speech recognition

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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

Can deep learning, along with big data, produce machines that can flexibly and reliably deal with human language? Speech Recognition and the Last 10 Percent Automated speech recognition—the task of transcribing spoken language into text in real time—was deep learning’s first major success in NLP, and I’d venture to say that it is AI’s most significant success to date in any domain. In 2012, at the same time that deep learning was revolutionizing computer vision, a landmark paper on speech recognition was published by research groups at the University of Toronto, Microsoft, Google, and IBM.2 These groups had been developing deep neural networks for various aspects of speech recognition: recognizing phonemes from acoustic signals, predicting words from combinations of phonemes, predicting phrases from combinations of words, and so on.

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According to a Google speech-recognition expert, the use of deep networks resulted in the “biggest single improvement in 20 years of speech research.”3 The same year, a new deep-network speech-recognition system was released to customers on Android phones; two years later it was released on Apple’s iPhone, with one Apple engineer commenting, “This was one of those things where the jump [in performance] was so significant that you do the test again to make sure that somebody didn’t drop a decimal place.”4 If you yourself happened to use any kind of speech-recognition technology both before and after 2012, you will have also noticed a very sharp improvement. Speech recognition, which before 2012 ranged from horribly frustrating to moderately useful, suddenly became very nearly perfect in some circumstances. I am now able to dictate all of my texts and emails on my phone’s speech-recognition app; just a few moments ago, I read the “Restaurant” story to my phone, using my normal speaking speed, and it correctly transcribed every word. What’s stunning to me is that speech-recognition systems are accomplishing all this without any understanding of the meaning of the speech they are transcribing. While the speech-recognition system on my phone can transcribe every word of my “Restaurant” story, I guarantee you that it doesn’t understand a thing about it, or about anything else.

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In 2012, at the same time that deep learning was revolutionizing computer vision, a landmark paper on speech recognition was published by research groups at the University of Toronto, Microsoft, Google, and IBM.2 These groups had been developing deep neural networks for various aspects of speech recognition: recognizing phonemes from acoustic signals, predicting words from combinations of phonemes, predicting phrases from combinations of words, and so on. According to a Google speech-recognition expert, the use of deep networks resulted in the “biggest single improvement in 20 years of speech research.”3 The same year, a new deep-network speech-recognition system was released to customers on Android phones; two years later it was released on Apple’s iPhone, with one Apple engineer commenting, “This was one of those things where the jump [in performance] was so significant that you do the test again to make sure that somebody didn’t drop a decimal place.”4 If you yourself happened to use any kind of speech-recognition technology both before and after 2012, you will have also noticed a very sharp improvement.

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!

Unlike at the board meeting, however, the voice interface belly flopped. Siri relied on a third-party company’s technology for speech recognition. But in a case of phenomenally bad timing, that company was experiencing technical problems on the day of the Apple showcase. “It was easily the worst demo that we ever did in the history of the company,” Kittlaus says. He told Siri, “Give me two tickets for the Cubs game,” and the speech recognition service interpreted his utterance as “The circus is going to be in town next week.” The founders were subsequently able to convince Apple that the speech-recognition glitch was only temporary. But they remained on edge in the months leading up to the launch of the Siri app.

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The first group of challenges were those that required engineering—speech recognition and language understanding, for example. They weren’t easy, but with enough time, effort, and resources, they could be cracked using technological methods that were already known to the world. The second category of problems, however, were those that required invention—wholly new approaches. Topping that list was a challenge known as far-field speech recognition. Wherever you were in a room, and whatever else was happening acoustically—music playing, baby crying, Klingons attacking—the device should be able to hear you. “Far-field speech recognition did not exist in any commercial product when we started on this project,” Hart says.

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The sound waves emanating from your mouth must be converted into words, a process known as automated speech recognition. Determining what you were trying to communicate with those words is called natural-language understanding. Formulating a suitable reply is natural-language generation. And finally, speech synthesis allows voice-computing devices to audibly reply. Each of these subprocesses has bedeviled computer scientists for decades. And each has been significantly advanced by deep learning, so we will spend the rest of this chapter examining how. Automated speech recognition The next time you get ready to swear at Siri for bungling your words, pump the brakes and consider the miracle of hearing.

Genius Makers: The Mavericks Who Brought A. I. To Google, Facebook, and the World by Cade Metz

AI winter, air gap, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, AlphaGo, Amazon Robotics, artificial general intelligence, Asilomar, autonomous vehicles, backpropagation, Big Tech, British Empire, Cambridge Analytica, carbon-based life, cloud computing, company town, computer age, computer vision, deep learning, deepfake, DeepMind, Demis Hassabis, digital map, Donald Trump, driverless car, drone strike, Elon Musk, fake news, Fellow of the Royal Society, Frank Gehry, game design, Geoffrey Hinton, Google Earth, Google X / Alphabet X, Googley, Internet Archive, Isaac Newton, Jeff Hawkins, Jeffrey Epstein, job automation, John Markoff, life extension, machine translation, Mark Zuckerberg, means of production, Menlo Park, move 37, move fast and break things, Mustafa Suleyman, new economy, Nick Bostrom, nuclear winter, OpenAI, PageRank, PalmPilot, pattern recognition, Paul Graham, paypal mafia, Peter Thiel, profit motive, Richard Feynman, ride hailing / ride sharing, Ronald Reagan, Rubik’s Cube, Sam Altman, Sand Hill Road, self-driving car, side project, Silicon Valley, Silicon Valley billionaire, Silicon Valley startup, Skype, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Ballmer, Steven Levy, Steven Pinker, tech worker, telemarketer, The Future of Employment, Turing test, warehouse automation, warehouse robotics, Y Combinator

Yann LeCun and Yoshua Bengio join him. 2007—Geoff Hinton coins the term “deep learning,” a way of describing neural networks. 2008—Geoff Hinton runs into Microsoft researcher Li Deng in Whistler, British Columbia. 2009—Geoff Hinton visits Microsoft Research lab in Seattle to explore deep learning for speech recognition. 2010—Abdel-rahman Mohamed and George Dahl, two of Hinton’s students, visit Microsoft. Demis Hassabis, Shane Legg, and Mustafa Suleyman found DeepMind. Stanford professor Andrew Ng pitches Project Marvin to Google chief executive Larry Page. 2011—University of Toronto researcher Navdeep Jaitly interns at Google in Montreal, building a new speech recognition system through deep learning. Andrew Ng, Jeff Dean, and Greg Corrado found Google Brain. Google deploys speech recognition service based on deep learning. 2012—Andrew Ng, Jeff Dean, and Greg Corrado publish the Cat Paper.

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Working alongside Vanhouke’s team, this new lab pushed the technology onto Android smartphones in less than six months. At first Google didn’t tell the world its speech recognition service had changed, and soon after it went live, Vanhoucke received a phone call from a small company that supplied a chip for the latest Android phones. This chip was supposed to remove background noise when you barked commands into your phone—a way of cleaning up the sound so that the speech system could more easily identify what was being said. But the company told Vanhoucke its chip had stopped working. It was no longer boosting the performance of the speech recognition service. As Vanhoucke listened to what this company was saying, it didn’t take long to realize what had happened.

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In early December 2009, Li Deng had once again made the drive from the NIPS conference in Vancouver to the NIPS workshops in Whistler. A year after running into Geoff Hinton inside the Whistler Hilton and stumbling onto his research with deep learning and speech recognition, Deng had organized a new workshop around the idea at the same spot high in the Canadian mountains. He and Hinton would spend the next few days explaining the finer points of “neural speech recognition” to the other researchers gathered in Whistler, walking them through the prototype under way at the Microsoft lab in Redmond. As he drove north, winding through the mountain roads, Deng carried three of these researchers in his SUV.

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

To the surprise of my colleagues, our effort turned out to be very successful, having succeeded in recognizing speech comprising a large vocabulary with high accuracy. After that experiment, all of our subsequent speech recognition efforts have been based on hierarchical hidden Markov models. Other speech recognition companies appeared to discover the value of this method independently, and since the mid-1980s most work in automated speech recognition has been based on this approach. Hidden Markov models are also used in speech synthesis—keep in mind that our biological cortical hierarchy is used not only to recognize input but also to produce output, for example, speech and physical movement.

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The technique we developed had substantially all of the attributes that I describe in the PRTM. It included a hierarchy of patterns with each higher level being conceptually more abstract than the one below it. For example, in speech recognition the levels included basic patterns of sound frequency at the lowest level, then phonemes, then words and phrases (which were often recognized as if they were words). Some of our speech recognition systems could understand the meaning of natural-language commands, so yet higher levels included such structures as noun and verb phrases. Each pattern recognition module could recognize a linear sequence of patterns from a lower conceptual level.

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There was not a lot known about the neocortex in the early 1980s, but based on my experience with a variety of pattern recognition problems, I assumed that the brain was also likely to be reducing its multidimensional data (whether from the eyes, the ears, or the skin) using a one-dimensional representation, especially as concepts rose in the neocortex’s hierarchy. For the speech recognition problem, the organization of information in the speech signal appeared to be a hierarchy of patterns, with each pattern represented by a linear string of elements with a forward direction. Each element of a pattern could be another pattern at a lower level, or a fundamental unit of input (which in the case of speech recognition would be our quantized vectors). You will recognize this situation as consistent with the model of the neocortex that I presented earlier.

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

By late 1997 and early 1998, large-vocabulary CSR without a domain limitation for dictating written documents (like this book) was commercially introduced.16 • Prediction: The three technologies required for a translating telephone (where you speak and listen in one language such as English, and your caller hears you and replies in another language such as German)—speaker-independent (not requiring training on a new speaker), continuous, large-vocabulary speech recognition; language translation; and speech synthesis—will each exist in sufficient quality for a first generation system by the late 1990s. Thus, we can expect “translating telephones with reasonable levels of performance for at least the more popular languages early in the first decade of the twenty-first century.” What Happened: Effective, speaker-independent speech recognition, MY LIFE WITH MACHINES: SOME HIGHLIGHTS I walked onstage and played a composition on an old upright piano.

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Artificial life Simulated organisms, each including a set of behavior and reproduction rules (a simulated “genetic code”), and a simulated environment. The simulated organisms simulate multiple generations of evolution. The term can refer to any self-replicating pattern. ASR See Automatic speech recognition. Automatic speech recognition (ASR) Software that recognizes human speech. In general, ASR systems include the ability to extract high-level patterns in speech data. BGM See Brain-generated music. Big bang theory A prominent theory on the beginning of the Universe: the cosmic explosion, from a single point of infinite density, that marked the beginning of the Universe billions of years ago.

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A key question in the twenty-first century is whether computers will achieve consciousness (which their human creators are considered to have). Continuous speech recognition (CSR) A software program that recognizes and records natural language. Crystalline computing A system in which data is stored in a crystal as a hologram, conceived by Stanford professor Lambertus Hesselink. This three-dimensional storage method requires a million atoms for each bit and could achieve a trillion bits of storage for each cubic centimeter. Crystalline computing also refers to the possibility of growing computers as crystals. CSR See Continuous speech recognition. Cybernetic artist A computer program that is able to create original artwork in poetry, visual art, or music.

AIQ: How People and Machines Are Smarter Together by Nick Polson, James Scott

Abraham Wald, Air France Flight 447, Albert Einstein, algorithmic bias, Amazon Web Services, Atul Gawande, autonomous vehicles, availability heuristic, basic income, Bayesian statistics, Big Tech, Black Lives Matter, Bletchley Park, business cycle, Cepheid variable, Checklist Manifesto, cloud computing, combinatorial explosion, computer age, computer vision, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, Donald Trump, Douglas Hofstadter, Edward Charles Pickering, Elon Musk, epigenetics, fake news, Flash crash, Grace Hopper, Gödel, Escher, Bach, Hans Moravec, Harvard Computers: women astronomers, Higgs boson, index fund, information security, Isaac Newton, John von Neumann, late fees, low earth orbit, Lyft, machine translation, Magellanic Cloud, mass incarceration, Moneyball by Michael Lewis explains big data, Moravec's paradox, more computing power than Apollo, natural language processing, Netflix Prize, North Sea oil, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, pattern recognition, Pierre-Simon Laplace, ransomware, recommendation engine, Ronald Reagan, Salesforce, self-driving car, sentiment analysis, side project, Silicon Valley, Skype, smart cities, speech recognition, statistical model, survivorship bias, systems thinking, the scientific method, Thomas Bayes, Uber for X, uber lyft, universal basic income, Watson beat the top human players on Jeopardy!, young professional

Today, though, it works shockingly well. At its tech conference in 2017, for example, Google boldly announced that machines had now reached parity with humans at speech recognition, with a per-word dictation error rate of 4.9%—drastically better than the 20–30% error rates common as recently as 2013. This quantum leap in linguistic performance is a huge reason why machines now seem so smart. One might argue, in fact, that human-level speech recognition is the last decade’s single most important breakthrough in AI. So when was the tipping point, and how did we get there? What are “word vectors,” and why are they so useful?

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From the 1950s through the 1970s, experts tried to get machines to understand natural language using this same top-down approach: (1) place constraints on human users, by restricting the grammar and vocabulary they can use; and (2) program the machines chock-full of translation rules: syntax, pronunciation, word choice … basically, all the rules you learned without trying as a child, together with all the grammar rules you learned from Mrs. Thistlebum in elementary school. This rules-based philosophy had worked great for programming languages. But it never worked very well for natural languages. A great example of how it went wrong is computer speech recognition. The very first speech-recognition systems were essentially toys. At the 1962 World’s Fair, for example, IBM showed off a machine that could recognize spoken English words—precisely 16 of them, and only if enunciated with painful clarity. In the 1970s there was a false dawn, in the form of a program called Harpy, created by researchers at Carnegie Mellon.

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Language became a prediction-rule problem based on input/output pairs, similar to the problems solved by Henrietta Leavitt, or that farmer in Japan who uses deep learning to classify cucumbers: • For speech recognition, you pair a voice recording (input = “ahstinbrekfustahkoz”) with the correct transcription (output = “Austin breakfast tacos”). • For translating English to Russian, you pair an English word or sentence (“reset”) with the correct Russian translation (“perezagruzka”). • For predicting sentiment, you pair a sentence (“What a delightful morning spent in line at the DMV”) with a human annotation (). And so on. In each case, the machine must use the data to learn a prediction rule that correctly maps inputs to outputs. In the 1980s, speech-recognition software based on this principle began to hit the market.

AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee

"World Economic Forum" Davos, AI winter, Airbnb, Albert Einstein, algorithmic bias, algorithmic trading, Alignment Problem, AlphaGo, artificial general intelligence, autonomous vehicles, barriers to entry, basic income, bike sharing, business cycle, Cambridge Analytica, cloud computing, commoditize, computer vision, corporate social responsibility, cotton gin, creative destruction, crony capitalism, data science, deep learning, DeepMind, Demis Hassabis, Deng Xiaoping, deskilling, Didi Chuxing, Donald Trump, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, fake news, full employment, future of work, general purpose technology, Geoffrey Hinton, gig economy, Google Chrome, Hans Moravec, happiness index / gross national happiness, high-speed rail, if you build it, they will come, ImageNet competition, impact investing, income inequality, informal economy, Internet of things, invention of the telegraph, Jeff Bezos, job automation, John Markoff, Kickstarter, knowledge worker, Lean Startup, low skilled workers, Lyft, machine translation, mandatory minimum, Mark Zuckerberg, Menlo Park, minimum viable product, natural language processing, Neil Armstrong, new economy, Nick Bostrom, OpenAI, pattern recognition, pirate software, profit maximization, QR code, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, risk tolerance, Robert Mercer, Rodney Brooks, Rubik’s Cube, Sam Altman, Second Machine Age, self-driving car, sentiment analysis, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, SoftBank, Solyndra, special economic zone, speech recognition, Stephen Hawking, Steve Jobs, strong AI, TED Talk, The Future of Employment, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, urban planning, vertical integration, Vision Fund, warehouse robotics, Y Combinator

International competitions frequently pit different computer vision or speech recognition teams against each other, with the competitors opening their work to scrutiny by other researchers. The speed of improvements in AI also drives researchers to instantly share their results. Many AI scientists aren’t trying to make fundamental breakthroughs on the scale of deep learning, but they are constantly making marginal improvements to the best algorithms. Those improvements regularly set new records for accuracy on tasks like speech recognition or visual identification. Researchers compete on the basis of these records—not on new products or revenue numbers—and when one sets a new record, he or she wants to be recognized and receive credit for the achievement.

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I told him that he was a great young researcher but that China lagged too far behind American speech-recognition giants like Nuance, and there were fewer customers in China for this technology. To his credit, Liu ignored that advice and poured himself into building iFlyTek. Nearly twenty years and dozens of AI competition awards later, iFlyTek has far surpassed Nuance in capabilities and market cap, becoming the most valuable AI speech company in the world. Combining iFlyTek’s cutting-edge capabilities in speech recognition, translation, and synthesis will yield transformative AI products, including simultaneous translation earpieces that instantly convert your words and voice into any language.

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At each step along the way, students’ time and performance on different problems feed into their student profiles, adjusting the subsequent problems to reinforce understanding. In addition, for classes such as English (which is mandatory in Chinese public schools), AI-powered speech recognition can bring top-flight English instruction to the most remote regions. High-performance speech recognition algorithms can be trained to assess students’ English pronunciation, helping them improve intonation and accent without the need for a native English speaker on site. From a teacher’s perspective, these same tools can be used to alleviate the burden of routine grading tasks, freeing up teachers to spend more time on the students themselves.

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

A Declaration of the Independence of Cyberspace, AI winter, airport security, Andy Rubin, Apollo 11, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, basic income, Baxter: Rethink Robotics, Bill Atkinson, Bill Duvall, bioinformatics, Boston Dynamics, Brewster Kahle, Burning Man, call centre, cellular automata, Charles Babbage, Chris Urmson, Claude Shannon: information theory, Clayton Christensen, clean water, cloud computing, cognitive load, collective bargaining, computer age, Computer Lib, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data acquisition, Dean Kamen, deep learning, DeepMind, deskilling, Do you want to sell sugared water for the rest of your life?, don't be evil, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dr. Strangelove, driverless car, dual-use technology, Dynabook, Edward Snowden, Elon Musk, Erik Brynjolfsson, Evgeny Morozov, factory automation, Fairchild Semiconductor, Fillmore Auditorium, San Francisco, From Mathematics to the Technologies of Life and Death, future of work, Galaxy Zoo, General Magic , Geoffrey Hinton, Google Glasses, Google X / Alphabet X, Grace Hopper, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, Hans Moravec, haute couture, Herbert Marcuse, hive mind, hype cycle, hypertext link, indoor plumbing, industrial robot, information retrieval, Internet Archive, Internet of things, invention of the wheel, Ivan Sutherland, Jacques de Vaucanson, Jaron Lanier, Jeff Bezos, Jeff Hawkins, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, John von Neumann, Kaizen: continuous improvement, Kevin Kelly, Kiva Systems, knowledge worker, Kodak vs Instagram, labor-force participation, loose coupling, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, military-industrial complex, Mitch Kapor, Mother of all demos, natural language processing, Neil Armstrong, new economy, Norbert Wiener, PageRank, PalmPilot, pattern recognition, Philippa Foot, pre–internet, RAND corporation, Ray Kurzweil, reality distortion field, Recombinant DNA, Richard Stallman, Robert Gordon, Robert Solow, Rodney Brooks, Sand Hill Road, Second Machine Age, self-driving car, semantic web, Seymour Hersh, shareholder value, side project, Silicon Valley, Silicon Valley startup, Singularitarianism, skunkworks, Skype, social software, speech recognition, stealth mode startup, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Strategic Defense Initiative, strong AI, superintelligent machines, tech worker, technological singularity, Ted Nelson, TED Talk, telemarketer, telepresence, telepresence robot, Tenerife airport disaster, The Coming Technological Singularity, the medium is the message, Thorstein Veblen, Tony Fadell, trolley problem, Turing test, Vannevar Bush, Vernor Vinge, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, We are as Gods, Whole Earth Catalog, William Shockley: the traitorous eight, zero-sum game

Gruber had spent his time designing for personal computers and the World Wide Web, not mobile phones, so hearing Kittlaus describe the future of computing was a revelation. In the mid-2000s, keyboards on mobile phones were a limiting factor and so it made more sense to include speech recognition. SRI had been at the forefront of speech recognition research for decades. Nuance, the largest independent speech recognition firm, got its start as an SRI spin-off, so Cheyer understood the capabilities of speech recognition well. “It’s not quite ready yet,” he said. “But it will be.” Gruber was thrilled. Cheyer had been the chief architect of the CALO project at SRI, and Kittlaus had deep knowledge of the mobile phone industry.

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“I’m here to announce”—he paused slightly for effect—“that the answer is still no.” The audience howled with laughter and broke into applause. He added, “But we’re getting there.” The Siri designers discovered early on that they could quickly improve cloud-based speech recognition. At that point, they weren’t using the SRI-inspired Nuance technology, but instead a rival system called Vlingo. Cheyer noticed that when speech recognition systems were placed on the Web, they were exposed to a torrent of data in the form of millions of user queries and corrections. This data set up a powerful feedback loop to train and improve Siri. The developers continued to believe that their competitive advantage would be that the Siri service represented a fundamental break with the dominant paradigm for finding information on the Web—the information search—exemplified by Google’s dramatically successful search engine.

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.), 240 Silicon Valley, inception of, 95–99, 100, 178, 256 Simon, Herbert, 108, 168, 215, 283 Simon (personal computer), 231 Singhal, Amit, 314 Singularity Institute, 17 Singularity Is Near, The (Kurzweil), 119 Sinofsky, Steven, 217 Siri (Apple), 277–323 Apple history and, 279–281 Apple’s acquisition of Siri, 281–282, 320–323 Cheyer’s early career and, 297–305 Gruber’s early career and, 277–279, 278, 282–297 IA versus AI issues, 12–13, 31, 190, 193–194, 282 Knowledge Navigator and, 188, 300, 304, 305–310, 317, 318 released as iPhone App, 319–320 Siri (company) founded, 310–320 Siu, Henry, 326 Sketchpad, 230–231, 306, 308 SLAM (simultaneous localization and mapping), 20–21, 37 Sloan Foundation, 180 smartphones, advent of, 239, 312 Snowden, Edward, 29 Software Psychology (Shneiderman), 188 Sol-20, 210–211 Sony, 23, 245, 246 Southworth, Lucy, 242 speech recognition. see language and speech recognition; Siri (Apple) Sproull, Bob, 231 SRI International CALO, 31, 297, 302–304, 310, 311 inception of, 3–4, 99–105 Intuitive Surgical and, 271 naming of, 2, 180 Prospector, 128 SRI name of, 180 Stanford Artificial Intelligence Laboratory (SAIL) Brooks and, 98–99, 196–201 McCarthy and, 7–8, 111–115 Salisbury and, 256 Stanford Cart, 120–122, 200–201 Stanford Artificial Intelligence Robot (STAIR), 259–260, 265 Stanford Industrial Park, 254–255 Stanford Knowledge Systems Laboratory, 293 Stanford Research Institute. see SRI International Stanford University Knowledge Engineering Laboratory, 133–134 Microsoft and, 189 Mycin, 127 Program on Liberation Technology, 16, 342 Stanford University Network, 134 Stanley project, 19–23, 35–36, 36 Starfish Prime, 126 Stochastic Neural Analog Reinforcement Calculator, 142 Strategic Defense Initiative (“Star Wars”), 134, 182, 183, 203 Strong artificial intelligence, 12, 26, 272 Suarez, Daniel, 45 Suitable Technologies, 243 Sun Microsystems, 134 Sutherland, Ivan, 230–232, 255, 306, 308 Symbolics, 128 Synergy, 134 Syntelligence, 128–130 Taylor, Robert, 31, 159–165 Teknowledge, 128, 134–140 Templeton, Brad, 333 Tenenbaum, Jay “Marty,” 289, 290 Terman, Frederick, 255 Tesla, 46 Tesler, Larry, 8, 138 Tether, Tony, 21–22, 28–32, 234, 236, 297, 302 Thinking Machines Corporation, 119 “Third Industrial Revolution” (IR3), 88, 89 Thrun, Sebastian Bradski and, 260, 263–265 goals of, xiii at Google, 35–40, 55–56, 152–153 Shashua and, 49–50 Stanley project and autonomous vehicles, 19–23, 35–36, 36, 59 time-sharing concept, 111–112, 197–198 Tools for Conviviality (Illich), 213–215 Total Information Awareness, 29 Toyoda, Akio, 90 Toyota, 60, 90 traffic jam assist, 45–46, 52, 57, 164 Trower, Tandy, 190, 327–332 Tufekci, Zeynep, 328–329 Turing, Alan, 13–14, 106 Turkle, Sherry, 173, 221–222 ubiquitous computing (UbiComp), xv, 193, 274, 281 Understanding Computers and Cognition (Flores, Winograd), 182, 188 United Auto Workers (UAW), 68–73 University of Massachusetts, 283–287 University of Pittsburgh, 127–128 University of Utah, 47 URBAN5, 308 Urmson, Chris, 56 U.S.

The Deep Learning Revolution (The MIT Press) by Terrence J. Sejnowski

AI winter, Albert Einstein, algorithmic bias, algorithmic trading, AlphaGo, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, autonomous vehicles, backpropagation, Baxter: Rethink Robotics, behavioural economics, bioinformatics, cellular automata, Claude Shannon: information theory, cloud computing, complexity theory, computer vision, conceptual framework, constrained optimization, Conway's Game of Life, correlation does not imply causation, crowdsourcing, Danny Hillis, data science, deep learning, DeepMind, delayed gratification, Demis Hassabis, Dennis Ritchie, discovery of DNA, Donald Trump, Douglas Engelbart, driverless car, Drosophila, Elon Musk, en.wikipedia.org, epigenetics, Flynn Effect, Frank Gehry, future of work, Geoffrey Hinton, Google Glasses, Google X / Alphabet X, Guggenheim Bilbao, Gödel, Escher, Bach, haute couture, Henri Poincaré, I think there is a world market for maybe five computers, industrial robot, informal economy, Internet of things, Isaac Newton, Jim Simons, John Conway, John Markoff, John von Neumann, language acquisition, Large Hadron Collider, machine readable, Mark Zuckerberg, Minecraft, natural language processing, Neil Armstrong, Netflix Prize, Norbert Wiener, OpenAI, orbital mechanics / astrodynamics, PageRank, pattern recognition, pneumatic tube, prediction markets, randomized controlled trial, Recombinant DNA, recommendation engine, Renaissance Technologies, Rodney Brooks, self-driving car, Silicon Valley, Silicon Valley startup, Socratic dialogue, speech recognition, statistical model, Stephen Hawking, Stuart Kauffman, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, Turing machine, Von Neumann architecture, Watson beat the top human players on Jeopardy!, world market for maybe five computers, X Prize, Yogi Berra

His literal translation of Aleksandr Pushkin’s Eugene Onegin into English, annotated with explanatory footnotes on the cultural background of the verses, made his point.11 Perhaps Google Translate will be able to translate Shakespeare someday by integrating across all of his poetry.12 Learning How to Listen Another holy grail of artificial intelligence is speech recognition. Until recently, speaker-independent speech recognition by computers was The Rise of Machine Learning 9 limited to narrow domains, such as airline reservations. Today, it is unlimited. A summer research project at Microsoft Research by an intern from the University of Toronto in 2012 dramatically improved the performance of Microsoft’s speech recognition system (figure 1.4).13 In 2016, a team at Microsoft announced that its deep learning network with 120 layers had achieved human-level performance on a benchmark test for multi-speaker speech recognition.14 The consequences of this breakthrough will ripple through society over the next few years, as computer keyboards are replaced by natural language interfaces.

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Just as typewriters became obsolete with the widespread use of Figure 1.4 Microsoft Chief Research Officer Rick Rashid in a live demonstration of automated speech recognition using deep learning on October 25, 2012, at an event in Tianjin, China. Before an audience of 2,000 Chinese, Rashid’s words, spoken in English, were recognized by the automated system, which first showed them in subtitles below Rashid’s screen image and then translated them into spoken Chinese. This high-wire act made newsfeeds worldwide. Courtesy of Microsoft Research. 10 Chapter 1 personal computers, so computer keyboards will someday become museum pieces. When speech recognition is combined with language translation, it will become possible to communicate across cultures in real time.

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For an early foray along these lines, see Andrej Karpathy, “The Unreasonable Effectiveness of Recurrent Neural Networks,” Andrej Karpathy Blog, posted May 21, 2015. http://karpathy.github.io/2015/05/21/rnn-effectiveness/. 13. G. Hinton, L. Deng, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, et al., “Deep Neural Networks for Acoustic Modeling in Speech Recognition,” IEEE Signal Processing Magazine 29, no. 6 (2012): 82–97. 14. W. Xiong, , J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, et al., “Achieving Human Parity in Conversational Speech Recognition,” Microsoft Research Technical Report MSR-TR-2016-71, revised February 2017. https://arxiv.org/pdf/ 1610.05256.pdf. 15. A. Esteva, B. Kuprel, R. A. Novoa, J. Ko J, S. M. Swetter, H. M. Blau, and S.

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

One of the things it learned to do was to discover a pattern that would fire if there was a cat of some sort in the center of the frame because that’s a relatively common occurrence in YouTube videos, so that was pretty cool. The other thing we did was to work with the speech recognition team on applying deep learning and deep neural networks to some of the problems in the speech recognition system. At first, we worked on the acoustic model, where you try to go from raw audio waveforms to a part-of-word sound, like “buh,” or “fuh,” or “ss”—the things that form words. It turned out we could use neural networks to do that much better than the previous system they were using. That got very significant decreases in word error rate for the speech recognition system. We then just started to look and collaborate with other teams around Google about what kinds of interesting perception problems that it had in the speech space or in the image recognition or video processing space.

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MARTIN FORD: That’s when it transitioned from being centered in universities to being in the mainstream domain at places like Google, Facebook, and Baidu? YOSHUA BENGIO: Exactly. The shift started slightly earlier, around 2010, with companies like Google, IBM, and Microsoft, who were working on neural networks for speech recognition. By 2012, Google had these neural networks on their Android smartphones. It was revolutionary for the fact that the same technology of deep learning could be used for both computer vision and speech recognition. It drove a lot of attention toward the field. MARTIN FORD: Thinking back to when you first started in neural networks, are you surprised at the distance things have come and the fact that they’ve become so central to what large companies, like Google and Facebook, are doing now?

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They went as interns to IBM and Microsoft, and a third student took their system to Google. The basic system that they had built was developed further, and over the next few years, all these companies’ labs converted to doing speech recognition using neural nets. Initially, it was just using neural networks for the frontend of their system, but eventually, it was using neural nets for the whole system. Many of the best people in speech recognition had switched to believing in neural networks before 2012, but the big public impact was in 2012, when the vision community, almost overnight, got turned on its head and this crazy approach turned out to win.

Automate This: How Algorithms Came to Rule Our World by Christopher Steiner

23andMe, Ada Lovelace, airport security, Al Roth, algorithmic trading, Apollo 13, backtesting, Bear Stearns, big-box store, Black Monday: stock market crash in 1987, Black-Scholes formula, call centre, Charles Babbage, cloud computing, collateralized debt obligation, commoditize, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, Donald Trump, Douglas Hofstadter, dumpster diving, financial engineering, Flash crash, G4S, Gödel, Escher, Bach, Hacker News, High speed trading, Howard Rheingold, index fund, Isaac Newton, Jim Simons, John Markoff, John Maynard Keynes: technological unemployment, knowledge economy, late fees, machine translation, Marc Andreessen, Mark Zuckerberg, market bubble, Max Levchin, medical residency, money market fund, Myron Scholes, Narrative Science, PageRank, pattern recognition, Paul Graham, Pierre-Simon Laplace, prediction markets, proprietary trading, quantitative hedge fund, Renaissance Technologies, ride hailing / ride sharing, risk tolerance, Robert Mercer, Sergey Aleynikov, side project, Silicon Valley, Skype, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, transaction costs, upwardly mobile, Watson beat the top human players on Jeopardy!, Y Combinator

Their hack was so revolutionary, in fact, that it not only changed speech translation software, but also speech recognition programs. Instead of trying to nail each word as it comes out of the speaker’s mouth, the latest and best speech recognition software looks for strings of words that make sense together. That way, it has an easy time distinguishing are from our. Are you going to the mall today won’t be mistaken with Our you going to the mall today because, simply, people never say our you going. Just as we learn grammar rules, so the machine-learning algorithm did as well. This method forms the backbone of the speech recognition programs we use today. Brown and Mercer’s breakthrough didn’t go unnoticed on Wall Street.

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For years, constructing a bot that could quantify spoken words and determine personalities and thoughts was impossible. The technology—software and hardware—just wasn’t ready. Speech recognition software—the ability of computers to capture and translate exactly what humans say—was a lost cause for decades. The software that did exist for the purpose was buggy and often wildly inaccurate. But in the early 1990s, two scientists at IBM’s research center dove into computerized speech recognition and translation, a field that had long failed to produce anything robust enough to be used in everyday situations. Peter Brown and Robert Mercer started by working on programs that translated one language to another, starting with French to English.

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In their freshman-year programming classes, many college engineers design a simple algorithm to flawlessly play the game of tic-tac-toe.3 In their program, the opposing, or human, player’s move forms the input. With that information, the algorithm produces an output in the way of its own moves. A student expecting an A on such a problem will produce an algorithm that never loses a game (but often plays to a draw). The algorithms used by a high-frequency trader or a speech recognition program work the same way. They’re fed inputs—perhaps the movements of different stock indices, currency rate fluctuations, and oil prices—with which they produce an output: say, buy GE stock. Algorithmic trading is nothing more than relying on an algorithm for the answers of when and what to buy and sell.

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!

Two members of Hinton’s lab, George Dahl and Abdel-rahman Mohamed, quickly demonstrated that it worked just as well for speech recognition as it did for image recognition. In 2009, the pair pitted their newly created speech recognition neural network up against the then-industry standard tools, which had been worked on for the past three decades. The deep learning net won. At this point, major companies began to take an interest. One of these was Google. In 2011, a PhD student of Hinton’s named Navdeep Jaitly was asked to tinker with Google’s speech recognition algorithms. He took one look at them and suggested gutting the entire system and replacing it with a deep neural network.

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‘I want to eat in the same restaurant I ate in last week,’ is a straightforward enough sentence, but to make it into something useful, an AI assistant such as Siri must not only use natural language processing to understand the concept you are talking about, but also use context to find the right rule in its programming to follow. The speech recognition used in Siri is the creation of Nuance Communications, arguably the most advanced speech recognition company in the world. ‘Our job is to figure out the logical assertions inherent in the question that is being asked, or the command that is being given,’ Nuance’s Distinguished Scientist Ron Kaplan tells me. ‘From that, you then have to be able to interpret and turn it into an executable command.

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For example, the Coca-Cola Bottling Company of Atlanta, Georgia, made headlines when it ‘hired’ an AI assistant called Hank to man its phone switchboard. Using what was then a state-of-the-art speech recognition system, Hank proved capable of answering some queries and redirecting calls for others. Like a prototype Siri, he was programmed with both an archive of useful information and a jovial personality. Ask him about Coca-Cola shareholder issues and he could tell you. Ask him about his personal life and he would answer that ‘virtual assistants are not allowed to have relationships’. (Alas, Hank’s speech recognition wasn’t perfect. Questioning him on whether he snorted coke would prompt him to say, ‘Of course!

Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again by Eric Topol

"World Economic Forum" Davos, 23andMe, Affordable Care Act / Obamacare, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic bias, AlphaGo, Apollo 11, artificial general intelligence, augmented reality, autism spectrum disorder, autonomous vehicles, backpropagation, Big Tech, bioinformatics, blockchain, Cambridge Analytica, cloud computing, cognitive bias, Colonization of Mars, computer age, computer vision, Computing Machinery and Intelligence, conceptual framework, creative destruction, CRISPR, crowdsourcing, Daniel Kahneman / Amos Tversky, dark matter, data science, David Brooks, deep learning, DeepMind, Demis Hassabis, digital twin, driverless car, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, fake news, fault tolerance, gamification, general purpose technology, Geoffrey Hinton, George Santayana, Google Glasses, ImageNet competition, Jeff Bezos, job automation, job satisfaction, Joi Ito, machine translation, Mark Zuckerberg, medical residency, meta-analysis, microbiome, move 37, natural language processing, new economy, Nicholas Carr, Nick Bostrom, nudge unit, OpenAI, opioid epidemic / opioid crisis, pattern recognition, performance metric, personalized medicine, phenotype, placebo effect, post-truth, randomized controlled trial, recommendation engine, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, Skinner box, speech recognition, Stephen Hawking, techlash, TED Talk, text mining, the scientific method, Tim Cook: Apple, traumatic brain injury, trolley problem, War on Poverty, Watson beat the top human players on Jeopardy!, working-age population

Whether it’s in English or Chinese, talking is more than two to three times faster than typing (both at the initial speech-transcription stage as well as when edited via keyboard) and has a significantly lower error rate in Chinese, a difficult language to type (Figure 12.2). It wasn’t until 2016 that speech recognition by AI came into its own, when Microsoft’s and Google’s speech recognition technologies matched our skill at typing, achieving a 5 percent error rate. By now, AI has surpassed human performance. FIGURE 12.1: The time it takes from introduction of a new technology to adoption by one in four Americans. Source: Adapted from: “Happy Birthday World Wide Web,” Economist (2014): www.economist.com/graphic-detail/2014/03/12/happy-birthday-world-wide-web.

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Versus M.D.”17 The adversarial relationship between humans and their technology, which had a long history dating back to the steam engine and the first Industrial Revolution, had been rekindled. 1936—Turing paper (Alan Turing) 1943—Artificial neural network (Warren McCullogh, Walter Pitts) 1955—Term “artificial intelligence” coined (John McCarthy), 1957—Predicted ten years for AI to beat human at chess (Herbert Simon) 1958—Perceptron (single-layer neural network) (Frank Rosenblatt) 1959—Machine learning described (Arthur Samuel) 1964—ELIZA, the first chatbot 1964—We know more than we can tell (Michael Polany’s paradox) 1969—Question AI viability (Marvin Minsky) 1986—Multilayer neural network (NN) (Geoffrey Hinton) 1989—Convolutional NN (Yann LeCun) 1991—Natural-language processing NN (Sepp Hochreiter, Jurgen Schmidhuber) 1997—Deep Blue wins in chess (Garry Kasparov) 2004—Self-driving vehicle, Mojave Desert (DARPA Challenge) 2007—ImageNet launches 2011—IBM vs. Jeopardy! champions 2011—Speech recognition NN (Microsoft) 2012—University of Toronto ImageNet classification and cat video recognition (Google Brain, Andrew Ng, Jeff Dean) 2014—DeepFace facial recognition (Facebook) 2015—DeepMind vs. Atari (David Silver, Demis Hassabis) 2015—First AI risk conference (Max Tegmark) 2016—AlphaGo vs.

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Even if Deep Blue didn’t have much of anything to do with deep learning, the technology’s day was coming. The founding of ImageNet by Fei-Fei Li in 2007 had historic significance. That massive database of 15 million labeled images would help catapult DNN into prominence as a tool for computer vision. In parallel, natural-language processing for speech recognition based on DNN at Microsoft and Google was moving into full swing. More squarely in the public eye was man versus machine in 2011, when IBM Watson beat the human Jeopardy! champions. Despite the relatively primitive AI that was used, which had nothing to do with deep learning networks and which relied on speedy access to Wikipedia’s content, IBM masterfully marketed it as a triumph of AI.

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

His formal report concluded, in diplomatic language, that future American government funding “should be spent hardheadedly toward important, realistic, and relatively short-range goals”. The real message was clear: up until then, money had been spent thoughtlessly on trivial, unrealistic or long-range goals. Pierce wrote privately in 1969 about the progress in another field, speech recognition, to the Journal of the Acoustical Society of America. This time, he was frank: …a general phonetic typewriter [ie, a speech-recognition system that would take voice input and produce text output] is simply impossible unless the typewriter has an intelligence and a knowledge of language comparable to those of a native speaker of English … The typical recognizer … builds or programs an elaborate system that either does very little or flops in an obscure way.

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In this case, instead of a mass of texts translated by humans from English to French, speech-recognition systems learn from a mass of recordings, paired with transcriptions of those recordings made by humans. Now the trick is to match not a string of English text to a string of French text, but a series of vibrations in the air to a string of words. Given enough data, computers can do exactly that, with increasing accuracy. Word-error rates have crept down gradually as computers have become more powerful, and are being fed more data. And the systems will get better if people continue using them. Every time a user uses a digital assistant with a speech-recognition system, the data becomes potential training data for the company that makes the system, as most requests are sent via the internet to the provider’s computers in the cloud.

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Neural networks were introduced for a few language-pairs for Google Translate in late 2016, leading to an immediate and dramatic improvement in Translate’s performance. That same year, Microsoft announced a speech-recognition system that made as few errors as a human transcriber. The system was powered by six neural networks, each of which tackled some parts of the problem better than others. None of these systems are perfect at the time of writing, and they almost certainly won’t be any time soon. “Deep learning” brought a sudden jump in quality in many language technologies, but it still cannot flexibly handle language like humans can. Translation and speech-recognition systems perform much better when their tasks are limited to a single domain, like medicine or law.

The Economic Singularity: Artificial Intelligence and the Death of Capitalism by Calum Chace

"World Economic Forum" Davos, 3D printing, additive manufacturing, agricultural Revolution, AI winter, Airbnb, AlphaGo, Alvin Toffler, Amazon Robotics, Andy Rubin, artificial general intelligence, augmented reality, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Berlin Wall, Bernie Sanders, bitcoin, blockchain, Boston Dynamics, bread and circuses, call centre, Chris Urmson, congestion charging, credit crunch, David Ricardo: comparative advantage, deep learning, DeepMind, Demis Hassabis, digital divide, Douglas Engelbart, Dr. Strangelove, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Fairchild Semiconductor, Flynn Effect, full employment, future of work, Future Shock, gender pay gap, Geoffrey Hinton, gig economy, Google Glasses, Google X / Alphabet X, Hans Moravec, Herman Kahn, hype cycle, ImageNet competition, income inequality, industrial robot, Internet of things, invention of the telephone, invisible hand, James Watt: steam engine, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kiva Systems, knowledge worker, lifelogging, lump of labour, Lyft, machine translation, Marc Andreessen, Mark Zuckerberg, Martin Wolf, McJob, means of production, Milgram experiment, Narrative Science, natural language processing, Neil Armstrong, new economy, Nick Bostrom, Occupy movement, Oculus Rift, OpenAI, PageRank, pattern recognition, post scarcity, post-industrial society, post-work, precariat, prediction markets, QWERTY keyboard, railway mania, RAND corporation, Ray Kurzweil, RFID, Rodney Brooks, Sam Altman, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, SoftBank, software is eating the world, speech recognition, Stephen Hawking, Steve Jobs, TaskRabbit, technological singularity, TED Talk, The future is already here, The Future of Employment, Thomas Malthus, transaction costs, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, universal basic income, Vernor Vinge, warehouse automation, warehouse robotics, working-age population, Y Combinator, young professional

We have seen before with the relative decline of seemingly invincible goliaths like IBM and Microsoft how fierce and fast-moving the competition is within the technology industry. This is one of the dynamics which is pushing AI forward so fast and so unstoppably. Image and speech recognition Deep learning has accelerated progress at tasks like image recognition, facial recognition, natural speech recognition and machine translation faster than anyone expected. In 2012, Google announced that an assembly of 16,000 processors looking at 10 million YouTube videos had identified – without being prompted – a particular class of objects. We call them cats.

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t=33 [lxxxviii] http://news.sciencemag.org/social-sciences/2015/02/facebook-will-soon-be-able-id-you-any-photo [lxxxix] http://www.computerworld.com/article/2941415/data-privacy/is-facial-recognition-a-threat-on-facebook-and-google.html [xc] http://www.wired.com/2016/01/2015-was-the-year-ai-finally-entered-the-everyday-world/ [xci] At the time of writing, April 2016, Aipoly is impressive, but far from perfect. [xcii] http://www.bloomberg.com/news/2014-12-23/speech-recognition-better-than-a-human-s-exists-you-just-can-t-use-it-yet.html [xciii] http://www.forbes.com/sites/parmyolson/2014/05/28/microsoft-unveils-near-real-time-language-translation-for-skype/ [xciv] http://www.technologyreview.com/news/544651/baidus-deep-learning-system-rivals-people-at-speech-recognition/#comments [xcv] https://youtu.be/V1eYniJ0Rnk?t=1 [xcvi] http://edge.org/response-detail/26780 [xcvii] http://techcrunch.com/2016/03/19/how-real-businesses-are-using-machine-learning/ [xcviii] http://www.latimes.com/business/technology/la-fi-cutting-edge-ibm-20160422-story.html [xcix] http://www.wired.com/2016/04/openai-elon-musk-sam-altman-plan-to-set-artificial-intelligence-free/ [c] http://www.strategyand.pwc.com/global/home/what-we-think/innovation1000/top-innovators-spenders#/tab-2015 [ci] 2013 data: http://www.ons.gov.uk/ons/rel/rdit1/gross-domestic-expenditure-on-research-and-development/2013/stb-gerd-2013.html [cii] http://insights.venturescanner.com/category/artificial-intelligence-2/ [ciii] http://techcrunch.com/2015/12/25/investing-in-artificial-intelligence/ [civ] http://www.wired.com/2015/11/google-open-sources-its-artificial-intelligence-engine/ [cv] https://www.theguardian.com/technology/2016/apr/13/google-updates-tensorflow-open-source-artificial-intelligence [cvi] http://www.wired.com/2015/12/facebook-open-source-ai-big-sur/ [cvii] The name Parsey McParseFace is a play on a jokey name for a research ship which received a lot of votes in a poll run by the British government in April 2016. http://www.wsj.com/articles/googles-open-source-parsey-mcparseface-helps-machines-understand-english-1463088180 [cviii] Assuming you don't count the Vatican as a proper country. http://www.ibtimes.co.uk/google-project-loon-provide-free-wifi-across-sri-lanka-1513136 [cix] https://setandbma.wordpress.com/2013/02/04/who-coined-the-term-big-data/ [cx] http://www.pcmag.com/encyclopedia/term/37701/amara-s-law [cxi] http://www.lrb.co.uk/v37/n05/john-lanchester/the-robots-are-coming [cxii] Haitz's Law states that the cost per unit of useful light emitted decreases exponentially [cxiii] http://computationalimagination.com/article_cpo_decreasing.php [cxiv] http://www.nytimes.com/2006/06/07/technology/circuits/07essay.html [cxv] . http://arstechnica.com/gadgets/2015/02/intel-forges-ahead-to-10nm-will-move-away-from-silicon-at-7nm/ [cxvi] .

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In January 2016 Baidu (often described as China's Google) showed off a system called DuLight which uses a camera to capture an image of something in front of you, sends the image to an app on your smartphone, which identifies the object and announces what it is. One application of this is to help blind people know what they are “looking” at.[xc] You can download a similar app called Aipoly for free at iTunes.[xci] Speech recognition systems that exceed human performance will be available in your smartphone soon.[xcii] Microsoft-owned Skype introduced real-time machine translation in March 2014: it is not yet perfect, but it is improving all the time. Microsoft CEO Satya Nadella revealed an intriguing discovery which he called transfer learning: “If you teach it English, it learns English,” he said.

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

Watts's software is capable of matching the intricacies that have been revealed in subtle experiments on human hearing and auditory discrimination. Watts has used his model as a preprocessor (front end) in speech-recognition systems and has demonstrated its ability to pick out one speaker from background sounds (the "cocktail party effect"). This is an impressive feat of which humans are capable but up until now had not been feasible in automated speech-recognition systems.90 Like human hearing, Watts's cochlea model is endowed with spectral sensitivity (we hear better at certain frequencies), temporal responses (we are sensitive to the timing of sounds, which create the sensation of their spatial locations), masking, nonlinear frequency-dependent amplitude compression (which allows for greater dynamic range—the ability to hear both loud and quiet sounds), gain control (amplification), and other subtle features.

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Another method that is good at applying probabilistic networks to complex sequences of information involves Markov models.170 Andrei Andreyevich Markov (1856–1922), a renowned mathematician, established a theory of "Markov chains," which was refined by Norbert Wiener (1894–1964) in 1923. The theory provided a method to evaluate the likelihood that a certain sequence of events would occur. It has been popular, for example, in speech recognition, in which the sequential events are phonemes (parts of speech). The Markov models used in speech recognition code the likelihood that specific patterns of sound are found in each phoneme, how the phonemes influence each other, and likely orders of phonemes. The system can also include probability networks on higher levels of language, such as the order of words.

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Rabiner, "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition," Proceedings of the IEEE 77 (1989): 257–86. For a mathematical treatment of Markov models, see http://jedlik.phy.bme.hu/~gerjanos/HMM/node2.html. 171. Kurzweil Applied Intelligence (KAI), founded by the author in 1982, was sold in 1997 for $100 million and is now part of ScanSoft (formerly called Kurzweil Computer Products, the author's first company, which was sold to Xerox in 1980), now a public company. KAI introduced the first commercially marketed large-vocabulary speech-recognition system in 1987 (Kurzweil Voice Report, with a ten-thousand-word vocabulary). 172.

Natural Language Annotation for Machine Learning by James Pustejovsky, Amber Stubbs

Amazon Mechanical Turk, bioinformatics, cloud computing, computer vision, crowdsourcing, easy for humans, difficult for computers, finite state, Free Software Foundation, game design, information retrieval, iterative process, language acquisition, machine readable, machine translation, natural language processing, pattern recognition, performance metric, power law, sentiment analysis, social web, sparse data, speech recognition, statistical model, text mining

Programs such as Google Translate are getting better and better, but the real killer app will be the BabelFish that translates in real time when you’re looking for the right train to catch in Beijing. Speech Recognition This is one of the most difficult problems in NLP. There has been great progress in building models that can be used on your phone or computer to recognize spoken language utterances that are questions and commands. Unfortunately, while these Automatic Speech Recognition (ASR) systems are ubiquitous, they work best in narrowly defined domains and don’t allow the speaker to stray from the expected scripted input (“Please say or type your card number now”).

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Key Word in Context (KWIC) is invented as a means of indexing documents and creating concordances. 1960s: Kucera and Francis publish A Standard Corpus of Present-Day American English (the Brown Corpus), the first broadly available large corpus of language texts. Work in Information Retrieval (IR) develops techniques for statistical similarity of document content. 1970s: Stochastic models developed from speech corpora make Speech Recognition systems possible. The vector space model is developed for document indexing. The London-Lund Corpus (LLC) is developed through the work of the Survey of English Usage. 1980s: The Lancaster-Oslo-Bergen (LOB) Corpus, designed to match the Brown Corpus in terms of size and genres, is compiled.

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For your own corpus, you may find yourself wanting to cover a wide variety of text, but it is likely that you will have a more specific task domain, and so your potential corpus will not need to include the full range of human expression. The Switchboard Corpus is an example of a corpus that was collected for a very specific purpose—Speech Recognition for phone operation—and so was balanced and representative of the different sexes and all different dialects in the United States. Early Use of Corpora One of the most common uses of corpora from the early days was the construction of concordances. These are alphabetical listings of the words in an article or text collection with references given to the passages in which they occur.

Tools and Weapons: The Promise and the Peril of the Digital Age by Brad Smith, Carol Ann Browne

"World Economic Forum" Davos, Affordable Care Act / Obamacare, AI winter, air gap, airport security, Alan Greenspan, Albert Einstein, algorithmic bias, augmented reality, autonomous vehicles, barriers to entry, Berlin Wall, Big Tech, Bletchley Park, Blitzscaling, Boeing 737 MAX, business process, call centre, Cambridge Analytica, Celtic Tiger, Charlie Hebdo massacre, chief data officer, cloud computing, computer vision, corporate social responsibility, data science, deep learning, digital divide, disinformation, Donald Trump, Eben Moglen, Edward Snowden, en.wikipedia.org, Hacker News, immigration reform, income inequality, Internet of things, invention of movable type, invention of the telephone, Jeff Bezos, Kevin Roose, Laura Poitras, machine readable, Mark Zuckerberg, minimum viable product, national security letter, natural language processing, Network effects, new economy, Nick Bostrom, off-the-grid, operational security, opioid epidemic / opioid crisis, pattern recognition, precision agriculture, race to the bottom, ransomware, Ronald Reagan, Rubik’s Cube, Salesforce, school vouchers, self-driving car, Sheryl Sandberg, Shoshana Zuboff, Silicon Valley, Skype, speech recognition, Steve Ballmer, Steve Jobs, surveillance capitalism, tech worker, The Rise and Fall of American Growth, Tim Cook: Apple, Wargames Reagan, WikiLeaks, women in the workforce

See Sejnowski for a thorough history of the developments that have led to advances in neural networks over the past two decades. Back to note reference 10. Dom Galeon, “Microsoft’s Speech Recognition Tech Is Officially as Accurate as Humans,” Futurism, October 20, 2016, https://futurism.com/microsofts-speech-recognition-tech-is-officially-as-accurate-as-humans/; Xuedong Huang, “Microsoft Researchers Achieve New Conversational Speech Recognition Milestone,” Microsoft Research Blog, Microsoft, August 20, 2017, https://www.microsoft.com/en-us/research/blog/microsoft-researchers-achieve-new-conversational-speech-recognition-milestone/. Back to note reference 11. The rise of superintelligence was first raised by I.J.

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Vision and speech recognition have long been among the holy grails for researchers in computer science. In 1995, when Bill Gates founded Microsoft Research, one of the first goals of Nathan Myhrvold, who headed the effort, was to recruit the top academics in vision and speech recognition. I still recall when Microsoft’s basic research team optimistically predicted in the 1990s that a computer would soon be able to understand speech as well as a human being. The optimism of Microsoft researchers was shared by experts across academia and the tech sector. The reality was that speech recognition took longer to improve than experts had predicted.

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It also required new breakthroughs in techniques needed to train multilayer neural networks,9 which started to come to fruition about a decade ago.10 The collective impact of these changes led to rapid and impressive advances in AI-based systems. In 2016, the team at Microsoft Research’s vision recognition system matched human performance in a specific challenge to identify a large number of objects in a library called ImageNet. They then did the same thing with speech recognition in a specific challenge called the Switchboard data set, achieving a 94.1 percent accuracy rate.11 In other words, computers were starting to perceive the world as well as human beings. The same phenomenon happened with translation of languages, which requires in part that computers understand the meaning of different words, including nuance and slang.

The New Division of Labor: How Computers Are Creating the Next Job Market by Frank Levy, Richard J. Murnane

Atul Gawande, business cycle, call centre, computer age, Computer Numeric Control, correlation does not imply causation, David Ricardo: comparative advantage, deskilling, digital divide, Frank Levy and Richard Murnane: The New Division of Labor, Gunnar Myrdal, hypertext link, index card, information asymmetry, job automation, knowledge economy, knowledge worker, low skilled workers, low-wage service sector, PalmPilot, pattern recognition, profit motive, Robert Shiller, Ronald Reagan, Salesforce, speech recognition, tacit knowledge, talking drums, telemarketer, The Wealth of Nations by Adam Smith, working poor

The explanation was plausible—a call center that used operators who read scripts on computer screens moved to a source of even cheaper labor. In fact, however, the work order had not been taken by a human operator but by a computer using speech recognition software. By reading menus to the caller, the software could prompt the caller to identify the problem as being in a refrigerator, specifically, the ice-maker. It could also prompt the caller to choose a time he would be at home from a list of times when technicians were available. The speech recognition software could recognize the caller’s phone number and establish that the home address was a “HOUSE” rather than an apartment. While the software was not yet good enough to recognize the home address itself, it had captured enough information to print up a work order and append it to the technician’s schedule.

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To make sense of what she sees, she must extract features from this information, understanding where an adult’s legs end and where another object begins. In a complex visual field, this feature extraction is extremely difficult to program even though most four-year-olds do it without thinking. Perception is an equally difficult problem in speech recognition, determining where words begin and end, factoring out the “ummm’s” and “like’s.” The second problem, for both people and computers, involves interpreting what is perceived—recognizing what it is we are seeing, hearing, tasting, and so on. This recognition involves comparing what is perceived to concepts or schemas stored in memory.

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In the case of Fannie Mae’s Desktop Underwriter, the analysis of previously issued mortgages provided the points assessed to different pieces of information in computing a total score. By contrast, in neural net soft- 26 CHAPTER 2 ware, the program is “trained” on samples of previously identified patterns to “learn” which characteristics of a set of information mark it as a pattern of interest. For example, training would enable speech recognition software to distinguish the digital pattern of the spoken word “BILL” from the digital pattern of “ROSE” and to distinguish each of them from the digital pattern for “SPAGHETTI.” But once software has identified “BILL,” there is still the problem of determining which meaning of “BILL” is intended.

The One Device: The Secret History of the iPhone by Brian Merchant

Airbnb, animal electricity, Apollo Guidance Computer, Apple II, Apple's 1984 Super Bowl advert, Black Lives Matter, Charles Babbage, citizen journalism, Citizen Lab, Claude Shannon: information theory, computer vision, Computing Machinery and Intelligence, conceptual framework, cotton gin, deep learning, DeepMind, Douglas Engelbart, Dynabook, Edward Snowden, Elon Musk, Ford paid five dollars a day, Frank Gehry, gigafactory, global supply chain, Google Earth, Google Hangouts, Higgs boson, Huaqiangbei: the electronics market of Shenzhen, China, information security, Internet of things, Jacquard loom, John Gruber, John Markoff, Jony Ive, Large Hadron Collider, Lyft, M-Pesa, MITM: man-in-the-middle, more computing power than Apollo, Mother of all demos, natural language processing, new economy, New Journalism, Norbert Wiener, offshore financial centre, oil shock, pattern recognition, peak oil, pirate software, profit motive, QWERTY keyboard, reality distortion field, ride hailing / ride sharing, rolodex, Shenzhen special economic zone , Silicon Valley, Silicon Valley startup, skeuomorphism, skunkworks, Skype, Snapchat, special economic zone, speech recognition, stealth mode startup, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, TED Talk, Tim Cook: Apple, Tony Fadell, TSMC, Turing test, uber lyft, Upton Sinclair, Vannevar Bush, zero day

Yet if you ask Siri where she—sorry, it, but more on that in a second—comes from, the reply is the same: “I, Siri, was designed by Apple in California.” But that isn’t the full story. Siri is really a constellation of features—speech-recognition software, a natural-language user interface, and an artificially intelligent personal assistant. When you ask Siri a question, here’s what happens: Your voice is digitized and transmitted to an Apple server in the Cloud while a local voice recognizer scans it right on your iPhone. Speech-recognition software translates your speech into text. Natural-language processing parses it. Siri consults what tech writer Steven Levy calls the iBrain—around 200 megabytes of data about your preferences, the way you speak, and other details.

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“So we now know a lot about what people want in life and what they want to say to a computer and what they say to an assistant. “We don’t give it to anyone outside the company—there’s a strong privacy policy. So we don’t even keep most of that data on the servers, if at all, for very long.… Speech recognition has gotten much better because we actually look at the data and run experiments on it.” He too is fully aware of Siri’s shortcomings. “Right now the illusion breaks down when either you have speech-recognition issue, or you have a question that isn’t a common question or a request with an uncommon way of saying it.… How chatty can it get? How companion-like could it really be? Who’s the audience for that?

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Before that, it was a research project at Stanford backed by the Defense Department with the aim of creating an artificially intelligent assistant. Before that, it was an idea that had bounced around the tech industry, pop culture, and the halls of academia for decades; Apple itself had an early concept of a voice-interfacing AI in the 1980s. Before that there was the Hearsay II, a proto-Siri speech-recognition system. And Gruber says it was the prime inspiration for Siri. Dabbala Rajagopal “Raj” Reddy was born in 1937 in a village of five hundred people south of Madras, India. Around then, the region was hit with a seven-year drought and subsequent famine. Reddy learned to write, he says, by carving figures in the sand.

The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution by Gregory Zuckerman

affirmative action, Affordable Care Act / Obamacare, Alan Greenspan, Albert Einstein, Andrew Wiles, automated trading system, backtesting, Bayesian statistics, Bear Stearns, beat the dealer, behavioural economics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Black Monday: stock market crash in 1987, blockchain, book value, Brownian motion, butter production in bangladesh, buy and hold, buy low sell high, Cambridge Analytica, Carl Icahn, Claude Shannon: information theory, computer age, computerized trading, Credit Default Swap, Daniel Kahneman / Amos Tversky, data science, diversified portfolio, Donald Trump, Edward Thorp, Elon Musk, Emanuel Derman, endowment effect, financial engineering, Flash crash, George Gilder, Gordon Gekko, illegal immigration, index card, index fund, Isaac Newton, Jim Simons, John Meriwether, John Nash: game theory, John von Neumann, junk bonds, Loma Prieta earthquake, Long Term Capital Management, loss aversion, Louis Bachelier, mandelbrot fractal, margin call, Mark Zuckerberg, Michael Milken, Monty Hall problem, More Guns, Less Crime, Myron Scholes, Naomi Klein, natural language processing, Neil Armstrong, obamacare, off-the-grid, p-value, pattern recognition, Peter Thiel, Ponzi scheme, prediction markets, proprietary trading, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, Robert Mercer, Ronald Reagan, self-driving car, Sharpe ratio, Silicon Valley, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, Steve Bannon, Steve Jobs, stochastic process, the scientific method, Thomas Bayes, transaction costs, Turing machine, Two Sigma

One sees the results of the chain but not the “states” that help explain the progression of the chain. Those not acquainted with baseball might throw their hands up when receiving updates of the number of runs scored each inning—one run in this inning, six in another, with no obvious pattern or explanation. Some investors liken financial markets, speech recognition patterns, and other complex chains of events to hidden Markov models. The Baum-Welch algorithm provided a way to estimate probabilities and parameters within these complex sequences with little more information than the output of the processes. For the baseball game, the Baum-Welch algorithm might enable even someone with no understanding of the sport to guess the game situations that produced the scores.

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Today, though, Baum’s algorithm, which allows a computer to teach itself states and probabilities, is seen as one of the twentieth century’s notable advances in machine learning, paving the way for breakthroughs affecting the lives of millions in fields from genomics to weather prediction. Baum-Welch enabled the first effective speech recognition system and even Google’s search engine. For all of the acclaim Baum-Welch brought Lenny Baum, most of the hundreds of other papers he wrote were classified, which grated on Julia. She came to believe her husband was getting neither the recognition nor the pay he deserved. The Baum children had little idea what their father was up to.

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One last thing got Patterson especially excited: if a potential recruit was miserable in their current job. “I liked smart people who were probably unhappy,” Patterson says. One day, after reading in the morning paper that IBM was slashing costs, Patterson became intrigued. He was aware of the accomplishments of the computer giant’s speech-recognition group and thought their work bore similarity to what Renaissance was doing. In early 1993, Patterson sent separate letters to Peter Brown and Robert Mercer, deputies of the group, inviting them to visit Renaissance’s offices to discuss potential positions. Brown and Mercer both reacted the exact same way—depositing Patterson’s letter in the closest trash receptacle.

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

., 221–22 smart homes, 71–72 Smith, Adam, 227 snopes.com, 108 social aggregation theorem, 220–21 Social Limits to Growth, The (Hirsch), 230 social media, and content selection algorithms, 8–9 softbots, 64 software systems, 248 solutions, searching for, 257–66 abstract planning and, 264–66 combinatorial complexity and, 258 computational activity, managing, 261–62 15-puzzle and, 258 Go and, 259–61 map navigation and, 257–58 motor control commands and, 263–64 24-puzzle and, 258 “Some Moral and Technical Consequences of Automation” (Wiener), 10 Sophia (robot), 126 specifications (of programs), 248 “Speculations Concerning the First Ultraintelligent Machine” (Good), 142–43 speech recognition, 6 speech recognition capabilities, 74–75 Spence, Mike, 117 SpotMini, 73 SRI, 41–42, 52 standard model of intelligence, 9–11, 13, 48–61, 247 StarCraft, 45 Stasi, 103–4 stationarity, 24 statistics, 10, 176 Steinberg, Saul, 88 stimulus–response templates, 67 Stockfish (chess program), 47 striving and enjoying, relation between, 121–22 subroutines, 34, 233–34 Summers, Larry, 117, 120 Summit machine, 34, 35, 37 Sunstein, Cass, 244 Superintelligence (Bostrom), 102, 145, 150, 167, 183 supervised learning, 58–59, 285–93 surveillance, 104 Sutherland, James, 71 “switch it off” argument, 160–61 synapses, 15, 16 Szilard, Leo, 8, 77, 150 tactile sensing problem, robots, 73 Taobao, 106 technological unemployment.

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Connections were made to the long-established disciplines of probability, statistics, and control theory. The seeds of today’s progress were sown during that AI winter, including early work on large-scale probabilistic reasoning systems and what later became known as deep learning. Beginning around 2011, deep learning techniques began to produce dramatic advances in speech recognition, visual object recognition, and machine translation—three of the most important open problems in the field. By some measures, machines now match or exceed human capabilities in these areas. In 2016 and 2017, DeepMind’s AlphaGo defeated Lee Sedol, former world Go champion, and Ke Jie, the current champion—events that some experts predicted wouldn’t happen until 2097, if ever.6 Now AI generates front-page media coverage almost every day.

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Yann LeCun’s team at AT&T Labs didn’t write special algorithms to recognize “8” by searching for curvy lines and loops; instead, they improved on existing neural network learning algorithms to produce convolutional neural networks. Those networks, in turn, exhibited effective character recognition after suitable training on labeled examples. The same algorithms can learn to recognize letters, shapes, stop signs, dogs, cats, and police cars. Under the headline of “deep learning,” they have revolutionized speech recognition and visual object recognition. They are also one of the key components in AlphaZero as well as in most of the current self-driving car projects. If you think about it, it’s hardly surprising that progress towards general AI is going to occur in narrow-AI projects that address specific tasks; those tasks give AI researchers something to get their teeth into.

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

In an essay defending this view and his predictions about technological milestones he wrote: Basically, we are looking for biologically inspired methods that can accelerate work in AI, much of which has progressed without significant insight as to how the brain performs similar functions. From my own work in speech recognition, I know that our work was greatly accelerated when we gained insights as to how the brain prepares and transforms auditory information. Back in the 1990s, Kurzweil Computer Technologies broke new ground in voice recognition with applications designed to let doctors dictate medical reports. Kurzweil sold the company, and it became one of the roots of Nuance Communications, Inc. Whenever you use Siri it is Nuance’s algorithms that perform the speech recognition part of its magic. Speech recognition is the art of translating the spoken word to text (not to be confused with NLP, extracting meaning from written words).

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A force so unstable and mysterious, nature achieved it in full just once—intelligence. Chapter One The Busy Child artificial intelligence (abbreviation: AI) noun the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. —The New Oxford American Dictionary, Third Edition On a supercomputer operating at a speed of 36.8 petaflops, or about twice the speed of a human brain, an AI is improving its intelligence. It is rewriting its own program, specifically the part of its operating instructions that increases its aptitude in learning, problem solving, and decision making.

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Through several well-funded projects, IBM pursues AGI, and DARPA seems to be backing every AGI project I look into. So, again, why not Google? When I asked Jason Freidenfelds, from Google PR, he wrote: … it’s much too early for us to speculate about topics this far down the road. We’re generally more focused on practical machine learning technologies like machine vision, speech recognition, and machine translation, which essentially is about building statistical models to match patterns—nothing close to the “thinking machine” vision of AGI. But I think Page’s quotation sheds more light on Google’s attitudes than Freidenfelds’s. And it helps explain Google’s evolution from the visionary, insurrectionist company of the 1990s, with the much touted slogan DON’T BE EVIL, to today’s opaque, Orwellian, personal-data-aggregating behemoth.

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

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

We can go one step further with a model like this: The states form a Markov chain, as before, but we don’t get to see them; we have to infer them from the observations. This is called a hidden Markov model, or HMM for short. (Slightly misleading, because it’s the states that are hidden, not the model.) HMMs are at the heart of speech-recognition systems like Siri. In speech recognition, the hidden states are written words, the observations are the sounds spoken to Siri, and the goal is to infer the words from the sounds. The model has two components: the probability of the next word given the current one, as in a Markov chain, and the probability of hearing various sounds given the word being pronounced.

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“The PageRank citation ranking: Bringing order to the Web,”* by Larry Page, Sergey Brin, Rajeev Motwani, and Terry Winograd (Stanford University technical report, 1998), describes the PageRank algorithm and its interpretation as a random walk over the web. Statistical Language Learning,* by Eugene Charniak (MIT Press, 1996), explains how hidden Markov models work. Statistical Methods for Speech Recognition,* by Fred Jelinek (MIT Press, 1997), describes their application to speech recognition. The story of HMM-style inference in communication is told in “The Viterbi algorithm: A personal history,” by David Forney (unpublished; online at arxiv.org/pdf/cs/0504020v2.pdf). Bioinformatics: The Machine Learning Approach,* by Pierre Baldi and Søren Brunak (2nd ed., MIT Press, 2001), is an introduction to the use of machine learning in biology, including HMMs.

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Ironically, Lenat has belatedly embraced populating Cyc by mining the web, not because Cyc can read, but because there’s no other way. Even if by some miracle we managed to finish coding up all the necessary pieces, our troubles would be just beginning. Over the years, a number of research groups have attempted to build complete intelligent agents by putting together algorithms for vision, speech recognition, language understanding, reasoning, planning, navigation, manipulation, and so on. Without a unifying framework, these attempts soon hit an insurmountable wall of complexity: too many moving parts, too many interactions, too many bugs for poor human software engineers to cope with. Knowledge engineers believe AI is just an engineering problem, but we have not yet reached the point where engineering can take us the rest of the way.

The Quants by Scott Patterson

Alan Greenspan, Albert Einstein, AOL-Time Warner, asset allocation, automated trading system, Bear Stearns, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, Black Monday: stock market crash in 1987, Black Swan, Black-Scholes formula, Blythe Masters, Bonfire of the Vanities, book value, Brownian motion, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, Carl Icahn, centralized clearinghouse, Claude Shannon: information theory, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, Doomsday Clock, Dr. Strangelove, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial engineering, Financial Modelers Manifesto, fixed income, Glass-Steagall Act, global macro, Gordon Gekko, greed is good, Haight Ashbury, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, index fund, invention of the telegraph, invisible hand, Isaac Newton, Jim Simons, job automation, John Meriwether, John Nash: game theory, junk bonds, Kickstarter, law of one price, Long Term Capital Management, Louis Bachelier, low interest rates, mandelbrot fractal, margin call, Mark Spitznagel, merger arbitrage, Michael Milken, military-industrial complex, money market fund, Myron Scholes, NetJets, new economy, offshore financial centre, old-boy network, Paul Lévy, Paul Samuelson, Ponzi scheme, proprietary trading, quantitative hedge fund, quantitative trading / quantitative finance, race to the bottom, random walk, Renaissance Technologies, risk-adjusted returns, Robert Mercer, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Savings and loan crisis, Sergey Aleynikov, short selling, short squeeze, South Sea Bubble, speech recognition, statistical arbitrage, The Chicago School, The Great Moderation, The Predators' Ball, too big to fail, transaction costs, value at risk, volatility smile, yield curve, éminence grise

Then the trail goes cold. At first blush, speech recognition and investing would appear to have little in common. But beneath the surface, there are striking connections. Computer models designed to map human speech depend on historical data that mimic acoustic signals. To operate most efficiently, speech recognition programs monitor the signals and, based on probability functions, try to guess what sound is coming next. The programs constantly make such guesses to keep up with the speaker. Financial models are also made up of data strings. By glomming complex speech recognition models onto financial data, say a series of soybean prices, Renaissance can discern a range of probabilities for the future directions of prices.

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By glomming complex speech recognition models onto financial data, say a series of soybean prices, Renaissance can discern a range of probabilities for the future directions of prices. If the odds become favorable … if you have an edge … It’s obviously not so simple—if it were, every speech recognition expert in the world would be running a hedge fund. There are complicated issues involving the quality of the data and whether the patterns discovered are genuine. But there is clearly a powerful connection between speech recognition and investing that Renaissance is exploiting to the hilt. A clue to the importance of speech recognition to Renaissance’s broader makeup is that Brown and Mercer were named co-CEOs of Renaissance Technologies after Simons stepped down in late 2009.

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Renaissance has applied that skill to enormous strings of market numbers, such as tick-by-tick data in oil prices, while looking at other relationships the data have with assets such as the dollar or gold. Another clue can be found in the company’s decision in the early 1990s to hire several individuals with expertise in the obscure, decidedly non–Wall Street field of speech recognition. In November 1993, Renaissance hired Peter Brown and Robert Mercer, founders of a speech recognition group at IBM’s Thomas J. Watson Research Center in Yorktown Heights, New York, in the hills of Westchester County. Brown came to be known as a freakishly hard worker at the fund, often spending the night at Renaissance’s East Setauket headquarters on a Murphy bed with a whiteboard tacked to the bottom of it.

Designing Search: UX Strategies for Ecommerce Success by Greg Nudelman, Pabini Gabriel-Petit

access to a mobile phone, Albert Einstein, AltaVista, augmented reality, barriers to entry, Benchmark Capital, business intelligence, call centre, cognitive load, crowdsourcing, folksonomy, information retrieval, Internet of things, Neal Stephenson, Palm Treo, performance metric, QR code, recommendation engine, RFID, search costs, search engine result page, semantic web, Silicon Valley, social graph, social web, speech recognition, text mining, the long tail, the map is not the territory, The Wisdom of Crowds, web application, zero-sum game, Zipcar

Figure 15-8: Custom sort control implemented via popover in the ThirstyPocket iPhone app Changing Search Paradigms Because of the unique mix of constraints and opportunities that mobile application design presents, this design space is rich with possibilities for changing the existing paradigms for search and finding. Consider speech recognition, for example. Although, on the desktop, speech recognition does not yet enjoy widespread popularity and use, mobile represents an entirely different context—where speech recognition can offer an ideal solution. Not interpreting a spoken word correctly on a mobile device might not be quite as big a deal as it is on the desktop because the accuracy of speech recognition may actually approach, if not exceed, that of typing on a mobile phone’s awkward mini-keyboard. For some mobile contexts, like driving, speech recognition may even offer a way to access full-featured search when typing is not available.

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For some mobile contexts, like driving, speech recognition may even offer a way to access full-featured search when typing is not available. Combine speech recognition with the use of an accelerometer and magnetometer, enabling gestural input, and you have the Google Mobile search application for the iPhone, shown in Figure 15-9. Figure 15-9: Google Mobile iPhone app Google’s iPhone application recognizes the gesture of a person’s swinging the phone up to his ear to know when to record a search command. When the user speaks, the search engine accepts and interprets his voice commands and then serves up search results. This user interface implements what is literally a game-changing design paradigm because its designers have taken the time to truly consider the mobile context of use and map natural interactions such as speech and gestures to mobile device functions.

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The Amazon Remembers feature discussed in Chapter 15 is already moving toward creating a high-touch experience in which human operators can augment an image-based search user interface. For the ultimate in service, these specially trained salespeople would be ready to chat the moment a customer opens an ecommerce application. Through speech recognition and speech synthesis technology, people could communicate with their virtual personal assistant, who’s a great multitasker and is always ready to chat, whether they need help shopping or tutoring on various life matters. References Clark, Josh. “iPad Design Headaches.” Design4Mobile, September 20–24, 2010.

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

The bad news is that it could only handle digits—well behind the kind of speech recognition program we tend to curse in modern automated telephone systems. It is interesting that when speech-recognition pioneer Ray Kurzweil was writing about Hal’s capabilities back in the late 1990s, he expected that we would be using speech to dictate to personal computer applications as the norm before 2001. In reality, such systems are still not used on most computers today. The shift Kurzweil expected has been much slower than he anticipated and may never come. Although the speech recognition technology built into my iMac is quite good, I very rarely use it.

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v=ajsCY8SjJ1Y, accessed September 3, 2014. The first computer music, produced at the University of Manchester, is described in B. Jack Copeland, Turing: Pioneer of the Information Age (Oxford, UK: Oxford University Press, 2012), pp. 163–64. Information in the history of speech recognition from the Raymond Kurzweil section, “When will HAL Understand what we are Saying? Computer Speech Recognition and Understanding,” in David G. Stork (ed.), Hal’s Legacy (Cambridge, MA: MIT Press, 2000), pp. 145–50. Apple’s Knowledge Navigator appears at a number of locations on YouTube including www.youtube.com/watch?v=QRH8eimU_20, accessed September 3, 2014.

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Over and above a few novelties and handling the kind of request you might expect to make of an electronic PA—booking appointments, looking something up online, planning a route, or playing music—it rapidly becomes clear that Siri is not capable of a real conversation, falling down on both of the key challenges of parsing and of understanding speech. Siri’s voice recognition is surprisingly effective, but there are times when it can struggle. Nonstandard accents can throw it easily—there isn’t a speech-recognition system yet that doesn’t fail with some Glasgow or Downeast Maine accents. And the way we speak as a matter of course involves slurring, running words together in a way that we never notice, but which a machine is forced to encounter and deal with. This doesn’t mean that it is not possible for a machine to understand speech.

Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy by George Gilder

23andMe, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, AlphaGo, AltaVista, Amazon Web Services, AOL-Time Warner, Asilomar, augmented reality, Ben Horowitz, bitcoin, Bitcoin Ponzi scheme, Bletchley Park, blockchain, Bob Noyce, British Empire, Brownian motion, Burning Man, business process, butterfly effect, carbon footprint, cellular automata, Claude Shannon: information theory, Clayton Christensen, cloud computing, computer age, computer vision, crony capitalism, cross-subsidies, cryptocurrency, Danny Hillis, decentralized internet, deep learning, DeepMind, Demis Hassabis, disintermediation, distributed ledger, don't be evil, Donald Knuth, Donald Trump, double entry bookkeeping, driverless car, Elon Musk, Erik Brynjolfsson, Ethereum, ethereum blockchain, fake news, fault tolerance, fiat currency, Firefox, first square of the chessboard, first square of the chessboard / second half of the chessboard, floating exchange rates, Fractional reserve banking, game design, Geoffrey Hinton, George Gilder, Google Earth, Google Glasses, Google Hangouts, index fund, inflation targeting, informal economy, initial coin offering, Internet of things, Isaac Newton, iterative process, Jaron Lanier, Jeff Bezos, Jim Simons, Joan Didion, John Markoff, John von Neumann, Julian Assange, Kevin Kelly, Law of Accelerating Returns, machine translation, Marc Andreessen, Mark Zuckerberg, Mary Meeker, means of production, Menlo Park, Metcalfe’s law, Money creation, money: store of value / unit of account / medium of exchange, move fast and break things, Neal Stephenson, Network effects, new economy, Nick Bostrom, Norbert Wiener, Oculus Rift, OSI model, PageRank, pattern recognition, Paul Graham, peer-to-peer, Peter Thiel, Ponzi scheme, prediction markets, quantitative easing, random walk, ransomware, Ray Kurzweil, reality distortion field, Recombinant DNA, Renaissance Technologies, Robert Mercer, Robert Metcalfe, Ronald Coase, Ross Ulbricht, Ruby on Rails, Sand Hill Road, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Singularitarianism, Skype, smart contracts, Snapchat, Snow Crash, software is eating the world, sorting algorithm, South Sea Bubble, speech recognition, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, stochastic process, Susan Wojcicki, TED Talk, telepresence, Tesla Model S, The Soul of a New Machine, theory of mind, Tim Cook: Apple, transaction costs, tulip mania, Turing complete, Turing machine, Vernor Vinge, Vitalik Buterin, Von Neumann architecture, Watson beat the top human players on Jeopardy!, WikiLeaks, Y Combinator, zero-sum game

With clusters of supercomputers running at sufficient velocity, you could beat every short-term market you could access and measure. In 2009 Simons retired and named Mercer and Brown co-CEOs of his company. Mercer’s IBM boss, Fred Jelinek, was a protégé of the MIT information theorist Robert Fano and a student of Claude Shannon. He saw speech recognition as an information theory problem—an acoustic signal and a noisy channel. Citing the content-neutral concept behind his speech-recognition successes, Jelinek proudly declared, “Every time I fire a linguist, the performance improves.” Renaissance’s approach similarly spurns any direct kibitzing from fundamental analysts or anyone who knows anything special about particular companies.

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These chips compute the car’s response to lidar, radar, ultrasound, and camera signals that free the missile to descend from the outer space of Elon Musk’s domains and enter the ever-changing high-entropy world beyond Google Maps. Dally barks his command: “Navigate to California Avenue Caltrain station,” and the car crisply responds. Dally comments, “In the last couple years speech recognition has become dramatically better. Thirty-percent better. Two years ago it was not really capable of getting it right. But now with machine learning on our Tegra chips, it gets it right every time.” Benefiting are all the users of Amazon’s Alexa, Apple’s Siri, Microsoft’s Cortana, Google’s Go.

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Simultaneously with the dogs and cats crisis in 2012, the leader of the Google Brain research team, Jeff Dean, raised the stakes by telling Urs Hölzle, Google’s data center dynamo, “We need another Google.” Dean meant that Google would have to double the capacity of its data centers just to accommodate new demand for its Google Now speech recognition services on Android smartphones. Late in the year, Bill Dally provided an answer. Over breakfast at Dally’s favorite Palo Alto café, his Stanford colleague Andrew Ng, who worked with Dean at Google Brain, was complaining about the naming of cats. Sixteen thousand costly microprocessor cores seemed inefficient.

Machine, Platform, Crowd: Harnessing Our Digital Future by Andrew McAfee, Erik Brynjolfsson

"World Economic Forum" Davos, 3D printing, additive manufacturing, AI winter, Airbnb, airline deregulation, airport security, Albert Einstein, algorithmic bias, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, Andy Rubin, AOL-Time Warner, artificial general intelligence, asset light, augmented reality, autism spectrum disorder, autonomous vehicles, backpropagation, backtesting, barriers to entry, behavioural economics, bitcoin, blockchain, blood diamond, British Empire, business cycle, business process, carbon footprint, Cass Sunstein, centralized clearinghouse, Chris Urmson, cloud computing, cognitive bias, commoditize, complexity theory, computer age, creative destruction, CRISPR, crony capitalism, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, Dean Kamen, deep learning, DeepMind, Demis Hassabis, discovery of DNA, disintermediation, disruptive innovation, distributed ledger, double helix, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, everywhere but in the productivity statistics, Evgeny Morozov, fake news, family office, fiat currency, financial innovation, general purpose technology, Geoffrey Hinton, George Akerlof, global supply chain, Great Leap Forward, Gregor Mendel, Hernando de Soto, hive mind, independent contractor, information asymmetry, Internet of things, inventory management, iterative process, Jean Tirole, Jeff Bezos, Jim Simons, jimmy wales, John Markoff, joint-stock company, Joseph Schumpeter, Kickstarter, Kiva Systems, law of one price, longitudinal study, low interest rates, Lyft, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Marc Andreessen, Marc Benioff, Mark Zuckerberg, meta-analysis, Mitch Kapor, moral hazard, multi-sided market, Mustafa Suleyman, Myron Scholes, natural language processing, Network effects, new economy, Norbert Wiener, Oculus Rift, PageRank, pattern recognition, peer-to-peer lending, performance metric, plutocrats, precision agriculture, prediction markets, pre–internet, price stability, principal–agent problem, Project Xanadu, radical decentralization, Ray Kurzweil, Renaissance Technologies, Richard Stallman, ride hailing / ride sharing, risk tolerance, Robert Solow, Ronald Coase, Salesforce, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, slashdot, smart contracts, Snapchat, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Pinker, supply-chain management, synthetic biology, tacit knowledge, TaskRabbit, Ted Nelson, TED Talk, the Cathedral and the Bazaar, The Market for Lemons, The Nature of the Firm, the strength of weak ties, Thomas Davenport, Thomas L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, Travis Kalanick, Two Sigma, two-sided market, Tyler Cowen, Uber and Lyft, Uber for X, uber lyft, ubercab, Vitalik Buterin, warehouse robotics, Watson beat the top human players on Jeopardy!, winner-take-all economy, yield management, zero day

depth=1#x0026;hl=en#x0026;prev=search#x0026;rurl=translate.google.com#x0026;sl=ja#x0026;sp=nmt4#x0026;u=http://www.fukoku-life.co.jp/about/news/download/20161226.pdf. 84 In October of 2016: Allison Linn, “Historic Achievement: Microsoft Researchers Reach Human Parity in Conversational Speech Recognition,” Microsoft (blog), October 18, 2016, http://blogs.microsoft.com/next/2016/10/18/historic-achievement-microsoft-researchers-reach-human-parity-conversational-speech-recognition/#sm.0001d0t49dx0veqdsh21cccecz0e3. 84 “I must confess that I never thought”: Mark Liberman, “Human Parity in Conversational Speech Recognition,” Language Log (blog), October 18, 2016, http://languagelog.ldc.upenn.edu/nll/?p=28894. 84 “Every time I fire a linguist”: Julia Hirschberg, “ ‘Every Time I Fire a Linguist, My Performance Goes Up,’ and Other Myths of the Statistical Natural Language Processing Revolution” (speech, 15th National Conference on Artificial Intelligence, Madison, WI, July 29, 1998). 84 “AI-first world”: Julie Bort, “Salesforce CEO Marc Benioff Just Made a Bold Prediction about the Future of Tech,” Business Insider, May 18, 2016, http://www.businessinsider.com/salesforce-ceo-i-see-an-ai-first-world-2016-5. 85 “Many businesses still make important decisions”: Marc Benioff, “On the Cusp of an AI Revolution,” Project Syndicate, September 13, 2016, https://www.project-syndicate.org/commentary/artificial-intelligence-revolution-by-marc-benioff-2016-09.

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But the hardest part of customer service to automate has not been finding an answer, but rather the initial step: listening and understanding. Speech recognition and other aspects of natural language processing have been tremendously difficult problems in artificial intelligence since the dawn of the field, for all of the reasons described earlier in this chapter. The previously dominant symbolic approaches have not worked well at all, but newer ones based on deep learning are making progress so quickly that it has surprised even the experts. In October of 2016, a team from Microsoft Research announced that a neural network they had built had achieved “human parity in conversational speech recognition,” as the title of their paper put it.

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One of Logic Theorist’s proofs, in fact, was so much more elegant than the one in the book that Russell himself “responded with delight” to it. Simon announced that he and his colleagues had “invented a thinking machine.” Other challenges, however, proved much less amenable to a rule-based approach. Decades of research in speech recognition, image classification, language translation, and other domains yielded unimpressive results. The best of these systems achieved much worse than human-level performance, and the worst were memorably bad. According to a 1979 collection of anecdotes, for example, researchers gave their English-to-Russian translation utility the phrase “The spirit is willing, but the flesh is weak.”

Invisible Women by Caroline Criado Perez

"Hurricane Katrina" Superdome, Affordable Care Act / Obamacare, algorithmic bias, augmented reality, Bernie Sanders, Cambridge Analytica, collective bargaining, crowdsourcing, data science, Diane Coyle, Donald Trump, falling living standards, first-past-the-post, gender pay gap, gig economy, glass ceiling, Grace Hopper, Hacker Ethic, independent contractor, Indoor air pollution, informal economy, lifelogging, low skilled workers, mental accounting, meta-analysis, Nate Silver, new economy, obamacare, Oculus Rift, offshore financial centre, pattern recognition, phenotype, post-industrial society, randomized controlled trial, remote working, Sheryl Sandberg, Silicon Valley, Simon Kuznets, speech recognition, stem cell, Stephen Hawking, Steven Levy, tech bro, the built environment, urban planning, women in the workforce, work culture , zero-sum game

_tid=f0a12b58-f81d-11e6-af6b-00000aab0f26&acdnat=1487671995_41cfe19ea98e87fb7e3e693bdddaba6e; http://www.sciencedirect.com/science/article/pii/S1050641108001909 18 https://www.theverge.com/circuitbreaker/2016/7/14/12187580/keecok1-hexagon-phone-for-women 19 https://www.theguardian.com/technology/askjack/2016/apr/21/can-speech-recognition-software-help-prevent-rsi 20 https://makingnoiseandhearingthings.com/2016/07/12/googles-speech-recognition-has-a-gender-bias/ 21 http://blog-archive.griddynamics.com/2016/01/automatic-speech-recognition-services.html 22 https://www.autoblog.com/2011/05/31/women-voice-command-systems/ 23 https://www.ncbi.nlm.nih.gov/pubmed/27435949 24 American Roentgen Ray Society (2007), ‘Voice Recognition Systems Seem To Make More Errors With Women’s Dictation’, ScienceDaily, 6 May 2007; Rodger, James A. and Pendharkar, Parag C. (2007), ‘A field study of database communication issues peculiar to users of a voice activated medical tracking application’, Decision Support Systems, 43:1 (1 February 2007), 168–80, https://doi.org/10.1016/j.dss.2006.08.005. 25 American Roentgen Ray Society (2007) 26 http://techland.time.com/2011/06/01/its-not-you-its-it-voice-recognition-doesnt-recognize-women/ 27 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2994697/ 28 http://www.aclweb.org/anthology/P08-1044 29 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2790192/ 30 http://www.aclweb.org/anthology/P08-1044 31 http://groups.inf.ed.ac.uk/ami/corpus/; http://www1.icsi.berkeley.edu/Speech/papers/gelbart-ms/numbers/; http://www.voxforge.org/ 32 http://www.natcorp.ox.ac.uk/corpus/index.xml?

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Voice recognition has also been suggested as a solution to smartphone-associated RSI,19 but this actually isn’t much of a solution for women, because voice-recognition software is often hopelessly male-biased. In 2016, Rachael Tatman, a research fellow in linguistics at the University of Washington, found that Google’s speech-recognition software was 70% more likely to accurately recognise male speech than female speech20 – and it’s currently the best on the market.21 Clearly, it is unfair for women to pay the same price as men for products that deliver an inferior service to them. But there can also be serious safety implications.

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After five failed attempts I suggested she tried lowering the pitch of her voice. It worked first time. As voice-recognition software has become more sophisticated, its use has branched out to numerous fields, including medicine, where errors can be just as grave. A 2016 paper analysed a random sample of a hundred notes dictated by attending emergency physicians using speech-recognition software, and found that 15% of the errors were critical, ‘potentially leading to miscommunication that could affect patient care’.23 Unfortunately these authors did not sex-disaggregate their data, but papers that have, report significantly higher transcription error rates for women than men.24 Dr Syed Ali, the lead author of one of the medical dictation studies, observed that his study’s ‘immediate impact’ was that women ‘may have to work somewhat harder’ than men ‘to make the [voice recognition] system successful’.25 Rachael Tatman agrees: ‘The fact that men enjoy better performance than women with these technologies means that it’s harder for women to do their jobs.

Text Analytics With Python: A Practical Real-World Approach to Gaining Actionable Insights From Your Data by Dipanjan Sarkar

bioinformatics, business intelligence, business logic, computer vision, continuous integration, data science, deep learning, Dr. Strangelove, en.wikipedia.org, functional programming, general-purpose programming language, Guido van Rossum, information retrieval, Internet of things, invention of the printing press, iterative process, language acquisition, machine readable, machine translation, natural language processing, out of africa, performance metric, premature optimization, recommendation engine, self-driving car, semantic web, sentiment analysis, speech recognition, statistical model, text mining, Turing test, web application

A question is posed to a computer and a human, and the test is passed if it is impossible to say which of the answers given was given by the human. Over time, a lot of progress has been made in this area by using techniques like speech synthesis, analysis, syntactic parsing, and contextual reasoning. But one chief limitation for speech recognition systems still remains: They are very domain specific and will not work if the user strays even a little bit from the expected scripted inputs needed by the system. Speech-recognition systems are now found in many places, from desktop computers to mobile phones to virtual assistance systems. Question Answering Systems Question Answering Systems (QAS) are built upon the principle of Question Answering, based on using techniques from NLP and information retrieval (IR).

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The Philosophy of Language Language Acquisition and Usage Linguistics Language Syntax and Structure Words Phrases Clauses Grammar Word Order Typology Language Semantics Lexical Semantic Relations Semantic Networks and Models Representation of Semantics Text Corpora Corpora Annotation and Utilities Popular Corpora Accessing Text Corpora Natural Language Processing Machine Translation Speech Recognition Systems Question Answering Systems Contextual Recognition and Resolution Text Summarization Text Categorization Text Analytics Summary Chapter 2:​ Python Refresher Getting to Know Python The Zen of Python Applications:​ When Should You Use Python?​ Drawbacks:​ When Should You Not Use Python?​

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In his spare time he loves reading, gaming, and watching popular sitcoms and football. About the Technical Reviewer Shanky Sharma Currently leading the AI team at Nextremer India, Shanky Sharma’s work entails implementing various AI and machine learning–related projects and working on deep learning for speech recognition in Indic languages. He hopes to grow and scale new horizons in AI and machine learning technologies. Statistics intrigue him and he loves playing with numbers, designing algorithms, and giving solutions to people. He sees himself as a solution provider rather than a scripter or another IT nerd who codes.

Hands-On Machine Learning With Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron

AlphaGo, Amazon Mechanical Turk, Anton Chekhov, backpropagation, combinatorial explosion, computer vision, constrained optimization, correlation coefficient, crowdsourcing, data science, deep learning, DeepMind, don't repeat yourself, duck typing, Elon Musk, en.wikipedia.org, friendly AI, Geoffrey Hinton, ImageNet competition, information retrieval, iterative process, John von Neumann, Kickstarter, machine translation, natural language processing, Netflix Prize, NP-complete, OpenAI, optical character recognition, P = NP, p-value, pattern recognition, pull request, recommendation engine, self-driving car, sentiment analysis, SpamAssassin, speech recognition, stochastic process

This paper revived the interest of the scientific community and before long many new papers demonstrated that Deep Learning was not only possible, but capable of mind-blowing achievements that no other Machine Learning (ML) technique could hope to match (with the help of tremendous computing power and great amounts of data). This enthusiasm soon extended to many other areas of Machine Learning. Fast-forward 10 years and Machine Learning has conquered the industry: it is now at the heart of much of the magic in today’s high-tech products, ranking your web search results, powering your smartphone’s speech recognition, and recommending videos, beating the world champion at the game of Go. Before you know it, it will be driving your car. Machine Learning in Your Projects So naturally you are excited about Machine Learning and you would love to join the party! Perhaps you would like to give your homemade robot a brain of its own?

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Caution Don’t jump into deep waters too hastily: while Deep Learning is no doubt one of the most exciting areas in Machine Learning, you should master the fundamentals first. Moreover, most problems can be solved quite well using simpler techniques such as Random Forests and Ensemble methods (discussed in Part I). Deep Learning is best suited for complex problems such as image recognition, speech recognition, or natural language processing, provided you have enough data, computing power, and patience. Other Resources Many resources are available to learn about Machine Learning. Andrew Ng’s ML course on Coursera and Geoffrey Hinton’s course on neural networks and Deep Learning are amazing, although they both require a significant time investment (think months).

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In contrast, a spam filter based on Machine Learning techniques automatically notices that “For U” has become unusually frequent in spam flagged by users, and it starts flagging them without your intervention (Figure 1-3). Figure 1-3. Automatically adapting to change Another area where Machine Learning shines is for problems that either are too complex for traditional approaches or have no known algorithm. For example, consider speech recognition: say you want to start simple and write a program capable of distinguishing the words “one” and “two.” You might notice that the word “two” starts with a high-pitch sound (“T”), so you could hardcode an algorithm that measures high-pitch sound intensity and use that to distinguish ones and twos.

Amazon Unbound: Jeff Bezos and the Invention of a Global Empire by Brad Stone

activist fund / activist shareholder / activist investor, air freight, Airbnb, Amazon Picking Challenge, Amazon Robotics, Amazon Web Services, autonomous vehicles, Bernie Sanders, big data - Walmart - Pop Tarts, Big Tech, Black Lives Matter, business climate, call centre, carbon footprint, Clayton Christensen, cloud computing, Colonization of Mars, commoditize, company town, computer vision, contact tracing, coronavirus, corporate governance, COVID-19, crowdsourcing, data science, deep learning, disinformation, disintermediation, Donald Trump, Downton Abbey, Elon Musk, fake news, fulfillment center, future of work, gentrification, George Floyd, gigafactory, global pandemic, Greta Thunberg, income inequality, independent contractor, invisible hand, Jeff Bezos, John Markoff, Kiva Systems, Larry Ellison, lockdown, Mahatma Gandhi, Mark Zuckerberg, Masayoshi Son, mass immigration, minimum viable product, move fast and break things, Neal Stephenson, NSO Group, Paris climate accords, Peter Thiel, Ponzi scheme, Potemkin village, private spaceflight, quantitative hedge fund, remote working, rent stabilization, RFID, Robert Bork, Ronald Reagan, search inside the book, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Snapchat, social distancing, SoftBank, SpaceX Starlink, speech recognition, Steve Ballmer, Steve Jobs, Steven Levy, tech billionaire, tech bro, techlash, TED Talk, Tim Cook: Apple, Tony Hsieh, too big to fail, Tragedy of the Commons, two-pizza team, Uber for X, union organizing, warehouse robotics, WeWork

After graduating in the late 1990s, Prasad passed on the dot-com boom and worked for the Cambridge, Massachusetts–based defense contractor BBN Technologies (later acquired by Raytheon) on some of the first speech recognition and natural language systems. At BBN, he worked on one of the first in-car speech recognition systems and automated directory assistance services for telephone companies. In 2000, he worked on another system that automatically transcribed courtroom proceedings. Accurately recording conversation from multiple microphones placed around a courtroom introduced him to the challenges of far-field speech recognition. At the start of the project, he said that eighty out of every hundred words were incorrect; but within the first year, they cut it down to thirty-three.

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By the time he was following Bezos around, the facial hair was gone and Hart was a rising corporate star. “You sort of feel like you’re an assistant coach watching John Wooden, you know, perhaps the greatest basketball coach ever,” Hart said of his time as the TA. Hart remembered talking to Bezos about speech recognition one day in late 2010 at Seattle’s Blue Moon Burgers. Over lunch, Hart demonstrated his enthusiasm for Google’s voice search on his Android phone by saying, “pizza near me,” and then showing Bezos the list of links to nearby pizza joints that popped up on-screen. “Jeff was a little skeptical about the use of it on phones, because he thought it might be socially awkward,” Hart remembered.

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There Greg Hart finally described “this little device, about the size of a Coke can, that would sit on your table and you could ask it natural language questions and it would be a smart assistant,” recalled Yap’s VP of research, Jeff Adams, a two-decade veteran of the speech industry. “Half of my team were rolling their eyes, saying ‘oh my word, what have we gotten ourselves into.’ ” After the meeting, Adams delicately told Hart and Lindsay that their goals were unrealistic. Most experts believed that true “far-field speech recognition”—comprehending speech from up to thirty-two feet away, often amid crosstalk and background noise—was beyond the realm of established computer science, since sound bounces off surfaces like walls and ceilings, producing echoes that confuse computers. The Amazon executives responded by channeling Bezos’s resolve.

Applied Artificial Intelligence: A Handbook for Business Leaders by Mariya Yao, Adelyn Zhou, Marlene Jia

Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, artificial general intelligence, autonomous vehicles, backpropagation, business intelligence, business process, call centre, chief data officer, cognitive load, computer vision, conceptual framework, data science, deep learning, DeepMind, en.wikipedia.org, fake news, future of work, Geoffrey Hinton, industrial robot, information security, Internet of things, iterative process, Jeff Bezos, job automation, machine translation, Marc Andreessen, natural language processing, new economy, OpenAI, pattern recognition, performance metric, price discrimination, randomized controlled trial, recommendation engine, robotic process automation, Salesforce, self-driving car, sentiment analysis, Silicon Valley, single source of truth, skunkworks, software is eating the world, source of truth, sparse data, speech recognition, statistical model, strong AI, subscription business, technological singularity, The future is already here

A Neural Network for Machine Translation, at Production Scale [blog post]. Retrieved from: https://research.googleblog.com/2016/09/a-neural-network-for-machine.html” (5) “Huang, X.D. (2017, August 20). Microsoft researchers achieve new conversational speech recognition milestone [blog post]. Retrieved from http://www.microsoft.com/en-us/research/blog/microsoft-researchers-achieve-new-conversational-speech-recognition-milestone/” (6) “Customer Case Studies. (n.d.). Retrieved from http://blog.clarifai.com/customer-case-studies/” (7) http://probcomp.csail.mit.edu/ (8) Reading List. (n.d.). MIT Probabilistic Computing Project.

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Deep Learning Deep learning is a subfield of machine learning that builds algorithms by using multi-layered artificial neural networks, which are mathematical structures loosely inspired by how biological neurons fire. Neural networks were invented in the 1950s, but recent advances in computational power and algorithm design—as well as the growth of big data—have enabled deep learning algorithms to approach human-level performance in tasks such as speech recognition and image classification. Deep learning, in combination with reinforcement learning, enabled Google DeepMind’s AlphaGo to defeat human world champions of Go in 2016, a feat that many experts had considered to be computationally impossible. Much media attention has been focused on deep learning, and an increasing number of sophisticated technology companies have successfully implemented deep learning for enterprise-scale products.

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Much media attention has been focused on deep learning, and an increasing number of sophisticated technology companies have successfully implemented deep learning for enterprise-scale products. Google replaced previous statistical methods for machine translation with neural networks to achieve superior performance.(4) Microsoft announced in 2017 that they had achieved human parity in conversational speech recognition.(5) Promising computer vision startups like Clarifai employ deep learning to achieve state-of-the-art results in recognizing objects in images and video for Fortune 500 brands.(6) While deep learning models outperform older machine learning approaches to many problems, they are more difficult to develop because they require robust training of data sets and specialized expertise in optimization techniques.

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

Let's begin with some near-term applications. These are the things that seem obvious, like replacing tubes in a radio with transistors or building calculators with a microprocessor. And we can start by looking at some areas that AI tried to tackle but couldn't solve— speech recognition, vision, and smart cars. * * * If you have ever tried to use speech recognition software to enter text on a personal computer, you know how dumb it can be. Like Searle's Chinese Room, the computer has no understanding of what is being said. The few times I tried these products, I grew frustrated. If there was any noise in the room, from a dropped pencil to someone speaking to me, extra words would appear on my screen.

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However, the words overlap and interfere, and pieces of sound drop out because of noise. You would find it extremely difficult to separate words and recognize them. These obstacles are what speech recognition software struggles with today. Engineers have discovered that by using probabilities of word transitions, they can improve the software's accuracy somewhat. For example, they use rules of grammar to decide between homonyms. This is a very simple form of prediction, but the systems are still dumb. Today's speech recognition software succeeds only in highly constrained situations in which the number of words you might say at any given moment is limited.

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Yet humans perform many language-related tasks easily, because our cortex understands not only words but sentences and the context within which they are spoken. We anticipate ideas, phrases, and individual words. Our cortical model of the world does this automatically. So we can expect that cortexlike memory systems will transform fallible speech recognition into robust speech understanding. Instead of programming in probabilities for single word transitions, a hierarchical memory will track accents, words, phrases, and ideas and use them to interpret what is being said. Like a person, such an intelligent machine could distinguish between various speech events— for example, a discussion between you and a friend in the room, a phone conversation, and editing commands for a book.

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

23andMe, Affordable Care Act / Obamacare, airport security, Apollo 11, barriers to entry, Berlin Wall, big data - Walmart - Pop Tarts, Black Swan, book scanning, book value, business intelligence, business process, call centre, cloud computing, computer age, correlation does not imply causation, dark matter, data science, double entry bookkeeping, Eratosthenes, Erik Brynjolfsson, game design, hype cycle, IBM and the Holocaust, index card, informal economy, intangible asset, Internet of things, invention of the printing press, Jeff Bezos, Joi Ito, lifelogging, Louis Pasteur, machine readable, machine translation, Marc Benioff, Mark Zuckerberg, Max Levchin, Menlo Park, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, obamacare, optical character recognition, PageRank, paypal mafia, performance metric, Peter Thiel, Plato's cave, post-materialism, random walk, recommendation engine, Salesforce, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, smart grid, smart meter, social graph, sparse data, speech recognition, Steve Jobs, Steven Levy, systematic bias, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, Thomas Davenport, Turing test, vertical integration, Watson beat the top human players on Jeopardy!

But what really interested Amazon, explains Andreas Weigend, Amazon’s former chief scientist, was getting hold of data on what AOL users were looking at and buying, which would improve the performance of its recommendation engine. Poor AOL never realized this. It only saw the data’s value in terms of its primary purpose—sales. Clever Amazon knew it could reap benefits by putting the data to a secondary use. Or take the case of Google’s entry into speech recognition with GOOG-411 for local search listings, which ran from 2007 to 2010. The search giant didn’t have its own speech-recognition technology so needed to license it. It reached an agreement with Nuance, the leader in the field, which was thrilled to have landed such a prized client. But Nuance was then a big-data dunderhead: the contract didn’t specify who got to retain the voice-translation records, and Google kept them for itself.

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Analyzing the data lets one score the probability that a given digitized snippet of voice corresponds to a specific word. This is essential for improving speech-recognition technology or creating a new service altogether. At the time Nuance perceived itself as in the business of software licensing, not data crunching. As soon as it recognized its error, it began striking deals with mobile operators and handset manufacturers to use its speech-recognition service—so that it could gather up the data. The value in data’s reuse is good news for organizations that collect or control large datasets but currently make little use of them, such as conventional businesses that mostly operate offline.

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See also imprecision and big data, [>]–[>], [>], [>], [>], [>] in database design, [>]–[>], [>] and measurement, [>]–[>], [>] necessary in sampling, [>], [>]–[>] Excite, [>] Experian, [>], [>], [>], [>], [>] expertise, subject-area: role in big data, [>]–[>] explainability: big data and, [>]–[>] Facebook, [>], [>], [>]–[>], [>]–[>], [>], [>], [>], [>] data processing by, [>] datafication by, [>], [>] IPO by, [>]–[>] market valuation of, [>]–[>] uses “data exhaust,” [>] Factual, [>] Fair Isaac Corporation (FICO), [>], [>] Farecast, [>]–[>], [>], [>], [>], [>], [>], [>], [>], [>] finance: big data in, [>]–[>], [>], [>] Fitbit, [>] Flickr, [>]–[>] FlightCaster.com, [>]–[>] floor covering, touch-sensitive: and datafication, [>] Flowers, Mike: and government use of big data, [>]–[>], [>] flu: cell phone data predicts spread of, [>]–[>] Google predicts spread of, [>]–[>], [>], [>], [>], [>], [>], [>], [>] vaccine shots, [>]–[>] FlyOnTime.us, [>]–[>], [>]–[>] Ford, Henry, [>] Ford Motor Company, [>]–[>] Foursquare, [>], [>] Freakonomics (Leavitt), [>]–[>] free will: justice based on, [>]–[>] vs. predictive analytics, [>], [>], [>], [>]–[>] Galton, Sir Francis, [>] Gasser, Urs, [>] Gates, Bill, [>] Geographia (Ptolemy), [>] geospatial location: cell phone data and, [>]–[>], [>]–[>] commercial data applications, [>]–[>] datafication of, [>]–[>] insurance industry uses data, [>] UPS uses data, [>]–[>] Germany, East: as police state, [>], [>], [>] Global Positioning System (GPS) satellites, [>]–[>], [>], [>], [>] Gnip, [>] Goldblum, Anthony, [>] Google, [>], [>], [>], [>], [>], [>], [>], [>] artificial intelligence at, [>] as big-data company, [>] Books project, [>]–[>] data processing by, [>] data-reuse by, [>]–[>], [>], [>] Flu Trends, [>], [>], [>], [>], [>], [>] gathers GPS data, [>], [>], [>] Gmail, [>], [>] Google Docs, [>] and language translation, [>]–[>], [>], [>], [>], [>] MapReduce, [>], [>] maps, [>] PageRank, [>] page-ranking by, [>] predicts spread of flu, [>]–[>], [>], [>], [>], [>], [>], [>], [>] and privacy, [>]–[>] search-term analytics by, [>], [>], [>], [>], [>], [>] speech-recognition at, [>]–[>] spell-checking system, [>]–[>] Street View vehicles, [>], [>]–[>], [>], [>] uses “data exhaust,” [>]–[>] uses mathematical models, [>]–[>], [>] government: and open data, [>]–[>] regulation and big data, [>]–[>], [>] surveillance by, [>]–[>], [>]–[>] Graunt, John: and sampling, [>] Great Britain: open data in, [>] guilt by association: profiling and, [>]–[>] Gutenberg, Johannes, [>] Hadoop, [>], [>] Hammerbacher, Jeff, [>] Harcourt, Bernard, [>] health care: big data in, [>]–[>], [>], [>] cell phone data in, [>], [>]–[>] predictive analytics in, [>]–[>], [>] Health Care Cost Institute, [>] Hellend, Pat: “If You Have Too Much Data, Then ‘Good Enough’ Is Good Enough,” [>] Hilbert, Martin: attempts to measure information, [>]–[>] Hitwise, [>], [>] Hollerith, Herman: and punch cards, [>], [>] Hollywood films: profits predicted, [>]–[>] Honda, [>] Huberman, Bernardo: and social networking analysis, [>] human behavior: datafication and, [>]–[>], [>]–[>] human perceptions: big data changes, [>] IBM, [>] and electric automobiles, [>]–[>] founded, [>] and language translation, [>]–[>], [>] Project Candide, [>]–[>] ID3, [>] “If You Have Too Much Data, Then ‘Good Enough’ Is Good Enough” (Hellend), [>] Import.io, [>] imprecision.

The Future of Technology by Tom Standage

air freight, Alan Greenspan, barriers to entry, business process, business process outsourcing, call centre, Clayton Christensen, computer vision, connected car, corporate governance, creative destruction, disintermediation, disruptive innovation, distributed generation, double helix, experimental economics, financial engineering, Ford Model T, full employment, hydrogen economy, hype cycle, industrial robot, informal economy, information asymmetry, information security, interchangeable parts, job satisfaction, labour market flexibility, Larry Ellison, Marc Andreessen, Marc Benioff, market design, Menlo Park, millennium bug, moral hazard, natural language processing, Network effects, new economy, Nicholas Carr, optical character recognition, PalmPilot, railway mania, rent-seeking, RFID, Salesforce, seminal paper, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, six sigma, Skype, smart grid, software as a service, spectrum auction, speech recognition, stem cell, Steve Ballmer, Steve Jurvetson, technological determinism, technology bubble, telemarketer, transcontinental railway, vertical integration, Y2K

In computing, says Microsoft’s Mr Breese, “the holy grail of simplicity is I-just-wanna-talk-to-my-computer”, so that the computer can “anticipate my needs”. The technical term for this is speech recognition. “Speech makes the screen deeper,” says X.D. Huang, Microsoft’s expert on the subject. “Instead of a limited drop-down menu, thousands of functions can be brought to the foreground.” The only problem is that the idea is almost certainly unworkable. People confuse speech recognition with language understanding, argues Mr Norman. But to achieve language understanding, you first have to crack the problem of artificial intelligence (ai), which has eluded scientists for half a century.

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Just think how difficult it would be to teach somebody to tie a shoelace or to fold an origami object by using words alone, without a diagram or a demonstration. “What we imagine systems of speech-understanding to be is really mind-reading,” says Mr Norman. “And not just mind-reading of thoughts, but of perfect thoughts, of solutions to problems that don’t yet exist.” The idea that speech recognition is the key to simplicity, Mr Norman says, is therefore “just plain silly”. He concludes that the only way to achieve simplicity is to have gadgets that explicitly and proudly do less (he calls these “information appliances”). Arguably, the iPod proves him right. Its success so far stems from its relative modesty of ambition: it plays songs but does little else.

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Once work has moved abroad, however, it joins the same cycle of automation and innovation that pushes technology forward everywhere. Optical-character-recognition software is automating the work of Indian data-entry workers. Electronic airline tickets are eliminating some of the ticket-reconciliation work airlines carry out in India. Eventually, natural-language speech recognition is likely to automate some of the call-centre work that is currently going to India, says Steve Rolls of Convergys, the world’s largest call-centre operator. All this helps to promote outsourcing and the building of production platforms in India. ge is selling gecis, its Indian financial-services administrator, and Citibank, Deutsche Bank and others have disposed of some of their Indian it operations.

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

Abraham Wald, Alan Greenspan, Bayesian statistics, bioinformatics, Bletchley Park, British Empire, classic study, Claude Shannon: information theory, Daniel Kahneman / Amos Tversky, data science, double helix, Dr. Strangelove, driverless car, Edmond Halley, Fellow of the Royal Society, full text search, government statistician, Henri Poincaré, Higgs boson, industrial research laboratory, Isaac Newton, Johannes Kepler, John Markoff, John Nash: game theory, John von Neumann, linear programming, longitudinal study, machine readable, machine translation, meta-analysis, Nate Silver, p-value, Pierre-Simon Laplace, placebo effect, prediction markets, RAND corporation, recommendation engine, Renaissance Technologies, Richard Feynman, Richard Feynman: Challenger O-ring, Robert Mercer, Ronald Reagan, seminal paper, speech recognition, statistical model, stochastic process, Suez canal 1869, Teledyne, the long tail, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, Turing machine, Turing test, uranium enrichment, We are all Keynesians now, Yom Kippur War

During the 1970s IBM had two competing teams working on a similar problem, speech recognition. One group, filled with linguists, studied the rules of grammar. The other group, led by Mercer and Brown, who later went to RenTech, was filled with mathematically inclined communications specialists, computer scientists, and engineers. They took a different tack, replaced logical grammar with Bayes’ rule, and were ignored for a decade. Mercer’s ambition was to make computers do intelligent things, and voice recognition seemed to be the way to make this happen. For both Mercer and Brown speech recognition was a problem about taking a signal that had passed through a noisy channel like a telephone and then determining the most probable sentence that the speaker had in mind.

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It allows its users to assess uncertainties when hundreds or thousands of theoretical models are considered; combine imperfect evidence from multiple sources and make compromises between models and data; deal with computationally intensive data analysis and machine learning; and, as if by magic, find patterns or systematic structures deeply hidden within a welter of observations. It has spread far beyond the confines of mathematics and statistics into high finance, astronomy, physics, genetics, imaging and robotics, the military and antiterrorism, Internet communication and commerce, speech recognition, and machine translation. It has even become a guide to new theories about learning and a metaphor for the workings of the human brain. One of the surprises is that Bayes, as a buzzword, has become chic. Stanford University biologist Stephen H. Schneider wanted a customized cancer treatment, called his logic Bayesian, got his therapy, went into remission, and wrote a book about the experience.

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At first they worked their way through old, out-of-copyright children’s books; 1,000 words from a U.S. Patent Office experiment with laser technology; and 60 million words of Braille-readable text from the American Printing House for the Blind. At an international acoustic, speech, and signal meeting the group wore identical T-shirts printed with the words “Fundamental Equation of Speech Recognition” followed by Bayes’ theorem. They developed “a bit of swagger, I’m ashamed to say,” Mercer recalled. “We were an obnoxious bunch back in those days.” In a major breakthrough in the late 1980s they gained access to French and English translations of the Canadian parliament’s daily debates, about 100 million words in computer-readable form.

Data-Ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else by Steve Lohr

"World Economic Forum" Davos, 23andMe, Abraham Maslow, Affordable Care Act / Obamacare, Albert Einstein, Alvin Toffler, Bear Stearns, behavioural economics, big data - Walmart - Pop Tarts, bioinformatics, business cycle, business intelligence, call centre, Carl Icahn, classic study, cloud computing, computer age, conceptual framework, Credit Default Swap, crowdsourcing, Daniel Kahneman / Amos Tversky, Danny Hillis, data is the new oil, data science, David Brooks, driverless car, East Village, Edward Snowden, Emanuel Derman, Erik Brynjolfsson, everywhere but in the productivity statistics, financial engineering, Frederick Winslow Taylor, Future Shock, Google Glasses, Ida Tarbell, impulse control, income inequality, indoor plumbing, industrial robot, informal economy, Internet of things, invention of writing, Johannes Kepler, John Markoff, John von Neumann, lifelogging, machine translation, Mark Zuckerberg, market bubble, meta-analysis, money market fund, natural language processing, obamacare, pattern recognition, payday loans, personalized medicine, planned obsolescence, precision agriculture, pre–internet, Productivity paradox, RAND corporation, rising living standards, Robert Gordon, Robert Solow, Salesforce, scientific management, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, SimCity, six sigma, skunkworks, speech recognition, statistical model, Steve Jobs, Steven Levy, The Design of Experiments, the scientific method, Thomas Kuhn: the structure of scientific revolutions, Tony Fadell, unbanked and underbanked, underbanked, Von Neumann architecture, Watson beat the top human players on Jeopardy!, yottabyte

Returning to Harvard, Hammerbacher got his job back at the library, a condition of his financial aid, and attended classes more regularly. One was a small math seminar on probability. The hands-on project was to write a software program for speech recognition. It seemed a good test bed for math, since calculating probabilities and matching patterns in sound frequencies is crucial in speech recognition. Not incidentally, the instructor was Paul Bamberg, a cofounder of Dragon Systems, a commercial pioneer in speech recognition software. The programming involved tasks like implementing a fast Fourier transform algorithm, which converts time or space to frequency, and vice versa. The seminar was for students with serious math muscles, and there were only five students in the class.

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Humans understand things in good part largely because of their experience of the real world. Computers lack that advantage. Advances in artificial intelligence mean that machines can increasingly see, read, listen, and speak, in their way. And a very different way, it is. As Frederick Jelinek, a pioneer in speech recognition and natural-language processing at IBM, once explained by way of analogy: “Airplanes don’t flap their wings.” To get a sense of how computers build knowledge, let’s look at Carnegie Mellon University’s Never-Ending Language Learning system, or NELL. Since 2010, NELL has been steadily scanning hundreds of millions of Web pages for text patterns that it uses to learn facts, more than 2.3 million so far, with an estimated accuracy of 87 percent.

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By December of 2013, however, Krugman had become more impressed by advances in computing and he wrote an article, published on the Times’s Web site, explaining why he thinks Gordon is “probably wrong.” A decade ago, Krugman writes, “the field of artificial intelligence had marched from failure to failure. But something has happened—things that were widely regarded as jokes not long ago, like speech recognition, machine translation, self-driving cars, and so on, have suddenly become more or less working reality.” Data and software, Krugman observes, have forged the path to working artificial intelligence. “They’re using big data and correlations and so on,” he writes, “to implement algorithms—mindless algorithms, you might say.

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

New York: Cambridge University Press, 2001. Baldwin, Roberto. “Netflix Gambles on Big Data to Become the HBO of Streaming.” WIRED, November 29, 2012. http://www.wired.com/2012/11/netflix-data-gamble. “Behind Apple’s Siri Lies Nuance’s Speech Recognition.” Forbes. Accessed May 28, 2014. http://www.forbes.com/sites/rogerkay/2014/03/24/behind-apples-siri-lies-nuances-speech-recognition. Belsky, Scott. “The Interface Layer: Where Design Commoditizes Tech.” Medium, May 30, 2014. https://medium.com/bridge-collection/the-interface-layer-when-design-commoditizes-tech-e7017872173a. Bendeich, Mark. “Foxconn Says Underage Workers Used in China Plant.”

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Kay, Paul, Brent Berlin, Luisa Maffi, William R. Merrifield, and Richard Cook. The World Color Survey. 1st ed. Stanford, Calif.: Center for the Study of Language and Information, 2011. Kay, Roger. “Behind Apple’s Siri Lies Nuance’s Speech Recognition.” Forbes, March 24, 2014. http://www.forbes.com/sites/rogerkay/2014/03/24/behind-apples-siri-lies-nuances-speech-recognition/#3b1b09f8421c. Kim, Larry. “How Many Ads Does Google Serve in a Day?” Business 2 Community. Published November 2, 2012. Accessed May 30, 2014. http://www.business2community.com/online-marketing/how-many-ads-does-google-serve-in-a-day-0322253.

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This is a classic computational pragmatist approach to a problem, charting an effective computability pathway through the morass of language by depending on trial and error, treating spoken language just like any other complex system. In this sense Siri is as much a listening service as it is an answering one. Over time Siri has presumably collected billions of records of successful and unsuccessful interactions, providing a valuable resource in improving speech recognition.15 Apple claims the data it retains is anonymized, but this policy is unsurprisingly troubling to privacy advocates.16 While we get personalized service, Siri is effectively a single collective machine, learning from these billions of data points under the supervision of its engineers. Like so many other big data, algorithmic machines, it depends on a deep well, a cistern of human attention and input that serves as an informational reservoir for computational inference.

The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future by Orly Lobel

2021 United States Capitol attack, 23andMe, Ada Lovelace, affirmative action, Airbnb, airport security, Albert Einstein, algorithmic bias, Amazon Mechanical Turk, augmented reality, barriers to entry, basic income, Big Tech, bioinformatics, Black Lives Matter, Boston Dynamics, Charles Babbage, choice architecture, computer vision, Computing Machinery and Intelligence, contact tracing, coronavirus, corporate social responsibility, correlation does not imply causation, COVID-19, crowdsourcing, data science, David Attenborough, David Heinemeier Hansson, deep learning, deepfake, digital divide, digital map, Elon Musk, emotional labour, equal pay for equal work, feminist movement, Filter Bubble, game design, gender pay gap, George Floyd, gig economy, glass ceiling, global pandemic, Google Chrome, Grace Hopper, income inequality, index fund, information asymmetry, Internet of things, invisible hand, it's over 9,000, iterative process, job automation, Lao Tzu, large language model, lockdown, machine readable, machine translation, Mark Zuckerberg, market bubble, microaggression, Moneyball by Michael Lewis explains big data, natural language processing, Netflix Prize, Network effects, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, occupational segregation, old-boy network, OpenAI, openstreetmap, paperclip maximiser, pattern recognition, performance metric, personalized medicine, price discrimination, publish or perish, QR code, randomized controlled trial, remote working, risk tolerance, robot derives from the Czech word robota Czech, meaning slave, Ronald Coase, Salesforce, self-driving car, sharing economy, Sheryl Sandberg, Silicon Valley, social distancing, social intelligence, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, surveillance capitalism, tech worker, TechCrunch disrupt, The Future of Employment, TikTok, Turing test, universal basic income, Wall-E, warehouse automation, women in the workforce, work culture , you are the product

As it turns out, however, they do not always listen to everyone equally. Speech recognition exemplifies how partial training data has led machines to learn more about white men’s speech patterns and less about those of women and people of color. Case in point: Google’s speech recognition is 13 percent more accurate for men than it is for women.22 Testing a variety of speech activation technologies has shown that virtual assistants are more likely to understand male users than female users. If the user is a woman of color, the rate of accurately understanding her speech drops further. In one study testing speech recognition of different accents, English spoken with an Indian accent only had a 78 percent accuracy rate; recognition of English spoken with a Scottish accent was only 53 percent accurate.

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In 2019, in partnership with the German Ministry for Economic Cooperation and Development, Mozilla increased its efforts to collect local language data in Africa through an initiative called Common Voice and Deep Speech.25 The data set is already being used in voice assistant technologies such as Mycroft, an open-source voice assistant named after Sherlock Holmes’s elder brother, and the Brazilian Portuguese medical transcription tool Iara Health. Kelly Davis, head of machine learning at Mozilla, describes the profound significance of focusing on underresourced languages and language preservation in correcting the imbalance of languages in mainstream speech recognition technology. He says that we should look at speech recognition as a public resource. This theme of conceptualizing advances in technologies, vastly aided through data collection, as a public good must become a recurring one as we strive to build equality machines. Voice and speech—like many other types of information that are making our machines smarter—are intimately tied to our autonomous selves, from our genetic makeup and our health information to our behavioral and emotional responses to different decision-making environments.

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Still, undoubtedly the increased understanding of English-speaking males is something of a “Big Five” effect: most voice recognition platforms are made by five companies—Amazon, Apple, Google, Meta, and Microsoft, which themselves are disproportionately staffed and led by white males. This kind of deficiency in speech recognition is relatively easy to remedy. The fix involves increasing the range and diversity of the data that we feed technology. A more diverse range of voices in the video and sound fed to algorithms will result in those algorithms’ improved ability to interpret a broader range of speech patterns. Diversity in, diversity out.

On the Future: Prospects for Humanity by Martin J. Rees

23andMe, 3D printing, air freight, Alfred Russel Wallace, AlphaGo, Anthropocene, Asilomar, autonomous vehicles, Benoit Mandelbrot, biodiversity loss, blockchain, Boston Dynamics, carbon tax, circular economy, CRISPR, cryptocurrency, cuban missile crisis, dark matter, decarbonisation, DeepMind, Demis Hassabis, demographic transition, Dennis Tito, distributed ledger, double helix, driverless car, effective altruism, Elon Musk, en.wikipedia.org, Geoffrey Hinton, global village, Great Leap Forward, Higgs boson, Hyperloop, Intergovernmental Panel on Climate Change (IPCC), Internet of things, James Webb Space Telescope, Jeff Bezos, job automation, Johannes Kepler, John Conway, Large Hadron Collider, life extension, mandelbrot fractal, mass immigration, megacity, Neil Armstrong, Nick Bostrom, nuclear winter, ocean acidification, off-the-grid, pattern recognition, precautionary principle, quantitative hedge fund, Ray Kurzweil, Recombinant DNA, Rodney Brooks, Search for Extraterrestrial Intelligence, sharing economy, Silicon Valley, smart grid, speech recognition, Stanford marshmallow experiment, Stanislav Petrov, stem cell, Stephen Hawking, Steven Pinker, Stuxnet, supervolcano, technological singularity, the scientific method, Tunguska event, uranium enrichment, Walter Mischel, William MacAskill, Yogi Berra

The vein pattern in our eyes allows the use of ‘iris recognition’ software—a substantial improvement on fingerprints or facial recognition. This is precise enough to unambiguously identify individuals, among the 1.3 billion Indians. And it is a foretaste of the benefits that can come from future advances in AI. Speech recognition, face recognition, and similar applications use a technique called generalised machine learning. This operates in a fashion that resembles how humans use their eyes. The ‘visual’ part of human brains integrates information from the retina through a multistage process. Successive layers of processing identify horizontal and vertical lines, sharp edges, and so forth; each layer processes information from a ‘lower’ layer and then passes its output to other layers.8 The basic machine-learning concepts date from the 1980s; an important pioneer was the Anglo-Canadian Geoff Hinton.

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But there are still limitations. The hardware underlying AlphaGo used hundreds of kilowatts of power. In contrast, the brain of Lee Sedol, AlphaGo’s Korean challenger, consumes about thirty watts (like a lightbulb) and can do many other things apart from play board games. Sensor technology, speech recognition, information searches, and so forth are advancing apace. So (albeit with a more substantial lag) is physical dexterity. Robots are still clumsier than a child in moving pieces on a real chessboard, tying shoelaces, or cutting toenails. But here too there is progress. In 2017, Boston Dynamics demonstrated a fearsome-looking robot called Handel (a successor to the earlier four-legged Big Dog), with wheels as well as two legs, that is agile enough to perform back flips.

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See also food production AI (artificial intelligence): airplanes flown using, 93–94; benefits and risks of, 5, 63; concern about decisions by, 89, 116; facial recognition and, 84, 85, 89, 90, 101; game-playing computers, 86–87, 88, 103, 106, 191; gene combinations identified with, 68; human-level intelligence and, 102–8, 119; inorganic intelligences, 151, 152–53, 159, 169–70; iris recognition and, 84–85; jobs affected by, 91–92; machine learning and, 85, 89, 143; now at very early stage, 91; personalisation of online learning by, 98–99; as possible threat to civilisation, 109–10; posthuman evolution and, 153, 178; privacy concerns regarding, 90; responsible innovation in, 106, 218, 219, 225; self-awareness and, 107, 153; self-driving vehicles, 92–95, 102–3; speech recognition and, 85, 88; in warfare, 101. See also robots air traffic control, 108 Alcor, 81–82 Aldrin, Buzz, 138 aliens, intelligent: communicating through shared mathematical culture, 160, 168; with different perception of reality, 160, 190; early history of speculation on, 126–27; Earth’s history seen by, 1–2; likelihood of, 154–56, 162; possibly pervading the cosmos, 8, 156; search for, 156–64.

Your Computer Is on Fire by Thomas S. Mullaney, Benjamin Peters, Mar Hicks, Kavita Philip

"Susan Fowler" uber, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, A Declaration of the Independence of Cyberspace, affirmative action, Airbnb, algorithmic bias, AlphaGo, AltaVista, Amazon Mechanical Turk, Amazon Web Services, American Society of Civil Engineers: Report Card, An Inconvenient Truth, Asilomar, autonomous vehicles, Big Tech, bitcoin, Bletchley Park, blockchain, Boeing 737 MAX, book value, British Empire, business cycle, business process, Californian Ideology, call centre, Cambridge Analytica, carbon footprint, Charles Babbage, cloud computing, collective bargaining, computer age, computer vision, connected car, corporate governance, corporate social responsibility, COVID-19, creative destruction, cryptocurrency, dark matter, data science, Dennis Ritchie, deskilling, digital divide, digital map, don't be evil, Donald Davies, Donald Trump, Edward Snowden, en.wikipedia.org, European colonialism, fake news, financial innovation, Ford Model T, fulfillment center, game design, gentrification, George Floyd, glass ceiling, global pandemic, global supply chain, Grace Hopper, hiring and firing, IBM and the Holocaust, industrial robot, informal economy, Internet Archive, Internet of things, Jeff Bezos, job automation, John Perry Barlow, Julian Assange, Ken Thompson, Kevin Kelly, Kickstarter, knowledge economy, Landlord’s Game, Lewis Mumford, low-wage service sector, M-Pesa, Mark Zuckerberg, mass incarceration, Menlo Park, meta-analysis, mobile money, moral panic, move fast and break things, Multics, mutually assured destruction, natural language processing, Neal Stephenson, new economy, Norbert Wiener, off-the-grid, old-boy network, On the Economy of Machinery and Manufactures, One Laptop per Child (OLPC), packet switching, pattern recognition, Paul Graham, pink-collar, pneumatic tube, postindustrial economy, profit motive, public intellectual, QWERTY keyboard, Ray Kurzweil, Reflections on Trusting Trust, Report Card for America’s Infrastructure, Salesforce, sentiment analysis, Sheryl Sandberg, Silicon Valley, Silicon Valley ideology, smart cities, Snapchat, speech recognition, SQL injection, statistical model, Steve Jobs, Stewart Brand, tacit knowledge, tech worker, techlash, technoutopianism, telepresence, the built environment, the map is not the territory, Thomas L Friedman, TikTok, Triangle Shirtwaist Factory, undersea cable, union organizing, vertical integration, warehouse robotics, WikiLeaks, wikimedia commons, women in the workforce, Y2K

Many of the early speech technologies used the now thirty-year-old corpus Switchboard, from the University of Pennsylvania’s Linguistic Consortium, to train on; the accents represented there are largely American Midwestern. The result is that speech recognition error rates for marginalized voices are higher than others; as Paul notes, “a typical database of American voices, for example, would lack poor, uneducated, rural, non-white, non-native English voices. The more of those categories you fall into, the worse speech recognition is for you.”46 Work by Caliksan-Islam, Bryson, and Narayanan has empirically demonstrated what we have already suspected: that beyond just marginalization of people, “human-like semantic biases result from the application of standard machine learning to ordinary language.”47 Simply put, speech technologies replicate existing language biases.

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“Usage of Content Languages for Websites,” W3Techs, accessed March 31, 2018, W3techs.com/technologies/overview/content_language/all. 37. Daniela Hernandez, “How Voice Recognition Systems Discriminate against People with Accents: When Will There be Speech Recognition for the Rest of Us?” Fusion (August 21, 2015), http://fusion.net/story/181498/speech-recognition-ai-equality/. 38. Braj B. Kachru, “The English Language in the Outer Circle,” World Englishes 3 (2006): 241–255. 39. Salikoko Mufwene, “The Legitimate and Illegitimate Offspring of English,” in World Englishes 2000, ed. Larry E. Smith and Michael L.

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These include but are not limited to: • A variety of what I would describe as “first-order,” more rudimentary, blunt tools that are long-standing and widely adopted, such as keyword ban lists for content and user profiles, URL and content filtering, IP blocking, and other user-identifying mechanisms;13 • More sophisticated automated tools such as hashing technologies used in products like PhotoDNA (used to automate the identification and removal of child sexual exploitation content; other engines based on this same technology do the same with regard to terroristic material, the definitions of which are the province of the system’s owners);14 • Higher-order AI tools and strategies for content moderation and management at scale, examples of which might include: ◦ Sentiment analysis and forecasting tools based on natural language processing that can identify when a comment thread has gone bad or, even more impressive, when it is in danger of doing so;15 ◦ AI speech-recognition technology that provides automatic, automated captioning of video content;16 ◦ Pixel analysis (to identify, for example, when an image or a video likely contains nudity);17 ◦ Machine learning and computer vision-based tools deployed toward a variety of other predictive outcomes (such as judging potential for virality or recognizing and predicting potentially inappropriate content).18 Computer vision was in its infancy when I began my research on commercial content moderation.

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

As always, competition between the cloud providers is a powerful driver of innovation, and Amazon’s deep learning tools for the AWS platform are likewise becoming easier to use. Along with the development tools, all the cloud services offer pre-built deep learning components that are ready to be used out of the box and incorporated into applications. Amazon, for example, offers packages for speech recognition and natural language processing and a “recommendation engine” that can make suggestions in the same way that online shoppers or movie watchers are shown alternatives that are likely to be of interest.16 The most controversial example of this kind of prepackaged capability is AWS’s Rekognition service, which makes it easy for developers to deploy facial recognition technology.

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Much of this was funneled through the Advanced Research Projects Agency, or ARPA. One especially important center of APRA-funded research was the Stanford Research Institute, which later separated from Stanford University to become SRI International. SRI’s Artificial Intelligence Center, established in 1966, did groundbreaking work in areas like language translation and speech recognition. The lab also created the first truly autonomous robot, a machine capable of turning AI-powered reasoning into physical interaction with the environment. Nearly half a century after its founding, SRI’s Artificial Intelligence Center would spin off a startup company with a new personal assistant called Siri that would be acquired by Apple in 2010.

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In the 1990s, Schmidhuber and his students developed a special type of neural network that implemented “long short-term memory,” or LSTM. With LSTM, networks are able to “remember” data from the past and incorporate it into the current analysis. This has proven to be of critical importance in areas like speech recognition and language translation, where the context created by words that came previously has a huge impact on accuracy. Companies like Google, Amazon and Facebook all rely heavily on LSTM, and Schmidhuber feels that it is the work of his team, rather than that of the more celebrated North American researchers, that underlies much of AI’s recent progress.

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

By 1952, Bell had developed a system for single-digit speech recognition but it was extremely limited. In 1969, however, John Pierce, one of Bell’s leading engineers, wrote an open letter to the Acoustical Society of America criticising speech recognition at Bell and compared it to “schemes for turning water into gasoline, extracting gold from the sea, curing cancer, or going to the moon”. Ironically, one month after Pierce published his open letter, Neil Armstrong landed on the moon. Regardless, Bell Labs still had its funding for speech recognition pulled soon after. By 1993, speech recognition systems developed by Ray Kurzweil could recognise 20,000 words (uttered one word at a time), but accuracy was limited to about 10 per cent.

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By 1993, speech recognition systems developed by Ray Kurzweil could recognise 20,000 words (uttered one word at a time), but accuracy was limited to about 10 per cent. In 1997, Bill Gates was pretty bullish on speech recognition, predicting that “In this 10-year time frame, I believe that we’ll not only be using the keyboard and the mouse to interact, but during that time we will have perfected speech recognition and speech output well enough that those will become a standard part of the interface.”25 In the year 2000, it was still a decade away. The big breakthroughs came with the application of Markov models and Deep Learning models or neural networks, basically better computer performance and bigger source databases.

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

Albert Einstein, Boeing 747, cloud computing, Computing Machinery and Intelligence, David Attenborough, double helix, Drosophila, elephant in my pajamas, finite state, Gregor Mendel, illegal immigration, Joan Didion, language acquisition, Loebner Prize, mass immigration, Maui Hawaii, meta-analysis, MITM: man-in-the-middle, natural language processing, out of africa, phenotype, rolodex, Ronald Reagan, Sapir-Whorf hypothesis, Saturday Night Live, speech recognition, Steven Pinker, Strategic Defense Initiative, tacit knowledge, theory of mind, transatlantic slave trade, Turing machine, Turing test, twin studies, Yogi Berra

But this advantage can be enjoyed only by a high-tech speech recognizer, one that has some kind of knowledge of how vocal tracts blend sounds. The human brain, of course, is a high-tech speech recognizer, but no one knows how it succeeds. For this reason psychologists who study speech perception and engineers who build speech recognition machines keep a close eye on each other’s work. Speech recognition may be so hard that there are only a few ways it could be solved in principle. If so, the way the brain does it may offer hints as to the best way to build a machine to do it, and how a successful machine does it may suggest hypotheses about how the brain does it.

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Ray Jackendoff and I think this is not the whole story, for reasons we explained in our paper debating Chomsky. Chapter 6: The Sounds of Silence. Speech recognition technology has advanced tremendously and is now inescapable in telephone information systems. But as everyone who has been trapped in “voice-mail jail” knows, the systems are far from foolproof (“I’m sorry, but I did not understand what you said”). And here is how the novelist Richard Powers described his recent experience with a state-of-the-art speech recognition program: “This machine is a master of speakos and mondegreens. Just as we might hear the…Psalms avow that ‘Shirley, good Mrs.

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A finite inventory of phonemes is sampled and permuted to define words, and the resulting strings of phonemes are then massaged to make them easier to pronounce and understand before they are actually articulated. I will trace out these steps for you and show you how they shape some of our everyday encounters with speech: poetry and song, slips of the ear, accents, speech recognition machines, and crazy English spelling. One easy way to understand speech sounds is to track a glob of air through the vocal tract into the world, starting in the lungs. When we talk, we depart from our usual rhythmic breathing and take in quick breaths of air, then release them steadily, using the muscles of the ribs to counteract the elastic recoil force of the lungs.

Human + Machine: Reimagining Work in the Age of AI by Paul R. Daugherty, H. James Wilson

3D printing, AI winter, algorithmic management, algorithmic trading, AlphaGo, Amazon Mechanical Turk, Amazon Robotics, augmented reality, autonomous vehicles, blockchain, business process, call centre, carbon footprint, circular economy, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, data science, deep learning, DeepMind, digital twin, disintermediation, Douglas Hofstadter, driverless car, en.wikipedia.org, Erik Brynjolfsson, fail fast, friendly AI, fulfillment center, future of work, Geoffrey Hinton, Hans Moravec, industrial robot, Internet of things, inventory management, iterative process, Jeff Bezos, job automation, job satisfaction, knowledge worker, Lyft, machine translation, Marc Benioff, natural language processing, Neal Stephenson, personalized medicine, precision agriculture, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, robotic process automation, Rodney Brooks, Salesforce, Second Machine Age, self-driving car, sensor fusion, sentiment analysis, Shoshana Zuboff, Silicon Valley, Snow Crash, software as a service, speech recognition, tacit knowledge, telepresence, telepresence robot, text mining, the scientific method, uber lyft, warehouse automation, warehouse robotics

“Illuminating Data,” Texas Medical Center, August 24, 2014, http://www.tmc.edu/news/2014/08/illuminating-data/. 24.George Wang, “Texas Medical Center and Ayasdi to Create a World-Class Center for Complex Data Research and Innovation,” Ayasdi, November 13, 2013, https://www.ayasdi.com/company/news-and-events/press/pr-texas-medical-center-and-ayasdi-to-create-a-world-class-center-for-complex-data-research-and-innovation/. 25.Khari Johnson, “Google’s Tensorflow Team Open-Sources Speech Recognition Dataset for DIY AI,” VentureBeat, August 24, 2017, https://venturebeat.com/2017/08/24/googles-tensorflow-team-open-sources-speech-recognition-dataset-for-diy-ai/. 26.Adam Liptak, “Sent to Prison by a Software Program’s Secret Algorithms,” New York Times, May 1, 2017, https://www.nytimes.com/2017/05/01/us/politics/sent-to-prison-by-a-software-programs-secret-algorithms.html?

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Neural networks that convert audio signals to text signals in a variety of languages. Applications include translation, voice command and control, audio transcription, and more. Natural language processing (NLP). A field in which computers process human (natural) languages. Applications include speech recognition, machine translation, and sentiment analysis. AI Applications Component Intelligent agents. Agents that interact with humans via natural language. They can be used to augment human workers working in customer service, human resources, training, and other areas of business to handle FAQ-type inquiries.

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See marketing and sales Salesforce, 85–86, 196 Samsung, 96–97 Samuel, Arthur, 41, 60 scale, 160 Schaefer, Markus, 148 scheduling agents, 196 Schnur, Steve, 194 scientific method, 69–77 hypotheses in, 72–74 observation in, 69–72 testing in, 74–77 SEB, 55–56, 59, 143–145, 160 second wave of business transformation, 5, 19, 47 security, IT, 56–58 semi-supervised learning, 62 Sensabot, 192 sensors in agriculture, 35–37 product development and, 29 retail shopping, 160–165 in robotic arms, 24–26 sentiment tracking, 176 SEW-Eurodrive, 149 Shah, Julie, 120 Shah, Uman, 98 Shannon, Claude, 40 Siemens, 23, 210 Sight Machine, 27 SigOpt, 77 Siri, 11, 96–97, 118, 146 6sense, 92 skills amplification of, 7 developing, 15–16 fusion, 12, 15–16, 181, 183–206 human vs. machine, 20–21, 105–106, 151 in manufacturing, 38 in marketing and sales, 100 in R&D, 83 Slack, 196 smart glasses, 143 smart mirrors, 87–88, 100 social media, 98, 176 software design, AI-enabled, 3 generative design, 135–137, 139, 141 Sophie, 119 SparkCognition, 58 speech recognition, 66 speech to text, 64 Spiegel, Eric, 210 Standup Bot, 196 State Farm, 99 Steele, Billy, 76 Stitch Fix, 110–111, 152, 204 Store No. 8, 162 Summer Olympics, 98 supervised learning, 60 supply chains, 19–39 data, 12, 15 sustaining, 107, 114–115, 179 jobs in, 126–132 See also missing middle S Voice, 96–97 Swedberg, Claire, 31 symbiosis, 7–8 symbol-based systems, 24, 41 Symbotic, 32–33 symmetry, 130 Systematica, 167 task performance training, 116 Tatsu, 196 Tay, 168–169 Taylor, Harriet, 91 telepresence robots, 159 Tempo, 176 Tesla, 67–68, 83, 190 testing, 74–77 Texas Medical Center, 178 Textio, 196 text recognition, 66 third wave transformation, 4–6 adaptive processes in, 19–21 time, rehumanizing, 12, 186–189 time-and-motion analyses, 4 Toyota Research Institute, 166 training/retraining, 15, 107, 114–115, 208 augmentation in, 143 auto technicians, 158–160 crowdsourcing/outsourcing, 120–121 curriculum development for, 178–179 data for, 121–122 education for AI, 132–133 empathy, 117–118 employee willingness toward, 185 feedback loops in, 174 for fusion skills, 211–213 holistic melding and, 200–201 human expertise and, 194–195 interaction modeling, 120 jobs in training AI systems, 100, 114–122 personality, 118–119 reciprocal apprenticing and, 12, 201–202 worldview and localization, 119–120 See also missing middle transparency, 213 transparency analysts, 125 trust of machines vs. humans, 166–168, 172–173 moral crumple zones and, 169–172 Twitter, 168–169 Uber, 44, 95, 169 uncanny valley, 116 Unilever, 51–52 Universal Robotics, 23 University of Ottawa, 70 University of Pennsylvania, 167 University of Pittsburgh Medical Center (UPMC), 188 unmanned vehicles, 28 unsupervised learning, 61–62 US Department of Justice, 45 user interfaces, 140 user needs, discovering, 156 V8, 98 Vallaeys, Frederick, 99 Vectra, 58 vehicle design anthropologists, 113–114 vehicles, autonomous, 67–68, 166–167, 189, 190 Vertesi, Janet, 201 vertical farms, 36 video recognition, 66 Virgin Trains, 47–48, 50, 59 vision.

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

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

It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 and can therefore potentially provide low-cost universal access to vital diagnostic care.”16 Machines are not just turning into better translators and diagnosticians. They are becoming better listeners, too. Speech recognition technology is improving at staggering speed. In 2016, Microsoft announced a milestone in reaching human parity in transcribing conversations. And in August 2017, a research paper published by Microsoft’s AI team revealed additional improvements, reducing the error rate from 6 percent to 5 percent.17 And like image recognition technology promises to replace doctors in diagnostic tasks, advances in speech recognition and user interfaces promise to replace workers in some interactive tasks. As we all know, Apple’s Siri, Google Assistant, and Amazon’s Alexa rely on natural user interfaces to recognize spoken words, interpret their meanings, and respond to them accordingly.

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According to Cisco, worldwide internet traffic will increase nearly threefold over the next five years, reaching 3.3 zettabytes per year by 2021.8 To put this number in perspective, researchers at the University of California, Berkeley estimate that the information contained in all books worldwide is around 480 terabytes, while a text transcript of all the words ever spoken by humans would amount to some five exabytes.9 Data can justly be regarded as the new oil. As big data gets bigger, algorithms get better. When we expose them to more examples, they improve their performance in translation, speech recognition, image classification, and many other tasks. For example, an ever-larger corpus of digitalized human-translated text means that we are able to better judge the accuracy of algorithmic translators in reproducing observed human translations. Every United Nations report, which is always translated by humans into six languages, gives machine translators more examples to learn from.10 And as the supply of data expands, computers do better.

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As we all know, Apple’s Siri, Google Assistant, and Amazon’s Alexa rely on natural user interfaces to recognize spoken words, interpret their meanings, and respond to them accordingly. Using speech recognition technology and natural language processing, a company called Clinc is now developing a new AI voice assistant to be used in drive-through windows of fast-food restaurants like McDonald’s and Taco Bell.18 And in 2018, Google announced that it is building AI technology to replace workers in call centers. Virtual agents will answer the phone when a customer calls. If a customer request involves something the algorithm cannot yet do, he or she will automatically be rerouted to a human agent.

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

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, Alan Greenspan, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, American Society of Civil Engineers: Report Card, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, basic income, Baxter: Rethink Robotics, Boston Dynamics, British Empire, business cycle, business intelligence, business process, call centre, carbon tax, Charles Lindbergh, Chuck Templeton: OpenTable:, clean water, combinatorial explosion, computer age, computer vision, congestion charging, congestion pricing, corporate governance, cotton gin, creative destruction, crowdsourcing, data science, David Ricardo: comparative advantage, digital map, driverless car, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, Fairchild Semiconductor, falling living standards, Filter Bubble, first square of the chessboard / second half of the chessboard, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, full employment, G4S, game design, general purpose technology, global village, GPS: selective availability, Hans Moravec, happiness index / gross national happiness, illegal immigration, immigration reform, income inequality, income per capita, indoor plumbing, industrial robot, informal economy, intangible asset, inventory management, James Watt: steam engine, Jeff Bezos, Jevons paradox, jimmy wales, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Khan Academy, Kiva Systems, knowledge worker, Kodak vs Instagram, law of one price, low skilled workers, Lyft, Mahatma Gandhi, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Mars Rover, mass immigration, means of production, Narrative Science, Nate Silver, natural language processing, Network effects, new economy, New Urbanism, Nicholas Carr, Occupy movement, oil shale / tar sands, oil shock, One Laptop per Child (OLPC), pattern recognition, Paul Samuelson, payday loans, post-work, power law, price stability, Productivity paradox, profit maximization, Ralph Nader, Ray Kurzweil, recommendation engine, Report Card for America’s Infrastructure, Robert Gordon, Robert Solow, Rodney Brooks, Ronald Reagan, search costs, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Simon Kuznets, six sigma, Skype, software patent, sovereign wealth fund, speech recognition, statistical model, Steve Jobs, Steven Pinker, Stuxnet, supply-chain management, TaskRabbit, technological singularity, telepresence, The Bell Curve by Richard Herrnstein and Charles Murray, the Cathedral and the Bazaar, the long tail, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Vernor Vinge, warehouse robotics, Watson beat the top human players on Jeopardy!, winner-take-all economy, Y2K

We could have pulled over, found the portable GPS and turned it on, typed in our destination, and waited for our routing, but we didn’t want to exchange information that way. We wanted to speak a question and hear and see (because a map was involved) a reply. Siri provided exactly the natural language interaction we were looking for. A 2004 review of the previous half-century’s research in automatic speech recognition (a critical part of natural language processing) opened with the admission that “Human-level speech recognition has proved to be an elusive goal,” but less than a decade later major elements of that goal have been reached. Apple and other companies have made robust natural language processing technology available to hundreds of millions of people via their mobile phones.10 As noted by Tom Mitchell, who heads the machine-learning department at Carnegie Mellon University: “We’re at the beginning of a ten-year period where we’re going to transition from computers that can’t understand language to a point where computers can understand quite a bit about language.”11 Digital Fluency: The Babel Fish Goes to Work Natural language processing software is still far from perfect, and computers are not yet as good as people at complex communication, but they’re getting better all the time.

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Exponential progress has made possible many of the advances discussed in the previous chapter. IBM’s Watson draws on a plethora of clever algorithms, but it would be uncompetitive without computer hardware that is about one hundred times more powerful than Deep Blue, its chess-playing predecessor that beat the human world champion, Garry Kasparov, in a 1997 match. Speech recognition applications like Siri require lots of computing power, which became available on mobile phones like Apple’s iPhone 4S (the first phone that came with Siri installed). The iPhone 4S was about as powerful, in fact, as Apple’s top-of-the-line Powerbook G4 laptop had been a decade earlier. As all of these innovations show, exponential progress allows technology to keep racing ahead and makes science fiction reality in the second half of the chessboard.

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These three forces are yielding breakthroughs that convert science fiction into everyday reality, outstripping even our recent expectations and theories. What’s more, there’s no end in sight. The advances we’ve seen in the past few years, and in the early sections of this book—cars that drive themselves, useful humanoid robots, speech recognition and synthesis systems, 3D printers, Jeopardy!-champion computers—are not the crowning achievements of the computer era. They’re the warm-up acts. As we move deeper into the second machine age we’ll see more and more such wonders, and they’ll become more and more impressive. How can we be so sure?

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

A mouse forces you to manipulate things with one hand alone; Bill develops interfaces that can use both hands. A perennial contender on the short list for the next big interface is speech recognition, promising to let us talk to our computers as naturally as we talk to each other. Appealing as that is, it has a few serious problems. It would be tiring if we had to spend the day speaking continuously to get anything done, and it would be intrusive if our conversations with other people had to be punctuated by our conversations with our machines. Most seriously, even if speech recognition systems worked perfectly (and they don't}, the result is no better than if the commands had been typed.

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(author of book on nuclear magnetic resonance), 155 expectations from computers, 4 eyeglasses, 58 factoring, 15 6 fashion and wearable computers, 55-56 Federal Communications Commission (FCC), 99 FedEx,47-48,203-4 Festo, 69 Feynmann, Richard, 158, 161, 166-67 First Amendment, 99 "Fish" circuit board, 144 Fletcher, Richard, 153 flexible work groups, 180, 192 focus groups, 75 Ford Motor Company, 78 Frankfurt book fair, 13 Fredkin, Ed, 132 freedom of speech and the press, 98-99 free markets versus central planning, 88-89 functional magnetic resonance imaging, 140-41 furnace, information, 201 furniture that can see, 169-70, 179, 193,202 futures traders, 77-78 fuzzy logic, 107, 109, 120-21 genome, editing the, 212 Global Positioning System (GPS) receivers, 152, 166, 177-78 GM, 78 gold standard, 79 Gore, AI, 60 Government Performance and Results Act (GRPA), 173-74 GPS receivers, 152, 166, 177-78 Grinstein, Geoff, 165 groupware, 59 Grover, Lov, 162 INDEX Gutenberg and movable metal type, 18-19 Hamanaka, Yasuo, 77 Harvard University, 195 Hawking, Stephen, 74 Hawley, Michael, 54-55, 195, 203 health care industry, 204 Helmholtz, Hermann, 39 Hewlett Packard, 52, 203 high-definition television, 6 Hilbert, David, 127 holograms, 142 Human-Computer Interaction community, 140 IBM, 63, 128, 159, 160, 165, 176 "Deep Blue," 129-30 Iguchi, Toshihide, 77, 86 Imitation Game, see Turing test implants, 211-12 industry: digital revolution and, 192 media Lab and, 107, 169-84, 202-7 research and development and, see research and development wearable computers and, 47-48 infant seats, device to disable, 170-71, 180 inflight videos, 111 Information Theory, 128 Institute for Theoretical Physics, 160 insurance, 209 Intel, 78, 156 intellectual property, 181, 194 intelligence agencies, quantum computers and, 159 interfaces, computer, 140-47, 156 mind control, 140-41 speech recognition, 140 3D graphics, 141-42 Windows-and-mice, 106, 139, 147 Internet and World Wide Web, 3-4, 6-7,9-10,58-59,213 attempts to regulate, 99 INDEX birth of, 79-80 developing countries and, 210-11 educational implications of, 193 electronic commerce and, 80-81 IP protocol, 89 search engines, 134 Things That Think and, 207 IRCAM, 33 Ishii, Hiroshi, 145 Jacobson, Joseph, 15-17, 72, 202 James II, King, 98 jargon, technology, 107-21 Jobs, Steve, 139 Josza, Richard, 158 Kasparov, Gary, 129, 133, 134-35 Kavafian, Ani, 49-50 Kay, Alan, 137, 138-39 Kaye, Joseph, 9 Kennedy administration, 185 Kepler, Johannes, 113 Land, Edwin, 187 Landauer, Rolf, 176, 177 laptop computers: backlighting of screen, 14 books versus, 14-15 complicated instructions for running, 97-98 invention of, 137 Leeson, Nick, 77, 86 Legoland, 68 Legos, 68-69, 71, 73, 146--47, 193 Leibniz, Gottfried, 131 Leo X, Pope, 95, 96 libraries and the electronic book, 20-23 Lind, James, 210 Lippman, Andy, 203 Lloyd, Seth, 158, 162 LOGO programming language, 138, 147 Lorenz, Edward, 113, 114 Lotus, 101 + 221 Lovelace, Lady Ada, 125 Luther, Martin, 95-97, 106 Luthiers, 39, 41 machine tools, 71, 75 characteristics of, 64 Machover, Tod, 33, 49, 169, 203, 206 Maelzel, Johann, 124 magnetic resonance imaging (MRI), 154 magnetoencephalography (MEG), 140--41 mainframes, 75, 151 characteristics of early, 63 Mann, Steve, 45--46, 47, 57-58 Manutius, Aldus, 20 Margolus, Norman, 132 Marketplace:Households product, 101 Massachusetts Institute of Technology (MIT), 65, 113, 128-29, 132, 138, 158, 195 Architecture Machine Group, 186 Media Lab, see Massachusetts Institute of Technology (MIT) Media Lab Massachusetts Institute of Technology (MIT) Media Lab, 6, 15, 33, 45-50, 55-56, 57, 68-69, 105, 110, 146--47, 179, 180-84, 185-97 attempts to copy, 194 Digital Life, 203 hidden purpose of, 185-86 industry and, 107, 169-71, 180-84, 202-7 News in the Future, 202-3 organization of, 194, 197 origins of, 185-86 student education in, 187-97 Things That Think, 202-7 unique qualities of, 194-95 Mathews, Max, 36 mature technologies, 10 Maxwell's demon, 175-76 222 Memex, 139 MEMS (Micro-Electro-Mechanical Systems), 72 microencapsulation, 15-16 micropayments, 82 Microsoft, 78, 139, 158, 203 microtubules, 162-63 MIDI, 90 milling machine, 66 mind control as computer interface, 140-41 minicomputers, 52, 138 Minsky, Marvin, 33, 117, 135, 2012 MIT, see Massachusetts Institute of Technology (MIT) modems, 188 money: distinction between atom-dollars and bit-dollars, 83-85 gold standard, 79 pennies, 82 smart money, see smart money as tangible asset, 79, 91 Moog, Bob, 30 Moog synthesizer, 30 Moore, Gordon, 156 Moore's law, 155-57, 163 Motorola, 99-100, 203 mouse, 106, 139, 142-43 multimedia, 109, 110-11 music and computers, 27-44 competing with a Stradivarius, 32-33,39-42 critical reaction to digital instruments, 37-38 designing and writing for smart instruments, 27-44, 143-44, 187 enhancing technology of the cello, 27-29 first electronic music ensemble, 31 limits of instruments and players, 32 MIDI protocol, 90 physics of musical instruments, 40 + INDEX purposes of smart instruments, 33, 42-43 synthesizers, 30-31 Mysterzum Cosmographicum, 113 nanotechnology, 161 Nass, Clifford, 54 National Highway Traffic Safety Administration, 170 National Science Foundation, 172-73, 174, 178 NEC, 170-71 Negroponte, Nicholas, 183, 185, 186 Netscape, 78 networking, 59-60 open standards for, 90 neural networks, 107, 109, 117-20, 164 Newton, Isaac, 131 Newton's theory of gravitation, 114 Nike, 202, 203 Ninety-five Theses, 96, 98 Nixon, Richard, 79 nuclear bombs, 171, 172, 178-79 nuclear magnetic resonance (NMR), 154-55, 160 numerically controlled (NC) mills, 66, 67-68 Office of Scientific Research and Development, 172 Oppenheimer, j.

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., 171, 172 Santa Fe Institute, 118 Satellites, communications, 99-100 Science-The Endless Frontier, 172 search engines, 134 security versus privacy, 57 224 + semiconductor industry, 72 Sensormatic, 153 Shannon,C~ud~5, 128,176,188-90 shoe, computer in a, 50, 52, 102-3, 179 shoplifting tags, 153 Shor, Peter, 158, 159 Silicon Graphics, 140 Simon, Dan, 158 skepticism about technological advances, 122 Small, David, 22-23 Smalltalk, 138 smart cards, 81, 152 smart money, 77-91 cryptography and, 80-81 as digital information, 80 distinction between atom-dollars and bit-dollars, 83-85 freeing money from legacy as tangible asset, 79, 91 global currency market, 83 linking algorithms with money, 86-88 paying-as-you-go, 82 precedent for, 80 standards for, 88-91 smart name badges, 206 Smith, Joshua, 144, 170-71 sociology of science, 119 software, 7, 53, 156 belief in magic bullets, 121 CAD, 73 for children, 138 remarkable descriptions of, 108-9 upgrades, 98, 108-9 Soviet Union, 121-22 speech recognition, 140 spirit chair, 169-70, 179, 193, 202 spread-spectrum coding techniques, 165, 166 standards: computer, 88-90, 126 smart money, 88-91 Stanford Research Institute, 139 INDEX Stanford University, 54 Starner, Thad, 47, 57-58 Steane, Andy, 159 Steelcase, 202, 203, 204 Stradivarius, designing digital instrument to compete with, 32-33,39-42 Strickon, Joshua, 55 Sumitomo, 77 supercomputers, 151, 177, 199 surveillance, 57 Swatch Access watches, 152 Szilard, Leo, 176 technology: Bill of Things' Rights, 104 Bill of Things Users' Rights, 102 daily use of, 58 freedom of technological expression, 103 imposing on our lives, 95, 100-2 invisible and unobtrusive, 44, 200, 211 jargon, 107-22 mature, 10 musical instruments incorporating available, 38 wisdom in old technologies, 19, 24 telemarketing, 95, 101 telephones, 175 access to phone numbers, 100 invasion in our lives, 95, 101 satellite, 99-100 smart cards, 81 widespread dissemination of, 99 television, 10, 99, 202 high-definition, 6 Termen, Lev, 144 Tetzel, Johann, 96 "There's Plenty of Room at the Bottom," 161 thermodynamics, 175, 176 Things That Think, 202-7 privacy and, 207-10 stratification of society and, 210-11 INDEX 3D graphics interface, 141-42 3D printer, 64-65, 70-71 3001: The Final Odyssey (Clarke), 51 Toffoli, Tomaso, 132 transistors: invention of the, 175 study of, 179 Turing, Alan, 127-28, 131, 135, 166 Turing test, 128, 131, 133-34, 135 281, 210-11 Underkoffler, John, 145-46 U.S.

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

Decision trees 5. Decision tables 6. Regression 7. Artificial Neural Networks 1. Introduction 2. Perceptrons 3. Sigmoid perceptrons 4. Using perceptron networks 5. Hype and real neurons 8. 9. 10. 11. 12. 6. Support vector machines 7. Unsupervised learning 8. Competing technologies Speech and Vision 1. Speech recognition 2. Hidden Markov models 3. Words and language 4. 3D graphics 5. Machine vision 6. 3D vs 2.5D 7. Kinetics Robots 1. Automata 2. Robotics 3. Sensing environment 4. Motion Planning 5. Movement and Balance 6. Robocup 7. Other robots 8. Humanistic 9. Robots leaving the factory Programs writing Programs 1.

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Instead, it contains patterns and images and loose associations that can either be analyzed directly in order to make predictions or be abstracted into symbolic knowledge which can then be reasoned about more deeply. The practical concerns of a robot are then addressed, namely to be able to hear, see and move. Speech recognition is now a practical technology that may see increased usage in small devices that lack keyboards. Machine vision is a critical aspect of understanding the environment in which a robot lives. It is a huge area of research in which much has been achieved but the problem is far from solved. A robot also has to move its limbs and body, which involves several non-trivial problems.

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The best results can be achieved by using multiple decision trees and then averaging the results. When considering error rates, humans that carefully examine images are said to have an error rate of 0.2%, whereas post office workers quickly sorting mail had an error rate of 2.5%. So all of the automated systems had better than human performance in practice. Speech and Vision Speech recognition One achievement of modern artificial intelligence research is the ability to understand spoken speech. After a little work training a system, people may abandon their keyboards and simply talk to their computers. This is particularly useful in situations for those with busy hands or disabilities.

The Digital Divide: Arguments for and Against Facebook, Google, Texting, and the Age of Social Netwo Rking by Mark Bauerlein

Alvin Toffler, Amazon Mechanical Turk, Andrew Keen, business cycle, centre right, citizen journalism, collaborative editing, computer age, computer vision, corporate governance, crowdsourcing, David Brooks, digital divide, disintermediation, folksonomy, Frederick Winslow Taylor, Future Shock, Hacker News, Herbert Marcuse, Howard Rheingold, invention of movable type, invention of the steam engine, invention of the telephone, Jaron Lanier, Jeff Bezos, jimmy wales, Kevin Kelly, knowledge worker, late fees, Lewis Mumford, Mark Zuckerberg, Marshall McLuhan, means of production, meta-analysis, moral panic, Network effects, new economy, Nicholas Carr, PageRank, PalmPilot, peer-to-peer, pets.com, radical decentralization, Results Only Work Environment, Saturday Night Live, scientific management, search engine result page, semantic web, Silicon Valley, slashdot, social graph, social web, software as a service, speech recognition, Steve Jobs, Stewart Brand, technology bubble, Ted Nelson, the long tail, the strength of weak ties, The Wisdom of Crowds, Thorstein Veblen, web application, Yochai Benkler

For example, if a majority of users start clicking on the fifth item on a particular search results page more often than the first, Google’s algorithms take this as a signal that the fifth result may well be better than the first, and eventually adjust the results accordingly. Now consider an even more current search application, the Google Mobile Application for the iPhone. The application detects the movement of the phone to your ear, and automatically goes into speech recognition mode. It uses its microphone to listen to your voice, and decodes what you are saying by referencing not only its speech recognition database and algorithms, but also the correlation to the most frequent search terms in its search database. The phone uses GPS or cell-tower triangulation to detect its location, and uses that information as well. A search for “pizza” returns the result you most likely want: the name, location, and contact information for the three nearest pizza restaurants.

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And while some of the databases referenced by the application—such as the mapping of GPS coordinates to addresses—are “taught” to the application, others, such as the recognition of speech, are “learned” by processing large, crowdsourced data sets. Clearly, this is a “smarter” system than what we saw even a few years ago. Coordinating speech recognition and search, search results and location, is similar to the “hand-eye” coordination the baby gradually acquires. The Web is growing up, and we are all its collective parents. >>> cooperating data subsystems In our original Web 2.0 analysis, we posited that the future “Internet operating system” would consist of a series of interoperating data subsystems.

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. >>> how the web learns: explicit vs. implicit meaning But how does the Web learn? Some people imagine that for computer programs to understand and react to meaning, meaning needs to be encoded in some special taxonomy. What we see in practice is that meaning is learned “inferentially” from a body of data. Speech recognition and computer vision are both excellent examples of this kind of machine learning. But it’s important to realize that machine learning techniques apply to far more than just sensor data. For example, Google’s ad auction is a learning system, in which optimal ad placement and pricing are generated in real time by machine learning algorithms.

Bezonomics: How Amazon Is Changing Our Lives and What the World's Best Companies Are Learning From It by Brian Dumaine

activist fund / activist shareholder / activist investor, AI winter, Airbnb, Amazon Robotics, Amazon Web Services, Atul Gawande, autonomous vehicles, basic income, Bernie Sanders, Big Tech, Black Swan, call centre, Cambridge Analytica, carbon tax, Carl Icahn, Chris Urmson, cloud computing, corporate raider, creative destruction, Danny Hillis, data science, deep learning, Donald Trump, Elon Musk, Erik Brynjolfsson, Fairchild Semiconductor, fake news, fulfillment center, future of work, gig economy, Glass-Steagall Act, Google Glasses, Google X / Alphabet X, income inequality, independent contractor, industrial robot, Internet of things, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Kevin Roose, Lyft, Marc Andreessen, Mark Zuckerberg, military-industrial complex, money market fund, natural language processing, no-fly zone, Ocado, pets.com, plutocrats, race to the bottom, ride hailing / ride sharing, Salesforce, Sand Hill Road, self-driving car, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Snapchat, speech recognition, Steve Jobs, Stewart Brand, supply-chain management, TED Talk, Tim Cook: Apple, too big to fail, Travis Kalanick, two-pizza team, Uber and Lyft, uber lyft, universal basic income, warehouse automation, warehouse robotics, wealth creators, web application, Whole Earth Catalog, work culture

Chapter 7: Sexy Alexa For centuries humans: William of Malmesbury, Chronicle of the Kings of England, Bk. II, Ch. x, 181, c. 1125. The first breakthrough: Melanie Pinola, “Speech Recognition Through the Decades,” PC World, November 2, 2011. Around that time: Andrew Myers, “Stanford’s John McCarthy, Seminal Figure of Artificial Intelligence, Dies at 84,” Stanford Report, October 25, 2011. By the 1980s: Pinola, “Speech Recognition Through the Decades.” A product called Dragon: Ibid. By 2010, computing: Bianca Bosker, “Siri Rising: The Inside Story of Siri’s Origins—and Why She Could Overshadow the iPhone,” Huffington Post, December 6, 2017.

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He defined it as machines that can perform human tasks, such as understanding language, recognizing objects and sounds, learning, and problem solving. By the 1980s, talking dolls, such as Worlds of Wonder’s Julie, could respond to simple questions from a child, but it wasn’t until the next decade that the first serious speech recognition software hit the market. A product called Dragon could process simple speech without the speaker having to pause awkwardly between each word. Despite this progress, over the next two decades, voice recognition as well as other types of AI programming largely disappointed its supporters, periodically entering into what the academic community dubbed AI winters—periods when progress and funding would dry up.

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See also AI flywheel health-care industry and, 222 job losses and, 143, 248, 267, 271 Prime Video and, 104 societal and ethical challenges of, 90–91 vision recognition and, 109 warehouse controls using, 128 artists, and automation, 143 ASOS, 9, 97, 117, 194 Atlantic, The (magazine), 172–73, 259 auctions, 42 Audrey speech recognition system, 107 Aurora, 175 Australia, drones used in, 179–80 automation air traffic control with, 179 Amazon employees’ concerns about job loss from, 248 business models and, 270 customer data from shopping with, 191 discontent from threat of, 240 disruptive nature of, 127, 139 economic growth and, 250 grocery stores with, 139–41 job losses and, 9, 12, 126–27, 141–43, 241–42, 248, 267 warehouses with, 124, 128, 129–30, 135, 136–37, 143 automobiles.

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

Because human beings were believed to understand speech by understanding context, the view was that artificial intelligence would in the end be achieved by, essentially, modelling human intelligence and human beings’ ways of processing information and of thinking about the world around them. This would require systems that had common sense and general knowledge. However, speech recognition was eventually cracked through brute-force processing, massive data retrieval and storage capability, and statistics. This means, for example, that a good speech recognition system that ‘hears’ the sentence ‘my last visit to the office took two hours too long’ can correctly spell the ‘to’, ‘two’, and ‘too’. It can do this not because it understands the context of the usage of these words as human beings do, but because it can determine, statistically, that ‘to’ is much more likely immediately to precede ‘the office’ than ‘two’ or ‘too’.

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But some weak systems are becoming increasingly capable and can outperform human beings, even though they do not ‘think’ or operate in the same way as we think we do. We learned this many years ago in the context of speech recognition, another branch of AI (or at least it was regarded as such in the early days). As we explain in section 4.9, the challenge of developing systems that could recognize human speech was eventually met through a combination of brute-force processing and statistics. An advanced speech recognition system that can distinguish between ‘abominable’ and ‘a bomb in a bull’ does so not by understanding the broader context of these utterances in the way that human beings do, but by statistical analysis of a large database of documents that confirm, for instance, other words that are likely to be collocated or associated with ‘bull’.

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The term ‘artificial intelligence’ was coined by John McCarthy in 1955, and in the thirty years or so that followed a wide range of systems, techniques, and technologies were brought under its umbrella (the terms used in the mid-1980s are included in parentheses): the processing and translation of natural language (natural language processing); the recognition of the spoken word (speech recognition); the playing of complex games such as chess (game-playing); the recognition of images and objects of the physical world (vision and perception); learning from examples and precedents (machine learning); computer programs that can themselves generate programs (automatic programming); the sophisticated education of human users (intelligent computer-aided instruction); the design and development of machines whose physical movements resembled those of human beings (robotics), and intelligent problem-solving and reasoning (intelligent knowledge-based systems or expert systems).103 Our project at the University of Oxford (1983–6) focused on theoretical and philosophical aspects of this last category—expert systems—as applied in the law.

Talk Is Cheap: Switching to Internet Telephones by James E. Gaskin

Debian, decentralized internet, end-to-end encryption, Ford Model T, packet switching, PalmPilot, peer-to-peer, peer-to-peer model, satellite internet, Silicon Valley, Skype, speech recognition, telemarketer

If you don't have a fairly recent computer or separate sound card, and have a choice of analog headset or USB headset in the price range you're comfortable with, try the USB headset first. Some experts disagree, but more lean toward USB. * * * Note: Stop MumblingIf you use speech recognition software, or are curious and want to try it, analog headsets will help. One of the strong recommendations by speech recognition software vendors is to use a quality microphone, and the headset in the medium and high range will do an excellent job. * * * 4.2.1. Quick and Cheap (Less Than $30) This category isn't the biggest, but you will have plenty of choices.

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If you don't have a fairly recent computer or separate sound card, and have a choice of analog headset or USB headset in the price range you're comfortable with, try the USB headset first. Some experts disagree, but more lean toward USB. * * * Note: Stop MumblingIf you use speech recognition software, or are curious and want to try it, analog headsets will help. One of the strong recommendations by speech recognition software vendors is to use a quality microphone, and the headset in the medium and high range will do an excellent job. * * * 4.2.1. Quick and Cheap (Less Than $30) This category isn't the biggest, but you will have plenty of choices.

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Everest chat defaults file transfers history instant messaging IRC (Internet Relay Chat) chat within teleconferencing combination Wi-Fi cellular phones conference calling 2nd confidentiality and configuring cordless phone costs Dutch hip-hop and encrypted connection encryption 2nd equipment requirements features firewalls and Forwarder enhancement future features Instant Messaging and file transfer Instant Messaging tricks KaZaA and MoneyBookers and operating systems and password and email, changing PayPal and PDA support Pocket PC 2nd presence feature ringtones signing up SIP versus SkypeOut service Sound Set Up Guide web site support forum Sysgration SkyGenie adapter technical answers web site technical details they don't mention traditional telephone network and (SkypeOut) troubleshooting Voicemail 2nd Vonage versus wireless USB headset Skype Answering Machine (SAM) Skype for Business features SkypeIn service Skypeing, verbified noun SkypeOut 2nd tracking usage SkypePlus SkypeVM softphones definition open source peer-to-peer telephone providers systems sound, measuring speech recognition software SummitCircle web site Index [SYMBOL] [A] [B] [C] [D] [E] [F] [G] [H] [I] [K] [L] [M] [N] [O] [P] [Q] [R] [S] [T] [U] [V] [W] [X] [Z] teleconferences Telemarketer Block Teleo, business-oriented softphone integrated with Microsoft Office telephones adapters bandwidth systems versus Internet Telephony three-way calling Time Warner 2nd TiVo phone links toll-free numbers virtual Touchtone service fee TowerStream WiMAX vendor TPC (The Phone Company) traditional phone companies Internet Telephony from SBC, Qwest, and BellSouth Verizon and AT&T traditional phone services Index [SYMBOL] [A] [B] [C] [D] [E] [F] [G] [H] [I] [K] [L] [M] [N] [O] [P] [Q] [R] [S] [T] [U] [V] [W] [X] [Z] Universal Service USB handsets 2nd phones Index [SYMBOL] [A] [B] [C] [D] [E] [F] [G] [H] [I] [K] [L] [M] [N] [O] [P] [Q] [R] [S] [T] [U] [V] [W] [X] [Z] Verizon 2nd videophones 2nd services viral marketing virtual numbers 2nd 3rd Viseon VocalTec VoFi (VoIP over Wi-Fi) VoiceGlo USB handset voicemail 2nd Voicemail Skype and VoicePulse 2nd VoiceWing VoIP (Voice over Internet Protocol) VON (Voice on the Net) Vonage 2nd 3rd 911 service and Bandwidth Saver Basic 500 plan business details they don't mention competitors Dashboard interface encryption, lack of firewalls and Great Benefits Help pages rebooting router Skype versus standard features technical details they don't mention troubleshooting Viseon and voicemail voicemail alerts voicemail management Wi-Fi cell phone handset Vonage, Inc.

Driverless: Intelligent Cars and the Road Ahead by Hod Lipson, Melba Kurman

AI winter, Air France Flight 447, AlphaGo, Amazon Mechanical Turk, autonomous vehicles, backpropagation, barriers to entry, butterfly effect, carbon footprint, Chris Urmson, cloud computing, computer vision, connected car, creative destruction, crowdsourcing, DARPA: Urban Challenge, deep learning, digital map, Donald Shoup, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, General Motors Futurama, Geoffrey Hinton, Google Earth, Google X / Alphabet X, Hans Moravec, high net worth, hive mind, ImageNet competition, income inequality, industrial robot, intermodal, Internet of things, Jeff Hawkins, job automation, Joseph Schumpeter, lone genius, Lyft, megacity, Network effects, New Urbanism, Oculus Rift, pattern recognition, performance metric, Philippa Foot, precision agriculture, RFID, ride hailing / ride sharing, Second Machine Age, self-driving car, Silicon Valley, smart cities, speech recognition, statistical model, Steve Jobs, technoutopianism, TED Talk, Tesla Model S, Travis Kalanick, trolley problem, Uber and Lyft, uber lyft, Unsafe at Any Speed, warehouse robotics

In contrast, deep-learning software would focus on the cat’s identifying visual features—perhaps its pointy ears and tail—and would quickly (and correctly) surmise that although the cat appears in an unusual setting, it’s still a cat. Deep learning has transformed the study of artificial perception and is being applied with great success to speech recognition and other activities that require software to deal with information that presents itself in quirky and imperfect ways. In the past few years, in search of deep-learning expertise, entire divisions of automotive companies have migrated to Silicon Valley. Deep learning is why software giants like Google and Baidu, already armed with expertise in managing huge banks of data and building intelligent software, are giving the once-invincible automotive giants a run for their money.

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We may be finally seeing the resolution of Moravec’s paradox, as roboticists and computer scientists find creative new ways to apply deep learning to automate artificial perception and response. Since 2012, deep learning has given driverless cars the ability to “see,” and has improved the language comprehension of speech-recognition software. In a high-profile demonstration of its power and versatility, in 2016, deep-learning software enabled Google’s AlphaGo program to trounce the world’s best players of go, a board game considered by many to be more challenging than chess. To encourage third-party developers to build intelligent applications using their software tools, Google, Microsoft, and Facebook have each launched their own version of an open source deep-learning development platform.

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The year following SuperVision’s triumph, the winner achieved a 11.2 percent error rate, with runners-up close behind at 12 percent and 13 percent; all used customized variants of deep-learning convolutional neural networks.10 In 2014, a team from Google achieved 6.66 percent error and a team from the University of Oxford achieved 7.1 percent error.11 In 2015, a team of researchers at Microsoft’s Beijing research lab (led by principal researcher Jian Sun) used a network that was 152-layers deep to win first place in all three categories.12 Most remarkably, Microsoft’s team achieved a 3.57 percent error rate, surpassing for the first time the previously unbeatable 5 percent error rate of human-level perception.13 After these triumphs, suddenly alternative research approaches in machine vision became obsolete. The string of watershed breakthroughs in object recognition quickly spilled out of the computer vision field into all other areas of artificial-intelligence research. Derivatives of the algorithm that ran SuperVision oozed into other AI fields, such as speech recognition and text generation. The final remaining barrier to the development of driverless cars—software capable of artificial perception—finally began to melt away. Soon after this big success, the pieces started coming together. Nvidia launched a deep-learning card that implemented a derivative of Krizhevsky’s Supervision network on low-power hardware.

Inventors at Work: The Minds and Motivation Behind Modern Inventions by Brett Stern

Apple II, augmented reality, autonomous vehicles, bioinformatics, Build a better mousetrap, business process, cloud computing, computer vision, cyber-physical system, distributed generation, driverless car, game design, Grace Hopper, human-factors engineering, Richard Feynman, Silicon Valley, skunkworks, Skype, smart transportation, speech recognition, statistical model, stealth mode startup, Steve Jobs, Steve Wozniak, the market place, value engineering, Yogi Berra

One of the products we came out with about a decade after the Speak & Spell was the Julie doll. She was a doll that had speech recognition on it. Stern: And how did that go? Frantz: Well, there were other things that made it die a short death. It happened to be that they brought it out in late 1987, which if you remember, there was the crash in the stock market, and start-ups didn’t do very well through that crash. But I had been working with toy companies for years trying to add speech recognition to their products, and it really came down to this silly notion at that time that speech recognition did not work. Stern: Did the companies know how the technology worked in those situations?

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Did the companies come to you looking for something? Or were you going to the companies, saying, “I have a great solution.”? Frantz: A little bit of both. I went after companies, saying, “We have this new technology—now what can you do with it?” And companies came to me saying things like, “We have a brilliant idea, and all we need is your speech recognition capability to make it work.” Stern: So back in the day, what was the prior art? Or what else was going on in the industry with this technology? Frantz: Well, that particular one was mostly used in military systems to do specific things, of which you spent more time training the user than training the product.

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advice artificial vision baseball cards brainstorming chip cloud confidence consumer corporate setting digital signal processing DRAM failures family background and education final words of wisdom firing definition IC-integrated circuit development ideation process inanimate object innovation definition inspiration integrated circuit intrapreneurs Julie doll, speech recognition manufacturing capacity marketplace mentor military systems moral judgment plan to retire positive and the negative balance presentation problem vs. solution professional heroes reputations serial innovator skill set solution and implementation spontaneity Starbucks team effort and responsibility transistor-transistor logic (TTL) US PTO variations G, H, I Gass, S.

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

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

Take children’s dolls as an example. Historically these were inert, rather poor representations of the human form. They are already becoming more realistic and more intelligent. Owners of “Amazing Amanda” can chat with their doll and “intelligence” is available in the form of facial recognition, speech recognition and accessories impregnated with radio-frequency identification devices (RFID). If you’re a bit older (and presumably no wiser) you can even buy a physically realistic, life-sized “love partner” for US$7,000 from a company called realdoll.com. But you ain’t seen nothing yet. In a few years’ time you will be able to personalize your doll’s face (to your own choice or, more likely, to resemble a celebrity), communicate with your doll by telephone or email, have real conversations and experience your entire life history through the eyes, ears (and nose) of your doll.

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The true test for artificial intelligence dates to 1950, when the British mathematician Alan Turing suggested the criterion of humans submitting statements through a machine and then not being able to tell whether the responses had come from another person or the machine. The 1960s and 1970s saw a great deal of progress in AI, but real breakthroughs failed to materialize. Instead, scientists and developers focused on specific problems such as speech recognition, text recognition and computer vision. However, we may be less than ten years away from seeing Turing’s AI vision become a reality. For instance, a company in Austin, Texas has developed a product called Cyc. It is much like a “chatbot” except that, if it answers Science and Technology 45 a question incorrectly, you can correct it and Cyc will learn from its mistakes.

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During the twentieth century people were paid to accumulate and apply information. The acquisition and analysis of data are logical left-brain activities, but, as Daniel Pink points out in his book A Whole New Mind, they are activities that are fast disappearing thanks to developments in areas such as computing. For instance, speech recognition and GPS systems are replacing people for taxi bookings, while sites like completemycase.com are giving mediocre lawyers a run for their money. So dump that MBA and get an arts education instead. Better still, do both. One fascinating statistic I came across recently is that 12 years ago 61% of McKinsey’s new US recruits had MBAs.

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!

Cameras capture speakers for video conferencing no matter where they sit or stand in the room—and also record facial expressions and body language. (The video might be analyzed later by the computer system, and insights drawn from it can be given back to the speakers so they see how people reacted to specific moments of a presentation.) Acoustic arrays help with speech recognition and processing. The idea is to create the richest possible environment for people’s interactions with data and one another in order to enhance the group’s collective intelligence—a term used by MIT’s Thomas Malone. Dario says, “The goal is to have the system partner with the humans inside the cognitive environments to transform the decision-making activities of teams that are dealing with very complex problems.”10 Imagine how a large oil company might use cognitive capabilities in such a space a few years from now.

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Experimental self-driving vehicles are capable of using vision and other senses to navigate through cities without colliding with buses, running over pedestrians, or getting speeding tickets. Yet, as of now, robots remain firmly in the von Neumann computing paradigm. They must be programmed in advance by people to deal with nearly every situation they encounter. Already, learning systems are playing important roles in advances in speech recognition and image recognition. Some voice-recognition systems, for instance, get better at understanding an individual’s manner of speech the more they interact. But the scientific community is just at the beginning of making machines that learn like humans do, so we’re just scratching the surface of the potential of computer sensing.

Dealers of Lightning by Michael A. Hiltzik

Apple II, Apple's 1984 Super Bowl advert, beat the dealer, Bill Atkinson, Bill Duvall, Bill Gates: Altair 8800, Boeing 747, business cycle, Charles Babbage, computer age, creative destruction, Douglas Engelbart, Dynabook, Edward Thorp, El Camino Real, Fairchild Semiconductor, financial engineering, index card, Ivan Sutherland, Jeff Rulifson, John Markoff, Joseph Schumpeter, L Peter Deutsch, luminiferous ether, Marshall McLuhan, Menlo Park, military-industrial complex, Multics, oil shock, popular electronics, reality distortion field, Robert Metcalfe, Ronald Reagan, Silicon Valley, speech recognition, Steve Ballmer, Steve Crocker, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, the medium is the message, The Soul of a New Machine, Vannevar Bush, Whole Earth Catalog, zero-sum game

The Systems Science Lab was to take over development of a laser-driven computer printer whose inventor, a Webster engineer, had come west after failing to interest his bosses in its potential. SSL researchers would also investigate optical memories, a technology that would eventually give rise to today’s compact disc and CD-ROM, and speech recognition by computer. Taylor’s Computer Science Lab was to pursue his pet interest in graphics while developing specifications for a basic center-wide computer system. And GSLwas assigned studies in solid-state technologies, including the electrical and optical qualities of crystals. Pake warned his superiors that under the projected growth curve the Porter Drive complex, which at the time housed everyone comfortably, would certainly burst its seams by the close of 1971.

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“I just came to introduce you to your next-door neighbor,” Biegelsen said, leading Thornburg into the adjoining warren. “This is George. I thought you guys should get together because you shared a similar research interest in grad school.” Thornburg was perplexed. He understood George to be working on speech recognition and he had come in as a thin-film metallurgist. “Really?” the neighbor asked. “What did you do you work in?” That was all the voluble Thornburg needed to set off on a thorough explication of his doctoral career, not excepting the time he had to change themes in midcourse thanks to the preemptive publication of a thesis on the same subject by a guy from Oregon named George White.

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Inside the building it was impossible to pass within a few yards of Kay’s door without sensing a gravitational tug. Perhaps his most important recruit was swept into his orbit that way, never to leave. Dan Ingalls had come to PARC on a temporary contract to help George White set up the SDS Sigma 3 he had acquired for his work in speech recognition. “My office ended up across the hall from Alan’s,” Ingalls said. “I kept noticing that I was more interested in what I was hearing across the hall than in the speech work I was hired to do. These conversa-tions I was eavesdropping on were all about open-ended computer science stuff, which I was interested in.

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

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

Yet the computers at web search companies are constantly performing these computations. In this chapter, on the other hand, we examine an area in which humans have a natural advantage: the field of pattern recognition. Pattern recognition is a subset of artificial intelligence and includes tasks such as face recognition, object recognition, speech recognition, and handwriting recognition. More specific examples would include the task of determining whether a given photograph is a picture of your sister, or determining the city and state written on a hand-addressed envelope. Thus, pattern recognition can be defined more generally as the task of getting computers to act “intelligently” based on input data that contains a lot of variability.

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Yet this apparent stumbling block for AI was convincingly eradicated in 1997, when IBM's Deep Blue computer beat world champion Garry Kasparov. Meanwhile, the success stories of AI were gradually creeping into the lives of ordinary people too. Automated telephone systems, servicing customers through speech recognition, became the norm. Computer-controlled opponents in video games began to exhibit human-like strategies, even including personality traits and foibles. Online services such as Amazon and Netflix began to recommend items based on automatically inferred individual preferences, often with surprisingly pleasing results.

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See also digital signature; RSA select operation server; secure SHA Shakespeare, William Shamir, Adi Shannon, Claude Shannon-Fano coding shared secret; definition of; length of shared secret mixture shorter-symbol trick signature: digital (see digital signature); handwritten Silicon Valley simple checksum. See checksum simulation: of the brain; of random surfer Singh, Simon SizeChecker.exe Sloane, N. J. A. smartphone. See phone snoop social network software; download; reliability of; signed software engineering sources spam. See also web spam speech recognition spirituality spreadsheet SQL staircase checksum. See checksum Stanford University Star Trek statistics Stein, Clifford stochastic gradient descent Strohman, Trevor structure: in data; in a web page. See also database, table structure query sunglasses problem. See neural network supercomputer support vector machine surfer authority score symbol table.

Tech Titans of China: How China's Tech Sector Is Challenging the World by Innovating Faster, Working Harder, and Going Global by Rebecca Fannin

"World Economic Forum" Davos, Adam Neumann (WeWork), Airbnb, augmented reality, autonomous vehicles, Benchmark Capital, Big Tech, bike sharing, blockchain, call centre, cashless society, Chuck Templeton: OpenTable:, clean tech, cloud computing, computer vision, connected car, corporate governance, cryptocurrency, data is the new oil, data science, deep learning, Deng Xiaoping, Didi Chuxing, digital map, disruptive innovation, Donald Trump, El Camino Real, electricity market, Elon Musk, fake news, family office, fear of failure, fulfillment center, glass ceiling, global supply chain, Great Leap Forward, income inequality, industrial robot, information security, Internet of things, invention of movable type, Jeff Bezos, Kickstarter, knowledge worker, Lyft, Mark Zuckerberg, Mary Meeker, megacity, Menlo Park, money market fund, Network effects, new economy, peer-to-peer lending, personalized medicine, Peter Thiel, QR code, RFID, ride hailing / ride sharing, Sand Hill Road, self-driving car, sharing economy, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart transportation, Snapchat, social graph, SoftBank, software as a service, South China Sea, sovereign wealth fund, speech recognition, stealth mode startup, Steve Jobs, stock buybacks, supply-chain management, tech billionaire, TechCrunch disrupt, TikTok, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, urban planning, Vision Fund, warehouse automation, WeWork, winner-take-all economy, Y Combinator, young professional

The Ministry of Science and Technology in China has earmarked specialties for each of these Chinese tech titans in its master plan for AI global dominance: Baidu for autonomous driving, Alibaba for smart-city initiatives, and Tencent for computer vision in medical diagnoses. The Chinese government also has designated two startups to lead AI development: SenseTime for facial recognition and iFlytek for speech recognition. Baidu, Alibaba, and Tencent are all powering up in autonomous driving, and each has a specialty focus area in AI. Baidu has its DuerOS line of smart household goods and Apollo, an open platform for self-driving technology solutions, and detoured on the AI journey several years before Google in 2015.

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China has an advantage based on large numbers of well-trained AI talent, a supportive government policy, and access to a vast amount of data sets powering AI and gleaned from China’s world-leading number of internet and mobile phone users, he notes. In the age of AI, data is the new oil, so China is the new Saudi Arabia, says Lee, author of AI Superpowers.4 His venture investment firm in Beijing, Sinovation Ventures, which I’ve visited multiple times, is betting on AI’s future. Lee, who is widely known for his pioneering work in speech recognition and artificial intelligence, is an investor in five Chinese AI companies worth more than $1 billion. Two that are in the forefront are Megvii, a Chinese developer of facial recognition system Face++, and 4Paradigm, a machine learning software for detecting fraud in insurance and banking. I’ve known Lee since 2006, when he was running Google China, and I’ve watched his career flourish as a China tech investor from starting Sinovation Ventures in 1999 and as a world-leading AI expert.

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The AI-based edtech startup was founded in 2012 by Wang Yi, a Princeton PhD in computer science and former Google product manager in charge of analytics and cloud computing. Yi’s startup is disrupting the online education sector by helping Chinese people learn to speak English through AI-powered interactive, customized courses accessed on mobile phones. Its AI technology crunches data to feed a speech recognition engine that can provide feedback on pronunciation, grammar, and vocabulary. This being China, LAIX integrates games and social sharing into its mobile app to make for a more fun, interactive learning experience. Riding high on China’s growing trend toward online knowledge platforms, LAIX attracted 110 million registered users in 2018, including 2.5 million who paid for courses for the full year.

Succeeding With AI: How to Make AI Work for Your Business by Veljko Krunic

AI winter, Albert Einstein, algorithmic trading, AlphaGo, Amazon Web Services, anti-fragile, anti-pattern, artificial general intelligence, autonomous vehicles, Bayesian statistics, bioinformatics, Black Swan, Boeing 737 MAX, business process, cloud computing, commoditize, computer vision, correlation coefficient, data is the new oil, data science, deep learning, DeepMind, en.wikipedia.org, fail fast, Gini coefficient, high net worth, information retrieval, Internet of things, iterative process, job automation, Lean Startup, license plate recognition, minimum viable product, natural language processing, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, six sigma, smart cities, speech recognition, statistical model, strong AI, tail risk, The Design of Experiments, the scientific method, web application, zero-sum game

An ML pipeline could span the whole community Whole communities and subfields of ML are formed around the pipelines that emerge early, showing that ossification of the ML pipeline can affect not only a single organization, but a whole community. An example is the NLP community, which often uses a relatively standardized form of the pipeline. Historical efforts of the speech recognition community also have led to a standardized pipeline [90]. In some situations, whole AI communities might be facing the possibility that the current standard pipeline needs to be changed. For example, the advance of deep learning caused a need for significant changes in the traditional speech recognition pipeline [90]. The ML pipeline’s ossification is compounded if data science and data engineering are in separate groups in the organization.

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This section explains those new capabilities. What’s new with AI and big data is that automated analysis has become cheaper, faster, better, and (using big data systems) capable of operating on much larger datasets. Analysis that used to require human involvement is now possible to do with computers in areas like image and speech recognition. Thanks to this new AI-powered capability, whole Sense/Analyze/React loops became viable in these contexts, when it wasn’t economical to apply them before. Examples of AI making automation viable The following are some examples where an introduction of AI makes it possible to automate tasks that previously required a human to perform them:  Automated translations from one language to another—Language translation is nothing new and is something that humans have done since the beginning of time.

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See machine learning (ML) pipelines mlr package 131 Modified National Institute of Standards and Technology (MNIST) 126–127 monotonic profit curves 154, 156 moonshots 58, 76–77 N natural language processing (NLP) 157–158, 202, 206–207 AutoML 211 machine learning pipeline 116–120 Nest 30, 198 Ng, Andrew 5 non-monotonic profit curves 154, 156, 158–159 non-unique (ambiguous) profit curves 155, 157 nonlinearity 176–178 O organizational silos 108–109 ossification of machine learning pipeline 118–120 addressing 123–125 causes of 118–120 example of 121–123 P pet monitors 43 pre-segmentation 151 product AI as fully autonomous product 41–42 AI as part of larger 38, 40–42 evolution of capabilities of 42 packaging AI as 38, 43–44 wide applicability of 44–45 profit curve accounting for influence of time on 187–188 arguments against 109 better defined by data science team 103–104 constructing 99–102 defined 97–98 evolution of over time 186–187 improvement over time 107 in academia 102–103 nonlinearity (convexity) in 176–178 not limited to supervised learning 106 precision depends on business problem 106 sophistication of mathematical analysis 100 profit curves in MinMax analysis categories of 157 categories of profit curves 154 complex profit curves 157–159 mental calculations 159 Provost, F. 102 pseudo experiments 180 publishing industry 66, 71 R recommendation engines 17, 32, 83–84 reinforcement learning 205 research questions defining 69–73 best practices for business leaders 72–73 INDEX contractual language of technical domain 70–72 misaligned business and research questions 72 understanding business concepts 70 linking business problems and business decisions based on technical metric 88–90 questions answered by metric 87–88 right business metric 93–94 right research question 87 understandability of business metric 91–93 linking business questions and 70–73, 85–94 Roomba 41–42 root mean square error (RMSE) metric 19–20, 88–93, 99–101 rule engines 144, 199, 211 S safety and security issues 41–42, 117–118, 198–200 autonomous vehicles 198 disagreement between AI engineering and safety engineering 210 heuristics for building safe systems 199 importance of human involvement 200 in MinMax analysis 154 local vs. global models 198–199 non-monotonic profit curves 156 ossification of machine learning pipeline 121–123 Sculley, D. 118 Sense/Analyze/React loop 29–31 AI methods and data 63–65 applicability of 30–32 elements of 29–30 finding business questions for AI to answer 60–62 monetization with 33–37 not limited to AI 213 prioritizing projects 59 speed in closing 30 sensitivity analysis 166–172, 175–185 CLUE 172–175 common critiques to 181–183 defined 166 design of experiments 180–181 detecting nonlinearity 176–178 enhancing quality of data analyzing data-producing stage 184 collapsing two stages of pipeline into one 184 example of using results 171–172 global sensitivity analysis 170–171 increasing/decreasing accuracy by 1% 169 interactions between pipeline stages addressing 179 effect of 179 introducing errors 185 local sensitivity analysis 167–170 MinMax analysis vs. 138 not limited to AI 214 recent advancements in field of 184–185 supervised vs.unsupervised learning 183 sentiment analysis 32 smart parking meters 142–146 smart speakers 40 smart thermostats 30, 38, 198 smart, internet-connected oven 64–65 speech recognition 31, 120 stock market investments 137 streaming analytics 30 Support-Vector Machines (SVMs) 129, 134 T team dynamics 128–129 technical metrics 17 business metrics vs. 20 escaping into the wild 89 linking to business metrics 19–20, 96–106 need for 97 poorly understood 91–93 presenting directly 88–90 technology smokescreens 91 Tesla 202 three-state, yes/no/maybe classification of results 191–192 threshold 67 timebox approach 191 timing diagrams 188–190 transplant projects 75–76 trend estimates 152–153 263 264 U uncanny valley concept 156 unicorns 47–50 acquiring skillsets of 48 data engineers 48–49 data science 47–48 gap analysis 49–50 INDEX MinMax analysis 146–147 selecting 101–102, 132, 143 sensitivity analysis 172, 181–183 timing diagrams 189 video surveillance systems 62, 78 ossification of machine learning pipeline 121–123 voice recognition 35, 206 V W vacuum cleaning robots 38, 41–42 value threshold defined 98 evolution of over time 186–187, 189 improving machine learning pipeline over time 187–188 Wikimedia Foundation 197 Z zero-shot learning 205 DATA SCIENCE/AI Succeeding with AI See first page Veljko Krunic S ucceeding with AI requires talent, tools, and money.

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

The most obvious example is your smartphone. It is probably the last inanimate thing you touch before you go to sleep at night and the first thing you touch in the morning. It has more processing power than the computers that NASA used to send Neil Armstrong to the moon in 1969. It uses AI algorithms to offer predictive text and speech recognition services, and these features improve year by year as the algorithms are improved. Many of the apps we download to our phones also employ AI to make themselves useful to us. The AI in our phones becomes more powerful with each generation of phone as their processing power increases, the bandwidth of the phone networks improve, cloud storage becomes better and cheaper, and we become more relaxed about sharing enough of our personal data for the AIs to “understand” us better.

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Andrej Markov was a Russian mathematician who died in 1922 and in the type of model that bears his name the next step depends only on the current step, and not any previous steps. A Hidden Markov Model (often abbreviated to HMM because they are so useful) is one where the current state is only partially observable. They are particularly useful in speech recognition and handwriting recognition systems. Deep learning Deep learning is a subset of machine learning. Its algorithms use several layers of processing, each taking data from previous layers and passing an output up to the next layer. The nature of the output may vary according to the nature of the input, which is not necessarily binary, just on or off, but can be weighted.

Radical Technologies: The Design of Everyday Life by Adam Greenfield

3D printing, Airbnb, algorithmic bias, algorithmic management, AlphaGo, augmented reality, autonomous vehicles, bank run, barriers to entry, basic income, bitcoin, Black Lives Matter, blockchain, Boston Dynamics, business intelligence, business process, Californian Ideology, call centre, cellular automata, centralized clearinghouse, centre right, Chuck Templeton: OpenTable:, circular economy, cloud computing, Cody Wilson, collective bargaining, combinatorial explosion, Computer Numeric Control, computer vision, Conway's Game of Life, CRISPR, cryptocurrency, David Graeber, deep learning, DeepMind, dematerialisation, digital map, disruptive innovation, distributed ledger, driverless car, drone strike, Elon Musk, Ethereum, ethereum blockchain, facts on the ground, fiat currency, fulfillment center, gentrification, global supply chain, global village, Goodhart's law, Google Glasses, Herman Kahn, Ian Bogost, IBM and the Holocaust, industrial robot, informal economy, information retrieval, Internet of things, Jacob Silverman, James Watt: steam engine, Jane Jacobs, Jeff Bezos, Jeff Hawkins, job automation, jobs below the API, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, John von Neumann, joint-stock company, Kevin Kelly, Kickstarter, Kiva Systems, late capitalism, Leo Hollis, license plate recognition, lifelogging, M-Pesa, Mark Zuckerberg, means of production, megacity, megastructure, minimum viable product, money: store of value / unit of account / medium of exchange, natural language processing, Network effects, New Urbanism, Nick Bostrom, Occupy movement, Oculus Rift, off-the-grid, PalmPilot, Pareto efficiency, pattern recognition, Pearl River Delta, performance metric, Peter Eisenman, Peter Thiel, planetary scale, Ponzi scheme, post scarcity, post-work, printed gun, proprietary trading, RAND corporation, recommendation engine, RFID, rolodex, Rutger Bregman, Satoshi Nakamoto, self-driving car, sentiment analysis, shareholder value, sharing economy, Shenzhen special economic zone , Sidewalk Labs, Silicon Valley, smart cities, smart contracts, social intelligence, sorting algorithm, special economic zone, speech recognition, stakhanovite, statistical model, stem cell, technoutopianism, Tesla Model S, the built environment, The Death and Life of Great American Cities, The Future of Employment, Tony Fadell, transaction costs, Uber for X, undersea cable, universal basic income, urban planning, urban sprawl, vertical integration, Vitalik Buterin, warehouse robotics, When a measure becomes a target, Whole Earth Review, WikiLeaks, women in the workforce

Chief among these are a multi-core central processing unit; a few gigabits of nonvolatile storage (and how soon that “giga-” will sound quaint); and one or more ancillary chips dedicated to specialized functions. Among the latter are the baseband processor, which manages communication via the phone’s multiple antennae; light and proximity sensors; perhaps a graphics processing unit; and, of increasing importance, a dedicated machine-learning coprocessor, to aid in tasks like speech recognition. The choice of a given chipset will determine what operating system the handset can run; how fast it can process input and render output; how many pictures, songs and videos it can store on board; and, in proportion to these capabilities, how much it will cost at retail. Thanks to its Assisted GPS chip—and, of course, the quarter-trillion-dollar constellation of GPS satellites in their orbits twenty million meters above the Earth—the smartphone knows where it is at all times.

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The material form of such speakers is all but irrelevant, though, as their primary job is to function as the physical presence of and portal onto a service—specifically, a branded “virtual assistant.” Google, Microsoft, Amazon and Apple each offer their own such assistant, based on natural-language speech recognition; no doubt further competitors and market entrants will have appeared by the time this book sees print. Almost without exception, these assistants are given female names, voices and personalities, presumably based on research conducted in North America indicating that users of all genders prefer to interact with women.7 Apple’s is called Siri, Amazon’s Alexa; Microsoft, in dubbing their agent Cortana, has curiously chosen to invoke a character from their Halo series of games, polluting that universe without seeming to garner much in return.

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And if this sense of effortlessness will never truly be achievable via handset, it is precisely what an emerging class of wearable mediators aims to provide for its users. The first of this class to reach consumers was the ill-fated Google Glass, which mounted a high-definition, forward-facing camera, a head-up reticle and the microphone required by its natural-language speech recognition interface on a lightweight aluminum frame. While Glass posed any number of aesthetic, practical and social concerns—all of which remain to be convincingly addressed, by Google or anyone else—it does at least give us a way to compare hands-free, head-mounted AR with the handset-based approach.

Bank 3.0: Why Banking Is No Longer Somewhere You Go but Something You Do by Brett King

3D printing, Abraham Maslow, additive manufacturing, Airbus A320, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, Apollo 11, Apollo 13, Apollo Guidance Computer, asset-backed security, augmented reality, barriers to entry, behavioural economics, bitcoin, bounce rate, business intelligence, business process, business process outsourcing, call centre, capital controls, citizen journalism, Clayton Christensen, cloud computing, credit crunch, crowdsourcing, disintermediation, en.wikipedia.org, fixed income, George Gilder, Google Glasses, high net worth, I think there is a world market for maybe five computers, Infrastructure as a Service, invention of the printing press, Jeff Bezos, jimmy wales, Kickstarter, London Interbank Offered Rate, low interest rates, M-Pesa, Mark Zuckerberg, mass affluent, Metcalfe’s law, microcredit, mobile money, more computing power than Apollo, Northern Rock, Occupy movement, operational security, optical character recognition, peer-to-peer, performance metric, Pingit, platform as a service, QR code, QWERTY keyboard, Ray Kurzweil, recommendation engine, RFID, risk tolerance, Robert Metcalfe, self-driving car, Skype, speech recognition, stem cell, telepresence, the long tail, Tim Cook: Apple, transaction costs, underbanked, US Airways Flight 1549, web application, world market for maybe five computers

UBank has the second-highest Facebook fan-base among the Australian banks, and ironically has more than its parent, NAB. The same can be said for First Direct in the UK, which has fantastic advocacy and is heavily engaged on Twitter, whereas its parent, HSBC, doesn’t yet have a brand Twitter account. In Bank 2.0 I also talked about the use of advances in speech recognition, enabling customers to issue spoken commands. Obviously the next generation of this speech recognition technology can be seen in Apple’s recent launch of Siri. In Siri’s patent application, various possibilities are hinted at, including being a voice agent providing assistance for “automated teller machines”.4 In fact, SRI (the creator of Siri™) and BBVA recently announced a collaboration to introduce Lola5, a Siri-like technology, to customers through the Internet and via voice.

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They found the man’s conversation with his phone ‘creepy,’ without any of the natural pauses and voice inflections that occur in a discussion between two people.” —New York Times article, “Oh, for the Good Old Days of Rude Cellphone Gabbers”, 2 December 2011 The same problem presents itself with the use of IVR in the near term. Speech recognition is not yet good enough for natural speech. But clearly it’s getting better, and fast. Avatars replacing IVRs The key advantage to integrating natural speech recognition to replace current IVR menus will be that IVRs will start to become more human again. The logical extension of this technology married with avatars are automated customer service representatives that will look and sound like a real person and be able to answer simple “canned” questions and respond to issues such as “What is my account balance?”

Future Politics: Living Together in a World Transformed by Tech by Jamie Susskind

3D printing, additive manufacturing, affirmative action, agricultural Revolution, Airbnb, airport security, algorithmic bias, AlphaGo, Amazon Robotics, Andrew Keen, Apollo Guidance Computer, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, Big Tech, bitcoin, Bletchley Park, blockchain, Boeing 747, brain emulation, Brexit referendum, British Empire, business process, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Cass Sunstein, cellular automata, Citizen Lab, cloud computing, commons-based peer production, computer age, computer vision, continuation of politics by other means, correlation does not imply causation, CRISPR, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, digital divide, digital map, disinformation, distributed ledger, Donald Trump, driverless car, easy for humans, difficult for computers, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, Erik Brynjolfsson, Ethereum, ethereum blockchain, Evgeny Morozov, fake news, Filter Bubble, future of work, Future Shock, Gabriella Coleman, Google bus, Google X / Alphabet X, Googley, industrial robot, informal economy, intangible asset, Internet of things, invention of the printing press, invention of writing, Isaac Newton, Jaron Lanier, John Markoff, Joseph Schumpeter, Kevin Kelly, knowledge economy, Large Hadron Collider, Lewis Mumford, lifelogging, machine translation, Metcalfe’s law, mittelstand, more computing power than Apollo, move fast and break things, natural language processing, Neil Armstrong, Network effects, new economy, Nick Bostrom, night-watchman state, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, payday loans, Philippa Foot, post-truth, power law, price discrimination, price mechanism, RAND corporation, ransomware, Ray Kurzweil, Richard Stallman, ride hailing / ride sharing, road to serfdom, Robert Mercer, Satoshi Nakamoto, Second Machine Age, selection bias, self-driving car, sexual politics, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart contracts, Snapchat, speech recognition, Steve Bannon, Steve Jobs, Steve Wozniak, Steven Levy, tech bro, technological determinism, technological singularity, technological solutionism, the built environment, the Cathedral and the Bazaar, The Structural Transformation of the Public Sphere, The Wisdom of Crowds, Thomas L Friedman, Tragedy of the Commons, trolley problem, universal basic income, urban planning, Watson beat the top human players on Jeopardy!, work culture , working-age population, Yochai Benkler

AI systems are now close to surpassing humans in their ability to translate natural languages, recognize faces, and mimic human speech.3 Self-driving vehicles using AI are widely expected to become commonplace in the next few years (Ford is planning a mass-market model by 2021).4 In 2016 Microsoft unveiled a speech-recognition AI system that can transcribe human conversation with the same number of errors, or fewer, as professional human transcriptionists.5 Researchers at Oxford University developed an AI system capable of lip-reading with 93 per cent accuracy, as against a 60 per cent success rate among professional lip-readers.6 AI systems can already write articles about sports, business, and finance.7 In 2014, the Associated Press began using algorithms to computerize the production of hundreds of formerly handcrafted earnings reports, producing fifteen times as many as before.8 AI systems have directed films and created movie trailers.9 AI ‘chatbots’ (systems that can ‘chat’ to you) will soon be taking orders at restaurants.10 OUP CORRECTED PROOF – FINAL, 26/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS Increasingly Capable Systems 31 Ominously, engineers have even built an AI system capable of writing entire speeches in support of a specified political party.11 It’s bad enough that politicians frequently sound like soulless robots; now we have soulless robots that sound like politicians.

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Taking one specific set of affordances, there are many people who are currently considered disabled whose freedom of action will be significantly enlarged in the digital lifeworld. Voice-controlled robots will do the bidding of people with limited mobility. Self-driving vehicles will make it easier to get around.Those unable to speak or hear will be able to use gloves that can turn sign language into writing.18 Speech recognition software embedded in ‘smart’ eyewear could allow all sounds—speech, alarms, sirens—to be captioned and read by the wearer.19 Brain interfaces will allow people with communication difficulties to ‘type’ messages to other people using only their thoughts.20 OUP CORRECTED PROOF – FINAL, 30/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS 170 FUTURE POLITICS As for freedom of thought, in the short time we’ve lived with digital technology we’ve already witnessed an explosion in the ­creation and communication of information.

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Peter Campbell, ‘Ford Plans Mass-market Self-driving Car by 2021’, Financial Times, 16 August 2016 <https://www.ft.com/content/ d2cfc64e-63c0-11e6-a08a-c7ac04ef00aa#axzz4HOGiWvHT> (accessed 28 November 2017); David Millward, ‘How Ford Will Create a New Generation of Driverless Cars’, Telegraph, 27 February 2017 <http://www.telegraph.co.uk/business/2017/02/27/ford-seeks-­ pioneer-new-generation-driverless-cars/> (accessed 28 November 2017). 5. Wei Xiong et al.,‘Achieving Human Parity in Conversational Speech Recognition’, arXiv, 17 February 2017 <https://arxiv.org/abs/ 1610.05256> (accessed 28 November 2017). 6. Yannis M. Assael et al.,‘LipNet: End-to-End Sentence-level Lipreading’, arXiv, 16 December 2016 <https://arxiv.org/abs/1611.01599> (accessed 6 December 2017). 7. Laura Hudson, ‘Some Like it Bot’, FiveThirtyEight, 29 September 2016 <http://fivethirtyeight.com/features/some-like-it-bot/> (accessed 28 November 2017). 8.

Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

agricultural Revolution, AI winter, Albert Einstein, algorithmic trading, anthropic principle, Anthropocene, anti-communist, artificial general intelligence, autism spectrum disorder, autonomous vehicles, backpropagation, barriers to entry, Bayesian statistics, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, Computing Machinery and Intelligence, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, Demis Hassabis, demographic transition, different worldview, Donald Knuth, Douglas Hofstadter, driverless car, Drosophila, Elon Musk, en.wikipedia.org, endogenous growth, epigenetics, fear of failure, Flash crash, Flynn Effect, friendly AI, general purpose technology, Geoffrey Hinton, Gödel, Escher, Bach, hallucination problem, Hans Moravec, income inequality, industrial robot, informal economy, information retrieval, interchangeable parts, iterative process, job automation, John Markoff, John von Neumann, knowledge worker, Large Hadron Collider, longitudinal study, machine translation, megaproject, Menlo Park, meta-analysis, mutually assured destruction, Nash equilibrium, Netflix Prize, new economy, Nick Bostrom, Norbert Wiener, NP-complete, nuclear winter, operational security, optical character recognition, paperclip maximiser, pattern recognition, performance metric, phenotype, prediction markets, price stability, principal–agent problem, race to the bottom, random walk, Ray Kurzweil, recommendation engine, reversible computing, search costs, social graph, speech recognition, Stanislav Petrov, statistical model, stem cell, Stephen Hawking, Strategic Defense Initiative, strong AI, superintelligent machines, supervolcano, synthetic biology, technological singularity, technoutopianism, The Coming Technological Singularity, The Nature of the Firm, Thomas Kuhn: the structure of scientific revolutions, time dilation, Tragedy of the Commons, transaction costs, trolley problem, Turing machine, Vernor Vinge, WarGames: Global Thermonuclear War, Watson beat the top human players on Jeopardy!, World Values Survey, zero-sum game

There are robotic pets and cleaning robots, lawn-mowing robots, rescue robots, surgical robots, and over a million industrial robots.64 The world population of robots exceeds 10 million.65 Modern speech recognition, based on statistical techniques such as hidden Markov models, has become sufficiently accurate for practical use (some fragments of this book were drafted with the help of a speech recognition program). Personal digital assistants, such as Apple’s Siri, respond to spoken commands and can answer simple questions and execute commands. Optical character recognition of handwritten and typewritten text is routinely used in applications such as mail sorting and digitization of old documents.66 Machine translation remains imperfect but is good enough for many applications.

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The DART tool for automated logistics planning and scheduling was used in Operation Desert Storm in 1991 to such effect that DARPA (the Defense Advanced Research Projects Agency in the United States) claims that this single application more than paid back their thirty-year investment in AI.68 Airline reservation systems use sophisticated scheduling and pricing systems. Businesses make wide use of AI techniques in inventory control systems. They also use automatic telephone reservation systems and helplines connected to speech recognition software to usher their hapless customers through labyrinths of interlocking menu options. AI technologies underlie many Internet services. Software polices the world’s email traffic, and despite continual adaptation by spammers to circumvent the countermeasures being brought against them, Bayesian spam filters have largely managed to hold the spam tide at bay.

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One can readily imagine improved versions of this technology—perhaps a next-generation implant could plug into Broca’s area (a region in the frontal lobe involved in language production) and pick up internal speech.73 But whilst such a technology might assist some people with disabilities induced by stroke or muscular degeneration, it would hold little appeal for healthy subjects. The functionality it would provide is essentially that of a microphone coupled with speech recognition software, which is already commercially available—minus the pain, inconvenience, expense, and risks associated with neurosurgery (and minus at least some of the hyper-Orwellian overtones of an intracranial listening device). Keeping our machines outside of our bodies also makes upgrading easier.

Rise of the Machines: A Cybernetic History by Thomas Rid

1960s counterculture, A Declaration of the Independence of Cyberspace, agricultural Revolution, Albert Einstein, Alistair Cooke, Alvin Toffler, Apple II, Apple's 1984 Super Bowl advert, back-to-the-land, Berlin Wall, Bletchley Park, British Empire, Brownian motion, Buckminster Fuller, business intelligence, Charles Babbage, Charles Lindbergh, Claude Shannon: information theory, conceptual framework, connected car, domain-specific language, Douglas Engelbart, Douglas Engelbart, Dr. Strangelove, dumpster diving, Extropian, full employment, game design, global village, Hacker News, Haight Ashbury, Herman Kahn, Howard Rheingold, Ivan Sutherland, Jaron Lanier, job automation, John Gilmore, John Markoff, John Perry Barlow, John von Neumann, Kevin Kelly, Kubernetes, Marshall McLuhan, Menlo Park, military-industrial complex, Mitch Kapor, Mondo 2000, Morris worm, Mother of all demos, Neal Stephenson, new economy, New Journalism, Norbert Wiener, offshore financial centre, oil shale / tar sands, Oklahoma City bombing, operational security, pattern recognition, public intellectual, RAND corporation, Silicon Valley, Simon Singh, Snow Crash, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, systems thinking, technoutopianism, Telecommunications Act of 1996, telepresence, The Hackers Conference, Timothy McVeigh, Vernor Vinge, We are as Gods, Whole Earth Catalog, Whole Earth Review, Y2K, Yom Kippur War, Zimmermann PGP

A third problem identified by Licklider was the speedy storage and retrieval of vast quantities of information and data. Licklider suspected that graphical interfaces and speech recognition would be highly desirable. A military commander, for instance, would need fast decisions. The notion of a ten-minute war would be overstated, yes, but it would be dangerous to assume that leaders would have more than ten minutes for critical decisions in wartime. Only speech recognition was fast enough as a human-machine interface; an officer in battle or a senior executive in a company could hardly be taken “away from his work to teach him to type,” Licklider quipped.

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Only speech recognition was fast enough as a human-machine interface; an officer in battle or a senior executive in a company could hardly be taken “away from his work to teach him to type,” Licklider quipped. It would probably take five years, he concluded in 1960, to achieve practically significant speech recognition on a “truly symbiotic level” of real-time man-machine interaction.84 In 1962, Licklider moved on to the Pentagon’s Advanced Research Projects Agency, ARPA. He became the first director of ARPA’s newly founded Information Processing Techniques Office, a research and funding organization tasked to improve military command-and-control systems. At ARPA, Licklider continued to work toward improved man-machine communication. He especially supported university-based research projects working on time-sharing over long distances, just as the air force had done.

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(Ken Goffman), 185, 218 60 Minutes, 335–37 Skipjack (Clipper algorithm), 275–76 sleep, 142–43 smart weapons, 300 Socialist Review, 151 social networking, 192 social science, 51 Solaris operating systems, 314 Solar Sunrise, 314–15 “Some Moral and Technical Consequences of Automation” (Wiener), 120–21 Sonoma State University, 222 “Sorcerer’s Apprentice, The” (Goethe), 93–95 South Carolina Research Authority, 319 Sovereign Individual, The (Rees-Mogg and Davidson), 285–87 Soviet Union Albe Archer exercise, 208 and automation, 110 collapse of, 246 first nuclear detonation, 75–76 and space race, 126, 127, 142 Sputnik I, 123 space; See also cyberspace electronic vs. outer, ix–x helmet-mounted sights, 198–206 space race, 123 space travel, 123, 125–28, 142–43 Spacewar video game, 181–84 speech recognition, 146 Sperry, Elmer Ambrose, 13–14 Sperry, Roger Wolcott, 65 Sperry cams, 22–23 Sperry Gyroscope, 13–14 and automated anti-aircraft weapons, 37–38 and automation, 110 and control systems, 36–37 Alfred Crimi’s work for, 14–16 and gun directors, 23 Spirit of St. Louis, 11 spirituality, cybernetics and, 348 Sputnik I, 123 Stalingrad, 35 Stanford University, 68, 181 Star Wars (film), 199–200, 204, 295–96 State Department, US, 276 Stenger, Nicole, 232–35 Steps to an Ecology of Mind (Bateson), 174–80, 227 Sterling, Bruce, 232, 242 Stibitz, George, 30, 33, 34 Stimpy (hacker), 315 Stimson, Henry, 74 Stoll, Clifford, 308 Stone, Allucquère Rosanne, 234 Strategic Information and Operations Center (SIOC), 321, 333 Strauss, Erwin, 287 structural unemployment, 109 Stuka dive-bombers, 24 subconscious mind, 163 subservience, intelligence and, 72 suffering, human, 90–91 Sun Microsystems, 314 super cockpit, 199, 202, 206 supercomputers, 77, 78, 146, 187, 324 surveillance, domestic vs. foreign, 273 survival, brain and, 63 symbiosis, 145–46 systems and cybernetic analysis, 51 and environment, 57–61, 63–67 taboos, 89–92 TACOM, See Tank-automotive and Armaments Command tactile sensation/feedback, 129–30, 135, 138 Tank-automotive and Armaments Command (TACOM), 132, 133, 135 Tanksley, Mike, 305 taxation, 267, 286 Technical Memo Number 82, 253 technological evolution, biological evolution and, 121–22 technology myths, xiv–xvi Telecommunications Act of 1996, 244 teledildonics, 235–37 telephone, 22 Tenenbaum, Ehud “The Analyzer,” 315 Tenet, George, 307 Terminal Compromise (Schwartau), 308 Terminator (film), 154, 155 Terminator 2 (film), 154 terms, new, 349–50 Texas Towers, 77 theodicy, 91 theoretical genetics, 119 “Theory of Self-Reproducing Automata” (von Neumann lectures), 115–16 Third Reich, 43 Thule, Greenland, early warning site, 99 thyraton tube, 27 Tibbets, Paul, 45 Tien, Lee, 270 Time magazine, 3–4, 53, 306 time-sharing, 146, 147, 182 TiNi, 241 Tizard, Sir Henry, 19 Toffler, Alvin, 308–10 Toffler, Heidi, 308–9 Tomahawk cruise missile, 303 TRADOC (US Army Training and Doctrine Command), 299; See also Field Manual (FM) 100-5 Tresh, Tom, 164–65 tribes/tribalism, 193 Trinity College, Oxford, 148 Trips Festival, 193 True Names (Vinge) and crypto anarchy, 258–59, 292–93 and cyberspace, 206–8, 212 and cypherpunk, 265, 266 and Habitat, 229 as inspiration for HavenCo Ltd., 288 Tim May and, 258–59 “Truly SAGE System, The”(Licklider), 144 Truman, Harry S., 75 TRW, 238 Tuve, Merle, 28 2001: A Space Odyssey (Clarke), 120–22 2001: A Space Odyssey (film), 149, 343 Übermensch, 140, 291 “Ultimate Offshore Startup, The” (Wired article), 289 ultraintelligent machines, 148–49 unemployment automated factories and, 109–10 automation and, 83, 100 cybernation and, 104 United Arab Emirates, 315 United Kingdom, 317 US Air Force automated defense systems, 71 cyberspace research, 196–206 forward-deployed early warning sites, 99 military cyborg research, 128–29 and virtual space, 195 US Army and cyborg research, 131–32 SCR-268 radar, 18 and V-2 missile, 43–72 US Army General Staff, 11 US Army Medical Corps, 85 US Army Natick Laboratories, 137 US Army Training and Doctrine Command (TRADOC), 299 US Court of Appeals for the Ninth Circuit, 276 US Navy Office of Naval Research, 136–37 US Pacific Command (PACOM), 311–13 US Secret Service, 238–39 unit key, 274 University of Birmingham, 19 University of California at Berkeley, 168, 172 University of Cincinnati, 317 University of Pennsylvania, 114 University of Texas, 231 University of Toronto, 316, 320 utopia in 1960s–1990s view of cybernetics, 5 dystopia vs., 6 and Halacy’s vision of cyborgs, 141 as mindset of 1950s research, 117–18 thinking machines and, 4 V-1 (Vergeltungswaffe 1) flying bomb, 39–42 V-2 ballistic missile, 43–44, 73 vacuum tubes, 27, 28, 96, 114 Valley, George, 76, 79–82 Valley Committee (Air Defense Systems Engineering Committee), 76 Vatis, Michael, 321, 334 VAX computer, 191, 194, 200 VCASS (visually coupled airborne systems simulator), 198–206 vehicles, cyborgs vs., 133 Viet Cong, 131 Vietnam War, 295 aftereffects of, 298–99 amputees, 142 and cyborg research, 131–32 and fourth-generation fighter-bombers, 197 and smart weapons, 300 Vinge, Vernor and crypto anarchy, 292–93 and cybernetic myth, 344 and cyberspace, 206–8 and cypherpunk, 265, 266 William Gibson and, 212 and Habitat, 229 as inspiration for HavenCo Ltd., 288 on limitations of IO devices, 228–29 Tim May and, 258–59 and singularity, 149 violence, 267, 285–86 Virginia Military Institute, 270 virtual reality (VR), 220–21 and Cyberthon, 240–43 Jaron Lanier and, 212–19 VCASS, 198–206 virtual space in 1980s, 195–96 and cyberwar, 304–5 and military research, 196–206 in science fiction, 206–8 viruses, 115, 150; See also computer viruses visually coupled airborne systems simulator (VCASS), 198–206 von Bertalanffy, Ludwig, 52 von Braun, Wernher, 43 von Foerster, Heinz, 52 Vonnegut, Kurt, 86–87, 129 von Neumann, John, 52 on brain–computer similarities, 114 and cybernetic myth, 344 and ENIAC, 114–15 and Player Piano, 87 and self-replicating machines, 118 virus studies, 115–16 VPL DataGlove, 226 VPL Research, 213–16, 241, 243 VR, See virtual reality VT (variable-time) fuse, 26–27, 40, 41, 67 Walhfred Anderson (fictional character), 87–88 Walker, John, 219–20, 225 “walking truck” (quadruped cyborg), 134–35 Wall Street Journal, 221 Walter, W.

The Dawn of Eurasia: On the Trail of the New World Order by Bruno Macaes

active measures, Berlin Wall, Brexit referendum, British Empire, computer vision, deep learning, Deng Xiaoping, different worldview, digital map, Donald Trump, energy security, European colonialism, eurozone crisis, failed state, Francis Fukuyama: the end of history, gentrification, geopolitical risk, global value chain, illegal immigration, intermodal, iterative process, land reform, liberal world order, Malacca Straits, mass immigration, megacity, middle-income trap, open borders, Parag Khanna, savings glut, scientific worldview, Silicon Valley, South China Sea, speech recognition, Suez canal 1869, The Brussels Effect, trade liberalization, trade route, Transnistria, young professional, zero-sum game, éminence grise

As if to confirm my theory, I would be going to the Almaty Opera that night to see a performance of Bizet’s The Pearl Fishers, whose action takes place on the beach in Ceylon and whose main character is a priestess of Brahma called Leila. 5 Chinese Dreams TECHNO ORIENTALISM ‘But you need an algorithm for English and another for Chinese in speech recognition, so machines will have their own national identity.’ My interlocutor smiled. ‘Not at all. The algorithm is pretty much universal.’ ‘What do you mean?’ ‘It learns to recognize speech and the learning process works equally for every language. Feed it the data and it will learn Latin or Sanskrit.

…

Eventually, the top layer yields the output: a dog match in the example above. In this, machine intelligence comes to resemble the way a large array of neurons works in the human brain. Speech and image recognition are among the most immediate applications of deep learning. Yuanqing told me how Baidu had been able to develop practically infallible speech recognition applications, even if the user chooses to whisper to his device rather than speak. They were now concentrating their efforts on how to apply deep learning to automated driving. Applying it to prediction systems still lies considerably in the future, but the future is getting closer each day.

…

Be that as it may, the point will serve to illustrate how two scientific civilizations may differ substantially. Patterns and rules dictated by a scientific culture are still dependent on the everyday world from which they are abstracted. If you walk in a Chinese city today, applications from deep learning can be seen all around you. Speech recognition software is so reliable that lots of young people now dictate their university essays. If you take a picture of some object that has caught your fancy, special software can take you directly to a website selling it. If you have a car accident, it is easy to pull out your smartphone, take a photo, and use image recognition to determine the damage and file an insurance claim.

The Future of the Brain: Essays by the World's Leading Neuroscientists by Gary Marcus, Jeremy Freeman

23andMe, Albert Einstein, backpropagation, bioinformatics, bitcoin, brain emulation, cloud computing, complexity theory, computer age, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data acquisition, data science, deep learning, Drosophila, epigenetics, Geoffrey Hinton, global pandemic, Google Glasses, ITER tokamak, iterative process, language acquisition, linked data, mouse model, optical character recognition, pattern recognition, personalized medicine, phenotype, race to the bottom, Richard Feynman, Ronald Reagan, semantic web, speech recognition, stem cell, Steven Pinker, supply-chain management, synthetic biology, tacit knowledge, traumatic brain injury, Turing machine, twin studies, web application

The successful phase resetting of neuronal oscillations provides time constants (or optimal temporal integration windows) for parsing and decoding speech signals. It has been shown recently in both behavioral and physiological experiments that eliminating such oscillatory phase-resetting operations compromises speech intelligibility. Such studies connect the neural infrastructure provided by neural oscillations to well-known perceptual challenges in speech recognition. An emerging generalization suggests that acoustic signals must contain an “edge,” that is, an acoustic discontinuity that the listeners use to chunk the signal at the appropriate temporal granularity. Acoustic edges in speech are likely to play an important causal role in the successful perceptual analysis of complex auditory signals, and this type of perceptual analysis is closely linked to the existence and causal force of cortical oscillations.

…

In the 1990s, journals and conferences were filled with demonstrations that showed how it was supposedly possible to capture simple cognitive and linguistic phenomena in any number of fields (such as models of how children acquired English past-tense verbs). But as Steven Pinker and I showed, the details were rarely correct empirically; more than that, nobody was ever able to turn a neural network into a functioning system for understanding language. Today neural networks have finally found a valuable home—in machine learning, especially in speech recognition and image classification, due in part to innovative work by researchers such as Geoff Hinton and Yann LeCun. But the utility of neural networks as models of mind and brain remains marginal, useful, perhaps, in aspects of low-level perception but of limited utility in explaining more complex, higher-level cognition.

…

Many neurons specialize in detecting low-level properties of images, and some neurons that are further up the chain of command represent more abstract entities, like faces versus houses, and in some instances, even particular individuals (most notoriously, Jennifer Aniston, in work by Itzhak Fried, Christof Koch, and their collaborators). The “Aniston” cells even seem to respond cross-modally, responding to written words as well as to photographs. Hierarchies of feature detectors have now also found practical application, in the modern-day neural networks that I mentioned earlier, in speech recognition and image class ific ation. So-called deep learning, for example, is a successful machine-learning variation on the theme of hierarchical feature detection, using many layers of feature detectors. But just because some of the brain is composed of feature detectors doesn’t mean that all of it is.

Where Wizards Stay Up Late: The Origins of the Internet by Katie Hafner, Matthew Lyon

air freight, Bill Duvall, Charles Babbage, Compatible Time-Sharing System, computer age, conceptual framework, Donald Davies, Douglas Engelbart, Douglas Engelbart, fault tolerance, Hush-A-Phone, information retrieval, Ivan Sutherland, John Markoff, Kevin Kelly, Leonard Kleinrock, Marc Andreessen, Menlo Park, military-industrial complex, Multics, natural language processing, OSI model, packet switching, RAND corporation, RFC: Request For Comment, Robert Metcalfe, Ronald Reagan, seminal paper, Silicon Valley, Skinner box, speech recognition, Steve Crocker, Steven Levy, The Soul of a New Machine

It could “teach the kids French” and “continue teaching them, while they sleep.” At the advertised price of $4,000, the thing seemed a steal. Phil Karlton of Carnegie-Mellon was the first to alert the Msg-Group, on May 26, 1977. His site on the ARPANET was heavily involved in exploring artificial intelligence, speech recognition, and related research problems, so he knew a thing or two about robots. The android and its inventor had attracted a fair amount of national press attention, most of it favorable. Quasar’s sales pitch had also caught the attention of Consumer Reports, which ran a skeptical item on it in the June issue, just out.

…

Brian Reid and a colleague, Mark Fox, from the Carnegie-Mellon Artificial Intelligence Lab, posted an offbeat report to everyone in the MsgGroup, giving them a personal account of their inspection of the domestic robot, “Sam Strugglegear,” at a large department store in downtown Pittsburgh. People in the research community, knowing of CMU’s pioneering AI work, had been calling the Lab to ask how it was possible for Quasar’s robot to be so much better at speech recognition than anything CMU had produced. Rising to the challenge, a four-member team from CMU had done the fieldwork. “They found a frightening sight,” reported Reid and Fox. In the men’s department, among the three-piece suits, was a five-feet-two-inch “aerosol can on wheels, talking animatedly” to a crowd.

…

Sloan School of Management smileys Snuper Computer sound localization Southern California, University of, Information Sciences Institute (ISI) Soviet Union perceived threat of nuclear attack from Sputnik launched by technological race between U.S. and space exploration Space Physics Analysis Network (SPAN) speech recognition speed-reading Sputnik, Sputnik II, Stanford Research Institute (SRI) ARPA host site at Network Information Center (NIC) at oNLine System of Stanford University Artificial Intelligence Laboratory at Business School of Computer Science Department at first network test at Medical School of Star Wars program stationary communications satellites Stefferud, Einar store-and-forward network Strategic Air Command submarines Sun (Stanford University Network) Microsystems SURAnet Sussex University Sutherland, Ivan Swarthmore College synchronizer bugs System Development Corporation (SDC) TALK tape recorders Taylor, Bob at ARPA networking concept advanced by TCP (Transmission-Control Protocol) TCP/IP telecommunications British history of long distance real-time systems of survivability of technology of teleconferences telegraphy TELENET telephone system analog signals of British circuit switching mechanism of computer network transmission through dedicated lines in direct dialing in line tests in long-distance network of service and equipment of vulnerability of see also AT&T; Bell System Teletype Model Model television Telnet Tenex Terminal IMPs, see TIPs terminals terrorist groups Texas, University of (UT) Texas Instruments Thach, Truett Thacker, Chuck Thomas, Charles Thoreau, Henry David 3Com 360 Model 50 computers Thrope, Marty Time time clocks time-sharing TIPs (Terminal IMPs) Tomlinson, Ray transistors transmission-control protocol (TCP) Trusted Information Systems Turing, Alan 2001: A Space Odyssey TX-0 TX-2 UCLA ARPANET host site at computer science department at first network test at Network Measurement Center at School of Public Health at “Under Libra: Weights and Measures” (Merrill) Ungermann-Bass Union Carbide United Nations (UN) Univac UNIX USING Utah, University of ARPANET host site at U-2 spy planes UUCP Van Nuys High School Vezza, Al Vietnam War antiwar movement against virtual reality conversations in Vittal, John Walden, Dave programming work of real-time systems expertise Walker, Steve Washington Hilton Hotel Washington University Watergate scandal Weizenbaum, Joseph Welchman, Gordon Wessler, Barry Western Union Telegraph Company Whirlwind White House Office of Telecommunications Policy at wind tunnels Wingfield, Mike Wired Wisconsin, University of Woods, Don workstation computers World War II World Wide Web Xerox Corporation Palo Alto Research Center X-Y position indicator for a display system see mouse Yale University York, Herbert

Radical Markets: Uprooting Capitalism and Democracy for a Just Society by Eric Posner, E. Weyl

3D printing, activist fund / activist shareholder / activist investor, Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, anti-communist, augmented reality, basic income, Berlin Wall, Bernie Sanders, Big Tech, Branko Milanovic, business process, buy and hold, carbon footprint, Cass Sunstein, Clayton Christensen, cloud computing, collective bargaining, commoditize, congestion pricing, Corn Laws, corporate governance, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, Donald Trump, Elon Musk, endowment effect, Erik Brynjolfsson, Ethereum, feminist movement, financial deregulation, Francis Fukuyama: the end of history, full employment, gamification, Garrett Hardin, George Akerlof, global macro, global supply chain, guest worker program, hydraulic fracturing, Hyperloop, illegal immigration, immigration reform, income inequality, income per capita, index fund, informal economy, information asymmetry, invisible hand, Jane Jacobs, Jaron Lanier, Jean Tirole, Jeremy Corbyn, Joseph Schumpeter, Kenneth Arrow, labor-force participation, laissez-faire capitalism, Landlord’s Game, liberal capitalism, low skilled workers, Lyft, market bubble, market design, market friction, market fundamentalism, mass immigration, negative equity, Network effects, obamacare, offshore financial centre, open borders, Pareto efficiency, passive investing, patent troll, Paul Samuelson, performance metric, plutocrats, pre–internet, radical decentralization, random walk, randomized controlled trial, Ray Kurzweil, recommendation engine, rent-seeking, Richard Thaler, ride hailing / ride sharing, risk tolerance, road to serfdom, Robert Shiller, Ronald Coase, Rory Sutherland, search costs, Second Machine Age, second-price auction, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, special economic zone, spectrum auction, speech recognition, statistical model, stem cell, telepresence, Thales and the olive presses, Thales of Miletus, The Death and Life of Great American Cities, The Future of Employment, The Market for Lemons, The Nature of the Firm, The Rise and Fall of American Growth, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thorstein Veblen, trade route, Tragedy of the Commons, transaction costs, trickle-down economics, Tyler Cowen, Uber and Lyft, uber lyft, universal basic income, urban planning, Vanguard fund, vertical integration, women in the workforce, Zipcar

If simple, early problems have much greater value than later, more complex ones, data will have diminishing value. However, if later, harder problems are more valuable than earlier, easier ones, then data’s marginal value may increase as more data become available. A classic example of this is speech recognition. Early ML systems for speech recognition achieved gains in accuracy more quickly than did later systems. However, a speech recognition system with all but very high accuracy is mostly useless, as it takes so much time for the user to correct the errors it makes. This means that the last few percentage points of accuracy may make a bigger difference for the value of a system than the first 90% does.

Ghost Work: How to Stop Silicon Valley From Building a New Global Underclass by Mary L. Gray, Siddharth Suri

"World Economic Forum" Davos, Affordable Care Act / Obamacare, AlphaGo, Amazon Mechanical Turk, Apollo 13, augmented reality, autonomous vehicles, barriers to entry, basic income, benefit corporation, Big Tech, big-box store, bitcoin, blue-collar work, business process, business process outsourcing, call centre, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, cognitive load, collaborative consumption, collective bargaining, computer vision, corporate social responsibility, cotton gin, crowdsourcing, data is the new oil, data science, deep learning, DeepMind, deindustrialization, deskilling, digital divide, do well by doing good, do what you love, don't be evil, Donald Trump, Elon Musk, employer provided health coverage, en.wikipedia.org, equal pay for equal work, Erik Brynjolfsson, fake news, financial independence, Frank Levy and Richard Murnane: The New Division of Labor, fulfillment center, future of work, gig economy, glass ceiling, global supply chain, hiring and firing, ImageNet competition, independent contractor, industrial robot, informal economy, information asymmetry, Jeff Bezos, job automation, knowledge economy, low skilled workers, low-wage service sector, machine translation, market friction, Mars Rover, natural language processing, new economy, operational security, passive income, pattern recognition, post-materialism, post-work, power law, race to the bottom, Rana Plaza, recommendation engine, ride hailing / ride sharing, Ronald Coase, scientific management, search costs, Second Machine Age, sentiment analysis, sharing economy, Shoshana Zuboff, side project, Silicon Valley, Silicon Valley startup, Skype, software as a service, speech recognition, spinning jenny, Stephen Hawking, TED Talk, The Future of Employment, The Nature of the Firm, Tragedy of the Commons, transaction costs, two-sided market, union organizing, universal basic income, Vilfredo Pareto, Wayback Machine, women in the workforce, work culture , Works Progress Administration, Y Combinator, Yochai Benkler

The types of micro-tasks available to workers on UHRS are not surprising if you think about the products that Microsoft sells. Workers review voice recordings, rating the sound quality of the recorded clip. They check written text to ensure it’s not peppered with adult content. Another popular task is translation. Microsoft’s strength in speech recognition and machine translation comes from the ghost work of people training algorithms with accurate data sets. They create them by listening to short audio recordings of one sentence in one language, typically English, and entering the translation of the sentence in their mother tongue in an Excel file.

…

“You’re just basically hitting two keys to either start or stop your subtitle to sync it up with the video. It’s a great program.” You could say that it’s as easy as playing a video game. Automatically recognizing and translating language looks easy in some ways because people are accustomed to the everyday nature of tools like Siri, Cortana, and Alexa. Automating human speech recognition and translation is a fundamental part of artificial intelligence that grew into a field called natural language processing. Natural language processing was helped immensely by the internet’s capacity to amass tons of examples of people writing and speaking in various languages. Yet capturing dialogue in video, particularly action scenes that change the mood and meaning of an actor’s words, remains a difficult task for a computer program to understand, let alone translate into different languages.

…

., 19 robots, xviii–xxiii Romney, Mitt, xii Rosie the Riveter, 47 S S&P Global Market Intelligence, 62 safety, workplace algorithmic cruelty, 86 Bangladesh Accord, 193–94 for full-time employment, 60, 97 Good Work Code, 157 industrial era, 45–46 unraveling of, xxiii–xxiv workspaces, 190 safety net, for workers, 189–92 Sanjay, 128–29 Sanjeev, 126 scaffolding technique, 149–50, 164, 240 n11 scams, 104, 122, 125 scheduling 80/20 rule, 103, 118 always-on workers, 104, 105, 126, 150–51, 158–59, 170, 190 control over, 96, 99–100, 108, 157 employer control over, xxvi, 48 experimentalists, 104, 126, 150–51 just-in-time scheduling, 100, 235 n11 MTurk, 5, 79 as priority, 147, 150, 155, 164 Treaty of Detroit, 48 Sears, Mark, 141, 143, 149 self-improvement, 100, 110–13 sentiment analysis, 19 Service Employees International Union, 158–59, 191 service jobs, growth of, 97 Shah, Palak, 157 shared workspaces, 180–81 Singh, Manmohan, 55 skilled work, 39, 51, 97 skills, learning, 100, 110–13 skills gap, 230 n26 Skype, 23, 132, 179 slavery, 40–41, 226 n2 Smart Glasses, 167–68 Smith, Aaron, 219 n2, 242 n2 Smith, Adam, 58 social consequences, algorithmic cruelty, 68–69 social entrepreneurship, 147–55 social environment forums as, 132–33, 164, 239 n8 job validation, 95 need for, 178–80, 233 n6 requesters on, 73–74 in workplaces, 121–23, 173–74 See also collaboration Software Technology Parks of India (STPI), 55 SpaceX, xviii Sparrow Cycling, 142 speech recognition, 30 spinning jenny, 43, 173 Star, Susan Leigh, 238 n2 Starbucks, 28, 100 Stern, Andy, 191 Strauss, Anselm, 238 n1 strikes, 47, 48 subcontracting, Industrial Revolution, 41–42 success, changing definition of, 97–98 Suchman, Lucy, 238 n3 support collaboration, 121–23, 133–37 for on-demand work, 105 as requirement, 162 of workers, 21, 140–43, 149, 240 n11 See also double bottom line; forums Suri, Siddharth, xxvii–xxix, 221 n23 surveys LeadGenius, 224 n27 market surveys, 3, 19 on payment, 90–91 as task, 87, 116, 219 n2, 242 n2 worker motivation, 100 T Taft, Robert A., 48 Taft-Hartley Act, 48–49, 54, 228 n20 Taste of the World, 14 Taylor, Frederick, 227 n6 Team Genius, 88–90 teamwork, 24, 28, 160–61, 164, 182–83 technology AI. see artificial intelligence (AI) APIs. see application programming interface (API) automation, xviii–xxiii, 173–77, 176–77, 243 n5 computers. see computers machinery, 42, 43–44, 58–59, 227 n5 paradox of automation, xxii, 36, 170, 173, 175 Technology, Entertainment and Design (TED).

Four Battlegrounds by Paul Scharre

2021 United States Capitol attack, 3D printing, active measures, activist lawyer, AI winter, AlphaGo, amateurs talk tactics, professionals talk logistics, artificial general intelligence, ASML, augmented reality, Automated Insights, autonomous vehicles, barriers to entry, Berlin Wall, Big Tech, bitcoin, Black Lives Matter, Boeing 737 MAX, Boris Johnson, Brexit referendum, business continuity plan, business process, carbon footprint, chief data officer, Citizen Lab, clean water, cloud computing, commoditize, computer vision, coronavirus, COVID-19, crisis actor, crowdsourcing, DALL-E, data is not the new oil, data is the new oil, data science, deep learning, deepfake, DeepMind, Demis Hassabis, Deng Xiaoping, digital map, digital rights, disinformation, Donald Trump, drone strike, dual-use technology, Elon Musk, en.wikipedia.org, endowment effect, fake news, Francis Fukuyama: the end of history, future of journalism, future of work, game design, general purpose technology, Geoffrey Hinton, geopolitical risk, George Floyd, global supply chain, GPT-3, Great Leap Forward, hive mind, hustle culture, ImageNet competition, immigration reform, income per capita, interchangeable parts, Internet Archive, Internet of things, iterative process, Jeff Bezos, job automation, Kevin Kelly, Kevin Roose, large language model, lockdown, Mark Zuckerberg, military-industrial complex, move fast and break things, Nate Silver, natural language processing, new economy, Nick Bostrom, one-China policy, Open Library, OpenAI, PalmPilot, Parler "social media", pattern recognition, phenotype, post-truth, purchasing power parity, QAnon, QR code, race to the bottom, RAND corporation, recommendation engine, reshoring, ride hailing / ride sharing, robotic process automation, Rodney Brooks, Rubik’s Cube, self-driving car, Shoshana Zuboff, side project, Silicon Valley, slashdot, smart cities, smart meter, Snapchat, social software, sorting algorithm, South China Sea, sparse data, speech recognition, Steve Bannon, Steven Levy, Stuxnet, supply-chain attack, surveillance capitalism, systems thinking, tech worker, techlash, telemarketer, The Brussels Effect, The Signal and the Noise by Nate Silver, TikTok, trade route, TSMC

The Chinese surveillance industry continues to grow at a breakneck pace, growing by an estimated 18 percent in 2019. Chinese firms participating in domestic surveillance are top-tier global technology companies. Hikvision specializes in surveillance cameras; telecom providers Huawei and ZTE in data storage and system integration; Alibaba in cloud computing services and big data analysis; iFLYTEK in speech recognition; SenseTime and SenseNets in facial recognition; and Huawei subsidiary HiSilicon in chips. Huawei, SenseTime, and Megvii have denied their technology is used to profile or target groups. Yet all eight technology companies are suppliers for Skynet or Sharp Eyes. Chinese facial recognition firms are some of the best in the world.

…

Inside, we were shown a small museum of iFLYTEK AI wonders: voice cloning, speech-to-text, text-to-speech, real-time language translation, and more. iFLYTEK is a global leader in AI voice technology. In 2017, they were named by MIT Technology Review the sixth smartest company in the world. In 2018, they won twelve awards in international AI and speech recognition competitions. iFLYTEK representatives gave a tour of the many applications for their AI systems, including in business, education, and health care. Their goal was to “enable machines to listen & speak, understand & think.” iFLYTEK had a voice-activated smart home system, like Amazon’s Alexa.

…

Lin spoke positively of iFLYTEK’s relationship with the government, saying that the company has joint labs with the government and that the government encourages innovation and likes iFLYTEK. Likewise, he said that the company advises the government on regulations and policy. Lin was very open about their partnership with the local government in Xinjiang on voice translation. He said that in 2011 they began a partnership with Xinjiang University to amass data on speech recognition for the Uighur language and, as a result of this partnership, had built a translation service to help Uighurs learn Chinese. He said that in Xinjiang the government was a “big customer.” Independent investigations of iFLYTEK operations in Xinjiang tell a more sinister story. iFLYTEK subsidiaries have signed deals to sell “voiceprint” collection systems to Xinjiang police and a “strategic cooperation framework agreement” with the Xinjiang prison administration bureau, according to investigations by Reuters and Human Rights Watch.

Seriously Curious: The Facts and Figures That Turn Our World Upside Down by Tom Standage

"World Economic Forum" Davos, agricultural Revolution, augmented reality, autonomous vehicles, Big Tech, blood diamond, business logic, corporate governance, CRISPR, deep learning, Deng Xiaoping, Donald Trump, Dr. Strangelove, driverless car, Elon Musk, failed state, financial independence, gender pay gap, gig economy, Gini coefficient, high net worth, high-speed rail, income inequality, index fund, industrial robot, Internet of things, invisible hand, it's over 9,000, job-hopping, Julian Assange, life extension, Lyft, M-Pesa, Mahatma Gandhi, manufacturing employment, mega-rich, megacity, Minecraft, mobile money, natural language processing, Nelson Mandela, plutocrats, post-truth, price mechanism, private spaceflight, prosperity theology / prosperity gospel / gospel of success, purchasing power parity, ransomware, reshoring, ride hailing / ride sharing, Ronald Coase, self-driving car, Silicon Valley, Snapchat, South China Sea, speech recognition, stem cell, supply-chain management, transaction costs, Uber and Lyft, uber lyft, undersea cable, US Airways Flight 1549, WikiLeaks, zoonotic diseases

Then a new approach emerged, based on machine learning – a technique in which computers are trained using lots of examples, rather than being explicitly programmed. For speech recognition, computers are fed sound files on the one hand, and human-written transcriptions on the other. The system learns to predict which sounds should result in what transcriptions. In translation, the training data are source-language texts and human-made translations. The system learns to match the patterns between them. One thing that improves both speech recognition and translation is a “language model” – a bank of knowledge about what (for example) English sentences tend to look like.

Trillion Dollar Coach: The Leadership Playbook of Silicon Valley's Bill Campbell by Eric Schmidt, Jonathan Rosenberg, Alan Eagle

Apple's 1984 Super Bowl advert, augmented reality, Ben Horowitz, cloud computing, El Camino Real, Erik Brynjolfsson, fear of failure, Jeff Bezos, longitudinal study, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Mary Meeker, Menlo Park, meta-analysis, PalmPilot, Sand Hill Road, shareholder value, Sheryl Sandberg, Silicon Valley, speech recognition, Steve Ballmer, Steve Jobs, Susan Wojcicki, Tim Cook: Apple

There was the time AT&T offered to pay tens of millions of dollars to license Tellme’s software. Tellme made the first cloud-based speech recognition platform for large businesses and provided the service that answered the phones when you called companies like FedEx, Fidelity, and American Airlines. The problem with the AT&T offer was that they wanted to create a competitive product to Tellme’s; in fact, the offer was contingent upon Tellme getting out of the cloud speech recognition business altogether. Oh, and if the deal didn’t happen, AT&T, who was at the time Tellme’s largest customer, would pull all of its business.

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

One must phrase questions in a simple form, since WolframAlpha is designed first to compute answers from tables of data, and only secondarily to attempt to handle complicated grammar. Siri processes spoken inquiries, whereas Watson processes transcribed questions. Researchers generally approach processing speech (speech recognition) as a separate problem from processing text. There is more room for error when a system attempts to transcribe spoken language before also interpreting it, as Siri does. Siri includes a dictionary of humorous canned responses. If you ask Siri about its origin with, “Who’s your daddy?” it will respond, “I know this must mean something . . . everybody keeps asking me this question.”

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“Oh dear,” says God, “I hadn’t thought of that,” and promptly disappears in a puff of logic. AI faces analogous self-destruction because, once you get a computer to do something, you’ve necessarily trivialized it. We conceive of as yet unmet “intelligent” objectives that appear big, impressive, and unwieldy, such as transcribing the spoken word (speech recognition) or defeating the world chess champion. They aren’t easy to achieve, but once we do pass such benchmarks, they suddenly lose their charm. After all, computers can manage only mechanical tasks that are well understood and well specified. You might be impressed by its lightning-fast speed, but its electronic execution couldn’t hold any transcendental or truly humanlike qualities.

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Apparently, they’ve predicted you’re going to switch to a competitor, because they are offering a huge discount on the iPhone 13. 7. Internet search. As it’s your colleague’s kid’s birthday, you query for a toy store that’s en route. Siri, available through your car’s audio, has been greatly improved—better speech recognition and proficiently tailored interaction. 8. Driver inattention. Your seat vibrates as internal sensors predict your attention has wavered—perhaps you were distracted by a personalized billboard a bit too long. 9. Collision avoidance. A stronger vibration plus a warning sound alert you to a potential imminent collision—possibly with a child running toward the curb or another car threatening to run a red light. 10.

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