speech recognition

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Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

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

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. 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.

In the speech-recognition literature, the most commonly used performance metric is “word-error rate” on large collections of short audio segments. While the word-error-rate performance of state-of-the-art speech-recognition systems applied to these collections is at or above “human level,” there are several reasons to argue that when more realistic measures are used (for example, noisy or accented speech, important words, ambiguous language), speech-recognition performance by machines is still significantly below that of humans. A good overview of some of these arguments is given in A. Hannun, “Speech Recognition Is Not Solved,” accessed Dec. 7, 2018, awni.github.io/speech-recognition.   6.  A good, though technical, overview of how modern speech-recognition algorithms work is given in J.H.L. Hansen and T. Hasan, “Speaker Recognition by Machines and Humans: A Tutorial Review,” IEEE Signal Processing Magazine 32, no. 6 (2015): 74–99.   7.  

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.


pages: 392 words: 108,745

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

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

When the founders arrived at Apple headquarters to do a demonstration, they faced a table crowded with people eager to see what they had built. 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. At least one prominent Silicon Valley investor had told the founders that the notion of talking to your phone rather than simply using an app or doing a web search was, well, stupid.

In September 2011 Amazon acquired Yap, a North Carolina–based company that specialized in cloud-based speech recognition. Engineers at Lab126—the company’s hardware skunk works in Sunnyvale, California, where the Kindle had been created—worked on designing the device itself. In 2012 Doppler added an office in Boston, which, thanks to all of the city’s academic institutions, was a hotbed of natural-language-processing talent. In October 2012 Amazon acquired a Cambridge, UK–based company called Evi, which specialized in automatically answering spoken questions. And in January 2013 Doppler bought out Ivona, a Polish company that produced synthetic computer voices. Big picture, the problems that the Doppler team had to solve could be divided into two categories. 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. “We didn’t know if we could solve it.” Rohit Prasad, a scientist whom Amazon hired in April 2013 to oversee Doppler’s natural-language processing, was uniquely qualified to help out. In the 1990s Prasad had done far-field research for the U.S. military, which wanted a system that could transcribe what everyone was saying in a meeting.


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

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

I established what was considered a “skunk works” project (an organizational term for a project off the beaten path that has little in the way of formal resources) that consisted of me, one part-time programmer, and an electrical engineer (to create the frequency filter bank). 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. HHMMs are also used in systems that understand the meaning of natural-language sentences, which represents going up the conceptual hierarchy.

This work was the predecessor to today’s widespread commercial systems that recognize and understand what we are trying to tell them (car navigation systems that you can talk to, Siri on the iPhone, Google Voice Search, and many others). 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. Each input had parameters for importance, size, and variability of size.

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. Human speech, therefore, is produced by a hierarchy of linear patterns in the brain. If we could simply examine these patterns in the brain of the person speaking, it would be a simple matter to match her new speech utterances against her brain patterns and understand what the person was saying.


pages: 696 words: 143,736

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

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

National Automated Highway System (NAHS) consortium is predicting implementation of these systems during the first decade of the twenty-first century.15 • Prediction: Continuous speech recognition (CSR) with large vocabularies for specific tasks will emerge in the early 1990s.What Happened: Whoops. Large-vocabulary domain-specific CSR did not emerge until around 1996. 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.

Fields within AI include knowledge-based systems, expert systems, pattern recognition, automatic learning, natural-language understanding, robotics, and others. 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. Big crunch A theory that the Universe will eventually lose momentum in expanding and contract and collapse in an event that is the opposite of the big bang.

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. Cybernetic artists will become increasingly commonplace starting in 2009.


pages: 301 words: 85,126

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

Air France Flight 447, Albert Einstein, Amazon Web Services, Atul Gawande, autonomous vehicles, availability heuristic, basic income, Bayesian statistics, business cycle, Cepheid variable, Checklist Manifesto, cloud computing, combinatorial explosion, computer age, computer vision, Daniel Kahneman / Amos Tversky, Donald Trump, Douglas Hofstadter, Edward Charles Pickering, Elon Musk, epigenetics, Flash crash, Grace Hopper, Gödel, Escher, Bach, Harvard Computers: women astronomers, index fund, Isaac Newton, John von Neumann, late fees, low earth orbit, Lyft, 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, p-value, pattern recognition, Pierre-Simon Laplace, ransomware, recommendation engine, Ronald Reagan, self-driving car, sentiment analysis, side project, Silicon Valley, Skype, smart cities, speech recognition, statistical model, survivorship bias, the scientific method, Thomas Bayes, Uber for X, uber lyft, universal basic income, Watson beat the top human players on Jeopardy!, young professional

This purely data-driven approach to language may seem naïve, and until recently we simply didn’t have enough data or fast-enough computers to make it work. 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? Why is data so important here—why can’t you just get a machine to follow linguistic rules that we write down explicitly, the same way you teach a third-grader to understand English grammar, or a machine to understand Python?

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. Harpy recognized exactly 1,011 words, about as many as a small toddler.

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. These systems could recognize a few thousand words, but only if you spoke … like … a … robot.


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

AI winter, Airbnb, Albert Einstein, algorithmic trading, artificial general intelligence, autonomous vehicles, barriers to entry, basic income, business cycle, cloud computing, commoditize, computer vision, corporate social responsibility, creative destruction, crony capitalism, Deng Xiaoping, deskilling, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, full employment, future of work, gig economy, Google Chrome, happiness index / gross national happiness, if you build it, they will come, ImageNet competition, 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, mandatory minimum, Mark Zuckerberg, Menlo Park, minimum viable product, natural language processing, new economy, 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, special economic zone, speech recognition, Stephen Hawking, Steve Jobs, strong AI, The Future of Employment, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, urban planning, Y Combinator

But AI experiments are perfectly replicable, and algorithms are directly comparable. They simply require those algorithms to be trained and tested on identical data sets. 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.

But when we asked him to accept our scholarship offer and become a Microsoft intern and then an employee, he declined. He wanted to start his own AI speech company. 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. It’s the kind of product that will soon revolutionize international travel, business, and culture, and unlock vast new stores of time, productivity, and creativity in the process.

While the whiz kids must complete higher-level problems that challenge them, the students who have yet to fully grasp the material are given more fundamental questions and perhaps extra drills. 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.


pages: 413 words: 119,587

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

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

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. Moreover, Cheyer had access to a team of great programmers who were equipped with the necessary skills to build an assistant.

That allowed him to plug the mainframe-based speech recognition system into his system. SRI’s speech technology—which was a research activity that had started with Shakey—would be spun out the next year as a separate start-up, Nuance Communications, which initially pioneered voice applications for call centers. He did the same with SRI handwriting recognition technologies. He built a demonstration system that used voice and pen input to approximate a software secretary. It automated calendar tasks and handled email, contact lists, and databases, and he started experimenting with virtual assistance tasks, like using maps to find restaurants and movie theaters. Cheyer walked the halls and sampled the different projects at the laboratory, like natural language understanding, speech recognition, cooperating robots, and machine vision.

“Do we think that this Knowledge Navigator vision is possible today?” he asked. “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.


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

AI winter, Albert Einstein, algorithmic trading, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, 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, delayed gratification, discovery of DNA, Donald Trump, Douglas Engelbart, Drosophila, Elon Musk, en.wikipedia.org, epigenetics, Flynn Effect, Frank Gehry, future of work, 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, John Conway, John Markoff, John von Neumann, Mark Zuckerberg, Minecraft, natural language processing, Netflix Prize, Norbert Wiener, orbital mechanics / astrodynamics, PageRank, pattern recognition, prediction markets, randomized controlled trial, recommendation engine, Renaissance Technologies, Rodney Brooks, self-driving car, Silicon Valley, Silicon Valley startup, Socratic dialogue, speech recognition, statistical model, Stephen Hawking, 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!, 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.

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. Star Trek’s Universal Translator is within our reach (figure 1.4). Why did it take so long for speech recognition and language translation by computers to reach human levels of performance?

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. Thrun, “Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks,” Nature 542, no. 7639 (2017): 115–118. 16.


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Architects of Intelligence by Martin Ford

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

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? YOSHUA BENGIO: Of course, we didn’t expect that. We’ve had a series of important and surprising breakthroughs with deep learning. I mentioned earlier that speech recognition came around 2010, and then computer vision around 2012. A couple of years later, in 2014 and 2015, we had breakthroughs in machine translation that ended up being used in Google Translate in 2016. 2016 was also the year we saw the breakthroughs with AlphaGo.

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.

Two different graduate students at Toronto showed in 2009 that you could make a better speech recognizer using deep learning. 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. MARTIN FORD: If you read the press now, you get the impression that neural networks and deep learning are equivalent to artificial intelligence—that it’s the whole field.


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

23andMe, Ada Lovelace, airport security, Al Roth, algorithmic trading, backtesting, big-box store, Black-Scholes formula, call centre, cloud computing, collateralized debt obligation, commoditize, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, Donald Trump, Douglas Hofstadter, dumpster diving, Flash crash, G4S, Gödel, Escher, Bach, High speed trading, Howard Rheingold, index fund, Isaac Newton, John Markoff, John Maynard Keynes: technological unemployment, knowledge economy, late fees, Marc Andreessen, Mark Zuckerberg, market bubble, medical residency, money market fund, Myron Scholes, Narrative Science, PageRank, pattern recognition, Paul Graham, Pierre-Simon Laplace, prediction markets, 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

“Only in this way can one hope to hear the quiet call of (marqué d’un asterisque/starred) or the whisper of (qui s’est fait bousculer/embattled),” wrote Brown in a paper summarizing their research.6 Brown and Mercer then built a set of algorithms that tried to anticipate what words would come next based on what preceded it. 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. They left IBM in 1993 for Renaissance Technologies, the hedge fund.

Inventing a better tool in this industry could be worth multiple billions of dollars every year. 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.

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. Building an algorithm with many variables is more difficult than building one to play tic-tac-toe, but the idea is identical.


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

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

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. Despite being initially sceptical, Jaitly’s boss agreed to let him try. The program outperformed the system Google had been fine-tuning for years. In 2012, Google incorporated deep learning speech recognition into its Android mobile platform.

The result is a way of turning requests into actions. ‘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. If the question is “Can I get a dinner reservation at twelve o’clock?”

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! I like all the products of the Coca-Cola Company.’) Microsoft tried its own version of a Hank-like virtual assistant with less success.


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

23andMe, Affordable Care Act / Obamacare, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, augmented reality, autonomous vehicles, bioinformatics, blockchain, cloud computing, cognitive bias, Colonization of Mars, computer age, computer vision, conceptual framework, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, dark matter, David Brooks, digital twin, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, fault tolerance, George Santayana, Google Glasses, ImageNet competition, Jeff Bezos, job automation, job satisfaction, Joi Ito, Mark Zuckerberg, medical residency, meta analysis, meta-analysis, microbiome, natural language processing, new economy, Nicholas Carr, nudge unit, pattern recognition, performance metric, personalized medicine, phenotype, placebo effect, randomized controlled trial, recommendation engine, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, speech recognition, Stephen Hawking, text mining, the scientific method, Tim Cook: Apple, War on Poverty, Watson beat the top human players on Jeopardy!, working-age population

As my friend Ben Greenberg at WebMD said, “It’s pretty darn likely that our grandkids will laugh at us for ever using a keyboard.”8 But it goes well beyond that. 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.

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. Go (Silver, Demis Hassabis) 2017—AlphaGo Zero vs. Go (Silver, Demis Hassabis) 2017—Libratus vs. poker (Noam Brown, Tuomas Sandholm) 2017—AI Now Institute launched TABLE 4.2: The AI timeline.

The other was that “at least [Deep Blue] didn’t enjoy beating me.”18 These will be important themes in our discussion of what AI can (and can’t) do for medicine. 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. The ensuing decade has seen remarkable machine performance.


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Talk on the Wild Side by Lane Greene

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

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. A lot of money and time are spent. No simple, clear, sure knowledge is gained. The work has been an experience, not an experiment. He went on to compare speech-recognition to schemes to turn water into gasoline, and said that “to sell suckers, one uses deceit and offers glamor.”3 In just over a decade, automated language processing had gone from a problem that would be solved within a few years to being equated – by a distinguished technology pioneer – with quackery, even fraud.

Here, too, computers need training data to learn from. 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.

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. This allows the software to make much better guesses about the appropriate output, by focusing on vocabulary and turns of phrase that are common to those domains.


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

3D printing, additive manufacturing, agricultural Revolution, AI winter, Airbnb, artificial general intelligence, augmented reality, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Berlin Wall, Bernie Sanders, bitcoin, blockchain, call centre, Chris Urmson, congestion charging, credit crunch, David Ricardo: comparative advantage, Douglas Engelbart, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Flynn Effect, full employment, future of work, gender pay gap, gig economy, Google Glasses, Google X / Alphabet X, 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, knowledge worker, lifelogging, lump of labour, Lyft, Marc Andreessen, Mark Zuckerberg, Martin Wolf, McJob, means of production, Milgram experiment, Narrative Science, natural language processing, new economy, Occupy movement, Oculus Rift, 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, software is eating the world, speech recognition, Stephen Hawking, Steve Jobs, TaskRabbit, technological singularity, The Future of Employment, Thomas Malthus, transaction costs, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, universal basic income, Vernor Vinge, working-age population, Y Combinator, young professional

[lxxxii] Improving that ratio is a key aim for the search giant. 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.[lxxxiii] Two years later, Microsoft researchers announced that their system – called Adam – could distinguish between the two breeds of corgi dogs.

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. “Then you teach it Mandarin: it learns Mandarin, but it also becomes better at English, and quite frankly none of us know exactly why.”[xciii] In December 2015, Baidu announced that its speech recognition system Deep Speech 2 performed better than humans with short phrases out of context.[xciv] It uses deep learning techniques to recognise Mandarin.

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

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

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. The actual probabilities in the models are trained on actual speech and language data, so the method is self-organizing. Markov modeling was one of the methods my colleagues and I used in our own speech-recognition development.171 Unlike phonetic approaches, in which specific rules about phoneme sequences are explicitly coded by human linguists, we did not tell the system that there are approximately forty-four phonemes in English, nor did we tell it what sequences of phonemes were more likely than others.

The software is not based on reproducing each individual neuron and connection, as is the cerebellum model described above, but rather the transformations performed by each region. 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.

SpamBayes spam filter, http://spambayes.sourceforge.net. 170. Lawrence R. 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. Here is the basic schema for a neural net algorithm. Many variations are possible, and the designer of the system needs to provide certain critical parameters and methods, detailed below.


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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, game design, information retrieval, iterative process, natural language processing, pattern recognition, performance metric, sentiment analysis, social web, speech recognition, statistical model, text mining

Machine Translation The holy grail of NLP applications, this was the first major area of research and engineering in the field. 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”). Document classification This is one of the most successful areas of NLP, wherein the task is to identify in which category (or bin) a document should be placed.

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. The COBUILD (Collins Birmingham University International Language Database) dictionary is published, the first based on examining usage from a large English corpus, the Bank of English.

Because of this, they attempt to embody a large cross section of existing texts, though whether they succeed in representing percentages of texts in the world is debatable (but also not terribly important). 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.


pages: 482 words: 121,173

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

Affordable Care Act / Obamacare, AI winter, airport security, Albert Einstein, augmented reality, autonomous vehicles, barriers to entry, Berlin Wall, Boeing 737 MAX, business process, call centre, Celtic Tiger, chief data officer, cloud computing, computer vision, corporate social responsibility, Donald Trump, Edward Snowden, en.wikipedia.org, immigration reform, income inequality, Internet of things, invention of movable type, invention of the telephone, Jeff Bezos, Mark Zuckerberg, minimum viable product, national security letter, natural language processing, Network effects, new economy, pattern recognition, precision agriculture, race to the bottom, ransomware, Ronald Reagan, Rubik’s Cube, school vouchers, self-driving car, Shoshana Zuboff, Silicon Valley, Skype, speech recognition, Steve Ballmer, Steve Jobs, The Rise and Fall of American Growth, Tim Cook: Apple, 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. Good, a British mathematician who worked as a cryptologist at Bletchley Park.

Similarly, machines have been able to hear since Thomas Edison invented the phonograph in 1877. But no machine could understand and transcribe as accurately as a human being. 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. The goal for both vision and speech was to enable computers to perceive the world with an accuracy rate that would match that of human beings, which is less than 100 percent.

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. Quickly, the public began to worry when articles appeared asking if an AI-based computer could think fully by itself and reason at superhuman speeds, leading to machines that could take over the world.


pages: 187 words: 55,801

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, 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, pattern recognition, profit motive, Robert Shiller, Robert Shiller, Ronald Reagan, speech recognition, talking drums, telemarketer, The Wealth of Nations by Adam Smith, working poor

They’re still learning the procedures down there and they must be having trouble with addresses. 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.

The four-year-old begins her walk with a two-dimensional set of photons projected onto her retina. 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. The schemas in Stephen Saltz’s memory are the product of extensive study and experience.

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. An everyday example is the Graffiti handwriting recognition software in the Palm OS. Like the assembly-line robot, the software’s task is simplified by requiring the user to write each letter in a certain style—a requirement that dramatically reduces the patterns the software must recognize.


pages: 416 words: 129,308

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

Airbnb, animal electricity, Apple II, Apple's 1984 Super Bowl advert, citizen journalism, Claude Shannon: information theory, computer vision, conceptual framework, Douglas Engelbart, Dynabook, Edward Snowden, Elon Musk, Ford paid five dollars a day, Frank Gehry, global supply chain, Google Earth, Google Hangouts, Internet of things, Jacquard loom, John Gruber, John Markoff, Jony Ive, 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, ride hailing / ride sharing, rolodex, Silicon Valley, Silicon Valley startup, skunkworks, Skype, Snapchat, special economic zone, speech recognition, stealth mode startup, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Tim Cook: Apple, Turing test, uber lyft, Upton Sinclair, Vannevar Bush, zero day

One of those AIs, of course, assists us with everyday routines—Siri answered one billion user requests per week in 2015, and two billion in 2016—and the other embodies our deepest fears about machine sentience gone awry. 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. If your question can be answered by the phone itself (“Would you set my alarm for eight a.m.?”)

“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? Is it kids? Is it shut-ins? “But there are certain things you see it doesn’t do right now.

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. He had difficulty with language, switching from his local dialect to English-only classes when he went to college, where professors spoke with Irish, Scottish, and Italian accents.


pages: 407 words: 104,622

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

affirmative action, Affordable Care Act / Obamacare, Albert Einstein, Andrew Wiles, automated trading system, backtesting, Bayesian statistics, beat the dealer, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, blockchain, Brownian motion, butter production in bangladesh, buy and hold, buy low sell high, Claude Shannon: information theory, computer age, computerized trading, Credit Default Swap, Daniel Kahneman / Amos Tversky, diversified portfolio, Donald Trump, Edward Thorp, Elon Musk, Emanuel Derman, endowment effect, Flash crash, George Gilder, Gordon Gekko, illegal immigration, index card, index fund, Isaac Newton, John Meriwether, John Nash: game theory, John von Neumann, Loma Prieta earthquake, Long Term Capital Management, loss aversion, Louis Bachelier, mandelbrot fractal, margin call, Mark Zuckerberg, More Guns, Less Crime, Myron Scholes, Naomi Klein, natural language processing, obamacare, p-value, pattern recognition, Peter Thiel, Ponzi scheme, prediction markets, 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 Jobs, stochastic process, the scientific method, Thomas Bayes, transaction costs, Turing machine

A hidden Markov process is one in which the chain of events is governed by unknown, underlying parameters or variables. 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.

“The Baum-Welch algorithm gets you closer to the final answer by giving you better probabilities,” Welch explains. Baum usually minimized the importance of his accomplishment. 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. The few times they asked, he told them his work was classified.

Simons needed to trust that his staffers weren’t going to take that information and run off to a competitor. 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. They’d reconsider after experiencing family upheaval, laying the groundwork for dramatic change at Jim Simons’s company, and the world as a whole


pages: 416 words: 112,268

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

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

., 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.

The field became far more mathematical. 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.

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. (There’s a reason people don’t say, “Staring out the window is the mother of invention.”)


pages: 294 words: 81,292

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

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

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). After Siri translates your query into text, its three other main talents come into play: its NLP facility, searching a vast knowledge database, and interacting with Internet search providers, such as OpenTable, Movietickets, and Wolfram|Alpha.

This book explores the plausibility of losing control of our future to machines that won’t necessarily hate us, but that will develop unexpected behaviors as they attain high levels of the most unpredictable and powerful force in the universe, levels that we cannot ourselves reach, and behaviors that probably won’t be compatible with our survival. 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.

That sounds like AGI to me. 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.


pages: 396 words: 117,149

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

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

Markov chains turn up everywhere and are one of the most intensively studied topics in mathematics, but they’re still a very limited kind of probabilistic model. 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. (How exactly to do the inference is a fascinating problem that we’ll turn to after the next section.)

“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.

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.


pages: 374 words: 114,600

The Quants by Scott Patterson

Albert Einstein, asset allocation, automated trading system, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, Black Swan, Black-Scholes formula, Blythe Masters, Bonfire of the Vanities, Brownian motion, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, 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, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, fixed income, 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, job automation, John Meriwether, John Nash: game theory, Kickstarter, law of one price, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, margin call, merger arbitrage, money market fund, Myron Scholes, NetJets, new economy, offshore financial centre, old-boy network, Paul Lévy, Paul Samuelson, Ponzi scheme, 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, Sergey Aleynikov, short selling, 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

Internet searches on any of these names will spit out a series of academic papers written in the early to mid-1990s. 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.

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. “It’s a statistical game,” said Nick Patterson, a former Renaissance analyst and trader who’d previously done work as a cryptographer for the British and U.S. governments. “You discern phenomena in the market.

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. Worried about his health, he became an avid squash player because he deduced that it was the most efficient method of exercising.


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, business intelligence, call centre, crowdsourcing, information retrieval, Internet of things, performance metric, QR code, recommendation engine, RFID, search engine result page, semantic web, Silicon Valley, social graph, social web, speech recognition, text mining, 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. 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.

What better way to grow a brand than to build personal relationships with young, affluent people before they realize their full earning—and spending—potential. 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. Morville, Peter. Callender, Jeffery. Search Patterns: Design for Discovery. O’Reilly Media, 2010.


pages: 315 words: 92,151

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

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

The 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. Many aren’t even aware that it exists. This is because the pioneers of speech recognition were so focused on the intellectual challenges of decoding speech that they forgot that such a system also needs to do something useful.

Internet video of dog saying “sausages” on YouTube at www.youtube.com/watch?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. The claim that the Eugene Goostman chatbot passed the Turing Test is described in BBC News, “Computer AI passes Turing test in ‘world first,’” accessed September 2, 2014, at www.bbc.co.uk/news/technology-27762088.

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. I have just dictated this sentence into my Mac using the built-in software, the factors as you can see it can slip up.


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

"Robert Solow", 3D printing, additive manufacturing, AI winter, Airbnb, airline deregulation, airport security, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, backtesting, barriers to entry, bitcoin, blockchain, 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, crony capitalism, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, Dean Kamen, discovery of DNA, disintermediation, disruptive innovation, distributed ledger, double helix, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, everywhere but in the productivity statistics, family office, fiat currency, financial innovation, George Akerlof, global supply chain, Hernando de Soto, hive mind, information asymmetry, Internet of things, inventory management, iterative process, Jean Tirole, Jeff Bezos, jimmy wales, John Markoff, joint-stock company, Joseph Schumpeter, Kickstarter, law of one price, longitudinal study, Lyft, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Marc Andreessen, Mark Zuckerberg, meta analysis, meta-analysis, Mitch Kapor, moral hazard, multi-sided market, Myron Scholes, natural language processing, Network effects, new economy, Norbert Wiener, Oculus Rift, PageRank, pattern recognition, peer-to-peer lending, performance metric, plutocrats, Plutocrats, precision agriculture, prediction markets, pre–internet, price stability, principal–agent problem, Ray Kurzweil, Renaissance Technologies, Richard Stallman, ride hailing / ride sharing, risk tolerance, Ronald Coase, 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, TaskRabbit, Ted Nelson, The Market for Lemons, The Nature of the Firm, Thomas Davenport, Thomas L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, ubercab, 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.

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. Their system was more accurate than professional human transcriptionists both for discussions on an assigned topic and for open-ended conversations among friends and family members.

It was able to prove thirty-eight of the theorems in the second chapter of Principia Mathematica, a landmark book on the foundations of math by Alfred North Whitehead and Bertrand Russell. 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.” The program responded with the Russian equivalent of “The whisky is agreeable, but the meat has gone bad.”


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Invisible Women by Caroline Criado Perez

Affordable Care Act / Obamacare, augmented reality, Bernie Sanders, collective bargaining, crowdsourcing, Diane Coyle, Donald Trump, falling living standards, first-past-the-post, gender pay gap, gig economy, glass ceiling, Grace Hopper, Hacker Ethic, Indoor air pollution, informal economy, lifelogging, low skilled workers, mental accounting, meta analysis, meta-analysis, Nate Silver, new economy, obamacare, Oculus Rift, offshore financial centre, pattern recognition, phenotype, post-industrial society, randomized controlled trial, remote working, Silicon Valley, Simon Kuznets, speech recognition, stem cell, Stephen Hawking, Steven Levy, the built environment, urban planning, women in the workforce, 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?

In China, women and men with smaller hands can buy the Keecoo K1 which, with its hexagonal design, is trying to account for women’s hand size: good.18 But it has less processing power and comes with in-built air-brushing: bad. Very bad. 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. Voice-recognition software in cars, for example, is meant to decrease distractions and make driving safer.

Immediately after writing these pages I was with my mother in her Volvo Cross-Country watching her try and fail to get the voice-recognition system to call her sister. 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, computer vision, continuous integration, en.wikipedia.org, general-purpose programming language, Guido van Rossum, information retrieval, Internet of things, invention of the printing press, iterative process, 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

Machine translation performed by Google Translate Over time, machine translation systems are getting better providing translations in real time as you speak or write into the application. Speech Recognition Systems This is perhaps the most difficult application for NLP. Perhaps the most difficult test of intelligence in artificial intelligence systems is the Turing Test. This test is defined as a test of intelligence for a computer. 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.

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?​ Python Implementations and Versions Installation and Setup Which Python Version?​

Dipanjan’s interests include learning about new technology, financial markets, disruptive startups, data science, and more recently, artificial intelligence and deep learning. 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. He loves heavy metal and trekking and giving back to society, which, he believes, is the task of every engineer.


pages: 1,331 words: 163,200

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

Amazon Mechanical Turk, Anton Chekhov, combinatorial explosion, computer vision, constrained optimization, correlation coefficient, crowdsourcing, don't repeat yourself, Elon Musk, en.wikipedia.org, friendly AI, ImageNet competition, information retrieval, iterative process, John von Neumann, Kickstarter, natural language processing, Netflix Prize, NP-complete, 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? Make it recognize faces? Or learn to walk around? Or maybe your company has tons of data (user logs, financial data, production data, machine sensor data, hotline stats, HR reports, etc.), and more than likely you could unearth some hidden gems if you just knew where to look; for example: Segment customers and find the best marketing strategy for each group Recommend products for each client based on what similar clients bought Detect which transactions are likely to be fraudulent Predict next year’s revenue And more Whatever the reason, you have decided to learn Machine Learning and implement it in your projects.

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). There are also many interesting websites about Machine Learning, including of course Scikit-Learn’s exceptional User Guide.

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. Obviously this technique will not scale to thousands of words spoken by millions of very different people in noisy environments and in dozens of languages.


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

Airbnb, Amazon Web Services, artificial general intelligence, autonomous vehicles, business intelligence, business process, call centre, chief data officer, computer vision, conceptual framework, en.wikipedia.org, future of work, industrial robot, Internet of things, iterative process, Jeff Bezos, job automation, Marc Andreessen, natural language processing, new economy, pattern recognition, performance metric, price discrimination, randomized controlled trial, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, skunkworks, software is eating the world, source of truth, speech recognition, statistical model, strong AI, technological singularity

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. 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.

Retrieved November 16, 2017, from http://en.wikipedia.org/wiki/Symbolic_artificial_intelligence” (4) “Le, Q.V., & Schuster, M. (2016, September 27). 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. Retrieved November 16, 2017, from http://probcomp.org/reading-list/ (9) Optical computing.


pages: 246 words: 81,625

On Intelligence by Jeff Hawkins, Sandra Blakeslee

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

The second approach is to think about the long-term trends, like Moore's Law, that can help us imagine the applications that could possibly be part of our future. 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. The recognition error rates were high.

As the conversation flows, you have no idea what it is about, but you try to pick out the words in isolation. 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. 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.

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. It won't be easy to build these machines. To fully understand human language, a machine will have to experience and learn what humans do.


pages: 304 words: 82,395

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

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. 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.

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. They may sit on untapped informational geysers. Some companies may have collected data, used it once (if at all), and just kept it around because of low storage cost—in “data tombs,” as data scientists call the places where such old info resides. Internet and technology companies are on the front lines of harnessing the data deluge, since they collect so much information just by being online and are ahead of the rest of industry in analyzing it.

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.


pages: 561 words: 120,899

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

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

As part of this endeavor, linguists like Noam Chomsky developed structural rules for English sentences, subjects, verbs, adjectives, and grammar but failed to produce an algorithm that could explain why one string of words makes an English sentence while another string does not. 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.

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.

They needed bodies of text focused on a fairly small topic, but nothing as adult as the New York Times. 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. From them, IBM extracted about three million pairs of sentences, almost 99% of which were actual translations of one another.


The Future of Technology by Tom Standage

air freight, 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, full employment, hydrogen economy, industrial robot, informal economy, information asymmetry, interchangeable parts, job satisfaction, labour market flexibility, Marc Andreessen, market design, Menlo Park, millennium bug, moral hazard, natural language processing, Network effects, new economy, Nicholas Carr, optical character recognition, railway mania, rent-seeking, RFID, 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, technology bubble, telemarketer, transcontinental railway, Y2K

But that may be promising too much, because what makes real-life assistants helpful is that they are able to make sense of their bosses’ inchoate ramblings. 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. In fact, the challenge goes beyond ai, according to Mr Norman, and to the heart of semantics.

The common strand was an attempt to capture or mimic human abilities using machines. That said, different groups of researchers attacked different problems, from speech recognition to chess playing, in different ways; ai unified the field in name only. But it was a term that captured the public’s imagination. Most researchers agree that the high-water mark for ai occurred around 1985. A public reared on science-fiction movies and excited by the growing power of home computers had high expectations. For years, ai researchers had implied that a breakthrough was just around the corner. (“Within a generation the problem of creating ‘artificial intelligence’ will be substantially solved,” Dr Minsky said in 1967.) Prototypes of medical-diagnosis programs, speech recognition software and expert systems appeared to be making progress. The 1985 conference of L 336 ROBOTS AND ARTIFICIAL INTELLIGENCE the American Association of Artificial Intelligence (aaai) was, recalls Eric Horvitz, now a researcher at Microsoft, attended by thousands of people, including many interested members of the public and entrepreneurs looking for the next big thing.

In fact, the challenge goes beyond ai, according to Mr Norman, and to the heart of semantics. 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. In the same vein, other vendors, such as Sun Microsystems, have for years been promoting radically stripped-down devices called “network computers” or “thin clients” that do nothing but access the internet, where the real action is.


pages: 239 words: 70,206

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

"Robert Solow", 23andMe, Affordable Care Act / Obamacare, Albert Einstein, big data - Walmart - Pop Tarts, bioinformatics, business cycle, business intelligence, call centre, cloud computing, computer age, conceptual framework, Credit Default Swap, crowdsourcing, Daniel Kahneman / Amos Tversky, Danny Hillis, data is the new oil, David Brooks, East Village, Edward Snowden, Emanuel Derman, Erik Brynjolfsson, everywhere but in the productivity statistics, Frederick Winslow Taylor, Google Glasses, impulse control, income inequality, indoor plumbing, industrial robot, informal economy, Internet of things, invention of writing, Johannes Kepler, John Markoff, John von Neumann, lifelogging, Mark Zuckerberg, market bubble, meta analysis, meta-analysis, money market fund, natural language processing, obamacare, pattern recognition, payday loans, personalized medicine, precision agriculture, pre–internet, Productivity paradox, RAND corporation, rising living standards, Robert Gordon, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, 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, unbanked and underbanked, underbanked, Von Neumann architecture, Watson beat the top human players on Jeopardy!

(Hammerbacher would later buy the retirement home in South Carolina for his parents, telling them, “Think of it as a good return on the investment of all your hard work.”) 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.

Our notions of “knowledge,” “meaning,” and “understanding” don’t really apply to how this technology works. 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.

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. But if they can take people’s place, does it matter?” Krugman’s tentative conversion is noteworthy because it comes from someone of his stature who has a deep understanding of the economy.


pages: 285 words: 86,853

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

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

PLoS Computational Biology 7 (6) (June 2011). doi:10.1371/journal.pcbi.1002072. Baker, C. Edwin. Media, Markets, and Democracy. 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.” Reuters, October 17, 2012. http://www.reuters.com/article/2012/10/17/us-foxconn-teenagers-idUSBRE89F1U620121017.

TODAY.com, July 8, 2014. http://www.today.com/money/netflix-tagger-shares-what-its-have-internets-dream-job-1D79900963. 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. Kim, Queena. “What Happens at Netflix When House of Cards Goes Live.” Marketplace. NPR, February 27, 2015. http://www.marketplace.org/topics/business/what-happens-netflix-when-house-cards-goes-live.

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.


pages: 193 words: 51,445

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

23andMe, 3D printing, air freight, Alfred Russel Wallace, Asilomar, autonomous vehicles, Benoit Mandelbrot, blockchain, cryptocurrency, cuban missile crisis, dark matter, decarbonisation, demographic transition, distributed ledger, double helix, effective altruism, Elon Musk, en.wikipedia.org, global village, Hyperloop, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jeff Bezos, job automation, Johannes Kepler, John Conway, life extension, mandelbrot fractal, mass immigration, megacity, nuclear winter, pattern recognition, quantitative hedge fund, Ray Kurzweil, 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, Yogi Berra

Indians now have an electronic identity card that makes it easier for them to register for welfare benefits. This card doesn’t need passwords. 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.

When the energy management of its large data farms was handed over to a machine, Google claimed energy savings of 40 percent. 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.

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.


pages: 385 words: 111,113

Augmented: Life in the Smart Lane by Brett King

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

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. 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.

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. However, the models that we have today are limited because they still don’t learn language. These algorithms don’t learn language like a human; they identify a phrase through recognition, look it up on a database and then deliver an appropriate response. Recognising speech and being able to carry on a conversation are two very different achievements.


pages: 661 words: 187,613

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

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

Consonant and vowels are being signaled simultaneously, greatly increasing the rate of phonemes per second, as I noted at the beginning of this chapter, and there are many redundant sound cues to a given phoneme. 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. Early in the history of speech research, it became clear that human listeners might somehow take advantage of their expectations of the kinds of things a speaker is likely to say.

Children zero in on a word’s meaning by exercising their “theory of mind” or intuitive psychology, deducing what a sensible speaker is probably referring to in the context. 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. Murphy, shall follow me all the days of my life,’ my tablet has changed ‘book tour’ to ‘back to work’ and ‘I truly couldn’t see’ to ‘a cruelly good emcee.’”

The sounds of language, then, are put together in several steps. 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. (If we did not, our speech would sound like the pathetic whine of a released balloon.)


pages: 523 words: 61,179

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

3D printing, AI winter, algorithmic trading, Amazon Mechanical Turk, augmented reality, autonomous vehicles, blockchain, business process, call centre, carbon footprint, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, digital twin, disintermediation, Douglas Hofstadter, en.wikipedia.org, Erik Brynjolfsson, friendly AI, future of work, industrial robot, Internet of things, inventory management, iterative process, Jeff Bezos, job automation, job satisfaction, knowledge worker, Lyft, natural language processing, personalized medicine, precision agriculture, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Rodney Brooks, Second Machine Age, self-driving car, sensor fusion, sentiment analysis, Shoshana Zuboff, Silicon Valley, software as a service, speech recognition, telepresence, telepresence robot, text mining, the scientific method, uber lyft

“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?_r=0. 27.Tim Lang, “Why Google’s PAIR Initiative to Take Bias out of AI Will Never Be Complete,” VentureBeat, July 18, 2017, https://venturebeat.com/2017/07/18/why-googles-pair-initiative-to-take-bias-out-of-ai-will-never-be-complete/.

Applications include computational speech, and audio and audiovisual processing. Speech to text. 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. Collaborative robotics (cobots). Robots that operate at slower speeds and are fitted with sensors to enable safe collaboration with human workers.

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.


pages: 626 words: 167,836

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

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

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. 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.

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.

“Germany Starts Facial Recognition Tests at Rail Station,” 2017, New York Post, December 17. 15. N. Coudray et al., 2018, “Classification and Mutation Prediction from Non–Small Cell Lung Cancer Histopathology Images Using Deep Learning,” Nature Medicine 24 (10): 1559–1567. 16. A. Esteva et al., 2017, “Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks,” Nature 542 (7639): 115. 17. W. Xiong et al., 2017, “The Microsoft 2017 Conversational Speech Recognition System,” Microsoft AI and Research Technical Report MSR-TR-2017-39, August, https://www.microsoft.com/en-us/research/wp-content/uploads/2017/08/ms_swbd17-2.pdf. 18. M. Burns, 2018, “Clinc Is Building a Voice AI System to Replace Humans in Drive-Through Restaurants,” TechCrunch, https://techcrunch.com/video/clinc-is-building-a-voice-ai-system-to-replace-humans-in-drive-through-restaurants/. 19.


pages: 339 words: 88,732

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

"Robert Solow", 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, 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, British Empire, business cycle, business intelligence, business process, call centre, Charles Lindbergh, Chuck Templeton: OpenTable:, clean water, combinatorial explosion, computer age, computer vision, congestion charging, corporate governance, creative destruction, crowdsourcing, David Ricardo: comparative advantage, digital map, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, 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, global village, 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, 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, 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, pattern recognition, Paul Samuelson, payday loans, post-work, price stability, Productivity paradox, profit maximization, Ralph Nader, Ray Kurzweil, recommendation engine, Report Card for America’s Infrastructure, Robert Gordon, Rodney Brooks, Ronald Reagan, 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 Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Tyler Cowen: Great Stagnation, Vernor Vinge, 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.

The ASCI Red was taken out of service in 2006. 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.

—Pierre Teilhard de Chardin THE PREVIOUS FIVE CHAPTERS laid out the outstanding features of the second machine age: sustained exponential improvement in most aspects of computing, extraordinarily large amounts of digitized information, and recombinant innovation. 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? Because the exponential, digital, and recombinant powers of the second machine age have made it possible for humanity to create two of the most important one-time events in our history: the emergence of real, useful artificial intelligence (AI) and the connection of most of the people on the planet via a common digital network.


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When Things Start to Think by Neil A. Gershenfeld

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

To give you an idea of the low level of that discussion, one of the most thoughtful HCI researchers, Bill Buxton (chief scientist at Silicon Graphics), is known for the insight that people have two hands. 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. So much of the frustration in using a computer is not the effort to enter the commands, it's figuring out how to tell it to do what you want, or trying to interpret just what it has done.

(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.

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

Pattern Matching 1. Symbols 2. The post/zip code problem 3. Case based reasoning 4. 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. The task of man 2. Recursive compilation 3. Quines 4. Reasoning about program logic 5. Automating program generation 6.

All of these systems require the world to be modelled as discrete symbols. Unfortunately, the real world is not neatly packaged as symbols. 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. The last program that a human need ever write is the program that can write other programs as well as people do.

Decision tables have also been effective in image analysis, although their effectiveness largely depends on the tests that can be applied to the image. 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. As small devices without keyboards such as smart phones and tablets become more powerful this technology is likely to become more widely used.


pages: 407 words: 103,501

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

Amazon Mechanical Turk, Andrew Keen, business cycle, centre right, citizen journalism, collaborative editing, computer age, computer vision, corporate governance, crowdsourcing, David Brooks, disintermediation, Frederick Winslow Taylor, 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, Mark Zuckerberg, Marshall McLuhan, means of production, meta analysis, meta-analysis, moral panic, Network effects, new economy, Nicholas Carr, PageRank, peer-to-peer, pets.com, Results Only Work Environment, Saturday Night Live, 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 Wisdom of Crowds, Thorstein Veblen, web application

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.

It’s getting smart enough to understand some things (such as where we are) without us having to tell it explicitly. And that’s just the beginning. 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. The Google Mobile Application provides one example of how such a data-driven operating system might work.

Vendors who are competing with a winnertakes-all mind-set would be advised to join together to enable systems built from the best-of-breed data subsystems of cooperating companies. >>> 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. In other cases, meaning is “taught” to the computer.


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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 Web Services, Atul Gawande, autonomous vehicles, basic income, Bernie Sanders, Black Swan, call centre, Chris Urmson, cloud computing, corporate raider, creative destruction, Danny Hillis, Donald Trump, Elon Musk, Erik Brynjolfsson, future of work, gig economy, Google Glasses, Google X / Alphabet X, income inequality, industrial robot, Internet of things, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Lyft, Marc Andreessen, Mark Zuckerberg, money market fund, natural language processing, pets.com, plutocrats, Plutocrats, race to the bottom, ride hailing / ride sharing, Sand Hill Road, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Snapchat, speech recognition, Steve Jobs, Stewart Brand, supply-chain management, Tim Cook: Apple, too big to fail, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, wealth creators, web application, Whole Earth Catalog

The papers also revealed: Peter Kafka, “Netflix Is Finally Sharing (Some of) Its Audience Numbers for Its TV Shows and Movies,” Recode, January 17, 2019. 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. Alexa and the Echo were hits: Amazon 2018 Annual Letter to Shareholders, April 11, 2019.

Around that time a Stanford computer professor, John McCarthy, coined the term “artificial intelligence.” 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.

See also Alexa Bezos and development of, 49, 111 goal of becoming part of people’s lives with, 14–15 Internet of Things and, 123–24 introduction of, 26 Prime Video use with, 26 shopping assistance with, 115–16 Amazon Flex, 23, 172–73 Amazon 4-star stores, 24, 167 AmazonFresh, 170–71, 189 Amazon Go stores, 13, 24, 111, 139–41, 143, 167 Amazonia (Marcus), 41 Amazon Lending, 13, 233–34 Amazon Music, 10, 26, 80, 97, 98, 100, 220, 260 Amazon Pay, 234 Amazon Prime, 93–105, 109 addictive nature of, 98–99 AI flywheel and, 94, 99, 102, 115 Alexa and, 115 all-you-can-eat aspect of shipping and services with, 99 Amazon ecosystem around, 94 Amazon’s income from, 154 Amazon’s sale of its own products and, 153 annual fee for, 14 Bezos’s focus on shortening delivery time by, 22 corporate synergy with other Amazon services, 98 cross-category spending in, 100 customer data from, 101 customer service and selection in, 104–5 decision to launch, 95–96 free delivery options from, 4, 10, 14, 22, 80, 98, 101, 154, 171, 186 free services with, 97, 101–2 free shipping debate over, 96–97 growth in number of members of, 94 health-care purchases and, 226 household percentage having, 15 merchant’s expenses for, 146 naming of, 96 number of shoppers in, 14 1-Click design and, 18, 98, 232–33 Prime Video and new members in, 102–3 shopping behavior change with, 98 spending by members of, 95, 100 as stand-alone business within Amazon, 97–98 streaming music service with, 26 streaming video service with, 25 Whole Foods discount with, 97, 101, 168, 260 Amazon Prime Air, 179 Amazon Prime Now, 22, 171 Amazon Restaurants food delivery business, 64 Amazon Robotics Challenge, 138 Amazon Studios, 101–2 Amazon Visa card, 234 Amazon Web Services (AWS), 51–52 Bezos’s creation of, 7, 218 Bezos’s long-term vision for, 63–64 medical records using, 225 Prime with free storage space on, 97 profitability of, 25, 64, 65 small business use of, 10–11 ambient computing, 111 American Civil Liberties Union (ACLU), 36 American Culture and Faith Institute, 240 Anderson, Chris, 18 Anderson, Joel, 186 Anderson, Sterling, 175 Andreessen, Marc, 248 Android operating system, 14, 64, 225 Android TV, 237 Ant Financial, 198, 234–35 antitrust law, 257–68, 271 academic arguments for breakup under, 258–59 Amazon lobbyists on, 247 congressional testimony on, 258 critics and proposed breakup of Amazon under, 255, 257–58, 261, 263–64, 266–67 Department of Justice review in, 257, 267 European investigations under, 259 evidence showing lack of violation of, 259–60 historical background to, 264–66 Apollo software platform, 176 Apple AI skills and customer knowledge of, 8, 114 brand value of, 16 corporate campus of, 75 economic power of, 265–66 global wealth gap and, 271 health-care innovation and, 90, 222, 225 identification with founder, 53 iOS operating system of, 225 iWatch from, 222 Jobs’s working culture at, 55 Siri voice assistant app from, 108 Apple Music, 26, 98 Apple Pay, 234, 235 Apple TV, 237 Arcadia Group, 223 Aronowitz, Nona Willis, 16–17 artificial intelligence (AI) Alexa and voice recognition and, 108–9, 111, 112–13 Amazon’s application of, 270 Amazon smart speakers and, 109–10 Bezonomics and companies’ adaptation to, 125 black box and, 91, 147 business plans driven by, 4, 6, 86, 125, 269–70 buying model and, 85–87 coining of term, 107 connected devices and, 124 disruptive nature of, 125 doctors’ diagnosis using, 27 early voice recognition and, 108 Echo’s use of, 26 expectations for future of, 112–13 flywheel model and, 5, 88. 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.


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

Debian, packet switching, peer-to-peer, peer-to-peer model, Silicon Valley, Skype, speech recognition, telemarketer

A decent sound card in a recent computer should be plenty fast enough to eliminate this problem, whether it's a genuine concern or one manufactured by the USB crowd. 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. Most lean toward the top of the range, but they're still less than 30 bucks (or so).

A decent sound card in a recent computer should be plenty fast enough to eliminate this problem, whether it's a genuine concern or one manufactured by the USB crowd. 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. Most lean toward the top of the range, but they're still less than 30 bucks (or so).

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.


pages: 742 words: 137,937

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

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

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’.

Its putative weakness lies in AI’s inability to replicate the human brain together with consciousness. 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’.

This was an exciting time for AI, the heyday of what has since been called the era of GOFAI (good old-fashioned AI). 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.


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

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

A software program using a traditional rule-based approach would falter in confusion if presented with an image of a cat riding a bicycle. 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.

Deep-learning software is breaking down decades-old barriers in artificial intelligence research, demonstrating human-level capacity to recognize objects in digital images, even when the objects are depicted in unusual contexts or in varying levels of light. 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.

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. Nvidia’s target commercial application was driverless cars.


pages: 486 words: 132,784

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, game design, Grace Hopper, Richard Feynman, Silicon Valley, skunkworks, Skype, smart transportation, speech recognition, statistical model, stealth mode startup, Steve Jobs, Steve Wozniak, the market place, Yogi Berra

Frantz: Yes, but the marketplace, as usual, doesn’t understand when it works correctly and when it doesn’t work. 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? Did the companies come to you looking for something? Or were you going to the companies, saying, “I have a great solution.”?

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. There were just too many problems. A lot of it was how you match the problem to the technology. Stern: Your career has been in a corporate setting. Can you talk about that as far as being an inventor person, and then being part of a team that went out and developed the technology or commercialized it?

advice artificial intelligence business side charge coupled device/CCD childhood days CMOS commercialization failures family background and education frustration Gentex hobby ideation process industry collaboration inspiration intellectual property iPhone Jet Propulsion Lab licensing mentors motivation NASA tech transfer plan to retire portable videophones self-funded project skill sets team effort web camera Frantz, G. 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. business plan proposal business problems CAD software consortium Consumer Product Safety Commission duct tape electronics design engineering company exclusivity failure hobby woodworkers/job shops industrial table-saw market industry influenced society inspiration/solutions inventions inventors IP process jobs Legos and built things licensing negotiations and discussions Mattel, Yakima Products motivation method nondisclosure agreement particular skill sets patent attorneys patents Paul Carter physics background portable Portland, Oregon preorders prototype provisional patent application SawStop works shockwave stuff lying table saws technical backgrounds technology trade association University of California venture capital industry woodworking Worker’s Comp, insurance costs Greiner, H.


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

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

Otherwise we imbued personality into objects via our imagination. This won’t be the case in the future. 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.

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. But Cyc still isn’t very intelligent, which is possibly why author, scientist and futurist Ray Kurzweil made a public bet with Mitchell Kapor, the founder of Lotus, that a computer would pass the Turing test by 2029.

The third revolution will be the shift from left-brain to right-brain economic production. 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. Now it’s around 40%. This may be partly because of an oversupply of MBAs in the domestic market or the outsourcing of data analysis to cheaper countries.


pages: 118 words: 35,663

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

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

Other screens designed for aiding group interactions can be positioned around the room by moving them via tracks suspended from the ceiling. 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.

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. “The excitement with machine learning is figuring out how we build a computer to recognize the things in the world and name them,” says John Smith, a senior manager in intelligent information management at IBM Research.


pages: 559 words: 157,112

Dealers of Lightning by Michael A. Hiltzik

Apple II, Apple's 1984 Super Bowl advert, beat the dealer, Bill Duvall, Bill Gates: Altair 8800, business cycle, computer age, creative destruction, Douglas Engelbart, Dynabook, Edward Thorp, El Camino Real, index card, Jeff Rulifson, John Markoff, Joseph Schumpeter, Marshall McLuhan, Menlo Park, oil shock, popular electronics, 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, Vannevar Bush, Whole Earth Catalog, zero-sum game

In a corporate memo dated January 4, 1971, he outlined an ambitious program for his group of twenty-three, augmented by another eight or ten professionals due to start work over the following few months. 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.

Thornburg was still unpacking his things on his very first day when Biegelsen, who on the strength of his three months’ tenure already ranked as a seasoned PARC veteran, showed up at his office door. “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. “I’m pleased to meet you,” his neighbor said, smiling.

At that stage nobody really knew how to do this stuff anyway, so I tended to hire people who could buy into the romance of the whole thing, because you could go a really good distance on romance.” 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. One day I walked over and said, ‘Hey, what are you up to?’ And that led to his talking about his whole picture of personal computing and how one might make a simple job of a lot of the important things through some new language.”


pages: 250 words: 73,574

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

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

This task is so vast that, in practice, it is impossible for a human. 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. The word “intelligently” is in quotation marks here for good reason: the question of whether computers can ever exhibit true intelligence is highly controversial.

For years, many believed that the intuition and insight of human chess champions would beat any computer program, which must necessarily rely on a deterministic set of rules rather than intuition. 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. Indeed, our very perceptions of these tasks have been fundamentally altered by the progress of artificial intelligence.

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. See database, table; virtual table tag Tale of Two Cities, A target value Taylor, A.


pages: 269 words: 70,543

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

Airbnb, augmented reality, autonomous vehicles, blockchain, call centre, cashless society, Chuck Templeton: OpenTable:, cloud computing, computer vision, connected car, corporate governance, cryptocurrency, data is the new oil, Deng Xiaoping, digital map, disruptive innovation, Donald Trump, El Camino Real, Elon Musk, family office, fear of failure, glass ceiling, global supply chain, income inequality, industrial robot, Internet of things, invention of movable type, Jeff Bezos, Kickstarter, knowledge worker, Lyft, Mark Zuckerberg, 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, software as a service, South China Sea, sovereign wealth fund, speech recognition, stealth mode startup, Steve Jobs, supply-chain management, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, urban planning, 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. Alipay uses facial recognition for payments, and Alibaba has an AI cloud platform called City Brain that crunches data and determines patterns for better urban planning.

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.

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.


pages: 144 words: 43,356

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

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

If it all suddenly disappeared they would notice, but its omnipresence has become unremarkable, like air. 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.

The system would test the accuracy of the linkages and the probabilities by running large sets of actual data through the model, and end up (hopefully) with a reliably predictive model. 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. The number of layers can vary too, with anything above ten layers seen as very deep learning.


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

3D printing, additive manufacturing, Airbus A320, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, asset-backed security, augmented reality, barriers to entry, 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, M-Pesa, Mark Zuckerberg, mass affluent, Metcalfe’s law, microcredit, mobile money, more computing power than Apollo, Northern Rock, Occupy movement, 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, Tim Cook: Apple, transaction costs, underbanked, US Airways Flight 1549, web application

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.

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?”


pages: 410 words: 119,823

Radical Technologies: The Design of Everyday Life by Adam Greenfield

3D printing, Airbnb, augmented reality, autonomous vehicles, bank run, barriers to entry, basic income, bitcoin, blockchain, business intelligence, business process, call centre, cellular automata, centralized clearinghouse, centre right, Chuck Templeton: OpenTable:, cloud computing, collective bargaining, combinatorial explosion, Computer Numeric Control, computer vision, Conway's Game of Life, cryptocurrency, David Graeber, dematerialisation, digital map, disruptive innovation, distributed ledger, drone strike, Elon Musk, Ethereum, ethereum blockchain, facts on the ground, fiat currency, global supply chain, global village, Google Glasses, IBM and the Holocaust, industrial robot, informal economy, information retrieval, Internet of things, James Watt: steam engine, Jane Jacobs, Jeff Bezos, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, joint-stock company, Kevin Kelly, Kickstarter, late capitalism, 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, Occupy movement, Oculus Rift, Pareto efficiency, pattern recognition, Pearl River Delta, performance metric, Peter Eisenman, Peter Thiel, planetary scale, Ponzi scheme, post scarcity, post-work, RAND corporation, recommendation engine, RFID, rolodex, Satoshi Nakamoto, self-driving car, sentiment analysis, shareholder value, sharing economy, 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, transaction costs, Uber for X, undersea cable, universal basic income, urban planning, urban sprawl, 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.

As we might by now expect of networked things, nothing about the physical form of these objects goes any way at all toward conveying their purpose or intended mode of function: Amazon’s Echo is a simple cylinder, and its Echo Dot that same cylinder hacked down to a puck, while the Google Home presents as a beveled ovoid. 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.

The sole genuine justification for AR is the idea that information is simply there, and can be assimilated without thought or effort. 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. Would either of the augmentation scenarios we explored be improved by moving the informational overlay from the phone to a wearable display?


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Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

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

Aside from the game AIs listed in Table 1, there are hearing aids with algorithms that filter out ambient noise; route-finders that display maps and offer navigation advice to drivers; recommender systems that suggest books and music albums based on a user’s previous purchases and ratings; and medical decision support systems that help doctors diagnose breast cancer, recommend treatment plans, and aid in the interpretation of electrocardiograms. 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.

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. Software using AI components is responsible for automatically approving or declining credit card transactions, and continuously monitors account activity for signs of fraudulent use.

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. But what about the dream of bypassing words altogether and establishing a connection between two brains that enables concepts, thoughts, or entire areas of expertise to be “downloaded” from one mind to another?


pages: 533

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

3D printing, additive manufacturing, affirmative action, agricultural Revolution, Airbnb, airport security, Andrew Keen, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, bitcoin, blockchain, brain emulation, British Empire, business process, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Cass Sunstein, cellular automata, cloud computing, computer age, computer vision, continuation of politics by other means, correlation does not imply causation, crowdsourcing, cryptocurrency, digital map, distributed ledger, Donald Trump, easy for humans, difficult for computers, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, Filter Bubble, future of work, 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, lifelogging, Metcalfe’s law, mittelstand, more computing power than Apollo, move fast and break things, move fast and break things, natural language processing, Network effects, new economy, night-watchman state, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, payday loans, 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 Jobs, Steve Wozniak, Steven Levy, technological singularity, the built environment, The Structural Transformation of the Public Sphere, The Wisdom of Crowds, Thomas L Friedman, universal basic income, urban planning, Watson beat the top human players on Jeopardy!, working-age population

There are thousands of activities formerly done only by humans that digital systems can now do faster, more efficiently, with greater precision, and at a different order of magnitude. 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.

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.

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.


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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, Apple II, Apple's 1984 Super Bowl advert, back-to-the-land, Berlin Wall, British Empire, Brownian motion, Buckminster Fuller, business intelligence, Charles Lindbergh, Claude Shannon: information theory, conceptual framework, connected car, domain-specific language, Douglas Engelbart, Douglas Engelbart, dumpster diving, Extropian, full employment, game design, global village, Haight Ashbury, Howard Rheingold, Jaron Lanier, job automation, John Markoff, John von Neumann, Kevin Kelly, Kubernetes, Marshall McLuhan, Menlo Park, Mitch Kapor, Mother of all demos, new economy, New Journalism, Norbert Wiener, offshore financial centre, oil shale / tar sands, pattern recognition, RAND corporation, Silicon Valley, Simon Singh, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, technoutopianism, Telecommunications Act of 1996, telepresence, The Hackers Conference, Vernor Vinge, 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. 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.

(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.


pages: 287 words: 95,152

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

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

Perhaps its heart is in the seaside and the distant ocean. 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. Some algorithms have even invented their own languages.’ I had come to the Baidu Technology Park in the Haidian District of Beijing to meet Yuanqing Lin, Director of the Baidu Institute of Deep Learning.

Each unit in that second layer then integrates its inputs from the first layer and passes the result further up. 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. ‘How would you describe what is different about the way the Chinese approach technology?’ I asked him.

It is certainly plausible to think that intense forms of social interaction have been responsible for more efficient development and diffusion processes in China. 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. One university lecturer in Chengdu is using face recognition technology, not only to register attendance but also to help determine boredom levels among his students.


pages: 336 words: 93,672

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

23andMe, Albert Einstein, bioinformatics, bitcoin, brain emulation, cloud computing, complexity theory, computer age, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data acquisition, Drosophila, epigenetics, global pandemic, Google Glasses, iterative process, 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, 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. Why is the scope of neural networks so limited if the brain itself is so obviously a neural network?

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. Some of what the brain does can’t be captured well by feature detection; for example, human beings are glorious generalizers.


pages: 352 words: 96,532

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

air freight, Bill Duvall, computer age, conceptual framework, Donald Davies, Douglas Engelbart, Douglas Engelbart, fault tolerance, Hush-A-Phone, information retrieval, John Markoff, Kevin Kelly, Leonard Kleinrock, Marc Andreessen, Menlo Park, natural language processing, packet switching, RAND corporation, RFC: Request For Comment, Robert Metcalfe, Ronald Reagan, Silicon Valley, speech recognition, Steve Crocker, Steven Levy

It came equipped with a personality and speech, so that it could “interact in any human situation.” 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. At first Quasar seemed nothing but an amusing diversion from the MsgGroup’s main business.

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. Electric motors and a system of gears moved the device’s arms. The robot seemed conversant on any subject, recognized the physical features of customers, and moved freely in any direction.

SENDMSG Sesostris I, King of Egypt SF-Lovers Shakespeare, William Sigma-7 computer IMP interface with SiliconValley simple mail transfer protocol (SMTP) Skinner, B. F. 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


pages: 463 words: 105,197

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, Branko Milanovic, business process, buy and hold, carbon footprint, Cass Sunstein, Clayton Christensen, cloud computing, collective bargaining, commoditize, Corn Laws, corporate governance, crowdsourcing, cryptocurrency, Donald Trump, Elon Musk, endowment effect, Erik Brynjolfsson, Ethereum, feminist movement, financial deregulation, Francis Fukuyama: the end of history, full employment, George Akerlof, 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, 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, Plutocrats, pre–internet, random walk, randomized controlled trial, Ray Kurzweil, recommendation engine, rent-seeking, Richard Thaler, ride hailing / ride sharing, risk tolerance, road to serfdom, Robert Shiller, Robert Shiller, Ronald Coase, Rory Sutherland, 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 Wealth of Nations by Adam Smith, Thorstein Veblen, trade route, transaction costs, trickle-down economics, Uber and Lyft, uber lyft, universal basic income, urban planning, Vanguard fund, women in the workforce, Zipcar

Just as with classical statistics, there is a second critical question that determines the marginal value of data: how important it is to solve each of the problems data allow ML to tackle. 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. The marginal value grows to the extent that it allows this last gap to be filled.


pages: 346 words: 97,330

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

Affordable Care Act / Obamacare, Amazon Mechanical Turk, augmented reality, autonomous vehicles, barriers to entry, basic income, 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, collaborative consumption, collective bargaining, computer vision, corporate social responsibility, crowdsourcing, data is the new oil, deindustrialization, deskilling, don't be evil, Donald Trump, Elon Musk, employer provided health coverage, en.wikipedia.org, equal pay for equal work, Erik Brynjolfsson, financial independence, Frank Levy and Richard Murnane: The New Division of Labor, future of work, gig economy, glass ceiling, global supply chain, hiring and firing, ImageNet competition, industrial robot, informal economy, information asymmetry, Jeff Bezos, job automation, knowledge economy, low skilled workers, low-wage service sector, market friction, Mars Rover, natural language processing, new economy, passive income, pattern recognition, post-materialism, post-work, race to the bottom, Rana Plaza, recommendation engine, ride hailing / ride sharing, Ronald Coase, 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, The Future of Employment, The Nature of the Firm, transaction costs, two-sided market, union organizing, universal basic income, Vilfredo Pareto, women in the workforce, Works Progress Administration, Y Combinator

The availability of micro-tasks to workers on UHRS around the world largely revolves around Microsoft’s immediate needs to support a range of products and services delivered to more than 20 countries in 70 languages. 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. Other common types of work on UHRS are market surveys—often restricted by demographics like age, gender, and location—and a task called “sentiment analysis.”

They can also stop and start a video as often as they need. “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).


pages: 199 words: 56,243

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, Mark Zuckerberg, Menlo Park, meta analysis, meta-analysis, Sand Hill Road, shareholder value, Silicon Valley, speech recognition, Steve Ballmer, Steve Jobs, Tim Cook: Apple

And since there were lots of strategic decisions to be made, Mike had plenty of opportunities to put into practice Bill’s recommendations to decide based on first principles. 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. The deal had the potential to be lucrative and Tellme needed the money, so some members of the team were arguing to take it.


pages: 215 words: 59,188

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

agricultural Revolution, augmented reality, autonomous vehicles, blood diamonds, corporate governance, Deng Xiaoping, Donald Trump, Elon Musk, failed state, financial independence, gender pay gap, gig economy, Gini coefficient, high net worth, income inequality, index fund, industrial robot, Internet of things, invisible hand, 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, Plutocrats, price mechanism, 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

Funding for so-called natural-language processing went into hibernation for decades, until a renaissance in the late 1980s. 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. This narrows the system’s guesswork considerably. In recent years, machine-learning approaches have made rapid progress, for three reasons.


pages: 391 words: 105,382

Utopia Is Creepy: And Other Provocations by Nicholas Carr

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

The human input is then funneled back to the computer running the program. Mechanical Turk, in essence, turns people into code. The patent, as Amazon describes it, covers “a hybrid machine/human computing arrangement which advantageously involves humans to assist a computer to solve particular tasks, allowing the computer to solve the tasks more efficiently.” It specifies several applications of such a system, including speech recognition, text classification, image recognition, image comparison, speech comparison, transcription of speech, and comparison of music samples. Amazon also notes that “those skilled in the art will recognize that the invention is not limited to the embodiments described.” The patent goes into great detail about how the system might work in evaluating the skills and performance of the “human-operated nodes.”

“Google’s engineers have found ways to put more computing power behind [machine learning] than was previously possible,” writes Simonite, “creating neural networks that can learn without human assistance and are robust enough to be used commercially, not just as research demonstrations.” The company’s new artificial-intelligence algorithms “decide for themselves which features of data to pay attention to, and which patterns matter, rather than having humans decide that, say, colors and particular shapes are of interest to software trying to identify objects.” Google has begun applying its neural nets to speech-recognition and image-recognition tasks. And, according to one of the company’s engineers, Jeff Dean, the technology can already outperform people at some jobs. “We are seeing better than human-level performance in some visual tasks,” [Dean] says, giving the example of labeling, where house numbers appear in photos taken by Google’s Street View car, a job that used to be farmed out to many humans. “They’re starting to use neural nets to decide whether [an object in an image] is a house number or not,” says Dean, and they turn out to perform better than humans.

., 144–46 targeting information through, 13–14 writing tailored to, 89 see also Google searching, ontological connotations of, 144–46 Seasteading Institute, 172 Second Life, 25–27 second nature, 179 self, technologies of the, 118, 119–20 self-actualization, 120, 340 monitoring and quantification of, 163–65 selfies, 224 self-knowledge, 297–99 self-reconstruction, 339 self-tracking, 163–65 Selinger, Evan, 153 serendipity, internet as engine of, 12–15 SETI@Home, 149 sexbots, 55 Sex Pistols, 63 sex-reassignment procedures, 337–38 sexuality, 10–11 virtual, 39 Shakur, Tupac, 126 sharecropping, as metaphor for social media, 30–31 Shelley, Percy Bysshe, 88 Shirky, Clay, 59–61, 90, 241 Shop Class as Soulcraft (Crawford), 265 Shuster, Brian, 39 sickles, 302 silence, 246 Silicon Valley: American culture transformed by, xv–xxii, 148, 155–59, 171–73, 181, 241, 257, 309 commercial interests of, 162, 172, 214–15 informality eschewed by, 197–98, 215 wealthy lifestyle of, 16–17, 195 Simonite, Tom, 136–37 simulation, see virtual world Singer, Peter, 267 Singularity, Singularitarians, 69, 147 sitcoms, 59 situational overload, 90–92 skimming, 233 “Slaves to the Smartphone,” 308–9 Slee, Tom, 61, 84 SLExchange, 26 slot machines, 218–19 smart bra, 168–69 smartphones, xix, 82, 136, 145, 150, 158, 168, 170, 183–84, 219, 274, 283, 287, 308–9, 315 Smith, Adam, 175, 177 Smith, William, 204 Snapchat, 166, 205, 225, 316 social activism, 61–62 social media, 224 biases reinforced by, 319–20 as deceptively reflective, 138–39 documenting one’s children on, 74–75 economic value of content on, 20–21, 53–54, 132 emotionalism of, 316–17 evolution of, xvi language altered by, 215 loom as metaphor for, 178 maintaining one’s microcelebrity on, 166–67 paradox of, 35–36, 159 personal information collected and monitored through, 257 politics transformed by, 314–20 scrapbooks compared to, 185–86 self-validation through, 36, 73 traditional media slow to adapt to, 316–19 as ubiquitous, 205 see also specific sites social organization, technologies of, 118, 119 Social Physics (Pentland), 213 Society for the Suppression of Unnecessary Noise, 243–44 sociology, technology and, 210–13 Socrates, 240 software: autonomous, 187–89 smart, 112–13 solitude, media intrusion on, 127–30, 253 Songza, 207 Sontag, Susan, xx SoundCloud, 217 sound-management devices, 245 soundscapes, 244–45 space travel, 115, 172 spam, 92 Sparrow, Betsy, 98 Special Operations Command, U.S., 332 speech recognition, 137 spermatic, as term applied to reading, 247, 248, 250, 254 Spinoza, Baruch, 300–301 Spotify, 293, 314 “Sprite Sips” (app), 54 Squarciafico, Hieronimo, 240–41 Srinivasan, Balaji, 172 Stanford Encyclopedia of Philosophy, 68 Starr, Karla, 217–18 Star Trek, 26, 32, 313 Stengel, Rick, 28 Stephenson, Neal, 116 Sterling, Bruce, 113 Stevens, Wallace, 158 Street View, 137, 283 Stroop test, 98–99 Strummer, Joe, 63–64 Studies in Classic American Literature (Lawrence), xxiii Such Stuff as Dreams (Oatley), 248–49 suicide rate, 304 Sullenberger, Sully, 322 Sullivan, Andrew, xvi Sun Microsystems, 257 “surf cams,” 56–57 surfing, internet, 14–15 surveillance, 52, 163–65, 188–89 surveillance-personalization loop, 157 survival, technologies of, 118, 119 Swing, Edward, 95 Talking Heads, 136 talk radio, 319 Tan, Chade-Meng, 162 Tapscott, Don, 84 tattoos, 336–37, 340 Taylor, Frederick Winslow, 164, 237–38 Taylorism, 164, 238 Tebbel, John, 275 Technics and Civilization (Mumford), 138, 235 technology: agricultural, 305–6 American culture transformed by, xv–xxii, 148, 155–59, 174–77, 214–15, 229–30, 296–313, 329–42 apparatus vs. artifact in, 216–19 brain function affected by, 231–42 duality of, 240–41 election campaigns transformed by, 314–20 ethical hazards of, 304–11 evanescence and obsolescence of, 327 human aspiration and, 329–42 human beings eclipsed by, 108–9 language of, 201–2, 214–15 limits of, 341–42 master-slave metaphor for, 307–9 military, 331–32 need for critical thinking about, 311–13 opt-in society run by, 172–73 progress in, 77–78, 188–89, 229–30 risks of, 341–42 sociology and, 210–13 time perception affected by, 203–6 as tool of knowledge and perception, 299–304 as transcendent, 179–80 Technorati, 66 telegrams, 79 telegraph, Twitter compared to, 34 telephones, 103–4, 159, 288 television: age of, 60–62, 79, 93, 233 and attention disorders, 95 in education, 134 Facebook ads on, 155–56 introduction of, 103–4, 159, 288 news coverage on, 318 paying for, 224 political use of, 315–16, 317 technological adaptation of, 237 viewing habits for, 80–81 Teller, Astro, 195 textbooks, 290 texting, 34, 73, 75, 154, 186, 196, 205, 233 Thackeray, William, 318 “theory of mind,” 251–52 Thiel, Peter, 116–17, 172, 310 “Things That Connect Us, The” (ad campaign), 155–58 30 Days of Night (film), 50 Thompson, Clive, 232 thought-sharing, 214–15 “Three Princes of Serendip, The,” 12 Thurston, Baratunde, 153–54 time: memory vs., 226 perception of, 203–6 Time, covers of, 28 Time Machine, The (Wells), 114 tools: blurred line between users and, 333 ethical choice and, 305 gaining knowledge and perception through, 299–304 hand vs. computer, 306 Home and Away blurred by, 159 human agency removed from, 77 innovation in, 118 media vs., 226 slave metaphor for, 307–8 symbiosis with, 101 Tosh, Peter, 126 Toyota Motor Company, 323 Toyota Prius, 16–17 train disasters, 323–24 transhumanism, 330–40 critics of, 339–40 transparency, downside of, 56–57 transsexuals, 337–38 Travels and Adventures of Serendipity, The (Merton and Barber), 12–13 Trends in Biochemistry (Nightingale and Martin), 335 TripAdvisor, 31 trolls, 315 Trump, Donald, 314–18 “Tuft of Flowers, A” (Frost), 305 tugboats, noise restrictions on, 243–44 Tumblr, 166, 185, 186 Turing, Alan, 236 Turing Test, 55, 137 Twain, Mark, 243 tweets, tweeting, 75, 131, 315, 319 language of, 34–36 theses in form of, 223–26 “tweetstorm,” xvii 20/20, 16 Twilight Saga, The (Meyer), 50 Twitter, 34–36, 64, 91, 119, 166, 186, 197, 205, 223, 224, 257, 284 political use of, 315, 317–20 2001: A Space Odyssey (film), 231, 242 Two-Lane Blacktop (film), 203 “Two Tramps in Mud Time” (Frost), 247–48 typewriters, writing skills and, 234–35, 237 Uber, 148 Ubisoft, 261 Understanding Media (McLuhan), 102–3, 106 underwearables, 168–69 unemployment: job displacement in, 164–65, 174, 310 in traditional media, 8 universal online library, 267–78 legal, commercial, and political obstacles to, 268–71, 274–78 universe, as memory, 326 Urban Dictionary, 145 utopia, predictions of, xvii–xviii, xx, 4, 108–9, 172–73 Uzanne, Octave, 286–87, 290 Vaidhyanathan, Siva, 277 vampires, internet giants compared to, 50–51 Vampires (game), 50 Vanguardia, La, 190–91 Van Kekerix, Marvin, 134 vice, virtual, 39–40 video games, 223, 245, 303 as addictive, 260–61 cognitive effects of, 93–97 crafting of, 261–62 violent, 260–62 videos, viewing of, 80–81 virtual child, tips for raising a, 73–75 virtual world, xviii commercial aspects of, 26–27 conflict enacted in, 25–27 language of, 201–2 “playlaborers” of, 113–14 psychological and physical health affected by, 304 real world vs., xx–xxi, 36, 62, 127–30 as restrictive, 303–4 vice in, 39–40 von Furstenberg, Diane, 131 Wales, Jimmy, 192 Wallerstein, Edward, 43–44 Wall Street, automation of, 187–88 Wall Street Journal, 8, 16, 86, 122, 163, 333 Walpole, Horace, 12 Walters, Barbara, 16 Ward, Adrian, 200 Warhol, Andy, 72 Warren, Earl, 255, 257 “Waste Land, The” (Eliot), 86, 87 Watson (IBM computer), 147 Wealth of Networks, The (Benkler), xviii “We Are the Web” (Kelly), xxi, 4, 8–9 Web 1.0, 3, 5, 9 Web 2.0, xvi, xvii, xxi, 33, 58 amorality of, 3–9, 10 culturally transformative power of, 28–29 Twitter and, 34–35 “web log,” 21 Wegner, Daniel, 98, 200 Weinberger, David, 41–45, 277 Weizenbaum, Joseph, 236 Wells, H.


pages: 502 words: 107,657

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

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

Geography—What is the biggest city in Texas? Healthcare—What country has the highest average life expectancy? 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.” This should not be taken to imply adept human language processing.

It proves that you exist, and so therefore, by your own arguments, you don’t. QED.” “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. If it’s possible, it’s not intelligent.

You accept your smartphone’s offer to read to you a text message from your wireless carrier. 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.


pages: 918 words: 257,605

The Age of Surveillance Capitalism by Shoshana Zuboff

Amazon Web Services, Andrew Keen, augmented reality, autonomous vehicles, barriers to entry, Bartolomé de las Casas, Berlin Wall, bitcoin, blockchain, blue-collar work, book scanning, Broken windows theory, California gold rush, call centre, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, choice architecture, citizen journalism, cloud computing, collective bargaining, Computer Numeric Control, computer vision, connected car, corporate governance, corporate personhood, creative destruction, cryptocurrency, dogs of the Dow, don't be evil, Donald Trump, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, Ford paid five dollars a day, future of work, game design, Google Earth, Google Glasses, Google X / Alphabet X, hive mind, impulse control, income inequality, Internet of things, invention of the printing press, invisible hand, Jean Tirole, job automation, Johann Wolfgang von Goethe, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, knowledge economy, linked data, longitudinal study, low skilled workers, Mark Zuckerberg, market bubble, means of production, multi-sided market, Naomi Klein, natural language processing, Network effects, new economy, Occupy movement, off grid, PageRank, Panopticon Jeremy Bentham, pattern recognition, Paul Buchheit, performance metric, Philip Mirowski, precision agriculture, price mechanism, profit maximization, profit motive, recommendation engine, refrigerator car, RFID, Richard Thaler, ride hailing / ride sharing, Robert Bork, Robert Mercer, Second Machine Age, self-driving car, sentiment analysis, shareholder value, Shoshana Zuboff, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, slashdot, smart cities, Snapchat, social graph, social web, software as a service, speech recognition, statistical model, Steve Jobs, Steven Levy, structural adjustment programs, The Future of Employment, The Wealth of Nations by Adam Smith, Tim Cook: Apple, two-sided market, union organizing, Watson beat the top human players on Jeopardy!, winner-take-all economy, Wolfgang Streeck

The company describes itself “at the forefront of innovation in machine intelligence,” a term in which it includes machine learning as well as “classical” algorithmic production, along with many computational operations that are often referred to with other terms such as “predictive analytics” or “artificial intelligence.” Among these operations Google cites its work on language translation, speech recognition, visual processing, ranking, statistical modeling, and prediction: “In all of those tasks and many others, we gather large volumes of direct or indirect evidence of relationships of interest, applying learning algorithms to understand and generalize.”9 These machine intelligence operations convert raw material into the firm’s highly profitable algorithmic products designed to predict the behavior of its users.

As Urs Hölzle, Google’s senior vice president of technical infrastructure, put it, “The dirty secret behind [AI] is that they require an insane number of computations to just actually train the network.” If the company had tried to process the growing computational workload with traditional CPUs, he explained, “We would have had to double the entire footprint of Google—data centers and servers—just to do three minutes or two minutes of speech recognition per Android user per day.”27 With data center construction as the company’s largest line item and power as its highest operating cost, Google invented its way through the infrastructure crisis. In 2016 it announced the development of a new chip for “deep learning inference” called the tensor processing unit (TPU). The TPU would dramatically expand Google’s machine intelligence capabilities, consume only a fraction of the power required by existing processors, and reduce both capital expenditure and the operational budget, all while learning more and faster.28 Global revenue for AI products and services is expected to increase 56-fold, from $644 million in 2016 to $36 billion in 2025.29 The science required to exploit this vast opportunity and the material infrastructure that makes it possible have ignited an arms race among tech companies for the 10,000 or so professionals on the planet who know how to wield the technologies of machine intelligence to coax knowledge from an otherwise cacophonous data continent.

From the start, Pentland understood reality mining as the gateway to a new universe of commercial opportunities. In 2004 he asserted that cell phones and other wearable devices with “computational horsepower” would provide the “foundation” for reality mining as an “exciting new suite of business applications.” The idea was always that businesses could use their privileged grasp of “reality” to shape behavior toward maximizing business objectives. He describes new experimental work in which speech-recognition technology generated “profiles of individuals based on the words they use,” thus enabling a manager to “form a team of employees with harmonious social behavior and skills.”17 In their 2006 article, Pentland and Eagle explained that their data would be “of significant value in the workplace,” and the two jointly submitted a patent for a “combined short range radio network and cellular telephone network for interpersonal communications” that would add to the stock of instruments available for businesses to mine reality.18 Eagle told Wired that year that the reality mining study represented an “unprecedented data set about continuous human behavior” that would revolutionize the study of groups and offer new commercial applications.


pages: 561 words: 157,589

WTF?: What's the Future and Why It's Up to Us by Tim O'Reilly

4chan, Affordable Care Act / Obamacare, Airbnb, Alvin Roth, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, basic income, Bernie Madoff, Bernie Sanders, Bill Joy: nanobots, bitcoin, blockchain, Bretton Woods, Brewster Kahle, British Empire, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, Chuck Templeton: OpenTable:, Clayton Christensen, clean water, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, computer vision, corporate governance, corporate raider, creative destruction, crowdsourcing, Danny Hillis, data acquisition, deskilling, DevOps, Donald Davies, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, Firefox, Flash crash, full employment, future of work, George Akerlof, gig economy, glass ceiling, Google Glasses, Gordon Gekko, gravity well, greed is good, Guido van Rossum, High speed trading, hiring and firing, Home mortgage interest deduction, Hyperloop, income inequality, index fund, informal economy, information asymmetry, Internet Archive, Internet of things, invention of movable type, invisible hand, iterative process, Jaron Lanier, Jeff Bezos, jitney, job automation, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Kelly, Khan Academy, Kickstarter, knowledge worker, Kodak vs Instagram, Lao Tzu, Larry Wall, Lean Startup, Leonard Kleinrock, Lyft, Marc Andreessen, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, McMansion, microbiome, microservices, minimum viable product, mortgage tax deduction, move fast and break things, move fast and break things, Network effects, new economy, Nicholas Carr, obamacare, Oculus Rift, packet switching, PageRank, pattern recognition, Paul Buchheit, peer-to-peer, peer-to-peer model, Ponzi scheme, race to the bottom, Ralph Nader, randomized controlled trial, RFC: Request For Comment, Richard Feynman, Richard Stallman, ride hailing / ride sharing, Robert Gordon, Robert Metcalfe, Ronald Coase, Sam Altman, school choice, Second Machine Age, secular stagnation, self-driving car, SETI@home, shareholder value, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart contracts, Snapchat, Social Responsibility of Business Is to Increase Its Profits, social web, software as a service, software patent, spectrum auction, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, strong AI, TaskRabbit, telepresence, the built environment, The Future of Employment, the map is not the territory, The Nature of the Firm, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Davenport, transaction costs, transcontinental railway, transportation-network company, Travis Kalanick, trickle-down economics, Uber and Lyft, Uber for X, uber lyft, ubercab, universal basic income, US Airways Flight 1549, VA Linux, Watson beat the top human players on Jeopardy!, We are the 99%, web application, Whole Earth Catalog, winner-take-all economy, women in the workforce, Y Combinator, yellow journalism, zero-sum game, Zipcar

The iPhone leapt to dominance in the early mobile era not just because of its touch-screen interface and sleek, innovative design but because the App Store enabled a worldwide developer community to add features in the form of apps. Social media platforms like YouTube, Facebook, Twitter, Instagram, and Snapchat all gain their power by aggregating the contributions of billions of users. When people asked me what came after Web 2.0, I was quick to answer “collective intelligence applications driven by data from sensors rather than from people typing on keyboards.” Sure enough, advances in areas like speech recognition and image recognition, real-time traffic and self-driving cars, all depend on massive amounts of data harvested from sensors on connected devices. The current race in autonomous vehicles is a race not just to develop new algorithms, but to collect larger and larger amounts of data from human drivers about road conditions, and ever-more-detailed maps of the world created by millions of unwitting contributors.

After all, it was illegal for drivers to provide “livery services” without a license. It was Sunil Paul’s efforts to get the California Public Utilities Commission to accept the model that made it thinkable. Lyft jumped on the opportunity. Uber eventually followed. A more recent demonstration of how old thinking holds back even smart entrepreneurs is how long it took for the Amazon Echo to arrive, given that speech recognition has been a feature of smartphones since the 2011 launch of Apple’s Siri intelligent agent. Yet it was Amazon’s Alexa, not Siri or Google, that brought a seemingly minor change that made all the difference: Alexa was the first smart agent always listening to your commands without the need to first touch a button. Tony Fadell, one of the creators of the original iPod and the founder and former CEO of Nest, the company bought by Google for $3.4 billion to be the heart of its push into the connected home, gave me a clue when I ribbed him about Amazon stealing a huge march on him.

It is something profoundly different. In their 2009 paper, “The Unreasonable Effectiveness of Data,” (a homage in its title to Eugene Wigner’s classic 1960 talk, “The Unreasonable Effectiveness of Mathematics in the Natural Sciences”), Google machine learning researchers Alon Halevy, Peter Norvig, and Fernando Pereira explained the growing effectiveness of statistical methods in solving previously difficult problems such as speech recognition and machine translation. Much of the previous work had been grammar based. Could you construct what was in effect a vast piston engine that used its knowledge of grammar rules to understand human speech? Success had been limited. But that changed as more and more documents came online. A few decades ago, researchers relied on carefully curated corpora of human speech and writings that, at most, contained a few million words.


pages: 228 words: 65,953

The Six-Figure Second Income: How to Start and Grow a Successful Online Business Without Quitting Your Day Job by David Lindahl, Jonathan Rozek

bounce rate, California gold rush, Charles Lindbergh, financial independence, Google Earth, new economy, speech recognition, There's no reason for any individual to have a computer in his home - Ken Olsen

Some people have a way of engaging and asking questions that’s far more productive than other people who might as well be potted plants. You have three options for taking that recorded content and making it usable. First, you can simply listen to it and write down the sections you find the most helpful. I’m not a fan of that method but you may be. Second, you can use a tool like Dragon Naturally Speaking to have the computer transcribe your words. Speech-recognition software seems to be getting better by the month. What formerly was a pretty cumbersome and inaccurate process of using software to create transcripts has now become fairly workable. With the recent generation of software, the more you train it to understand your voice the more accurate it becomes. Third, you can hire a person or company to create a transcript of your audio. It’s the least amount of work for you and you’ll end up with a highly accurate printed version of what you covered.

See also Live events boot camps live tours and lunch/dinner one-day teleseminars/webinars videos of Shopping carts Size of business Slide charts Snagit software Snowball microphone Social media. See also Blogs Software Camtasia as content delivery tool domain name permutation software Dragon Naturally Speaking FTP tools HTML editing tools iPhone applications Joomla Microsoft Word MindManager shopping cart software Snagit speech-recognition software trial software Spam Special reports Specificity of claims Success advice on conventional approach to effort needed for excuses for lack of false barriers to implementation leading to proof of real dangers to Sullivan, Anthony “Sully,” Target market Techsmith.com Telephone services consulting hotlines customer contact by telephone iPhone applications teleseminars/webinars toll-free 24/7 recorded lines Templates, web site Terminology.


pages: 205 words: 20,452

Data Mining in Time Series Databases by Mark Last, Abraham Kandel, Horst Bunke

call centre, computer vision, discrete time, G4S, information retrieval, iterative process, NP-complete, p-value, pattern recognition, random walk, sensor fusion, speech recognition, web application

The similarity model is based on the Euclidean distance and they extend the ideas presented by Faloutsos et al. in [16], by computing the distances between multidimensional Minimum Bounding Rectangles. Another, more flexible way to describe similar but out-of-phase sequences can be achieved by using the Dynamic Time Warping (DTW) [38]. Berndt and Clifford [5] were the first that introduced this measure in the datamining community. Recent works on DTW are [27,31,45]. DTW has been first used to match signals in speech recognition [38]. DTW between two sequences A and B is described by the following equation: DTW (A, B) = Dbase (am , bn ) + min{DTW (Head(A), Head(B)), DTW (Head(A), B), DTW (A, Head(B))} where Dbase is some Lp Norm and Head(A) of a sequence A are all the elements of A except for am , the last one. Indexing Time-Series under Conditions of Noise 71 Fig. 2. The support of out-of-phase matching is important.

Some experimental results will be reported to demonstrate the median concept and to compare some of the considered algorithms. Keywords: String distance; set median string; generalized median string; online handwritten digits. 1. Introduction Strings provide a simple and yet powerful representation scheme for sequential data. In particular time series can be effectively represented by strings. Numerous applications have been found in a broad range of fields including computer vision [2], speech recognition, and molecular biology [13,34]. 173 174 X. Jiang, H. Bunke and J. Csirik A large number of operations and algorithms have been proposed to deal with strings [1,5,13,34,36]. Some of them are inherent to the special nature of strings such as the shortest common superstring and the longest common substring, while others are adapted from other domains. In data mining, clustering and machine learning, a typical task is to represent a set of (similar) objects by means of a single prototype.


pages: 259 words: 67,456

The Mythical Man-Month by Brooks, Jr. Frederick P.

finite state, HyperCard, Menlo Park, sorting algorithm, speech recognition, Steve Jobs, Tacoma Narrows Bridge, Turing machine

Unfortunately I cannot identify a body of technology that is unique to this field. . . . Most of the work is problem-specific, and some abstraction or creativity is required to see how to transfer it.5 I agree completely with this critique. The techniques used for speech recognition seem to have little in common with those used for image recognition, and both are different from those used in expert systems. I have a hard time seeing how image recognition, for example, will make any appreciable difference in programming practice. The same is true of speech recognition. The hard thing about building software is deciding what to say, not saying it. No facilitation of expression can give more than marginal gains. Expert systems technology, AI-2, deserves a section of its own. Expert systems. The most advanced part of the artificial intelligence art, and the most widely applied, is the technology for building expert systems.


Cartesian Linguistics by Noam Chomsky

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

Teuber, “Perception,” in the Handbook of Physiology, Neurophysiology, ed. J. Field, H. W. Magoun, V. E. Hall (Washington: American Physiological Society, 1960), vol III, chap. LXV. [Scientific research on perception since 140 Notes 1966 continues this theme; the literature is now massive. Chomsky sometimes refers to Marr 1981.] 124. For discussion and references in the areas of phonology and syntax respectively, see M. Halle and K. N. Stevens, “Speech Recognition: A Model and a Program for Research,” in Fodor and Katz (eds.), op. cit.; and G. A. Miller and N. Chomsky, “Finitary Models of Language Users,” part 2, in Handbook of Mathematical Psychology, ed. R. D. Luce, R. Bush, and E. Galanter (New York: John Wiley, 1963), vol. II.. 141 Bibliography Aarslef, H. “Leibniz on Locke on Language,” American Philosophical Quarterly, vol. 1, 1964, pp. 1–24, .

Mahwah, NJ: L. Erlbaum. Grammont, M. “Review of A. Gregoire, ‘Petit traité de linguistique,’” Revue des langues romanes, vol. 60, 1920. ———. Traité de phonétique, Delagrave, Paris, 1933. Gunderson, K. “Descartes, La Mettrie, Language and Machines,” Philosophy, vol. 39, 1964. Gysi, L. Platonism and Cartesianism in the Philosophy of Ralph Cudworth. Herbert Lang, Bern, 1962. Halle, M., and K. N. Stevens. “Speech Recognition: A Model and a Program for Research,” in Fodor and Katz, Structure of Language. Harnois, G. “Les théories du langage en France de 1660 à 1821,” Études Françaises, vol. 17, 1929. Harris, J. Works, ed. Earl of Malmesbury. London, 1801. Harris, Z. S. “Co-occurrence and Transformation in Linguistic Structure,” Language, vol. 33, 1957. pp. 283–340. Repr. in Fodor and Katz, Structure of Language.


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Coders at Work by Peter Seibel

Ada Lovelace, bioinformatics, cloud computing, Conway's Game of Life, domain-specific language, don't repeat yourself, Donald Knuth, fault tolerance, Fermat's Last Theorem, Firefox, George Gilder, glass ceiling, Guido van Rossum, HyperCard, information retrieval, Larry Wall, loose coupling, Marc Andreessen, Menlo Park, Metcalfe's law, Perl 6, premature optimization, publish or perish, random walk, revision control, Richard Stallman, rolodex, Ruby on Rails, Saturday Night Live, side project, slashdot, speech recognition, the scientific method, Therac-25, Turing complete, Turing machine, Turing test, type inference, Valgrind, web application

In the process of going around to these service bureaus, I wound up at the CDC service bureau in Stanford industrial park—typically you're working late at night because that's when it was less expensive—there was another guy there who had a Fortran program to do speech recognition. He had various speech samples and his program analyzed the spectra and grouped the phonemes and stuff like that. I started talking to him and I said, “Well, jeez, you want to run my program on yours?” So we did that and parted company. He called me up a couple of weeks later and said, “I've been hired by Xerox to do a speech-recognition project and I've got no one to help me with the nitty-gritty; would you like to work with me?” So I started consulting with him. That was George White, who went on for a long time to do speech recognition. That's how I got in with Xerox and also with Alan Kay, because it turned out that my office was across the hall from Alan's and I kept hearing conversations that I was more interested in than speech recognition. Seibel: Was the domain of speech recognition not that interesting or was it something about the programming involved?

Seibel: Was the domain of speech recognition not that interesting or was it something about the programming involved? Ingalls: Oh, it was interesting—it was fascinating. I ended up building up a whole personal-computing environment on this Sigma 3 minicomputer. It used card decks and Fortran was the main thing I had to work with. Out of that I built an interactive environment. I wrote a text editor in Fortran and then something so we could start submitting stuff remotely from a terminal. It wound up being a nice little computing environment, just done in this sort of strange way. Seibel: This desire for an interactive environment is a theme that's come up a bunch of times in your career. For example, you wrote the first Smalltalk in BASIC because that was the interactive environment you had at hand.


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The New Digital Age: Transforming Nations, Businesses, and Our Lives by Eric Schmidt, Jared Cohen

access to a mobile phone, additive manufacturing, airport security, Amazon Mechanical Turk, Amazon Web Services, anti-communist, augmented reality, Ayatollah Khomeini, barriers to entry, bitcoin, borderless world, call centre, Chelsea Manning, citizen journalism, clean water, cloud computing, crowdsourcing, data acquisition, Dean Kamen, drone strike, Elon Musk, failed state, fear of failure, Filter Bubble, Google Earth, Google Glasses, hive mind, income inequality, information trail, invention of the printing press, job automation, John Markoff, Julian Assange, Khan Academy, Kickstarter, knowledge economy, Law of Accelerating Returns, market fundamentalism, means of production, MITM: man-in-the-middle, mobile money, mutually assured destruction, Naomi Klein, Nelson Mandela, offshore financial centre, Parag Khanna, peer-to-peer, peer-to-peer lending, personalized medicine, Peter Singer: altruism, Ray Kurzweil, RFID, Robert Bork, self-driving car, sentiment analysis, Silicon Valley, Skype, Snapchat, social graph, speech recognition, Steve Jobs, Steven Pinker, Stewart Brand, Stuxnet, The Wisdom of Crowds, upwardly mobile, Whole Earth Catalog, WikiLeaks, young professional, zero day

All digital platforms will forge a common policy with respect to dangerous extremist videos online, just as they have coalesced in establishing policies governing child pornography. There is a fine line between censorship and security, and we must create safeguards accordingly. The industry will work as a whole to develop software that more effectively identifies videos with terrorist content. Some in the industry may even go so far as employing speech-recognition software that registers strings of keywords, or facial-recognition software that identifies known terrorists. Terrorism, of course, will never disappear, and it will continue to have a destructive impact. But as the terrorists of the future are forced to live in both the physical and the virtual world, their model of secrecy and discretion will suffer. There will be more digital eyes watching, more recorded interactions, and, as careful as even the most sophisticated terrorists are, even they cannot completely hide online.

They never know how long they’ll be in one place, when food will arrive or how to get some, where they can find firewood, water and health services, and what the security threats are. With registration and specialized platforms to address these concerns, IDPs will be able to receive alerts, navigate their new environment, and receive supplies and benefits from international aid organizations on the scene. Facial-recognition software will be heavily used to find lost or missing persons. With speech-recognition technology, illiterate users will be able to speak the names of relatives and the database will report if they are in the camp system. Online platforms and mobile phones will allow refugee camps to classify and organize their members according to their skills, backgrounds and interests. In today’s refugee camps, there are large numbers of people with relevant and needed skills (doctors, teachers, soccer coaches) whose participation is only leveraged in an ad hoc manner, mobilized slowly through word-of-mouth networks throughout the camps.

., itr.1, 5.1, 5.2 in schools selective memory self-control self-driving cars, itr.1, 1.1, 1.2 September 11, 2001, terrorist attacks of, 3.1, 5.1 Serbia, 4.1, 6.1 servers Shafik, Ahmed shanzhai network, 1.1 sharia Shia Islam Shia uprising Shiites Shock Doctrine, The (Klein), 7.1n short-message-service (SMS) platform, 4.1, 7.1 Shukla, Prakash Sichuan Hongda SIM cards, 5.1, 5.2, 5.3, 6.1, 6.2, nts.1 Singapore, 2.1, 4.1 Singer, Peter, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8 singularity SkyGrabber Skype, 2.1, 2.2, 2.3, 3.1, 5.1 sleeping rhythms Slim Helú, Carlos smart phones, itr.1, 1.1, 1.2, 5.1, 5.2, 7.1 in failed states peer-to-peer capability on Snapchat Snoad, Nigel social networking, 2.1, 4.1, 5.1 social-networking profiles social prosthetics social robots “socioeconomically at risk” people Solidarity Somalia, 2.1, 5.1, 5.2, 5.3, 6.1n, 210, 7.1, 7.2, 7.3 Sony South Africa, 4.1, 7.1 South Central Los Angeles Southern African Development Community (SADC) South Korea, 3.1, 3.2 South Sudan Soviet Union, 4.1, 6.1 Spain Speak2Tweet Special Weapons Observation Reconnaissance Detection System (SWORDS), 6.1, 6.2 speech-recognition technology spoofing Spotify Sputnik spyware, 3.1, 6.1 Stanford University statecraft State Department, U.S., 5.1, 7.1 states: ambition of future of Storyful, n Strategic Arms Limitation Talks (SALT) Stuxnet worm, 3.1, 3.2 suborbital space travel Sudan suggestion engines Summit Against Violent Extremism Sunni Web supersonic tube commutes supplements supply chains Supreme Council of the Armed Forces (SCAF) surveillance cameras Sweden switches Switzerland synthetic skin grafts Syria, 2.1, 3.1, 4.1, 4.2 uprising in Syrian Telecommunications Establishment tablets, 1.1, 1.2, 7.1 holographic Tacocopter Tahrir Square, 4.1, 4.2, 4.3 Taiwan Taliban, 2.1, 5.1, 7.1 TALON Tanzania technology companies, 2.1, 3.1 Tehran Telecom Egypt telecommunications, reconstruction of telecommunications companies Télécoms Sans Frontières television terrorism, terrorists, 4.1, 5.1, con.1 chat rooms of connectivity and cyber, 3.1n, 153–5, 5.1 hacking by Thailand Thomson Reuters Foundation thought-controlled robotic motion 3-D printing, 1.1, 2.1, 2.2, 5.1 thumbprints Tiananmen Square protest, 3.1, 4.1 Tibet time zones tissue engineers to-do lists Tor service, 2.1, 2.2, 2.3, 3.1, 5.1n Total Information Awareness (TIA) trade transmission towers transparency, 2.1, 4.1 “trespass to chattels” tort, n Trojan horse viruses, 2.1, 3.1 tsunami Tuareg fighters Tumblr Tunisia, 4.1, 4.2, 4.3, 4.4, 4.5 Turkey, 3.1, 3.2, 4.1, 5.1, 6.1 Tutsis Twa Twitter, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 3.1, 3.2, 4.1, 4.2, 5.1, 5.2, 6.1, 7.1, 7.2, nts.1 Uganda Uighurs, 3.1, 6.1 Ukraine unemployment UNESCO World Heritage Centre unique identification (UID) program United Arab Emirates, 2.1, 2.2, 2.3 United Kingdom, 2.1, 2.2, 2.3, 3.1 United Nations, 4.1, 5.1, 6.1, 7.1 United Nations Security Council, 3.1n, 214, 7.1 United Russia party United States, 3.1, 3.2, 3.3, 4.1, 5.1, 7.1 engineering sector in United States Agency for International Development (USAID) United States Cyber Command (USCYBERCOM) unmanned aerial vehicles (UAVs), 6.1, 6.2, 6.3, 6.4, 6.5 Ürümqi riots user-generated content Ushahidi vacuuming, 1.1, 1.2 Valspar Corporation Venezuela, 2.1, 2.2, 6.1 verification video cameras video chats video games videos Vietcong Vietnam vigilantism violence virtual espionage virtual governance virtual identities, itr.1, 2.1, 2.2 virtual juvenile records virtual kidnapping virtual private networks (VPNs), 2.1, 3.1 virtual reality virtual statehood viruses vitamins Vodafone, 4.1, 7.1 Vodafone/Raya voice-over-Internet-protocol (VoIP) calls, 2.1, 5.1 voice-recognition software, 1.1, 2.1, 5.1 Voilà VPAA statute, n Walesa, Lech walled garden Wall Street Journal, 97 war, itr.1, itr.2, 6.1 decline in Wardak, Abdul Rahim warfare: automated remote warlords, 2.1, 2.2 Watergate Watergate break-in Waters, Carol weapons of mass destruction wearable technology weibos, 62 Wen Jiabao Wenzhou, China West Africa whistle-blowers whistle-blowing websites Who Controls the Internet?


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

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

And it will be the actions of autonomous machines that will most drastically change the world—and perhaps what lies beyond. A TURNING POINT IN ARTIFICIAL INTELLIGENCE STEVE OMOHUNDRO Scientist, Self-Aware Systems; cofounder, Center for Complex Systems Research, University of Illinois Last year appears to have been a turning point for AI and robotics. Major corporations invested billions of dollars in these technologies. AI techniques, like machine learning, are now routinely used for speech recognition, translation, behavior modeling, robotic control, risk management, and other applications. McKinsey predicts that these technologies will create more than $50 trillion of economic value by 2025. If this is accurate, we should expect dramatically increased investment soon. The recent successes are being driven by cheap computer power and plentiful training data. Modern AI is based on the theory of “rational agents,” arising from work on microeconomics in the 1940s by John von Neumann and others.

What was needed was not only much more computer power but also a lot more data to train the network. After thirty years of research, a million-times improvement in computer power, and vast data sets from the Internet, we now know the answer to this question: Neural networks scaled up to twelve layers deep, with billions of connections, are outperforming the best algorithms in computer vision for object recognition and have revolutionized speech recognition. It’s rare for any algorithm to scale this well, which suggests that they may soon be able to solve even more difficult problems. Recent breakthroughs have been made that allow the application of deep learning to natural-language processing. Deep recurrent networks with short-term memory were trained to translate English sentences into French sentences at high levels of performance. Other deep-learning networks could create English captions for the content of images with surprising and sometimes amusing acumen.

More structure means more preconceptions, which can be useful in making sense of limited data but can result in biases that reduce performance. More flexibility means a greater ability to capture the patterns appearing in data but a greater risk of finding patterns that aren’t there. In artificial intelligence research, this tension between structure and flexibility manifests in different kinds of systems that can be used to solve challenging problems like speech recognition, computer vision, and machine translation. For decades, the systems that performed best on those problems came down on the side of structure: They were the result of careful planning, design, and tweaking by generations of engineers who thought about the characteristics of speech, images, and syntax and tried to build into the system their best guesses about how to interpret those particular kinds of data.


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The Nature of Technology by W. Brian Arthur

Andrew Wiles, business process, cognitive dissonance, computer age, creative destruction, double helix, endogenous growth, Geoffrey West, Santa Fe Institute, haute cuisine, James Watt: steam engine, joint-stock company, Joseph Schumpeter, Kenneth Arrow, Kevin Kelly, knowledge economy, locking in a profit, Mars Rover, means of production, Myron Scholes, railway mania, Silicon Valley, Simon Singh, sorting algorithm, speech recognition, technological singularity, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions

To do this I will go back to first principles and define technology from scratch. I will give technology three definitions that we will use throughout the book. The first and most basic one is that a technology is a means to fulfill a human purpose. For some technologies—oil refining—the purpose is explicit. For others—the computer—the purpose may be hazy, multiple, and changing. As a means, a technology may be a method or process or device: a particular speech recognition algorithm, or a filtration process in chemical engineering, or a diesel engine. It may be simple: a roller bearing. Or it may be complicated: a wavelength division multiplexer. It may be material: an electrical generator. Or it may be nonmaterial: a digital compression algorithm. Whichever it is, it is always a means to carry out a human purpose. The second definition I will allow is a plural one: technology as an assemblage of practices and components.

INDEX accounting, 85, 153, 197 agriculture, 10, 25, 154, 196 air inlet system, 40, 41 Airbus, 91 aircraft, 7, 10, 22, 182 design of, 72–73, 77, 91, 92–94, 108, 111–12, 120, 133, 136–37 detection of, 22, 39, 49, 73–74, 132 navigation and control of, 25, 30, 72–73, 93–94, 96, 108, 111–12, 132, 206 people and cargo processed by, 30, 32, 92–94 piston-and-propeller, 108, 111, 113, 120, 140–41 propulsion of, 108, 111–12, 120 radar surveillance, 41 stealth, 39–42 see also jet engines; specific aircraft aircraft carriers, 39–42 air traffic control, 132 algorithms, 6, 24, 25, 50, 53, 55, 80, 167, 178, 180–81, 206 digital compression, 28 sorting, 17, 30–31, 98 speech recognition, 28 text-processing, 153 altruism, 142 amplifiers, 69, 83, 167–68 analog systems, 71 anatomy, 13, 14, 32, 43 animals, 9, 53 bones and organs of, 13, 45, 187 genus of, 13 natural selection among, 16 see also vertebrates; specific animals archaeology, 45–46, 88 archaeomagnetic dating, 45 Architectural Digest, 175 architecture, 10, 32, 35, 41–42, 71, 73, 79, 81, 84, 98, 101, 116, 212–13 arithmetic, 81, 108, 125, 182 Armstrong oscillator, 102, 130 Arpanet, 156 artificial intelligence, 12, 215 arts, 15, 72, 77, 79 see also music; painting; poetry Astronomical Society, 74 astronomy, 47–50, 74 Atanasoff-Berry machine, 87 Atomic Energy Commission, U.S., 104 atomic power, 10, 24, 80, 103–5, 114–15, 160, 200 automobiles, 2, 10, 176, 180 autopoiesis, 2–3, 21, 24, 59, 167–70, 188 Babbage, Charles, 74, 75, 126 bacteria, 10, 119, 148, 207 banking, 149, 153–55, 192, 201, 209 bar-codes, 48 barges, 81–83 barometers, 47 batteries, 58, 59, 63 Bauhaus architecture, 212 beekeeping, 25 Bernoulli effect, 52 Bessemer process, 14, 75, 152, 153 biochemistry, 61, 119–20, 123–24, 147 biology, 10, 13, 16, 17, 18, 53, 54, 147–48, 187–88 evolution and, 13, 16, 107, 127–28, 188, 204 molecular, 147, 161, 188 technology and, 28, 61, 206–8 BIOS chip, 13 Black, Fischer, 154 black-bellied plover (pluvialis squatarola), 31 black box concept, 14, 18, 178 blacksmithing, 180 Boeing 737, 96 Boeing 747, 92–94, 109 Boeing 787, 32 bones, 13, 45, 187–88 Boot, Henry, 113 bows, 171 Boyer, Herbert, 148 brain: imaging of, 10 implanting electrodes in, 9 mental processes of, 9, 23, 56, 97, 112, 121–22, 193 parts of, 9, 10, 56, 208 bridges, 29, 109, 150 cable-stayed, 31, 70, 91 concrete, 99–100 bridging technologies, 83–84 bronze, 185 Brown, John Seely, 210 buildings, 47 design and construction of, 10, 71, 72 business, 54, 148, 149, 192, 205 practices of, 80–81, 83, 153, 157, 158–59, 209 Butler, Paul, 47–48, 49–50 Butler, Samuel, 16, 17 cables, 31, 70, 91 fiber optic, 69, 83 calculating devices, 74 canals, 81–83, 85, 150, 192 canoes, 16, 171 capacitors, 59, 69, 169 carbon-14, 45 carrier: air wing, 40, 42 battlegroup, 40–41 Cathcart, Brian, 160 cathedrals, 10 cathode-ray tubes, 57, 59 Cavendish Laboratory, 160 cavity magnetron, 113 Chain, Ernst, 120 Chargaff, Erwin, 77 chemistry, 25, 57, 66, 69, 159, 202, 205 industrial, 75, 162, 171 polymer, 162 Chicago Board of Trade, 156 “chunking,” 36–37, 50 clocks, 33, 36, 38, 49, 158, 198 atomic, 24, 206 cloning, 70 cloud chamber, 61 coal, 82, 83 Cockburn, Lord, 149 Cohen, Stanley, 148 combustion systems, 17, 19, 34, 50, 52, 53, 120 common sense, 65 communication, 66, 78 see also language; telecommunications compressors, 18–19, 34, 51–52, 65, 136–37, 168 computers, 10, 28, 33, 64, 71–73, 75, 80–81, 82, 85, 96, 153–55, 181–83, 203 evolution of, 87, 108–9, 125–26, 146, 150–51, 159, 168–69, 171 intrinsic capabilities of, 88–89 operating systems of, 12–13, 34–35, 36, 72–73, 79–80, 88, 108–9, 150, 156 programming of, 34–35, 53, 71, 88–89 see also algorithms; Internet computer science, 38, 98 concrete, 10, 73, 99–100 contracts, 54, 55, 153–54, 193, 201 derivatives, 154–55, 209 cooling systems, 103–4, 134–35 Copernicus, Nicolaus, 61 copper, 9, 58 cotton, 139, 196 Crick, Francis, 58, 61 Crooke’s tube, 57 Cuvier, Georges, 13 cyclotron, 115, 131 Darwin, Charles, 16, 17–18, 89, 102–3, 107, 127–28, 129, 132, 138, 142, 188, 203–4 “Darwin Among the Machines” (Butler), 16 Darwin’s mechanism, 18, 89, 138 data, 50, 146, 153 processing of, 70, 80–81, 83, 151 dating technologies, 45–46 David, Paul, 157–58 Dawkins, Richard, 102 deep craft, 159–60, 162, 164 de Forest, Lee, 167–68 Deligne, Pierre, 129 dendrochronology, 45 Descartes, René, 208, 211 diabetes, 175 Dickens, Charles, 197 digital technologies, 25, 28, 66, 71, 72, 79–80, 80–81, 82, 84, 117–18, 145, 154, 156, 206 “Digitization and the Economy” (Arthur), 4 DNA, 24, 77, 85, 169, 208 amplification of, 37, 70, 123–24 complementary base pairing in, 57–58, 61, 123–24 extraction and purification of, 61, 70 microarrays, 85 recombinant, 10, 148 replication of, 147 sequencing of, 6, 37, 70, 123–24 domaining, 71–76 definition of, 71–72 redomaining and, 72–74, 85, 151–56 domains, 69–85, 103, 108, 145–65, 171 choice of, 71–73, 101 deep knowledge of, 78–79 definitions of, 70, 80, 84, 145 discipline-based, 146 economy and, 149, 151–56, 163 effectiveness of, 75–76, 150 evolution and development of, 72, 84, 85, 88, 145–65 languages and grammar of, 69, 76–80, 147 mature, 149–50, 165 morphing of, 150–51 novel, 74–75, 152–53 styles defined by, 74–76 subdomains and sub-subdomains of, 71, 151, 165 worlds of, 80–85 Doppler effect, 48, 122 dynamo, 14 Eckert, J.


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Found in Translation: How Language Shapes Our Lives and Transforms the World by Nataly Kelly, Jost Zetzsche

airport security, Berlin Wall, Celtic Tiger, crowdsourcing, Donald Trump, glass ceiling, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, randomized controlled trial, Ray Kurzweil, Skype, speech recognition, Steve Jobs, the market place

Speech translation is not just the stuff of science fiction. It exists today, and it’s a field that is growing. But machines cannot yet fully replace humans when it comes to converting spoken language. A human can interpret simultaneously—listening and speaking at nearly the same time. For now, a machine works much more slowly. Actually, the machine has to complete three separate processes. First, a speech recognition program comprehends what was spoken in one language, converting it into text. Then, using automatic translation, the written text gets translated into a second language. For the final step, the machine vocalizes or speaks the translated version of the text. Because there are so many variables involved, speech translation presents even more obstacles to developers than text translation. Humans are fairly adept at looking past a speech impediment or unfamiliar accent, but machines are not.

Google has added speech options to its core translation product. The U.S. Department of Defense has spent millions upon millions of dollars over the years on various projects to automate the translation of speech. There are some promising examples of technologies that do a decent job when limited to certain settings or specific languages. There are even some tools that work reasonably well (after significant time spent in training the speech recognition portion) with a single user’s voice. Yet, despite plenty of investment from government organizations and private-sector firms, automated speech translation today does not even come close to doing what human interpreters can do. Enabling human beings who speak different languages to communicate with each other in real time without relying on a human interpreter is one of the final frontiers of translation technology.


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

Amazon Mechanical Turk, Bayesian statistics, centre right, combinatorial explosion, constrained optimization, correlation coefficient, crowdsourcing, en.wikipedia.org, iterative process, Netflix Prize, NP-complete, optical character recognition, P = NP, p-value, pattern recognition, performance metric, recommendation engine, self-driving car, SpamAssassin, speech recognition, statistical model

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. Obviously this technique will not scale to thousands of words spoken by millions of very different people in noisy environments and in dozens of languages.

Insufficient Quantity of Training Data For a toddler to learn what an apple is, all it takes is for you to point to an apple and say “apple” (possibly repeating this procedure a few times). Now the child is able to recognize apples in all sorts of colors and shapes. Genius. Machine Learning is not quite there yet; it takes a lot of data for most Machine Learning algorithms to work properly. Even for very simple problems you typically need thousands of examples, and for complex problems such as image or speech recognition you may need millions of examples (unless you can reuse parts of an existing model). The Unreasonable Effectiveness of Data In a famous paper published in 2001, Microsoft researchers Michele Banko and Eric Brill showed that very different Machine Learning algorithms, including fairly simple ones, performed almost identically well on a complex problem of natural language disambiguation8 once they were given enough data (as you can see in Figure 1-20).


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Hello World: Being Human in the Age of Algorithms by Hannah Fry

23andMe, 3D printing, Air France Flight 447, Airbnb, airport security, augmented reality, autonomous vehicles, Brixton riot, chief data officer, computer vision, crowdsourcing, DARPA: Urban Challenge, Douglas Hofstadter, Elon Musk, Firefox, Google Chrome, Gödel, Escher, Bach, Ignaz Semmelweis: hand washing, John Markoff, Mark Zuckerberg, meta analysis, meta-analysis, pattern recognition, Peter Thiel, RAND corporation, ransomware, recommendation engine, ride hailing / ride sharing, selection bias, self-driving car, Shai Danziger, Silicon Valley, Silicon Valley startup, Snapchat, speech recognition, Stanislav Petrov, statistical model, Stephen Hawking, Steven Levy, Tesla Model S, The Wisdom of Crowds, Thomas Bayes, Watson beat the top human players on Jeopardy!, web of trust, William Langewiesche

Amazon’s recommendation engine uses a similar idea, connecting your interests to those of past customers. It’s what led to the intriguing shopping suggestion that confronted Reddit user Kerbobotat after buying a baseball bat on Amazon: ‘Perhaps you’ll be interested in this balaclava?’11 Filtering: isolating what’s important Algorithms often need to remove some information to focus on what’s important, to separate the signal from the noise. Sometimes they do this literally: speech recognition algorithms, like those running inside Siri, Alexa and Cortana, first need to filter out your voice from the background noise before they can get to work on deciphering what you’re saying. Sometimes they do it figuratively: Facebook and Twitter filter stories that relate to your known interests to design your own personalized feed. The vast majority of algorithms will be built to perform a combination of the above.

(TV show) 97–9 John Carter (film) 180 Johnson, Richard 50, 51 Jones Beach 1 Jones, Robert 13–14 judges anchoring effect 73 bail, factors for consideration 73 decision-making consistency in 51 contradictions in 52–3 differences in 52 discretion in 53 unbiased 77 judges (continued) discrimination and bias 70–1, 75 intuition and considered thought 72 lawyers’ preference over algorithms 76–7 vs machines 59–61 offenders’ preference over algorithms 76 perpetuation of bias 73 sentencing 53–4, 63 use of algorithms 63, 64 Weber’s Law 74–5 Jukebox 192 junk algorithms 200 Just Noticeable Difference 74 justice 49–78 algorithms and 54–6 justification for 77 appeals process 51 Brixton riots 49–51 by country Australia 53 Canada 54 England 54 Ireland 54 Scotland 54 United States 53, 54 Wales 54 discretion of judges 53 discrimination 70–1 humans vs machines 59–61, 62–4 hypothetical cases (UK research) 52–3 defendants appearing twice 52–3 differences in judgement 52, 53 hypothetical cases (US research) 51–2 differences in judgements 52 differences in sentencing 52 inherent injustice 77 machine bias 65–71 maximum terms 54 purpose of 77–8 re-offending 54, 55 reasonable doubt 51 rehabilitation 55 risk-assessment algorithms 56 sentencing consistency in 51 mitigating factors in 53 substantial grounds 51 Kadoodle 15–16 Kahneman, Daniel 72 Kanevsky, Dr Jonathan 93, 95 kangaroos 128 Kant, Immanuel 185 Kasparov, Gary 5-7, 202 Kelly, Frank 87 Kerner, Winifred 188–9 Kernighan, Brian x Killingbeck 145, 146 Larson, Steve 188–9 lasers 119–20 Leibniz, Gottfried 184 Leroi, Armand 186, 192–3 level 0 (driverless technology) 131 level 1 (driverless technology) 131 level 2 (driverless technology) 131, 136 careful attention 134–5 level 3 (driverless technology) 131 technical challenge 136 level 4 (driverless technology) 131 level 5 (driverless technology) 131 Li Yingyun 45 Lickel, Charles 97–8 LiDAR (Light Detection and Ranging) 119–20 life insurance 109 ‘Lockdown’ (52Metro) 177 logic 8 logical instructions 8 London Bridge 172 London School of Economics (LSE) 129 Loomis, Eric 217n38 Los Angeles Police Department 152, 155 Lucas, Teghan 161–2, 163 machine-learning algorithms 10–11 neural networks 85–6 random forests 58–9 machines art and 194 bias in 65–71 diagnostic 98–101, 110–11 domination of humans 5-6 vs humans 59–61, 62–4 paradoxical relationship with 22–3 recognising images 84–7 superior judgement of 16 symbolic dominance over humans 5-6 Magic Test 199 magical illusions 18 mammogram screenings 94, 96 manipulation 39–44 micro-manipulation 42–4 Maple, Jack 147–50 Marx, Gary 173 mastectomies 83, 84, 92, 94 maternity wards, deaths on 81 mathematical certainty 68 mathematical objects 8 McGrayne, Sharon Bertsch 122 mechanized weaving machines 2 Medicaid assistance 16–17 medical conditions, algorithms for 96–7 medical records 102–7 benefits of algorithms 106 DeepMind 104–5 disconnected 102–3 misuse of data 106 privacy 105–7 medicine 79–112 in ancient times 80 cancer diagnoses study 79–80 complexity of 103–4 diabetic retinopathy 96 diagnostic machines 98–101, 110–11 choosing between individuals and the population 111 in fifteenth-century China 81 Hippocrates and 80 magic and 80 medical records 102–6 neural networks 85–6, 95, 96, 219–20n11 in nineteenth-century Europe 81 pathology 79, 82–3 patterns in data 79–81 predicting dementia 90–2 scientific base 80 see also Watson (IBM computer) Meehl, Paul 21–2 MegaFace challenge 168–9 Mercedes 125–6 microprocessors x Millgarth 145, 146 Mills, Tamara 101–2, 103 MIT Technology Review 101 modern inventions 2 Moses, Robert 1 movies see films music 176–80 choosing 176–8 diversity of charts 186 emotion and 189 genetic algorithms 191–2 hip hop 186 piano experiment 188–90 algorithm 188, 189–91 popularity 177, 178 quality 179, 180 terrible, success of 178–9 Music Lab 176–7, 179, 180 Musk, Elon 138 MyHeritage 110 National Geographic ­Genographic project 110 National Highway Traffic Safety Administration 135 Navlab 117 Netflix 8, 188 random forests 59 neural networks 85–6, 95, 119, 201, 219–20n11 driverless cars 117–18 in facial recognition 166–7 predicting performances of films 183 New England Journal of ­Medicine 94 New York City subway crime 147–50 anti-social behaviour 149 fare evasion 149 hotspots 148, 149 New York Police Department (NYPD) 172 New York Times 116 Newman, Paul 127–8, 130 NHS (National Health Service) computer virus in hospitals 105 data security record 105 fax machines 103 linking of healthcare records 102–3 paper records 103 prioritization of non-smokers for operations 106 nuclear war 18–19 Nun Study 90–2 obesity 106 OK Cupid 9 Ontario 169–70 openworm project 13 Operation Lynx 145–7 fingerprints 145 overruling algorithms correctly 19–20 incorrectly 20–1 Oxbotica 127 Palantir Technologies 31 Paris Auto Show (2016) 124–5 parole 54–5 Burgess’s forecasting power 55–6 violation of 55–6 passport officers 161, 164 PathAI 82 pathologists 82 vs algorithms 88 breast cancer research on corpses 92–3 correct diagnoses 83 differences of opinion 83–4 diagnosing cancerous tumours 90 sensitivity and 88 specificity and 88 pathology 79, 82 and biology 82–3 patterns in data 79–81, 103, 108 payday lenders 35 personality traits 39 advertising and 40–1 inferred by algorithm 40 research on 39–40 Petrov, Stanislav 18–19 piano experiment 188–90 pigeons 79–80 Pomerleau, Dean 118–19 popularity 177, 178, 179, 183–4 power 5–24 blind faith in algorithms 13–16 overruling algorithms 19–21 struggle between humans and algorithms 20–4 trusting algorithms 16–19 power of veto 19 Pratt, Gill 137 precision in justice 53 prediction accuracy of 66, 67, 68 algorithms vs humans 22, 59–61, 62–5 Burgess 55–6 of crime burglary 150–1 HunchLab algorithm 157–8 PredPol algorithm 152–7, 158 risk factor 152 Strategic Subject List algorithm 158 decision trees 56–8 dementia 90–2 prediction (continued) development of abnormalities 87, 95 homicide 62 of personality 39–42 of popularity 177, 178, 179, 180, 183–4 powers of 92–6 of pregnancy 29–30 re-offending criminals 55–6 recidivism 62, 63–4, 65 of successful films 180–1, 182–3, 183 superiority of algorithms 22 see also Clinical vs Statistical Prediction (Meehl); neural networks predictive text 190–1 PredPol (PREDictive POL­icing) 152–7, 158, 228–9n27 assessing locations at risk 153–4 cops on the dots 155–6 fall in crime 156 feedback loop 156–7 vs humans, test 153–4 target hardening 154–5 pregnancy prediction 29–30 prescriptive sentencing systems 53, 54 prioritization algorithms 8 prisons cost of incarceration 61 Illinois 55, 56 reduction in population 61 privacy 170, 172 false sense of 47 issues 25 medical records 105–7 overriding of 107 sale of data 36–9 probabilistic inference 124, 127 probability 8 ProPublica 65–8, 70 quality 179, 180 ‘good’ changing nature of 184 defining 184 quantifying 184–8 difficulty of 184 Washington Post experiment 185–6 racial groups COMPAS algorithm 65–6 rates of arrest 68 radar 119–20 RAND Corporation 158 random forests technique 56–9 rape 141, 142 re-offending 54 prediction of 55–6 social types of inmates 55, 56 recidivism 56, 62, 201 rates 61 risk scores 63–4, 65 regulation of algorithms 173 rehabilitation 55 relationships 9 Republican voters 41 Rhode Island 61 Rio de Janeiro–Galeão International Airport 132 risk scores 63–4, 65 Robinson, Nicholas 49, 50, 50–1, 77 imprisonment 51 Rossmo, Kim 142–3 algorithm 145–7 assessment of 146 bomb factories 147 buffer zone 144 distance decay 144 flexibility of 146 stagnant water pools 146–7 Operation Lynx 145–7 Rotten Tomatoes website 181 Royal Free NHS Trust 222–3n48 contract with DeepMind 104–5 access to full medical histories 104–5 outrage at 104 Rubin’s vase 211n13 rule-based algorithms 10, 11, 85 Rutherford, Adam 110 Safari browser 47 Sainsbury’s 27 Salganik, Matthew 176–7, 178 Schmidt, Eric 28 School Sisters of Notre Dame 90, 91 Science magazine 15 Scunthorpe 2 search engines 14–15 experiment 14–15 Kadoodle 15–16 Semmelweis, Ignaz 81 sensitivity, principle of 87, 87–8 sensors 120 sentencing algorithms for 62–4 COMPAS 63, 64 considerations for 62–3 consistency in 51 length of 62–3 influencing 73 Weber’s Law 74–5 mitigating factors in 53 prescriptive systems 53, 54 serial offenders 144, 145 serial rapists 141–2 Sesame Credit 45–6, 168 sexual attacks 141–2 shoplifters 170 shopping habits 28, 29, 31 similarity 187 Slash X (bar) 113, 114, 115 smallpox inoculation 81 Snowden, David 90–2 social proof 177–8, 179 Sorensen, Alan 178 Soviet Union detection of enemy missiles 18 protecting air space 18 retaliatory action 19 specificity, principle of 87, 87–8 speech recognition algorithms 9 Spotify 176, 188 Spotify Discover 188 Sreenivasan, Sameet 181–2 Stammer, Neil 172 Standford University 39–40 STAT website 100 statistics 143 computational 12 modern 107 NYPD 172 Stilgoe, Jack 128–9, 130 Strategic Subject List 158 subway crime see New York City subway crime supermarkets 26–8 superstores 28–31 Supreme Court of Wisconsin 64, 217n38 swine flu 101–2 Talley, Steve 159, 162, 163–4, 171, 230n47 Target 28–31 analysing unusual data ­patterns 28–9 expectant mothers 28–9 algorithm 29, 30 coupons 29 justification of policy 30 teenage pregnancy incident 29–30 target hardening 154–5 teenage pregnancy 29–30 Tencent YouTu Lab algorithm 169 Tesco 26–8 Clubcard 26, 27 customers buying behaviour 26–7 knowledge about 27 loyalty of 26 vouchers 27 online shopping 27–8 ‘My Favourites’ feature 27–8 removal of revealing items 28 Tesla 134, 135 autopilot system 138 full autonomy 138 full self-driving hardware 138 Thiel, Peter 31 thinking, ways of 72 Timberlake, Justin 175–6 Timberlake, Justin (artist) 175–6 Tolstoy, Leo 194 TomTom sat-nav 13–14 Toyota 137, 210n13 chauffeur mode 139 guardian mode 139 trolley problem 125–6 true positives 67 Trump election campaign 41, 44 trust 17–18 tumours 90, 93–4 Twain, Mark 193 Twitter 36, 37, 40 filtering 10 Uber driverless cars 135 human intervention 135 uberPOOL 10 United Kingdom (UK) database of facial images 168 facial recognition algorithms 161 genetic tests for Huntington’s disease 110 United States of America (USA) database of facial images 168 facial recognition algorithms 161 life insurance stipulations 109 linking of healthcare ­records 103 University of California 152 University of Cambridge research on personality traits 39–40 and advertising 40–1 algorithm 40 personality predictions 40 and Twitter 40 University of Oregon 188–90 University of Texas M.


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

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

Good predictions need not have good explanations. The owl can be a good hunter without understanding why the rat always goes from point A to point B. Some readers may be surprised to see that I have placed present-day learning machines squarely on rung one of the Ladder of Causation, sharing the wisdom of an owl. We hear almost every day, it seems, about rapid advances in machine learning systems—self-driving cars, speech-recognition systems, and, especially in recent years, deep-learning algorithms (or deep neural networks). How could they still be only at level one? The successes of deep learning have been truly remarkable and have caught many of us by surprise. Nevertheless, deep learning has succeeded primarily by showing that certain questions or tasks we thought were difficult are in fact not. It has not addressed the truly difficult questions that continue to prevent us from achieving humanlike AI.

Thanks in part to Bonaparte’s accuracy and speed, the NFI managed to identify remains from 294 of the 298 victims by December 2014. As of 2016, only two victims of the crash (both Dutch citizens) have vanished without a trace. Bayesian networks, the machine-reasoning tool that underlies the Bonaparte software, affect our lives in many ways that most people are not aware of. They are used in speech-recognition software, in spam filters, in weather forecasting, in the evaluation of potential oil wells, and in the Food and Drug Administration’s approval process for medical devices. If you play video games on a Microsoft Xbox, a Bayesian network ranks your skill. If you own a cell phone, the codes that your phone uses to pick your call out of thousands of others are decoded by belief propagation, an algorithm devised for Bayesian networks.

A few months later it played sixty online games against top human players without losing a single one, and in 2017 it was officially retired after beating the current world champion, Ke Jie. The one game it lost to Sedol is the only one it will ever lose to a human. All of this is exciting, and the results leave no doubt: deep learning works for certain tasks. But it is the antithesis of transparency. Even AlphaGo’s programmers cannot tell you why the program plays so well. They knew from experience that deep networks have been successful at tasks in computer vision and speech recognition. Nevertheless, our understanding of deep learning is completely empirical and comes with no guarantees. The AlphaGo team could not have predicted at the outset that the program would beat the best human in a year, or two, or five. They simply experimented, and it did. Some people will argue that transparency is not really needed. We do not understand in detail how the human brain works, and yet it runs well, and we forgive our meager understanding.


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Devil's Bargain: Steve Bannon, Donald Trump, and the Storming of the Presidency by Joshua Green

4chan, Affordable Care Act / Obamacare, Ayatollah Khomeini, Bernie Sanders, business climate, centre right, Charles Lindbergh, coherent worldview, collateralized debt obligation, conceptual framework, corporate raider, crony capitalism, currency manipulation / currency intervention, Donald Trump, Fractional reserve banking, Goldman Sachs: Vampire Squid, Gordon Gekko, guest worker program, illegal immigration, immigration reform, liberation theology, low skilled workers, Nate Silver, Nelson Mandela, nuclear winter, obamacare, Peace of Westphalia, Peter Thiel, quantitative hedge fund, Renaissance Technologies, Robert Mercer, Ronald Reagan, Silicon Valley, social intelligence, speech recognition, urban planning

Outside IBM, their unorthodox approach to translation was greeted with hostility (“the crude force of computers is not science,” huffed one linguist at a professional conference who reviewed their work). But pattern-hunting worked. A computer could learn to recognize patterns without regard for the rules of grammar and still produce a successful translation. “Statistical machine translation,” as the process became known, soon outpaced the old method and went on to become the basis of modern speech-recognition software and tools such as Google Translate. At Renaissance, Mercer and Brown applied this approach broadly to the markets, feeding all kinds of abstruse data into their computers in a never-ending hunt for hidden correlations. Sometimes they found them in strange places. Even by the paranoid standards of black-box quantitative hedge funds, Renaissance is notoriously secretive about its methods.

the Mercer family had given: Elise Viebeck and Matea Gold, “Pro-Trump Megadonor Is Part Owner of Breitbart News Empire, CEO Reveals,” Washington Post, February 24, 2017. According to The Washington Post, between 2009 and 2014, the family donated $35 million to conservative think tanks and at least $36.5 million to individual GOP races, www.washingtonpost.com/politics/pro-trump-megadonor-is-part-owner-of-breitbart-news-empire-ceo-reveals/2017/02/24/9f16eea4-fad8-11e6-9845-576c69081518_story.html?utm_term=.86e1f0f5e7c4. modern speech-recognition: Zachary Mider, “What Kind of Man Spends Millions to Elect Ted Cruz?,” Bloomberg Politics, January 20, 2016, https://www.bloomberg.com/politics/features/2016-01-20/what-kind-of-man-spends-millions-to-elect-ted-cruz-. “Bob told me he believed”: Jane Mayer, “The Reclusive Hedge-Fund Tycoon Behind the Trump Presidency,” New Yorker, March 27, 2017, www.newyorker.com/magazine/2017/03/27/the-reclusive-hedge-fund-tycoon-behind-the-trump-presidency.


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

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

To understand how they did it, we need to go deep. Deep learning neural networks, first mentioned in chapter 5 as one potential solution to improving military automatic target recognition in DARPA’s TRACE program, have been the driving force behind astounding gains in AI in the past few years. Deep neural networks have learned to play Atari, beat the world’s reigning champion at go, and have been behind dramatic improvements in speech recognition and visual object recognition. Neural networks are also behind the “fully automated combat module” that Russian arms manufacturer Kalashnikov claims to have built. Unlike traditional computer algorithms that operate based on a script of instructions, neural networks work by learning from large amounts of data. They are an extremely powerful tool for handling tricky problems that can’t be easily solved by prescribing a set of rules to follow.

Once trained, the AIs are purpose-built tools to solve narrow problems. AlphaGo can beat any human at go, but it can’t play a different game, drive a car, or make a cup of coffee. Still, the tools used to train AlphaGo are generalizable tools that can be used to build any number of special-purpose narrow AIs to solve various problems. Deep neural networks have been used to solve other thorny problems that have bedeviled the AI community for years, notably speech recognition and visual object recognition. A deep neural network was the tool used by the research team I witnessed autonomously find the crashed helicopter. The researcher on the project explained that he had taken an existing neural network that had already been trained on object recognition, stripped off the top few layers, then retrained the network to identify helicopters, which hadn’t originally been in its image dataset.

This hack exploits their weakness on the extremes, however, in the space of all possible images, which is virtually infinite. Because this vulnerability stems from the basic structure of the neural net, it is present in essentially every deep neural network commonly in use today, regardless of its specific design. It applies to visual object recognition neural nets but also to those used for speech recognition or other data analysis. This exploit has been demonstrated with song-interpreting AIs, for example. Researchers fed specially evolved noise into the AI, which sounds like nonsense to humans, but which the AI confidently interpreted as music. In some settings, the consequences of this vulnerability could be severe. Clune gave a hypothetical example of a stock-trading neural net that read the news.


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The Art of Invisibility: The World's Most Famous Hacker Teaches You How to Be Safe in the Age of Big Brother and Big Data by Kevin Mitnick, Mikko Hypponen, Robert Vamosi

4chan, big-box store, bitcoin, blockchain, connected car, crowdsourcing, Edward Snowden, en.wikipedia.org, Firefox, Google Chrome, Google Earth, Internet of things, Kickstarter, license plate recognition, Mark Zuckerberg, MITM: man-in-the-middle, pattern recognition, ransomware, Ross Ulbricht, self-driving car, Silicon Valley, Skype, Snapchat, speech recognition, Tesla Model S, web application, WikiLeaks, zero day, Zimmermann PGP

Your “watching info” includes the names of files stored on any USB drive you connect to your LG television—say, one that contains photos from your family vacation. Researchers carried out another experiment in which they created a mock video file and loaded it to a USB drive, then plugged it into their TV. When they analyzed network traffic, they found that the video file name was transmitted unencrypted within http traffic and sent to the address GB.smartshare.lgtvsdp.com. Sensory, a company that makes embedded speech-recognition solutions for smart products, thinks it can do even more. “We think the magic in [smart TVs] is to leave it always on and always listening,” says Todd Mozer, CEO of Sensory. “Right now [listening] consumes too much power to do that. Samsung’s done a really intelligent thing and created a listening mode. We want to go beyond that and make it always on, always listening no matter where you are.”11 Now that you know what your digital TV is capable of, you might be wondering: Can your cell phone eavesdrop when it’s turned off?

With the malware installed on your mobile phone, the gyroscope within the phone is now sensitive enough to pick up slight vibrations. The malware in this case, researchers say, can also pick up minute air vibrations, including those produced by human speech. Google’s Android operating system allows movements from the sensors to be read at 200 Hz, or 200 cycles per second. Most human voices range from 80 to 250 Hz. That means the sensor can pick up a significant portion of those voices. Researchers even built a custom speech-recognition program designed to interpret the 80–250 Hz signals further.9 Cui found something similar within the VoIP phones and printers. He found that the fine pins sticking out of just about any microchip within any embedded device today could be made to oscillate in unique sequences and therefore exfiltrate data over radio frequency (RF). This is what he calls a funtenna, and it is a virtual playground for would-be attackers.


pages: 324 words: 92,805

The Impulse Society: America in the Age of Instant Gratification by Paul Roberts

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, accounting loophole / creative accounting, activist fund / activist shareholder / activist investor, Affordable Care Act / Obamacare, American Society of Civil Engineers: Report Card, asset allocation, business cycle, business process, Cass Sunstein, centre right, choice architecture, collateralized debt obligation, collective bargaining, computerized trading, corporate governance, corporate raider, corporate social responsibility, creative destruction, crony capitalism, David Brooks, delayed gratification, disruptive innovation, double helix, factory automation, financial deregulation, financial innovation, fixed income, full employment, game design, greed is good, If something cannot go on forever, it will stop - Herbert Stein's Law, impulse control, income inequality, inflation targeting, invisible hand, job automation, John Markoff, Joseph Schumpeter, knowledge worker, late fees, Long Term Capital Management, loss aversion, low skilled workers, mass immigration, new economy, Nicholas Carr, obamacare, Occupy movement, oil shale / tar sands, performance metric, postindustrial economy, profit maximization, Report Card for America’s Infrastructure, reshoring, Richard Thaler, rising living standards, Robert Shiller, Robert Shiller, Rodney Brooks, Ronald Reagan, shareholder value, Silicon Valley, speech recognition, Steve Jobs, technoutopianism, the built environment, The Predators' Ball, the scientific method, The Wealth of Nations by Adam Smith, Thorstein Veblen, too big to fail, total factor productivity, Tyler Cowen: Great Stagnation, Walter Mischel, winner-take-all economy

Doi: http://www.people.com/people/archive/article/0,,20064026,00.html. Wolfe, Thomas. “The ‘Me’ Decade and the Third Great Awakening.” New York Magazine, August 23, 1976. Wood, Allen W. “Hegel on Education.” In Philosophy as Education, edited by Amélie O. Rorty. London: Routledge, 1998. Notes Chapter 1: More Better 1. Andrew Nusca, “Say Command: How Speech Recognition Will Change the World,” SmartPlanet, Issue 7, at http://www.smartplanet.com/blog/smart-takes/say-command-how-speech-recognition-will-change-the-world/19895tag=content;siu-container. 2. Apple video introducing Siri, at http://www.youtube.com/watchv=8ciagGASro0. 3. The Independent, 86–87 (1916), at http://books.google.com/booksid=IZAeAQAAMAAJ&lpg=PA108&ots=L5W1-w9EDW&dq=Edward%20Earle%20Purinton&pg=PA246#v=onepage&q=Edward%20Earle%20Purinton&f=false. 4.


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Moonwalking With Einstein by Joshua Foer

Albert Einstein, Asperger Syndrome, Berlin Wall, conceptual framework, deliberate practice, Fall of the Berlin Wall, Frank Gehry, lifelogging, mental accounting, patient HM, pattern recognition, Rubik’s Cube, speech recognition, Stephen Hawking, zero-sum game

See also specific titles Borges, Jorge Luis Born on a Blue Day (Tammet) Bradwardine, Thomas Brain and Mind Brainman brain(s) capacity of of chess masters computer and, seamless connection between as energetically expensive experimenting on of Kim Peek of London cabdrivers of mental athletes mysteries of neuroplasticity of part used while memorizing physical structure of random-access indexing system of temporary turn off of brain training software Bruno, Giordano Buddha Buzan, Tony appearance of author’s interview with awakening to art of memory BBC series of on education home of on intelligence on memory Mind Mapping skills and talents of travel schedule of World Memory Championships and writings of Byblos calendar calculating Cambridge Autism Research Center Camillo, Giulio cards. See speed cards Carroll, Lewis Carruthers, Mary Carvello, Creighton Charmadas Chase, Bill chess masters chicken sexers chunking Cicero Clemens, Samuel L. Clemons, Alonzo cochlear implants computer and brain, seamless connection between memory speech recognition Confessions (Augustine) context Cooke, Ed as author’s coach birthday party of career plans of classroom demonstration of family life of home of intellectual pursuits of memorization projects of memorizing techniques of personality of on remembering experiences speed cards and Tony Buzan and at World Memory Championships Cooke, Rod Cooke, Teen corpus callosum creativity, memory and cricket cultural literacy Cyrus, King Darnton, Robert Dead Reckoning: Calculating Without Instruments Dean, John, memory of deliberate practice De Oratore (Cicero) Dewey, John digital information, externalization of memory and digit span author’s SF’s test Discover Doerfler, Ronald Dottino, Tony “Double Deck’r Bust” Down, John Langdon Draschl, Corinna drawing dreams Du Bois, W.

See also Talented Tenth Santos, Chester savant syndrome definition of reasons for S (case study) compulsive remembering of inability to forget personal life of regimented memory of synesthesia of Scipio, Lucius Scoville, William scriptio continua scrolls self/identity Seneca the Elder Seneca the Younger SenseCam Serrell, Orlando SF (case study) Shakespeare Shass Pollak (Talmud Pole) Siffre, Michel Simonides of Ceos Simplicius Simpson’s in the Strand singularity skill acquisition “Skilled Memory Theory” Slate Small, Jocelyn Penny Smith, Steven Snyder, Allan Socrates song, as structuring device for language spatial navigation. See also memory palace(s)/art of memory speech speech recognition speed cards author as U.S. record holder author’s work on Ben Pridmore and Ed Cooke and techniques for memorizing at U.S. Memory Championships at World Memory Championships speed numbers sports records Squire, Larry Stanislavski, Konstantin stardom, author’s Stoll, Maurice Stratton, George Stromeyer, Charles surgeons SWAT officers swimming synesthesia testing for table of contents Talented Tenth Tammet, Daniel Anders Ericsson and Asperger’s syndrome of author and Ben Pridmore on childhood of epileptic seizure of Kim Peek compared to as mental mathematician numbers and online memory course of as psychic skepticism about study of synesthesia of at World Memory Championships tannaim Test of Genuineness for Synesthesia text(s).


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

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

As in other chapters, there will be many examples drawn from practical experience managing linguistic data, including data that has been collected in the course of linguistic fieldwork, laboratory work, and web crawling. 11.1 Corpus Structure: A Case Study The TIMIT Corpus was the first annotated speech database to be widely distributed, and it has an especially clear organization. TIMIT was developed by a consortium including Texas Instruments and MIT, from which it derives its name. It was designed to provide data for the acquisition of acoustic-phonetic knowledge and to support the development and evaluation of automatic speech recognition systems. The Structure of TIMIT Like the Brown Corpus, which displays a balanced selection of text genres and sources, TIMIT includes a balanced selection of dialects, speakers, and materials. For each of eight dialect regions, 50 male and female speakers having a range of ages and educational backgrounds each read 10 carefully chosen sentences. Two sentences, read by all speakers, were designed to bring out dialect variation: 407 (1) a. she had your dark suit in greasy wash water all year b. don’t ask me to carry an oily rag like that The remaining sentences were chosen to be phonetically rich, involving all phones (sounds) and a comprehensive range of diphones (phone bigrams).

Curation Versus Evolution As large corpora are published, researchers are increasingly likely to base their investigations on balanced, focused subsets that were derived from corpora produced for 414 | Chapter 11: Managing Linguistic Data entirely different reasons. For instance, the Switchboard database, originally collected for speaker identification research, has since been used as the basis for published studies in speech recognition, word pronunciation, disfluency, syntax, intonation, and discourse structure. The motivations for recycling linguistic corpora include the desire to save time and effort, the desire to work on material available to others for replication, and sometimes a desire to study more naturalistic forms of linguistic behavior than would be possible otherwise. The process of choosing a subset for such a study may count as a non-trivial contribution in itself.

For a certain period in the development of NLP, particularly during the 1980s, this premise provided a common starting point for both linguists and practitioners of NLP, leading to a family of grammar formalisms known as unification-based (or feature-based) grammar (see Chapter 9), and to NLP applications implemented in the Prolog programming language. Although grammar-based NLP is still a significant area of research, it has become somewhat eclipsed in the last 15–20 years due to a variety of factors. One significant influence came from automatic speech recognition. Although early work in speech processing adopted a model that emulated the kind of rule-based phonological phonology processing typified by the Sound Pattern of English (Chomsky & Halle, 1968), this turned out to be hopelessly inadequate in dealing with the hard problem of recognizing actual speech in anything like real time. By contrast, systems which involved learning patterns from large bodies of speech data were significantly more accurate, efficient, and robust.


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

AI winter, Andy Kessler, artificial general intelligence, asset allocation, Automated Insights, autonomous vehicles, basic income, Baxter: Rethink Robotics, business intelligence, business process, call centre, carbon-based life, Clayton Christensen, clockwork universe, commoditize, conceptual framework, dark matter, David Brooks, deliberate practice, deskilling, digital map, disruptive innovation, Douglas Engelbart, Edward Lloyd's coffeehouse, Elon Musk, Erik Brynjolfsson, estate planning, fixed income, follow your passion, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, game design, general-purpose programming language, global pandemic, Google Glasses, Hans Lippershey, haute cuisine, income inequality, index fund, industrial robot, information retrieval, intermodal, Internet of things, inventory management, Isaac Newton, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joi Ito, Khan Academy, knowledge worker, labor-force participation, lifelogging, longitudinal study, loss aversion, Mark Zuckerberg, Narrative Science, natural language processing, Norbert Wiener, nuclear winter, pattern recognition, performance metric, Peter Thiel, precariat, quantitative trading / quantitative finance, Ray Kurzweil, Richard Feynman, risk tolerance, Robert Shiller, Robert Shiller, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, six sigma, Skype, social intelligence, speech recognition, spinning jenny, statistical model, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, superintelligent machines, supply-chain management, transaction costs, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, Works Progress Administration, Zipcar

Some of the same technologies are being used to analyze and identify images. Humans are still better able to make subjective judgments on unstructured data, such as interpreting the meaning of a poem, or distinguishing between images of good neighborhoods and bad ones. But computers are making headway even on these fronts. Meanwhile, intelligent applications that already combine text, image, and speech recognition offer very welcome “human support” by making it easier for us to communicate with computers. As you probably know, it is very difficult for machines to deal with high levels of variation in speech accents, pronunciation, volume, background noise, and so forth. If you use Siri on your iPhone or have an Amazon Echo device, you know both the joy and the frustration. Yet even if the progress is not as fast as we would like, these systems are getting better all the time—as are tools for recognizing handwriting and identifying facial images.

This might involve translating words across languages, understanding questions posed by people in plain language, and answering in kind, or “reading” a text with sufficient understanding to summarize it—or create new passages in the same style. Machine translation has been around for a while, and like everything else digital, it gets better all the time. Written language translation has progressed much faster than spoken language, since no speech recognition is necessary, but both are becoming quite useful. Google Translate, for example, does a credible job of it using “statistical machine translation,” or looking at a variety of examples of translated work and determining which translation is most likely. IBM’s Watson is the first tool to be broadly capable of ingesting, analyzing, and “understanding” text to a sufficient degree to answer detailed questions on it.


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The Road Ahead by Bill Gates, Nathan Myhrvold, Peter Rinearson

Albert Einstein, Apple's 1984 Super Bowl advert, Berlin Wall, Bill Gates: Altair 8800, Bob Noyce, Bonfire of the Vanities, business process, California gold rush, Claude Shannon: information theory, computer age, Donald Knuth, first square of the chessboard, first square of the chessboard / second half of the chessboard, glass ceiling, global village, informal economy, invention of movable type, invention of the printing press, invention of writing, John von Neumann, knowledge worker, medical malpractice, Mitch Kapor, new economy, packet switching, popular electronics, Richard Feynman, Ronald Reagan, speech recognition, Steve Ballmer, Steve Jobs, Steven Pinker, Ted Nelson, telemarketer, the scientific method, The Wealth of Nations by Adam Smith, transaction costs, Turing machine, Turing test, Von Neumann architecture

I expect major new generations of Windows to come along every two to three years. The seeds of new competition are being sown constantly in research environments and garages around the world. For instance, the Internet is becoming so important that Windows will only thrive if it is clearly the best way to gain access to the Internet. All operating-system companies are rushing to find ways to have a competitive edge in providing Internet support. When speech recognition becomes genuinely reliable, this will cause another big change in operating systems. In our business things move too fast to spend much time looking back. I pay close attention to our mistakes, however, and try to focus on future opportunity. It's important to acknowledge mistakes and make sure you draw some lesson from them. It's also important to make sure no one avoids trying something because he thinks he'll be penalized for what happened or that management is not working to fix the problems.

These innovations will first show up in the mainstream in the high-volume office-productivity packages: word processors, spreadsheets, presentation packages, databases, and electronic mail. Some proponents claim these tools are so capable already that there will never be a need for newer versions. But there were those who thought that about software five and ten years ago. Over the next few years, as speech recognition, social interfaces, and connections to the information highway are incorporated into core applications, I think individuals and companies will find the productivity enhancements these improved applications will bring extremely attractive. The greatest improvement in productivity, and the greatest change in work habits, will be brought about because of networking. The original use for the PC was to make it easier to create documents that were printed on paper and shared by passing around the printed output.


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The End of Ownership: Personal Property in the Digital Economy by Aaron Perzanowski, Jason Schultz

3D printing, Airbnb, anti-communist, barriers to entry, bitcoin, blockchain, carbon footprint, cloud computing, conceptual framework, crowdsourcing, cryptocurrency, Donald Trump, Edward Snowden, en.wikipedia.org, endowment effect, Firefox, George Akerlof, Hush-A-Phone, information asymmetry, intangible asset, Internet Archive, Internet of things, Isaac Newton, loss aversion, Marc Andreessen, means of production, minimum wage unemployment, new economy, peer-to-peer, price discrimination, Richard Thaler, ride hailing / ride sharing, rolodex, self-driving car, sharing economy, Silicon Valley, software as a service, software patent, software studies, speech recognition, Steve Jobs, subscription business, telemarketer, The Market for Lemons, transaction costs, winner-take-all economy

However, those threats aren’t limited to intellectual property and DRM; they also include battles for control over information about our behavior and our inner lives. One troubling example is the Wi-Fi-enabled Hello Barbie doll from Mattel. This IoT Barbie looks like many of her predecessors but offers a unique feature. She can engage in conversation with a child and learn about them in the process. Barbie does this by recording her conversations and transmitting them via network connections to ToyTalk, a third-party cloud-based speech recognition service. ToyTalk then uses software and data analytics to analyze those conversations and deliver personalized responses. It’s an impressive trick, but the implications for our sense of ownership are quite shocking. For many children, talking to toy dolls is a way to share their unfiltered thoughts, dreams, and fears in a safe, private environment. But according to the terms of the Hello Barbie EULA, ToyTalk and its unnamed partners have wide latitude to make use of information about your child’s conversations in ways that few parents would anticipate: All information, materials and content ... is owned by ToyTalk or is used with permission. ...

But according to the terms of the Hello Barbie EULA, ToyTalk and its unnamed partners have wide latitude to make use of information about your child’s conversations in ways that few parents would anticipate: All information, materials and content ... is owned by ToyTalk or is used with permission. ... You agree that ToyTalk and its licensors and contractors may use, transcribe and store. ... Recordings and any speech data contained therein, including your voice and likeness as may be captured therein, to provide and maintain the ToyTalk App, to develop, tune, test, enhance or improve speech recognition technology and artificial intelligence algorithms, to develop acoustic and language models and for other research and development purposes. ... By using any Service, you consent to ToyTalk’s collection, use and/or disclosure of your personal information as described in this Policy. By allowing other people to use the Service via your account, you are confirming that you have the right to consent on their behalf to ToyTalk’s collection, use and disclosure of their personal information as described below.


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

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

Someone would have to build the system and then trust that the decisions it was making were the right ones. Winning and Losing Hinton kept working, workshopping the idea with his students as well as with Lecun and Bengio, and published papers beginning in 2006. By 2009, Hinton’s lab had applied deep neural nets for speech recognition, and a chance meeting with a Microsoft researcher named Li Deng meant that the technology could be piloted in a meaningful way. Deng, a Chinese deep-learning specialist, was a pioneer in speech recognition using large-scale deep learning. By 2010, the technique was being tested at Google. Just two years later, deep neural nets were being used in commercial products. If you used Google Voice and its transcription services, that was deep learning, and the technique became the basis for all the digital assistants we use today.


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The Launch Pad: Inside Y Combinator, Silicon Valley's Most Exclusive School for Startups by Randall Stross

affirmative action, Airbnb, AltaVista, always be closing, Amazon Mechanical Turk, Amazon Web Services, barriers to entry, Ben Horowitz, Burning Man, business cycle, California gold rush, call centre, cloud computing, crowdsourcing, don't be evil, Elon Musk, high net worth, index fund, inventory management, John Markoff, Justin.tv, Lean Startup, Marc Andreessen, Mark Zuckerberg, medical residency, Menlo Park, Minecraft, minimum viable product, Paul Buchheit, Paul Graham, Peter Thiel, QR code, Richard Feynman, Richard Florida, ride hailing / ride sharing, Sam Altman, Sand Hill Road, side project, Silicon Valley, Silicon Valley startup, Skype, social graph, software is eating the world, South of Market, San Francisco, speech recognition, Stanford marshmallow experiment, Startup school, stealth mode startup, Steve Jobs, Steve Wozniak, Steven Levy, TaskRabbit, transaction costs, Y Combinator

What a jackass!” The audience laughs, but Tan is not done. “Our system found Joe. And we’re going to find anyone else who tries to fuck with our customers!” The choice of verb draws extended laughter in the hall; someone even starts to clap. Tan resumes. “Now this is a really hard problem, but we’re the right team to solve this. My cofounder, Brandon, was most recently the tech lead for Android speech recognition—that’s a really hard machine-learning problem. I myself have worked at three startups and was CTO of BuzzLabs, acquired by IAC in April.” • Outside, the founders await their turns to present. They mill about, exchanging tidbits of news that dribble out from the show inside. These hours spent standing outside anxiously will turn out to be the time when members of the batch build bonds in a way they had not during the summer itself.

Abbott, Ryan, 46, 171, 174, 177, 180, 181 Adidas, 234 Adpop Media, 46–47, 122–23, 129 AeroFS, 231 Airbnb, 4, 43, 88, 95 AirTV, 103–4 circumvention, 177–78 investors seeking next, 207 Kutcher, Ashton, 265n1 marketplace, 179 Sift Science, 210 Vidyard, 103–5, 120 Aisle50, 51–52, 191, 208–9, 223, 233 Akamai, 101 Albertsons, 209 Allen, Paul, 16 AlphaLab, 41 Altair BASIC, 11, 68 AltaVista, 204 Altman, Sam, 220 on buzzwords, 18–19 CampusCred, 111–14 interviewing finalists, 11, 21 Rap Genius, 196–202 Science Exchange, 173 Sift Science, 75–76 speaking style, 114, 115, 196 YC partner, 63, 150 Amarasiriwardena, Thushan, 127–28 Amazon, 126 Interview Street, 213 Mechanical Turk, 89, 90 movie rentals, 106 web services, 32, 101, 131, 132 Andreessen Horowitz, 4, 66, 230 Andreessen, Marc, 1–2, 4, 215, 239 Android, 17, 122, 147, 212 NFC, 157, 158 speech recognition, 210 Andrzejewski, Alexa, 54 angel investors, 28, 86–87, 189–90 AnyAsq, 166–67 Anybots, 12, 27, 40, 63 AOL, 124, 126 AppJet, 64, 204 Apple, 69 App Store, 100, 127 cofounders, 161 headquarters, 251n1 iOS devices, 122, 127–28, 142, 147, 172, 187, 209, 212 Sequoia Capital, 3 Snapjoy, 187 Arrington, Michael, 48–49 The Art of Ass-Kicking blog (Shen), 9 Artix, 25, 27, 29 Ask Me Anything, 166 Auburn University, 29 Auctomatic, 64, 66, 159, 204 Austin, TX, 42 Australia, 17, 238 Ballinger, Brandon, 70–76, 121, 134–38, 209–10 Barbie, 53 Bard College at Simon’s Rock, 52 Beatles, 200 Bechtolsheim, Andy, 86 Bellingham, WA, 101 Benchmark Capital, 5 Berkeley (UC) CampusCred, 20, 111 graduates of, 9, 68, 135, 164 Information, School of, 89, 90 newspaper, 136 students, 20, 52–53, 135 Venture Lab Competition, 53 women in computer science, 53 Bernstam, Tikhon, 122, 166, 185–86, 212, 228, 230–31 Bernstein, Mikael, 213–14 Betaspring, 42 Bible, 127, 197, 199, 200 Bill of Rights, 197 Bing Nursery School, 52 Birmingham, AL, 29, 33, 51, 203, 223 BizPress, 125, 147–48, 192 BlackBerry, 157, 184 Blackwell, Trevor Anybots, 27 interviewing finalists, 10, 11–12, 32–33 Kiko, 16 Viaweb, 25 YC partner, 27, 40, 57, 63 Blank, Steve, 77 Blogger, 57 Blomfield, Tom, 191 Bloomberg, Michael, 227 Bloomberg TV, 55 Blurb.com, 12 BMW, 165 Books On Campus, 164 BoomStartup, 42 Boso, 57–58, 60 Boston, MA, 56 Boston University, 112 Boucher, Ross, 64 Boulder, CO, 41, 43, 53, 130 Box, 54 Boyd, E.


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The Open Revolution: New Rules for a New World by Rufus Pollock

Airbnb, discovery of penicillin, Donald Davies, Donald Trump, double helix, Hush-A-Phone, informal economy, Internet of things, invention of the wheel, Isaac Newton, Kickstarter, Live Aid, openstreetmap, packet switching, RAND corporation, Richard Stallman, software patent, speech recognition

The freedoms to use, build upon and share must be available to all, irrespective of borders, wealth or purpose. For example, information is not Open if it is available only to those in the United States, or if it may not be used to make a profit – or even used for military purposes. Distasteful though it may sometimes be, universality is especially important to the idea of Openness. An inventor may not want his speech-recognition software to be used to power drones that bomb people. However, rather as if Apple issued an edict that its computers were not to be used for trolling on the internet or posting terrorist videos, this would be impractical and unpoliceable. The power of Openness, like that of freedom of speech, lies in its being available, whatever people wish to do with it. To allow a myriad of restrictions would be to make the system unwieldy and the accumulation of specific conditions would be highly detrimental to creativity.


Data Mining: Concepts and Techniques: Concepts and Techniques by Jiawei Han, Micheline Kamber, Jian Pei

bioinformatics, business intelligence, business process, Claude Shannon: information theory, cloud computing, computer vision, correlation coefficient, cyber-physical system, database schema, discrete time, distributed generation, finite state, information retrieval, iterative process, knowledge worker, linked data, natural language processing, Netflix Prize, Occam's razor, pattern recognition, performance metric, phenotype, random walk, recommendation engine, RFID, semantic web, sentiment analysis, speech recognition, statistical model, stochastic process, supply-chain management, text mining, thinkpad, Thomas Bayes, web application

What can we do if we want to build a classifier for data where only some of the data are class-labeled, but most are not? Document classification, speech recognition, and information extraction are just a few examples of applications in which unlabeled data are abundant. Consider document classification, for example. Suppose we want to build a model to automatically classify text documents like articles or web pages. In particular, we want the model to distinguish between hockey and football documents. We have a vast amount of documents available, yet the documents are not class-labeled. Recall that supervised learning requires a training set, that is, a set of classlabeled data. To have a human examine and assign a class label to individual documents (to form a training set) is time consuming and expensive. Speech recognition requires the accurate labeling of speech utterances by trained linguists.

., Bagging, boosting, and C4.5, In: Portland, OR. Proc. 1996 Nat. Conf. Artificial Intelligence (AAAI’96), Vol. 1 (Aug. 1996), pp. 725–730. [RA87] Rissland, E.L.; Ashley, K., HYPO: A case-based system for trade secret law, In: Proc. 1st Int. Conf. Artificial Intelligence and Law Boston, MA. (May 1987), pp. 60–66. [Rab89] Rabiner, L.R., A tutorial on hidden Markov models and selected applications in speech recognition, Proc. IEEE 77 (1989) 257–286. [RBKK95] Russell, S.; Binder, J.; Koller, D.; Kanazawa, K., Local learning in probabilistic networks with hidden variables, In: Proc. 1995 Joint Int. Conf. Artificial Intelligence (IJCAI’95) Montreal, Quebec, Canada. (Aug. 1995), pp. 1146–1152. [RC07] Ramakrishnan, R.; Chen, B.-C., Exploratory mining in cube space, Data Mining and Knowledge Discovery 15 (2007) 29–54.

seeStructural Clustering Algorithm for Networks core vertex 531 illustrated 532 scatter plots 54 2-D data set visualization with 59 3-D data set visualization with 60 correlations between attributes 54–56 illustrated 55 matrix 56, 59 schemas integration 94 snowflake 140–141 star 139–140 science applications 611–613 search engines 28 search space pruning 263, 301 second guess heuristic 369 selection dimensions 225 self-training 432 semantic annotations applications 317, 313, 320–321 with context modeling 316 from DBLP data set 316–317 effectiveness 317 example 314–315 of frequent patterns 313–317 mutual information 315–316 task definition 315 Semantic Web 597 semi-offline materialization 226 semi-supervised classification 432–433, 437 alternative approaches 433 cotraining 432–433 self-training 432 semi-supervised learning 25 outlier detection by 572 semi-supervised outlier detection 551 sensitivity analysis 408 sensitivity measure 367 sentiment classification 434 sequence data analysis 319 sequences 586 alignment 590 biological 586, 590–591 classification of 589–590 similarity searches 587 symbolic 586, 588–590 time-series 586, 587–588 sequential covering algorithm 359 general-to-specific search 360 greedy search 361 illustrated 359 rule induction with 359–361 sequential pattern mining 589 constraint-based 589 in symbolic sequences 588–589 shapelets method 590 shared dimensions 204 pruning 205 shared-sorts 193 shared-partitions 193 shell cubes 160 shell fragments 192, 235 approach 211–212 computation algorithm 212, 213 computation example 214–215 precomputing 210 shrinking diameter 592 sigmoid function 402 signature-based detection 614 significance levels 373 significance measure 312 significance tests 372–373, 386 silhouette coefficient 489–490 similarity asymmetric binary 71 cosine 77–78 measuring 65–78, 79 nominal attributes 70 similarity measures 447–448, 525–528 constraints on 533 geodesic distance 525–526 SimRank 526–528 similarity searches 587 in information networks 594 in multimedia data mining 596 simple random sample with replacement (SRSWR) 108 simple random sample without replacement (SRSWOR) 108 SimRank 526–528, 539 computation 527–528 random walk 526–528 structural context 528 simultaneous aggregation 195 single-dimensional association rules 17, 287 single-linkage algorithm 460, 461 singular value decomposition (SVD) 587 skewed data balanced 271 negatively 47 positively 47 wavelet transforms on 102 slice operation 148 small-world phenomenon 592 smoothing 112 by bin boundaries 89 by bin means 89 by bin medians 89 for data discretization 90 snowflake schema 140 example 141 illustrated 141 star schema versus 140 social networks 524–525, 526–528 densification power law 592 evolution of 594 mining 623 small-world phenomenon 592see alsonetworks social science/social studies data mining 613 soft clustering 501 soft constraints 534, 539 example 534 handling 536–537 space-filling curve 58 sparse data 102 sparse data cubes 190 sparsest cuts 539 sparsity coefficient 579 spatial data 14 spatial data mining 595 spatiotemporal data analysis 319 spatiotemporal data mining 595, 623–624 specialized SQL servers 165 specificity measure 367 spectral clustering 520–522, 539 effectiveness 522 framework 521 steps 520–522 speech recognition 430 speed, classification 369 spiral method 152 split-point 333, 340, 342 splitting attributes 333 splitting criterion 333, 342 splitting rules. seeattribute selection measures splitting subset 333 SQL, as relational query language 10 square-error function 454 squashing function 403 standard deviation 51 example 51 function of 50 star schema 139 example 139–140 illustrated 140 snowflake schema versus 140 Star-Cubing 204–210, 235 algorithm illustration 209 bottom-up computation 205 example 207 for full cube computation 210 ordering of dimensions and 210 performance 210 shared dimensions 204–205 starnet query model 149 example 149–150 star-nodes 205 star-trees 205 compressed base table 207 construction 205 statistical data mining 598–600 analysis of variance 600 discriminant analysis 600 factor analysis 600 generalized linear models 599–600 mixed-effect models 600 quality control 600 regression 599 survival analysis 600 statistical databases (SDBs) 148 OLAP systems versus 148–149 statistical descriptions 24, 79 graphic displays 44–45, 51–56 measuring the dispersion 48–51 statistical hypothesis test 24 statistical models 23–24 of networks 592–594 statistical outlier detection methods 552, 553–560, 581 computational cost of 560 for data analysis 625 effectiveness 552 example 552 nonparametric 553, 558–560 parametric 553–558see alsooutlier detection statistical theory, in exceptional behavior disclosure 291 statistics 23 inferential 24 predictive 24 StatSoft 602, 603 stepwise backward elimination 105 stepwise forward selection 105 stick figure visualization 61–63 STING 479–481 advantages 480–481 as density-based clustering method 480 hierarchical structure 479, 480 multiresolution approach 481see alsocluster analysis; grid-based methods stratified cross-validation 371 stratified samples 109–110 stream data 598, 624 strong association rules 272 interestingness and 264–265 misleading 265 Structural Clustering Algorithm for Networks (SCAN) 531–532 structural context-based similarity 526 structural data analysis 319 structural patterns 282 structure similarity search 592 structures as contexts 575 discovery of 318 indexing 319 substructures 243 Student' t-test 372 subcube queries 216, 217–218 sub-itemset pruning 263 subjective interestingness measures 22 subject-oriented data warehouses 126 subsequence 589 matching 587 subset checking 263–264 subset testing 250 subspace clustering 448 frequent patterns for 318–319 subspace clustering methods 509, 510–511, 538 biclustering 511 correlation-based 511 examples 538 subspace search methods 510–511 subspaces bottom-up search 510–511 cube space 228–229 outliers in 578–579 top-down search 511 substitution matrices 590 substructures 243 sum of the squared error (SSE) 501 summary fact tables 165 superset checking 263 supervised learning 24, 330 supervised outlier detection 549–550 challenges 550 support 21 association rule 21 group-based 286 reduced 285, 286 uniform 285–286 support, rule 245, 246 support vector machines (SVMs) 393, 408–415, 437 interest in 408 maximum marginal hyperplane 409, 412 nonlinear 413–415 for numeric prediction 408 with sigmoid kernel 415 support vectors 411 for test tuples 412–413 training/testing speed improvement 415 support vectors 411, 437 illustrated 411 SVM finding 412 supremum distance 73–74 surface web 597 survival analysis 600 SVMs.


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Data Mining: Concepts, Models, Methods, and Algorithms by Mehmed Kantardzić

Albert Einstein, bioinformatics, business cycle, business intelligence, business process, butter production in bangladesh, combinatorial explosion, computer vision, conceptual framework, correlation coefficient, correlation does not imply causation, data acquisition, discrete time, El Camino Real, fault tolerance, finite state, Gini coefficient, information retrieval, Internet Archive, inventory management, iterative process, knowledge worker, linked data, loose coupling, Menlo Park, natural language processing, Netflix Prize, NP-complete, PageRank, pattern recognition, peer-to-peer, phenotype, random walk, RFID, semantic web, speech recognition, statistical model, Telecommunications Act of 1996, telemarketer, text mining, traveling salesman, web application

The process repeats iteratively for large training data sets. 7.7 SOMs SOMs, often called Kohonen maps, are a data visualization technique introduced by University of Helsinki Professor Teuvo Kohonen,. The main idea of the SOMs is to project the n-dimensional input data into some representation that could be better understood visually, for example, in a 2-D image map. The SOM algorithm is not only a heuristic model used to visualize, but also to explore linear and nonlinear relationships in high-dimensional data sets. SOMs were first used in the 1980s for speech-recognition problems, but later they become a very popular and often used methodology for a variety of clustering and classification-based applications. The problem that data visualization attempts to solve is: Humans simply cannot visualize high-dimensional data, and SOM techniques are created to help us visualize and understand the characteristics of these dimensional data. The SOM’s output emphasizes on the salient features of the data, and subsequently leads to the automatic formation of clusters of similar data items.

Also, practical experience shows that hexagonal grids give output results with a better quality. Finally, selection of distance measure is important as in any clustering algorithm. Euclidean distance is almost standard, but that does not mean that it is always the best. For an improved quality (isotropy) of the display, it is advisable to select the grid of the SOM units as hexagonal. The SOMs have been used in large spectrum of applications such as automatic speech recognition, clinical data analysis, monitoring of the condition of industrial plants and processes, classification from satellite images, analysis of genetic information, analysis of electrical signals from the brain, and retrieval from large document collections. Illustrative examples are given in Figure 7.18. Figure 7.18. SOM applications. (a) Drugs binding to human cytochrome; (b) interest rate classification; (c) analysis of book-buying behavior. 7.8 REVIEW QUESTIONS AND PROBLEMS 1.

The probabilities are combined to determine the final probability of the pattern produced by the MM. For example, with the MM shown in Figure 12.21, the probability that the MM takes the horizontal path from starting node to S2 is 0.4 × 0.7 = 0.28. Figure 12.21. A simple Markov Model. MM is derived based on the memoryless assumption. It states that given the current state of the system, the future evolution of the system is independent of its history. MMs have been used widely in speech recognition and natural language processing. Hidden Markov Model (HMM) is an extension to MM. Similar to MM, HMM consists of a set of states and transition probabilities. In a regular MM, the states are visible to the observer, and the state-transition probabilities are the only parameters. In HMM, each state is associated with a state-probability distribution. For example, assume that we were given a sequence of events in a coin toss: O = (HTTHTHH), where H = Head and T = Tail.


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

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

It would have been a tough position for any programmer, even one more bullheaded than McCusker, who enjoyed looking at problems from multiple perspectives. But it was also clear that he was having a hard time pulling a plan together because his life outside OSAF had begun to fall apart almost immediately after he joined the Chandler team. His marriage broke up, he grew depressed, and he came down with a miserable case of repetitive stress injury that forced him off the keyboard and onto a speech-recognition dictation system. Over the holidays he decided to change his first name from David to Rys, and soon he began to change his appearance, too: He let his hair grow long and dyed it blond. He began wearing lipstick. He blogged about his interest in an “alternative lifestyle,” though he warned his readers not to “speculate incorrectly”: “You almost certainly are getting something wrong. However, that’s not my problem, and I’m not particularly upset by being misunderstood.

As the project’s first big-splash Long Bet, Kapor wagered $20,000 (all winnings earmarked for worthy nonprofit institutions) that by 2029 no computer or “machine intelligence” will have passed the Turing Test. (To pass a Turing Test, typically conducted via the equivalent of instant messaging, a computer program must essentially fool human beings into believing that they are conversing with a person rather than a machine.) Taking the other side of the bet was Ray Kurzweil, a prolific inventor responsible for breakthroughs in electronic musical instruments and speech recognition who had more recently become a vigorous promoter of an aggressive species of futurism. Kurzweil’s belief in a machine that could ace the Turing Test was one part of his larger creed—that human history was about to be kicked into overdrive by the exponential acceleration of Moore’s Law and a host of other similar skyward-climbing curves. As the repeated doublings of computational power, storage capacity, and network speed start to work their magic, and the price of all that power continues to drop, according to Kurzweil, we will reach a critical moment when we can technologically emulate the human brain, reverse-engineering our own organic processors in computer hardware and software.


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The Blockchain Alternative: Rethinking Macroeconomic Policy and Economic Theory by Kariappa Bheemaiah

accounting loophole / creative accounting, Ada Lovelace, Airbnb, algorithmic trading, asset allocation, autonomous vehicles, balance sheet recession, bank run, banks create money, Basel III, basic income, Ben Bernanke: helicopter money, bitcoin, blockchain, Bretton Woods, business cycle, business process, call centre, capital controls, Capital in the Twenty-First Century by Thomas Piketty, cashless society, cellular automata, central bank independence, Claude Shannon: information theory, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, complexity theory, constrained optimization, corporate governance, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, cryptocurrency, David Graeber, deskilling, Diane Coyle, discrete time, disruptive innovation, distributed ledger, diversification, double entry bookkeeping, Ethereum, ethereum blockchain, fiat currency, financial innovation, financial intermediation, Flash crash, floating exchange rates, Fractional reserve banking, full employment, George Akerlof, illegal immigration, income inequality, income per capita, inflation targeting, information asymmetry, interest rate derivative, inventory management, invisible hand, John Maynard Keynes: technological unemployment, John von Neumann, joint-stock company, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, Kevin Kelly, knowledge economy, large denomination, liquidity trap, London Whale, low skilled workers, M-Pesa, Marc Andreessen, market bubble, market fundamentalism, Mexican peso crisis / tequila crisis, MITM: man-in-the-middle, money market fund, money: store of value / unit of account / medium of exchange, mortgage debt, natural language processing, Network effects, new economy, Nikolai Kondratiev, offshore financial centre, packet switching, Pareto efficiency, pattern recognition, peer-to-peer lending, Ponzi scheme, precariat, pre–internet, price mechanism, price stability, private sector deleveraging, profit maximization, QR code, quantitative easing, quantitative trading / quantitative finance, Ray Kurzweil, Real Time Gross Settlement, rent control, rent-seeking, Satoshi Nakamoto, Satyajit Das, savings glut, seigniorage, Silicon Valley, Skype, smart contracts, software as a service, software is eating the world, speech recognition, statistical model, Stephen Hawking, supply-chain management, technology bubble, The Chicago School, The Future of Employment, The Great Moderation, the market place, The Nature of the Firm, the payments system, the scientific method, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, too big to fail, trade liberalization, transaction costs, Turing machine, Turing test, universal basic income, Von Neumann architecture, Washington Consensus

The entity we will choose is Chatbots. A Chatbot is essentially a service, powered by rules and artificial intelligence (AI), that a user can interact with via a chat interface. The service could be anything ranging from functional to fun, and it could exist in any chat product (Facebook Messenger, Slack, telegram, text messages, etc.). Recent advancements in Natural Language Processing (NLP) and Automatic Speech Recognition (ASR), coupled with crowdsourced data inputs and machine learning techniques, now allow AI’s to not just understand groups of words but also submit a corresponding natural response to a grouping of words. That’s essentially the base definition of a conversation, except this conversation is with a “bot.” Does this mean that we’ll soon have technology that can pass the Turing test? Maybe not yet, but Chatbots seem to be making progress towards that objective.

Buiter • The Precariat: The New Dangerous Class (2011), Guy Standing • Inventing the Future: Postcapitalism and a World Without Work (2015), Nick Srnicek and Alex Williams • Raising the Floor: How a Universal Basic Income Can Renew Our Economy and Rebuild the American Dream (2016), Andy Stern 239 Index „„         A Aadhaar program, 80 Agent Based Computational Economics (ABCE) models complexity economists, 196 developments, 211–213 El Farol problem and minority games, 207–210 Kim-Markowitz Portfolio Insurers Model, 204 Santa Fe artificial stock market model, 205–207 Agent based modelling (ABM), 180–181 aggregate behavioural trends, 197 axiomatisation, linearization and generalization, 184 black-boxing, 199 bottom-up approach, 197 challenge, 198 computational modelling paradigm, 196 conceptualizing, individual agents, 198 EBM, 197 enacting agent interaction, 202–204 environmental factors, 198 environment creation, 201–202 individual agent, 199 parameters and modelling decisions, 199 simulation designing, 199–200 specifying agent behaviour, 200–201 Alaska, 147 Anti-Money Laundering (AML), 67 ARPANet, 54 Artificial Neural Networks (ANN), 222–224 Atlantic model, 75 Automatic Speech Recognition (ASR), 140 Autor-Levy-Murnane (ALM), 85 „„         B Bandits’ Club, 32 BankID system, 79 Basic Income Earth Network (BIEN), 143 Bitnation, 69 Blockchain, 45, 151 ARPANet, 54 break down points, 56–57 decentralized communication, 54 emails, 54 fiat currency, 123 functions, 55 Jiggery Pokery accounts, 107 malware, 54 protocols, 57 Satoshi, 55 skeleton keys, 54, 63–64 smart contract, 58 TCP/IP protocol, 54 technological and financial innovation, 54 trade finance, 101–102 Blockchain-based regulatory framework (BRF), 108 BlockVerify, 68 „„         C Capitalism, 83 ALM hypotheses and SBTC, 90 Blockchain and CoCo, 151 canonical model, 87 © Kariappa Bheemaiah 2017 K.


pages: 170 words: 45,121

Don't Make Me Think, Revisited: A Common Sense Approach to Web Usability by Steve Krug

collective bargaining, game design, job satisfaction, Kickstarter, Lean Startup, Mark Zuckerberg, speech recognition, Steve Jobs

New technologies and form factors are going to be introduced all the time, some of them involving dramatically different ways of interacting.8 8 Personally, I think talking to your computer is going to be one of the next big things. Recognition accuracy is already amazing; we just need to find ways for people to talk to their devices without looking, sounding, and feeling foolish. Someone who’s seriously working on the problems should give me a call; I’ve been using speech recognition software for 15 years, and I have a lot of thoughts about why it hasn’t caught on. Just make sure that usability isn’t being lost in the shuffle. And the best way to do this is by testing. Chapter 11. Usability as common courtesy WHY YOUR WEB SITE SHOULD BE A MENSCH1 1 Mensch: a German-derived Yiddish word originally meaning “human being.” A person of integrity and honor; “a stand-up guy”; someone who does the right thing.


pages: 372 words: 152

The End of Work by Jeremy Rifkin

banking crisis, Bertrand Russell: In Praise of Idleness, blue-collar work, cashless society, collective bargaining, computer age, deskilling, Dissolution of the Soviet Union, employer provided health coverage, Erik Brynjolfsson, full employment, future of work, general-purpose programming language, George Gilder, global village, hiring and firing, informal economy, interchangeable parts, invention of the telegraph, Jacques de Vaucanson, job automation, John Maynard Keynes: technological unemployment, knowledge economy, knowledge worker, land reform, low skilled workers, means of production, new economy, New Urbanism, Paul Samuelson, pink-collar, post-industrial society, Productivity paradox, Richard Florida, Ronald Reagan, Silicon Valley, speech recognition, strikebreaker, technoutopianism, Thorstein Veblen, Toyota Production System, trade route, trickle-down economics, women in the workforce, working poor, working-age population, Works Progress Administration

The ambitious effort, which has been dubbed the RealWorld Program, will attempt to develop what the Japanese call "flexible information processing" or "Soft Logic," the kind of intuitive thinking that people use when they make decisions. 3 Using new computers equipped with massive parallel processing, neural networks, and optical signals, the Japanese hope to create a new generation of intelligent machines that can read text, understand complex speech, interpret facial gestures and expressions, and even anticipate behavior. Intelligent machines equipped with rudimentary speech recognition already exist. Companies like BBN Systems and Technologies of Cambridge, Massachusetts, and Dragon Systems of Newton, Massachusetts, have developed computers with vocabularies of up to 30,000 words. 4 Some of the new thinking machines can recognize casual speech, carryon meaningful conversations, and even solicit additional information upon which to make decisions, provide recommendations, and answer questions.

Once a CD is selected, the robot's video display is used to process the payment. The robot's arm then selects the CD from the shelves and delivers it to the customer along with his or her receipt. A twentythree-year-old who shops regularly at the store says he prefers the robot to a human salesperson. "It's easy to use and it can't talk back to yoU."47 More sophisticated robots equipped with speech recognition and conversational abilities will likely be commonplace in department stores, convenience stores, fast-food restaurants, and other retail and service businesses by the early part of the next century. A large European super-discounter is experimenting with a new electronic technology that allows the customer to insert his or her credit card into a slot on the shelf holding the desired product.


pages: 588 words: 131,025

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

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

Wardrop, “Doctors Told to Prescribe Smartphone Apps to Patients,” Telegraph, February 22, 2012, http://www.telegraph.co.uk/health/healthnews/9097647/Doctors-told-to-prescribe-smartphone-apps-to-patients.html. 97. S. Curtis, “Digital Doctors: How Mobile Apps Are Changing Healthcare,” Telegraph, December 4, 2013, http://www.telegraph.co.uk/technology/news/10488778/Digital-doctors-how-mobile-apps-are-changing-healthcare.html. 98. “How Speech-Recognition Software Got So Good,” The Economist, April 22, 2014, http://www.economist.com/node/21601175/print. 99. J. Conn, “IT Experts Push Translator Systems to Convert Doc-Speak into ICD-10 Codes,” Modern Healthcare, May 3, 2014, http://www.modernhealthcare.com/article/20140503/MAGAZINE/305039969/1246/. 100. R. Rosenberger, “Siri, Take This Down: Will Voice Control Shape Our Writing?,” The Atlantic, August 2012, http://www.theatlantic.com/technology/print/2012/08/siri-take-this-down-will-voice-control-shape-our-writing/259624/. 101.

,” New Yorker, May 20, 2013, http://www.newyorker.com/reporting/2013/05/20/130520fa_fact_heller?printable=true&currentPage=all. 9. “When Waiting Is Not an Option,” The Economist, May 13, 2012, http://www.economist.com/node/21554157/print. 10. J. Weiner, “What Big Data Can’t Tell Us, but Kolstad’s Paper Suggests,” Penn LDI, April 24, 2014, http://ldi.upenn.edu/voices/2014/04/24/what-big-data-can-t-tell-us-but-kolstad-s-paper-suggests. 11. “How Speech-Recognition Software Got So Good,” The Economist, April 22, 2014, http://www.economist.com/node/21601175/print. 12. T. Lewin, “Master’s Degree Is New Frontier of Study Online,” New York Times, August 18, 2013, http://www.nytimes.com/2013/08/18/education/masters-degree-is-new-frontier-of-study-online.html. 13. M. M. Waldrop, “Massive Open Online Courses, aka MOOCs, Transform Higher Education and Science,” Scientific American, March 13, 2013, http://www.scientificamerican.com/article/massive-open-online-courses-transform-higher-education-and-science/. 14.


pages: 474 words: 130,575

Surveillance Valley: The Rise of the Military-Digital Complex by Yasha Levine

23andMe, activist fund / activist shareholder / activist investor, Airbnb, AltaVista, Amazon Web Services, Anne Wojcicki, anti-communist, Apple's 1984 Super Bowl advert, bitcoin, borderless world, British Empire, call centre, Chelsea Manning, cloud computing, collaborative editing, colonial rule, computer age, computerized markets, corporate governance, crowdsourcing, cryptocurrency, digital map, don't be evil, Donald Trump, Douglas Engelbart, Douglas Engelbart, drone strike, Edward Snowden, El Camino Real, Electric Kool-Aid Acid Test, Elon Musk, fault tolerance, George Gilder, ghettoisation, global village, Google Chrome, Google Earth, Google Hangouts, Howard Zinn, hypertext link, IBM and the Holocaust, index card, Jacob Appelbaum, Jeff Bezos, jimmy wales, John Markoff, John von Neumann, Julian Assange, Kevin Kelly, Kickstarter, life extension, Lyft, Mark Zuckerberg, market bubble, Menlo Park, Mitch Kapor, natural language processing, Network effects, new economy, Norbert Wiener, packet switching, PageRank, Paul Buchheit, peer-to-peer, Peter Thiel, Philip Mirowski, plutocrats, Plutocrats, private military company, RAND corporation, Ronald Reagan, Ross Ulbricht, Satoshi Nakamoto, self-driving car, sentiment analysis, shareholder value, side project, Silicon Valley, Silicon Valley startup, Skype, slashdot, Snapchat, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Telecommunications Act of 1996, telepresence, telepresence robot, The Bell Curve by Richard Herrnstein and Charles Murray, The Hackers Conference, uber lyft, Whole Earth Catalog, Whole Earth Review, WikiLeaks

Though most computer engineers thought of computers as little more than oversized calculators, Lick saw them as extensions of the human mind, and he became obsessed with designing machines that could be seamlessly coupled to human beings. In 1960, he published a paper that outlined his vision for the coming “man-computer symbiosis” and described in simple terms the kinds of computer components that needed to be invented to make it happen. The paper essentially described a modern multipurpose computer, complete with a display, keyboard, speech recognition software, networking capabilities, and applications that could be used in real time for a variety of tasks.27 It seems obvious to us now, but back then Lick’s ideas were visionary. His paper was widely circulated in defense circles and earned him an invitation by the Pentagon to do a series of lectures on the topic.28 “My first experience with computers had been listening to a talk by [mathematician John] von Neumann in Chicago back in nineteen forty-eight.

Clients included, according to Brand’s Media Lab, ABC, NBC, CBS, PBS, HBO, Warner Brothers, 20th Century Fox, and Paramount. IBM, Apple, Hewlett-Packard, Digital Equipment Corporation, Sony, NEC, Mitsubishi, and General Motors were also members, as were major newspapers and news publishing businesses: Time Inc., the Washington Post, and the Boston Globe. 92. Among other things, DARPA funded lab research on speech recognition technology that promised to identify people by their voices or to visually read their lips from a distance. 93. Todd Hertz, “How Computer Nerds Describe God,” Christianity Today, November 20, 2002. 94. “Wired was meant to be a lifestyle magazine as well as a technology guide,” writes John Cassidy in Dot.Con, a book about the dot-com bubble. “Sections like ‘Fetish’ and ‘Street Cred’ told readers which new gadgets to buy, while ‘Idees Forte’ and ‘Jargon Watch’ told them what to think and say.”


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The Blank Slate: The Modern Denial of Human Nature by Steven Pinker

affirmative action, Albert Einstein, Alfred Russel Wallace, anti-communist, British Empire, clean water, cognitive dissonance, Columbine, conceptual framework, correlation coefficient, correlation does not imply causation, cuban missile crisis, Daniel Kahneman / Amos Tversky, Defenestration of Prague, desegregation, epigenetics, Exxon Valdez, George Akerlof, germ theory of disease, ghettoisation, glass ceiling, Hobbesian trap, income inequality, invention of agriculture, invisible hand, Joan Didion, long peace, meta analysis, meta-analysis, More Guns, Less Crime, Murray Gell-Mann, mutually assured destruction, Norman Mailer, Peter Singer: altruism, phenotype, plutocrats, Plutocrats, Potemkin village, prisoner's dilemma, profit motive, QWERTY keyboard, Richard Feynman, Richard Thaler, risk tolerance, Robert Bork, Rodney Brooks, Saturday Night Live, social intelligence, speech recognition, Stanford prison experiment, stem cell, Steven Pinker, The Bell Curve by Richard Herrnstein and Charles Murray, the new new thing, theory of mind, Thomas Malthus, Thorstein Veblen, twin studies, ultimatum game, urban renewal, War on Poverty, women in the workforce, Yogi Berra, zero-sum game

Weizenbaum discussed an AI program by the computer scientists Alan Newell and Herbert Simon that relied on analogy: if it knew the solution to one problem, it applied the solution to other problems with a similar logical structure. This, Weizenbaum told us, was really designed to help the Pentagon come up with counterinsurgency strategies in Vietnam. The Vietcong had been said to “move in the jungle as fish move in water.” If the program were fed this information, he said, it could deduce that just as you can drain a pond to expose the fish, you can denude the jungle to expose the Vietcong. Turning to research on speech recognition by computer, he said that the only conceivable reason to study speech perception was to allow the CIA to monitor millions of telephone conversations simultaneously, and he urged the students in the audience to boycott the topic. But, he added, it didn’t really matter if we ignored his advice because he was completely certain—there was not the slightest doubt in his mind—that by the year 2000 we would all be dead.

But, he added, it didn’t really matter if we ignored his advice because he was completely certain—there was not the slightest doubt in his mind—that by the year 2000 we would all be dead. And with that inspiring charge to the younger generation he ended the talk. The rumors of our death turned out to be greatly exaggerated, and the other prophecies of the afternoon fared no better. The use of analogy in reasoning, far from being the work of the devil, is today a major research topic in cognitive science and is widely considered a key to what makes us smart. Speech-recognition software is routinely used in telephone information services and comes packaged with home computers, where it has been a godsend for the disabled and for people with repetitive strain injuries. And Weizenbaum’s accusations stand as a reminder of the political paranoia and moral exhibitionism that characterized university life in the 1970s, the era in which the current opposition to the sciences of human nature took shape.

slavery Slavs Sledgehammer slippery slopes Slovic, Paul Small, Meredith smell (olfactory) system Smith, Adam Smith, Edgar Smith, John Maynard Smith, Susan smoking Smolensky, Paul Smothers Brothers Smuts, Barbara Sober, Elliot social constructionism social contract Social Contract, The (Rousseau) Social Darwinism Hitler’s belief in social engineering socialism see also Marxism socialization, personality vs. social psychology, see psychology, social social reality social sciences sociobiology Sociobiology (Wilson) sociology Socrates Sokal, Alan Solzhenitsyn, Aleksandr Sommers, Christina Hoff Sontag, Susan soul see also Ghost in the Machine South Africa Southerners Soviet Union Sowell, Thomas Spanish Civil War spatial sense Specter, Arlen speech-recognition software Spencer, Herbert Sperber, Dan Sperry, Roger Spock, Benjamin Sponsel, Leslie sports Springsteen, Bruce Stalin, Joseph Standard Social Science Model see also social constructionism; social sciences Stardust Memories statistics status Stein, Gertrude Steinem, Gloria Steiner, George Steiner, Wendy stem cell research Stephen, James stepparenting stereotypes Stevens, Wallace Stich, Stephen Stills, Stephen Sting Stockhausen, Karlheinz Stoicism Stolba, Christine Stoppard, Tom Storey, Robert strict constructionism Strossen, Nadine Sullivan, Andrew Sullivan, Arthur Sulloway, Frank Summerhill (Neill) Superfund Act (1980) superorganism (group mind) supervisory attention system Supreme Court, U.S.


The Complete Android Guide: 3Ones by Kevin Purdy

car-free, card file, crowdsourcing, Firefox, Google Chrome, Google Earth, Googley, John Gruber, QR code, Skype, speech recognition, telemarketer, turn-by-turn navigation

Everyone in the U.S. or Canada who calls 1-800-GOOG-411 (1-800-466-4411) and says the business they're looking for, along with city and state, can be connected for free, or have additional information sent by text message. As you might have guessed, Google has been using all that speech—in particular, the phonemes of regional dialects—and the search results they're connected to in order to build a pretty huge speech recognition database. It is far, far from perfect, but it's also surprisingly good at times. Alternate Keyboards HTC's Keyboard, Included by Default on Phones with "Sense" Interfaces Not every phone comes with Google's own keyboard installed as the default, and not every phone has to keep it around. In fact, many of the phones manufactured by HTC—the Hero, the Droid Incredible, and the EVO 4G—feature their own keyboard configuration as part of the HTC Sense theme.


pages: 174 words: 56,405

Machine Translation by Thierry Poibeau

AltaVista, augmented reality, call centre, Claude Shannon: information theory, cloud computing, combinatorial explosion, crowdsourcing, easy for humans, difficult for computers, en.wikipedia.org, Google Glasses, information retrieval, Internet of things, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, natural language processing, Necker cube, Norbert Wiener, RAND corporation, Robert Mercer, Skype, speech recognition, statistical model, technological singularity, Turing test, wikimedia commons

The development of communications networks, mobile Internet, and the miniaturization of electronic devices also highlight the need to switch quickly to audio applications that are able to translate speech directly. Speech processing has been the subject of intensive research in recent decades, and performance is now acceptable. However, the task remains difficult since speech processing as well as machine translation have to be performed in real time, and errors are cumulative (i.e., if a word has not been properly analyzed by the speech recognition system, it will not be properly translated). Large companies producing connected tools (Apple, Google, Microsoft, or Samsung, to name a few) develop their own solutions and regularly buy start-ups in technological domains. They need to be first on the technological front and propose new features that may be an important source of revenue in the future. The future will likely see the integration of machine translation modules in new kinds of appliances, as seen in chapter 14.


Beautiful Data: The Stories Behind Elegant Data Solutions by Toby Segaran, Jeff Hammerbacher

23andMe, airport security, Amazon Mechanical Turk, bioinformatics, Black Swan, business intelligence, card file, cloud computing, computer vision, correlation coefficient, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, DARPA: Urban Challenge, data acquisition, database schema, double helix, en.wikipedia.org, epigenetics, fault tolerance, Firefox, Hans Rosling, housing crisis, information retrieval, lake wobegon effect, longitudinal study, Mars Rover, natural language processing, openstreetmap, prediction markets, profit motive, semantic web, sentiment analysis, Simon Singh, social graph, SPARQL, speech recognition, statistical model, supply-chain management, text mining, Vernor Vinge, web application

Using data collected from the API servers, user profiles, and activity data from the site itself, we were able to construct a model for scoring applications that allowed us to allocate invitations to the applications deemed most useful to users. The Unreasonable Effectiveness of Data In a recent paper, a trio of Google researchers distilled what they have learned from trying to solve some of machine learning’s most difficult challenges. When discussing the problems of speech recognition and machine translation, they state that, “invariably, simple models and a lot of data trump more elaborate models based on less data.” I don’t intend to debate their findings; certainly there are domains where elaborate models are successful. Yet based on their experiences, there does exist a wide class of problems for which more data and simple models are better. At Facebook, Hadoop was our tool for exploiting the unreasonable effectiveness of data.

If we are to base our models on large amounts of data, we’ll need data that is readily available “in the wild.” N-gram counts have this property: we can easily harvest a trillion words of naturally occurring text from the Web. On the other hand, labeled spelling corrections do not occur naturally, and thus we found only 40,000 of them. It is not a coincidence that the two most successful applications of natural language—machine translation and speech recognition—enjoy large corpora of examples available in the wild. In contrast, the task of syntactic parsing of sentences remains largely unrealized, in part because there is no large corpus of naturally occurring parsed sentences. It should be mentioned that our probabilistic data-driven methodology—maximize the probability over all candidates—is a special case of the rational data-driven methodology— maximize expected utility over all candidates.


pages: 523 words: 148,929

Physics of the Future: How Science Will Shape Human Destiny and Our Daily Lives by the Year 2100 by Michio Kaku

agricultural Revolution, AI winter, Albert Einstein, Asilomar, augmented reality, Bill Joy: nanobots, bioinformatics, blue-collar work, British Empire, Brownian motion, cloud computing, Colonization of Mars, DARPA: Urban Challenge, delayed gratification, double helix, Douglas Hofstadter, en.wikipedia.org, friendly AI, Gödel, Escher, Bach, hydrogen economy, I think there is a world market for maybe five computers, industrial robot, Intergovernmental Panel on Climate Change (IPCC), invention of movable type, invention of the telescope, Isaac Newton, John Markoff, John von Neumann, life extension, Louis Pasteur, Mahatma Gandhi, Mars Rover, mass immigration, megacity, Mitch Kapor, Murray Gell-Mann, new economy, oil shale / tar sands, optical character recognition, pattern recognition, planetary scale, postindustrial economy, Ray Kurzweil, refrigerator car, Richard Feynman, Rodney Brooks, Ronald Reagan, Search for Extraterrestrial Intelligence, Silicon Valley, Simon Singh, social intelligence, speech recognition, stem cell, Stephen Hawking, Steve Jobs, telepresence, The Wealth of Nations by Adam Smith, Thomas L Friedman, Thomas Malthus, trade route, Turing machine, uranium enrichment, Vernor Vinge, Wall-E, Walter Mischel, Whole Earth Review, X Prize

The translation won’t be perfect, since there are always problems with idioms, slang, and colorful expressions, but it will be good enough so you will understand the gist of what that person is saying. There are several ways in which scientists are making this a reality. The first is to create a machine that can convert the spoken word into writing. In the mid-1990s, the first commercially available speech recognition machines hit the market. They could recognize up to 40,000 words with 95 percent accuracy. Since a typical, everyday conversation uses only 500 to 1,000 words, these machines are more than adequate. Once the transcription of the human voice is accomplished, then each word is translated into another language via a computer dictionary. Then comes the hard part: putting the words into context, adding slang, colloquial expressions, etc., all of which require a sophisticated understanding of the nuances of the language.

With the ability to devour or rearrange whole star systems, there should be some footprint left behind by this rapidly expanding singularity. (His detractors say that he is whipping up a near-religious fervor around the singularity. However, his supporters say that he has an uncanny ability to correctly see into the future, judging by his track record.) Kurzweil cut his teeth on the computer revolution by starting up companies in diverse fields involving pattern recognition, such as speech recognition technology, optical character recognition, and electronic keyboard instruments. In 1999, he wrote a best seller, The Age of Spiritual Machines: When Computers Exceed Human Intelligence, which predicted when robots will surpass us in intelligence. In 2005, he wrote The Singularity Is Near and elaborated on those predictions. The fateful day when computers surpass human intelligence will come in stages.


pages: 660 words: 141,595

Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking by Foster Provost, Tom Fawcett

Albert Einstein, Amazon Mechanical Turk, big data - Walmart - Pop Tarts, bioinformatics, business process, call centre, chief data officer, Claude Shannon: information theory, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, data acquisition, David Brooks, en.wikipedia.org, Erik Brynjolfsson, Gini coefficient, information retrieval, intangible asset, iterative process, Johann Wolfgang von Goethe, Louis Pasteur, Menlo Park, Nate Silver, Netflix Prize, new economy, p-value, pattern recognition, placebo effect, price discrimination, recommendation engine, Ronald Coase, selection bias, Silicon Valley, Skype, speech recognition, Steve Jobs, supply-chain management, text mining, The Signal and the Noise by Nate Silver, Thomas Bayes, transaction costs, WikiLeaks

Working through case studies (either in theory or in practice) of data science applications helps prime the mind to see opportunities and connections to new problems that could benefit from data science. For example, in the late 1980s and early 1990s, one of the largest phone companies had applied predictive modeling—using the techniques we’ve described in this book—to the problem of reducing the cost of repairing problems in the telephone network and to the design of speech recognition systems. With the increased understanding of the use of data science for helping to solve business problems, the firm subsequently applied similar ideas to decisions about how to allocate a massive capital investment to best improve its network, and how to reduce fraud in its burgeoning wireless business. The progression continued. Data science projects for reducing fraud discovered that incorporating features based on social-network connections (via who-calls-whom data) into fraud prediction models improved the ability to discover fraud substantially.

., Implications for Managing the Data Science Team Solove, Daniel, Privacy, Ethics, and Mining Data About Individuals solution paths, changing, Data Understanding spam (target class), Example: Targeting Online Consumers With Advertisements spam detection systems, Example: Targeting Online Consumers With Advertisements specified class value, Supervised Versus Unsupervised Methods specified target value, Supervised Versus Unsupervised Methods speech recognition systems, Thinking Data-Analytically, Redux speeding up neighbor retrieval, Computational efficiency Spirited Away, Example: Evidence Lifts from Facebook “Likes” spreadsheet, implementation of Naive Bayes with, Evidence in Action: Targeting Consumers with Ads spurious correlations, * Example: Why Is Overfitting Bad? SQL, Database Querying squared errors, Regression via Mathematical Functions stable stock prices, The Task standard linear regression, Regression via Mathematical Functions Star Trek, Example: Evidence Lifts from Facebook “Likes” Starbucks, Applying Our Fundamental Concepts to a New Problem: Mining Mobile Device Data statistical draws, * Logistic Regression: Some Technical Details statistics calculating conditionally, Statistics field of study, Statistics summary, Statistics uses, Statistics stemming, Term Frequency, Example: Jazz Musicians Stillwell, David, Example: Evidence Lifts from Facebook “Likes” stock market, The Task stock price movement example, Example: Mining News Stories to Predict Stock Price Movement–Results Stoker (movie thriller), Term Frequency stopwords, Term Frequency, Term Frequency strategic considerations, Data and Data Science Capability as a Strategic Asset strategy, Implications for Managing the Data Science Team strength, in association mining, Co-occurrences and Associations: Finding Items That Go Together, Example: Beer and Lottery Tickets strongly dependent evidence, Advantages and Disadvantages of Naive Bayes structure, Machine Learning and Data Mining Structured Query Language (SQL), Database Querying structured thinking, Data Mining and Data Science, Revisited structuring, Business Understanding subjective priors, Applying Bayes’ Rule to Data Science subtasks, From Business Problems to Data Mining Tasks summary statistics, Statistics, Statistics Summit Technology, Inc., The Data Sun Ra, Example: Jazz Musicians supervised data, Introduction to Predictive Modeling: From Correlation to Supervised Segmentation–Introduction to Predictive Modeling: From Correlation to Supervised Segmentation, Summary supervised data mining classification, Supervised Versus Unsupervised Methods conditions, Supervised Versus Unsupervised Methods regression, Supervised Versus Unsupervised Methods subclasses, Supervised Versus Unsupervised Methods unsupervised vs., Supervised Versus Unsupervised Methods–Supervised Versus Unsupervised Methods supervised learning generating cluster descriptions with, * Using Supervised Learning to Generate Cluster Descriptions–* Using Supervised Learning to Generate Cluster Descriptions methods of, * Using Supervised Learning to Generate Cluster Descriptions term, Supervised Versus Unsupervised Methods supervised segmentation, Introduction to Predictive Modeling: From Correlation to Supervised Segmentation–Introduction to Predictive Modeling: From Correlation to Supervised Segmentation, Supervised Segmentation–Supervised Segmentation with Tree-Structured Models, Clustering attribute selection, Selecting Informative Attributes–Example: Attribute Selection with Information Gain creating, Supervised Segmentation with Tree-Structured Models entropy, Selecting Informative Attributes–Selecting Informative Attributes inducing, Supervised Segmentation with Tree-Structured Models performing, Introduction to Predictive Modeling: From Correlation to Supervised Segmentation purity of datasets, Selecting Informative Attributes–Selecting Informative Attributes regression problems and, Selecting Informative Attributes tree induction of, Supervised Segmentation with Tree-Structured Models–Supervised Segmentation with Tree-Structured Models tree-structured models for, Supervised Segmentation with Tree-Structured Models–Supervised Segmentation with Tree-Structured Models support vector machines, Optimizing an Objective Function, Example: Overfitting Linear Functions linear discriminants and, Support Vector Machines, Briefly–Support Vector Machines, Briefly, Support Vector Machines, Briefly non-linear, Support Vector Machines, Briefly, Nonlinear Functions, Support Vector Machines, and Neural Networks objective function, Support Vector Machines, Briefly parametric modeling and, Nonlinear Functions, Support Vector Machines, and Neural Networks–Nonlinear Functions, Support Vector Machines, and Neural Networks support, in association mining, Example: Beer and Lottery Tickets surge (stock prices), The Task surprisingness, Measuring Surprise: Lift and Leverage–Measuring Surprise: Lift and Leverage synonyms, Why Text Is Difficult syntactic similarity, semantic vs., The news story clusters T table models, Generalization, Holdout Data and Fitting Graphs tables, Models, Induction, and Prediction Tambe, Prasanna, Data Processing and “Big Data” Tamdhu single malt scotch, * Using Supervised Learning to Generate Cluster Descriptions Target, Data Science, Engineering, and Data-Driven Decision Making target variables, Models, Induction, and Prediction, Regression estimating value, Example: Attribute Selection with Information Gain evaluating, Flaws in the Big Red Proposal targeted ad example, Example: Targeting Online Consumers With Advertisements–Example: Targeting Online Consumers With Advertisements of Naive Bayes, Evidence in Action: Targeting Consumers with Ads privacy protection in Europe and, Privacy, Ethics, and Mining Data About Individuals targeting best prospects example, Targeting the Best Prospects for a Charity Mailing–A Brief Digression on Selection Bias tasks/techniques, Data Science, Engineering, and Data-Driven Decision Making, Other Data Science Tasks and Techniques–Summary associations, Co-occurrences and Associations: Finding Items That Go Together–Associations Among Facebook Likes bias, Bias, Variance, and Ensemble Methods–Bias, Variance, and Ensemble Methods classification, From Business Problems to Data Mining Tasks co-occurrence, Co-occurrences and Associations: Finding Items That Go Together–Associations Among Facebook Likes data reduction, Data Reduction, Latent Information, and Movie Recommendation–Data Reduction, Latent Information, and Movie Recommendation data-driven causal explanations, Data-Driven Causal Explanation and a Viral Marketing Example–Data-Driven Causal Explanation and a Viral Marketing Example ensemble method, Bias, Variance, and Ensemble Methods–Bias, Variance, and Ensemble Methods latent information, Data Reduction, Latent Information, and Movie Recommendation–Data Reduction, Latent Information, and Movie Recommendation link prediction, Link Prediction and Social Recommendation–Link Prediction and Social Recommendation market basket analysis, Associations Among Facebook Likes–Associations Among Facebook Likes overlap in, Regression Analysis principles underlying, From Business Problems to Data Mining Tasks profiling, Profiling: Finding Typical Behavior–Profiling: Finding Typical Behavior social recommendations, Link Prediction and Social Recommendation–Link Prediction and Social Recommendation variance, Bias, Variance, and Ensemble Methods–Bias, Variance, and Ensemble Methods viral marketing example, Data-Driven Causal Explanation and a Viral Marketing Example–Data-Driven Causal Explanation and a Viral Marketing Example Tatum, Art, Example: Jazz Musicians technology analytic, Data Preparation applying, Other Analytics Techniques and Technologies big-data, Data Processing and “Big Data” theory in data science vs., Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data Scientist–Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data Scientist term frequency (TF), Term Frequency–Term Frequency defined, Term Frequency in TFIDF, Combining Them: TFIDF inverse document frequency, combining with, Combining Them: TFIDF values for, Example: Jazz Musicians terms in documents, Representation supervised learning, Supervised Versus Unsupervised Methods unsupervised learning, Supervised Versus Unsupervised Methods weights of, Topic Models Terry, Clark, Example: Jazz Musicians test data, model building and, A General Method for Avoiding Overfitting test sets, Holdout Data and Fitting Graphs testing, holdout, From Holdout Evaluation to Cross-Validation text, Representing and Mining Text as unstructured data, Why Text Is Difficult–Why Text Is Difficult data, Representing and Mining Text fields, varying number of words in, Why Text Is Difficult importance of, Why Text Is Important Jazz musicians example, Example: Jazz Musicians–Example: Jazz Musicians relative dirtiness of, Why Text Is Difficult text processing, Representing and Mining Text text representation task, Representation–Combining Them: TFIDF text representation task, Representation–Combining Them: TFIDF bag of words approach to, Bag of Words data preparation, The Data–The Data data preprocessing, Data Preprocessing–Data Preprocessing defining, The Task–The Task inverse document frequency, Measuring Sparseness: Inverse Document Frequency–Measuring Sparseness: Inverse Document Frequency Jazz musicians example, Example: Jazz Musicians–Example: Jazz Musicians location mining as, Applying Our Fundamental Concepts to a New Problem: Mining Mobile Device Data measuring prevalence in, Term Frequency–Term Frequency measuring sparseness in, Measuring Sparseness: Inverse Document Frequency–Measuring Sparseness: Inverse Document Frequency mining news stories example, Example: Mining News Stories to Predict Stock Price Movement–Results n-gram sequence approach to, N-gram Sequences named entity extraction, Named Entity Extraction–Named Entity Extraction results, interpreting, Results–Results stock price movement example, Example: Mining News Stories to Predict Stock Price Movement–Results term frequency, Term Frequency–Term Frequency TFIDF value and, Combining Them: TFIDF topic models for, Topic Models–Topic Models TFIDF scores (TFIDF values), Data preparation applied to locations, Applying Our Fundamental Concepts to a New Problem: Mining Mobile Device Data text representation task and, Combining Them: TFIDF The Big Bang Theory, Example: Evidence Lifts from Facebook “Likes” The Colbert Report, Example: Evidence Lifts from Facebook “Likes” The Daily Show, Example: Evidence Lifts from Facebook “Likes” The Godfather, Example: Evidence Lifts from Facebook “Likes” The New York Times, Example: Hurricane Frances, What Data Can’t Do: Humans in the Loop, Revisited The Onion, Example: Evidence Lifts from Facebook “Likes” The Road (McCarthy), Term Frequency The Signal and the Noise (Silver), Evaluation, Baseline Performance, and Implications for Investments in Data The Sound of Music (film), Data Reduction, Latent Information, and Movie Recommendation The Stoker (film comedy), Term Frequency The Wizard of Oz (film), Data Reduction, Latent Information, and Movie Recommendation Thomson Reuters Text Research Collection (TRC2), Example: Clustering Business News Stories thresholds and classifiers, Ranking Instead of Classifying–Ranking Instead of Classifying and performance curves, Profit Curves time series (data), The Data Tobermory single malt scotch, Understanding the Results of Clustering tokens, Represe