Jeopardy! http://www.jeopardy.com/showguide/abouttheshow/showhistory/ 83 Computer Program to Take On ’Jeopardy!’, John Markoff, 2009. The New York Times. http://www.nytimes.com/2009/04/27/technology/27jeopardy.html 84 According to IBM, Watson is a workload optimised system designed for complex analytics, made possible by integrating massively parallel POWER7 processors and the IBM DeepQA software to answer Jeopardy!

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

3D printing, Airbnb, algorithmic trading, AlphaGo, Alvin Toffler, Amazon Web Services, anti-work, antiwork, artificial general intelligence, asset light, autonomous vehicles, basic income, behavioural economics, business cycle, cloud computing, collective bargaining, Computing Machinery and Intelligence, correlation does not imply causation, creative destruction, data is the new oil, data science, David Graeber, David Ricardo: comparative advantage, deep learning, DeepMind, deindustrialization, Demis Hassabis, deskilling, disintermediation, do what you love, Donald Trump, driverless car, Erik Brynjolfsson, fake news, feminist movement, Ford Model T, Frederick Winslow Taylor, future of work, Future Shock, general purpose technology, gig economy, global supply chain, income inequality, independent contractor, informal economy, Internet of things, Jarndyce and Jarndyce, Jarndyce and Jarndyce, job automation, job polarisation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, knowledge economy, Loebner Prize, low skilled workers, Lyft, Mark Zuckerberg, means of production, moral panic, Network effects, new economy, Nick Bostrom, off grid, pattern recognition, post-work, Ronald Coase, scientific management, Second Machine Age, self-driving car, sharing economy, SoftBank, Steve Jobs, strong AI, tacit knowledge, technological determinism, technoutopianism, TED Talk, The Chicago School, The Future of Employment, the market place, The Nature of the Firm, The Wealth of Nations by Adam Smith, Thorstein Veblen, Turing test, Uber for X, uber lyft, universal basic income, wealth creators, working poor

Broadly speaking, we found that humans still hold the competitive advantage in three broad domains: creativity, complex social interactions, and the perception and manipulation of irregular objects. To take one example, the state-of-the-­art of technology in reproducing human social interactions is best described by the Loebner Prize—a Turing test competition—where chatbots try to convince human judges that they are actually chatting with a person. Some pundits have argued that there was a breakthrough in 2014, when one chatbot actually managed to convince 30 percent of judges of it being a human. But it did so by pretending to be a 13-year-old boy speaking English as his second language.

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And if so, at what point would consciousness arrive? When there were ten people? A thousand? A million? Since the existence of consciousness is not a graded thing, it would have to suddenly appear when there were enough people together; or it would have to be already present, if in a more subtle It should be noted, though, that these days the Turing test has generally been abandoned as the way to test intelligence (see New Scientist 2017: 4–5, 19, 65–67). 1 102 T. Tozer form, when even just two were together. Both possibilities are absurd. The same absurdity would apply to the collection of parts making up the brain and to those making up the computer: neither could produce consciousness (i.e. a singular subjective experience) by virtue of being a collective.

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

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

Alan Greenspan, Albert Einstein, algorithmic trading, Amazon Mechanical Turk, Apollo 11, Apple's 1984 Super Bowl advert, backtesting, Black Swan, book scanning, bounce rate, business intelligence, business process, butter production in bangladesh, call centre, Charles Lindbergh, commoditize, computer age, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data is the new oil, data science, driverless car, en.wikipedia.org, Erik Brynjolfsson, Everything should be made as simple as possible, experimental subject, Google Glasses, happiness index / gross national happiness, information security, job satisfaction, Johann Wolfgang von Goethe, lifelogging, machine readable, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mass immigration, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, Norbert Wiener, personalized medicine, placebo effect, prediction markets, Ray Kurzweil, recommendation engine, risk-adjusted returns, Ronald Coase, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, Shai Danziger, software as a service, SpaceShipOne, speech recognition, statistical model, Steven Levy, supply chain finance, text mining, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Davenport, Turing test, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra, zero-sum game

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?

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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.

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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.

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

AltaVista, Amazon Web Services, Apollo 13, cloud computing, Computing Machinery and Intelligence, data science, en.wikipedia.org, Firefox, Guido van Rossum, information security, machine readable, meta-analysis, natural language processing, optical character recognition, random walk, self-driving car, Turing test, web application

By providing Tesseract with a large collection of text images with known values, Tesser‐ act can be “taught” to recognize the same font in the future with far greater precision and accuracy, even despite occasional background and positioning problems in the text. 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.

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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. It’s ironic that in the last 60 years we’ve gone from using these tests to test machines to using them to test ourselves, with mixed results. Google’s notoriously difficult reCAPTCHA, currently the most popular among security-conscious websites, blocks as many as 25% of legitimate human users from accessing a site.2 Most other CAPTCHAs are somewhat easier.

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

4chan, Albert Einstein, algorithmic management, AltaVista, Andrew Keen, augmented reality, Burning Man, Carrington event, cognitive dissonance, crowdsourcing, dematerialisation, disinformation, en.wikipedia.org, Evgeny Morozov, 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, Lewis Mumford, lifelogging, Loebner Prize, low earth orbit, Marshall McLuhan, McMansion, moral panic, Nicholas Carr, off-the-grid, pattern recognition, Plato's cave, pre–internet, Republic of Letters, Silicon Valley, Skype, Snapchat, social web, Steve Jobs, technological solutionism, TED Talk, the medium is the message, The Wisdom of Crowds, traumatic brain injury, Turing test

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.

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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.

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., 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.

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

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

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.

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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.

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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.

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

3D printing, Ada Lovelace, AI winter, Airbnb, Alvin Toffler, artificial general intelligence, augmented reality, barriers to entry, basic income, bitcoin, Bletchley Park, blockchain, brain emulation, Buckminster Fuller, Charles Babbage, cloud computing, computer age, computer vision, correlation does not imply causation, credit crunch, cryptocurrency, cuban missile crisis, deep learning, DeepMind, dematerialisation, Demis Hassabis, discovery of the americas, disintermediation, don't be evil, driverless car, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, everywhere but in the productivity statistics, Flash crash, friendly AI, Geoffrey Hinton, Google Glasses, hedonic treadmill, hype cycle, 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, machine translation, Mahatma Gandhi, means of production, mutually assured destruction, Neil Armstrong, Nicholas Carr, Nick Bostrom, paperclip maximiser, pattern recognition, peer-to-peer, peer-to-peer model, Peter Thiel, radical life extension, Ray Kurzweil, Robert Solow, 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, TED Talk, The future is already here, The Future of Employment, theory of mind, Turing machine, Turing test, universal basic income, Vernor Vinge, wage slave, Wall-E, zero-sum game

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.

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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.

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

Alvin Toffler, An Inconvenient Truth, Andrew Keen, Charles Babbage, computer age, corporate governance, Dennis Ritchie, deskilling, Douglas Engelbart, Douglas Engelbart, East Village, en.wikipedia.org, information retrieval, Internet Archive, invention of movable type, Ivan Sutherland, Jaron Lanier, Ken Thompson, knowledge economy, Lewis Mumford, machine translation, Marshall McLuhan, Mikhail Gorbachev, military-industrial complex, national security letter, Neal Stephenson, On the Economy of Machinery and Manufactures, optical character recognition, profit motive, QR code, RAND corporation, RFC: Request For Comment, scientific management, Shoshana Zuboff, Silicon Valley, Steve Jobs, tacit knowledge, technological determinism, The Structural Transformation of the Public Sphere, Turing test, WikiLeaks, Works Progress Administration

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. A little like the nominal blanks pervasive in eighteenth-­century letters (see chapter 1), captcha works as an “I know you know” game, where a computer and a reader both “know” which alphanumeric characters need to be filled in.

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Except for the images of alphanumeric characters, that is, word and image remain distinct in the ways they function and feel online, despite the apparent pictorial qualities of page images as they appear on screen and the ubiquity of digital images that include pictured text, text that has not been “seen” computationally (that is, encoded) as such. 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.

Talk on the Wild Side by Lane Greene

Affordable Care Act / Obamacare, Albert Einstein, Boris Johnson, deep learning, Donald Trump, ending welfare as we know it, experimental subject, facts on the ground, fake news, framing effect, Google Chrome, Higgs boson, illegal immigration, invisible hand, language acquisition, Large Hadron Collider, machine translation, meta-analysis, Money creation, moral panic, natural language processing, obamacare, public intellectual, Ronald Reagan, Sapir-Whorf hypothesis, Snapchat, sparse data, speech recognition, Steven Pinker, TED Talk, Turing test, Wall-E

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

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

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

Aaron Swartz, call centre, conceptual framework, confounding variable, correlation does not imply causation, crowdsourcing, death of newspapers, Desert Island Discs, Dr. Strangelove, drug harm reduction, en.wikipedia.org, experimental subject, Firefox, Flynn Effect, Helicobacter pylori, jimmy wales, John Snow's cholera map, Loebner Prize, meta-analysis, moral panic, nocebo, placebo effect, publication bias, selection bias, selective serotonin reuptake inhibitor (SSRI), seminal paper, Simon Singh, social distancing, statistical model, stem cell, Stephen Fry, sugar pill, the scientific method, Turing test, two and twenty, WikiLeaks

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). 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.

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Artificial intelligence, in the form of ‘Nanniebots’, is being used to catch paedophiles. Nanniebots are AI programs which hang out in internet chatrooms, allegedly spotting the signs of grooming. 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’.

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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.

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