speech recognition

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

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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.

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

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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.

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

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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.

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

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“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.

The Economic Singularity: Artificial intelligence and the death of capitalism by Calum Chace

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[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] .

pages: 502 words: 107,510

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

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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.

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

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

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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: 294 words: 81,292

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

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3D printing, AI winter, Amazon Web Services, artificial general intelligence, Automated Insights, Bernie Madoff, Bill Joy: nanobots, brain emulation, cellular automata, cloud computing, cognitive bias, computer vision, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Isaac Newton, Jaron Lanier, 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, 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: 374 words: 114,600

The Quants by Scott Patterson

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Albert Einstein, asset allocation, automated trading system, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Brownian motion, buttonwood tree, buy low sell high, capital asset pricing model, centralized clearinghouse, Claude Shannon: information theory, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, Doomsday Clock, Emanuel Derman, Eugene Fama: efficient market hypothesis, fixed income, Gordon Gekko, greed is good, Haight Ashbury, index fund, invention of the telegraph, invisible hand, Isaac Newton, job automation, John Nash: game theory, law of one price, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, margin call, merger arbitrage, NetJets, new economy, offshore financial centre, Paul Lévy, Ponzi scheme, quantitative hedge fund, quantitative trading / quantitative finance, race to the bottom, random walk, Renaissance Technologies, risk-adjusted returns, 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.

pages: 396 words: 117,149

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

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3D printing, Albert Einstein, Amazon Mechanical Turk, Arthur Eddington, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, crowdsourcing, Danny Hillis, data is the new oil, double helix, Douglas Hofstadter, Erik Brynjolfsson, experimental subject, Filter Bubble, future of work, global village, Google Glasses, Gödel, Escher, Bach, information retrieval, job automation, John Snow's cholera map, John von Neumann, Joseph Schumpeter, Kevin Kelly, lone genius, mandelbrot fractal, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, Narrative Science, Nate Silver, natural language processing, Netflix Prize, Network effects, NP-complete, P = NP, PageRank, pattern recognition, phenotype, planetary scale, pre–internet, random walk, Ray Kurzweil, recommendation engine, Richard Feynman, Richard Feynman, Second Machine Age, self-driving car, Silicon Valley, speech recognition, statistical model, Stephen Hawking, Steven Levy, Steven Pinker, superintelligent machines, the scientific method, The Signal and the Noise by Nate Silver, theory of mind, transaction costs, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, white flight

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: 304 words: 82,395

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

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

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: 239 words: 70,206

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

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23andMe, Affordable Care Act / Obamacare, Albert Einstein, big data - Walmart - Pop Tarts, bioinformatics, 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, John von Neumann, Mark Zuckerberg, market bubble, meta analysis, meta-analysis, 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: 561 words: 120,899

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

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

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

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air freight, barriers to entry, business process, business process outsourcing, call centre, Clayton Christensen, computer vision, connected car, corporate governance, disintermediation, distributed generation, double helix, experimental economics, full employment, hydrogen economy, industrial robot, informal economy, interchangeable parts, job satisfaction, labour market flexibility, 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: 339 words: 88,732

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

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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, Baxter: Rethink Robotics, British Empire, business intelligence, business process, call centre, clean water, combinatorial explosion, computer age, computer vision, congestion charging, corporate governance, crowdsourcing, David Ricardo: comparative advantage, 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, game design, global village, happiness index / gross national happiness, illegal immigration, immigration reform, income inequality, income per capita, indoor plumbing, industrial robot, informal economy, inventory management, James Watt: steam engine, Jeff Bezos, jimmy wales, job automation, 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, Mark Zuckerberg, Mars Rover, 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, payday loans, 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.

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

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Amazon Mechanical Turk, Andrew Keen, 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, Network effects, new economy, Nicholas Carr, PageRank, 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.

pages: 238 words: 46

When Things Start to Think by Neil A. Gershenfeld

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

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.

pages: 661 words: 187,613

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

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

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: 731 words: 134,263

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

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Debian, packet switching, 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

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

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.

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

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

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

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

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: 486 words: 132,784

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

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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, 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.

pages: 574 words: 164,509

Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

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agricultural Revolution, AI winter, Albert Einstein, algorithmic trading, anthropic principle, anti-communist, artificial general intelligence, autonomous vehicles, barriers to entry, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, demographic transition, Douglas Hofstadter, Drosophila, Elon Musk, en.wikipedia.org, 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 von Neumann, knowledge worker, 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

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: 559 words: 157,112

Dealers of Lightning by Michael A. Hiltzik

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Apple II, Apple's 1984 Super Bowl advert, Bill Duvall, Bill Gates: Altair 8800, computer age, Dynabook, El Camino Real, index card, Jeff Rulifson, Joseph Schumpeter, Marshall McLuhan, Menlo Park, oil shock, popular electronics, 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

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

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

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: 382 words: 120,064

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

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3D printing, additive manufacturing, 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, 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, London Interbank Offered Rate, M-Pesa, Mark Zuckerberg, mass affluent, microcredit, mobile money, more computing power than Apollo, Northern Rock, Occupy movement, optical character recognition, performance metric, platform as a service, QWERTY keyboard, Ray Kurzweil, recommendation engine, RFID, risk tolerance, self-driving car, Skype, speech recognition, stem cell, telepresence, Tim Cook: Apple, transaction costs, underbanked, 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: 509 words: 132,327

Rise of the Machines: A Cybernetic History by Thomas Rid

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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, Claude Shannon: information theory, conceptual framework, connected car, domain-specific language, Douglas Engelbart, dumpster diving, Extropian, full employment, game design, global village, Haight Ashbury, Howard Rheingold, Jaron Lanier, job automation, John von Neumann, Kevin Kelly, Marshall McLuhan, Menlo Park, 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, V2 rocket, 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: 336 words: 93,672

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

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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, Google Glasses, iterative process, linked data, mouse model, optical character recognition, pattern recognition, personalized medicine, phenotype, race to the bottom, Richard Feynman, Richard Feynman, Ronald Reagan, semantic web, speech recognition, stem cell, Steven Pinker, supply-chain management, Turing machine, 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

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air freight, Bill Duvall, computer age, conceptual framework, Douglas Engelbart, fault tolerance, Hush-A-Phone, information retrieval, Kevin Kelly, Menlo Park, natural language processing, packet switching, RAND corporation, RFC: Request For Comment, 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: 391 words: 105,382

Utopia Is Creepy: And Other Provocations by Nicholas Carr

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

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

Amazon: amazon.comamazon.co.ukamazon.deamazon.fr

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

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: 144 words: 43,356

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

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

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.

pages: 1,201 words: 233,519

Coders at Work by Peter Seibel

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

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.

pages: 348 words: 39,850

Data Scientists at Work by Sebastian Gutierrez

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Albert Einstein, algorithmic trading, bioinformatics, bitcoin, business intelligence, chief data officer, clean water, cloud computing, computer vision, continuous integration, correlation does not imply causation, crowdsourcing, data is the new oil, DevOps, domain-specific language, follow your passion, full text search, informal economy, information retrieval, Infrastructure as a Service, inventory management, iterative process, linked data, Mark Zuckerberg, microbiome, Moneyball by Michael Lewis explains big data, move fast and break things, natural language processing, Network effects, nuclear winter, optical character recognition, pattern recognition, Paul Graham, personalized medicine, Peter Thiel, pre–internet, quantitative hedge fund, quantitative trading / quantitative finance, recommendation engine, Renaissance Technologies, Richard Feynman, Richard Feynman, self-driving car, side project, Silicon Valley, Skype, software as a service, speech recognition, statistical model, Steve Jobs, stochastic process, technology bubble, text mining, the scientific method, web application

LeCun: It was never about data for me. For me, data was and is a means to an end. For me, it’s always been about the power of the model you can train, and so it’s about learning algorithms. The wide availability of data came way, way, way later—like 20 years after I started working on these questions. We started having large data sets or decent-sized data sets for things like handwriting recognition or speech recognition in the 1990s. In fact, I published one of those data sets—the MNIST data set, which is used very frequently for handwriting recognition. Now it’s not considered big at all, but at the time it was. The availability of data sets so large that you don’t even have time to look at any piece of data more than once because you have streaming data coming at you is a very recent phenomenon. A lot of the methods that I am interested in happen to scale very well in those situations, because I have always been a believer in things like stochastic gradient descent and similar techniques.

The work was labeled “neural nets” and basically not interesting for that reason. People just didn’t seem interested in digging past the title essentially. It’s only around 2007 or so that things started to take off. For deep learning, it was still a bit of a struggle for a while, particularly in computer vision. In computer vision, the transition to deep learning happened just last year. In speech recognition, it happened about three years ago, when people started to realize deep learning was working really well and it was beating everything else, and so there came a big rush to those methods. But it was a struggle for almost 10 years. Gutierrez: So you’ve done vision and audio. What’s next? LeCun: Natural language is what’s next. At Facebook, we have quite a lot of effort going on with deep learning for natural language.

And the funny thing is that Geoff was one of the people who changed their minds about this. I mean, he didn’t change his mind in the sense that he still thinks unsupervised learning is the way to go in the long run, and I believe this, and Andrew Ng believes this, and Yoshua Bengio believes this. But in terms of practical applications, he changed his mind in the sense that he started working on purely supervised convolutional nets like me. He applied this to speech recognition, image recognition, and contributed to changing the opinion of the community about the whole idea of deep learning. We’ve been sort of oscillating between the case of Geoff thinking unsupervised learning is the way to go in the long run, but sort of holding his nose and, you know, making supervised learning work because it actually works. And for me, it’s been more of a change of opinion over the years.

pages: 228 words: 65,953

The Six-Figure Second Income by Lindahl, David; Rozek, Jonathan

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bounce rate, California gold rush, financial independence, Google Earth, new economy, speech recognition

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

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4chan, call centre, computer vision, discrete time, 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.

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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.

pages: 297 words: 77,362

The Nature of Technology by W. Brian Arthur

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Andrew Wiles, business process, cognitive dissonance, computer age, double helix, Geoffrey West, Santa Fe Institute, haute cuisine, James Watt: steam engine, joint-stock company, Joseph Schumpeter, Kevin Kelly, knowledge economy, locking in a profit, Mars Rover, means of production, 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.

pages: 274 words: 73,344

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

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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.

Cartesian Linguistics by Noam Chomsky

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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.

pages: 525 words: 116,295

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

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3D printing, 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, 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, Julian Assange, Khan Academy, Kickstarter, knowledge economy, Law of Accelerating Returns, market fundamentalism, means of production, mobile money, mutually assured destruction, Naomi Klein, offshore financial centre, peer-to-peer lending, personalized medicine, Peter Singer: altruism, Ray Kurzweil, RFID, 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?

pages: 481 words: 125,946

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

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3D printing, agricultural Revolution, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, artificial general intelligence, augmented reality, autonomous vehicles, 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, Elon Musk, Emanuel Derman, endowment effect, epigenetics, Ernest Rutherford, experimental economics, Flash crash, friendly AI, Google Glasses, hive mind, income inequality, information trail, Internet of things, invention of writing, iterative process, Jaron Lanier, job automation, 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, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, 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.

pages: 329 words: 93,655

Moonwalking With Einstein by Joshua Foer

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Albert Einstein, Asperger Syndrome, Berlin Wall, conceptual framework, deliberate practice, Fall of the Berlin Wall, Frank Gehry, mental accounting, patient HM, pattern recognition, speech recognition, Stephen Hawking

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).

pages: 324 words: 92,805

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

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2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, accounting loophole / creative accounting, Affordable Care Act / Obamacare, American Society of Civil Engineers: Report Card, asset allocation, business process, Cass Sunstein, centre right, choice architecture, collateralized debt obligation, collective bargaining, corporate governance, corporate social responsibility, crony capitalism, David Brooks, delayed gratification, double helix, factory automation, financial deregulation, financial innovation, full employment, game design, greed is good, If something cannot go on forever, it will stop, impulse control, income inequality, inflation targeting, invisible hand, job automation, Joseph Schumpeter, knowledge worker, late fees, Long Term Capital Management, loss aversion, low skilled workers, 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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Only Humans Need Apply: Winners and Losers in the Age of Smart Machines by Thomas H. Davenport, Julia Kirby

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AI winter, Andy Kessler, artificial general intelligence, asset allocation, Automated Insights, autonomous vehicles, Baxter: Rethink Robotics, business intelligence, business process, call centre, carbon-based life, Clayton Christensen, clockwork universe, conceptual framework, dark matter, David Brooks, deliberate practice, deskilling, Edward Lloyd's coffeehouse, Elon Musk, Erik Brynjolfsson, estate planning, follow your passion, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, game design, general-purpose programming language, 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 Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Khan Academy, knowledge worker, labor-force participation, 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, Richard Feynman, risk tolerance, Robert Shiller, Robert Shiller, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, six sigma, Skype, 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.

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

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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, 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.

pages: 504 words: 89,238

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

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

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.

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

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agricultural Revolution, AI winter, Albert Einstein, 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, invention of movable type, invention of the telescope, Isaac Newton, John von Neumann, life extension, Louis Pasteur, Mahatma Gandhi, Mars Rover, megacity, Murray Gell-Mann, new economy, oil shale / tar sands, optical character recognition, pattern recognition, planetary scale, postindustrial economy, Ray Kurzweil, refrigerator car, Richard Feynman, Richard Feynman, Rodney Brooks, Ronald Reagan, Search for Extraterrestrial Intelligence, Silicon Valley, Simon Singh, 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.

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

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

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

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

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.

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The End of Work by Jeremy Rifkin

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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, 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: 189 words: 57,632

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

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

Of course, the Singularity isn't just a conceit for noodling with in the pages of the pulps: it's the subject of serious-minded punditry, futurism, and even science. Ray Kurzweil is one such pundit-futurist-scientist. He's a serial entrepreneur who founded successful businesses that advanced the fields of optical character recognition (machine-reading) software, text-to-speech synthesis, synthetic musical instrument simulation, computer-based speech recognition, and stock-market analysis. He cured his own Type-II diabetes through a careful review of the literature and the judicious application of first principles and reason. To a casual observer, Kurzweil appears to be the star of some kind of Heinlein novel, stealing fire from the gods and embarking on a quest to bring his maverick ideas to the public despite the dismissals of the establishment, getting rich in the process.

pages: 202 words: 59,883

Age of Context: Mobile, Sensors, Data and the Future of Privacy by Robert Scoble, Shel Israel

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Albert Einstein, Apple II, augmented reality, call centre, Chelsea Manning, cloud computing, connected car, Edward Snowden, Elon Musk, factory automation, Filter Bubble, Google Earth, Google Glasses, Internet of things, job automation, Kickstarter, Mars Rover, Menlo Park, New Urbanism, PageRank, pattern recognition, RFID, ride hailing / ride sharing, Saturday Night Live, self-driving car, sensor fusion, Silicon Valley, Skype, smart grid, social graph, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Tesla Model S, Tim Cook: Apple, urban planning, Zipcar

A free, government-published mobile app lets users post to a hot map that shows real-time air quality data in the same way that Libelium, the Spanish sensor company, provided radiation-level data in Japan after the Fukushima nuclear disaster. Teenybopper lockets. The iLocket from Dano is a $25 little heart-shaped locket that is connected to an Apple iOS mobile app. Targeted to young teens, iLocket lets users whisper their most personal secrets into an iPhone or iPad. The app uses speech recognition. Put in a favorite photo of a secret heartthrob and the app prints a photo that fits perfectly inside the locket. The killer part of the app is how it treats user privacy. Press the locket and the diary entries disappear and remain protected until the iLocket owner unlocks it by pressing the locket again to send a unique code to the iPad app. Marketers have already started to take contextual technology to the pre-adolescent level.

pages: 170 words: 45,121

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

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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: 274 words: 75,846

The Filter Bubble: What the Internet Is Hiding From You by Eli Pariser

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A Declaration of the Independence of Cyberspace, A Pattern Language, Amazon Web Services, augmented reality, back-to-the-land, Black Swan, borderless world, Build a better mousetrap, Cass Sunstein, citizen journalism, cloud computing, cognitive dissonance, crowdsourcing, Danny Hillis, data acquisition, disintermediation, don't be evil, Filter Bubble, Flash crash, fundamental attribution error, global village, Haight Ashbury, Internet of things, Isaac Newton, Jaron Lanier, Jeff Bezos, jimmy wales, Kevin Kelly, knowledge worker, Mark Zuckerberg, Marshall McLuhan, megacity, Netflix Prize, new economy, PageRank, paypal mafia, Peter Thiel, recommendation engine, RFID, sentiment analysis, shareholder value, Silicon Valley, Silicon Valley startup, social graph, social software, social web, speech recognition, Startup school, statistical model, stem cell, Steve Jobs, Steven Levy, Stewart Brand, technoutopianism, the scientific method, urban planning, Whole Earth Catalog, WikiLeaks, Y Combinator

Set loose on a hundred thousand patent applications in English and French, Translate could determine that when word showed up in the English document, mot was likely to show up in the corresponding French paper. And as users correct Translate’s work over time, it gets better and better. What Translate is doing with foreign languages Google aims to do with just about everything. Cofounder Sergey Brin has expressed his interest in plumbing genetic data. Google Voice captures millions of minutes of human speech, which engineers are hoping they can use to build the next generation of speech recognition software. Google Research has captured most of the scholarly articles in the world. And of course, Google’s search users pour billions of queries into the machine every day, which provide another rich vein of cultural information. If you had a secret plan to vacuum up an entire civilization’s data and use it to build artificial intelligence, you couldn’t do a whole lot better. As Google’s protobrain increases in sophistication, it’ll open up remarkable new possibilities.

pages: 381 words: 78,467

100 Plus: How the Coming Age of Longevity Will Change Everything, From Careers and Relationships to Family And by Sonia Arrison

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23andMe, 8-hour work day, Albert Einstein, Anne Wojcicki, artificial general intelligence, attribution theory, Bill Joy: nanobots, bioinformatics, Clayton Christensen, dark matter, East Village, en.wikipedia.org, epigenetics, Frank Gehry, Googley, income per capita, indoor plumbing, Jeff Bezos, Johann Wolfgang von Goethe, Law of Accelerating Returns, life extension, personalized medicine, Peter Thiel, placebo effect, post scarcity, Ray Kurzweil, rolodex, Silicon Valley, Simon Kuznets, Singularitarianism, smart grid, speech recognition, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Levy, Thomas Malthus, upwardly mobile, World Values Survey, X Prize

Microsoft cofounder Bill Gates has called Kurzweil “the best person I know at predicting the future of artificial intelligence.” Incredibly prolific, Kurzweil was the principal developer of the first print-to-speech reading machine for the blind, the first CCD flat-bed scanner, the first text-to-speech synthesizer, the first music synthesizer capable of re-creating the grand piano and other orchestral instruments, and the first commercially marketed, large-vocabulary speech recognition technology.17 That is, it’s safe to say that he is good at collecting information and translating it into usable ideas and products. In his book The Singularity Is Near, Kurzweil discusses how exponentially growing technology will have many important effects, such as pushing the growth of AI that will help humans solve longevity problems. He writes, “Human life expectancy is itself growing steadily and will accelerate rapidly, now that we are in the early stages of reverse engineering the information processes underlying life and disease.

Toast by Stross, Charles

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

The idea that, by 2000, 45% of the population of a post-imperial Britain would classify themselves as “middle class” would have struck a turn-of-the-century socialist as preposterous, and not even a lunatic would suggest that the world’s largest industry would be devoted to the design of imaginary machines—software—with no physical existence. We live in a world which, by the metrics of Victorian industrial consumption, is poverty stricken; nevertheless, we are richer than ever before. Apply our own metrics to the Victorian age and they appear poor. The definition of what is valuable changes over time, and with it change our social values. As AI and computer speech recognition pioneer Raymond Kurzweil pointed out in The Age of Sensual Machines, the first decade of the twenty-first century will see more change than the latter half of the twentieth. To hammer the last nail into the coffin of predictive SF, our personal values are influenced by our social environment. Our environment is in turn dependent on these economic factors. Human nature itself changes over time—and the rate of change of human nature is not constant.

pages: 288 words: 66,996

Travel While You Work: The Ultimate Guide to Running a Business From Anywhere by Mish Slade

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Airbnb, Atul Gawande, business process, Checklist Manifesto, cloud computing, crowdsourcing, Firefox, Google Chrome, Google Hangouts, Inbox Zero, job automation, Lyft, remote working, side project, Skype, speech recognition

Tap the camera icon to take a photo of any text (a menu, perhaps) and have the entire thing translated for you – which is so much quicker than doing it word-by-word. You do need data access for this, though. Speak and translate. Speak in your own language and Google Translate will display the translation, then the other person just taps on their own language to speak back to you. You can therefore have a complete Google-mediated conversation with someone – all you need is to trust Google's speech recognition not to mishear you and end up accidentally insulting their mother. Make full screen. Once you've translated a word or phrase, you can just go into the options and tap "make full screen" to show someone what you want to say without sending them scrambling for their reading glasses. Save to phrasebook. Tapping the star icon next to a translation will save it to your phrasebook, which synchronises across all your devices – so you can keep common phrases (like "Co ve jménu svaté sakra je s vaší Wifi v pořádku") at hand for when you need them.

pages: 268 words: 75,850

The Formula: How Algorithms Solve All Our Problems-And Create More by Luke Dormehl

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3D printing, algorithmic trading, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, big data - Walmart - Pop Tarts, call centre, Cass Sunstein, Clayton Christensen, computer age, death of newspapers, deferred acceptance, Edward Lorenz: Chaos theory, Erik Brynjolfsson, Filter Bubble, Flash crash, Florence Nightingale: pie chart, Frank Levy and Richard Murnane: The New Division of Labor, Google Earth, Google Glasses, High speed trading, Internet Archive, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, Kevin Kelly, Kodak vs Instagram, Marshall McLuhan, means of production, Nate Silver, natural language processing, Netflix Prize, pattern recognition, price discrimination, recommendation engine, Richard Thaler, Rosa Parks, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Slavoj Žižek, social graph, speech recognition, Steve Jobs, Steven Levy, Steven Pinker, Stewart Brand, the scientific method, The Signal and the Noise by Nate Silver, upwardly mobile, Wall-E, Watson beat the top human players on Jeopardy!, Y Combinator

Capable of scanning through political speeches in real time and informing us of when we are being lied to, the TruthTeller is an uncomfortable reminder of both our belief in algorithmic objectivity and our desire for easy answers. In a breathless article, Geek.com described it as “the most robust, automated way to tell whether a politician is lying or not, even more [accurate] than a polygraph test . . . because politicians are so delusional they end up genuinely believing their lies.” The algorithm works by using speech recognition technology developed by Microsoft, which converts audio signals into words, before handing the completed transcript over to a matching algorithm to comb through and compare alleged “facts” to a database of previously recorded, proven facts.35 Imagine the potential for manipulation should such a technology ever ascend beyond simple gimmickry to enjoy the ubiquity of, for instance, automated spell-checking algorithms.

pages: 666 words: 181,495

In the Plex: How Google Thinks, Works, and Shapes Our Lives by Steven Levy

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23andMe, AltaVista, Anne Wojcicki, Apple's 1984 Super Bowl advert, autonomous vehicles, book scanning, Brewster Kahle, Burning Man, business process, clean water, cloud computing, crowdsourcing, Dean Kamen, discounted cash flows, don't be evil, Douglas Engelbart, El Camino Real, fault tolerance, Firefox, Gerard Salton, Google bus, Google Chrome, Google Earth, Googley, HyperCard, hypertext link, IBM and the Holocaust, informal economy, information retrieval, Internet Archive, Jeff Bezos, Kevin Kelly, Mark Zuckerberg, Menlo Park, optical character recognition, PageRank, Paul Buchheit, Potemkin village, prediction markets, recommendation engine, risk tolerance, Sand Hill Road, Saturday Night Live, search inside the book, second-price auction, Silicon Valley, skunkworks, Skype, slashdot, social graph, social software, social web, spectrum auction, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Levy, Ted Nelson, telemarketer, trade route, traveling salesman, Vannevar Bush, web application, WikiLeaks, Y Combinator

“We didn’t have an Arabic speaker on the team but did the very best machine translation.” By not requiring native speakers, Google was free to provide translations to the most obscure language pairs. “You can always translate French to English or English to Spanish, but where else can you translate Hindi to Danish or Finnish or Norwegian?” A long-term problem in computer science had been speech recognition—the ability of computers to hear and understand natural language. Google applied Och’s techniques to teaching its vast clusters of computers how to make sense of the things humans said. It set up a telephone number, 1-800-GOOG-411, and offered a free version of what the phone companies used to call directory assistance. You would say the name and city of the business you wanted to call, and Google would give the result and ask if you wanted to be connected.

., 140 Playboy, 153–54, 155 pornography, blocking, 54, 97, 108, 173, 174 Postini, 241 Pregibon, Daryl, 118–19 Premium Sunset, 109, 112–13, 115 privacy: and Book Settlement, 363 and browsers, 204–12, 336–37 and email, 170–78, 211–12, 378 and Google’s policies, 10, 11, 145, 173–75, 333–35, 337–40 and Google Street View, 340–43 and government fishing expeditions, 173 and interest-based ads, 263, 334–36 and security breach, 268 and social networking, 378–79, 383 and surveillance, 343 Privacy International, 176 products: beta versions of, 171 “dogfooding,” 216 Google neglect of, 372, 373–74, 376, 381 in GPS meetings, 6, 135, 171 machine-driven, 207 marketing themselves, 77, 372 speed required in, 186 Project Database (PDB), 164 property law, 6, 360 Python, 18, 37 Qiheng, Hu, 277 Queiroz, Mario, 230 Rainert, Alex, 373, 374 Rajaram, Gokul, 106 Rakowski, Brian, 161 Randall, Stephen, 153 RankDex, 27 Rasmussen, Lars, 379 Red Hat, 78 Reese, Jim, 181–84, 187, 195, 196, 198 Reeves, Scott, 153 Rekhi, Manu, 373 Reyes, George, 70, 148 Richards, Michael, 251 robotics, 246, 351, 385 Romanos, Jack, 356 Rosenberg, Jonathan, 159–60, 281 Rosenstein, Justin, 369 Rosing, Wayne, 44, 55, 82, 155, 158–59, 186, 194, 271 Rubin, Andy, 135, 213–18, 220, 221–22, 226, 227–30, 232 Rubin, Robert, 148 Rubinson, Barry, 20–21 Rubinstein, Jon, 221 Sacca, Chris, 188–94 Salah, George, 84, 128, 129, 132–33, 166 Salinger Group, The, 190–91 Salton, Gerard, 20, 24, 40 Samsung, 214, 217 Samuelson, Pamela, 362, 365 Sandberg, Sheryl, 175, 257 and advertising, 90, 97, 98, 99, 107 and customer support, 231 and Facebook, 259, 370 Sanlu Group, 297–98 Santana, Carlos, 238 Schillace, Sam, 201–3 Schmidt, Eric, 107, 193 and advertising, 93, 95–96, 99, 104, 108, 110, 112, 114, 115, 117, 118, 337 and antitrust issues, 345 and Apple, 218, 220, 236–37 and applications, 207, 240, 242 and Book Search, 350, 351, 364 and China, 267, 277, 279, 283, 288–89, 305, 310–11, 313, 386 and cloud computing, 201 and financial issues, 69–71, 252, 260, 376, 383 and Google culture, 129, 135, 136, 364 and Google motto, 145 and growth, 165, 271 and IPO, 147–48, 152, 154, 155–57 on lawsuits, 328–29 and management, 4, 80–83, 110, 158–60, 165, 166, 242, 254, 255, 273, 386, 387 and Obama, 316–17, 319, 321, 346 and privacy, 175, 178, 383 and public image, 328 and smart phones, 216, 217, 224, 236 and social networking, 372 and taxes, 90 and Yahoo, 344, 345 and YouTube, 248–49, 260, 265 Schrage, Elliot, 285–87 Schroeder, Pat, 361 search: decoding the intent of, 59 failed, 60 freshness in, 42 Google as synonymous with, 40, 41, 42, 381 mobile, 217 organic results of, 85 in people’s brains, 67–68 real-time, 376 sanctity of, 275 statelessness of, 116, 332 verticals, 58 see also web searches search engine optimization (SEO), 55–56 search engines, 19 bigram breakage in, 51 business model for, 34 file systems for, 43–44 and hypertext link, 27, 37 information retrieval via, 27 and licensing fees, 77, 84, 95, 261 name detection in, 50–52 and relevance, 48–49, 52 signals to, 22 ultimate, 35 upgrades of, 49, 61–62 Search Engine Watch, 102 SearchKing, 56 SEC regulations, 149, 150–51, 152, 154, 156 Semel, Terry, 98 Sengupta, Caesar, 210 Seti, 65–67 Shah, Sonal, 321 Shapiro, Carl, 117 Shazeer, Noam, 100–102 Sheff, David, 153 Sherman Antitrust Act, 345 Shriram, Ram, 34, 72, 74, 79 Siao, Qiang, 277 Sidekick, 213, 226 signals, 21–22, 49, 59, 376 Silicon Graphics (SGI), 131–32 Silverstein, Craig, 13, 34, 35, 36, 43, 78, 125, 129, 139 Sina, 278, 288, 302 Singh, Sanjeev, 169–70 Singhal, Amit, 24, 40–41, 48–52, 54, 55, 58 Siroker, Dan, 319–21 skunkworks, 380–81 Skype, 233, 234–36, 322, 325 Slashdot, 167 Slim, Carlos, 166 SMART (Salton’s Magical Retriever of Text), 20 smart phones, 214–16, 217–22 accelerometers on, 226–28 carrier contracts for, 230, 231, 236 customer support for, 230–31, 232 direct to consumer, 230, 232 Nexus One, 230, 231–32 Smith, Adam, 360 Smith, Bradford, 333 Smith, Christopher, 284–86 Smith, Megan, 141, 158, 184, 258, 318, 350, 355–56 social graph, 374 social networking, 369–83 Sogou, 300 Sohu, 278, 300 Sony, 251, 264 Sooner (mobile operating system), 217, 220 Southworth, Lucinda, 254 spam, 53–57, 92, 241 Spector, Alfred, 65, 66–67 speech recognition, 65, 67 spell checking, 48 Spencer, Graham, 20, 28, 201, 375 spiders, 18, 19 Stanford University: and BackRub, 29–30 and Book Search, 357 Brin in, 13–14, 16, 17, 28, 29, 34 computer science program at, 14, 23, 27, 32 Digital Library Project, 16, 17 and Google, 29, 31, 32–33, 34 and MIDAS, 16 Page in, 12–13, 14, 16–17, 28, 29, 34 and Silicon Valley, 27–28 Stanley (robot), 246, 385 Stanton, Katie, 318, 321, 322, 323–25, 327 Stanton, Louis L., 251 State Department, U.S., 324–25 Steremberg, Alan, 18, 29 Stewart, Jon, 384 Stewart, Margaret, 207 Stricker, Gabriel, 186 Sullivan, Danny, 102 Sullivan, Stacy, 134, 140, 141, 143–44, 158–59 Summers, Larry, 90 Sun Microsystems, 28, 70 Swetland, Brian, 226, 228 Taco Town, 377 Tan, Chade-Meng, 135–36 Tang, Diane, 118 Taylor, Bret, 259, 370 Teetzel, Erik, 184, 197 Tele Atlas, 341 Tesla, Nikola, 13, 32, 106 Thompson, Ken, 241 3M, 124 Thrun, Sebastian, 246, 385–86 T-Mobile, 226, 227, 230 Tseng, Erick, 217, 227 Twentieth Century Fox, 249 Twitter, 309, 322, 327, 374–77, 387 Uline, 112 Universal Music Group, 261 Universal Search, 58–60, 294, 357 University of Michigan, 352–54, 357 UNIX, 54, 80 Upson, Linus, 210, 211–12 Upstartle, 201 Urchin Software, 114 users: in A/B tests, 61 data amassed about, 45–48, 59, 84, 144, 173–74, 180, 185, 334–37 feedback from, 65 focus on, 5, 77, 92 increasing numbers of, 72 predictive clues from, 66 and security breach, 268, 269 U.S.

pages: 677 words: 206,548

Future Crimes: Everything Is Connected, Everyone Is Vulnerable and What We Can Do About It by Marc Goodman

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23andMe, 3D printing, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, algorithmic trading, artificial general intelligence, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, Bill Joy: nanobots, bitcoin, Black Swan, blockchain, borderless world, Brian Krebs, business process, butterfly effect, call centre, Chelsea Manning, cloud computing, cognitive dissonance, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, data acquisition, data is the new oil, Dean Kamen, disintermediation, don't be evil, double helix, Downton Abbey, Edward Snowden, Elon Musk, Erik Brynjolfsson, Filter Bubble, Firefox, Flash crash, future of work, game design, Google Chrome, Google Earth, Google Glasses, Gordon Gekko, high net worth, High speed trading, hive mind, Howard Rheingold, hypertext link, illegal immigration, impulse control, industrial robot, Internet of things, Jaron Lanier, Jeff Bezos, job automation, John Harrison: Longitude, Jony Ive, Julian Assange, Kevin Kelly, Khan Academy, Kickstarter, knowledge worker, Kuwabatake Sanjuro: assassination market, Law of Accelerating Returns, Lean Startup, license plate recognition, litecoin, M-Pesa, Mark Zuckerberg, Marshall McLuhan, Menlo Park, mobile money, more computing power than Apollo, move fast and break things, Nate Silver, national security letter, natural language processing, obamacare, Occupy movement, Oculus Rift, offshore financial centre, optical character recognition, pattern recognition, personalized medicine, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, RAND corporation, ransomware, Ray Kurzweil, refrigerator car, RFID, ride hailing / ride sharing, Rodney Brooks, Satoshi Nakamoto, Second Machine Age, security theater, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart grid, smart meter, Snapchat, social graph, software as a service, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, supply-chain management, technological singularity, telepresence, telepresence robot, Tesla Model S, The Wisdom of Crowds, Tim Cook: Apple, trade route, uranium enrichment, Wall-E, Watson beat the top human players on Jeopardy!, Wave and Pay, We are Anonymous. We are Legion, web application, WikiLeaks, Y Combinator, zero day

Willow Garage’s PR2 can already fold clothes, grab a beer from the fridge, clean up after the dog, bake cookies, and cook a complete breakfast. From Japan to Europe and the United States, there are unprecedented amounts of research-and-development dollars flowing into robotics. Admittedly, some of these new developments sound like something out of a Philip K. Dick novel. For instance, nanny bots have already been launched in South Korea and Japan. They can play games and carry out limited conversations with speech recognition. Many use the robot’s eyes to transmit live video of your children to your computer or smart phone. NEC’s PaPeRo robot nanny also allows you to speak with your children directly or via text messages, which the robot can read to your child, and SoftBank’s Pepper proclaims that “it can read your child’s emotions and facial expressions and respond appropriately.” Though robo-nannies may prove helpful to sleep-deprived, overworked parents everywhere, another area of personal robotics that is expanding even more rapidly is that of elder-care bots.

NOAM CHOMSKY When the computer scientist John McCarthy coined the term “artificial intelligence” in 1956, he defined it succinctly as “the science and engineering of making intelligent machines.” Today artificial intelligence (AI) more broadly refers to the study and creation of information systems capable of performing tasks that resemble human problem-solving capabilities, using computer algorithms to do things that would normally require human intelligence, such as speech recognition, visual perception, and decision making. These computers and software agents are not self-aware or intelligent in the way people are; rather, they are tools that carry out functionalities encoded in them and inherited from the intelligence of their human programmers. This is the world of narrow or weak AI, and it surrounds us daily. Weak AI can be a powerful means for accomplishing specific and narrow tasks.

pages: 509 words: 92,141

The Pragmatic Programmer by Andrew Hunt, Dave Thomas

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A Pattern Language, Broken windows theory, business process, buy low sell high, c2.com, combinatorial explosion, continuous integration, database schema, domain-specific language, general-purpose programming language, Grace Hopper, if you see hoof prints, think horses—not zebras, index card, loose coupling, Menlo Park, MVC pattern, premature optimization, Ralph Waldo Emerson, revision control, Schrödinger's Cat, slashdot, sorting algorithm, speech recognition, traveling salesman, urban decay, Y2K

A blackboard system lets us decouple our objects from each other completely, providing a forum where knowledge consumers and producers can exchange data anonymously and asynchronously. As you might guess, it also cuts down on the amount of code we have to write. Blackboard Implementations Computer-based blackboard systems were originally invented for use in artificial intelligence applications where the problems to be solved were large and complex—speech recognition, knowledge-based reasoning systems, and so on. Modern distributed blackboard-like systems such as JavaSpaces and T Spaces [URL 50, URL 25] are based on a model of key/value pairs first popularized in Linda [CG90], where the concept was known as tuple space. With these systems, you can store active Java objects—not just data—on the blackboard, and retrieve them by partial matching of fields (via templates and wildcards) or by subtypes.

pages: 299 words: 91,839

What Would Google Do? by Jeff Jarvis

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23andMe, Amazon Mechanical Turk, Amazon Web Services, Anne Wojcicki, barriers to entry, Berlin Wall, business process, call centre, cashless society, citizen journalism, clean water, connected car, credit crunch, crowdsourcing, death of newspapers, disintermediation, diversified portfolio, don't be evil, fear of failure, Firefox, future of journalism, Google Earth, Googley, Howard Rheingold, informal economy, inventory management, Jeff Bezos, jimmy wales, Kevin Kelly, Mark Zuckerberg, moral hazard, Network effects, new economy, Nicholas Carr, PageRank, peer-to-peer lending, post scarcity, prediction markets, pre–internet, Ronald Coase, search inside the book, Silicon Valley, Skype, social graph, social software, social web, spectrum auction, speech recognition, Steve Jobs, the medium is the message, The Nature of the Firm, the payments system, The Wisdom of Crowds, transaction costs, web of trust, Y Combinator, Zipcar

I can imagine it using us to create a vast repository of our reviews and recommendations about establishments (“leave your review after the tone” or “rate the restaurant using your keypad”). Google may find yet another side door to make money. Tech publisher Tim O’Reilly speculated on his blog that Google wants to gather billions of voice samples as we ask for listings. That will make its speech recognition smarter, helping it get ready for the day when phones and computers respond to voice commands. Chris Anderson, editor of Wired magazine, projected that by 2012, Google could make $144 million in fees from users if it charged for directory assistance, but by foregoing that revenue it could instead make $2.5 billion in the voice-powered mobile search market. As with newspaper classifieds, the entire industry may shrink but the winner will grab the biggest share of what is left.

pages: 394 words: 108,215

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

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

Soon, she began following Kay around in the hallways, telling him she wanted to learn to program. Kay took her under his wing, and before long she was writing intricate low-level software for his project. Others came to Xerox and then were pulled into Kay’s orbit, because his group was talking about the most “supercool things” in an already cool place. Dan Ingalls was working on a separate speech-recognition project across the hallway from Kay’s office and soon found he couldn’t resist Kay’s ideas. Ingalls had come to Stanford in 1966 as a graduate student in electrical engineering. He had grown up in Cambridge, steeped in both old-world wealth and intellectual scholarship. His family had been Virginia landowners for generations, but his father was a Harvard Sanskrit scholar. During the Second World War, Daniel H.

Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage by Zdravko Markov, Daniel T. Larose

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Firefox, information retrieval, Internet Archive, iterative process, natural language processing, pattern recognition, random walk, recommendation engine, semantic web, speech recognition, statistical model, William of Occam

For example, the document “Mary loves John” can be represented by the set of 2-grams {[Mary, loves], [loves, John]} and “John loves Mary” by {[John, loves], [loves, Mary]}. Now these four 2-grams are the features that represent our documents. In this representation the documents do not have any overlap. We have already mentioned n-grams as a technique for approximate string matching but they are also popular in many other areas where the task is detecting subsequences such as spelling correction, speech recognition, and character recognition. Shingled document representation can be used for estimating document resemblance. Let us denote the set of shingles of size w contained in document d as S(d,w). That is, the set S(d,w) contains all w-grams obtained from document d. Note that T (d) = S(d,1), because terms are in fact 1-grams. Also, S(d,|d|) = d (i.e., the document itself is a w-gram, where w is equal to the size of the document).

pages: 292 words: 85,151

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It) by Salim Ismail, Yuri van Geest

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23andMe, 3D printing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, bioinformatics, bitcoin, Black Swan, blockchain, Burning Man, business intelligence, business process, call centre, chief data officer, Clayton Christensen, clean water, cloud computing, cognitive bias, collaborative consumption, collaborative economy, corporate social responsibility, cross-subsidies, crowdsourcing, cryptocurrency, dark matter, Dean Kamen, dematerialisation, discounted cash flows, distributed ledger, Edward Snowden, Elon Musk, en.wikipedia.org, ethereum blockchain, Galaxy Zoo, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, hiring and firing, Hyperloop, industrial robot, Innovator's Dilemma, Internet of things, Iridium satellite, Isaac Newton, Jeff Bezos, Kevin Kelly, Kickstarter, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, loose coupling, loss aversion, Lyft, Mark Zuckerberg, market design, means of production, minimum viable product, natural language processing, Netflix Prize, Network effects, new economy, Oculus Rift, offshore financial centre, p-value, PageRank, pattern recognition, Paul Graham, Peter H. Diamandis: Planetary Resources, Peter Thiel, prediction markets, profit motive, publish or perish, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Ronald Coase, Second Machine Age, self-driving car, sharing economy, Silicon Valley, skunkworks, Skype, smart contracts, Snapchat, social software, software is eating the world, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, subscription business, supply-chain management, TaskRabbit, telepresence, telepresence robot, Tony Hsieh, transaction costs, Tyler Cowen: Great Stagnation, urban planning, WikiLeaks, winner-take-all economy, X Prize, Y Combinator

As an example: In June 2012, a team at Google X built a neural network of 16,000 computer processors with one billion connections. After allowing it to browse ten million randomly selected YouTube video thumbnails for three days, the network began to recognize cats, without actually knowing the concept of “cats.” Importantly, this was without any human intervention or input. In the two years since, the capabilities of Deep Learning have improved considerably. Today, in addition to improving speech recognition, creating a more effective search engine (Ray Kurzweil is working on this within Google) and identifying individual objects, Deep Learning algorithms can also detect particular episodes in videos and even describe them in text, all without human input. Deep Learning algorithms can even play video games by figuring out the rules of the game and then optimizing performance. Think about the implications of this revolutionary breakthrough.

pages: 396 words: 107,814

Is That a Fish in Your Ear?: Translation and the Meaning of Everything by David Bellos

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Clapham omnibus, Claude Shannon: information theory, Douglas Hofstadter, Etonian, European colonialism, haute cuisine, invention of the telephone, invention of writing, natural language processing, Republic of Letters, speech recognition

A second threat to maintaining current language practice in international organizations is that some states may become unwilling to finance simultaneous interpretation into languages that are ceasing to be global vehicular tongues—but the replacement of Russian (for example) may prove politically impossible for many decades yet, and nobody has a clear idea of what might replace French. But the bigger threat looming on the horizon is something that’s going on right now in research labs in New Jersey and elsewhere. Using the technology of speech recognition that allows a widely available word processor to generate text from speech, alongside the speech synthesis systems that power today’s automated answering machines, the FAHQT target that current U.S. science policy encourages could well become FAHQST—fully automated, high-quality speech translation. Experimental systems not very far from commercial release already produce running English text from Spanish speech.

pages: 312 words: 91,538

The Fear Index by Robert Harris

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algorithmic trading, backtesting, banking crisis, dark matter, family office, Fellow of the Royal Society, fixed income, Flash crash, high net worth, implied volatility, mutually assured destruction, Renaissance Technologies, speech recognition

Instead, the humans that computers are replacing are members of the educated classes: translators, medical technicians, legal clerks, accountants, financial traders. ‘Computers are increasingly reliable translators in the sectors of commerce and technology. In medicine they can listen to a patient’s symptoms and are diagnosing illnesses and even prescribing treatment. In the law they search and evaluate vast amounts of complex documents at a fraction of the cost of legal analysts. Speech recognition enables algorithms to extract the meaning from the spoken as well as the written word. News bulletins can be analysed in real time. ‘When Hugo and I started this fund, the data we used was entirely digitalised financial statistics: there was almost nothing else. But over the past couple of years a whole new galaxy of information has come within our reach. Pretty soon all the information in the world – every tiny scrap of knowledge that humans possess, every little thought we’ve ever had that’s been considered worth preserving over thousands of years – all of it will be available digitally.

pages: 484 words: 104,873

Rise of the Robots: Technology and the Threat of a Jobless Future by Martin Ford

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3D printing, additive manufacturing, Affordable Care Act / Obamacare, AI winter, algorithmic trading, Amazon Mechanical Turk, artificial general intelligence, autonomous vehicles, banking crisis, Baxter: Rethink Robotics, Bernie Madoff, Bill Joy: nanobots, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chris Urmson, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, computer age, debt deflation, deskilling, diversified portfolio, Erik Brynjolfsson, factory automation, financial innovation, Flash crash, Fractional reserve banking, Freestyle chess, full employment, Goldman Sachs: Vampire Squid, High speed trading, income inequality, indoor plumbing, industrial robot, informal economy, iterative process, Jaron Lanier, job automation, John Maynard Keynes: technological unemployment, John von Neumann, Khan Academy, knowledge worker, labor-force participation, labour mobility, liquidity trap, low skilled workers, low-wage service sector, Lyft, manufacturing employment, McJob, moral hazard, Narrative Science, Network effects, new economy, Nicholas Carr, Norbert Wiener, obamacare, optical character recognition, passive income, performance metric, Peter Thiel, Plutocrats, plutocrats, post scarcity, precision agriculture, price mechanism, Ray Kurzweil, rent control, rent-seeking, reshoring, RFID, Richard Feynman, Richard Feynman, Rodney Brooks, secular stagnation, self-driving car, Silicon Valley, Silicon Valley startup, single-payer health, software is eating the world, sovereign wealth fund, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Steven Pinker, strong AI, Stuxnet, technological singularity, telepresence, telepresence robot, The Bell Curve by Richard Herrnstein and Charles Murray, The Coming Technological Singularity, Thomas L Friedman, too big to fail, Tyler Cowen: Great Stagnation, union organizing, Vernor Vinge, very high income, Watson beat the top human players on Jeopardy!, women in the workforce

Artificial neural networks were first conceived and experimented with in the late 1940s and have long been used to recognize patterns. However, the last few years have seen a number of dramatic breakthroughs that have resulted in significant advances in performance, especially when multiple layers of neurons are employed—a technology that has come to be called “deep learning.” Deep learning systems already power the speech recognition capability in Apple’s Siri and are poised to accelerate progress in a broad range of applications that rely on pattern analysis and recognition. A deep learning neural network designed in 2011 by scientists at the University of Lugano in Switzerland, for example, was able to correctly identify more than 99 percent of the images in a large database of traffic signs—a level of accuracy that exceeded that of human experts who competed against the system.

Pandora's Brain by Calum Chace

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3D printing, AI winter, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, brain emulation, Extropian, friendly AI, hive mind, Ray Kurzweil, self-driving car, Silicon Valley, Singularitarianism, Skype, speech recognition, stealth mode startup, Stephen Hawking, strong AI, technological singularity, theory of mind, Turing test, Wall-E

He looked away, turning back to his rucksack to avoid making Matt more self-conscious. Uncomfortable, Matt changed the subject. ‘So have you read any transhumanist literature?’ ‘Not really: as I say, just the odd article here and there. If you are curious about their ideas, perhaps you should read one of Ray Kurzweil’s books: he seems to be the best-known proponent. He’s an interesting chap, actually. He was a successful software developer – made a lot of money out of speech recognition software, if I remember right. He’s also written several books about an event called a Singularity, when the rate of technological progress becomes so fast that mere humans are unable to keep up, and we will have to upload our minds into computers. He thinks that this will happen remarkably soon – within your lifetime.’ ‘Yes, Carl said it was about uploading minds,’ Matt said. ‘But in my lifetime?

pages: 389 words: 109,207

Fortune's Formula: The Untold Story of the Scientific Betting System That Beat the Casinos and Wall Street by William Poundstone

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Albert Einstein, anti-communist, asset allocation, Benoit Mandelbrot, Black-Scholes formula, Brownian motion, buy low sell high, capital asset pricing model, Claude Shannon: information theory, computer age, correlation coefficient, diversified portfolio, en.wikipedia.org, Eugene Fama: efficient market hypothesis, high net worth, index fund, interest rate swap, Isaac Newton, Johann Wolfgang von Goethe, John von Neumann, Long Term Capital Management, Louis Bachelier, margin call, market bubble, market fundamentalism, Marshall McLuhan, New Journalism, Norbert Wiener, offshore financial centre, publish or perish, quantitative trading / quantitative finance, random walk, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, short selling, speech recognition, statistical arbitrage, The Predators' Ball, The Wealth of Nations by Adam Smith, transaction costs, traveling salesman, value at risk, zero-coupon bond

For instance, in 1978 Shannon investigated Perception Technology Corporation on behalf of Teledyne. Perception Technology was founded by an MIT physicist, Huseyin Yilmaz, whose training was largely in general relativity. During the visit with Shannon, Yilmaz spoke enthusiastically about physics, asserting that there was a “gap in Einstein’s equation” which Yilmaz had filled with an extra term. Yilmaz’s company, however, was involved in speech recognition. They had developed a secret “word spotter” that would allow intelligence agencies to automatically listen for key words like “missile” or “atomic” in tapped conversations. Another product allowed a computer to talk. Shannon’s pithy report warned Singleton that speech synthesis “is a very difficult field. Bell Telephone Laboratories spent many years and much manpower at this with little result…I had a curious feeling that the corporation is somewhat schizoid between corporate profits and general relativity.

pages: 371 words: 108,317

The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future by Kevin Kelly

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3D printing, A Declaration of the Independence of Cyberspace, AI winter, Airbnb, Albert Einstein, Amazon Web Services, augmented reality, bank run, barriers to entry, Baxter: Rethink Robotics, bitcoin, blockchain, book scanning, Brewster Kahle, Burning Man, cloud computing, computer age, connected car, crowdsourcing, dark matter, dematerialisation, Downton Abbey, Edward Snowden, Elon Musk, Filter Bubble, Freestyle chess, game design, Google Glasses, hive mind, Howard Rheingold, index card, indoor plumbing, industrial robot, Internet Archive, Internet of things, invention of movable type, invisible hand, Jaron Lanier, Jeff Bezos, job automation, Kevin Kelly, Kickstarter, linked data, Lyft, M-Pesa, Marshall McLuhan, means of production, megacity, Minecraft, multi-sided market, natural language processing, Netflix Prize, Network effects, new economy, Nicholas Carr, peer-to-peer lending, personalized medicine, placebo effect, planetary scale, postindustrial economy, recommendation engine, RFID, ride hailing / ride sharing, Rodney Brooks, self-driving car, sharing economy, Silicon Valley, slashdot, Snapchat, social graph, social web, software is eating the world, speech recognition, Stephen Hawking, Steven Levy, Ted Nelson, the scientific method, transport as a service, two-sided market, Uber for X, Watson beat the top human players on Jeopardy!, Whole Earth Review

A future office worker is not going to be pecking at a keyboard—not even a fancy glowing holographic keyboard—but will be talking to a device with a newly evolved set of hand gestures, similar to the ones we now have of pinching our fingers in to reduce size, pinching them out to enlarge, or holding up two L-shaped pointing hands to frame and select something. Phones are very close to perfecting speech recognition today (including being able to translate in real time), so voice will be a huge part of interacting with devices. If you’d like to have a vivid picture of someone interacting with a portable device in the year 2050, imagine them using their eyes to visually “select” from a set of rapidly flickering options on the screen, confirming with lazy audible grunts, and speedily fluttering their hands in their laps or at their waist.

pages: 319 words: 90,965

The End of College: Creating the Future of Learning and the University of Everywhere by Kevin Carey

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Albert Einstein, barriers to entry, Berlin Wall, business intelligence, carbon-based life, Claude Shannon: information theory, complexity theory, declining real wages, deliberate practice, discrete time, double helix, Douglas Engelbart, Downton Abbey, Drosophila, Firefox, Frank Gehry, Google X / Alphabet X, informal economy, invention of the printing press, inventory management, Khan Academy, Kickstarter, low skilled workers, Lyft, Mark Zuckerberg, meta analysis, meta-analysis, natural language processing, Network effects, open borders, pattern recognition, Peter Thiel, pez dispenser, ride hailing / ride sharing, Ronald Reagan, Sand Hill Road, self-driving car, Silicon Valley, Silicon Valley startup, social web, South of Market, San Francisco, speech recognition, Steve Jobs, technoutopianism, transcontinental railway, Vannevar Bush

For most people, information only moved in one direction through the airwaves—broadcast out—and the same was true for the growing network of coaxial cable. The only common way to exchange information in real time at a distance was over the copper wire telephone network, and that was designed for voice interaction. This happened to be one of the tasks computers couldn’t do very well. Although many advances have been made in electronic speech recognition, nobody has invented a computer that can listen and talk to you with the speed and facility of a person. When the Internet became available to ordinary people in the mid-1990s, many of these barriers began to fall. Connections to both powerful computers and to other people that had been limited to small populations in the defense and research sectors were suddenly available to millions and then billions worldwide.

pages: 481 words: 121,669

The Invisible Web: Uncovering Information Sources Search Engines Can't See by Gary Price, Chris Sherman, Danny Sullivan

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AltaVista, American Society of Civil Engineers: Report Card, bioinformatics, Brewster Kahle, business intelligence, dark matter, Douglas Engelbart, full text search, HyperCard, hypertext link, information retrieval, Internet Archive, joint-stock company, knowledge worker, natural language processing, pre–internet, profit motive, publish or perish, search engine result page, side project, Silicon Valley, speech recognition, stealth mode startup, Ted Nelson, Vannevar Bush, web application

The telltale sign of a dynamically generated page is the “?” symbol appearing in its URL. Technically, these pages are part of the visible Web. Crawlers can fetch any page that can be displayed in a Web browser, regardless of whether it’s a static page stored on a server or generated dynamically. A good example of this type of Invisible Web site is Compaq’s experimental SpeechBot search engine, which indexes audio and video content using speech recognition, and converts the streaming media files to viewable text (http://www.speech bot.com). Somewhat ironically, one could make a good argument that most search engine result pages are themselves Invisible Web content, since they generate dynamic pages on the fly in response to user search terms. Dynamically generated pages pose a challenge for crawlers. Dynamic pages are created by a script, a computer program that selects from various options to assemble a customized page.

pages: 519 words: 118,095

Your Money: The Missing Manual by J.D. Roth

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Airbnb, asset allocation, bank run, buy low sell high, car-free, Community Supported Agriculture, delayed gratification, diversification, diversified portfolio, estate planning, Firefox, fixed income, full employment, Home mortgage interest deduction, index card, index fund, late fees, mortgage tax deduction, Own Your Own Home, passive investing, Paul Graham, random walk, Richard Bolles, risk tolerance, Robert Shiller, Robert Shiller, speech recognition, traveling salesman, Vanguard fund, web application, Zipcar

Earning Extra Cash in Your Spare Time Maybe you don't have a productive hobby or an attic full of Stuff to sell, but that doesn't mean you can't make a little money on the side. Here are a handful of ways to add to your cash flow: Research studies You can earn quick cash by participating in medical research and marketing studies. I once earned $120 for spending an hour inside an MRI scanner while answering questions about money. Other folks have earned $150 for giving opinions on food packaging, $50 to record 40 minutes of audio for a speech-recognition program—and even $35 for watching porn! Colleges and companies are always looking for people to join their experiments and focus groups. To find studies in your area, check Craigslist.org's "miscellaneous jobs" section or scope out college newspapers and bulletin boards. Here's a short video from MSN Money that describes one study: http://tinyurl.com/MSNmoneystudies. Tutoring Are you good at math?

pages: 476 words: 132,042

What Technology Wants by Kevin Kelly

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Albert Einstein, Alfred Russel Wallace, Buckminster Fuller, c2.com, carbon-based life, Cass Sunstein, charter city, Clayton Christensen, cloud computing, computer vision, Danny Hillis, dematerialisation, demographic transition, double entry bookkeeping, en.wikipedia.org, Exxon Valdez, George Gilder, gravity well, hive mind, Howard Rheingold, interchangeable parts, invention of air conditioning, invention of writing, Isaac Newton, Jaron Lanier, John Conway, John von Neumann, Kevin Kelly, knowledge economy, Lao Tzu, life extension, Louis Daguerre, Marshall McLuhan, megacity, meta analysis, meta-analysis, new economy, out of africa, performance metric, personalized medicine, phenotype, Picturephone, planetary scale, RAND corporation, random walk, Ray Kurzweil, recommendation engine, refrigerator car, Richard Florida, Silicon Valley, silicon-based life, Skype, speech recognition, Stephen Hawking, Steve Jobs, Stewart Brand, Ted Kaczynski, the built environment, the scientific method, Thomas Malthus, Vernor Vinge, Whole Earth Catalog, Y2K

Unless an artificial mind behaves exactly like a human one, we don’t count it as intelligent. Sometimes we dismiss it by calling it “machine learning.” So while we weren’t watching, billions of tiny, insectlike artificial minds spawned deep into the technium, doing invisible, low-profile chores like reliably detecting credit-card fraud or filtering e-mail spam or reading text from documents. These proliferating microminds run speech recognition on the phone, assist in crucial medical diagnosis, aid stock-market analysis, power fuzzy-logic appliances, and guide automatic gearshifts and brakes in cars. A few experimental minds can even drive a car autonomously for a hundred miles. The future of the technium at first seems to point to bigger brains. But a bigger computer is not necessarily smarter, more sentient. And even when intelligence is demonstrably greater in biological minds, it is only weakly correlated to how many brain cells are present.

pages: 478 words: 149,810

We Are Anonymous: Inside the Hacker World of LulzSec, Anonymous, and the Global Cyber Insurgency by Parmy Olson

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4chan, Asperger Syndrome, bitcoin, call centre, Chelsea Manning, corporate governance, crowdsourcing, Firefox, hive mind, Julian Assange, Minecraft, Occupy movement, pirate software, side project, Skype, speech recognition, Stephen Hawking, Stuxnet, We are Anonymous. We are Legion, We are the 99%, web application, WikiLeaks, zero day

When the #press channel’s participants read over the press release, it sounded so dramatic and ominous that they decided something similar should be narrated in a video, too. A member of the group, whose nickname was VSR, created a YouTube account called Church0fScientology, and the group spent the next several hours finding uncopyrighted footage and music, then writing a video script that could be narrated by an automated voice. The speech recognition technology was so bad they had to go back and misspell most of the words—destroyed became “dee stroid,” for instance—to make it sound natural. The final script ended up looking like nonsense but sounding like normal prose. When they finally put it together, a Stephen Hawking–style robotic voice said over an image of dark clouds, “Hello, leaders of Scientology, we are Anonymous.” It climbed to new heights of hyperbole, vowing to “systematically dismantle the Church of Scientology in its current form.

pages: 310 words: 34,482

Makers at Work: Folks Reinventing the World One Object or Idea at a Time by Steven Osborn

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3D printing, A Pattern Language, additive manufacturing, air freight, Airbnb, augmented reality, autonomous vehicles, barriers to entry, Baxter: Rethink Robotics, c2.com, computer vision, crowdsourcing, dumpster diving, en.wikipedia.org, Firefox, future of work, Google Chrome, Google Glasses, Google Hangouts, Hacker Ethic, Internet of things, Iridium satellite, Khan Academy, Kickstarter, Mason jar, means of production, Minecraft, minimum viable product, Network effects, Oculus Rift, patent troll, popular electronics, Rodney Brooks, Shenzhen was a fishing village, side project, Silicon Valley, Skype, slashdot, social software, software as a service, special economic zone, speech recognition, subscription business, telerobotics, urban planning, web application, Y Combinator

I’ve often said—I don’t have enough energy to do this myself yet—I always thought it would be nice to just grab a recorder and go to these old-timers and plop it down in front of them and just have them tell stories: “Tell us the nitty-gritty little stories about what you did and how you solved problems in engineering,” and get those recorded because it’s pretty exciting. All this media right now—we’re recording audio today. It’s not very searchable, but speech recognition is going to become more and more searchable in the future. If we could at least capture that information now and someday down the road it will become searchable. Video will become more searchable, too. Osborn: If I had one question for them, it would be, “Tell me about abusing some components to do things that they were not designed to do, and what did you use it for?” Those are always the best kind of stories.

pages: 462 words: 142,240

Iron Sunrise by Stross, Charles

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blood diamonds, dumpster diving, gravity well, hiring and firing, industrial robot, life extension, loose coupling, mutually assured destruction, phenotype, planetary scale, postindustrial economy, RFID, side project, speech recognition, technological singularity, trade route, uranium enrichment, urban sprawl

Wednesday blinked furiously. “I want it back before someone else finds it,” she said, forcing a tone of spoiled pique. Trying to figure it out, whatever it was that Wednesday had stashed near the police station in Old Newfie, was infuriating, but he didn’t dare say so openly while they might be under surveillance. The combination of ultrawideband transceivers, reprogrammed liaison network nodes, and speech recognition software had turned the entire ship into a panopticon prison — one where mentioning the wrong words could get a passenger into a world of pain. Martin’s head hurt just thinking about it, and he had an idea from her tense, clipped answers to any questions he asked her that Rachel felt the same way. They made it through a sleepless night (Wednesday staked out the smaller room off to one side of the suite for herself) and a deeply boring breakfast served up by the suite’s fab.

pages: 390 words: 114,538

Digital Wars: Apple, Google, Microsoft and the Battle for the Internet by Charles Arthur

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AltaVista, Build a better mousetrap, Burning Man, cloud computing, credit crunch, crowdsourcing, disintermediation, don't be evil, en.wikipedia.org, Firefox, gravity well, Jeff Bezos, John Gruber, Mark Zuckerberg, Menlo Park, Network effects, PageRank, pre–internet, Robert X Cringely, Silicon Valley, Silicon Valley startup, skunkworks, Skype, slashdot, Snapchat, software patent, speech recognition, stealth mode startup, Steve Ballmer, Steve Jobs, the scientific method, Tim Cook: Apple, upwardly mobile

In March 1999, Gates gave him and his team the task of developing a tablet computer – an idea that had echoed around Silicon Valley for decades: a letterpad-sized, portable computer without a keyboard that you could write on directly. (Its forebears are seen on the original Star Trek series, and the Etch A Sketch.) As Gates explained to the New York Times that August, ‘We’re trying to see if we can produce a tablet PC and the software for it that will be sufficiently powerful and intuitive and inexpensive to capture the imagination and the marketplace.’1 He thought handwriting or speech recognition might replace the keyboard. Brass already knew he faced a huge challenge. It wasn’t over the quality of the idea; it was getting the right backing inside the company. He had experience of that already: when the group invented a system for displaying text on-screen with greater legibility, which they called ClearType, he was told by the Windows group that some of the colours made the display break, and by the Office group that the display wasn’t sharp, but ‘fuzzy’.

pages: 405 words: 117,219

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

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

This technology is valuable for Facebook as it aspires to increase the ways in which it serves its billions of customers – and the advertising industry – by extracting meaning from its colossal and ever-expanding archive of user-generated content. Google has a similar aspiration: it wants to use AI technology to understand context and meaning, and thus provide better search resources, video recognition, speech recognition and translation, increased security, and smarter services when it comes to Google’s social networks and e-commerce platforms. When Google spent half a billion dollars to acquire the British company Deep Mind, it was in fact hedging a bet that Artificial Intelligence will define the second machine age. In this chapter I shall explore what all this means. How close are we to truly intelligent machines – complete with self-awareness?

pages: 624 words: 180,416

For the Win by Cory Doctorow

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barriers to entry, Burning Man, double helix, Internet Archive, inventory management, loose coupling, Maui Hawaii, microcredit, New Journalism, Ponzi scheme, Post-materialism, post-materialism, random walk, RFID, Silicon Valley, skunkworks, slashdot, speech recognition, stem cell, Steve Jobs, Steve Wozniak, supply-chain management, technoutopianism, union organizing, urban renewal, wage slave

NOW WATCH THIS The words materialized in the middle of the rectangle on the distant wall. “Testing one two three,” Kettlewell said. TESTING ONE TWO THREE “Donde esta el bano?” WHERE IS THE BATHROOM “What is it?” said Suzanne. Her hand wobbled a little and the distant letters danced. WHAT IS IT “This is a new artifact designed and executed by five previously out-of-work engineers in Athens, Georgia. They’ve mated a tiny Linux box with some speaker-independent continuous speech recognition software, a free software translation engine that can translate between any of twelve languages, and an extremely high-resolution LCD that blocks out words in the path of the laser-pointer. “Turn this on, point it at a wall, and start talking. Everything said shows up on the wall, in the language of your choosing, regardless of what language the speaker was speaking.” All the while, Kettlewell’s words were scrolling by in black block caps on that distant wall: crisp, laser-edged letters.

pages: 797 words: 227,399

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

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

When he was seventeen, he appeared on the game show I’ve Got a Secret. His “secret” was a song composed by a computer that he had built. Soon after, Kurzweil created such inventions as an automated college application program, the first print-to-speech reading machine for the blind (considered the biggest advancement for the visually impaired since the Braille language in 1829), the first computer flatbed scanner, and the first large-vocabulary speech recognition system. The musician Stevie Wonder, who used one of Kurzweil’s reading machines, then urged him to invent an electronic music synthesizer that could re-create the sounds of pianos and other orchestral instruments. So Kurzweil did. As his inventions piled up, Forbes magazine called him “the Ultimate Thinking Machine” and “rightful heir to Thomas Edison.” Three different U.S. presidents have honored him, and in 2002 he was inducted into the National Inventors Hall of Fame.

pages: 598 words: 183,531

Hackers: Heroes of the Computer Revolution - 25th Anniversary Edition by Steven Levy

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air freight, Apple II, Bill Gates: Altair 8800, Buckminster Fuller, Byte Shop, computer age, computer vision, corporate governance, El Camino Real, game design, Hacker Ethic, hacker house, Haight Ashbury, John Conway, Mark Zuckerberg, Menlo Park, non-fiction novel, Paul Graham, popular electronics, RAND corporation, reversible computing, Richard Stallman, Silicon Valley, software patent, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Ted Nelson, Whole Earth Catalog, Y Combinator

As much as the hackers tried to make their own world on the ninth floor, it could not be done. The movement of key people was inevitable. And the harsh realities of funding hit Tech Square in the seventies: ARPA, adhering to the strict new Mansfield Amendment passed by Congress, had to ask for specific justification for many computer projects. The unlimited funds for basic research were drying up; ARPA was pushing some pet projects like speech recognition (which would have directly increased the government’s ability to mass-monitor phone conversations abroad and at home). Minsky thought the policy was a “losing” one, and distanced the AI lab from it. But there was no longer enough money to hire anyone who showed exceptional talent for hacking. And slowly, as MIT itself became more ensconced in training students for conventional computer studies, the Institute’s attitude to computer studies shifted focus somewhat.

pages: 834 words: 180,700

The Architecture of Open Source Applications by Amy Brown, Greg Wilson

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8-hour work day, anti-pattern, bioinformatics, c2.com, cloud computing, collaborative editing, combinatorial explosion, computer vision, continuous integration, create, read, update, delete, Debian, domain-specific language, en.wikipedia.org, fault tolerance, finite state, Firefox, friendly fire, linked data, load shedding, locality of reference, loose coupling, Mars Rover, MVC pattern, premature optimization, recommendation engine, revision control, side project, Skype, slashdot, social web, speech recognition, the scientific method, The Wisdom of Crowds, web application, WebSocket

This ability to get many different types of phone calls into and out of the system is one of Asterisk's main strengths. Once phone calls are made to and from an Asterisk system, there are many additional features that can be used to customize the processing of the phone call. Some features are larger pre-built common applications, such as voicemail. There are other smaller features that can be combined together to create custom voice applications, such as playing back a sound file, reading digits, or speech recognition. 1.1. Critical Architectural Concepts This section discusses some architectural concepts that are critical to all parts of Asterisk. These ideas are at the foundation of the Asterisk architecture. 1.1.1. Channels A channel in Asterisk represents a connection between the Asterisk system and some telephony endpoint (Figure 1.1). The most common example is when a phone makes a call into an Asterisk system.

pages: 701 words: 199,010

The Crisis of Crowding: Quant Copycats, Ugly Models, and the New Crash Normal by Ludwig B. Chincarini

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affirmative action, asset-backed security, automated trading system, bank run, banking crisis, Basel III, Bernie Madoff, Black-Scholes formula, buttonwood tree, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, discounted cash flows, diversification, diversified portfolio, family office, financial innovation, financial intermediation, fixed income, Flash crash, full employment, Gini coefficient, high net worth, hindsight bias, housing crisis, implied volatility, income inequality, interest rate derivative, interest rate swap, labour mobility, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, low skilled workers, margin call, market design, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, moral hazard, mortgage debt, Northern Rock, Occupy movement, oil shock, price stability, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Waldo Emerson, regulatory arbitrage, Renaissance Technologies, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, Sharpe ratio, short selling, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, systematic trading, The Great Moderation, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond

The fund uses complex mathematical models to execute trades, which are often automated and generated on a model’s signal. One-third of the fund’s employees have PhDs in fields such as statistics, economics, mathematics, and physics. The firm depends on models built by mathematician Leonard Baum, co-author of the Baum-Welch algorithm, which determine probabilities in (among other things) biology, automated speech recognition, and statistical computing. Simons hoped to harness Baum’s mathematical models to trade currencies. The models and techniques were modified over time, but stayed rooted in the quantitative discipline. Some of Medallion’s staff eventually left to start their own hedge funds. Sandor Strauss, for instance, started Merfin LLC using a similar methodology. 10. Zuckerman et al. (2007) and Sender et al. (2007). 11.

pages: 1,199 words: 332,563

Golden Holocaust: Origins of the Cigarette Catastrophe and the Case for Abolition by Robert N. Proctor

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bioinformatics, carbon footprint, clean water, corporate social responsibility, Deng Xiaoping, desegregation, facts on the ground, friendly fire, germ theory of disease, index card, Indoor air pollution, information retrieval, invention of gunpowder, John Snow's cholera map, language of flowers, life extension, New Journalism, optical character recognition, pink-collar, Ponzi scheme, Potemkin village, Ralph Nader, Ronald Reagan, speech recognition, stem cell, telemarketer, Thomas Kuhn: the structure of scientific revolutions, Triangle Shirtwaist Factory, Upton Sinclair, Yogi Berra

In 1993, for example, just to receive calls and process orders for its Marlboro Adventure Team promotion, Philip Morris established a new 450,000-square-foot “fulfillment facility” in Lafayette, Indiana, staffed by 350 employees, and a new Customer Service Telemarketing Facility in Kankakee, Illinois, with a staff of 25 to handle phone orders. Philip Morris in the year 2000 expanded its call-receiving capabilities, implementing natural-language speech recognition, standby promotional and apology mail packages, and a “new attitude” tailoring personal service to the individual smoker. Callers were given a personalized consumer ID and PIN to allow personal logins, and email and fax programs were installed to reach consumers more quickly. For a time the industry hoped to replace its telephonic contacts with fax, email, and web-based interactions, though phone calls apparently still remain important, with texting and interactive web 2.0 advertising close on their heels.77 Philip Morris is not the only tobacco company to engage outside firms for such purposes.

pages: 892 words: 91,000

Valuation: Measuring and Managing the Value of Companies by Tim Koller, McKinsey, Company Inc., Marc Goedhart, David Wessels, Barbara Schwimmer, Franziska Manoury

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air freight, barriers to entry, Basel III, BRICs, business climate, business process, capital asset pricing model, capital controls, cloud computing, compound rate of return, conceptual framework, corporate governance, corporate social responsibility, credit crunch, Credit Default Swap, discounted cash flows, distributed generation, diversified portfolio, energy security, equity premium, index fund, iterative process, Long Term Capital Management, market bubble, market friction, meta analysis, meta-analysis, new economy, p-value, performance metric, Ponzi scheme, price anchoring, purchasing power parity, quantitative easing, risk/return, Robert Shiller, Robert Shiller, shareholder value, six sigma, sovereign wealth fund, speech recognition, technology bubble, time value of money, too big to fail, transaction costs, transfer pricing, value at risk, yield curve, zero-coupon bond

They do this because they can acquire the technology more quickly than developing it themselves, avoid royalty 14 IBM Investor Briefing website, 2014. 610 MERGERS AND ACQUISITIONS payments on patented technologies, and keep the technology away from competitors. For example, Apple bought Siri (the automated personal assistant) in 2010 to enhance its iPhones. More recently, in 2014, Apple purchased Novauris Technologies, a speech recognition technology company, to further enhance Siri’s capabilities. In 2014, Apple also purchased Beats Electronics, which had recently launched a music-streaming service. One reason for the acquisition was to quickly offer its customers a music-streaming service, as the market was moving away from Apple’s iTunes business model of purchasing and downloading music. Cisco Systems, the network product and services company (with $49 billion in revenue in 2013), used acquisitions of key technologies to assemble a broad line of network solution products during the frenzied Internet growth period.