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pages: 666 words: 181,495

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

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, Donald Knuth, Douglas Engelbart, Douglas Engelbart, El Camino Real, fault tolerance, Firefox, Gerard Salton, Gerard Salton, Google bus, Google Chrome, Google Earth, Googley, HyperCard, hypertext link, IBM and the Holocaust, informal economy, information retrieval, Internet Archive, Jeff Bezos, John Markoff, Kevin Kelly, Kickstarter, Mark Zuckerberg, Menlo Park, one-China policy, optical character recognition, PageRank, Paul Buchheit, Potemkin village, prediction markets, recommendation engine, risk tolerance, Rubik’s Cube, Sand Hill Road, Saturday Night Live, search inside the book, second-price auction, selection bias, 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, turn-by-turn navigation, undersea cable, Vannevar Bush, web application, WikiLeaks, Y Combinator

The first assigned reading was their own paper, but later in the semester a class was devoted to a comparison of PageRank and Kleinberg’s work. In December, after the final projects were due, Page emailed the students a party invitation that also marked a milestone: “The Stanford Research Project is now The Next Generation Internet Search Company.” “Dress is Tiki Lounge wear,” the invitation read, “and bring something for the hot tub.” 2 “We want Google to be as smart as you.” Larry Page did not want to be Tesla’d. Google had quickly become a darling of everyone who used it to search the net. But at first so had AltaVista, and that search engine had failed to improve. How was Google, led by two talented but inexperienced youngsters, going to tackle the devilishly difficult problems of improving its service?

Sites were now spending time, energy, and technical wizardry to deconstruct Google’s processes and artificially boost page rank. The practice was called search engine optimization, or SEO. You could see their handiwork when you typed in the name of a hotel. The website of the actual hotel would not appear on the first page. Instead, the top results would be dominated by companies specializing in hotel bookings. This made Google less useful. Cutts went to Wayne Rosing and told him that the company really needed to work on stopping spam. Rosing told him to go ahead and try. A delicate balance was required. Legitimate businesses as well as shady ones partook in the sport. Highly paid consultants tried to reverse-engineer PageRank and other Google techniques. Even amateurs could partake in the hunt for “Google juice,” buying books like Search Engine Optimization for Dummies.

However, I hadn’t expected that instead of being attired in traditional T-shirts and jeans, the employees were decked out in costumes. I had come on Halloween. “Steven, meet Larry Page and Sergey Brin,” said Cindy, introducing me to the two young men who had founded the company as Stanford graduate students. Larry was dressed as a Viking, with a long-haired fur vest and a hat with long antlers protruding. Sergey was in a cow suit. On his chest was a rubber slab from which protruded huge, wart-specked teats. They greeted me cheerfully and we all retreated to a conference room where the Viking and the cow explained the miraculous powers of Google’s PageRank technology. That was the first of many interviews I would conduct at Google. Over the next few years, the company became a focus of my technology reporting at Newsweek. Google grew from the small start-up I had visited to a behemoth of more than 20,000 employees.

pages: 250 words: 73,574

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

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

For readers with a computer science background, Search Engines: Information Retrieval in Practice, by Croft, Metzler, and Strohman, is a good option for learning more about indexing and many other aspects of search engines. PageRank (chapter 3). The opening quotation by Larry Page is taken from an interview by Ben Elgin, published in Businessweek, May 3, 2004. Vannevar Bush's “As We May Think” was, as mentioned above, originally published in The Atlantic magazine (July 1945). Bishop's lectures (see above) contain an elegant demonstration of PageRank using a system of water pipes to emulate hyperlinks. The original paper describing Google's architecture is “The Anatomy of a Large-Scale Hypertextual Web Search Engine,” written by Google's co-founders, Sergey Brin and Larry Page, and presented at the 1998 World Wide Web conference. The paper includes a brief description and analysis of PageRank. A much more technical, wide-ranging analysis appears in Langville and Meyer's Google's PageRank and Beyond—but this book requires college-level linear algebra.

And here is where our story really begins: in the words of PC Magazine, Google's elite status was awarded for its “uncanny knack for returning extremely relevant results.” You may recall from the last chapter that the first commercial search engines had been launched four years earlier, in 1994. How could the garage-bound Google overcome this phenomenal four-year deficit, leapfrogging the already-popular Lycos and AltaVista in terms of search quality? There is no simple answer to this question. But one of the most important factors, especially in those early days, was the innovative algorithm used by Google for ranking its search results: an algorithm known as PageRank. The name “PageRank” is a pun: it's an algorithm that ranks web pages, but it's also the ranking algorithm of Larry Page, its chief inventor. Page and Brin published the algorithm in 1998, in an academic conference paper, “The Anatomy of a Large-scale Hypertextual Web Search Engine.”

As we already know, efficient matching is only half the story for an effective search engine: the other grand challenge is to rank the matching pages. And as we will see in the next chapter, the emergence of a new type of ranking algorithm was enough to eclipse AltaVista, vaulting Google into the forefront of the world of web search. 3 PageRank: The Technology That Launched Google The Star Trek computer doesn't seem that interesting. They ask it random questions, it thinks for a while. I think we can do better than that. —LARRY PAGE (Google cofounder) Architecturally speaking, the garage is typically a humble entity. But in Silicon Valley, garages have a special entrepreneurial significance: many of the great Silicon Valley technology companies were born, or at least incubated, in a garage. This is not a trend that began in the dot-com boom of the 1990s.

pages: 281 words: 95,852

The Googlization of Everything: by Siva Vaidhyanathan

1960s counterculture, activist fund / activist shareholder / activist investor, AltaVista, barriers to entry, Berlin Wall, borderless world, Burning Man, Cass Sunstein, choice architecture, cloud computing, computer age, corporate social responsibility, correlation does not imply causation, creative destruction, data acquisition, death of newspapers, don't be evil, Firefox, Francis Fukuyama: the end of history, full text search, global pandemic, global village, Google Earth, Howard Rheingold, informal economy, information retrieval, John Markoff, Joseph Schumpeter, Kevin Kelly, knowledge worker, libertarian paternalism, market fundamentalism, Marshall McLuhan, means of production, Mikhail Gorbachev, moral panic, Naomi Klein, Network effects, new economy, Nicholas Carr, PageRank, Panopticon Jeremy Bentham, pirate software, Ray Kurzweil, Richard Thaler, Ronald Reagan, side project, Silicon Valley, Silicon Valley ideology, single-payer health, Skype, Social Responsibility of Business Is to Increase Its Profits, social web, Steven Levy, Stewart Brand, technoutopianism, The Nature of the Firm, The Structural Transformation of the Public Sphere, Thorstein Veblen, urban decay, web application, zero-sum game

Unfortunately, universities have allowed Google to take the lead in and set the terms of the relationship. There is a strong cultural affinity between Google corporate culture and that of academia. Google’s founders, Sergey Brin and Larry Page, THE GOOGL I ZAT I ON OF ME MORY 187 met while pursuing PhDs in computer science at Stanford University.16 The foundational concept behind Google Web Search, the PageRank algorithm, emerged from an academic paper that Brin and Page wrote and published in 1999.17 Page did his undergraduate work at the University of Michigan and retains strong ties with that institution. Some of the most visionary Google employees, such as the University of California at Berkeley economist Hal Varian, suspended successful academic careers to join the company. So it’s not surprising that Google’s corporate culture reflects much of the best of academic work life: unstructured work time, horizontal management structures, multidirectional information and feedback flows, an altruistic sense of mission, recreation and physical activity integrated centrally into the “campus,” and an alarmingly relaxed dress code.

Links are a sort of currency on the Web because those who make Web pages usually understand that GOOGL E ’S WAYS A ND ME A N S 63 Google rewards them, but no such ethic exists generally among commercial sites. By relying on PageRank, Google has historically favored highly motivated and Web-savvy interests over truly popular, important, or valid interests. Being popular or important on the Web is not the same as being popular or important in the real world. Google tilts toward the geeky and Webby, as well as toward the new and loud. For example, if you search for “God” on Google Web Search, as I did on July 15, 2009, from my home in Virginia, you could receive a set of listings that reflect the peculiar biases of PageRank. The Wikipedia page for “God” ranks highest. That’s interesting for a number of reasons.

To accomplish the goal of generating a natural-language or semantic search system, search companies need two things: brilliant thinkers in the areas of linguistics, logic, and computer science, and massive collections of human-produced language on which computers can conduct complex statistical analysis. Many companies have the former. Only Google, Yahoo, and Microsoft have the latter. Of those, Google leads the pack. It’s no accident that Google has enthusiastically scanned and “read” millions of books from some of the world’s largest libraries. It wants to collect enough examples of grammar and diction in enough languages from enough places to generate the algorithms that can conduct naturallanguage searches. Google already deploys some elements of semantic analysis in its search process. PageRank is no longer flat and democratic. When I typed “What is the capital of Norway?” into Google in August 2010, the top result was “Oslo” from the Web Definitions site hosted by Princeton University. The second result was “Oslo” from Wikipedia.

pages: 290 words: 73,000

Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Umoja Noble

A Declaration of the Independence of Cyberspace, affirmative action, Airbnb, borderless world, cloud computing, conceptual framework, crowdsourcing, desegregation, Donald Trump, Edward Snowden, Filter Bubble, Firefox, Google Earth, Google Glasses, housing crisis, illegal immigration, immigration reform, information retrieval, Internet Archive, Jaron Lanier, Mitch Kapor, Naomi Klein, new economy, PageRank, performance metric, phenotype, profit motive, Silicon Valley, Silicon Valley ideology, Snapchat, Tim Cook: Apple, union organizing, women in the workforce, yellow journalism

Of course, this makes sense, because Google Search is in fact an advertising platform, not intended to solely serve as a public information resource in the way that, say, a library might. Google creates advertising algorithms, not information algorithms. To understand search in the context of this book, it is important to look at the description of the development of Google outlined by the former Stanford computer science graduate students and cofounders of the company, Sergey Brin and Larry Page, in “The Anatomy of a Large-Scale Hypertextual Web Search Engine.” Their paper, written in graduate school, serves as the architectural framework for Google’s PageRank. In addition, it is crucial to also look at the way that citation analysis, the foundational notion behind Brin and Page’s idea, works as a bibliometric project that has been extensively developed by library and information science scholars.

Judit Bar-Ilan, a professor of information science at Bar-Ilan University, has studied this practice to see if the effect of forcing results to the top of PageRank has a lasting effect on the result’s persistence, which can happen in well-orchestrated campaigns. In essence, Google bombing is the process of co-opting content or a term and redirecting it to unrelated content. Internet lore attributes the creation of the term “Google bombing” to Adam Mathes, who associated the term “talentless hack” with a friend’s website in 2001. Practices such as Google bombing (also known as Google washing) are impacting both SEO companies and Google alike. While Google is invested in maintaining the quality of search results in PageRank and policing companies that attempt to “game the system,” as Brin and Page foreshadowed, SEO companies do not want to lose ground in pushing their clients or their brands up in PageRank.48 SEO is the process of “using a range of techniques, including augmenting HTML code, web page copy editing, site navigation, linking campaigns and more, in order to improve how well a site or page gets listed in search engines for particular search topics,”49 in contrast to “paid search,” in which the company pays Google for its ads to be displayed when specific terms are searched.

This advertising mechanism is an essential part of how PageRank prioritizes ads on a page, and the association of certain keywords with particular industries, products, and services derives from this process, which works in tandem with PageRank. In order to make sense of the specific results in keyword searches, it is important to know how Google’s PageRank works, what commercial processes are involved in PageRank, how search engine optimization (SEO) companies have been developed to influence the process of moving up results,46 and how Google bombing47 occurs on occasion. Google bombing is the practice of excessively hyperlinking to a website (repeatedly coding HTML to link a page to a term or phrase) to cause it to rise to the top of PageRank, but it is also seen as a type of “hit and run” activity that can deliberately co-opt terms and identities on the web for political, ideological, and satirical purposes.

Understanding search engines: mathematical modeling and text retrieval by Michael W. Berry, Murray Browne

information retrieval, PageRank

Another more well-known, similar, linkage data approach is the PageRank algorithm developed by the founders of Google, Larry Page and Sergey Brin [49]. Page and Brin were graduate students at Stanford in 1998 when they published a paper describing the fundamental concepts of the PageRank algorithm, which later was used as the underlying algorithm that currently drives Google [49]. Unlike the HITS algorithm, where the results are created after the query is made, Google has the Web crawled and indexed ahead of time, and the links within these pages are analyzed before the query is ever entered by the user. Basically, Google looks not only at the number of links to The History of Meat and Potatoes website — referring to the earlier example — but also the importance of those referring links. Google determines how many other Meat and Potatoes websites are also being linked to the referring site and what is important about those sites.

This problem alone should provide ample fodder for research in large-scale link-based search technologies. 7.2.3 PageRank Summary Similar to HITS, PageRank can suffer from topic drift. The importance of a webpage (as defined by its query-independent PageRank score) does not necessarily reflect the relevance of the webpage to a user's query. Unpopular yet very relevant webpages may be missed with PageRank scoring. Some consider this a major weakness of Google [15]. On the other hand, the query independence of PageRank has made Google a great success in the speed and ease of web searching. A clear advantage of PageRank over the HITS approach lies in its resilience to spamming. Attempts to increase the number of inlinks to a webpage with hopes of increasing its PageRank are possible, but their global effects are limited. Some spam recognition techniques have been developed [81, 16].

As the original matrix A reflects the true link structure of the Web, it is highly desirable to make minimal perturbations to force irreducibility. 7.2.2 PageRank Implementation As mentioned in [63], PageRank is updated about once a month and does not require any analysis of the actual (semantic) content of the Web or of a user's queries. Google must find semantic matches (webpages) with a user's query first and then rank order the returned list according to PageRank. As mentioned earlier, the computation of PageRank is quite a challenge in and of itself. What can one do to make this computation tractable? As summarized in [49], approaches to compute PageRank via the power iteration may involve (i) parallelization of the sparse vector-matrix multiplications, (ii) partitioning of the stochastic iteration matrix [50, 54] (e.g., into a block of webpages with outlinks and another block of those without outlinks), (iii) extrapolation or aggregation techniques [40, 41] to speed up convergence to the stationary distribution vector x, and (iv) adaptive methods [39] which track the convergence of individual elements in Xj .

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

1960s counterculture, Ada Lovelace, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, AltaVista, Apple II, augmented reality, back-to-the-land, beat the dealer, Bill Gates: Altair 8800, bitcoin, Bob Noyce, Buckminster Fuller, Byte Shop,, call centre, citizen journalism, Claude Shannon: information theory, Clayton Christensen, commoditize, computer age, crowdsourcing, cryptocurrency, Debian, desegregation, Donald Davies, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dynabook, El Camino Real, Electric Kool-Aid Acid Test,, Firefox, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Hacker Ethic, Haight Ashbury, Howard Rheingold, Hush-A-Phone, HyperCard, hypertext link, index card, Internet Archive, Jacquard loom, Jaron Lanier, Jeff Bezos, jimmy wales, John Markoff, John von Neumann, Joseph-Marie Jacquard, Leonard Kleinrock, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Mitch Kapor, Mother of all demos, new economy, New Journalism, Norbert Wiener, Norman Macrae, packet switching, PageRank, Paul Terrell, pirate software, popular electronics, pre–internet, RAND corporation, Ray Kurzweil, RFC: Request For Comment, Richard Feynman, Richard Stallman, Robert Metcalfe, Rubik’s Cube, Sand Hill Road, Saturday Night Live, self-driving car, Silicon Valley, Silicon Valley startup, Skype, slashdot, speech recognition, Steve Ballmer, Steve Crocker, Steve Jobs, Steve Wozniak, Steven Levy, Steven Pinker, Stewart Brand, technological singularity, technoutopianism, Ted Nelson, The Coming Technological Singularity, The Nature of the Firm, The Wisdom of Crowds, Turing complete, Turing machine, Turing test, Vannevar Bush, Vernor Vinge, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, Whole Earth Review, wikimedia commons, William Shockley: the traitorous eight

s=Lost+Google+Tapes; John Ince, “Google Flashback—My 2000 Interviews,” Huffington Post, Feb. 6, 2012; Ken Auletta, Googled (Penguin, 2009); Battelle, The Search; Richard Brandt, The Google Guys (Penguin, 2011); Steven Levy, In the Plex (Simon & Schuster, 2011); Randall Stross, Planet Google (Free Press, 2008); David Vise, The Google Story (Delacorte, 2005); Douglas Edwards, I’m Feeling Lucky: The Confessions of Google Employee Number 59 (Mariner, 2012); Brenna McBride, “The Ultimate Search,” College Park magazine, Spring 2000; Mark Malseed, “The Story of Sergey Brin,” Moment magazine, Feb. 2007. 116. Author’s interview with Larry Page. 117. Larry Page interview, Academy of Achievement. 118. Larry Page interview, by Andy Serwer, Fortune, May 1, 2008. 119. Author’s interview with Larry Page. 120. Author’s interview with Larry Page. 121. Author’s interview with Larry Page. 122. Larry Page, Michigan commencement address. 123. Author’s interview with Larry Page. 124. Author’s interview with Larry Page. 125. Author’s interview with Larry Page. 126. Battelle, The Search, 1031. 127. Auletta, Googled, 28. 128. Interview with Larry Page and Sergey Brin, conducted by Barbara Walters, ABC News, Dec. 8, 2004. 129. Sergey Brin talk, Breakthrough Learning conference, Google headquarters, Nov. 12, 2009. 130. Malseed, “The Story of Sergey Brin.” 131.

McBride, “The Ultimate Search.” 133. Auletta, Googled, 31. 134. Auletta, Googled, 32. 135. Vise, The Google Story, 33. 136. Auletta, Googled, 39. 137. Author’s interview with Larry Page. 138. Author’s interview with Larry Page. 139. Terry Winograd interview, conducted by Bill Moggridge, 140. Author’s interview with Larry Page. 141. Craig Silverstein, Sergey Brin, Rajeev Motwani, and Jeff Ullman, “Scalable Techniques for Mining Causal Structures,” Data Mining and Knowledge Discovery, July 2000. 142. Author’s interview with Larry Page. 143. Author’s interview with Larry Page. 144. Larry Page, Michigan commencement address. 145. Vise, The Google Story, 10. 146. Larry Page, Michigan commencement address. 147. Battelle, The Search, 1183. 148.

As the project evolved, he and Brin conjured up more sophisticated ways to assess the value of each page, based on the number and quality of links coming into it. That’s when it dawned on the BackRub Boys that their index of pages ranked by importance could become the foundation for a high-quality search engine. Thus was Google born. “When a really great dream shows up,” Page later said, “grab it!”149 At first the revised project was called PageRank, because it ranked each page captured in the BackRub index and, not incidentally, played to Page’s wry humor and touch of vanity. “Yeah, I was referring to myself, unfortunately,” he later sheepishly admitted. “I feel kind of bad about it.”150 That page-ranking goal led to yet another layer of complexity. Instead of just tabulating the number of links that pointed to a page, Page and Brin realized that it would be even better if they could also assign a value to each of those incoming links.

pages: 496 words: 154,363

I'm Feeling Lucky: The Confessions of Google Employee Number 59 by Douglas Edwards

Albert Einstein, AltaVista, Any sufficiently advanced technology is indistinguishable from magic, barriers to entry, book scanning, Build a better mousetrap, Burning Man, business intelligence, call centre, commoditize, crowdsourcing, don't be evil, Elon Musk, fault tolerance, Googley, gravity well, invisible hand, Jeff Bezos, job-hopping, John Markoff, Kickstarter, Marc Andreessen, Menlo Park, microcredit, music of the spheres, Network effects, PageRank, performance metric,, Ralph Nader, risk tolerance, second-price auction, side project, Silicon Valley, Silicon Valley startup, slashdot, stem cell, Superbowl ad, Y2K

It's some kind of technical way to say "unrelated." I still don't really get it. But that didn't stop me from casually dropping it into conversations with engineers: "Oh, yeah, that press release is totally orthogonal to the ads we're running on Yahoo." Overture: The name assumed by the advertising network GoTo in October 2001. PageRank: An algorithm used for analyzing the relative importance of pages on the web. Written by, and named for, Google's co-founder Larry Page. PageRank's breakthrough approach was to look at the sites linking to a particular page to determine how many other websites deemed that page authoritative or important. Pay for inclusion: Some search engines accept payment from website owners to guarantee that their sites will be included in search results. These search engines don't necessarily guarantee the site prominent placement.

Except that when the "advanced features" were activated, they also gave Google a look at every page a user viewed. To tell you the PageRank of a site, Google needed to know what site you were visiting. The Toolbar sent that data back to Google if you let it, and Google would show you the green bar. The key was "if you let it," because you could also download a version of the toolbar that would not send any data back to Google. The user could make the choice, though Larry and the engineering team believed—and hoped—that most people wouldn't pass up the advanced features just because Google might learn their surfing habits. We're talking free extra data here. While knowing the PageRank of a page might have only nominal value to users, knowing the sites users visited would be tremendously valuable to Google. The PageRank indicator provided a justification for gathering it.

One of those products was a toolbar that tucked a search box right into users' browsers, enabling them to conduct Google searches without going to The product had been worked on by Joel Spolsky, a contract developer, based on prototypes developed by my UI team colleague Bay Chang. Googler David Watson created the first working version, and Eric Fredricksen finalized the software we actually launched. Larry was very keen to get it out the door. The Google toolbar came with more than just a search box. It had "advanced features." One displayed a green bar with a relative length approximating the PageRank of the web page the user was visiting. PageRank was Google's assessment of the importance of a page, determined by looking at the importance of the sites that linked to it. So, knowing a page's PageRank could give you a feel for whether or not Google viewed a site as reliable.

pages: 274 words: 75,846

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

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, Metcalfe’s law, Netflix Prize, new economy, PageRank, paypal mafia, Peter Thiel, recommendation engine, RFID, Robert Metcalfe, 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

But it would take two Stanford graduate students to apply the principles of machine learning to the whole world of online information. Click Signals As Jeff Bezos’s new company was getting off the ground, Larry Page and Sergey Brin, the founders of Google, were busy doing their doctoral research at Stanford. They were aware of Amazon’s success—in 1997, the dot-com bubble was in full swing, and Amazon, on paper at least, was worth billions. Page and Brin were math whizzes; Page, especially, was obsessed with AI. But they were interested in a different problem. Instead of using algorithms to figure out how to sell products more effectively, what if you could use them to sort through sites on the Web? Page had come up with a novel approach, and with a geeky predilection for puns, he called it PageRank. Most Web search companies at the time sorted pages using keywords and were very poor at figuring out which page for a given word was the most relevant.

Both companies’ primary advantage lies in the extraordinary number of people who trust them and use their services (remember lock-in?). According to Danny Sullivan’s Search Engine Land blog, Bing’s search results are “highly competitive” with Google’s, but it has a fraction of its more powerful rival’s users. It’s not a matter of math that keeps Google ahead, but the sheer number of people who use it every day. PageRank and the other major pieces of Google’s search engine are “actually one of the world’s worst kept secrets,” says Google fellow Amit Singhal. Google has also argued that it needs to keep its search algorithm under tight wraps because if it was known it’d be easier to game. But open systems are harder to game than closed ones, precisely because everyone shares an interest in closing loopholes.

Soon, the temptation to spin it off as a business was too great for the twenty-something cofounders to bear. In the Google mythology, it is PageRank that drove the company to worldwide dominance. I suspect the company likes it that way—it’s a simple, clear story that hangs the search giant’s success on a single ingenious breakthrough by one of its founders. But from the beginning, PageRank was just a small part of the Google project. What Brin and Page had really figured out was this: The key to relevance, the solution to sorting through the mass of data on the Web was ... more data. It wasn’t just which pages linked to which that Brin and Page were interested in. The position of a link on the page, the size of the link, the age of the page—all of these factors mattered. Over the years, Google has come to call these clues embedded in the data signals.

pages: 532 words: 139,706

Googled: The End of the World as We Know It by Ken Auletta

23andMe, AltaVista, Anne Wojcicki, Apple's 1984 Super Bowl advert, Ben Horowitz, bioinformatics, Burning Man, carbon footprint, citizen journalism, Clayton Christensen, cloud computing, Colonization of Mars, commoditize, corporate social responsibility, creative destruction, death of newspapers, disintermediation, don't be evil, facts on the ground, Firefox, Frank Gehry, Google Earth, hypertext link, Innovator's Dilemma, Internet Archive, invention of the telephone, Jeff Bezos, jimmy wales, John Markoff, Kevin Kelly, knowledge worker, Long Term Capital Management, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Network effects, new economy, Nicholas Carr, PageRank, Paul Buchheit, Peter Thiel, Ralph Waldo Emerson, Richard Feynman, Sand Hill Road, Saturday Night Live, semantic web, sharing economy, Silicon Valley, Skype, slashdot, social graph, spectrum auction, stealth mode startup, Stephen Hawking, Steve Ballmer, Steve Jobs, strikebreaker, telemarketer, the scientific method, The Wisdom of Crowds, Upton Sinclair, X Prize, yield management, zero-sum game

When a question is typed into the Google search box, the task is to divine the searcher’s intention: when you wrote “Jobs” in the query box, did you mean employment or Steve Jobs? The query may produce thousands of links, but the promise of Google—what Google considers its secret sauce—is that the ones that appear near the top of the search results will be more relevant to you. The company’s algorithms not only rank those links that generate the most traffic, and therefore are presumed to be more reliable, they also assign a slightly higher qualitative ranking to more reliable sources—like, for instance, a New York Times story. By mapping how many people click on a link, or found it interesting enough to link to, Google determines whether the link is “relevant” and assigns it a value. This quantified value is known as PageRank, after Larry Page. All this was interesting enough, but where the Google executives really got Karmazin’s attention was when they described the company’s advertising business, which accounted for almost all its revenues.

Norman, Design of Everyday Things, Basic Books, 1988. 37 an obsession of Larry’s: author interview with Larry Page, March 25, 2008. 38 disdained games like golf: author interview with Omid Kordestani, April 15, 2008. 38 “two swords sharpening each other”: author interview with John Battelle, March 20, 2008. 38 “they were not”: author interview with Terry Winograd, September 25, 2007. 38 Page and Brin’s breakthrough: Search, John Battelle. 39 “they didn’t have this false respect”: author interview with Rajeev Motwani, October 12, 2007. 39 snuck onto the loading dock: author interview with Terry Winograd: September 16, 2008. 39 “We wanted to finish school”: Page and Schmidt appearance at Stanford, May 1, 2002, available on YouTube. 40 “You guys can always come back”: author interview with Larry Page, March 25, 2008; confirmed in a May 5, 2008 e-mail to the author from Jeffrey Ullman. 40 They chose the name Google: Sergey Brin interview with John Ince on PodVentureZone, January 2000. 40 “two important features”: Page and Brin, “The Anatomy of a Large-Scale Hypertextual Web Search Engine”; a printed version, “The PageRank Citation Ranking: Bringing Order to the Web,” was published January 29, 1998, and is available on the Web. 40 “Brin and Page . . . are expressing a desire”: Nicholas Carr, Big Switch: Rewiring the World, From Edison to Google, W. W. Norton & Company, 2008. 41 “They were . . . part of an engineering tribe”: author interview with Lawrence Lessig, March 30, 2009. 41 “This is going to change the way”: author interview with Rajeev Motwani, October 12, 2007. 41 “free of many of the old prejudices”: Nicholas Negroponte, Being Digital, Alfred A.

See Google advertising cookies, use by Google as default search for browsers development of future threats mechanism of on mobile devices name, choosing versus other search engines PageRank Google Ventures “Google Version 2.0: The Calculating Predator,” Google Video Google Voice Googzilla concept Gore, Al on Apple board and Google Google, view of on Steve Jobs Gotlieb, Irwin on ads and smart phones career of on misuse of data observations on Google outlook for future GoTo GrandCentral Gravel, Mike Green initiatives Gross, Bill Grouf, Nick Group M Hands Off the Internet Harik, George Hashim, Smita Health records, Google Health Hecht, Albie Heiferman, Scott Hennessy Stanford L. Herskovitz, Marshall Hiring practices Hirschhorn, Jason Holden, Richard Hölzle, Urs Horowitz, Ben Horvath, Jane Huber, Jeff Hulu Hurley Chad Hybrid sites Idealism of founders and tech companies Iger, Robert Initial public offering (IPO).

Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman

cloud computing, crowdsourcing,, first-price auction, G4S, information retrieval, John Snow's cholera map, Netflix Prize, NP-complete, PageRank, pattern recognition, random walk, recommendation engine, second-price auction, sentiment analysis, social graph, statistical model, web application

Pedersen, “Combating link spam with trustrank,” Proc. 30th Intl. Conf. on Very Large Databases, pp. 576–587, 2004. [5]T.H. Haveliwala, “Efficient computation of PageRank,” Stanford Univ. Dept. of Computer Science technical report, Sept., 1999. Available as [6]T.H. Haveliwala, “Topic-sensitive PageRank,” Proc. 11th Intl. World-Wide- Web Conference, pp. 517–526, 2002 [7]J.M. Kleinberg, “Authoritative sources in a hyperlinked environment,” J. ACM 46:5, pp. 604–632, 1999. 1 Link spammers sometimes try to make their unethicality less apparent by referring to what they do as “search-engine optimization.” 2 The term PageRank comes from Larry Page, the inventor of the idea and a founder of Google. 3 They are so called because the programs that crawl the Web, recording pages and links, are often referred to as “spiders.”

Imagine, if you will, that the number of movies is extremely large, so counting ticket sales of each one separately is not feasible. 5 Link Analysis One of the biggest changes in our lives in the decade following the turn of the century was the availability of efficient and accurate Web search, through search engines such as Google. While Google was not the first search engine, it was the first able to defeat the spammers who had made search almost useless. Moreover, the innovation provided by Google was a nontrivial technological advance, called “PageRank.” We shall begin the chapter by explaining what PageRank is and how it is computed efficiently. Yet the war between those who want to make the Web useful and those who would exploit it for their own purposes is never over. When PageRank was established as an essential technique for a search engine, spammers invented ways to manipulate the PageRank of a Web page, often called link spam.1 That development led to the response of TrustRank and other techniques for preventing spammers from attacking PageRank. We shall discuss TrustRank and other approaches to detecting link spam.

Compute the hubs and authorities vectors, as a function of n. 5.6Summary of Chapter 5 ✦Term Spam: Early search engines were unable to deliver relevant results because they were vulnerable to term spam – the introduction into Web pages of words that misrepresented what the page was about. ✦The Google Solution to Term Spam: Google was able to counteract term spam by two techniques. First was the PageRank algorithm for determining the relative importance of pages on the Web. The second was a strategy of believing what other pages said about a given page, in or near their links to that page, rather than believing only what the page said about itself. ✦PageRank: PageRank is an algorithm that assigns a real number, called its PageRank, to each page on the Web. The PageRank of a page is a measure of how important the page is, or how likely it is to be a good response to a search query. In its simplest form, PageRank is a solution to the recursive equation “a page is important if important pages link to it.”

The Art of SEO by Eric Enge, Stephan Spencer, Jessie Stricchiola, Rand Fishkin

AltaVista, barriers to entry, bounce rate, Build a better mousetrap, business intelligence, cloud computing, dark matter,, Firefox, Google Chrome, Google Earth, hypertext link, index card, information retrieval, Internet Archive, Law of Accelerating Returns, linked data, mass immigration, Metcalfe’s law, Network effects, optical character recognition, PageRank, performance metric, risk tolerance, search engine result page, self-driving car, sentiment analysis, social web, sorting algorithm, speech recognition, Steven Levy, text mining, web application, wikimedia commons

To help you understand the origins of link algorithms, the underlying logic of which is still in force today, let’s take a look at the original PageRank algorithm in detail. The Original PageRank Algorithm The PageRank algorithm was built on the basis of the original PageRank thesis ( authored by Sergey Brin and Larry Page while they were undergraduates at Stanford University. In the simplest terms, the paper states that each link to a web page is a vote for that page. However, votes do not have equal weight. So that you can better understand how this works, we’ll explain the PageRank algorithm at a high level. First, all pages are given an innate but tiny amount of PageRank, as shown in Figure 7-1. Figure 7-1. Some PageRank for every page Pages can then increase their PageRank by receiving links from other pages, as shown in Figure 7-2.

, Facebook Shares/Links as a Ranking Factor, Facebook Likes Are Votes, Too, Google+ Shares as a Ranking Factor, Google +1s Are Also an Endorsement, Impact of Google+ on Google Rankings, Ranking, Ranking, Ranking, Ranking, Ranking, Analysis of Top-Ranking Sites and Pages analysis of top-ranking sites and pages, Analysis of Top-Ranking Sites and Pages analyzing ranking factors, Analyzing Ranking Factors, Other Ranking Factors benchmarking current search rankings, Benchmarking Current Rankings critical role of links in, How Search Engines Use Links engagement with website as factor, Measuring Content Quality and User Engagement Facebook Likes affecting, Facebook Likes Are Votes, Too Facebook Shares/links as ranking factors, Facebook Shares/Links as a Ranking Factor getting data from Google, Ranking Google +1s as endorsement, Google +1s Are Also an Endorsement Google+ Shares as ranking factor, Google+ Shares as a Ranking Factor impact of Google+ on Google rankings, Impact of Google+ on Google Rankings influence of links, How Links Influence Search Engine Rankings, How Search Engines Use Links, The Original PageRank Algorithm, The Original PageRank Algorithm, Additional Factors That Influence Link Value, Trust additional factors in link value, Additional Factors That Influence Link Value, Trust original PageRank algorithm, The Original PageRank Algorithm, The Original PageRank Algorithm scenarios where data is helpful, Ranking testing of new ranking factors, How Social Media and User Data Play a Role in Search Results and Rankings tools for data on, Ranking tweets as ranking factors, How big a ranking factor are Tweets?

However, as link-building expert Eric Ward points out, avoiding lower-PageRank domains simply because they are PageRank 3 or 4 is not necessarily a good thing, as you could be missing out on very relevant links that, in volume, contribute to your relevance. PageRank of the page Since pages that are relatively new (three to four months old) are shown on the Google Toolbar as having a PageRank of 0 (it can take Google that long to update the PageRank it shows on the Toolbar), and since so many valuable pages may have PageRanks that are only in the 1–3 range, it seems unwise to get caught up in the PageRank of a specific page. It is better to look at the domain and the attention it gives your page. However, PageRank can be valuable if the page has been around for a while. Inlinks to the page It can be useful to look at the links to the specific page you want to get a link from (or perhaps that already links to you).

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Programming Collective Intelligence by Toby Segaran

always be closing, correlation coefficient, Debian,, Firefox, full text search, information retrieval, PageRank, prediction markets, recommendation engine, slashdot, Thomas Bayes, web application

It's also possible that people are more interested in results that have attracted the attention of very popular sites. Next, you'll see how to make links from popular pages worth more in calculating rankings. The PageRank Algorithm The PageRank algorithm was invented by the founders of Google, and variations on the idea are now used by all the large search engines. This algorithm assigns every page a score that indicates how important that page is. The importance of the page is calculated from the importance of all the other pages that link to it and from the number of links each of the other pages has. Tip In theory, PageRank (named after one of its inventors, Larry Page) calculates the probability that someone randomly clicking on links will arrive at a certain page. The more inbound links the page has from other popular pages, the more likely it is that someone will end up there purely by chance.

After each iteration, the PageRank for each page gets closer to its true PageRank value. The number of iterations needed varies with the number of pages, but in the small set you're working with, 20 should be sufficient. Because the PageRank is time-consuming to calculate and stays the same no matter what the query is, you'll be creating a function that precomputes the PageRank for every URL and stores it in a table. This function will recalculate all the PageRanks every time it is run. Add this function to the crawler class: def calculatepagerank(self,iterations=20): # clear out the current PageRank tables self.con.execute('drop table if exists pagerank') self.con.execute('create table pagerank(urlid primary key,score)') # initialize every url with a PageRank of 1 self.con.execute('insert into pagerank select rowid, 1.0 from urllist') self.dbcommit( ) for i in range(iterations): print "Iteration %d" % (i) for (urlid,) in self.con.execute('select rowid from urllist'): pr=0.15 # Loop through all the pages that link to this one for (linker,) in self.con.execute( 'select distinct fromid from link where toid=%d' % urlid): # Get the PageRank of the linker linkingpr=self.con.execute( 'select score from pagerank where urlid=%d' % linker).fetchone( )[0] # Get the total number of links from the linker linkingcount=self.con.execute( 'select count(*) from link where fromid=%d' % linker).fetchone( )[0]pr+=0.85*(linkingpr/linkingcount) self.con.execute( 'update pagerank set score=%f where urlid=%d' % (pr,urlid)) self.dbcommit( ) This function initially sets the PageRank of every page to 1.0.

The nature of many machine-learning algorithms is that they can continue to learn as new information arrives. Real-Life Examples There are many sites on the Internet currently collecting data from many different people and using machine learning and statistical methods to benefit from it. Google is likely the largest effort—it not only uses web links to rank pages, but it constantly gathers information on when advertisements are clicked by different users, which allows Google to target the advertising more effectively. In Chapter 4 you'll learn about search engines and the PageRank algorithm, an important part of Google's ranking system. Other examples include web sites with recommendation systems. Sites like Amazon and Netflix use information about the things people buy or rent to determine which people or items are similar to one another, and then make recommendations based on purchase history.

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Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard

1960s counterculture, A Declaration of the Independence of Cyberspace, Ada Lovelace, AI winter, Airbnb, Amazon Web Services, autonomous vehicles, availability heuristic, barriers to entry, Bernie Sanders, bitcoin, Buckminster Fuller, Chris Urmson, Clayton Christensen, cloud computing, cognitive bias, complexity theory, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, digital map, disruptive innovation, Donald Trump, Douglas Engelbart, easy for humans, difficult for computers, Electric Kool-Aid Acid Test, Elon Musk, Firefox, gig economy, global supply chain, Google Glasses, Google X / Alphabet X, Hacker Ethic, Jaron Lanier, Jeff Bezos, John von Neumann, Joi Ito, Joseph-Marie Jacquard, life extension, Lyft, Mark Zuckerberg, mass incarceration, Minecraft, minimum viable product, Mother of all demos, move fast and break things, move fast and break things, Nate Silver, natural language processing, PageRank, payday loans, paypal mafia, performance metric, Peter Thiel, price discrimination, Ray Kurzweil, ride hailing / ride sharing, Ross Ulbricht, Saturday Night Live, school choice, self-driving car, Silicon Valley, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, Tesla Model S, the High Line, The Signal and the Noise by Nate Silver, theory of mind, Travis Kalanick, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, women in the workforce

“What’s necessary about them can be replicated, but when it comes to more sophisticated robots, they have to be male.”8 Minsky’s world view is even behind the scenes in the founding of Internet search, which most of us use every day. As PhD students at Stanford, Larry Page and Sergei Brin invented PageRank, the revolutionary search algorithm that led to the two founding Google. Larry Page is the son of Carl Victor Page Sr., an artificial intelligence professor at Michigan who would have read Minsky extensively and interacted with him at AI conferences. Larry Page’s PhD advisor at Stanford was Terry Winograd, who counts Minsky as a professional mentor. Winograd’s PhD advisor at MIT was Seymour Papert—Minsky’s longtime collaborator and business partner. A number of Google executives, like Raymond Kurzweil, are Minsky’s former graduate students. Minsky was a Gladwellian connector. As far back as the 1950s, there were only a handful of places in the whole country of millions of people where computing machines were—and Marvin Minsky was in all of these places, hanging around, doing math, and building things and tinkering and hanging out.

Therefore, they built a search engine that would calculate how many incoming links pointed to a given web page, then they ran an equation to generate a ranking called PageRank based on the number of incoming links and the ranking of the outgoing links on a page. They reasoned that web users would act just like academics: each web user would create web pages that linked to other pages that each user considered good. A popular page, one with a large number of incoming links, was ranked higher than a page with fewer incoming links. PageRank was named after one of the grad students, Larry Page. Page and his partner, Sergei Brin, went on to commercialize their algorithm and create Google, one of the most influential companies in the world. For a long time, PageRank worked beautifully. The popular web pages were the good ones—in part because there was so little content on the web that good was not a very high threshold.

ALVINN, a self-driving van, launched at CMU in 1989.3 There was a stroke of enormous good fortune during the development period. Google founder Larry Page happened to become very interested in digital mapping. He attached a bunch of cameras to the outside of a panel van and drove around Mountain View, California, filming the landscape and turning the images into maps. Google then turned the van project into its massive Google Street View mapping program. Page’s vision fit nicely with tech developed by the previously mentioned CMU professor Sebastian Thrun, who was active with the DARPA Challenge team. Thrun and his students developed a program that knit street photos together into maps. Thrun moved from CMU to Stanford. Google bought his tech and folded it into Google Street View. Something important happened in hardware at this point too. Video and 3-D take up huge amounts of memory space.

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

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

Entrepreneur and AI maker Peter Voss speculated that had Aristotle possessed Einstein’s knowledge base, he could’ve come up with the theory of general relativity. The Google, Inc. search engine in particular has multiplied worker productivity, especially in occupations that call for research and writing. Tasks that formerly required time-consuming research—a trip to the library to pore over books and periodicals, perform Lexis/Nexis searches, and look up experts and write or phone them—are now fast, easy, and cheap. Much of this increased productivity is due, of course, to the Internet itself. But the vast ocean of information it holds is overwhelming without intelligent tools to extract the small fraction you need. How does Google do it? Google’s proprietary algorithm called PageRank gives every site on the entire Internet a score of 0 to 10. A score of 1 on PageRank (allegedly named after Google cofounder Larry Page, not because it ranks Web pages) means a page has twice the “quality” of a site with a PageRank of 0.

reqstyleid=10&mode=form&rsid=&reqsrcid=ChicagoInterview&more=yes&nameCnt=1 (accessed June 10, 2010). Google’s proprietary algorithm called PageRank: Geordie, “Learn How Google Works: in Gory Detail,” PPC Blog (blog), 2011, (accessed October 10, 2011). mankind’s primary tool: Schwartz, Evan, “The Mobile Device is Becoming Humankind’s Primary Tool,” Technology Review, November 29, 2010, (accessed December 4, 2011). you merely think of a question: Carr, Nicholas, “When Google Grows Up,”, January 11, 2008, (accessed March 10, 2011). You are never lost: Kharif, Olga, “Google Uses AI to Make Search Smarter,” Bloomberg Businessweek, September 21, 2010, (accessed April 5, 2012).

., July 12, 2011, (accessed August 28, 2012). People always make the assumption: Memepunks, “Google A.I. a Twinkle in Larry Page’s Eye,” May 26, 2006, (accessed May 3, 2011). Even the Google camera cars: Streitfeld, David, “Google Is Faulted for Impeding U.S. Inquiry on Data Collection,” New York Times, sec. technology, April 14, 2012, (accessed May 3, 2012). It doesn’t take Google glasses: In December 2012, Ray Kurzweil joined Google as Director of Engineering to work on projects involving machine learning and language processing. In the development of AGI, this is a landmark event, and a sobering one.

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

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

Their efficiency at modeling a particular ideology of time and space, denominated in microseconds, is gradually reshaping the entire ecosystem they inhabit. In this way HFT algorithms replace the original structure of value in securities trading, the notion of a share owned and traded as an investment in future economic success, with a new structure of value that is based on process. Valuing Culture The arbitrage of process is central to Google’s business model; one of the world’s largest companies (now in the form of Alphabet Corporation) is built on the valuation of cultural information. The very first PageRank algorithms created by Larry Page and Sergei Brin at Stanford in 1996 marked a new era in the fundamental problem of search online. Brin and Page’s innovation was to use the inherent ontological structure of the web itself to evaluate knowledge. Pages on university websites were likely to be better sources of information than those on commercial sites; a news article that had already been linked to by hundreds of others was a stronger source of information than one that had only a few references elsewhere.

So too, we now expect the Internet to serve as a utility that provides dependable, and perhaps fungible, kinds of information. PageRank and the complementary algorithms Google has developed since its launch in 1998 started as sophisticated finding aids for that awkward, adolescent Internet. But the company and the web’s spectacular expansion since then has turned their assumptions into rationalizing laws, just as Diderot’s framework of interlinked topics has shaped untold numbers of encyclopedias, indexes, and lists. At some point during the “search wars” of the mid-2000s, when Google cemented its dominance, an inversion point occurred where the observational system of PageRank became a deterministic force in the cultural fabric of the web. Google now runs roughly two-thirds of searches online, and a vibrant industry of “search engine optimization” exists to leverage and game Google’s algorithms to lure traffic and advertising.10 At its heart, PageRank catalogs human acts of judgment, counting up the links and relative attention millions of people have paid to different corners of the web.

That temporal arbitrage inevitably led to a renegotiation of value in financial terms as well: time is money, especially time measured by milliseconds multiplied across millions of servers. For this, Google needed AdSense. From a business perspective, PageRank creates a basic index for the circulation of ideas, an essential currency in the economy of attention.14 When Google began selling advertisements against its search results with the market bidding system AdSense, it succeeded in monetizing that attention at a previously unimaginable scale. In 2013, Google earned over $55 billion in revenue, of which more than 90 percent came from advertising.15 Now the company will help you register the domain name, build the website, analyze the traffic, and serve the ads for the site, which its algorithms will then index and rank. In 2014, Google exceeded the market capitalization of ExxonMobil, leaving it second only to Apple among the most valuable companies in the world.16 The typical Google advertisement nets the company some tiny fraction of a penny to serve up to a customer, but over the volume of the tens of billions of ads it serves each day, those fractions add up to a kind of minimal transaction cost for using the Internet, collected by its most powerful gatekeeper.17 The functionality of AdSense is in fact a kind of HFT arbitrage in its own right: every time a user navigates to a site serving advertisements via Google’s network, a rapid auction takes place for the marketers with the highest bids to serve their ads.

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

activist fund / activist shareholder / activist investor, AltaVista, Build a better mousetrap, Burning Man, cloud computing, commoditize, credit crunch, crowdsourcing, disintermediation, don't be evil,, 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 new new thing, the scientific method, Tim Cook: Apple, turn-by-turn navigation, upwardly mobile

They had wanted to call it ‘Googol’ (an enormous number –10 to the hundredth power – to represent the vastness of the net, but also as a mathematical in-joke; Page and Brin love maths jokes). But that was taken. They settled on ‘Google’. Had Gates known about them, he might have worried, briefly. But there was no way Gates could have easily known about it – except by spending lots and lots of time surfing the web. The scientific paper describing how Google chose its results wasn’t formally published until the end of December 1998; a paper describing how ‘PageRank’, the system used to determine what order the search results should be delivered in – with the ‘most relevant’ (as determined by the rest of the web) first – wasn’t deposited with Stanford University’s online publishing service until 1999.15 The duo incorporated Google as a company on 4 September 1998, while they were renting space in the garage of Susan Wojcicki.

This ebook published in 2014 by Kogan Page Limited 2nd Floor, 45 Gee Street London EC1V 3RS UK © Charles Arthur, 2012, 2014 E-ISBN 978 0 7494 7204 7 Full imprint details Contents Introduction 01 1998 Bill Gates and Microsoft Steve Jobs and Apple Bill Gates and Steve Jobs Larry Page, Sergey Brin and Google Internet search Capital thinking 02 Microsoft antitrust Steve Ballmer The antitrust trial The outcome of the trial 03 Search: Google versus Microsoft The beginnings of search Google Search and Microsoft Bust Link to money Boom Random access Google and the public consciousness Project Underdog Preparing for battle Do it yourself Going public Competition Cultural differences Microsoft’s relaunched search engine Friends Microsoft’s bid for Yahoo Google’s identity The shadow of antitrust Still underdog 04 Digital music: Apple versus Microsoft The beginning of iTunes Gizmo, Tokyo iPod design Marketing the new product Meanwhile, in Redmond: Microsoft iPods and Windows Music, stored Celebrity marketing iTunes on Windows iPod mini The growth of iTunes Music Store Apple and the mobile phone Stolen!

At the end of the day it was worth $346.7 billion; Microsoft was worth $214.3 billion and Google $185.1 billion. Compared to the end of 1998 (Apple $5.54 billion, Microsoft $344.6 billion, Google $10 million), the aggregate wealth of the companies had more than doubled. Microsoft, though, had shrunk by 40 per cent, after being outdistanced first in search, then in digital music and then in smartphones – in the latter category by both companies. The companies had changed enormously. Google was soon to celebrate its 13th birthday, having roared from a three-person garage start-up to web giant; it was struggling too with having nearly 29,000 staff worldwide. Larry Page, once more the chief executive, was forcing the divisions to justify themselves, getting divisional heads to explain their projects in soundbite-length memos. His greatest concern was that Google was getting too big and slow to act: ‘Large companies are their own worst enemy’, he said in September.

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

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

Driving was the original metaphor for interactive computing, but today Google’s vision has changed the metaphor. The new analogy will be closer to traveling in an elevator or a train without human intervention. In Google’s world you will press a button and be taken to your destination. This conception of transportation undermines several notions that are deeply ingrained in American culture. In the last century the car became synonymous with the American ideal of freedom and independence. That era is now ending. What will replace it? It is significant that Google is instrumental in changing the metaphor. In one sense the company began as the quintessential intelligence augmentation, or IA, company. The PageRank algorithm Larry Page developed to improve Internet search results essentially mined human intelligence by using the crowd-sourced accumulation of human decisions about valuable information sources.

In the audience was Scott Hassan, the former Stanford graduate student who had done the original heavy lifting for Google as the first PageRank algorithm programmer, the basis for the company’s core search engine. It’s time to build an AI robot, Ng told the group. He said his dream was to put a robot in every home. The idea resonated with Hassan. A student in computer science first at the State University of New York at Buffalo, he then entered graduate programs in computer science at both Washington University in St. Louis and Stanford, but dropped out of both programs before receiving an advanced degree. Once he was on the West Coast, he had gotten involved with Brewster Kahle’s Internet Archive Project, which sought to save a copy of every Web page on the Internet. Larry Page and Sergey Brin had given Hassan stock for programming PageRank, and Hassan also sold E-Groups, another of his information retrieval projects, to Yahoo!

., 240–241 DARPA Advanced Research Projects Agency as precursor to, 30, 110, 111–112, 164, 171 ARPAnet, 164, 196 autonomous cars and Grand Challenge, 24, 26, 27–36, 40 CALO and, 31, 297, 302–304, 310, 311 Dugan and, 236 Engelbart and, 6 Licklider and, 11 LRASM, 26–27 Moravec and, 119 Pratt and, 235–236 Robotics Challenge, 227–230, 234, 236–238, 244–254, 249, 333–334 Rosen and, 102 Taylor and, 160 Darrach, Brad, 103–105 Dartmouth Summer Research Project on Artificial Intelligence, 105, 107–109, 114, 143 DataLand, 307 Davis, Ruth, 102–103 “Declaration of the Independence of Cyberspace, A” (Barlow), 173 DeepMind Technologies, 91, 337–338 Defense Science Board, 27 de Forest, Lee, 98 “demons,” 190 Dendral, 113–114, 127 Diebold, John, 98 Diffie, Whitfield, 8, 112 Digital Equipment Corporation, 112, 285 direct manipulation, 187 Djerassi, Carl, 113 Doerr, John, 7 Dompier, Steve, 211–212 Dreyfus, Hubert, 177–178, 179 drone delivery research, 247–248 Dubinsky, Donna, 154 Duda, Richard, 128, 129 Dugan, Regina, 236 Duvall, Bill, 1–7 Earnest, Les, 120, 199 Earth Institute, 59 Edgerton, Germeshausen, and Grier (EG&G), 127 e-discovery software, 78 E-Groups, 259 elastic actuation, 236–237 electronic commerce, advent of, 289, 301–302 electronic stability control (ESC), 46 Elementary Perceiver and Memorizer (EPAM), 283 “Elephants Don’t Play Chess” (Brooks), 201 Eliza, 14, 113, 172–174, 221 email, advent of, 290, 310 End of Work, The (Rifkin), 76–77 Engelbart, Doug. see also SRI International on exponential power of computers, 118–119 IA versus AI debate and, 165–167 on intelligence augmentation (IA), xii, 5–7, 31 Minsky and, 17 “Mother of All Demos” (1968) by, 62 NLS, 5–7, 172, 197 Rosen and, 102 Siri and, 301, 316–317 Engineers and the Price System, The (Veblen), 343 Enterprise Integration Technologies, 289, 291 ethical issues, 324–344. see also intelligence augmentation (IA) versus AI; labor force of autonomous cars, 26–27, 60–61 decision making and control, 341–342 Google on, 91 human-in-the-loop debates, 158–165, 167–169, 335 of labor force, 68–73, 325–332 scientists’ responsibility and, 332–341, 342–344 “techno-religious” issues, 116–117 expert systems, defined, 134–141, 285 Facebook, 83, 156–158, 266–267 Fast-SLAM, 37 Feigenbaum, Ed, 113, 133–136, 167–169, 283, 287–288 Felsenstein, Lee, 208–215 Fernstedt, Anders, 71 “field robotics,” 233–234 Fishman, Charles, 81 Flextronics, 68 Flores, Fernando, 179–180, 188 Foot, Philippa, 60 Ford, Martin, 79 Ford Motor Company, 70 Forstall, Scott, 322 Foxconn, 93, 208, 248 Friedland, Peter, 292 Galaxy Zoo, 219–220 Gates, Bill, 305, 329–330 General Electric (GE), 68–69 General Magic, 240, 315 General Motors (GM), 32–35, 48–50, 52, 53, 60 Genetic Finance, 304 Genghis (robot), 202 Geometrics, 127 George, Dileep, 154 Geraci, Robert, 85, 116–117 Gerald (digital light field), 271 Giant Brains, or Machines That Think (Berkeley), 231 Gibson, William, 23–24 Go Corp., 141 God & Golem, Inc. (Wiener), 75, 211 GOFAI (Good Old-Fashioned Artificial Intelligence), 108–109, 186 “Golemic Approach, The” (Felsenstein), 212–213 “golemics,” 75, 208–215 Google Android, 43, 239, 248, 320 autonomous cars and, 35–45, 51–52, 54–59, 62–63 Chauffeur, 43 DeepMind Technologies and, 91, 337–338 Google Glass, 23, 38 Google Now, 12–13, 341 Google X Laboratory, 152–153 Human Brain Project, 153–154 influence of early AI history on, 99 Kurzweil and, 85 PageRank algorithm, 62, 92, 259 robotic advancement by, 241–244, 248–255, 256, 260–261 70-20-10 rule of, 39 Siri’s development and, 314–315 Street View cars, 39, 42–43, 54 X Lab, 38, 55–56 Gordon, Robert J., 87–89 Gou, Terry, 93, 248 Gowen, Rhia, 277–279 Granakis, Alfred, 70 Grand Challenge (DARPA), 24, 26, 27–36, 40 “Grand Traverse,” 234 Green, David A., 80 Grendel (rover), 203 Grimson, Eric, 47 Gruber, Tom, xiii–xiv, 277–279, 278, 282–297, 310–323, 339 Grudin, Jonathan, 15, 170, 193, 342 Guzzoni, Didier, 303 hacker culture, early, 110–111, 174 Hart, Peter, 101–102, 103, 128, 129 Hassan, Scott, 243, 259–260, 267, 268, 271 Hawkins, Jeff, 85, 154 Hayon, Gaby, 50 Hearsay-II, 282–283 Heartland Robotics (Rethink Robotics), 204–208 Hecht, Lee, 135, 139 Hegel, G.

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The Internet Is Not the Answer by Andrew Keen

"Robert Solow", 3D printing, A Declaration of the Independence of Cyberspace, Airbnb, AltaVista, Andrew Keen, augmented reality, Bay Area Rapid Transit, Berlin Wall, bitcoin, Black Swan, Bob Geldof, Burning Man, Cass Sunstein, citizen journalism, Clayton Christensen, clean water, cloud computing, collective bargaining, Colonization of Mars, computer age, connected car, creative destruction, cuban missile crisis, David Brooks, disintermediation, disruptive innovation, Donald Davies, Downton Abbey, Edward Snowden, Elon Musk, Erik Brynjolfsson, Fall of the Berlin Wall, Filter Bubble, Francis Fukuyama: the end of history, Frank Gehry, Frederick Winslow Taylor, frictionless, full employment, future of work, gig economy, global village, Google bus, Google Glasses, Hacker Ethic, happiness index / gross national happiness, income inequality, index card, informal economy, information trail, Innovator's Dilemma, Internet of things, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, Joi Ito, Joseph Schumpeter, Julian Assange, Kevin Kelly, Kickstarter, Kodak vs Instagram, Lean Startup, libertarian paternalism, lifelogging, Lyft, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Martin Wolf, Metcalfe’s law, move fast and break things, move fast and break things, Nate Silver, Nelson Mandela, Network effects, new economy, Nicholas Carr, nonsequential writing, Norbert Wiener, Norman Mailer, Occupy movement, packet switching, PageRank, Panopticon Jeremy Bentham, Paul Graham, peer-to-peer, peer-to-peer rental, Peter Thiel, plutocrats, Plutocrats, Potemkin village, precariat, pre–internet, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Robert Metcalfe, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Skype, smart cities, Snapchat, social web, South of Market, San Francisco, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, TaskRabbit, Ted Nelson, telemarketer, The Future of Employment, the medium is the message, the new new thing, Thomas L Friedman, Travis Kalanick, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, urban planning, Vannevar Bush, Whole Earth Catalog, WikiLeaks, winner-take-all economy, working poor, Y Combinator

It seems like a win-win for everyone, of course—one of those supposedly virtuous circles that Sergey Brin and Larry Page built into PageRank. We all get free tools and the Internet entrepreneurs get to become superrich. KPCB cofounder Tom Perkins, whose venture fund has made billions from its investments in Google, Facebook, and Twitter, would no doubt claim that the achievement of what he called Silicon Valley’s “successful one percent” is resulting in more jobs and general prosperity. But as always with something that’s too good to be true, there’s a catch. The problem, of course, is that we are all working for Facebook and Google for free, manufacturing the very personal data that makes their companies so valuable. So Google, with its mid-2014 market cap of over $400 billion, needs to employ only 46,000 people.

By crawling the entire Web and indexing all its pages and links, they turned the Web into what Brin, a National Science Foundation fellow at Stanford, identified as “a big equation.” The end result of this gigantic math project was an algorithm they called PageRank, which determined the relevance of the Web page based on the number and quality of its incoming links. “The more prominent the status of the page that made the link, the more valuable the link was and the higher it would rise when calculating the ultimate PageRank number of the web page itself,” explains Steven Levy in In the Plex, his definitive history of Google.62 In the spirit of Norbert Wiener’s flight path predictor device, which relied on a continuous stream of information that flowed back and forth between the gun and its operator, the logic of the Google algorithm was dependent on a self-regulating system of hyperlinks flowing around the Web. Page and Brin’s creation represented the realization of Licklider’s man-computer symbiosis.

They rose to $347 million in 2002, then to just under a billion dollars in 2003 and to almost $2 billion in 2004, when the six-year-old company went public in a $1.67 billion offering that valued it at $23 billion. By 2014, Google had become the world’s second most valuable company, after Apple, with a market cap of over $400 billion, and Brin and Page were the two wealthiest young men in the world, with fortunes of around $30 billion apiece. In vivid contrast with Amazon, Google’s profits were also astonishing. In 2012, its operational profits were just under $14 billion from revenues of $50 billion. In 2013, Google “demolished” Wall Street expectations and returned operational profits of over $15 billion from revenues of nearly $60 billion.71 Larry Page’s response to John Doerr’s question when they first met in 1999 had turned out to be a dramatic underestimation of just “how big” Google could become. And the company is still growing. By 2014, Google had joined Amazon as a winner-take-all company.

pages: 252 words: 74,167

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

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

In this way, working towards achieving consciousness in a machine is a little like the way Google is perfecting their search engine. Larry Page and Sergey Brin began at Stanford with their PageRank algorithm, which remains the kernel of the Google empire. PageRank ranked pages according to the quality and number of incoming links to each page. But while PageRank remains a crucially important algorithm, Google has since enhanced it with 200 different unique signals, or what it refers to as ‘clues’, which make informed guesses about what it is that users are looking for. As Google engineers explain, ‘These signals include things like the terms on websites, the freshness of content [and] your region,’ in addition to PageRank. Human consciousness could well reside in a similar number of clues: a combination of life’s training data and millions of years of evolution, which we call instinct.

To find a specific word or phrase from the index, please use the search feature of your ebook reader. 2001: A Space Odyssey (1968) 2, 228, 242–4 2045 Initiative 217 accountability issues 240–4, 246–8 Active Citizen 120–2 Adams, Douglas 249 Advanced Research Projects Agency (ARPA) 19–20, 33 Affectiva 131 Age of Industry 6 Age of Information 6 agriculture 150–1, 183 AI Winters 27, 33 airlines, driverless 144 algebra 20 algorithms 16–17, 59, 67, 85, 87, 88, 145, 158–9, 168, 173, 175–6, 183–4, 186, 215, 226, 232, 236 evolutionary 182–3, 186–8 facial recognition 10–11, 61–3 genetic 184, 232, 237, 257 see also back-propagation AliveCor 87 AlphaGo (AI Go player) 255 Amazon 153, 154, 198, 236 Amy (AI assistant) 116 ANALOGY program 20 Analytical Engine 185 Android 59, 114, 125 animation 168–9 Antabi, Bandar 77–9 antennae 182, 183–5 Apple 6, 35, 56, 65, 90–1, 108, 110–11, 113–14, 118–19, 126–8, 131–2, 148–9, 158, 181, 236, 238–9, 242 Apple iPhone 108, 113, 181 Apple Music 158–9 Apple Watch 66, 199 architecture 186 Artificial Artificial Intelligence (AAI) 153, 157 Artificial General Intelligence (AGI) 226, 230–4, 239–40, 254 Artificial Intelligence (AI) 2 authentic 31 development problems 23–9, 32–3 Good Old-Fashioned (Symbolic) 22, 27, 29, 34, 36, 37, 39, 45, 49–52, 54, 60, 225 history of 5–34 Logical Artificial Intelligence 246–7 naming of 19 Narrow/Weak 225–6, 231 new 35–63 strong 232 artificial stupidity 234–7 ‘artisan economy’ 159–61 Asimov, Isaac 227, 245, 248 Athlone Industries 242 Atteberry, Kevan J. 112 Automated Land Vehicle in a Neural Network (ALVINN) 54–5 automation 141, 144–5, 150, 159 avatars 117, 193–4, 196–7, 201–2 Babbage, Charles 185 back-propagation 50–3, 57, 63 Bainbridge, William Sims 200–1, 202, 207 banking 88 BeClose smart sensor system 86 Bell Communications 201 big business 31, 94–6 biometrics 77–82, 199 black boxes 237–40 Bletchley Park 14–15, 227 BMW 128 body, machine analogy 15 Bostrom, Nick 235, 237–8 BP 94–95 brain 22, 38, 207–16, 219 Brain Preservation Foundation 219 Brain Research Through Advanced Innovative Neurotechnologies 215–16 brain-like algorithms 226 brain-machine interfaces 211–12 Breakout (video game) 35, 36 Brin, Sergey 6–7, 34, 220, 231 Bringsjord, Selmer 246–7 Caenorhabditis elegans 209–10, 233 calculus 20 call centres 127 Campbell, Joseph 25–6 ‘capitalisation effect’ 151 cars, self-driving 53–56, 90, 143, 149–50, 247–8 catering 62, 189–92 chatterbots 102–8, 129 Chef Watson 189–92 chemistry 30 chess 1, 26, 28, 35, 137, 138–9, 152–3, 177, 225 Cheyer, Adam 109–10 ‘Chinese Room, the’ 24–6 cities 89–91, 96 ‘clever programming’ 31 Clippy (AI assistant) 111–12 clocks, self-regulating 71–2 cognicity 68–9 Cognitive Assistant that Learns and Organises (CALO) 112 cognitive psychology 12–13 Componium 174, 176 computer logic 8, 10–11 Computer Science and Artificial Intelligence Laboratory (CSAIL) 96–7 Computer-Generated Imagery (CGI) 168, 175, 177 computers, history of 12–17 connectionists 53–6 connectomes 209–10 consciousness 220–1, 232–3, 249–51 contact lenses, smart 92 Cook, Diane 84–6 Cook, Tim 91, 179–80 Cortana (AI assistant) 114, 118–19 creativity 163–92, 228 crime 96–7 curiosity 186 Cyber-Human Systems 200 cybernetics 71–4 Dartmouth conference 1956 17–18, 19, 253 data 56–7, 199 ownership 156–7 unlabelled 57 death 193–8, 200–1, 206 Deep Blue 137, 138–9, 177 Deep Knowledge Ventures 145 Deep Learning 11–12, 56–63, 96–7, 164, 225 Deep QA 138 DeepMind 35–7, 223, 224, 245–6, 255 Defense Advanced Research Projects Agency (DARPA) 33, 112 Defense Department 19, 27–8 DENDRAL (expert system) 29–31 Descartes, René 249–50 Dextro 61 DiGiorgio, Rocco 234–5 Digital Equipment Corporation (DEC) 31 Digital Reasoning 208–9 ‘Digital Sweatshops’ 154 Dipmeter Advisor (expert system) 31 ‘do engines’ 110, 116 Dungeons and Dragons Online (video game) 197 e-discovery firms 145 eDemocracy 120–1 education 160–2 elderly people 84–6, 88, 130–1, 160 electricity 68–9 Electronic Numeric Integrator and Calculator (ENIAC) 12, 13, 92 ELIZA programme 129–30 Elmer and Elsie (robots) 74–5 email filters 88 employment 139–50, 150–62, 163, 225, 238–9, 255 eNeighbor 86 engineering 182, 183–5 Enigma machine 14–15 193–7 ethical issues 244–8 Etsy 161 Eurequa 186 Eve (robot scientist) 187–8 event-driven programming 79–81 executives 145 expert systems 29–33, 47–8, 197–8, 238 Facebook 7, 61–2, 63, 107, 153, 156, 238, 254–5 facial recognition 10–11, 61–3, 131 Federov, Nikolai Fedorovich 204–5 feedback systems 71–4 financial markets 53, 224, 236–7 Fitbit 94–95 Flickr 57 Floridi, Luciano 104–5 food industry 141 Ford 6, 230 Foxbots 149 Foxconn 148–9 fraud detection 88 functional magnetic resonance imaging (fMRI) 211 Furbies 123–5 games theory 100 Gates, Bill 32, 231 generalisation 226 genetic algorithms 184, 232, 237, 257 geometry 20 glial cells 213 Go (game) 255 Good, Irving John 227–8 Google 6–7, 34, 58–60, 67, 90–2, 118, 126, 131, 155–7, 182, 213, 238–9 ‘Big Dog’ 255–6 and DeepMind 35, 245–6, 255 PageRank algorithm 220 Platonic objects 164, 165 Project Wing initiative 144 and self-driving cars 56, 90, 143 Google Books 180–1 Google Brain 61, 63 Google Deep Dream 163–6, 167–8, 184, 186, 257 Google Now 114–16, 125, 132 Google Photos 164 Google Translate 11 Google X (lab) 61 Government Code and Cypher School 14 Grain Marketing Adviser (expert system) 31 Grímsson, Gunnar 120–2 Grothaus, Michael 69, 93 guilds 146 Halo (video game) 114 handwriting recognition 7–8 Hank (AI assistant) 111 Hawking, Stephen 224 Hayworth, Ken 217–21 health-tracking technology 87–8, 92–5 Healthsense 86 Her (film, 2013) 122 Herd, Andy 256–7 Herron, Ron 89–90 High, Rob 190–1 Hinton, Geoff 48–9, 53, 56, 57–61, 63, 233–4 hive minds 207 holograms 217 HomeChat app 132 homes, smart 81–8, 132 Hopfield, John 46–7, 201 Hopfield Nets 46–8 Human Brain Project 215–16 Human Intelligence Tasks (HITs) 153, 154 hypotheses 187–8 IBM 7–11, 136–8, 162, 177, 189–92 ‘IF THEN’ rules 29–31 ‘If-This-Then-That’ rules 79–81 image generation 163–6, 167–8 image recognition 164 imagination 178 immortality 204–7, 217, 220–1 virtual 193–8, 201–4 inferences 97 Infinium Robotics 141 information processing 208 ‘information theory’ 16 Instagram 238 insurance 94–5 Intellicorp 33 intelligence 208 ambient 74 ‘intelligence explosion’ 228 top-down view 22, 25, 246 see also Artificial Intelligence internal combustion engine 140–1, 150–1 Internet 10, 56 disappearance 91 ‘Internet of Things’ 69, 70, 83, 249, 254 invention 174, 178, 179, 182–5, 187–9 Jawbone 78–9, 92–3, 254 Jennings, Ken 133–6, 138–9, 162, 189 Jeopardy!

As a result, Google’s Platonic objects (that perfect ‘essence of chair’) sprouted some unusual appendages, such as long fleshy arms that hung from Deep Dream’s idealised dumbbells like pink lengths of rubber tubing. As Google software engineers Alexander Mordvintsev and Mike Tyka pointed out in a blog post: ‘There are dumbbells in there all right, but it seems no picture of a dumbbell is complete without a muscular weightlifter there to lift them. In this case, the network failed to completely distill the essence of a dumbbell. Maybe it’s never been shown a dumbbell without an arm holding it.’ Normally, Google would correct what it called ‘these kinds of training mishaps’. With Deep Dream it decided to go in the opposite direction. The result was surrealistic landscapes which seemed to owe more to Salvador Dalí or H. P. Lovecraft than Google co-founders Larry Page and Sergey Brin. The team allowed the neural network to accentuate whatever eccentricities it discovered.

pages: 368 words: 96,825

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

3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, Charles Lindbergh, cloud computing, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, dematerialisation, deskilling, disruptive innovation, Elon Musk,, Exxon Valdez, fear of failure, Firefox, Galaxy Zoo, Google Glasses, Google Hangouts, gravity well, ImageNet competition, industrial robot, Internet of things, Jeff Bezos, John Harrison: Longitude, John Markoff, Jono Bacon, Just-in-time delivery, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, loss aversion, Louis Pasteur, low earth orbit, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Mars Rover, meta analysis, meta-analysis, microbiome, minimum viable product, move fast and break things, Narrative Science, Netflix Prize, Network effects, Oculus Rift, optical character recognition, packet switching, PageRank, pattern recognition, performance metric, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, Ray Kurzweil, recommendation engine, Richard Feynman, ride hailing / ride sharing, risk tolerance, rolodex, self-driving car, sentiment analysis, shareholder value, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart grid, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, superconnector, technoutopianism, telepresence, telepresence robot, Turing test, urban renewal, web application, X Prize, Y Combinator, zero-sum game

v=9pmPa_KxsAM. 40 Joann Muller, “No Hands, No Feet: My Unnerving Ride in Google’s Driverless Car,” Forbes, March 21, 2013, 41 Robert Hof, “10 Breakthrough Technologies 2013: Deep Learning,” MIT Technology Review, April 23, 2013, 42 Steven Levy, “Google’s Larry Page on Why Moon Shots Matter,” Wired, January 17, 2013, 43 Larry Page, “Beyond Today—Larry Page—Zeitgeist 2012.” 44 Larry Page, “Google+: Calico Announcement,” Google+, September 2013, 45 Harry McCracken and Lev Grossman, “Google vs. Death,” Time, September 30, 2013, 46 Jason Calacanis, “#googlewinseverything (part 1),” Launch, October 30, 2013,

v=G-0KJF3uLP8. 31 “About Blue Origin,” Blue Origin, July 2014, 32 Alistair Barr, “Amazon testing delivery by drone, CEO Bezos Says,” USA Today, December 2, 2013, referencing a 60 Minutes interview with Jeff Bezos, 33 Jay Yarow, “Jeff Bezos’ Shareholder Letter Is Out,” Business Insider, April 10, 2014, 34 “Larry Page Biography,” Academy of Achievement, January 21, 2011, 35 Marcus Wohlsen, “Google Without Larry Page Would Not Be Like Apple Without Steve Jobs,” Wired, October 18, 2013, 36 Google Inc., 2012, Form 10-K 2012, retrieved from SEC Edgar website: 37 Larry Page, “Beyond Today—Larry Page—Zeitgeist 2012,” Google Zeitgeist, Zeitgeist Minds, May 22, 2012, 38 Matt Ridley, The Rational Optimist: How Prosperity Evolves (New York: HarperCollins, 2010). 39 Larry Page, “Google I/O 2013: Keynote,” Google I/O 2013, Google Developers, May 15, 2013,

This led to a partnership with another Stanford PhD student, Sergey Brin, and a research project nicknamed BackRub, which led to the page-rank algorithm that became Google. Not surprisingly, neither Brin nor Page ever finished their PhDs. Instead, in 1998, they dropped out and started up and changed history. The PageRank algorithm democratized access to information, or as a recent article in Wired put it: “Search, Google’s core product, is itself wondrous. Unlike shiny new gadgets, however, Google search has become such an expected part of the internet’s fabric that it has become mundane.”35 Meanwhile, YouTube became the dominant video platform on the web, Chrome the most popular browser, and Android the most prolific mobile phone operating system ever. To put this in perspective, today a Masai warrior in the heart of Kenya who has a smartphone and access to Google has—at his fingertips—access to the same level of information that the president of the United States did eighteen years ago.

pages: 474 words: 130,575

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

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

Sergey Brin’s Home Page, Stanford University, accessed June 11, 2004, 30. Battelle, The Search, 73. 31. John Ince, “The Lost Google Tapes,” January 2000, quoted in Walter Isaacson’s The Innovators, chap. 11. 32. “It’s all recursive. It’s all a big circle,” Larry Page later explained at a computer forum a few years after launching Google. “Navigating Cyberspace,” PC forum held in Scottsdale, AZ, 2001, quoted in Steven Levy’s In the Plex, 21. 33. John Battelle, “The Birth of Google,” Wired, August 1, 2005. 34. Ince, “The Lost Google Tapes,” quoted in Isaacson, The Innovators, chap. 11. 35. Sergey Brin and Larry Page, “The PageRank Citation Ranking: Bringing Order to the Web,” Stanford University InfoLab, January 29, 1998, 36.

Steering Committee on the Changing Nature of Telecommunications/Information Infrastructure, National Research Council, The Changing Nature of Telecommunications/Information Infrastructure (Washington, DC: National Academies Press, 1995). 37. David A. Vise, The Google Story (New York: Delacorte Press, 2005). 38. Battelle, The Search, 62. 39. Levy, In the Plex, 47. 40. Ibid., 48. 41. Alex Chitu, “Google in 2000,” Google System, December 28, 2007, 42. Edwards, I’m Feeling Lucky, chap. 11. 43. “Google’s Revenue Worldwide from 2002 to 2016 (in Billion U.S. Dollars),” Statista, April 11, 2017, 44. “Google Advertising Revenue, Billions of Dollars,” Vox, accessed July 6, 2017, 45. Sergey Brin and Larry Page also understood that Google was going to change—and in fact needed to change—people’s expectations of privacy.

One thing was certain in the wake of the AOL release: search logs provided an unadulterated look into the details of people’s inner lives, with all the strangeness, embarrassing quirks, and personal anguish those details divulged. And Google owned it all. You Have Spy Mail It’s April 2004 and Google is in crisis mode. Sergey Brin and Larry Page set up a war room and bring top executives from across the company together to deal with a dangerous development. They aren’t hunting for terrorists this time, but repelling an attack in progress. About a month earlier, Google had started to roll out the beta version of Gmail, its email service. It was a big deal for the young company, representing its first product offering beyond search. At the beginning, everything was going smoothly. Then events quickly spiraled out of control. Gmail aimed to poach users from established email providers such as Microsoft and Yahoo. To do that, Google shocked everyone by offering one gigabyte of free storage space with every account—an incredible amount of space at the time, considering Microsoft’s Hotmail offered just two megabytes of free storage.

pages: 299 words: 91,839

What Would Google Do? by Jeff Jarvis

23andMe, Amazon Mechanical Turk, Amazon Web Services, Anne Wojcicki, barriers to entry, Berlin Wall, business process, call centre, cashless society, citizen journalism, clean water, commoditize, connected car, credit crunch, crowdsourcing, death of newspapers, different worldview, disintermediation, diversified portfolio, don't be evil, fear of failure, Firefox, future of journalism, G4S, 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, old-boy network, 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, WikiLeaks, Y Combinator, Zipcar

The challenge is finding and supporting it. That is where Google comes in. Google can’t and shouldn’t do it all; we still need curators, editors, teachers—and ad salespeople—to find and nurture the best. But Google provides the infrastructure for a culture of choice. Google’s algorithms and its business model work because Google trusts us. That was the ding moment that led Sergey Brin and Larry Page to found their company: the realization that by tracking what we click on and link to, we would lead them to the good stuff and they, in turn, could lead others to it. “Good,” of course, is too relative and loaded a term. “Relevant” is a better description for what Google’s PageRank delivers. As the company explains on its site: PageRank relies on the uniquely democratic nature of the web by using its vast link structure as an indicator of an individual page’s value.

If any institution relies more on permanence than hastiness, God’s does. Google, like God, values permanence. In its search results, Google gives more credence to sites that have been online long enough to build a reputation over time via clicks and links—this is the essence of PageRank. As a result, Google’s search has been better at delivering completeness and relevance than currency. Google is not great at surfacing the latest links on a topic. Google has fresh links in its database because it constantly and quickly scrapes the web to find the latest content, but until those new entrants gather more links and clicks, it’s hard for Google’s algorithms to know what to make of them. Could this be a chink in Google’s armor? Life is live Just as Google and the rest of us start to get our hands around currency—finding the latest—the web speeds up even more.

It is an exercise in enlightened self-interest. Google and its megaplexes of servers are gigantic consumers of electricity with a growing impact on the economy and the earth. Google is not free of atoms’ drag. If Google can help create cleaner, cheaper electricity anywhere it operates, it will improve its own bottom line (the cost of power has been approaching that of the computers themselves in Google’s P&L). It will mitigate charges that Google is becoming a major contributor to carbon pollution. Google will have the flexibility to put servers most anywhere on earth, expanding its reach (Google has even patented the idea of wave-powered, water-cooled server farms on platforms in oceans). And the company will get due credit for helping to save the planet. “Our primary goal is not to fix the world,” Larry Page has said. But wouldn’t that be a nice fringe benefit?

pages: 407 words: 103,501

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

Amazon Mechanical Turk, Andrew Keen, business cycle, centre right, citizen journalism, collaborative editing, computer age, computer vision, corporate governance, crowdsourcing, David Brooks, disintermediation, Frederick Winslow Taylor, Howard Rheingold, invention of movable type, invention of the steam engine, invention of the telephone, Jaron Lanier, Jeff Bezos, jimmy wales, Kevin Kelly, knowledge worker, late fees, Mark Zuckerberg, Marshall McLuhan, means of production, meta analysis, meta-analysis, moral panic, Network effects, new economy, Nicholas Carr, PageRank, peer-to-peer,, 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

Second, because the blogging community is so highly self-referential, bloggers paying attention to other bloggers magnifies their visibility and power . . . like Wikipedia, blogging harnesses collective intelligence as a kind of filter . . . much as PageRank produces better results than analysis of any individual document, the collective attention of the blogosphere selects for value. PageRank is Google’s algorithm—its mathematical formula—for ranking search results. This is another contribution, according to its touters, to access to information, and therefore yet another boon to “democracy.” PageRank keeps track of websites that are the most linked to—that are the most popular. It is, in fact, the gold standard of popularity in Web culture. What O’Reilly is saying, in plain English, is that the more people blog, and the more blogs link to each other, the more highly ranked the most popular blogs will be.

The company has declared that its mission is “to organize the world’s information and make it universally accessible and useful.” It seeks to develop “the perfect search engine,” which it defines as something that “understands exactly what you mean and gives you back exactly what you want.” In Google’s view, information is a kind of commodity, a utilitarian resource that can be mined and processed with industrial efficiency. The more pieces of information we can “access” and the faster we can extract their gist, the more productive we become as thinkers. Where does it end? Sergey Brin and Larry Page, the gifted young men who founded Google while pursuing doctoral degrees in computer science at Stanford, speak frequently of their desire to turn their search engine into an artificial intelligence, a HAL-like machine that might be connected directly to our brains.

You use your iPhone camera to take a photo of a map that contains details not found on generic mapping applications such as Google maps—say, a trailhead map in a park, or another hiking map. Use the phone’s GPS to set your current location on the map. Walk a distance away, and set a second point. Now your iPhone can track your position on that custom map image as easily as it can on Google maps. Some of the most fundamental and useful services on the Web have been constructed in this way, by recognizing and then teaching the overlooked regularity of what at first appears to be unstructured data. Ti Kan, Steve Scherf, and Graham Toal, the creators of CDDB, realized that the sequence of track lengths on a CD formed a unique signature that could be correlated with artist, album, and song names. Larry Page and Sergey Brin realized that a link is a vote. Marc Hedlund at Wesabe realized that every credit card swipe is also a vote, that there is hidden meaning in repeated visits to the same merchant.

Alpha Girls: The Women Upstarts Who Took on Silicon Valley's Male Culture and Made the Deals of a Lifetime by Julian Guthrie

Airbnb, Apple II, barriers to entry, blockchain, Bob Noyce, call centre, cloud computing, credit crunch, disruptive innovation, Elon Musk, equal pay for equal work, fear of failure, game design, glass ceiling, hiring and firing, Jeff Bezos, Louis Pasteur, Lyft, Mark Zuckerberg, Menlo Park, Mitch Kapor, new economy, PageRank, peer-to-peer,, phenotype, place-making, Ronald Reagan, Rosa Parks, Sand Hill Road, Silicon Valley, Silicon Valley startup, Skype, Snapchat, software as a service, South of Market, San Francisco, stealth mode startup, Steve Jobs, Steve Wozniak, TaskRabbit, Tim Cook: Apple, Travis Kalanick, uber lyft, unpaid internship, upwardly mobile, urban decay, web application, William Shockley: the traitorous eight, women in the workforce

They decided that a googol, the number one with a hundred zeroes after it, better reflected the amount of data they were trying to sift through. From googol, they landed on the more user-friendly Google. Brin and Page, while working on their doctorates, had taken the Web crawler they developed and figured out how to calculate the tendency of Web-browsing people to congregate on specific pages, returning the most relevant page rather than a relevant page. Other search engines defined the concept of relevance as the relationship between a page and a query. The page would be relevant to the question if the terms of the query appeared more often than average on that page. PageRank, by contrast—named after Larry Page and with a patent pending—was a property of the page itself. PageRank didn’t just crawl the web; it returned the most popular things first. Theresia loved the geeky aspect of it: that the measure of the importance of Web pages was computed by solving an equation of 500 million variables and two billion terms.

At the same time that she was assessing which ideas had merit, she was encouraged to find her own deals and develop her own areas of specialty. Shortly after starting, Theresia was told of a meeting coming up with two Stanford PhD students who had created a new algorithm for search. It was her job to meet the students, Sergey Brin and Larry Page, and visit with them before they pitched the partners on their new page-rank algorithm. It was called Google. SONJA Sonja treated Kim Davis, her partner in the F5 deal, to a trip to the Four Seasons on the Big Island to celebrate their lucrative triumph. It was Sonja’s way of thanking her friend for showing her the deal. The women, both single, both in their early thirties, spent their days lounging by the pool, watching waves, and snorkeling in the nearby lagoon.

He used to Rollerblade around Stanford’s computer science building, and now he played full-contact roller hockey with Google employees at lunch. Larry communicated more through gestures and body language, unsettling employees with a raised eyebrow, a lowered tone, or lack of eye contact. The two were known to fight like brothers, but they were more similar than dissimilar. Both were the sons of high-powered intellects steeped in computer science. Sergey was interested in data mining—analyzing large amounts of data to discover patterns—and Larry was interested in downloading the Web, which Sergey considered the most interesting data to be mined. Theresia had also done her homework on Google. Brin and Page had originally called their company BackRub. The process of ranking Web pages based on how many other Web pages linked back to them was called PageRank. But neither name lasted long.

pages: 23 words: 5,264

Designing Great Data Products by Jeremy Howard, Mike Loukides, Margit Zwemer

AltaVista, Filter Bubble, PageRank, pattern recognition, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, text mining

Our objective and available levers, what data we already have and what additional data we will need to collect, determine the models we can build. The models will take both the levers and any uncontrollable variables as their inputs; the outputs from the models can be combined to predict the final state for our objective. Step 4 of the Drivetrain Approach for Google is now part of tech history: Larry Page and Sergey Brin invented the graph traversal algorithm PageRank and built an engine on top of it that revolutionized search. But you don’t have to invent the next PageRank to build a great data product. We will show a systematic approach to step 4 that doesn’t require a PhD in computer science. The Model Assembly Line: A case study of Optimal Decisions Group Optimizing for an actionable outcome over the right predictive models can be a company’s most important strategic decision.

While their models were good at finding relevant websites, the answer the user was most interested in was often buried on page 100 of the search results. Then, Google came along and transformed online search by beginning with a simple question: What is the user’s main objective in typing in a search query? The four steps in the Drivetrain Approach. Google realized that the objective was to show the most relevant search result; for other companies, it might be increasing profit, improving the customer experience, finding the best path for a robot, or balancing the load in a data center. Once we have specified the goal, the second step is to specify what inputs of the system we can control, the levers we can pull to influence the final outcome. In Google’s case, they could control the ranking of the search results. The third step was to consider what new data they would need to produce such a ranking; they realized that the implicit information regarding which pages linked to which other pages could be used for this purpose.

We call it the Drivetrain Approach, inspired by the emerging field of self-driving vehicles. Engineers start by defining a clear objective: They want a car to drive safely from point A to point B without human intervention. Great predictive modeling is an important part of the solution, but it no longer stands on its own; as products become more sophisticated, it disappears into the plumbing. Someone using Google’s self-driving car is completely unaware of the hundreds (if not thousands) of models and the petabytes of data that make it work. But as data scientists build increasingly sophisticated products, they need a systematic design approach. We don’t claim that the Drivetrain Approach is the best or only method; our goal is to start a dialog within the data science and business communities to advance our collective vision.

pages: 281 words: 71,242

World Without Mind: The Existential Threat of Big Tech by Franklin Foer

artificial general intelligence, back-to-the-land, Berlin Wall, big data - Walmart - Pop Tarts, big-box store, Buckminster Fuller, citizen journalism, Colonization of Mars, computer age, creative destruction, crowdsourcing, data is the new oil, don't be evil, Donald Trump, Double Irish / Dutch Sandwich, Douglas Engelbart, Edward Snowden, Electric Kool-Aid Acid Test, Elon Musk, Fall of the Berlin Wall, Filter Bubble, global village, Google Glasses, Haight Ashbury, hive mind, income inequality, intangible asset, Jeff Bezos, job automation, John Markoff, Kevin Kelly, knowledge economy, Law of Accelerating Returns, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, means of production, move fast and break things, move fast and break things, new economy, New Journalism, Norbert Wiener, offshore financial centre, PageRank, Peace of Westphalia, Peter Thiel, planetary scale, Ray Kurzweil, self-driving car, Silicon Valley, Singularitarianism, software is eating the world, Steve Jobs, Steven Levy, Stewart Brand, strong AI, supply-chain management, the medium is the message, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas L Friedman, Thorstein Veblen, Upton Sinclair, Vernor Vinge, Whole Earth Catalog, yellow journalism

The New York Times’s John Markoff, our most important chronicler of the technologists, says that Kurzweil “represents a community of many of Silicon Valley’s best and brightest,” ranks that include the finest minds at Google. • • • LARRY PAGE LIKES TO IMAGINE that he never escaped academia. Google, after all, began as a doctoral dissertation—and the inspiration for the search engine came from his connoisseurship of academic papers. As the son of a professor, he knew how researchers judge their own work. They look at the number of times it gets cited by other papers. His eureka moment arrived when he saw how the Web mimicked the professoriate. Links were just like citations—both were, in their way, a form of recommendation. The utility of a Web page could be judged by tabulating the number of links it received on other pages. When he captured this insight in an algorithm, he punningly named it for himself: PageRank. Research is a pursuit Page cherishes, and in which Google invests vast sums—last year it spent nearly $12.5 billion on R&D and on projects that it won’t foreseeably monetize.

The aphorism became widely known only after the company’s CEO, Eric Schmidt, inadvertently mentioned it in an interview with Wired, an act of blabbing that frustrated many in the company, who understood how the motto would make Google a slow-moving target for ridicule. (Google eventually retired the motto.) When Larry Page issues his pronouncements, they are unusually earnest. And the talking points that he repeats often are a good measure of his true, supersized intentions. He has a talent for sentences that are at once self-effacing and impossibly grandiose: “We’re at maybe 1% of what is possible. Despite the faster change, we’re still moving slow relative to the opportunities we have.” To understand Page’s intentions, it’s necessary to examine the varieties of artificial intelligence. The field can be roughly divided in two. There’s a school of incrementalists, who cherish everything that has been accomplished to date—victories like the PageRank algorithm or the software that allows ATMs to read the scrawled writing on checks.

“represents a community of many of Silicon Valley’s best and brightest”: John Markoff, Machines of Loving Grace (HarperCollins, 2015), 85. Google invests vast sums: Alphabet Inc., Research & Development Expenses, 2015, Google Finance. “Google is not a conventional company”: Larry Page and Sergey Brin, “Letter from the Founders: ‘An Owner’s Manual’ for Google’s Shareholders,” August 2004. The aphorism became widely known only: Josh McHugh, “Google vs. Evil,” Wired, January 2003. “We’re at maybe 1%”: Greg Kumparak, “Larry Page Wants Earth to Have a Mad Scientist Island,” TechCrunch, May 15, 2003. “This is the culmination of literally 50 years”: Robert D. Hof, “Deep Learning,” Technology Review, “The Google policy on a lot of things is to get right up to the creepy line”: Sara Jerome, “Schmidt: Google gets ‘right up to the creepy line’,” The Hill, October 1, 2010.

pages: 721 words: 197,134

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

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

As the popularity of Internet applications explodes, it is expected that one of the most important data-mining issues for years to come will be the problem of effectively discovering knowledge on the Web. 11.5 PAGERANK ALGORITHM PageRank was originally published by Sergey Brin and Larry Page, the co-creators of Google. It likely contributed to the early success of Google. PageRank provides a global ranking of nodes in a graph. For search engines it provides a query-independent, authority ranking of all Web pages. PageRank has similar goals of finding authoritative Web pages to that of the HITS algorithm. The main assumption behind the PageRank algorithm is that every link from page a to page b is a vote by page a for page b. Not all votes are equal. Votes are weighted by the PageRank score of the originating node. PageRank is based on the random surfer model. If a surfer were to randomly select a starting Web page, and at each time step the surfer were to randomly select a link on the current Web page, then PageRank could be seen as the probability that this random surfer is on any given page.

From the values given in Figure 11.5b we can see that node 5 has the highest PageRank by far and also has the highest in-degree or edges pointing in to it. Surprising perhaps is that node 3 has the next highest score, having a score higher than node 4, which has more in-edges. The reason is that node 6 has only one out-edge pointing to node 3, while the edges pointing to node 4 are each one of multiple out-edges. Lastly, as expected, the lowest ranked edges are those with no in-edges, nodes 1 and 2. One of the main contributions of Google’s founders is implementation and experimental evaluation of the PageRank algorithm. They included a database of web sites with 161 million links, and the algorithm converge in 45 iterations. Repeated experiments with 322 million links converged in 52 iterations. These experiments were evidence that PageRank converges in log(n) time where n is number of links, and it is applicable for a growing Web.

For example, if the Web-page connections are those given in Figure 11.4, and the current node under consideration were node B, then the following values would hold through all iterations: N = 3, In(B) = {A,C}, |Out(A)| = |{B,C}| = 2, and |Out(C)| = |{B}| = 1. The values for Pr(A) and Pr(C) would vary depending on the calculations from the previous iterations. The result is a recursive definition of PageRank. To calculate the PageRank of a given node, one must calculate the PageRank of all nodes with edges pointing into that given node. Figure 11.4. First example used to demonstrate PageRank. Often PageRank is calculated using an iterative approach where all nodes are given an initial value for Pr of 1/N. Then during a single iteration we calculate what the PageRank of each node would be according to the current values of all nodes linking to that node. This process is repeated until the change between iterations is below some predetermined threshold or the maximum number of iterations is achieved.

pages: 320 words: 87,853

The Black Box Society: The Secret Algorithms That Control Money and Information by Frank Pasquale

Affordable Care Act / Obamacare, algorithmic trading, Amazon Mechanical Turk, American Legislative Exchange Council, asset-backed security, Atul Gawande, bank run, barriers to entry, basic income, Berlin Wall, Bernie Madoff, Black Swan, bonus culture, Brian Krebs, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chelsea Manning, Chuck Templeton: OpenTable:, cloud computing, collateralized debt obligation, computerized markets, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, cryptocurrency, Debian, don't be evil, drone strike, Edward Snowden,, Fall of the Berlin Wall, Filter Bubble, financial innovation, financial thriller, fixed income, Flash crash, full employment, Goldman Sachs: Vampire Squid, Google Earth, Hernando de Soto, High speed trading, hiring and firing, housing crisis, informal economy, information asymmetry, information retrieval, interest rate swap, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, Julian Assange, Kevin Kelly, knowledge worker, Kodak vs Instagram, kremlinology, late fees, London Interbank Offered Rate, London Whale, Marc Andreessen, Mark Zuckerberg, mobile money, moral hazard, new economy, Nicholas Carr, offshore financial centre, PageRank, pattern recognition, Philip Mirowski, precariat, profit maximization, profit motive, quantitative easing, race to the bottom, recommendation engine, regulatory arbitrage, risk-adjusted returns, Satyajit Das, search engine result page, shareholder value, Silicon Valley, Snapchat, social intelligence, Spread Networks laid a new fibre optics cable between New York and Chicago, statistical arbitrage, statistical model, Steven Levy, the scientific method, too big to fail, transaction costs, two-sided market, universal basic income, Upton Sinclair, value at risk, WikiLeaks, zero-sum game

Mark Walters, “How Does Google Rank Websites?” SEOmark. Available at /how-does-google-rank-websites/. Amy N. Langville and Carl D. Meyer, “Deeper inside PageRank,” Internet Mathematics, 1 (2004): 335–380. Langville and Meyer, Google’s PageRank and Beyond. 41. Siva Vaidhyanathan, The Googlization of Everything (And Why We Should Worry) (Berkeley: University of California Press, 2010). 42. Ibid. 43. “Trust Us—We’re Geniuses and You’re Not—The Arrival of Google,” Searchless in Paradise (blog), February 19, 2013, /page/2/. Google’s current mission statement is “to organize the world’s information and make it universally accessible and useful.” “Company Overview,” Google. Available at /about /company/. For Google’s recent dominance, see Matt McGee, “Google Now #1 Search Engine In Czech Republic; 5 Countries to Go for Global Domination” (Jan. 2011).

It rates sites on relevance and on importance. The more web pages link to a given page, the more authoritative Google deems it. (For those who need to connect to a page but don’t want to promote it, Google promises not to count links that include a “rel:nofollow” tag.) The voting is weighted; web pages that are themselves linked to by many other pages have more authority than unconnected ones. This is the core of the patented “PageRank” method behind Google’s success.36 PageRank’s hybrid of egalitarianism (anyone can link) and elitism (some links count more than others) both reflected and inspired powerful modes of ordering web content.37 It also caused new problems. The more Google revealed about its ranking algorithms, the easier it was to manipulate them.38 Thus THE HIDDEN LOGICS OF SEARCH 65 began the endless cat-and-mouse game of “search engine optimization,” and with it the rush to methodological secrecy that makes search the black box business that it is.

The more Google revealed about its ranking algorithms, the easier it was to manipulate them.38 Thus THE HIDDEN LOGICS OF SEARCH 65 began the endless cat-and-mouse game of “search engine optimization,” and with it the rush to methodological secrecy that makes search the black box business that it is. The original PageRank patent, open for all to see, clandestinely accumulated a thick crust of tweaks and adjustments intended to combat web baddies: the “link farms” (sites that link to other sites only to goose their Google rankings), the “splogs” (spam blogs, which farm links in the more dynamic weblog format); and the “content farms” (which rapidly and clumsily aggregate content based on trending Google searches, so as to appear at the top of search engine result pages, or SERPs). Beneath the façade of sleek interfaces and neatly ordered results, guerrilla war simmers between the search engineers and the spammers.39 The war with legitimate content providers is just as real, if colder. Search engine optimizers parse speeches from Google the way Kremlinologists used to pore over the communiqués of Soviet premiers, looking for ways to improve their showing without provoking the “Google Death Penalty” that de-indexes sites caught gaming the system.

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Don't Be Evil: How Big Tech Betrayed Its Founding Principles--And All of US by Rana Foroohar

"side hustle", accounting loophole / creative accounting, Airbnb, AltaVista, autonomous vehicles, banking crisis, barriers to entry, Bernie Madoff, Bernie Sanders, bitcoin, book scanning, Brewster Kahle, Burning Man, call centre, cashless society, cleantech, cloud computing, cognitive dissonance, Colonization of Mars, computer age, corporate governance, creative destruction, Credit Default Swap, cryptocurrency, data is the new oil, death of newspapers, Deng Xiaoping, disintermediation, don't be evil, Donald Trump, drone strike, Edward Snowden, Elon Musk,, Erik Brynjolfsson, Etonian, Filter Bubble, future of work, game design, gig economy, global supply chain, Gordon Gekko, greed is good, income inequality, informal economy, information asymmetry, intangible asset, Internet Archive, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, Kenneth Rogoff, life extension, light touch regulation, Lyft, Mark Zuckerberg, Marshall McLuhan, Martin Wolf, Menlo Park, move fast and break things, move fast and break things, Network effects, new economy, offshore financial centre, PageRank, patent troll, paypal mafia, Peter Thiel,, price discrimination, profit maximization, race to the bottom, recommendation engine, ride hailing / ride sharing, Robert Bork, Sand Hill Road, search engine result page, self-driving car, shareholder value, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, smart cities, Snapchat, South China Sea, sovereign wealth fund, Steve Jobs, Steven Levy, subscription business, supply-chain management, TaskRabbit, Telecommunications Act of 1996, The Chicago School, the new new thing, Tim Cook: Apple, too big to fail, Travis Kalanick, trickle-down economics, Uber and Lyft, Uber for X, uber lyft, Upton Sinclair, WikiLeaks, zero-sum game

But what would? Here, Larry Page relied on an insight from his parents’ background in academia, where the most desirable papers on a topic were never the ones that just repeated a term or name endlessly, but the one that other papers cited most frequently. On the Web, the equivalent to those citations were the hyperlinks, which meant that their search engine would need a way to tally up all these hyperlinks. So Page and Brin developed a program they called BackRub, because it tracked links back to other documents. Essentially, BackRub unleashed millions of tiny electronic messengers called bots to crawl all over as many documents as they could reach and tag each one with a code that only BackRub could detect and then tally up all its “back links.” The resulting summary was called PageRank, an opportune pun on Page’s name that was fully intended.

CHAPTER 1 A Summary of the Case “Don’t be evil” is the famous first line of Google’s original Code of Conduct, what seems today like a quaint relic of the company’s early days, when the crayon colors of the Google logo still conveyed the cheerful, idealistic spirit of the enterprise. How long ago that feels. Of course, it would be unfair to accuse Google of being actively evil. But evil is as evil does, and some of the things that Google and other Big Tech firms have done in recent years have not been very nice. When Larry Page and Sergey Brin first dreamed up the idea for Google as Stanford graduate students, they probably didn’t imagine that the shiny apple of knowledge that was their search engine would ever get anyone expelled from paradise (as many Google executives have been over a variety of scandals in recent years).

As Steven Levy writes in In the Plex, “the DoubleClick deal radically broadened the scope of the information Google collected about everyone’s browsing activity on the Internet.”20 Competitors and regulators alike questioned the deal, which eventually went through, in large part because Chicago School thinking didn’t really leave any room for a good antitrust argument against it (despite the fact that it would allow Google to essentially control the vast majority of advertising online). But Google questioned it, too, at least in terms of the ramifications for its own bottom line. Larry Page and Sergey Brin were at first reluctant to combine the data and information that could be harvested via cookies on its own platform with what could now be garnered via DoubleClick (which was, of course, now a part of Google itself). But eventually, under pressure to grow, the company relented. Thanks to the merger, “Google became the only company,” writes Levy, “with the ability to pull together user data on both the fat head and the long tail of the Internet. The question was, would Google aggregate that data to track the complete activity of internet users? The answer was yes.”21 While Google had long promised users that it would ask their permission if it ever used their data for anything other than the purposes for which they’d given it (that is, for whatever individual search, or email, social media, or map functions they’d signed up for), it had begun combining and selling all the data it had on users to the highest bidder.

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Valley of Genius: The Uncensored History of Silicon Valley (As Told by the Hackers, Founders, and Freaks Who Made It Boom) by Adam Fisher

Airbnb, Albert Einstein, AltaVista, Apple II, Apple's 1984 Super Bowl advert, augmented reality, autonomous vehicles, Bob Noyce, Brownian motion, Buckminster Fuller, Burning Man, Byte Shop, cognitive dissonance, disintermediation, don't be evil, Donald Trump, Douglas Engelbart, Dynabook, Elon Musk, frictionless, glass ceiling, Hacker Ethic, Howard Rheingold, HyperCard, hypertext link, index card, informal economy, information retrieval, Jaron Lanier, Jeff Bezos, Jeff Rulifson, John Markoff, Jony Ive, Kevin Kelly, Kickstarter, knowledge worker, life extension, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Maui Hawaii, Menlo Park, Metcalfe’s law, Mother of all demos, move fast and break things, move fast and break things, Network effects, new economy, nuclear winter, PageRank, Paul Buchheit, paypal mafia, peer-to-peer, Peter Thiel,, pez dispenser, popular electronics, random walk, risk tolerance, Robert Metcalfe, rolodex, self-driving car, side project, Silicon Valley, Silicon Valley startup, skunkworks, Skype, social graph, social web, South of Market, San Francisco, Startup school, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Ted Nelson, telerobotics, The Hackers Conference, the new new thing, Tim Cook: Apple, tulip mania, V2 rocket, Whole Earth Catalog, Whole Earth Review, Y Combinator

So at the time to have a computer with four gigabytes of main memory was crazy, but it turns out that there was one computer in the computer science department that did have that, and that was in the graphics lab. They had a really, really big machine that had a whole bunch of memory and they were using it for graphics. So Larry got access to that big computer and we basically ran the algorithm on it for a couple of hours, and once it computed it, it was done. Sergey Brin: Every web page has a number. Larry Page: Then we were like, “Wow, this is really good. It ranks things in the order you expect them!” John Markoff: It’s a very simple idea: You saw the most popular things first. PageRank was an algorithm that looked at what other humans thought was significant—as demonstrated by other people linking to them—and used that as a mechanism for ordering search results. Sergey Brin: And we produced a search engine called BackRub. It was fairly primitive, it only actually looked at the titles of the web pages, but it was already working better than the available search engines at the time in terms of producing relevant results.

Scott Hassan: Larry came up with the idea of doing random walk, but Larry didn’t know how to compute it. Sergey looked at it and said, “Oh, that looks like computing the eigenvector of a matrix!” Sergey Brin: Basically we convert the entire web into a big equation, with several hundred million variables, which are the page ranks of all the web pages and billions of terms, which are the links. And we’re able to solve that equation. Terry Winograd: You can get fancy about it in formal terms. But in informal terms, PageRank was the implementation of that intuition. Scott Hassan: So Sergey just saw that and was like, “Okay great, I’m going to need a computer with four gigabytes of main memory to compute this.” So at the time to have a computer with four gigabytes of main memory was crazy, but it turns out that there was one computer in the computer science department that did have that, and that was in the graphics lab.

They were crashing them with traffic just through word of mouth. Larry Page: We caused the whole Stanford network to go down. For some significant amount of time nobody could log in to any computers at Stanford. Heather Cairns: And they were, essentially, nicely asked to leave because of that. Larry Page: Stanford said, “You guys can come back and finish your PhDs if you don’t succeed.” David Cheriton: They thought they had a big challenge of raising money, I thought that money wasn’t the big problem, and so I kind of put myself in the spot of having to prove this, so I contacted Andy Bechtolsheim. Sergey Brin: One of the founders of SUN Microsystems, and a Stanford alum. David Cheriton: Andy said he was interested, and we arranged to meet at my house, and Andy pulled up in his Porsche and they put on this demonstration of the Google search engine, and there was a certain amount of discussion.

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

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

The result is complete gibberish, of course, but if we let each letter depend on several previous letters instead of just one, it starts to sound more like the ramblings of a drunkard, locally coherent even if globally meaningless. Still not enough to pass the Turing test, but models like this are a key component of machine-translation systems, like Google Translate, which lets you see the whole web in English (or almost), regardless of the language the pages were originally written in. PageRank, the algorithm that gave rise to Google, is itself a Markov chain. Larry Page’s idea was that web pages with many incoming links are probably more important than pages with few, and links from important pages should themselves count for more. This sets up an infinite regress, but we can handle it with a Markov chain. Imagine a web surfer going from page to page by randomly following links: the states of this Markov chain are web pages instead of characters, making it a vastly larger problem, but the math is the same.

“First links in the Markov chain,” by Brian Hayes (American Scientist, 2013), recounts Markov’s invention of the eponymous chains. “Large language models in machine translation,”* by Thorsten Brants et al. (Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2007), explains how Google Translate works. “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.

., 128 building blocks and, 128–129, 134 schemas, 129 survival of the fittest programs, 131–134 The Genetical Theory of Natural Selection (Fisher), 122 Genetic programming, 52, 131–133, 240, 244, 245, 252, 303–304 sex and, 134–137 Genetic Programming (Koza), 136 Genetic search, 241, 243, 249 Genome, poverty of, 27 Gentner, Dedre, 199 Ghani, Rayid, 17 The Ghost Map (Johnson), 182–183 Gibson, William, 289 Gift economy, 279 Gleevec, 84 Global Alliance for Genomics and Health, 261 Gödel, Escher, Bach (Hofstadter), 200 Good, I. J., 286 Google, 9, 44, 291 A/B testing and, 227 AdSense system, 160 communication with learner, 266–267 data gathering, 272 DeepMind and, 222 knowledge graph, 255 Master Algorithm and, 282 Naïve Bayes and, 152 PageRank and, 154, 305 problem of induction and, 61 relational learning and, 227–228 search results, 13 value of data, 274 value of learning algorithms, 10, 12 Google Brain network, 117 Google Translate, 154, 304 Gould, Stephen Jay, 127 GPS, 212–214, 216, 277 Gradient descent, 109–110, 171, 189, 193, 241, 243, 249, 252, 257–258 Grammars, formal, 36–37 Grandmother cell, perceptron and, 99–100 Graphical models, 240, 245–250 Graphical user interfaces, 236 The Guns of August (Tuchman), 178 Handwritten digit recognition, 189, 195 Hart, Peter, 185 Hawking, Stephen, 47, 283 Hawkins, Jeff, 28, 118 Hebb, Donald, 93, 94 Hebb’s rule, 93, 94, 95 Heckerman, David, 151–152, 159–160 Held-out data, accuracy of, 75–76 Help desks, 198 Hemingway, Ernest, 106 Heraclitus, 48 Hidden Markov model (HMM), 154–155, 159, 210, 305 Hierarchical structure, Markov logic network with, 256–257 Hill climbing, 135, 136, 169, 189, 252 Hillis, Danny, 135 Hinton, Geoff, 103, 104, 112, 115, 137, 139 The Hitchhiker’s Guide to the Galaxy (Adams), 130 HIV testing, Bayes’ theorem and, 147–148 HMM.

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

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

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, [>]–[>], [>]–[>] 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, [>]–[>], [>]–[>], [>]–[>] 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), [>], [>] imprecision.

Falls to Lowest Level Since 2008,” Bloomberg, August 13, 2012 ( est-level-since-2008-as-vix-falls.html). [>] Google’s 24 petabytes per day—Thomas H. Davenport, Paul Barth, and Randy Bean, “How ‘Big Data’ Is Different,” Sloan Review, July 30, 2012, pp. 43–46 ( Facebook stats—Facebook IPO prospectus, “Form S-1 Registration Statement,” U.S. Securities and Exchange Commission, February 1, 2012 ( YouTube stats—Larry Page, “Update from the CEO,” Google, April 2012 ( Number of tweets—Tomio Geron, “Twitter’s Dick Costolo: Twitter Mobile Ad Revenue Beats Desktop on Some Days,” Forbes, June 6, 2012 (

With big data, these problems may arise more frequently or have larger consequences. Google, as we’ve shown in many examples, runs everything according to data. That strategy has obviously led to much of its success. But it also trips up the company from time to time. Its co-founders, Larry Page and Sergey Brin, long insisted on knowing all job candidates’ SAT scores and their grade point averages when they graduated from college. In their thinking, the first number measured potential and the second measured achievement. Accomplished managers in their forties who were being recruited were hounded for the scores, to their outright bafflement. The company even continued to demand the numbers long after its internal studies showed no correlation between the scores and job performance. Google ought to know better, to resist being seduced by data’s false charms.

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

1960s counterculture, 3D printing, 4chan, Ada Lovelace, additive manufacturing, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, basic income, Benevolent Dictator For Life (BDFL), Berlin Wall, bioinformatics, bitcoin, blockchain, Buckminster Fuller, Burning Man, call centre, carbon footprint, carbon-based life, Cass Sunstein, Celebration, Florida, charter city, clean water, cloud computing, connected car, corporate governance, crowdsourcing, cryptocurrency, dark matter, David Graeber, deglobalization, dematerialisation, disintermediation, distributed generation, don't be evil, Douglas Engelbart, Douglas Engelbart, Edward Snowden, Elon Musk,, Eratosthenes, Ethereum, ethereum blockchain, facts on the ground, Flash crash, Frank Gehry, Frederick Winslow Taylor, future of work, Georg Cantor, gig economy, global supply chain, Google Earth, Google Glasses, Guggenheim Bilbao, High speed trading, Hyperloop, illegal immigration, industrial robot, information retrieval, Intergovernmental Panel on Climate Change (IPCC), intermodal, Internet of things, invisible hand, Jacob Appelbaum, Jaron Lanier, Joan Didion, John Markoff, Joi Ito, Jony Ive, Julian Assange, Khan Academy, liberal capitalism, lifelogging, linked data, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, Masdar, McMansion, means of production, megacity, megastructure, Menlo Park, Minecraft, MITM: man-in-the-middle, Monroe Doctrine, Network effects, new economy, offshore financial centre, oil shale / tar sands, packet switching, PageRank, pattern recognition, peak oil, peer-to-peer, performance metric, personalized medicine, Peter Eisenman, Peter Thiel, phenotype, Philip Mirowski, Pierre-Simon Laplace, place-making, planetary scale, RAND corporation, recommendation engine, reserve currency, RFID, Robert Bork, Sand Hill Road, self-driving car, semantic web, sharing economy, Silicon Valley, Silicon Valley ideology, Slavoj Žižek, smart cities, smart grid, smart meter, social graph, software studies, South China Sea, sovereign wealth fund, special economic zone, spectrum auction, Startup school, statistical arbitrage, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, Superbowl ad, supply-chain management, supply-chain management software, TaskRabbit, the built environment, The Chicago School, the scientific method, Torches of Freedom, transaction costs, Turing complete, Turing machine, Turing test, undersea cable, universal basic income, urban planning, Vernor Vinge, Washington Consensus, web application, Westphalian system, WikiLeaks, working poor, Y Combinator

In the longer term, a Google Energy may be a key player in the retail and wholesaling of renewables and the management of both consumer and municipality-facing smart grids. The company sees the pairing of bits and electrons as part of its vocation in ways that others simply cannot: Google Energy, Google Glass, Google Ideas, Google Car, Google Robotics, Google Earth. Google Space. Google Time. Google AI. Google Grossraum. Google Sovereignty, Google World. For the Google platform model for the Cloud Polis, these are all based on a grand vision encompassing (at least) information cosmopolitanism, search, advertising, physicalized information, and global infrastructure. Google (and now Alphabet) is a company founded on an algorithm.62 The original PageRank algorithm was Larry Page's attempt to organize the entire World Wide Web according to something like the peer citation models that quantify which academic papers are most influential and relevant.

Steven Carstensen, “Google's Public DNS Intercepted in Turkey,” Google Online Security Blog, March 29, 2014, 66.  A current line of my research looks at convergences of machine sensing and animal sensation. The larger domain of “search” underwrites both and sometimes enables that convergence. 67.  Yann Moulier Boutang and Ed Emery, Cognitive Capitalism (Cambridge: Polity Press, 2011). 68.  Pasquinelli writes on this conjunction within Google's algorithmic phylum: “First and foremost Google's power is understood from the perspective of value production (in different forms: attention value, cognitive value, network value, etc.): the biopolitical consequences of its data monopoly come logically later.” Matteo Pasquinelli, “Google's PageRank Algorithm: A Diagram of the Cognitive Capitalism and the Rentier of the Common Intellect,” in Deep Search: The Politics of Search beyond Google, ed.

It's not my interest to revisit or revitalize Cold War ideologies (or evangelize twentieth-century economic ideologies, as should be clear by now) and so will offer instead an update and correction of this joke.56 The most significant indirect contribution was not Apollo; rather it was Google. Is that still even a joke? The PageRank algorithm that formed the initial core of Search was based on “collective evaluation” as opposed to expert evaluation, which would be more expensive, slower and less reliable when dealing with massive amounts of unstructured and dynamic data. As described by Massimo Franceschet in his article “PageRank: Standing on the Shoulders of Giants,” which locates Google's algorithmic methods in the long and diverse history of econometric, sociometric, and bibliometric information evaluation and calculative techniques, “PageRank introduced an original notion of quality of information found on the Web: the collective intelligence of the Web, formed by the opinions of the millions of people that populate this universe, is exploited to determine the importance, and ultimately the quality, of that information.”

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Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World With OKRs by John Doerr

Albert Einstein, Bob Noyce, cloud computing, collaborative editing, commoditize, crowdsourcing, Firefox, Frederick Winslow Taylor, Google Chrome, Google Earth, Google X / Alphabet X, Haight Ashbury, Jeff Bezos, job satisfaction, Khan Academy, knowledge worker, Menlo Park, meta analysis, meta-analysis, PageRank, Paul Buchheit, Ray Kurzweil, risk tolerance, self-driving car, side project, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, Steven Levy, subscription business, web application, Yogi Berra, éminence grise

Sylvia Mathews Burwell, a Gates alumna, ported the process to the federal Office of Management and Budget and later to the Department of Health and Human Services, where it helped the U.S. government fight Ebola. But perhaps no organization, not even Intel, has scaled OKRs more effectively than Google. While conceptually simple, Andy Grove’s regimen demands rigor, commitment, clear thinking, and intentional communication. We’re not just making some list and checking it twice. We’re building our capacity, our goal muscle, and there is always some pain for meaningful gain. Yet Google’s leaders have never faltered. Their hunger for learning and improving remains insatiable. As Eric Schmidt and Jonathan Rosenberg observed in their book How Google Works , OKRs became the “simple tool that institutionalized the founders’ ‘think big’ ethos.” In Google’s early years, Larry Page set aside two days per quarter to personally scrutinize the OKRs for each and every software engineer. (I’d sit in on some of those reviews, and Larry’s analytical legerdemain—his preternatural ability to find coherence in so many moving parts—was unforgettable.)

When a few companies prevailed upon him to accept stock, he funneled the proceeds into his philanthropic organization. In 2001, after helping persuade Google’s founders to hire Eric Schmidt as CEO, I advised Eric that he needed Bill as his coach. Eric was a rightfully proud man who’d already served as CEO and chairman at Novell, and my suggestion offended him—“I know what I’m doing,” he said. So it wasn’t love at first sight for him and Bill. But in less than a year, Eric’s self-review showed how far he’d come around: “ Bill Campbell has been very helpful in coaching all of us. In hindsight, his role was needed from the beginning. I should have encouraged this structure sooner, ideally the moment I started at Google.” Bill considered his Google mandate open-ended. He coached Larry Page and Sergey Brin—and Susan Wojcicki and Sheryl Sandberg and Jonathan Rosenberg and Google’s whole executive team. He did it in his characteristic style, one part Zen and one part Bud Light.

Davidow, Marketing High Technology: An Insider’s View (New York: Free Press, 1986). “the gospel of 10x” : Steven Levy, “Big Ideas: Google’s Larry Page and the Gospel of 10x,” Wired , March 30, 2013. “tend to assume that” : Eric Schmidt and Jonathan Rosenberg, How Google Works (New York: Grand Central Publishing, 2014). “The way Page sees it” : Levy, “Big Ideas.” start of the period : Interview with Bock. In pursuing high-effort : Locke and Latham, “Building a Practically Useful Theory of Goal Setting and Task Motivation.” “You know, in our business” : iOPEC seminar, 1992. CHAPTER 13: Stretch: The Google Chrome Story “If you want your car” : Laszlo Bock, Work Rules!: Insights from Inside Google That Will Transform How You Live and Lead (New York: Grand Central Publishing, 2015). “If you set a crazy” : Ibid.

pages: 229 words: 67,869

So You've Been Publicly Shamed by Jon Ronson

4chan, AltaVista, Berlin Wall, Broken windows theory, Burning Man, Clive Stafford Smith, cognitive dissonance, Desert Island Discs, different worldview, don't be evil, Donald Trump, drone strike, Google Hangouts, illegal immigration, Menlo Park, PageRank, Ralph Nader, Rosa Parks, Silicon Valley, Skype, Stanford prison experiment, Steve Jobs, urban planning, WikiLeaks

Which for me would be the most fantastic website to chance upon, but for everyone else, less so. But then two students at Stanford, Larry Page and Sergey Brin, had their idea. Why not build a search engine that ranked websites by popularity instead? If someone is linking to your page, that’s one vote. A link, they figured, is like a citation - a nod of respect. If the page linking to your page has a lot of links into it, then that page counts for more votes. An esteemed person bestowing their admiration upon you is worth more than some loner doing the same. And that was it. They called their invention PageRank, after Larry Page, and as soon as they turned the algorithm on, us early searchers were spellbound. This was why Farukh needed to create LinkedIn and Tumblr and Twitter pages for Lindsey. They come with a built-in high PageRank. The Google algorithm prejudges them as well liked.

It was a producer at Radiolab - Tim Howard - who put me in touch with their former contributor, Jonah Lehrer. So my thanks to them for that too. The Murderer Next Door was published by Penguin in 2005. Some background information on the Zumba prostitute ring in Kennebunk came from the story ‘Modern-Day Puritans Wring Hands Over Zumba Madam’s List Of Shame’ by Patrik Jonsson, which was published in the Christian Science Monitor on 13 October 2012. For more on Larry Page and Sergey Brin’s days at Stanford, I recommend ‘The Birth of Google’ by John Battelle, which was published in Wired magazine in August 2005. All my information about the Stasi came from Anna Funder’s brilliant Stasiland: Stories from Behind the Berlin Wall, published by Granta in 2003 and by Harper Perennial in 2011. My research into the terrible story of Lindsay Armstrong took me to ‘She Couldn’t Take Any More’, which was written by Kirsty Scott and published in the Guardian on 2 August 2002.

It’s about the companies that dominate the data flows of the Internet.’ Now, I suddenly wondered. Did Google make money from the destruction of Justine Sacco? Could a figure be calculated? And so I joined forces with a number-crunching researcher, Solvej Krause, and began writing to economists and analysts and online ad revenue people. Some things were known. In December 2013, the month of Justine’s annihilation, 12.2 billion Google searches took place - a figure that made me feel less worried about the possibility that people were sitting inside Google headquarters personally judging me. Google’s ad revenue for that month was $4.69 billion. Which meant they made an average of $0.38 for every search query. Every time we typed anything into Google: 38 cents to Google. Of those 12.2 billion searches that December, 1.2 million were people searching the name Justine Sacco.

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Where Good Ideas Come from: The Natural History of Innovation by Steven Johnson

Ada Lovelace, Albert Einstein, Alfred Russel Wallace, carbon-based life, Cass Sunstein, cleantech, complexity theory, conceptual framework, cosmic microwave background, creative destruction, crowdsourcing, data acquisition, digital Maoism, digital map, discovery of DNA, Dmitri Mendeleev, double entry bookkeeping, double helix, Douglas Engelbart, Douglas Engelbart, Drosophila, Edmond Halley, Edward Lloyd's coffeehouse, Ernest Rutherford, Geoffrey West, Santa Fe Institute, greed is good, Hans Lippershey, Henri Poincaré, hive mind, Howard Rheingold, hypertext link, invention of air conditioning, invention of movable type, invention of the printing press, invention of the telephone, Isaac Newton, Islamic Golden Age, James Hargreaves, James Watt: steam engine, Jane Jacobs, Jaron Lanier, Johannes Kepler, John Snow's cholera map, Joseph Schumpeter, Joseph-Marie Jacquard, Kevin Kelly, lone genius, Louis Daguerre, Louis Pasteur, Mason jar, mass immigration, Mercator projection, On the Revolutions of the Heavenly Spheres, online collectivism, packet switching, PageRank, patent troll, pattern recognition, price mechanism, profit motive, Ray Oldenburg, Richard Florida, Richard Thaler, Ronald Reagan, side project, Silicon Valley, silicon-based life, six sigma, Solar eclipse in 1919, spinning jenny, Steve Jobs, Steve Wozniak, Stewart Brand, The Death and Life of Great American Cities, The Great Good Place, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, transaction costs, urban planning

A platform adapted for scholarship was exapted for shopping, and sharing photos, and watching pornography—along with a thousand other uses that would have astounded Berners-Lee when he created his first HTML-based directories in the early nineties. When Sergey Brin and Larry Page decided to use links between Web pages as digital votes endorsing the content of those pages, they were exapting Berners-Lee’s original design: they took a trait adapted for navigation—the hypertext link—and used it as a vehicle for assessing quality. The result was PageRank, the original algorithm that made Google into the behemoth that it is today. The literary historian Franco Moretti has persuasively documented the role of exaptation in the evolution of the novel. An author conceives a new kind of narrative device to address a specific, local need in a work he or she is writing.

The most telling contrast between Google and the FBI lies in the story of Krishna Bharat, who now holds the title of “principal scientist” at Google. In the weeks after 9/11, Bharat found himself overwhelmed by the amount of news information available about the attacks and the imminent war in Afghanistan. It occurred to him that it would be useful to create a software tool that could organize all those stories into useful clusters of relevance, so that you could see at a glance all the latest stories from around the Web about the search for bin Laden, or the cleanup efforts at Ground Zero, or the Bush administration’s case for military retaliation. Bharat decided to use his 20-percent time to build a system called StoryRank—modeled after the original PageRank algorithm that Google’s search engine relies on—to organize and cluster news items.

Most engineers end up drifting from idea to idea, and the vast majority of those ideas never turn into an official Google product. But every now and then, one of those hunches blooms into something significant. AdSense, Google’s platform that allows bloggers and Web publishers to run Google ads on their sites, was partially generated during 20-percent time. In 2009, AdSense was responsible for more than $5 billion of Google’s earnings, nearly a third of their total for the year. Orkut, one of the largest social network sites in India and Brazil, originated in the Innovation Time Off of a Turkish Google engineer named Orkut Büyükkökten. Google’s popular mail platform, Gmail, has roots in an Innovation Time Off project as well. Marissa Mayer, Google’s vice president of Search Products and User Experience, claims that over 50 percent of Google’s new products derive from Innovation Time Off hunches.

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

23andMe, 3D printing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, Ben Horowitz, bioinformatics, bitcoin, Black Swan, blockchain, Burning Man, business intelligence, business process, call centre, chief data officer, Chris Wanstrath, Clayton Christensen, clean water, cloud computing, cognitive bias, collaborative consumption, collaborative economy, commoditize, corporate social responsibility, cross-subsidies, crowdsourcing, cryptocurrency, dark matter, Dean Kamen, dematerialisation, discounted cash flows, disruptive innovation, distributed ledger, Edward Snowden, Elon Musk,, Ethereum, ethereum blockchain, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, hiring and firing, Hyperloop, industrial robot, Innovator's Dilemma, intangible asset, Internet of things, Iridium satellite, Isaac Newton, Jeff Bezos, Joi Ito, Kevin Kelly, Kickstarter, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, lifelogging, loose coupling, loss aversion, low earth orbit, Lyft, Marc Andreessen, Mark Zuckerberg, market design, means of production, minimum viable product, natural language processing, Netflix Prize, NetJets, Network effects, new economy, Oculus Rift, offshore financial centre, PageRank, pattern recognition, Paul Graham, paypal mafia, peer-to-peer, peer-to-peer model, 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, Travis Kalanick, Tyler Cowen: Great Stagnation, uber lyft, urban planning, WikiLeaks, winner-take-all economy, X Prize, Y Combinator, zero-sum game

Recommendation: Hire both internal and external Black Ops teams and have them establish startups with a combined goal of defeating one another and disrupting the mother ship. Copy Google[X] At a Singularity University event three years ago, Larry Page told Salim he’d heard good things about Brickhouse and asked whether Google should set up something similar. Salim’s recommendation was no; he believed it would only evoke the same immune system response he’d experienced at Yahoo. Page’s response was cryptic: “What would a Brickhouse for atoms look like?” he asked. We now know what he meant. In launching the Google[X] lab, Google has taken the classic skunkworks approach to new product development further than anyone ever imagined. Google[X] offers two fascinating new extensions to the traditional approach. First, it aims for moonshot-quality ideas (e.g., life extension, autonomous vehicles, Google Glass, smart contact lenses, Project Loon, etc.).

Dependencies or Prerequisites • Increase loyalty to ExO • Drives exponential growth • Validates new ideas, and learning • Allows agility and rapid implementation • Amplifies ideation • MTP • Engagement • Authentic and transparent leadership • Low threshold to participate • P2P value creation Algorithms In 2002, Google’s revenues were less than a half-billion dollars. Ten years later, its revenues had jumped 125x and the company was generating a half-billion dollars every three days. At the heart of this staggering growth was the PageRank algorithm, which ranks the popularity of web pages. (Google doesn’t gauge which page is better from a human perspective; its algorithms simply respond to the pages that deliver the most clicks.) Google isn’t alone. Today, the world is pretty much run on algorithms. From automotive anti-lock braking to Amazon’s recommendation engine; from dynamic pricing for airlines to predicting the success of upcoming Hollywood blockbusters; from writing news posts to air traffic control; from credit card fraud detection to the 2 percent of posts that Facebook shows a typical user—algorithms are everywhere in modern life.

* ( ) We don’t do any meaningful data analysis ( ) We collect and analyze data mostly via reporting systems ( ) We use Machine Learning algorithms to analyze data and drive actionable decisions ( ) Our products and services are built around algorithms and machine learning (e.g. PageRank) 11) Do you share strategic data assets internally across the company or expose them externally to your community?* ( ) We don’t share data, even between departments ( ) We have data shared between departments (e.g. use internal dashboards, activity streams and wiki pages) ( ) We expose some data to key suppliers (e.g. EDI interfaces or via APIs) ( ) We expose some data to our external ecosystem via open APIs (e.g. Flickr, Google, Twitter, Ford) Interfaces and Scalable Processes 12) Do you have specialized processes for managing the output of externalities within your internal organization? [by externalities, we mean Staff on Demand, Community/Crowd, Algorithms, Leased Assets and Engagement]* ( ) We don’t leverage externalities or we have no special processes to capture or manage externalities ( ) We have dedicated staff to manage externalities (e.g.

pages: 317 words: 84,400

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

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

Being able to identify pecking-order top dogs automatically on a wide scale could lead to new approaches in politics, management, sales, and marketing. This model of ranking things based on small clues of influence is the same calculus that drives PageRank, Google’s algorithm, named after cofounder Larry Page, which steers Web traffic to sites the Web regards as authoritative on the subject being searched. Important Web sites are called hubs and influencers. Google gives more credence in its search results to sites that are often linked to by influential sites and hubs. If these sites commonly refer to, say, a particular flight-booking search engine as the best one while concurrently linking to it, it’s likely that this Web site will rise to the top of Google’s results. By looking at where the influential sites link, Google’s algorithm can quickly determine what to show for any query a user might type in. Sorting humans can be done much the same way.

., 3, 218 “Explanation of Binary Arithmetic” (Leibniz), 58 ExxonMobil, 50 Facebook, 198–99, 204–6, 214 graph theory and, 70 face-reading algorithms, 129, 161 Falchuk, Myron, 157 Farmville, 206 fat tails, 63–64 FBI, 137 FedEx, 116 Ferguson, Lynne, 87 Fermat, Pierre, 66–67 fiber: dark, 114–20, 122 lit, 114 fiber optic cables, 117, 124, 192 Fibonacci, Leonardo, 56–57 Fibonacci sequence, 57 Fidelity, 50 finance, probability theory and, 66 financial markets, algorithms’ domination of, 24 financial sector, expansion of, 184, 191 see also Wall Street Finkel, Eli, 145 Finland, 130 First New York Securities, 4 Fisher, Helen, 144 Flash Crash of 2010, 2–5, 48–49, 64, 184 Forbes magazine, 8 foreign exchange, golden mean and, 57 Fortran, 12, 38 Fortune 500 companies, Kahler’s methods at, 176 Fourier, Joseph, 105–6 Fourier series, 105–7 Fourier transforms, 82 401K plans, 50 Fox News, 137 fractal geometry, 56 France, 61, 66, 80, 121, 147 Frankfurt, 121 fraud, eLoyalty bots and, 193 French-English translation software, 178–79 From Darkness, Light, 99 galaxies, orbital patterns of, 56 gambling: algorithms and, 127–35 probability theory and, 66, 67 game theory, 58 algorithms and, 129–31 and fall of Soviet Union, 136 in organ donor networks, 147–49 in politics, 136 sports betting and, 133–35 terrorism prevention by, 135–40 gastroenterology, 157 Gauss, Carl Friedrich, 61–65 Gaussian copula, 65, 189 Gaussian distributions, 63–64 Gaussian functions, 53 GE, 209, 213 Geffen, 87 General Mills, 130 General Motors, 201 genes, algorithmic scanning of, 159, 160 geometry, 55 of carbon, 70 fractal, 56 George IV, king of England, 62 Germany, 26, 61, 90 West, 19 Getco, 49, 116, 118 Glenn, John, 175 gluten, 157 Gmail, 71, 196 Gödel, Escher, Bach: An Eternal Golden Braid (Hofstadter), 97 gold, 21, 27 Gold and Stock Telegraph Company, 123 Goldberg, David, 219 golden mean, 56–57 Goldman Sachs, 116, 119, 204, 213 bailout of, 191 engineering and science talent hired by, 179, 186, 187, 189 Hull Trading bought by, 46 Peterffy’s buyout offer from, 46 Gomez, Dominic, 87 goodwill, 27 Google, 47, 71, 124, 192, 196, 207, 213, 219 algorithm-driven cars from, 215 PageRank algorithm of, 213–14 Gorbachev, Mikhail, 136 Göttingen, 122 Göttingen, University of, 59, 65 grain prices, hedging algorithm for, 130 grammar, algorithms for, 54 Grammy awards, 83 graph theory, 69–70 Great Depression, 123 Greatest Trade Ever, The (Zuckerman), 202 Greece, rioting in, 2–3 Greenlight Capital, 128 Greenwich, Conn., 47, 48 Griffin, Blake, 142 Griffin, Ken, 128, 190 Groopman, Jerome, 156 Groupon, 199 growth prospects, 27 Guido of Arezzo, 91 guitars: Harrison’s twelve–string Rickenbacker, 104–5, 107–9 Lennon’s six–string, 104, 107–8 hackers: as algorithm creators, 8, 9, 178 chat rooms for, 53, 124 as criminals, 7–8 for gambling, 135 Leibniz as, 60 Lovelace as, 73 online, 53 poker played by, 128 Silicon Valley, 8 on Wall Street, 17–18, 49, 124, 160, 179, 185, 201 Wall Street, dawn of hacker era on, 24–27 haiku, algorithm-composed, 100–101 Haise, Fred, 165–67 Hal 9000, 7 Hammerbacher, Jeffrey, 201–6, 209, 216 Handel, George Frideric, 68, 89, 91 Hanover, 62 Hanto, Ruthanne, 151 Hardaway, Penny, 143 “Hard Day’s Night, A,” opening chord of, 104–10 hardware: escalating war of, 119–25 Leibniz’s binary system and, 61 Harrah’s, 135 Harrison, George, 103–5, 107–10 on Yahoo!

., 140, 165 Nobel Prize, 23, 106 North Carolina, 48, 204 Northwestern University, 145, 186 Kellogg School of Management at, 10 Novak, Ben, 77–79, 83, 85, 86 NSA, 137 NuclearPhynance, 124 nuclear power, 139 nuclear weapons, in Iran, 137, 138–39 number theory, 65 numerals: Arabic-Indian, 56 Roman, 56 NYSE composite index, 40, 41 Oakland Athletics, 141 Obama, Barack, 46, 218–19 Occupy Wall Street, 210 O’Connor & Associates, 40, 46 OEX, see S&P 100 index Ohio, 91 oil prices, 54 OkCupid, 144–45 Olivetti home computers, 27 opera, 92, 93, 95 Operation Match, 144 opinions-driven people, 173, 174, 175 OptionMonster, 119 option prices, probability and statistics in, 27 options: Black-Scholes formula and, 23 call, 21–22 commodities, 22 definition of, 21 pricing of, 22 put, 22 options contracts, 30 options trading, 36 algorithms in, 22–23, 24, 114–15 Oregon, University of, 96–97 organ donor networks: algorithms in, 149–51, 152, 214 game theory in, 147–49 oscilloscopes, 32 Outkast, 102 outliers, 63 musical, 102 outputs, algorithmic, 54 Pacific Exchange, 40 Page, Larry, 213 PageRank, 213–14 pairs matching, 148–51 pairs trading, 31 Pakistan, 191 Pandora, 6–7, 83 Papanikolaou, Georgios, 153 Pap tests, 152, 153–54 Parham, Peter, 161 Paris, 56, 59, 121 Paris Stock Exchange, 122, 201 partial differential equations, 23 Pascal, Blaise, 59, 66–67 pathologists, 153 patient data, real-time, 158–59 patterns, in music, 89, 93, 96 Patterson, Nick, 160–61 PayPal, 188 PCs, Quotron data for, 33, 37, 39 pecking orders, social, 212–14 Pennsylvania, 115, 116 Pennsylvania, University of, 49 pension funds, 202 Pentagon, 168, 144 Perry, Katy, 89 Persia, 54 Peru, 91 Peterffy, Thomas: ambitions of, 27 on AMEX, 28–38 automated trading by, 41–42, 47–48, 113, 116 background and early career of, 18–20 Correlator algorithm of, 42–45 early handheld computers developed by, 36–39, 41, 44–45 earnings of, 17, 37, 46, 48, 51 fear that algorithms have gone too far by, 51 hackers hired by, 24–27 independence retained by, 46–47 on index funds, 41–46 at Interactive Brokers, 47–48 as market maker, 31, 35–36, 38, 51 at Mocatta, 20–28, 31 Nasdaq and, 11–18, 32, 42, 47–48, 185 new technology innovated by, 15–16 options trading algorithm of, 22–23, 24 as outsider, 31–32 profit guidelines of, 29 as programmer, 12, 15–16, 17, 20–21, 26–27, 38, 48, 62 Quotron hack of, 32–35 stock options algorithm as goal of, 27 Timber Hill trading operation of, see Timber Hill traders eliminated by, 12–18 trading floor methods of, 28–34 trading instincts of, 18, 26 World Trade Center offices of, 11, 39, 42, 43, 44 Petty, Tom, 84 pharmaceutical companies, 146, 155, 186 pharmacists, automation and, 154–56 Philips, 159 philosophy, Leibniz on, 57 phone lines: cross-country, 41 dedicated, 39, 42 phones, cell, 124–25 phosphate levels, 162 Physicians’ Desk Reference (PDR), 146 physicists, 62, 157 algorithms and, 6 on Wall Street, 14, 37, 119, 185, 190, 207 pianos, 108–9 Pincus, Mark, 206 Pisa, 56 pitch, 82, 93, 106 Pittsburgh International Airport, security algorithm at, 136 Pittsburgh Pirates, 141 Pius II, Pope, 69 Plimpton, George, 141–42 pneumonia, 158 poetry, composed by algorithm, 100–101 poker, 127–28 algorithms for, 129–35, 147, 150 Poland, 69, 91 Polyphonic HMI, 77–79, 82–83, 85 predictive algorithms, 54, 61, 62–65 prescriptions, mistakes with, 151, 155–56 present value, of future money streams, 57 pressure, thriving under, 169–70 prime numbers, general distribution pattern of, 65 probability theory, 66–68 in option prices, 27 problem solving, cooperative, 145 Procter & Gamble, 3 programmers: Cope as, 92–93 at eLoyalty, 182–83 Peterffy as, 12, 15–16, 17, 20–21, 26–27, 38, 48, 62 on Wall Street, 13, 14, 24, 46, 47, 53, 188, 191, 203, 207 programming, 188 education for, 218–20 learning, 9–10 simple algorithms in, 54 Progress Energy, 48 Project TACT (Technical Automated Compatibility Testing), 144 proprietary code, 190 proprietary trading, algorithmic, 184 Prussia, 69, 121 PSE, 40 pseudocholinesterase deficiency, 160 psychiatry, 163, 171 psychology, 178 Pu, Yihao, 190 Pulitzer Prize, 97 Purdue University, 170, 172 put options, 22, 43–45 Pythagorean algorithm, 64 quadratic equations, 63, 65 quants (quantitative analysts), 6, 46, 124, 133, 198, 200, 202–3, 204, 205 Leibniz as, 60 Wall Street’s monopoly on, 183, 190, 191, 192 Queen’s College, 72 quizzes, and OkCupid’s algorithms, 145 Quotron machine, 32–35, 37 Rachmaninoff, Sergei, 91, 96 Radiohead, 86 radiologists, 154 radio transmitters, in trading, 39, 41 railroad rights-of-way, 115–17 reactions-based people, 173–74, 195 ReadyForZero, 207 real estate, 192 on Redfin, 207 recruitment, of math and engineering students, 24 Redfin, 192, 206–7, 210 reflections-driven people, 173, 174, 182 refraction, indexes of, 15 regression analysis, 62 Relativity Technologies, 189 Renaissance Technologies, 160, 179–80, 207–8 Medallion Fund of, 207–8 retirement, 50, 214 Reuter, Paul Julius, 122 Rhode Island hold ‘em poker, 131 rhythms, 82, 86, 87, 89 Richmond, Va., 95 Richmond Times-Dispatch, 95 rickets, 162 ride sharing, algorithm for, 130 riffs, 86 Riker, William H., 136 Ritchie, Joe, 40, 46 Rochester, N.Y., 154 Rolling Stones, 86 Rondo, Rajon, 143 Ross, Robert, 143–44 Roth, Al, 147–49 Rothschild, Nathan, 121–22 Royal Society, London, 59 RSB40, 143 runners, 39, 122 Russia, 69, 193 intelligence of, 136 Russian debt default of 1998, 64 Rutgers University, 144 Ryan, Lee, 79 Saint Petersburg Academy of Sciences, 69 Sam Goody, 83 Sandberg, Martin (Max Martin), 88–89 Sandholm, Tuomas: organ donor matching algorithm of, 147–51 poker algorithm of, 128–33, 147, 150 S&P 100 index, 40–41 S&P 500 index, 40–41, 51, 114–15, 218 Santa Cruz, Calif., 90, 95, 99 satellites, 60 Savage Beast, 83 Saverin, Eduardo, 199 Scholes, Myron, 23, 62, 105–6 schools, matching algorithm for, 147–48 Schubert, Franz, 98 Schwartz, Pepper, 144 science, education in, 139–40, 218–20 scientists, on Wall Street, 46, 186 Scott, Riley, 9 scripts, algorithms for writing, 76 Seattle, Wash., 192, 207 securities, 113, 114–15 mortgage-backed, 203 options on, 21 Securities and Exchange Commission (SEC), 185 semiconductors, 60, 186 sentence structure, 62 Sequoia Capital, 158 Seven Bridges of Königsberg, 69, 111 Shannon, Claude, 73–74 Shuruppak, 55 Silicon Valley, 53, 81, 90, 116, 188, 189, 215 hackers in, 8 resurgence of, 198–211, 216 Y Combinator program in, 9, 207 silver, 27 Simons, James, 179–80, 208, 219 Simpson, O.

pages: 472 words: 117,093

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

"Robert Solow", 3D printing, additive manufacturing, AI winter, Airbnb, airline deregulation, airport security, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, backtesting, barriers to entry, bitcoin, blockchain, British Empire, business cycle, business process, carbon footprint, Cass Sunstein, centralized clearinghouse, Chris Urmson, cloud computing, cognitive bias, commoditize, complexity theory, computer age, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, Dean Kamen, discovery of DNA, disintermediation, disruptive innovation, distributed ledger, double helix, Elon Musk,, Erik Brynjolfsson, Ethereum, ethereum blockchain, everywhere but in the productivity statistics, family office, fiat currency, financial innovation, George Akerlof, global supply chain, Hernando de Soto, hive mind, information asymmetry, Internet of things, inventory management, iterative process, Jean Tirole, Jeff Bezos, jimmy wales, John Markoff, joint-stock company, Joseph Schumpeter, Kickstarter, law of one price, longitudinal study, Lyft, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Marc Andreessen, Mark Zuckerberg, meta analysis, meta-analysis, Mitch Kapor, moral hazard, multi-sided market, Myron Scholes, natural language processing, Network effects, new economy, Norbert Wiener, Oculus Rift, PageRank, pattern recognition, peer-to-peer lending, performance metric, plutocrats, Plutocrats, precision agriculture, prediction markets, pre–internet, price stability, principal–agent problem, Ray Kurzweil, Renaissance Technologies, Richard Stallman, ride hailing / ride sharing, risk tolerance, Ronald Coase, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, slashdot, smart contracts, Snapchat, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Pinker, supply-chain management, TaskRabbit, Ted Nelson, The Market for Lemons, The Nature of the Firm, Thomas Davenport, Thomas L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, ubercab, Watson beat the top human players on Jeopardy!, winner-take-all economy, yield management, zero day

The algorithm that Page and Brin developed created a rank of every page and was called “PageRank.” Their paper describing this approach, titled “The Anatomy of a Large-Scale Hypertextual Web Search Engine,” was presented in April 1998 at the Seventh International World-Wide Web Conference in Brisbane, Australia. The company that the pair created to put this approach into practice—initially called BackRub, but later renamed Google—was founded in September 1998 in Silicon Valley. Google changed the world with the realization that even though the crowd’s online content was uncontrolled, it wasn’t disorganized. It, in fact, had an extremely elaborate and fine-grained structure, but not one that was consciously decided on by any core group of humans. Instead, it was a structure that emerged from the content itself, once it was analyzed by the company’s PageRank algorithm and all of its relatives.

All 129,864,880 of You,” Google Books Search (blog), August 5, 2010, 231 about 30 million are available: Khazar University Library and Information Center, “10 Largest Libraries of the World,” accessed February 6, 2017, 231 approximately 45 billion pages: Antal van den Bosch, Toine Bogers, and Maurice de Kunder, “Estimating Search Engine Index Size Variability: A 9-Year Longitudinal Study,” Scientometrics, July 27, 2015,; Maurice de Kunder, “The Size of the World Wide Web (the Internet),”, accessed February 6, 2017, 231 at least 25 million of those books: Stephen Heyman, “Google Books: A Complex and Controversial Experiment,” New York Times, October 28, 2015, 231 an estimated 80 million videos are on YouTube alone: Chris Desadoy, “How Many Videos Have Been Uploaded to YouTube?” Quora, March 31, 2015, 233 “The Internet is the world’s largest library”: Quote verified via personal communication with Allen Paulos, March 2017. 233 Their paper describing this approach: Sergey Brin and Larry Page, “The Anatomy of a Large-Scale Hypertextual Web Search Engine,” paper presented at the Seventh International World-Wide Web Conference, Brisbane, Australia, 1998, 234 “Act in good faith”: Wikipedia, “Wikipedia:Five Pillars,” last modified February 6, 2017, at 10:52, 236 “the ‘data’ from which the economic calculus starts”: Friedrich A.

Bertram’s Mind, The” (AI-generated prose), 121 MySpace, 170–71 Naam, Ramez, 258n Nakamoto, Satoshi, 279–85, 287, 296–97, 306, 312 Nakamoto Institute, 304 Nappez, Francis, 190 Napster, 144–45 NASA, 15 Nasdaq, 290–91 National Association of Realtors, 39 National Enquirer, 132 National Institutes of Health, 253 National Library of Australia, 274 Naturalis Historia (Pliny the Elder), 246 natural language processing, 83–84 “Nature of the Firm, The” (Coase), 309–10 Navy, US, 72 negative prices, 216 Nelson, Ted, 33 Nelson, Theodore, 229 Nesbitt, Richard, 45 Netflix, 187 Netscape Navigator, 34 network effects, 140–42 defined, 140 diffusion of platforms and, 205–6 O2O platforms and, 193 size of network and, 217 Stripe and, 174 Uber’s market value and, 219 networks, Cambrian Explosion and, 96 neural networks, 73–74, 78 neurons, 72–73 Newell, Allen, 69 Newmark, Craig, 138 New Republic, 133 news aggregators, 139–40 News Corp, 170, 171 newspapers ad revenue, 130, 132, 139 publishing articles directly on Facebook, 165 Newsweek, 133 New York City Postmates in, 185 taxi medallion prices before and after Uber, 201 UberPool in, 9 New York Times, 73, 130, 152 Ng, Andrew, 75, 96, 121, 186 Nielsen BookScan, 293, 294 99Degrees Custom, 333–34 99designs, 261 Nixon, Richard, 280n Nokia, 167–68, 203 noncredentialism, 241–42 Norman, Robert, 273–74 nugget ice, 11–14 Nuomi, 192 Nupedia, 246–48 Obama, Barack, election of 2012, 48–51 occupancy rates, 221–22 oDesk, 188 Office of Personnel Management, US, 32 oil rigs, 100 on-demand economy, future of companies in, 320 online discussion groups, 229–30 online payment services, 171–74 online reviews, 208–10 O2O (online to offline) platforms, 185–98 business-to-business, 188–90 consumer-oriented, 186–88 defined, 186 as engines of liquidity, 192–96 globalization of, 190–92 interdisciplinary insights from data compiled by, 194 for leveraging assets, 196–97 and machine learning, 194 Opal (ice maker), 13–14 Open Agriculture Initiative, 272 openness (crowd collaboration principle), 241 open platforms curation and, 165 downsides, 164 importance of, 163–65 as key to success, 169 open-source software; See also Linux Android as, 166–67 development by crowd, 240–45 operating systems, crowd-developed, 240–45 Oracle, 204 O’Reilly, Tim, 242 organizational dysfunction, 257 Oruna, 291 Osindero, Simon, 76 Osterman, Paul, 322 Ostrom, Elinor, 313 outcomes, clear (crowd collaboration principle), 243 outsiders in automated investing, 270 experts vs., 252–75 overall evaluation criterion, 51, 290 Owen, Ivan, 273, 274 Owen, Jennifer, 274n ownership, contracts and, 314–15 Page, Larry, 233 PageRank, 233 Pahlka, Jennifer, 163 Painting Fool, The, 117 Papa John’s Pizza, 286 Papert, Seymour, 73 “Paperwork Mine,” 32 Paris, France, terrorist attack (2015), 55 Parker, Geoffrey, 148 parole, 39–40, 10 Paulos, John Allen, 233 payments platforms, 171–74 peer reviews, 208–10 peer-to-peer lending, 263 peer-to-peer platforms, 144–45, 298 Peloton, 177n Penthouse magazine, 132 People Express, 181n, 182 Perceptron, 72–74 Perceptrons: An Introduction to Computational Geometry (Minsky and Papert), 73 perishing/perishable inventory and O2O platforms, 186 and revenue management, 181–84 risks in managing, 180–81 personal drones, 98 perspectives, differing, 258–59 persuasion, 322 per-transaction fees, 172–73 Pew Research Center, 18 p53 protein, 116–17 photography, 131 physical environments, experimentation in development of, 62–63 Pindyck, Robert, 196n Pinker, Steven, 68n piracy, of recorded music, 144–45 Plaice, Sean, 184 plastics, transition from molds to 3D printing, 104–7 Platform Revolution (Parker, Van Alstyne, and Choudary), 148 platforms; See also specific platforms business advantages of, 205–11 characteristics of successful, 168–74 competition between, 166–68 and complements, 151–68 connecting online and offline experience, 177–98; See also O2O (online to offline) platforms consumer loyalty and, 210–11 defined, 14, 137 diffusion of, 205 economics of “free, perfect, instant” information goods, 135–37 effect on incumbents, 137–48, 200–204 elasticity of demand, 216–18 future of companies based on, 319–20 importance of being open, 163–65; See also open platforms and information asymmetries, 206–10 limits to disruption of incumbents, 221–24 multisided markets, 217–18 music industry disruption, 143–48 network effect, 140–42 for nondigital goods/services, 178–85; See also O2O (online to offline) platforms and perishing inventory, 180–81 preference for lower prices by, 211–21 pricing elasticities, 212–13 product as counterpart to, 15 and product maker prices, 220–21 proliferation of, 142–48 replacement of assets with, 6–10 for revenue management, 181–84 supply/demand curves and, 153–57 and unbundling, 145–48 user experience as strategic element, 169–74 Playboy magazine, 133 Pliny the Elder, 246 Polanyi, Michael, 3 Polanyi’s Paradox and AlphaGo, 4 defined, 3 and difficulty of comparing human judgment to mathematical models, 42 and failure of symbolic machine learning, 71–72 and machine language, 82 and problems with centrally planned economies, 236 and System 1/System 2 relationship, 45 Postmates, 173, 184–85, 205 Postmates Plus Unlimited, 185 Postrel, Virginia, 90 Pratt, Gil, 94–95, 97, 103–4 prediction data-driven, 59–60 experimentation and, 61–63 statistical vs. clinical, 41 “superforecasters” and, 60–61 prediction markets, 237–39 premium brands, 210–11 presidential elections, 48–51 Priceline, 61–62, 223–24 price/pricing data-driven, 47; See also revenue management demand curves and, 154 elasticities, 212–13 loss of traditional companies’ power over, 210–11 in market economies, 237 and prediction markets, 238–39 product makers and platform prices, 220 supply curves and, 154–56 in two-sided networks, 213–16 Principia Mathematica (Whitehead and Russell), 69 print media, ad revenue and, 130, 132, 139 production costs, markets vs. companies, 313–14 productivity, 16 products as counterpart to platforms, 15 loss of profits to platform providers, 202–4 pairing free apps with, 163 platforms’ effect on, 200–225 threats from platform prices, 220–21 profitability Apple, 204 excessive use of revenue management and, 184 programming, origins of, 66–67 Project Dreamcatcher, 114 Project Xanadu, 33 proof of work, 282, 284, 286–87 prose, AI-generated, 121 Proserpio, Davide, 223 Prosper, 263 protein p53, 116–17 public service, 162–63 Pullman, David, 131 Pullum, Geoffrey, 84 quantitative investing firms (quants), 266–70 Quantopian, 267–70 Quinn, Kevin, 40–41 race cars, automated design for, 114–16 racism, 40, 51–52, 209–10 radio stations as complements to recorded music, 148 in late 1990s, 130 revenue declines (2000–2010), 135 Ramos, Ismael, 12 Raspbian, 244 rationalization, 45 Raymond, Eric, 259 real-options pricing, 196 reasoning, See System 1/System 2 reasoning rebundling, 146–47 recommendations, e-commerce, 47 recorded music industry in late 1990s, 130–31 declining sales (1999-2015), 134, 143 disruption by platforms, 143–48 Recording Industry Association of America (RIAA), 144 redlining, 46–47 Redmond, Michael, 2 reengineering, business process, 32–35 Reengineering the Corporation (Hammer and Champy), 32, 34–35, 37 regulation financial services, 202 Uber, 201–2, 208 Reichman, Shachar, 39 reinforcement learning, 77, 80 Renaissance Technologies, 266, 267 Rent the Runway, 186–88 Replicator 2 (3D printer), 273 reputational systems, 209–10 research and development (R&D), crowd-assisted, 11 Research in Motion (RIM), 168 residual rights of control, 315–18 “Resolution of the Bitcoin Experiment, The” (Hearn), 306 resource utilization rate, 196–97 restaurants, robotics in, 87–89, 93–94 retail; See also e-commerce MUEs and, 62–63 Stripe and, 171–74 retail warehouses, robotics in, 102–3 Rethinking the MBA: Business Education at a Crossroads (Datar, Garvin, and Cullen), 37 revenue, defined, 212 revenue management defined, 47 downsides of, 184–85 O2O platforms and, 193 platforms for, 181–84 platform user experience and, 211 problems with, 183–84 Rent the Runway and, 187 revenue-maximizing price, 212–13 revenue opportunities, as benefit of open platforms, 164 revenue sharing, Spotify, 147 reviews, online, 208–10 Ricardo, David, 279 ride services, See BlaBlaCar; Lyft; Uber ride-sharing, 196–97, 201 Rio Tinto, 100 Robohand, 274 robotics, 87–108 conditions for rapid expansion of, 94–98 DANCE elements, 95–98 for dull, dirty, dangerous, dear work, 99–101 future developments, 104–7 humans and, 101–4 in restaurant industry, 87–89 3D printing, 105–7 Rocky Mountain News, 132 Romney, Mitt, 48, 49 Roosevelt, Teddy, 23 Rosenblatt, Frank, 72, 73 Rovio, 159n Roy, Deb, 122 Rubin, Andy, 166 Ruger, Ted, 40–41 rule-based artificial intelligence, 69–72, 81, 84 Russell, Bertrand, 69 Sagalyn, Raphael, 293n Saloner, Garth, 141n Samsung and Android, 166 and Linux, 241, 244 sales and earnings deterioration, 203–4 San Francisco, California Airbnb in, 9 Craigslist in, 138 Eatsa in, 87 Napster case, 144 Postmates in, 185 Uber in, 201 Sanger, Larry, 246–48 Sato, Kaz, 80 Satoshi Nakamoto Institute, 304 scaling, cloud and, 195–96 Schiller, Phil, 152 Schumpeter, Joseph, 129, 264, 279, 330 Scott, Brian, 101–2 second machine age origins of, 16 phase one, 16 phase two, 17–18 secular trends, 93 security lanes, automated, 89 Sedol, Lee, 5–6 self-checkout kiosks, 90 self-driving automobiles, 17, 81–82 self-justification, 45 self-organization, 244 self-selection, 91–92 self-service, at McDonald’s, 92 self-teaching machines, 17 Seychelles Trading Company, 291 Shanghai Tower, 118 Shapiro, Carl, 141n Shaw, David, 266 Shaw, J.

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

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

Videos of the talks by discussants at the Grand Challenges for Science in the 21st Century conference can be found at =Grand+Challenges++for+Science+in+the+21st+Century. 2. See W. Brian Arthur, The Nature of Technology: What It Is and How It Evolves (New York: Free Press, 2009). 3. George A. Cowan, Manhattan Project to the Santa Fe Institute: The Memoirs of George A. Cowan (Albuquerque: University of New Mexico Press, 2010). 4. Google’s PageRank algorithm, which was invented by Google founders Larry Page and Sergey Brin, uses links to a webpage to rank the importance of pages on the Internet. It has since been elaborated with many layers of algorithms to manipulate the bias on searches. 5. A. D. I. Kramer, J. E. Guillory, and J. T. Hancock, “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks,” Proceedings of the National Academy of Sciences of the United States of America 111, no. 24 (2014): 8788–8790. 6.

(Sejnowski’s wife), 44, 174, 203, 224, 269, 271 on the brain, 174 Ed Posner and, 44, 163 Francis Crick and, 269, 319n3 parallel distributed processing (PDP) and, 203 perceptron and, 44, 44f Sejnowski and, 161, 163, 269–270 Index SEXNET talk, 161 Stephen Wolfram and, 203 writings, 44f, 286n7, 291nn8–9, 313n2, 314n6 Golomb, Solomon “Sol” Wolf (Beatrice’s father), 220–224, 222f, 271, 273 Goodfellow, Ian, 135 Google, 20, 191, 205 deep learning and, ix, 7, 192 Geoffrey Hinton and, ix, 191, 273 PageRank algorithm, 311n4 self-driving cars, ix, 4, 6 TensorFlow and, 205–206 tensor processing unit (TPU), 7, 205 Google Assistant, 192 Google Brain, 191–192, 273 Google Translate, ix, 7, 8, 8f, 117, 191 Google X, 4 Gopnik, Alison, 317n10 Gould, Stephen Jay, 312n14 Gradient descent, 112 Grand Challenges for Science in the 21st Century conference, 195 Grandmother cell hypothesis, 235, 237 Grandmother cells, 235, 236f, 237–238 Graphics processing units (GPUs), 205 Graves, Alex, 259, 318n25 Gray, Michael S., 44f Greenspan, Ralph J., 316n21 Griffin, Donald R., 277 Groh, Jennifer M., 315n12 Gross, Charles G., 56, 64, 293n3 Grossberg, Stephen, 92, 297n5 Gross domestic intangibles (GDI), 193 Gross domestic product (GDP), 193 Guggenheim Museum Bilbao, 72, 72f Gutmann, Amy, 226f Halgren, Eric, 227, 228f, 314n12 Handwritten zip codes, learning to recognize, 104, 105f, 106 Hanson, David, 179f, 308n16 Hardy, Godfrey H., 223, 314n5 Index Harris, Kristen M., 121, 300n18 Hassabis, Demis, 19f, 20, 159, 288n36, 317n15 Hasson, Uri, 78, 295n18 Hawking, Stephen, 24, 125 Haykin, Simon, 154, 291n13, 305nn16–17 He, Kaiming, 129 Hebb, Donald O., 79, 101, 298n16, 313n11 Hebbian synaptic plasticity, 79, 95b, 101–102, 133, 213 Hecht-Nielsen, Robert, 118 Heeger, David J., 295n18 Helmholtz, Hermann von, 63, 225f, 314n10 Helmholtz Club, 63, 293n2 Hemingway, Ernest, 7–8 Herault, Jeanny, 81, 295n1 Hertz, John A., 94f Hidden target distribution, 241, 242f Hidden targets, 241 Hidden units (in neural networks), 103f, 113, 114f, 116, 119, 128, 132–133, 237–238 backprop networks with, 111b, 118, 148 in Boltzmann machine, 98b, 101, 102, 104, 106, 109 layers of, 47, 72, 74, 98b, 104, 106, 111b, 128, 153 perceptron and, 106, 109 simple cells compared with, 72, 74 Hillis, Danny, 229 Hinton, Geoffrey Everest, 91, 92, 96, 113, 117, 127–129, 271, 272 Boltzmann machine and, 49, 79, 104, 105f, 106, 110, 112, 127 Carnegie Mellon and, 60f, 117, 117f characterizations of, ix Charles Smith and, 61 computing with networks and, 273 David Rumelhart and, 109, 110, 112 329 deep learning and, 129f, 141f dropout technique and, 120 education, 50 George Boole and, 54 Google and, ix, 191, 273 neural networks and, 49, 165, 207 overview, 49–51 photographs, 50f, 60f, 117f, 129f positions held by, 51, 52, 61, 99, 127, 141, 191, 310n41 students, 24, 104, 128, 165 and the workings of the brain, 49–51 workshops, 1, 49, 50f, 52, 54, 60f, 109 writings, 1, 79, 97f, 100f, 103f, 105f, 112, 132f, 165, 286n13, 292n6, 297n12, 298n14, 298n20, 298n22, 299n4, 300nn14–15, 302n7, 303n17 Hippocampus (HC), 76f, 94, 101, 121, 236f Hit the opponent pieces, 148 HMAX, 128 Ho, Yu-Chi, 299n2 Hochreiter, Sepp, 134 Hodgkin, Alan, 32 Hoff, Ted, 39 Hofstadter, Douglas R., 224 Holland, John H., 312n15 Hollom, P.

., 40, 299n1 Number theory, 220–224 Oakley, Barbara, 22, 186, 187f, 188, 189, 271–272, 301n29, 309n32 Obama, Barack, 226f Object recognition, 29, 37, 40, 51 computer, 71 in cluttered scenes, 29 Index computers recognizing objects in images, 3 improvements in, 128 lighting and, 27 deep learning and, 128, 133, 149, 158, 165, 203 neural networks and, 238 perceptron and, 46, 48f visual cortex and, 131f, 133 Oja, Erkki, 295n5 Oligodendrocytes, 306n2 Olshausen, Bruno, 296n7 One-hundred-step rule, Feldman’s, 91 Operating systems, 227, 229, 230f Optimization, 110, 112 Optimization problems, convex vs. nonconvex, 119 Orgel, Leslie, 245, 246f, 251, 259 Orgel’s second rule, 245–247 Oriented complex cell, 66 Oriented simple cell, 66 Orwell, George, 306n5 Osindero, S., 105f Otto (company), 24–25, 191 Overfitting, 43, 113, 119 Page, Larry, 311n4 PageRank algorithm, 311n4 Palmer, Richard G., 94f Pandemonium, 39, 40f Papert, Seymour A., 79, 109, 262, 289n2 Perceptrons, 1, 47, 48f, 255–256, 255f, 291n14, 317n18 photograph, 255f Parallel distributed processing (PDP), 32, 109, 118 Parallel Distributed Processing (Rumelhart and McClelland), 110f, 118 Parallel Distributed Processing (PDP) Group, 51, 106 Parallel Models of Associative Memory workshop, 1 335 Parallel processing, 39, 205, 225–227, 229 Parvizi, Josef, 316n17 Pashler, Harold, 184 Pattern recognition, 37, 39, 46, 123, 135, 142, 149.

pages: 387 words: 119,409

Work Rules!: Insights From Inside Google That Will Transform How You Live and Lead by Laszlo Bock

Airbnb, Albert Einstein, AltaVista, Atul Gawande, Black Swan, book scanning, Burning Man, call centre, Cass Sunstein, Checklist Manifesto, choice architecture, citizen journalism, clean water, correlation coefficient, crowdsourcing, Daniel Kahneman / Amos Tversky, deliberate practice,, experimental subject, Frederick Winslow Taylor, future of work, Google Earth, Google Glasses, Google Hangouts, Google X / Alphabet X, Googley, helicopter parent, immigration reform, Internet Archive, longitudinal study, Menlo Park, mental accounting, meta analysis, meta-analysis, Moneyball by Michael Lewis explains big data, nudge unit, PageRank, Paul Buchheit, Ralph Waldo Emerson, Rana Plaza, random walk, Richard Thaler, Rubik’s Cube, self-driving car, shareholder value, side project, Silicon Valley, six sigma, statistical model, Steve Ballmer, Steve Jobs, Steven Levy, Steven Pinker, survivorship bias, TaskRabbit, The Wisdom of Crowds, Tony Hsieh, Turing machine, winner-take-all economy, Y2K

Nicole Mowbray, “Oprah’s path to power,” The Observer, March 2, 2003, 19. Adam Lashinsky, “Larry Page: Google should be like a family,” Fortune, January 19, 2012, 20. Larry Page’s University of Michigan Commencement Address, 21. Mark Malseed, “The Story of Sergey Brin,” Moment, February–March 2007, 22. Steven Levy, In the Plex: How Google Thinks, Works, and Shapes Our Lives (New York: Simon & Schuster, 2011). 23. John Battelle, “The Birth of Google,” Wired, August 2005, “Our History in Depth,” Google, 24. Those investments would eventually be worth in excess of $1 billion.

Pierce, “Mervin Joe Kelly, 1894–1971” (Washington, DC: National Academy of Sciences, 1975), 34. “Google Search Now Supports Cherokee,” Google (official blog), March 25, 2011, 35. “Some Weekend Work That Will (Hopefully) Enable More Egyptians to Be Heard,” Google (official blog), January 31, 2011, 36. Lashinsky, “Larry Page: Google should be like a family.” 37. Edgar H. Schein, Organizational Culture and Leadership (San Francisco: Jossey-Bass, 2010). 38. Googlegeist (our annual employee survey), 2013. 39. The RescueTime blog estimated that visitors to our website that day spent a total of 5,350,000 hours (twenty-six seconds each) playing Les Paul’s guitar. “Google Doodle Strikes Again! 5.35 Million Hours Strummed,” RescueTime (blog), June 9, 2011, 40.

By 2008, it contained one trillion (1,000,000,000,000!). According to Jesse Alpert and Nissan Hajaj from our search team, we’ve made our search engine more comprehensive and efficient: “Our systems have come a long way since the first set of Web data Google processed to answer queries. Back then, we did everything in batches: One workstation could compute the PageRank graph [the algorithm that prioritizes search results] on 26 million pages in a couple of hours, and that set of pages would be used as Google’s index for a fixed period of time. Today [in 2008], Google crawls the Web continuously, collecting updated page information and re-processing the entire Web-link graph several times per day. This graph of one trillion URLs is similar to a map made up of one trillion intersections. So multiple times every day, we do the computational equivalent of fully exploring every intersection of every road in the United States.

pages: 477 words: 75,408

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

3D printing, additive manufacturing, agricultural Revolution, AI winter, Airbnb, artificial general intelligence, augmented reality, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Berlin Wall, Bernie Sanders, bitcoin, blockchain, call centre, Chris Urmson, congestion charging, credit crunch, David Ricardo: comparative advantage, Douglas Engelbart, Elon Musk,, Erik Brynjolfsson, Flynn Effect, full employment, future of work, gender pay gap, gig economy, Google Glasses, Google X / Alphabet X, ImageNet competition, income inequality, industrial robot, Internet of things, invention of the telephone, invisible hand, James Watt: steam engine, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, knowledge worker, lifelogging, lump of labour, Lyft, Marc Andreessen, Mark Zuckerberg, Martin Wolf, McJob, means of production, Milgram experiment, Narrative Science, natural language processing, new economy, Occupy movement, Oculus Rift, PageRank, pattern recognition, post scarcity, post-industrial society, post-work, precariat, prediction markets, QWERTY keyboard, railway mania, RAND corporation, Ray Kurzweil, RFID, Rodney Brooks, Sam Altman, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, software is eating the world, speech recognition, Stephen Hawking, Steve Jobs, TaskRabbit, technological singularity, The Future of Employment, Thomas Malthus, transaction costs, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, universal basic income, Vernor Vinge, working-age population, Y Combinator, young professional

The pages were indexed by an algorithm called PageRank, which scored each web page according to how many other web pages linked to it. This algorithm, while ingenious, was not itself an example of artificial intelligence. Over time, Google Search has become unquestionably AI-powered. In August 2013, Google executed a major update of its search function by introducing Hummingbird, which enables the service to respond appropriately to questions phrased in natural language, such as, “what's the quickest route to Australia?”[lxxix] It combines AI techniques of natural language processing with colossal information resources (including Google's own Knowledge Graph, and of course Wikipedia) to analyse the context of the search query and make the response more relevant. PageRank wasn't dropped, but instead became just one of the 200 or so techniques that are now deployed to provide answers.

In February 2015 he told a CBS interviewer “Eventually I think most jobs will be replaced, like 75-80% of people are probably not going to work for a living... There are a few people starting to talk about it.”[xlv] Federico Pistono Federico Pistono is a young Italian lecturer and social entrepreneur. He attracted considerable attention with his 2012 book “Robots Will Steal Your Job, But That's OK”. A range of eminent people, including Google's Larry Page, were drawn to its optimistic and discursive style. (Google re-named itself Alphabet in October 2015, but most people still call it Google, so in this book I’ll mostly follow that convention.) After making a forceful case that future automation will render most people unemployed, Pistono argues that there is no need to worry. Much of the book is taken up with musing on the nature of happiness – the word features in the titles of a quarter of its chapters.

As recently as the late 20th century, knowledge workers could spend hours each day looking for information. Today, less than twenty years after Google was incorporated in 1998, we have something close to omniscience. At the press of a button or two, you can access pretty much any knowledge that humans have ever recorded. To our great-grandparents, this would surely have been more astonishing than flying cars. (Some people are so impressed by Google Search that they have established a Church of Google, and offer nine proofs that Google is God, including its omnipresence, near-onmiscience, potential immortality, and responses to prayer.[lxxvii] Admittedly, at the time of writing, there are only 427 registered devotees, or “readers”, at their meeting-place, a page on the internet community site Reddit.[lxxviii]) In the early days, Google Search was achieved by indexing large amounts of the web with software agents called crawlers, or spiders.

pages: 393 words: 115,217

Loonshots: How to Nurture the Crazy Ideas That Win Wars, Cure Diseases, and Transform Industries by Safi Bahcall

accounting loophole / creative accounting, Albert Einstein, Apple II, Apple's 1984 Super Bowl advert, Astronomia nova, British Empire, Cass Sunstein, Charles Lindbergh, Clayton Christensen, cognitive bias, creative destruction, disruptive innovation, diversified portfolio, double helix, Douglas Engelbart, Douglas Engelbart, Edmond Halley, Gary Taubes, hypertext link, invisible hand, Isaac Newton, Johannes Kepler, Jony Ive, knowledge economy, lone genius, Louis Pasteur, Mark Zuckerberg, Menlo Park, Mother of all demos, Murray Gell-Mann, PageRank, Peter Thiel, Philip Mirowski, Pierre-Simon Laplace, prediction markets, pre–internet, Ralph Waldo Emerson, RAND corporation, random walk, Richard Feynman, Richard Thaler, side project, Silicon Valley, six sigma, Solar eclipse in 1919, stem cell, Steve Jobs, Steve Wozniak, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Tim Cook: Apple, tulip mania, Wall-E, wikimedia commons, yield management

It began as a sustaining innovation from the largest company in the country. It was initially much more expensive than a vacuum tube ($20 vs. $1). It first sold to high-end customers like the military. Later, of course, the transistor got cheaper and disrupted nearly every market. ONLINE SEARCH To fast-forward a few decades: could Google, when it began, say that it had developed a disruptive innovation? Larry Page and Sergey Brin’s improved algorithm for prioritizing internet search results, PageRank, was incrementally more helpful to users than results from the many other existing search engines. It was a “sustaining” innovation, by the definitions above. WALMART When Sam Walton opened stores in rural areas, far from big cities, was he thinking it might be a strategic, disruptive innovation? “Man, I was all set to become a big-city department store owner,” he wrote about opening his first store.

Of those, 3,452 (0.1 percent) are one degree removed; 403,920 (17 percent), two degrees; and 1,504,560 (64 percent), three degrees. cited more than Einstein’s paper on relativity: The Watts-Strogatz 1998 paper is followed closely by the Barabási-Alberts 1999 paper, which proposed a similar concept, adding the idea of “preferential attachment”: nodes with more links get friended more. In other words, popular kids get liked more. (The same principle underlies Google’s PageRank search algorithm.) According to the curated list maintained by the high-energy physics database INSPIRE, the two highest-cited papers in “fundamental” physics (excluding materials science and calculational techniques) are Steven Weinberg’s 1967 paper on the standard model of particle physics (5,905 citations) and Juan Maldacena’s 1999 paper on string theory (4,651 citations). Citations do not necessarily reflect importance; Einstein’s papers, for example, are rarely cited now because the ideas have been so integrated.

Scott Fleming, Ian Fokker, Anthony (F-VIIa) Folkman, Judah forest fires Framingham Heart Study franchise cycle (“dangerous, virtuous cycle”) definition of as phase of organization, above the magic number in large empires (China, India, Merck, Microsoft) and movie industry and pharmaceutical industry free-rider bonus problem, thumb-twiddling Friendster, False Fail of Futureworld Galambos, Louis Galileo “gardener, not a Moses” gas-mask puzzle Gates, Bill Gedankenexperiment (thought experiment) Gell-Mann, Murray Genentech compared with Pixar See also Avastin genetic engineering (protein drugs) Gladwell, Malcolm GlaxoSmithKline Gleevec Goddard, Robertn Goldstein, Joseph Goldwasser, Eugene Google and Android engineering group PageRank as S-type loonshot Gore, Bill Greenspan, Alan Hammersley, John Hardegen, Reinhard heart disease and diet (Keys) and statins, cholesterol lowering See also familial hypercholesterolemia; Framingham Heart Study; statins Hemingway, Ernest. See also Omission, Theory of herapathite Hiroshima (atomic bomb). See also nuclear weapons Hitler, Adolf. See Nazi Germany Hollywood studios origin, fleeing Edison Holmes, Sherlock Hooke, Robert bouncing shoes, marijuana experiments compared with Jef Raskin Hopkins, Harry Howery, Ken Huygens, Christiaan Hyland, Lawrence IBM Ibn Sina (Avicenna) ibrutinib (Miller’s piranha) IKEA incentives, see compensation incentives; behavioral economics India, empire of dominance then decline.

pages: 606 words: 157,120

To Save Everything, Click Here: The Folly of Technological Solutionism by Evgeny Morozov

3D printing, algorithmic trading, Amazon Mechanical Turk, Andrew Keen, augmented reality, Automated Insights, Berlin Wall, big data - Walmart - Pop Tarts, Buckminster Fuller, call centre, carbon footprint, Cass Sunstein, choice architecture, citizen journalism, cloud computing, cognitive bias, creative destruction, crowdsourcing, data acquisition, Dava Sobel, disintermediation, East Village,, Fall of the Berlin Wall, Filter Bubble, Firefox, Francis Fukuyama: the end of history, frictionless, future of journalism, game design, Gary Taubes, Google Glasses, illegal immigration, income inequality, invention of the printing press, Jane Jacobs, Jean Tirole, Jeff Bezos, jimmy wales, Julian Assange, Kevin Kelly, Kickstarter, license plate recognition, lifelogging, lone genius, Louis Pasteur, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, moral panic, Narrative Science, Nelson Mandela, Nicholas Carr, packet switching, PageRank, Parag Khanna, Paul Graham, peer-to-peer, Peter Singer: altruism, Peter Thiel,, placebo effect, pre–internet, Ray Kurzweil, recommendation engine, Richard Thaler, Ronald Coase, Rosa Parks, self-driving car, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, Slavoj Žižek, smart meter, social graph, social web, stakhanovite, Steve Jobs, Steven Levy, Stuxnet, technoutopianism, the built environment, The Chicago School, The Death and Life of Great American Cities, the medium is the message, The Nature of the Firm, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, transaction costs, urban decay, urban planning, urban sprawl, Vannevar Bush, WikiLeaks

See Perversity-futility-jeopardy triad Galileo Galison, Peter Galton, Francis Gambling addiction Game mechanics Games and gamification and humanitarianism and smartphones vs. reality Gamification and adversarial design and degrading environment, enjoyment in and efficiency vs. inefficiency and games and games vs. reality literature and motivation and rewards vs. citizenship Gardner, James Garland, David Gasto Público Bahiense (website) Gatekeepers Gates, Bill Gates, Kelly Gawker Gender discrimination Generativity theory Genetic engineering Gertner, Joe Ghonim, Wael Gillespie, Tarleton Global Integrity Godin, Benoit Google AdSense and advertising and algorithms and algorithms, and democracy and algorithms, neutrality and objectivity of and badges and citizenship and content business and ethics GPS-enabled Android phones and Huffington Post and information organization and legal challenges and mirror imagery and openness PageRank Places and predictive policing and privacy Project Glass goggles and scientific credentials and self-driving cars values and WiFi networks and Zagat Google+ Google Autocomplete Google Buzz Google News and badges Google Scholar Government and networks role of Government, US, and WikiLeaks GPS driving data GPS-enabled Android phones (Google) Grafton, Anthony Graham, Paul Grant, Ruth Green, Donald Green, Shane Greenwald, Glenn Guernica Gutenberg, Johannes Gutshot-detection systems Hanrahan, Nancy Harvey, David Hayek, Friedrich Heald, David Health and gamification monitoring device Heller, Nathaniel Hibbing, John Hierarchies, and networks Hieronymi, Pamela Highlighting and shading Hildebrandt, Mireille Hill, Kashmir Hirschman, Albert Historians, and Internet debate History as irrelevant of technology Hoffman, Reid Holiday, Ryan Holocaust Horkheimer, Max Howard Dean for Iowa Game Huffington Post, The Humanitarianism, and games Hypocrisy Illich, Ivan Image-recognition software Imperfection Impermium Incentives Information cascades theory Information consumption self-tracking of Information emperors Information industries and government history of Information organization Information-processing imperative Information reductionism Information technology InfoWorld (website) Innovation and justice and technology unintended consequences of Innovation talk Institutions, and networks Intel Intermediaries.

Thus, in explaining why they present search results the way they do, Google’s website tells us that “democracy on the Web works”—by which they mean that everybody gets a say by voting for their favorite website with links, which are then counted by Google’s PageRank algorithm in order to determine which results should come on top. Theirs is a very peculiar definition of “democracy.” For one, the idea of equality on which Google search is based is quite shallow: yes, everyone can vote with “links”—but those who have the resources to generate more links, perhaps by paying influential sites to link to them, or to game the system through search engine optimization have much more power than those who don’t. It’s anything but “one person, one vote.” At best, this is more of an oligarchy than a democracy. Besides, Google’s ranking algorithm considers at least two hundred other factors—for example, the loading speed of the website—in addition to how many other sites link to a particular page.

See National Endowment for the Arts Nelson, Mark Networks News industry and international news, and technological intervention Newspaper industry Newspapers Newton, Sir Isaac Nietzsche, Friedrich Noise-abatement campaigns Norms adaptation of, and technology revision of, and technological enforcement Nostalgia Noveck, Beth Nuclear age Nudging Numeric imagination Nussbaum, Martha Nutrition, and quantification Nyberg, David Oakeshott, Michael Obama, Barack and open government and the Pirates Obesity Object-recognition technology Occupy Wall Street On-line shopping O’Neill, Onora Online data, longevity of Online learning Online profiling Online/offline divide Open government Open-government data Open Handset Alliance Openness and Google See also Transparency Openness fundamentalism Originality Ortega y Gasset, José Otter, Chris Page, Larry PageRank (Google) Palantir Paparazzi Pariser, Eli Parking system (California) Parks, Rosa Pasteur, Louis Paul, Ron Payer, Peter Payne, Brent PayPal Paywall Peppet, Scott Perfection, and situational crime prevention Personal analytics Perversity-futility-jeopardy triad Peters, John Durham Pharmaceutical industry Philips company Philosophy vs. psychology PhotoDNA (Microsoft) Pirates Places (Google) Plant watering system Play, and games Pocket registrator Political backpacking Political change Political information Political parties Political Reform Act of 1974 (California) Politics and ambiguity and consumerism and fact checking and gamification and hypocrisy and imperfection and mendacity and networks and the Pirates and proxy voting and technocracy and technology and technorationalists and technoscapists and transparency and two-party system Politwoops Poole, Steven Populism vs. expertise Post, David Potholes, and smartphones Power, Michael Power and control Predictive policing dangers of and Facebook and social networks, surveillance of See also Crime prevention PredPol Print culture Printing press as agent of change and the Internet Privacy and digital natives Internet and online data, longevity of and self-tracking and tracking See also Self-disclosure Problem solving Professors Profiling, online Project Glass goggles (Google) Projectors Proposition 8 (California) Protestant Reformation Proust, Marcel Proxy voting Pseudo-crime Psychology vs. philosophy Public broadcasting Public engagement Public information Public information databases Public life, and memes Public relations industry Publishing industry, and gatekeepers Putin, Vladimir Quantification critique of deficiency in and education ethics of in the future and marketing budgets and narrative imagination vs. numeric imagination and needs/desires/necessities and nutrition and water and energy consumption feedback devices and water and energy consumption metering systems Quantified Self movement and authenticity beginning of and correlations and hunches and narrative imagination See also Lifelogging; Self-tracking Quick Response Codes Racial discrimination Radical agenda Radio erratic appliance Rand, Ayn Rapid Content Analysis for Law Enforcement Rate My Professors (website) RateMyDrive Rational-choice theory (RCT) RCT.

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Elsewhere, U.S.A: How We Got From the Company Man, Family Dinners, and the Affluent Society to the Home Office, BlackBerry Moms,and Economic Anxiety by Dalton Conley

assortative mating, call centre, clean water, commoditize, dematerialisation, demographic transition, Edward Glaeser, extreme commuting, feminist movement, financial independence, Firefox, Frank Levy and Richard Murnane: The New Division of Labor, Home mortgage interest deduction, income inequality, informal economy, Jane Jacobs, Joan Didion, John Maynard Keynes: Economic Possibilities for our Grandchildren, knowledge economy, knowledge worker, labor-force participation, late capitalism, low skilled workers, manufacturing employment, mass immigration, McMansion, mortgage tax deduction, new economy, off grid, oil shock, PageRank, Ponzi scheme, positional goods, post-industrial society, post-materialism, principal–agent problem, recommendation engine, Richard Florida, rolodex, Ronald Reagan, Silicon Valley, Skype, statistical model, The Death and Life of Great American Cities, The Great Moderation, The Wealth of Nations by Adam Smith, Thomas Malthus, Thorstein Veblen, transaction costs, women in the workforce, Yom Kippur War

There were free bikes left at various stations. I hopped on one and found my way around to Building 40. As I wove through the main central area, I passed a huge dinosaur skeleton (bought by Google co-founder Larry Page on eBay) posed so that it was chasing a flock of pink lawn flamingos, a huge sandbox with a volleyball net strung over it, and an herb and tomato garden growing out of plastic “Earthboxes” meant for high-density soilless agriculture in the developing world. Above the Google reception desk a liquid crystal display offered a scrolling list of searches going on across the world. It is said that Google employees can see how news breaks across the globe in real time by watching searches on, say Britney Spears or a major earthquake, spreading across the globe like an information tsunami.

In many ways such network-based categorizations are more insidious that the hackneyed groupings based on race, class, gender, religion, or any other demographic characteristic: The rules of assignment are not made explicit; there is no totem; and the group is, in fact, a group-less group. This first point is fairly straightforward: Although the programmers in Palo Alto may know the formulas that go into the recommendation process, we certainly don’t.1∗ In fact, the Amazon (or Google pagerank) formula may even be beyond the knowledge of any single programmer in the same way that a modern, industrial machine such as the automobile is too complicated for any single line worker or engineer to fathom in its entirety. Second, there is no totem to these groups. Ironically, by tailoring our consumer choices so narrowly to our previous preferences (as they align with the preferences of others), we create a situation of a group of one—myself—in which my uniqueness fails to create an individual because it is not created from the overlap of meaningful groups of “others” but rather from a formula based on purchases recommending purchases.

If you are popular—in other words, if you get a high page rank thanks to lots of other Web pages pointing toward yours within the category delineated by the search terms—then there is no need to pay for anything. But if you are trying to nudge your way into the top ten to get noticed, you have two options: try to game the Google algorithm (which evolves to outsmarting the gamers); or pay dollars. In this way, the abstract notion of network popularity—or rather, of positional status—becomes, like everything else, monetized. Another aspect of the new economics can be seen in these Google AdWords: the blunting of competition. The mechanism behind the auctioning off of paid links to be billed on a click-through basis was not invented by Google but by the company, which later became Overture Services Inc.

pages: 298 words: 43,745

Understanding Sponsored Search: Core Elements of Keyword Advertising by Jim Jansen

AltaVista, barriers to entry, Black Swan, bounce rate, business intelligence, butterfly effect, call centre, Claude Shannon: information theory, complexity theory, correlation does not imply causation,, first-price auction, information asymmetry, information retrieval, intangible asset, inventory management, life extension, linear programming, longitudinal study, megacity, Nash equilibrium, Network effects, PageRank, place-making, price mechanism, psychological pricing, random walk, Schrödinger's Cat, sealed-bid auction, search engine result page, second-price auction, second-price sealed-bid, sentiment analysis, social web, software as a service, stochastic process, telemarketer, the market place, The Present Situation in Quantum Mechanics, the scientific method, The Wisdom of Crowds, Vickrey auction, Vilfredo Pareto, yield management

Page jacking: theft of a page from the original site and publication of a copy (or nearcopy) at another site (Source: Marketing (see Chapter 2 model). Page request: the opportunity for an HTML document to appear on a browser window as a direct result of a user’s interaction with a Web site (Source: IAB) (see Chapter 2 model). Page view: request to load a single HTML page (Source: Marketing (see Chapter 2 model). PageRank (PR): the Google technology developed at Stanford University for placing importance on pages and Web sites. At one point, PageRank (PR) was a major factor in rankings. Today it is one of hundreds of factors in the algorithm that determines a page’s rankings (Source: SEMPO) (see Chapter 2 model). Paid Inclusion: refers to the process of paying a fee to a search engine in order to be included in that search engine or directory. Also known as guaranteed inclusion.

Hart does a statistical analysis using attributes of the people on the list, noting that there is a clustering by location and time. Hart credits this clustering to particular societies’ ability to communicate more effectively. This increased ability to communicate has a positive effect on the society’s ability to innovate. With this viewpoint, sponsored search (as the economy engine of the Web) is a significant social enhancer. Given that Google was the search platform that really took the sponsoredsearch concept and made it the economic engine of the Web, Sergey Brin and Larry Page really deserve credit for shaping the Web and Internet as we know it. Their efforts were most influential. By the way, there were two other interesting correlations that Hart discovered with the people on his list: There were high occurrences of gout and no living descendents. Nothing to do with sponsored search, but I found it interesting.

Berlin: Springer, pp. 177–206. [11] Voge, K. and McCaffrey, C. 2000. Google Launches Self-Service Advertising Program. (October 23). Retrieved January 6, 2011, from [12] Krane, D. and McCaffrey, C. 2002. Google Introduces New Pricing For Popular Self-Service Online Advertising Program. (February 20). Retrieved January 6, 2011, from http://www. [13] Google. 2010. Google, Corporate Information, Our Philosophy. Retrieved July 13, 2010, from [14] Saracevic, T. 1975. “Relevance: A Review of and a Framework for the Thinking on the Notion in Information Science.” Journal of the American Society of Information Science, vol. 26(6), pp. 321–343. [15] Battelle, J. 2005. The Search: How Google and Its Rivals Rewrote the Rules of Business and Transformed Our Culture.

pages: 561 words: 157,589

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

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

The output of these functions is a score that you can think of as the target of a master fitness function designed to optimize relevance. Some of these functions, like PageRank, have names, and even research papers explaining them. Others are trade secrets known only to the engineering teams that create and manage them. Many of them represent fundamental improvements in the art of search. For example, Google’s addition of what it called “the Knowledge Graph” allowed it to build on known associations between various kinds of entities, such as dates, people, places, and organizations, understanding for instance that a person might be “born on,” an “employee of,” a “daughter of” or “mother of,” “living in,” and so on. This work was based on a database created by a company called Metaweb, which Google acquired in 2010. When Metaweb unveiled its project in March 2007, I wrote enthusiastically, “They are building new synapses for the global brain.”

O’Reilly Media (at the time still called O’Reilly & Associates) was one of the earliest sites on the web and we published a lot of content—rich, high-quality pages that were especially relevant to the web’s early adopters—so we had many, many inbound links. This gave us a very high page rank. At one point early in Google’s history, someone published “the Google alphabet”—the top result for searching on a single letter. My company owned the letter o. But what about O’Reilly Auto Parts, a Fortune 500 company? They didn’t even show up on the first page of search results. For a brief time, until they came up with a proper algorithmic fix, Google divided pages like these into two parts. In the case of Glacier Bay, the national park occupied the top half of the search results page, with the bottom half given over to sinks, toilets, and faucets. In the case of O’Reilly, Bill O’Reilly and I came to share the top half while O’Reilly Auto Parts got the lower half. Eventually, Google improved the ranking algorithms sufficiently to interleave the results on the page.

The US Air Force had originally launched GPS satellites for military purposes, but after a crucial policy decision made by President Reagan, agreed to open up the system for commercial use, much as Google decided to open up its Maps platform. No longer just an application, GPS became a platform, resulting in a wave of innovation from the private and public sector and a market now worth more than $26 billion. Gov 2.0 started to mean something much more profound than getting federal agencies on social media. Washington insiders started talking about what we could achieve as a country with government functioning as a platform on which anyone could build. CENTRAL PARK AND THE APP STORE It’s easy to forget just how generative government interventions can be. Larry Page and Sergey Brin’s research project at Stanford, which led to Google, was funded by the National Science Foundation’s Digital Library program. Were the NSF an investor rather than a grant maker for the public good, that investment alone would have repaid more than the entire NSF budget for the years the grant was made.

pages: 370 words: 105,085

Joel on Software by Joel Spolsky

AltaVista, barriers to entry,, commoditize, George Gilder, index card, Jeff Bezos, knowledge worker, Metcalfe's law, Mitch Kapor, Network effects, new economy, PageRank, Paul Graham, profit motive, Robert X Cringely, shareholder value, Silicon Valley, Silicon Valley startup, six sigma, slashdot, Steve Ballmer, Steve Jobs, the scientific method, thinkpad, VA Linux, web application

In defense of the computer scientists, this is something nobody even noticed until they starting indexing gigantic corpora the size of the Internet. But somebody noticed. Larry Page and Sergey Brin over at Google realized that ranking the pages in the right order was more important than grabbing every possible page. Their PageRank algorithm1 is a great way to sort the zillions of results so that the one you want is probably in the top ten. Indeed, search for Joel on Software on Google and you'll see that it comes up first. On Altavista, it's not even on the first five pages, after which I gave up looking for it. __________ 1. See Antialiased Text Antialiasing was invented way back in 1972 at the Architecture Machine Group of MIT, which was later incorporated into the famous Media Lab.

Web sites become flexible services that can interact, and exchange and leverage each other's data. That's a "feature" of this exciting .NET architecture. The fact that it is so broad, vague, and high level that it doesn't mean anything at all doesn't seem to be bothering anyone. Or how about: Microsoft .NET makes it possible to find services and people with which to interact. Oh, joy! Five years after Altavista went live, and two years after Larry Page and Sergei Brin actually invented a radically better search engine (Google), Microsoft is pretending like there's no way to search on the Internet and they're going to solve this problem for us. The whole document is exactly like that. There are two things going on here. Microsoft has some great thinkers. When great thinkers think about problems, they start to see patterns. They look at the problem of people sending each other word-processor files, and then they look at the problem of people sending each other spreadsheets, and they realize that there's a general pattern: sending files.

Not-Invented-Here syndrome–2nd Old New Thing weblog Oliver, Jamie on-site, in-person interviews one step builds online discussion forums open issues in functional specifications–2nd open source software–2nd, 3rd–4th HP IBM Java–2nd Netscape–2nd Sun–2nd operating systems APIs. See APIs history–2nd opportunity cost options, stock expensing–2nd value of organic business model original estimates in software schedules–2nd OS X output from successful programs oversimplifying condescension own products, using–2nd P Page, Larry PageRank algorithm Palmerston, Lord, quote by paper companies paper prototyping–2nd Pascal language productivity in–2nd strings in passionate employees, looking for Paterson, Tim patterns pay incentive–2nd, 3rd programmer service PC-DOS operating system history licensing Peopleware–2nd, 3rd performance measurement system based string concatenation–2nd XML data with SELECT statements–2nd performance reviews–2nd PhDs as employees phone screening pictures in functional specifications Pipeline online service–2nd pivot tables plain text plane travel planning in Extreme Programming, 2nd functional specs in.

pages: 239 words: 56,531

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

Albert Einstein, Andrew Keen, anti-globalists, Apple II, Berlin Wall, British Empire, Brownian motion, Buckminster Fuller, Burning Man, business cycle, butterfly effect, computer age, creative destruction, crowdsourcing, cuban missile crisis, Dissolution of the Soviet Union, don't be evil, Douglas Engelbart, Douglas Engelbart, Dynabook, East Village, Edward Lorenz: Chaos theory, Fall of the Berlin Wall, Francis Fukuyama: the end of history, Frank Gehry, Grace Hopper, gravity well, Guggenheim Bilbao, Honoré de Balzac, Howard Rheingold, invention of movable type, Isaac Newton, Jacquard loom, Jane Jacobs, Jeff Bezos, John Markoff, John von Neumann, Kickstarter, Mark Zuckerberg, Marshall McLuhan, Mercator projection, Metcalfe’s law, Mother of all demos, mutually assured destruction, Nelson Mandela, Network effects, new economy, Norbert Wiener, PageRank, pattern recognition, peer-to-peer, planetary scale, plutocrats, Plutocrats, post-materialism, Potemkin village, RFID, Richard Feynman, Richard Stallman, Robert Metcalfe, Robert X Cringely, Schrödinger's Cat, Search for Extraterrestrial Intelligence, SETI@home, Silicon Valley, Skype, social software, spaced repetition, Steve Ballmer, Steve Jobs, Steve Wozniak, Ted Nelson, the built environment, The Death and Life of Great American Cities, the medium is the message, Thomas L Friedman, Turing machine, Turing test, urban planning, urban renewal, Vannevar Bush, walkable city, Watson beat the top human players on Jeopardy!, William Shockley: the traitorous eight

Eric S. Raymond, The Cathedral and the Bazaar: Musings on Linux and Open Source by an Accidental Revolutionary (Cambridge, MA: O’Reilly, 1999), available at <>. 29 . In the corporation’s own words, from “Ten Things Google Has Found to Be True,” available at <>: PageRank™ “evaluates all of the sites linking to a web page and assigns them a value, based in part on the sites linking to them. By analyzing the full structure of the web, Google is able to determine which sites have been ‘voted’ the best sources of information by those most interested in the information they offer. This technique actually improves as the web gets bigger, as each new site is another point of information and another vote to be counted.” 30.

The Web 1.0 bubble laid much “dark fiber” across the world, as companies built far broader networks than they could ever use profitably, and after the crash, others have since benefited from that infrastructure to restructure the ways we conceive of and engage with the Internet. No one company has so palpably benefited and defined this shift than Google, the search algorithm that became a company and then a verb, as noted earlier. Google was an intentional misspelling of the word “googol,” the mathematical term for 174 HOW THE COMPUTER BECAME OUR CULTURE MACHINE a one followed by ten zeros. The company became a networked Ourobors, that creature from Greek mythology that devours its own tail and encircles the world. What cofounders Larry Page and Sergey Brin created was a relentless innovation and acquisition machine, powered by users and advertisers alike. They challenged the old masters, from Microsoft to Yahoo! and infiltrated everything from libraries to desktops, enmeshing everyone from pornographers to cartographers, from anticorporate bloggers to CEOs.

Building on the installed base of all these users as the new millennium looms, the Hosts— World Wide Web inventor Tim Berners-Lee and open-source guru Linus Torvalds—link these disparate personal machines into a huge web, concentrating on communication as much as technology, pushing participation to the next level. The sixth generation, that of the Searchers—named after but hardly limited to Larry Page and Sergey Brin of Google, the search algorithm that became a company and then a verb—aggregated so much information and so many experiences that they rendered simulation and participation ubiquitous. There are three default ways of telling the history of computing, and the interesting thing is that people rarely tend to blend the narratives. There is the technical and scientific history of computing, which is frankly the least understood and disseminated.

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The Signal and the Noise: Why So Many Predictions Fail-But Some Don't by Nate Silver

"Robert Solow", airport security, availability heuristic, Bayesian statistics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, big-box store, Black Swan, Broken windows theory, business cycle, buy and hold, Carmen Reinhart, Claude Shannon: information theory, Climategate, Climatic Research Unit, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, complexity theory, computer age, correlation does not imply causation, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, Daniel Kahneman / Amos Tversky, diversification, Donald Trump, Edmond Halley, Edward Lorenz: Chaos theory,, equity premium, Eugene Fama: efficient market hypothesis, everywhere but in the productivity statistics, fear of failure, Fellow of the Royal Society, Freestyle chess, fudge factor, George Akerlof, global pandemic, haute cuisine, Henri Poincaré, high batting average, housing crisis, income per capita, index fund, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), Internet Archive, invention of the printing press, invisible hand, Isaac Newton, James Watt: steam engine, John Nash: game theory, John von Neumann, Kenneth Rogoff, knowledge economy, Laplace demon, locking in a profit, Loma Prieta earthquake, market bubble, Mikhail Gorbachev, Moneyball by Michael Lewis explains big data, Monroe Doctrine, mortgage debt, Nate Silver, negative equity, new economy, Norbert Wiener, PageRank, pattern recognition,, Pierre-Simon Laplace, prediction markets, Productivity paradox, random walk, Richard Thaler, Robert Shiller, Robert Shiller, Rodney Brooks, Ronald Reagan, Saturday Night Live, savings glut, security theater, short selling, Skype, statistical model, Steven Pinker, The Great Moderation, The Market for Lemons, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transaction costs, transfer pricing, University of East Anglia, Watson beat the top human players on Jeopardy!, wikimedia commons

This metaphor is borrowed from Bill Wyman, a music critic for the Chicago Reader, who ranked it as the greatest moment in rock history. Bill Wyman, “The 100 Greatest Moments in Rock History,” Chicago Reader, September 28, 1995. 44. Campbell, Hoane Jr., and Feng-hsiung, “Deep Blue.” 45. Larry Page, “PageRank: Bringing Order to the Web,” Stanford Digital Library Project, August 18, 1997. 46. “How Search Works,” by Google via YouTube, March 4, 2010. 47. Per interview with Vasik Rajlich. 48. “Amateurs beat GMs in PAL / CSS Freestyle,” ChessBase News. 49. Kasparov, “The Chess Master and the Computer.”

You probably should have formed a better search query, but since you didn’t, Google can convene a panel of 1,000 people who made the same request, show them a wide variety of Web pages, and have them rate the utility of each one on a scale of 0 to 10. Then Google would display the pages to you in order of the highest to lowest average rating. Google cannot do this for every search request, of course—not when they receive hundreds of millions of search requests per day. But, Varian told me, they do use human evaluators on a series of representative search queries. Then they see which statistical measurements are best correlated with these human judgments about relevance and usefulness. Google’s best-known statistical measurement of a Web site is PageRank,45 a score based on how many other Web pages link to the one you might be seeking out. But PageRank is just one of two hundred signals that Google uses46 to approximate the human evaluators’ judgment.

., 396 O’Meara, Christopher, 36 Omori’s Law, 477 On-base percentage (OBP), 95, 106, 314, 471 O’Neal, Shaquille, 233–34, 235, 236, 237 options traders, 364 order, complexity and, 173 outliers, 65, 425–28, 452 out of sample, 43–44, 420 Overcoming Bias (blog), 201 overconfidence, 179–83, 191, 203, 323–24, 386, 443, 454 in stock market trading, 359–60, 367 overeating, 503 overfitting, 163–68, 166, 191, 452n, 478 earthquake predictions and, 168–71, 185 over-under line, 239–40, 257, 286 ozone, 374 Ozonoff, Alex, 218–19, 223, 231, 483 Pacific countries, 379 Pacific Ocean, 419 Pacific Poker, 296–97 Page, Clarence, 48, 467 PageRank, 291 Pakistan, 434–35 Palin, Sarah, 59 Palm, 361, 362 panics, financial, 38, 195 Papua New Guinea, 228 Pareto principle, 312–13, 314, 315, 316n, 317, 496 Paris, 2 Parkfield, Calif., 158–59, 174 partisanship, 13, 56, 57, 58, 60, 64, 92, 130, 200, 378, 411, 452 Party Poker, 296, 319 patents, 7–8, 8, 411, 411n, 460, 514 pattern detection, 12, 281, 292 Pearl Harbor, 10, 412–13, 414, 415–17, 419–20, 423, 426, 444, 510 Pearl Harbor: Warning and Decisions (Wohlstetter), 415, 416, 418, 419–20 PECOTA, 9, 74–75, 78, 83, 84, 85–86 scouts vs., 88–90, 90, 91, 102, 105, 106–7 Pecota, Bill, 88 Pedroia, Dustin, 74–77, 85, 89, 97, 101–5 penicillin, 119 pensions, 24, 27, 34, 356, 463 P/E (price-to-earnings) ratio, 348, 349, 350–51, 354, 365, 369, 500 Perry, Rick, 59, 217 persistence, 131, 132, 132 personal income, 481 Peru, 210 Petit, Yusemiro, 89 Petty, William, 212 pharmaceuticals, 411 Philadelphia Phillies, 286 Pielke, Roger, Jr., 177n pigs, 209 Pippen, Scottie, 235, 236 pitchers, 88, 90, 92 Pitch f/x, 100–101, 106–7 Pittsburgh, Pa., 207–8, 228, 230 Pittsburgh, University of, 225–26 plate discipline, 96 Plato, 2 pneumonia, 205 Poe, Edgar Allan, 262–64, 282, 289 Poggio, Tomaso, 12, 231 point spread, 239 poker, 10, 16, 59–60, 63, 66, 256, 284, 294–328, 343, 362, 494–95 Bayesian reasoning in, 299, 301, 304, 306, 307, 322–23 boom in, 294, 296, 314–15, 319, 323 competition in, 313 computer’s playing of, 324 fish in, 312, 316, 317–19 inexperience of mid-2000s players in, 315 limit hold ’em, 311, 322, 322 luck vs. skill in, 321–23 no-limit hold ’em, 300–308, 309–11, 315–16, 316, 318, 324n, 495 online, 296–97, 310 plausible win rates in, 323 predictions in, 297–99, 311–15 random play in, 310 results in, 327 river in, 306, 307, 494 signal and noise in, 295 suckers in, 56, 237, 240, 317–18, 320 Texas hold ’em, 298–302 volatility of, 320, 322, 328, 318 PokerStars, 296, 320 Poland, 52 Polgar, Susan, 281 polio vaccine, 206 political partisanship, see partisanship political polls, see polls politics, political science, 11, 14–15, 16, 53, 426 failures of predictions on, 11, 14–15, 47–50, 49, 53, 55–59, 64, 67–68, 157, 162, 183, 249, 314 small amount of data in, 80 polls, 61–63, 62, 68, 70, 426 biases in, 252–53 frequentist approach to, 252 individual vs. consensus, 335 margin of error in, 62, 65, 176, 252, 452 outlier, 65 prediction interval in, 183n Popper, Karl, 14, 15 Population Bomb, The (Ehrlich and Ehrlich), 212–13 pork, 210 Portland Trail Blazers, 234, 235–37, 489 positive feedback, 38, 39, 368 posterior possibility, 244 power-law distribution, 368n, 427, 429–31, 432, 437, 438, 441, 442 precision, accuracy vs., 46, 46, 225 predestination, 112 Predicting the Unpredictable: The Tumultuous Science of Earthquake Prediction (Hough), 157 prediction, 1, 16 computers and, 292 consensus, 66–67, 331–32, 335–36 definition of, 452n Enlightenment debates about, 112 in era of big data, 9, 10, 197, 250 fatalism and, 5 feedback on, 183 forecasting vs., 5, 149 by foxes, see foxes of future returns of stocks, 330–31, 332–33 of global warming, 373–76, 393, 397–99, 401–6, 402, 507 in Google searches, 290–91 by hedgehogs, see hedgehogs human ingenuity and, 292 of Hurricane Katrina, 108–10, 140–41, 388 as hypothesis-testing, 266–67 by IPCC, 373–76, 389, 393, 397–99, 397, 399, 401, 507 in Julius Caesar, 5 lack of demand for accuracy in, 202, 203 long-term progress vs. short-term regress and, 8, 12 Pareto principle of, 312–13, 314 perception and, 453–54, 453 in poker, 297–99, 311–15 probability and, 243 quantifying uncertainty of, 73 results-oriented thinking and, 326–28 scientific progress and, 243 self-canceling, 219–20, 228 self-fulfilling, 216–19, 353 as solutions to problems, 14–16 as thought experiments, 488 as type of information-processing, 266 of weather, see weather forecasting prediction, failures of: in baseball, 75, 101–5 of CDO defaults, 20–21, 22 context ignored in, 43 of earthquakes, 7, 11, 143, 147–49, 158–61, 168–71, 174, 249, 346, 389 in economics, 11, 14, 40–42, 41, 45, 53, 162, 179–84, 182, 198, 200–201, 249, 388, 477, 479 financial crisis as, 11, 16, 20, 30–36, 39–42 of floods, 177–79 of flu, 209–31 of global cooling, 399–400 housing bubble as, 22–23, 24, 25–26, 28–29, 32–33, 42, 45 overconfidence and, 179–83, 191, 203, 368, 443 overfitting and, 185 on politics, 11, 14–15, 47–50, 49, 53, 55–59, 64, 67–68, 157, 162, 183, 249, 314 as rational, 197–99, 200 recessions, 11 September 11, 11 in stock market, 337–38, 342, 343–46, 359, 364–66 suicide bombings and, 424 by television pundits, 11, 47–50, 49, 55 Tetlock’s study of, 11, 51, 52–53, 56–57, 64, 157, 183, 443, 452 of weather, 21–22, 114–18 prediction interval, 181-183, 193 see also margin of error prediction markets, 201–3, 332–33 press, free, 5–6 Price, Richard, 241–42, 490 price discovery, 497 Price Is Right, 362 Principles of Forecasting (Armstrong), 380 printing press, 1–4, 6, 13, 17, 250, 447 prior probability, 244, 245, 246, 252, 255, 258–59, 260, 403, 406–7, 433n, 444, 451, 490, 497 probability, 15, 61–64, 63, 180, 180, 181 calibration and, 134–36, 135, 136, 474 conditional, 240, 300; see also Bayes’s theorem frequentism, 252 and orbit of planets, 243 in poker, 289, 291, 297, 302–4, 302, 306, 307, 322–23 posterior, 244 predictions and, 243 prior, 244, 245, 246, 252, 255, 258–59, 260, 403, 406–7, 433n, 444, 451, 490, 498 rationality and, 242 as waypoint between ignorance and knowledge, 243 weather forecasts and, 195 probability distribution, of GDP growth, 201 probability theory, 113n productivity paradox, 7–8 “Programming a Computer for Playing Chess” (Shannon), 265–66 progress, forecasting and, 1, 4, 5, 7, 112, 243, 406, 410–11, 447 prospect theory, 64 Protestant Reformation, 4 Protestant work ethic, 5 Protestants, worldliness of, 5 psychology, 183 Public Opinion Quarterly, 334 PURPLE, 413 qualitative information, 100 quantitative information, 72–73, 100 Quantum Fund, 356 quantum mechanics, 113–14 Quebec, 52 R0 (basic reproduction number), 214–15, 215, 224, 225, 486 radar, 413 radon, 143, 145 rain, 134–37, 473, 474 RAND database, 511 random walks, 341 Rapoport, David C., 428 Rasskin-Gutman, Diego, 269 ratings agencies, 463 CDOs misrated by, 20–21, 21, 22, 26–30, 36, 42, 43, 45 housing bubble missed by, 22–23, 24, 25–26, 28–29, 42, 45, 327 models of, 13, 22, 26, 27, 29, 42, 45, 68 profits of, 24–25 see also specific agencies rationality, 183–84 biases as, 197–99, 200 of markets, 356–57 as probabilistic, 242 Reagan, Ronald, 50, 68, 160, 433, 466, 390, 409 real disposable income per capita, 67 recessions, 42 double dip, 196 failed predictions of, 177, 187, 194 in Great Moderation, 190 inflation-driven, 191 of 1990, 187, 191 since World War II, 185 of 2000-1, 187, 191 of 2007-9, see Great Recession, 78 Red Cross, 158 Red River of the North, 177–79 regression analysis, 100, 401, 402, 498, 508 regulation, 13, 369 Reinhart, Carmen, 39–40, 43 religion, 13 Industrial Revolution and, 6 religious extremism, 428 religious wars of sixteenth and seventeenth centuries, 2, 6 Remote Sensing Systems, 394 Reno, Nev., 156–57, 157, 477 reserve clause, 471 resolution, as measure of forecasts, 474 results-oriented thinking, 326–28 revising predictions, see Bayesian reasoning Ricciardi, J.

pages: 302 words: 74,350

I Hate the Internet: A Novel by Jarett Kobek

Anne Wojcicki, Burning Man, disruptive innovation, East Village, Edward Snowden, Golden Gate Park, Google bus, Google Glasses, Google X / Alphabet X, immigration reform, indoor plumbing, informal economy, Jeff Bezos, liberation theology, Mark Zuckerberg, MITM: man-in-the-middle, Norman Mailer, nuclear winter, packet switching, PageRank, Peter Thiel, quantitative easing, Ray Kurzweil, rent control, Ronald Reagan, Silicon Valley, Steve Jobs, technological singularity, Triangle Shirtwaist Factory, union organizing, V2 rocket, Vernor Vinge, wage slave, Whole Earth Catalog

Hephaestus was the out-classed God, like Larry Page was the outclassed CEO who wrested back control of the company in 2011 and forced it to start a social networking platform which everyone thought was terrible. Then Larry page bought Motorola, a maker of cellphones that was losing money and continued to bleed money. Christine didn’t know it, but by 2014, Google would sell Motorola at a $12,000,000,000 loss. Just like Hephaestus had a sham marriage to Aphrodite that required keeping up appearances, Larry Page was considered a good CEO because Google’s core business of advertising made so much money that no one noticed that Larry Page was bad at his job and operated off the principle that unexamined growth was a successful strategy for the future. Sergey Brin, the other co-founder, was like Dionysius, the god of sex and drugs and revelry. Sergey Brin had rebranded himself as the head of Google X, Google’s nonsense experimental lab which developed faddish technologies like wearable computers and cars that could drive themselves and dogs that didn’t need to clean their genitals.

Everyone in Silicon Valley loved Ray Kurzweil. He was their High Priest of Intolerable Bullshit. He was the Seer of Pseudoscience. He worked for Google. He was a director of engineering. Like Marissa Mayer, who Christine identified with Elpis, the Greek goddess of hope. There was no way you could be Marissa Mayer without hope. When she worked at Google, she had at some point dated Larry Page while helping out on all kinds of projects that went nowhere, like Google Books, which she called, “Google’s Moon Shot.” Google Books was Google’s attempt to steal the intellectual property of every writer in America by offering free copies of their work in an unusable system. Mayer had parlayed her experience with the unusable system of Google Books into being CEO and President of Yahoo!, which was a company that offered products which no one used.

“They can’t say that they work in advertising. So they lie about what they do. Google wants us to believe that they’re changing the world and offering a million services for free and that we’re all part of the same team, but they’re lying. All Google does is serve advertisements. Nothing else makes money. “They are liars and I pray to liars.” Christine saw all the founders and key players in Silicon Valley as new gods, like the New Gods created by Jack Kirby while he worked-for-hire at DC Comics, and Christine arranged them accordingly. Larry Page, the CEO and co-founder of Google, was like Hephaestus because Hephaestus was the physically debilitated God of artisans and creators. Hephaestus was the out-classed God, like Larry Page was the outclassed CEO who wrested back control of the company in 2011 and forced it to start a social networking platform which everyone thought was terrible.

pages: 207 words: 57,959

Little Bets: How Breakthrough Ideas Emerge From Small Discoveries by Peter Sims

Amazon Web Services, Black Swan, Clayton Christensen, complexity theory, David Heinemeier Hansson, deliberate practice, discovery of penicillin, endowment effect, fear of failure, Frank Gehry, Guggenheim Bilbao, Jeff Bezos, knowledge economy, lateral thinking, Lean Startup, longitudinal study, loss aversion, meta analysis, meta-analysis, PageRank, Richard Florida, Richard Thaler, Ruby on Rails, Silicon Valley, statistical model, Steve Ballmer, Steve Jobs, Steve Wozniak, theory of mind, Toyota Production System, urban planning, Wall-E

So, if you wanted to search for books about Joan of Arc, the Joan of Arc book that was cited the most by other Joan of Arc sources would appear first. This insight was the core of their now famous PageRank algorithm. Yet, even after they realized how powerful their search algorithm was and formulated their much more ambitious goal to “organize all the world’s information,” they still had not identified the company’s breakthrough revenue engine. Until 2002, most web advertising sales, including Google’s, came from banner ads that would appear at the top of search result pages. Prices were negotiated on a fixed-fee basis such that Google would price ad deals at, for instance, a million dollars and flash the display ad when it deemed appropriate. Borrowing an idea from a company called (renamed Overture), Google then created AdWords, an automated auction-based system that allowed advertisers to display ads next to specific search terms, such as “hockey” or “flowers.”

One of the most common things I would hear people say was that they would do something new—take an unconventional career path or start a company—but that they needed a great idea first. I had worked before then as a venture capital investor, and in that work, I had learned that most successful entrepreneurs don’t begin with brilliant ideas—they discover them. Ironically, this would include the biggest business idea to come out of Stanford in decades. Google founders Larry Page and Sergey Brin didn’t set out to create one of the fastest-growing startup companies in history; they didn’t even start out seeking to revolutionize the way we search for information on the web. Their first goal, as collaborators on the Stanford Digital Library Project, was to solve a much smaller problem: how to prioritize library searches online. In working through possibilities for doing so, their clever innovation was to realize that the best way to prioritize the results was to measure how many other citations referred to a source.

Is it to convey knowledge, as the current system is weighted, or it to inspire and nurture an ability to constantly learn? Probing into this puzzle, Gregersen and Dyer were intrigued to learn that a number of the innovators in their study went to Montessori schools, where they learned to follow their curiosity. The Montessori learning method, founded by Maria Montessori, emphasizes self-directed student learning, particularly for young children. Well-known Montessori alums include Google’s founders Sergei Brin and Larry Page, who credit their Montessori education as a major factor behind their success, Jeff Bezos, and computer game pioneer Will Wright, as well as Julia Child. The innovators got encouragement to pursue their intrinsic interests from parents, teachers, neighbors, other family members, and the like. As Gregersen shared, “We were struck by the stories they told about being sustained by people who cared about experimentation and exploration.”

pages: 224 words: 64,156

You Are Not a Gadget by Jaron Lanier

1960s counterculture, accounting loophole / creative accounting, additive manufacturing, Albert Einstein, call centre, cloud computing, commoditize, crowdsourcing, death of newspapers, different worldview, digital Maoism, Douglas Hofstadter, Extropian, follow your passion, hive mind, Internet Archive, Jaron Lanier, jimmy wales, John Conway, John von Neumann, Kevin Kelly, Long Term Capital Management, Network effects, new economy, packet switching, PageRank, pattern recognition, Ponzi scheme, Ray Kurzweil, Richard Stallman, Silicon Valley, Silicon Valley startup, slashdot, social graph, stem cell, Steve Jobs, Stewart Brand, Ted Nelson, telemarketer, telepresence, The Wisdom of Crowds, trickle-down economics, Turing test, Vernor Vinge, Whole Earth Catalog

It is utterly strange to hear my many old friends in the world of digital culture claim to be the true sons of the Renaissance without realizing that using computers to reduce individual expression is a primitive, retrograde activity, no matter how sophisticated your tools are. Rejection of the Idea of Quality Results in a Loss of Quality The fragments of human effort that have flooded the internet are perceived by some to form a hive mind, or noosphere. These are some of the terms used to describe what is thought to be a new superintelligence that is emerging on a global basis on the net. Some people, like Larry Page, one of the Google founders, expect the internet to come alive at some point, while others, like science historian George Dyson, think that might already have happened. Popular derivative terms like “blogosphere” have become commonplace. A fashionable idea in technical circles is that quantity not only turns into quality at some extreme of scale, but also does so according to principles we already understand.

Visualize, if you will, the most transcendently messy, hirsute, and otherwise eccentric pair of young nerds on the planet. They were in their early twenties. The scene was an uproariously messy hippie apartment in Cambridge, Massachusetts, in the vicinity of MIT. I was one of these men; the other was Richard Stallman. Why are so many of the more sophisticated examples of code in the online world—like the page-rank algorithms in the top search engines or like Adobe’s Flash—the results of proprietary development? Why did the adored iPhone come out of what many regard as the most closed, tyrannically managed software-development shop on Earth? An honest empiricist must conclude that while the open approach has been able to create lovely, polished copies, it hasn’t been so good at creating notable originals.

When businesses rushed in to capitalize on what had happened, there was something of a problem, in that the content aspect of the web, the cultural side, was functioning rather well without a business plan. Google came along with the idea of linking advertising and searching, but that business stayed out of the middle of what people actually did online. It had indirect effects, but not direct ones. The early waves of web activity were remarkably energetic and had a personal quality. People created personal “homepages,” and each of them was different, and often strange. The web had flavor. Entrepreneurs naturally sought to create products that would inspire demand (or at least hypothetical advertising opportunities that might someday compete with Google) where there was no lack to be addressed and no need to be filled, other than greed. Google had discovered a new permanently entrenched niche enabled by the nature of digital technology.

pages: 230 words: 76,655

Choose Yourself! by James Altucher

Airbnb, Albert Einstein, Bernie Madoff, bitcoin, cashless society, cognitive bias, dark matter, Elon Musk, estate planning, Mark Zuckerberg, money market fund, Network effects, new economy, PageRank, passive income, pattern recognition, payday loans, Peter Thiel, Ponzi scheme, Rodney Brooks, rolodex, Saturday Night Live, sharing economy, short selling, side project, Silicon Valley, Skype, software as a service, Steve Jobs, superconnector, Uber for X, Vanguard fund, Y2K, Zipcar

I’d rather spend 2,000 hours each on five areas of life with very steep learning curves, get in the top 10 percent of all of those, rather than spend 10,000 hours on one area of life. * * * Idea Sex If you combine two areas of life and get reasonably good at both and then combine them, then you are suddenly the best in the world at the combination. Google is a great example. Larry Page was an academic at heart but he built a search engine. Then he combined it with the idea of how academics rank the value of their papers. Putting the two together gave him the basic algorithm of Google, dubbed PageRank, and Google became the best search engine in the world. * * * The 80-20 Rule Tim Ferriss talks about this in his various books, and it’s a notion that’s been around for a long time. It was originally discovered when someone noticed that 80 percent of the land in Italy was owned by 20 percent of the people.

Ten books I can write (Ex: The Choose Yourself Guide to an Alternative Education, etc). Ten business ideas for Google/Amazon/Twitter. Ten people I can send ideas to. Ten podcast ideas I can do, or videos I can shoot (“Lunch with James,” a video podcast where I just have lunch with people over Skype and we chat). Ten industries where I can remove the middleman. Ten ways to make old posts of mine and make books out of them. Ten ways I can surprise Claudia. (Actually, more like one hundredways. That’s hard work!) Ten items I can put on my “ten list ideas I usually write” list. Ten people I want to be friends with and I figure out what the next steps are to contact them (Azaelia Banks, I’m coming after you! Larry Page better watch out also.) Ten things I learned yesterday. Ten things I can do differently today.

But understanding demographic trends and how to use them to take advantage of the coming monopolies in society will be of great benefit. Idea Sex (get good at coming up with ideas. Then combine them. Master the intersection) The 1% Rule (every week try to get better 1% physically, emotionally, mentally) The Google Rule - give constantly to the people in your network. The value of your network increases linearly if you get to know more people, but EXPONENTIALLY if the people you know, get to know and help each other. Note that Google measures its success by how quickly it sends you to other websites where you can help. But then…where do you return to when you need more help?...Google. Failure is a Myth: how to fail so that a failure turns into a new beginning. Turn the word “failure” into “experiment”. Become the scientist of your life, the explorer of your future. The $2 Bill Rule: simple tools to increase productivity The Secrets of Mastery.

pages: 239 words: 80,319

Lurking: How a Person Became a User by Joanne McNeil

4chan, A Declaration of the Independence of Cyberspace, Ada Lovelace, Airbnb, AltaVista, Amazon Mechanical Turk, Burning Man, Chelsea Manning, Chris Wanstrath, citation needed, cloud computing, crowdsourcing, delayed gratification, dematerialisation, don't be evil, Donald Trump, drone strike, Edward Snowden, Elon Musk, feminist movement, Firefox, Google Earth, Google Glasses, Google Hangouts, helicopter parent, Internet Archive, invention of the telephone, Jeff Bezos, jimmy wales, l'esprit de l'escalier, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, means of production, Menlo Park, moral panic, move fast and break things, move fast and break things, Network effects, packet switching, PageRank, pre–internet, profit motive, QAnon, recommendation engine, Saturday Night Live, Shoshana Zuboff, Silicon Valley, slashdot, Snapchat, social graph, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, technoutopianism, Ted Nelson, Tim Cook: Apple, trade route, Turing complete, We are the 99%, web application, white flight, Whole Earth Catalog

The sand turned to gold because they collected enough. “Google has single-handedly cut into my ability to bullshit,” Owen Wilson’s character complains in the 2013 fish-out-of-water comedy The Internship, in which he and Vince Vaughn maunder into “Noogler”—new Google hire—positions. The overarching punch line of the film is how Silicon Valley redefined what counts as an alpha guy. Wilson and Vaughn might be the prom kings of the Hollywood Hills, but the sky is the limit to Larry Page and Sergey Brin’s privilege. Historians of technology love tales of lone geniuses saving the world, and a lasting collaboration such as Page and Brin’s is unusual, while at the same time it explains Google’s scope. If Sergey Brin is the colored letters in the Google logo, cofounder Larry Page is the blank white background. Sergey Brin, the extroverted, more politically and culturally minded cofounder, often roller-skated through the office and wore those weird toe sneakers.

“Mirroring the world,” while impossible, was coherent with the company’s story and execution. After all, Google’s most immediate scandals back then related to how well—how invasively well—PageRank worked. Predictive search—the words that appear in autocomplete when a user enters a query—can snitch on someone’s past. Because of it, I have turned up the names of people’s spouses and ex-spouses and estranged children, which I never intended to find out—these autocompletes indicate what other users googled in sessions before me. What it calls “relevancy” might seem, to an individual, like a personal invasion, with secrets spilled to other users who never even asked to know—information for the sake of providing information. From 2004 until 2012, Google seemed determined to create a digital copy of everything. It was scanning all the streets and all the books in the world, or so they wished for you to believe.

Why bother uploading videos to any service other than YouTube, where it will be stored on Google servers, which are reasonably secure? Yet Google could make a mistake or shutter one of its products without alerting you. You—a user—or a school, or an institution, or another body smaller than Google now have habits shaped by Google’s influence. The ICA is a museum, which has standards and practices of archiving, collecting, and preserving objects and information. If Google had never had a hand in the event, the video probably would be available today. The consequence of Google’s “mirror” stage was that public institutions relaxed certain functions and services that Google tools appeared to provide—and for free. When Google Street View launched in 2007, I thought of it in terms of Google’s ambition to “mirror the world.” The audacity of the experiment is what excited me.

pages: 459 words: 103,153

Adapt: Why Success Always Starts With Failure by Tim Harford

Andrew Wiles, banking crisis, Basel III, Berlin Wall, Bernie Madoff, Black Swan, car-free, carbon footprint, Cass Sunstein, charter city, Clayton Christensen, clean water, cloud computing, cognitive dissonance, complexity theory, corporate governance, correlation does not imply causation, creative destruction, credit crunch, Credit Default Swap, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, Dava Sobel, Deep Water Horizon, Deng Xiaoping, disruptive innovation, double entry bookkeeping, Edmond Halley,, Erik Brynjolfsson, experimental subject, Fall of the Berlin Wall, Fermat's Last Theorem, Firefox, food miles, Gerolamo Cardano, global supply chain, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, Jane Jacobs, Jarndyce and Jarndyce, Jarndyce and Jarndyce, John Harrison: Longitude, knowledge worker, loose coupling, Martin Wolf, mass immigration, Menlo Park, Mikhail Gorbachev, mutually assured destruction, Netflix Prize, New Urbanism, Nick Leeson, PageRank, Piper Alpha, profit motive, Richard Florida, Richard Thaler, rolodex, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, South China Sea, special economic zone, spectrum auction, Steve Jobs, supply-chain management, the market place, The Wisdom of Crowds, too big to fail, trade route, Tyler Cowen: Great Stagnation, web application, X Prize, zero-sum game

Yet it was hard to forget seeing peer monitoring in action: the instant correction of a problem, no matter how small and no matter what the hierarchical relationship might be between head of safety and tea lady. 4 Google’s corporate strategy: have no corporate strategy At Hinkley Point, the key priority is ensuring that the power station operates exactly as planned, without deviation. But at other companies, the challenge is to do something new every day, and nowhere is this truer than at Google. The company’s CEO Eric Schmidt had a surprise when he walked into Larry Page’s office in 2002. Page is the co-creator of Google and the man who gave his name to the idea at the company’s foundation: its PageRank search algorithm. But Page had something rather different to show Schmidt: a machine he’d built himself which cut off the bindings of books and then scanned their pages into a digital format. Page had been trying to figure out whether it might be possible for Google to scan the world’s books into searchable form.

If any company can be said to embrace trying new things in the expectation that many will fail, it is Google. Marissa Mayer, the vice-president who helped Larry Page bodge together the first book scanner, says that 80 per cent of Google’s products will fail – but that doesn’t matter, because people will remember the ones that stick. Fair enough: Google’s image seems to be untarnished by the indifferent performances of Knol, a Google service vaguely similar to Wikipedia which didn’t seem to catch on; or SearchMash, a testbed for alternative Google search products which was labelled ‘Google’s Worst Ever Product’ by one search expert and has now been discontinued. According to the influential TechRepublic website, two of the five worst technology products of 2009 came from Google – and they were major Google products at that, Google Wave and the Android 1.0 operating system for mobile phones.

Rather than instructing an intern to rig something up, or commissioning analysis from a consulting firm, he teamed up with Marissa Mayer, a Google vice-president, to see how fast two people could produce an image of a 300-page book. Armed with a plywood frame, a pair of clamps, a metronome and a digital camera, two of Google’s most senior staff tried out the project themselves. (The book went from paper to pixels in forty minutes.) Larry Page regarded the time he devoted to the project not as something he could do because he was Google’s founder and could do whatever he wanted, but as something to which he was entitled because every engineer at Google had the same deal. Famous, Google has a ‘20 per cent time’ policy: any engineer (and some other employees) is allowed to spend one fifth of his or her time on any project that seems worthwhile. Google News, Google Suggest, Adsense and the social networking site Orkut are all projects that emerged from these personal projects, along with half of all Google’s successful products – and an astonishing portfolio of failures.

Remix: Making Art and Commerce Thrive in the Hybrid Economy by Lawrence Lessig

Amazon Web Services, Andrew Keen, Benjamin Mako Hill, Berlin Wall, Bernie Sanders, Brewster Kahle, Cass Sunstein, collaborative editing, commoditize, disintermediation, don't be evil, Erik Brynjolfsson, Internet Archive, invisible hand, Jeff Bezos, jimmy wales, Joi Ito, Kevin Kelly, Larry Wall, late fees, Mark Shuttleworth, Netflix Prize, Network effects, new economy, optical character recognition, PageRank, peer-to-peer, recommendation engine, revision control, Richard Stallman, Ronald Coase, Saturday Night Live, SETI@home, sharing economy, Silicon Valley, Skype, slashdot, Steve Jobs, The Nature of the Firm, thinkpad, transaction costs, VA Linux, yellow journalism

That innovation rewards others and Amazon both. 80706 i-xxiv 001-328 r4nk.indd 126 8/12/08 1:55:16 AM T W O EC O NO MIE S: C O MMERC I A L A ND SH A RING 127 Google Without a doubt, the most famous example of Internet success is Google. Founded at Stanford by two students (the first URL was, the company radically improved the effectiveness of Internet searches. Rather than selling placement (which can often corrupt the results) or relying upon humans to index (which would be impossible given the vast scale of the Internet), the first Google algorithms ordered search results based upon how the Net linked to the results—a process called PageRank, referring not to “page” as in Web page, but “Page” as in Larry Page, Google cofounder and developer of the technique.11 If many Web sites linked to a particular site, that site would be ranked higher in the returned list than another Web site that had few links. Google thus built upon the knowledge the Web revealed to deliver back to the Web a product of extraordinary value.

., “,”, available at link #57 (last visited July 31, 2007). These numbers reflect sales only. According to reports, Amazon’s net deficit is still high— $2 billion as of 2005. 10. Ibid., available at link #58 (last visited July 31, 2007). 11. Wikipedia contributors, “Larry Page,” Wikipedia: The Free Encyclopedia, available at link #59 (last visited July 31, 2007). 12. Verne Kopytoff, “Google Shares Top $400: Search Engine No. 3 in Market Cap Among Firms in Bay Area,” San Francisco Chronicle, November 18, 2005; Yahoo! Finance, “GOOG: Key Statistics for Google Inc,” Capital IQ, available at link #60 (last visited July 5, 2007). 13. Keen, The Cult of the Amateur, 135. 14. The point was made long before by Nicholas Negroponte. “A best-seller in 1990, Nicholas Negroponte’s Being Digital drew a sharp contrast between ‘passive old media’ and ‘interactive new media,’ predicting the collapse of broadcast networks in favor of an era of narrowcasting and niche media on demand: ‘What will happen to broadcast television over the next five years is so phenomenal that it’s difficult to comprehend.’ ” Jenkins, Convergence Culture, 5. 15.

But it is false if it suggests that da Vinci wasn’t responsible for the great value the Mona Lisa is. Like Amazon, Google also offers its tools as a platform for others to build upon. We’ll see this more below as we consider Google Application Programming Interfaces (APIs). And more successfully than anyone, Google has built an advertising business into the heart of technology. Web pages can be served with very smartly selected ads; users can buy searches in Google to promote their own products. The complete range of Google products is vast. But one feature of all of them is central to the argument I want to make here. Practically everything Google offers helps Google build an extraordinary database of knowledge about what people want, and how those wants relate to the Web. Every click you make in the Google universe adds to that database. With each click, Google gets smarter. Three Keys to These Three Successes These familiar stories of Internet success reveal three keys to success in this digital economy.

pages: 538 words: 147,612

All the Money in the World by Peter W. Bernstein

Albert Einstein, anti-communist, Berlin Wall, Bill Gates: Altair 8800, call centre, Charles Lindbergh, corporate governance, corporate raider, creative destruction, currency peg, David Brooks, Donald Trump, estate planning, family office, financial innovation, George Gilder, high net worth, invisible hand, Irwin Jacobs: Qualcomm, Jeff Bezos, job automation, job-hopping, John Markoff, Long Term Capital Management, Marc Andreessen, Martin Wolf, Maui Hawaii, means of production, mega-rich, Menlo Park, Mikhail Gorbachev, new economy, Norman Mailer, PageRank, Peter Singer: altruism, pez dispenser, popular electronics, Renaissance Technologies, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Sand Hill Road, school vouchers, Search for Extraterrestrial Intelligence, shareholder value, Silicon Valley, Silicon Valley startup, stem cell, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, the new new thing, Thorstein Veblen, too big to fail, traveling salesman, urban planning, wealth creators, William Shockley: the traitorous eight, women in the workforce

One of the youngest people16 ever to make the list was Steve Jobs (number 49 on the 2006 Forbes 400 list), who at age twenty-seven boasted a $100 million fortune, thanks to his success with Apple Computer. Bill Gates was also one of the youngest; in 1986 he joined the list at age thirty, with $315 million. And then came the Google guys: In 1998 Google’s founders17, Larry Page (number 13 on the 2006 Forbes 400 list) and Sergey Brin (number 12 on the 2006 list), both then just in their mid-twenties, formally incorporated Google and hired their first employee while working on a graduate student project at Stanford University. This became the prototype for the phenomenally successful search engine. In 2004, a year after Google went public, Brin and Page joined the list, each with a fortune of $4 billion that has since ballooned to $14.1 billion and $14 billion, respectively. * * * At Home in Woodside Once they make their fortunes, many of the most successful Silicon Valley entrepreneurs head to the historic Silicon Valley town of Woodside.

Four years later, his fortune had increased to $4.9 billion. Sergey Brin Larry Page August 1, 1973 December 1,1972 Google 2004 The Google guys weren’t rich enough to make the Forbes list when they were 30, but at 31 and 32, respectively, they were each worth $4 billion. In 2006, Brin, 33, and Page, 34, were each worth $14.1 billion. * * * Before long, everyone at Stanford was Googling. And it was not much longer before the venture capitalists, many of whom were headquartered just a few miles up the road from Stanford on Sand Hill Road, came knocking with proposals in hand. One of them, Vinod Khosla50 of Kleiner Perkins Caufield & Byers, showed up with an offer from Excite, a company in which he was invested, to buy Google for $750,000. But Brin and Page held out for $1.6 million, and the deal fell through.

It worked so well that he took the company public in 1978 and grew it into the world’s largest overnight delivery service. He more than recouped his investment: FedEx brought him a personal net worth of $2.2 billion in 2006. Yahoo, the popular Web portal, and Google were both born at Stanford, under strikingly similar circumstances. Yahoo founders David Filo and Jerry Yang were Stanford graduate students when they designed a system for operating an Internet directory. The duo found the idea so compelling that they put their PhDs on hold in the mid-1990s to devote full attention to the Yahoo project. Now Filo and Yang are each billionaires twice over. Meanwhile, in 1998 Google cofounders Larry Page and Sergey Brin were working toward their PhDs in computer science at Stanford when they started running the now wildly popular search engine. The pair currently shares the company’s presidency; their 2006 net worth came to about $14 billion each.

pages: 392 words: 108,745

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

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

Instead, the keywords that a person typed into the search box were simply matched with those occurring on web pages. This matching, to be sure, was a sophisticated process; search engine experts believed that Google’s PageRank system for ordering search results involved more than two hundred different factors. But search engines were still just making statistically backed best guesses at what people wanted to know. So they hedged their bets and presented long lists of links. True Knowledge, by contrast, aimed for the heterodox goal of providing single correct answers. “When we started, there were people at Google who were completely allergic to what we were doing,” Tunstall-Pedoe says. He argued with one senior Google employee who rejected the notion of there even being such a thing as a single correct reply to any given question. “Just even the idea of a one-shot answer to a search was taboo.”

“What does that suggest to you?” “I beat up the bookie who did not pay off, and I thought he might use his friends in the underworld to get even with me.” Parry and Eliza and other early chatbots, while entertaining, didn’t impress everyone. One notable detractor was Terry Winograd, a graduate student at MIT in the late 1960s. (Decades later, as a professor at Stanford, he would serve as the thesis advisor for Google cofounder Larry Page.) Winograd was underwhelmed by Eliza because she didn’t really understand what people were saying. She didn’t really understand anything. In his PhD dissertation, Winograd laid out a loftier vision. For computers to really converse with people, he wrote, they needed to have actual knowledge. They needed to employ reasoning and make logical inferences. Enabling such capabilities would be difficult, Winograd knew, because meaning isn’t conveyed by words alone.

, USA Today, December 27, 2016, 223 “By buying a smart speaker”: Adam Clark Estes, “Don’t Buy Anyone an Echo,” Gizmodo, December 5, 2017, 226 “The home is a special intimate place”: Isabelle Olsson, “Google Event October 4 2017 New Google Home Mini,” October 4, 2017, San Francisco, 227 “My Google Home Mini was inadvertently spying on me”: Artem Russakovskii, “Google is permanently nerfing all Home Minis because mine spied on everything I said 24/7,” Android Police, October 10, 2017, 227 “allowed Google to intercept and record private conversations”: letter from the Electronic Privacy Information Center to the Consumer Product Safety Commission, October 13, 2017, 228 “Conversation history with Google Home”: “Data security & privacy on Google Home,” Google Home Help website, accessed July 30, 2018, 228 “the legal standard of ‘reasonable expectation of privacy’ is eviscerated”: Joel Reidenberg, email to author, August 1, 2018. 228 According to a Google transparency report: “Requests for user information,” Google Transparency Report, accessed July 30, 2018, 229 “Bluetooth LE typically has a range”: Paul Stone, “Hacking Unicorns with Web Bluetooth,” Context, February 27, 2018, 230 “It’s not that the risks are particularly any different”: Troy Hunt, “Data from connected CloudPets teddy bears leaked and ransomed, exposing kids’ voice messages,” personal blog, February 28, 2017, 230 That’s what a team of researchers: Guoming Zhang et al., “DolphinAttack: Inaudible Voice Commands,” 24th ACM Conference on Computer and Communications Security (2017): 103–17, 231 “If in connection with such a review”: “Hello Barbie Messaging/Q&A,” Mattel consumer information document, 2015, 232 “Will personal assistants be responsible”: Robert Harris, “What Religion is Hello Barbie?”

pages: 499 words: 144,278

Coders: The Making of a New Tribe and the Remaking of the World by Clive Thompson

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 4chan, 8-hour work day, Ada Lovelace, AI winter, Airbnb, Amazon Web Services, Asperger Syndrome, augmented reality, Ayatollah Khomeini, barriers to entry, basic income, Bernie Sanders, bitcoin, blockchain, blue-collar work, Brewster Kahle, Brian Krebs, Broken windows theory, call centre, cellular automata, Chelsea Manning, clean water, cloud computing, cognitive dissonance, computer vision, Conway's Game of Life, crowdsourcing, cryptocurrency, Danny Hillis, David Heinemeier Hansson, don't be evil, don't repeat yourself, Donald Trump, dumpster diving, Edward Snowden, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Ethereum, ethereum blockchain, Firefox, Frederick Winslow Taylor, game design, glass ceiling, Golden Gate Park, Google Hangouts, Google X / Alphabet X, Grace Hopper, Guido van Rossum, Hacker Ethic, HyperCard, illegal immigration, ImageNet competition, Internet Archive, Internet of things, Jane Jacobs, John Markoff, Jony Ive, Julian Assange, Kickstarter, Larry Wall, lone genius, Lyft, Marc Andreessen, Mark Shuttleworth, Mark Zuckerberg, Menlo Park, microservices, Minecraft, move fast and break things, move fast and break things, Nate Silver, Network effects, neurotypical, Nicholas Carr, Oculus Rift, PageRank, pattern recognition, Paul Graham, paypal mafia, Peter Thiel, pink-collar, planetary scale, profit motive, ransomware, recommendation engine, Richard Stallman, ride hailing / ride sharing, Rubik’s Cube, Ruby on Rails, Sam Altman, Satoshi Nakamoto, Saturday Night Live, self-driving car, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, single-payer health, Skype, smart contracts, Snapchat, social software, software is eating the world, sorting algorithm, South of Market, San Francisco, speech recognition, Steve Wozniak, Steven Levy, TaskRabbit, the High Line, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, universal basic income, urban planning, Wall-E, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y Combinator, Zimmermann PGP, éminence grise

But in the last few years, dissatisfaction with the job began to creep in. He’d begun to hate the long 1.5-hour rides to work on the “Google buses.” Apart from being a huge chunk of time in traffic, the buses had become a lightning rod for San Franciscans furious at how the influx of rich tech workers was jacking up rents in the city. Meanwhile, some employees were uneasy with Google’s executives’ recent overtures to the Trump administration; Larry Page had met with Trump in a tech roundtable soon after the president took office, which also annoyed a few vocal employees. Breisacher and other colleagues had also been lately finding that Google executives were increasingly offering vague hand waves when, in weekly staff meetings, employees raised ethical objections about company activities. “You’d get this wishy-washy answer, the type of answer you’d get from politicians,” Breisacher says: That’s a good point you raised, and we’re looking into it.

In the TV show Silicon Valley, the night before the startup is about to go down in flames onstage at the TechCrunch conference, the coder-founder Richard has an epiphany and—again, in a single night—rewrites his entire compression algorithm, nearly doubling its performance and trouncing his competition. The hacker Cameron Howe of the TV show Halt and Catch Fire, as a favor to her friend’s firm, creates what is essentially the Google PageRank algorithm. It’s so artful that the firm’s resident head of software wincingly admits he can’t even understand how it works; she’s that good. This belief in the unicorn programmer isn’t just a piece of pop culture. Indeed, in the real world of software, it’s so well known that the concept has a name: the “10X” coder. As the moniker suggests, it describes a programmer who is provably better, multiple times so, than the average code monkey.

He’s right: The list of small-person or one-person innovators is long. The first version of Photoshop was created by two brothers; the version of BASIC that launched Microsoft in 1975 was hacked together in weeks by a young Bill Gates, his former schoolmate Paul Allen, and a Harvard freshman Monte Davidoff. An early and influential blogging tool, LiveJournal, was written by Brad Fitzpatrick. The breakthrough search algorithm that led to Google was a product of two students, Larry Page and Sergey Brin; YouTube was a trio of coworkers; Snapchat a trio (or, the level of the code, one person, Bobby Murphy). BitTorrent was entirely a creation of Bram Cohen, and Bitcoin was reputedly the work of a lone coder, the pseudonymous “Satoshi Nakamoto.” John Carmack created the 3-D-graphics engines that helped usher in the multi-billion-dollar industry of first-person shooter video games.

pages: 418 words: 128,965

The Master Switch: The Rise and Fall of Information Empires by Tim Wu

accounting loophole / creative accounting, Alfred Russel Wallace, Apple II, barriers to entry, British Empire, Burning Man, business cycle, Cass Sunstein, Clayton Christensen, commoditize, corporate raider, creative destruction, disruptive innovation, don't be evil, Douglas Engelbart, Douglas Engelbart, Howard Rheingold, Hush-A-Phone, informal economy, intermodal, Internet Archive, invention of movable type, invention of the telephone, invisible hand, Jane Jacobs, John Markoff, Joseph Schumpeter, Menlo Park, open economy, packet switching, PageRank, profit motive, road to serfdom, Robert Bork, Robert Metcalfe, Ronald Coase, sexual politics, shareholder value, Silicon Valley, Skype, Steve Jobs, Steve Wozniak, Telecommunications Act of 1996, The Chicago School, The Death and Life of Great American Cities, the market place, The Wisdom of Crowds, too big to fail, Upton Sinclair, urban planning, zero-sum game

The firm harvests the best of the Internet, organizing the worldwide chaos in a useful way, and asks its users to navigate this order via their own connections; by relying on the sweat of others for content and carriage, Google can focus on its central mission: search. From its founding, the firm was dedicated to performing that function with clear superiority; it famously pioneered an algorithm called PageRank, which arranged search hits by importance rather than sheer numerical incidence, thereby making search more intelligent. The company resolved to stand or fall on the strength of that competitive edge. As Google’s CEO, Eric Schmidt, explained to me once, firms like the old AT&T or Western Union “had to build the entire supply chain. We are specialized. We understand that infrastructure is not the same thing as content. And we do infrastructure better than anyone else.” Google, between content and transport Unlike AOL Time Warner, Google doesn’t need to try to steer users anywhere in particular.

This may seem an improbably shaky foundation to build a firm on, but perhaps that is the genius of it. If that seems a bit abstract, it is well to remember that Google is an unusually academic company in origins and sensibility. Larry Page, one of the two founders, described his personal ambitions this way: “I decided I was either going to be a professor or start a company.” Just as Columbia University effectively financed FM radio in the 1930s, Stanford got Google started. With its original Web address, the operation relied on university hardware and software and the efforts of graduate students. “At one point,” as John Battelle writes in The Search, the early Google “consumed nearly half of Stanford’s entire network bandwidth.”15 Google’s corporate design remains both its greatest strength and its most serious vulnerability. It is what makes the firm so remarkably well adapted to the Internet environment, as a native species, so to speak.

That’s the advantage. On the other hand, Google’s lack of vertical integration leaves it vulnerable, rather like a medieval city without a wall.* He who controls the wires or airwaves can potentially destroy Google, for it is only via these means that Google reaches its customers. To use the search engine and other utilities, you need Internet access, not a service Google now provides (with trivial exceptions). To have such access, you need to pay an Internet Service Provider—typically your telephone or cable company. Meanwhile, Google itself must also pay for Internet service, a fact that, conceptually at least, puts the firm and its customers on an equal footing: both are subscription users of the Internet. And so whoever controls those connection services can potentially block Google—or any other site or content, as well as the individual user, for that matter.

pages: 528 words: 146,459

Computer: A History of the Information Machine by Martin Campbell-Kelly, William Aspray, Nathan L. Ensmenger, Jeffrey R. Yost

Ada Lovelace, air freight, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Apple's 1984 Super Bowl advert, barriers to entry, Bill Gates: Altair 8800, borderless world, Buckminster Fuller, Build a better mousetrap, Byte Shop, card file, cashless society, cloud computing, combinatorial explosion, computer age, deskilling, don't be evil, Donald Davies, Douglas Engelbart, Douglas Engelbart, Dynabook, fault tolerance, Fellow of the Royal Society, financial independence, Frederick Winslow Taylor, game design, garden city movement, Grace Hopper, informal economy, interchangeable parts, invention of the wheel, Jacquard loom, Jeff Bezos, jimmy wales, John Markoff, John von Neumann, Kickstarter, light touch regulation, linked data, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Mitch Kapor, natural language processing, Network effects, New Journalism, Norbert Wiener, Occupy movement, optical character recognition, packet switching, PageRank, pattern recognition, Pierre-Simon Laplace, pirate software, popular electronics, prediction markets, pre–internet, QWERTY keyboard, RAND corporation, Robert X Cringely, Silicon Valley, Silicon Valley startup, Steve Jobs, Steven Levy, Stewart Brand, Ted Nelson, the market place, Turing machine, Vannevar Bush, Von Neumann architecture, Whole Earth Catalog, William Shockley: the traitorous eight, women in the workforce, young professional

Using a “web crawler” to gather back-link data (that is, the websites that linked to a particular site), Page, now teamed up with Brin, created their “PageRank” algorithm based on back-links ranked by importance—the more prominent the linking site, the more influence it would have on the linked site’s page rank. They insightfully reasoned that this would provide the basis for more useful web searches than any existing tools and, moreover, that there would be no need to hire a corps of indexing staff. Thus was born their “search engine,” Backrub, renamed Google shortly before they launched the URL in September 1997. The name was a modification of a friend’s suggestion of googol—a term referring to the number 1 followed by 100 zeros. Brin misspelled the term as google, but the Internet address for googol was already taken so the catchy misspelling stuck.

was not without competition: Lycos, Excite, and a dozen others had come up with the same concept, and listing and information search services became one of the first established categories of the web. One question remained: How to pay for the service? The choices included subscriptions, sponsorship, commissions, or advertising. As with early broadcasting, advertising was the obvious choice. Another firm focused on helping users find information on the web—Google Inc.—soon demonstrated how lucrative web advertising could be. Yahoo! was already well established when two other Stanford University doctoral students, Larry Page and Sergey Brin, began work on the Stanford Digital Library Project (funded in part by the National Science Foundation)—research that would not only forever change the process of finding things on the Internet but also, in time, lead to an unprecedentedly successful web advertising model. Page became interested in a dissertation project on the mathematical properties of the web, and found strong support from his adviser Terry Winograd, a pioneer of artificial intelligence research on natural language processing.

The symbolic return of Silicon Valley to glory came with the success of Google. In 2004 Google’s public offering valued the company at more than $26 billion. By 2007 Google facilitated more searches than all other search and listing services combined. That year Google achieved revenue of $16.6 billion and net income of $4.2 billion. Google continues to dominate the search field with 1.7 trillion annual searches (in 2011, representing roughly a two-thirds share). While search-based advertising revenue remained its primary source of income, Google successfully moved into e-mail services (Gmail), maps and satellite photos, Internet video (with its 2006 acquisition of YouTube), cloud computing, digitizing books, and other endeavors. More recently, Google has also been an important participant in open-source mobile platforms that are transforming computing.

pages: 706 words: 202,591

Facebook: The Inside Story by Steven Levy

active measures, Airbnb, Airbus A320, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, augmented reality, Ben Horowitz, blockchain, Burning Man, business intelligence, cloud computing, computer vision, crowdsourcing, cryptocurrency, don't be evil, Donald Trump, East Village, Edward Snowden, El Camino Real, Elon Musk, Firefox, Frank Gehry, glass ceiling, indoor plumbing, Jeff Bezos, John Markoff, Jony Ive, Kevin Kelly, Kickstarter, Lyft, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Menlo Park, Metcalfe’s law, MITM: man-in-the-middle, move fast and break things, move fast and break things, natural language processing, Network effects, Oculus Rift, PageRank, Paul Buchheit, paypal mafia, Peter Thiel,, post-work, Ray Kurzweil, recommendation engine, Robert Mercer, Robert Metcalfe, rolodex, Sam Altman, Sand Hill Road, self-driving car, sexual politics, Shoshana Zuboff, side project, Silicon Valley, Silicon Valley startup, slashdot, Snapchat, social graph, social software, South of Market, San Francisco, Startup school, Steve Ballmer, Steve Jobs, Steven Levy, Steven Pinker, Tim Cook: Apple, web application, WikiLeaks, women in the workforce, Y Combinator, Y2K

Facebook’s Growth team, which had continued to track the remarkable Onavo data, recognized immediately the danger of WhatsApp in enemy hands. Zuckerberg’s new priority was now buying Koum and Acton’s messaging company. The acquisition machinery cranked up for what would be its biggest and most expensive quest. Meanwhile, Google reached out again. This time it was CEO Larry Page offering the meeting. It went no better than Google’s previous effort. The enigmatic Page was a half hour late. He did ask that if they ever did go on sale, to allow Google to make an offer. Mark Zuckerberg wasn’t going to let that happen. The Onavo numbers told him that WhatsApp was becoming a global powerhouse, possibly blocking Facebook’s own messaging efforts around the world. It had 450 million users, including 40 million users in India, 30 million in Mexico.

His office was not in New York City or even the financial district in San Francisco but on Sand Hill Road in Menlo Park, where the big VC firms made their bets. He had worked on the Google IPO and recently on LinkedIn’s. And he was friendly with Sheryl Sandberg. (As usual, other investment banks and advisers joined in, including Goldman Sachs and JPMorgan.) Zuckerberg had firm ideas about the way Facebook’s stock structure would operate. The key factor was keeping himself in control, presumably forever, by creating two levels of shareholders, with the top level—the one where he had the overwhelming majority of shares—given dominance in any vote. It was similar to schemes that let family-owned newspaper companies, like that of his mentor Don Graham, control the company for decades while owning a minority of the company. It had also been adopted by Larry Page and Sergey Brin of Google. But Facebook’s plan topped theirs in how much control a single founder had.

It worked this way: you went to the Buddy Zoo site and submitted your Buddy List. The program would then do an analysis, yielding all sorts of insights: Find out which buddies you have in common with your friends. Measure how popular you are. Detect cliques you’re part of. See a visualization of your Buddy List. View your Prestige, computed the way Google computes PageRank to rank web pages. See the degrees of separation between different screen names. The effectiveness of the program depended in part on a lot of people submitting their lists so Buddy Zoo could garner a huge data set. To D’Angelo’s astonishment, that wasn’t a problem. D’Angelo had posted games he’d written before, and never gotten more than a hundred or so downloads.

pages: 855 words: 178,507

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

Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, AltaVista, bank run, bioinformatics, Brownian motion, butterfly effect, citation needed, Claude Shannon: information theory, clockwork universe, computer age, conceptual framework, crowdsourcing, death of newspapers, discovery of DNA, Donald Knuth, double helix, Douglas Hofstadter,, Eratosthenes, Fellow of the Royal Society, Gödel, Escher, Bach, Henri Poincaré, Honoré de Balzac, index card, informal economy, information retrieval, invention of the printing press, invention of writing, Isaac Newton, Jacquard loom, Jaron Lanier, jimmy wales, Johannes Kepler, John von Neumann, Joseph-Marie Jacquard, lifelogging, Louis Daguerre, Marshall McLuhan, Menlo Park, microbiome, Milgram experiment, Network effects, New Journalism, Norbert Wiener, Norman Macrae, On the Economy of Machinery and Manufactures, PageRank, pattern recognition, phenotype, Pierre-Simon Laplace, pre–internet, Ralph Waldo Emerson, RAND corporation, reversible computing, Richard Feynman, Rubik’s Cube, Simon Singh, Socratic dialogue, Stephen Hawking, Steven Pinker, stochastic process, talking drums, the High Line, The Wisdom of Crowds, transcontinental railway, Turing machine, Turing test, women in the workforce

When the publishers of the Oxford English Dictionary began digitizing its contents in 1987 (120 typists; an IBM mainframe), they estimated its size at a gigabyte. A gigabyte also encompasses the entire human genome. A thousand of those would fill a terabyte. A terabyte was the amount of disk storage Larry Page and Sergey Brin managed to patch together with the help of $15,000 spread across their personal credit cards in 1998, when they were Stanford graduate students building a search-engine prototype, which they first called BackRub and then renamed Google. A terabyte is how much data a typical analog television station broadcasts daily, and it was the size of the United States government’s database of patent and trademark records when it went online in 1998. By 2010, one could buy a terabyte disc drive for a hundred dollars and hold it in the palm of one hand.

Kolmogorov in Perspective. History of Mathematics, vol. 20. Translated by Harold H. McFaden. N.p.: American Mathematical Society, London Mathematical Society, 2000. Krutch, Joseph Wood. Edgar Allan Poe: A Study in Genius. New York: Knopf, 1926. Kubát, Libor, and Jirí Zeman. Entropy and Information in Science and Philosophy. Amsterdam: Elsevier, 1975. Langville, Amy N., and Carl D. Meyer. Google’s Page Rank and Beyond: The Science of Search Engine Rankings. Princeton, N.J.: Princeton University Press, 2006. Lanier, Jaron. You Are Not a Gadget. New York: Knopf, 2010. Lanouette, William. Genius in the Shadows. New York: Scribner’s, 1992. Lardner, Dionysius. “Babbage’s Calculating Engines.” Edinburgh Review 59, no. 120 (1834): 263–327. ———. The Electric Telegraph. Revised and rewritten by Edward B.

“African Talking Drums and Oral Noetics.” New Literary History 8, no. 3 (1977): 411–29. ———. Interfaces of the Word. Ithaca, N.Y.: Cornell University Press, 1977. ———. Orality and Literacy: The Technologizing of the Word. London: Methuen, 1982. Oslin, George P. The Story of Telecommunications. Macon, Ga.: Mercer University Press, 1992. Page, Lawrence, Sergey Brin, Rajeev Motwani, and Terry Winograd. “The Pagerank Citation Ranking: Bringing Order to the Web.” Technical Report SIDL-WP-1999-0120, Stanford University InfoLab (1998). Available online at Pain, Stephanie. “Mr. Babbage and the Buskers.” New Scientist 179, no. 2408 (2003): 42. Paine, Albert Bigelow. In One Man’s Life: Being Chapters from the Personal & Business Career of Theodore N. Vail.

pages: 809 words: 237,921

The Narrow Corridor: States, Societies, and the Fate of Liberty by Daron Acemoglu, James A. Robinson

Affordable Care Act / Obamacare, agricultural Revolution, AltaVista, Andrei Shleifer, bank run, Berlin Wall, British Empire, California gold rush, central bank independence, centre right, collateralized debt obligation, collective bargaining, colonial rule, Computer Numeric Control, conceptual framework, Corn Laws, corporate governance, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, Dava Sobel, David Ricardo: comparative advantage, Deng Xiaoping, discovery of the americas, double entry bookkeeping, Edward Snowden,, equal pay for equal work, European colonialism, Ferguson, Missouri, financial deregulation, financial innovation, Francis Fukuyama: the end of history, full employment, income inequality, income per capita, industrial robot, information asymmetry, interest rate swap, invention of movable type, Isaac Newton, James Watt: steam engine, John Harrison: Longitude, joint-stock company, Kula ring, labor-force participation, land reform, Mahatma Gandhi, manufacturing employment, mass incarceration, Maui Hawaii, means of production, megacity, Mikhail Gorbachev, Nelson Mandela, obamacare, openstreetmap, out of africa, PageRank, pattern recognition, road to serfdom, Ronald Reagan, Skype, spinning jenny, Steven Pinker, the market place, transcontinental railway, War on Poverty, WikiLeaks

While its competitors, such as Yahoo! and AltaVista, ranked websites by the number of times they included the term being searched for, the founders of Google, Sergei Brin and Larry Page, came up with a much better approach when they were graduate students at Stanford University. This approach, which came to be called the PageRank algorithm, ranked a web page according to its relevance estimated from how many other pages also mentioning the search term linked to this website. Because this algorithm was much better at suggesting relevant websites to users, Google’s market share of Internet searches grew quickly. Once it had a large market share, Google could use more data from user searches to refine its algorithm, making it even better and more dominant. These dynamics got stronger once data from Internet searches started being used for artificial intelligence applications, for example, for translation and pattern recognition.

The size of the largest companies relative to the rest of the economy is at an all-time high. The tech giants Alphabet (Google), Amazon, Apple, Facebook, and Microsoft have a combined market value (as measured by their stock market valuations) equivalent to over 17 percent of U.S. gross domestic product. The same number for the five largest companies in 1900, when policy makers and society became alarmed about the power of large corporations, was less than 6 percent. This huge increase in concentration appears to have several causes. The most important is the nature of the technology of these new companies, which creates what economists call “winner take all” dynamics. Take Google, for instance. Founded in 1998, when there were already several successful search engines for the Internet, Google quickly distinguished itself because of its superior search algorithm.

The irony of the NSA debacle is that, even though the agency appears to have clearly and massively overstepped its purported boundaries and unconstitutionally collected information against American civilians, it has done so in a distorted version of the public-private partnership; it relied on private contractors and it compelled (or received the cooperation of willing) phone companies such as AT&T and Verizon and tech giants such as Google, Microsoft, Facebook, and Yahoo! to share their customers’ data. The Paradoxical American Leviathan It is possible, perhaps even compelling, to see the rise of the American Leviathan as a success story—a society committed to liberty, a Constitution enshrining rights and protections, a state born with shackles on and remaining and evolving in the corridor because of the weight of the shackles.

pages: 918 words: 257,605

The Age of Surveillance Capitalism by Shoshana Zuboff

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

Eric Schmidt, “Alphabet’s Eric Schmidt: We Should Embrace Machine Learning—Not Fear It,” Newsweek, January 10, 2017, 4. Richard Waters, “FT Interview with Google Co-founder and CEO Larry Page,” Financial Times, October 31, 2014, 5. Marcus Wohlsen, “Larry Page Lays Out His Plan for Your Future,” Wired, March 2014, 6. Waters, “FT Interview with Google Co-founder”; Vinod Khosla, “Fireside Chat with Google Co-founders, Larry Page and Sergey Brin,” Khosla Ventures, July 3, 2014, 7. Miguel Helft, “Fortune Exclusive: Larry Page on Google,” Fortune, December 11, 2012, 8.

Vinod Khosla, “Fireside Chat with Google Co-Founders, Larry Page and Sergey Brin,” Khosla Ventures, July 3, 2014, 24. Holman W. Jenkins, “Google and the Search for the Future,” Wall Street Journal, August 14, 2010, 25. See Lillian Cunningham, “Google’s Eric Schmidt Expounds on His Senate Testimony,” Washington Post, September 30, 2011, 26. Pascal-Emmanuel Gobry, “Eric Schmidt to World Leaders at EG8: Don’t Regulate Us, or Else,” Business Insider, May 24, 2011, 27.

.…”90 When the Court of Justice’s decision was announced, the “smart money” said that it could never happen in the US, where the internet companies typically seek cover behind the First Amendment as justification for their “permissionless innovation.”91 Some technology observers called the ruling “nuts.”92 Google’s leaders sneered at the decision. Reporters characterized Google cofounder Sergey Brin as “joking” and “dismissive.” When asked about the ruling during a Q&A at a prominent tech conference, he said, “I wish we could just forget the ruling.”93 In response to the ruling, Google CEO and cofounder Larry Page recited the catechism of the firm’s mission statement, assuring the Financial Times that the company “still aims to ‘organise the world’s information and make it universally accessible and useful.’” Page defended Google’s unprecedented information power with an extraordinary statement suggesting that people should trust Google more than democratic institutions: “In general, having the data present in companies like Google is better than having it in the government with no due process to get that data, because we obviously care about our reputation.

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

Albert Einstein, Apple II, augmented reality, call centre, Chelsea Manning, cloud computing, connected car, Edward Snowden, Edward Thorp, Elon Musk, factory automation, Filter Bubble, G4S, Google Earth, Google Glasses, Internet of things, job automation, John Markoff, Kickstarter, lifelogging, Marc Andreessen, Mars Rover, Menlo Park, Metcalfe’s law, New Urbanism, PageRank, pattern recognition, RFID, ride hailing / ride sharing, Robert Metcalfe, 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, ubercab, urban planning, Zipcar

Not Another Day Scoble was the 107th person to receive a Google Glass prototype. He put them on and immediately started posting short notes on his social networks about his experience. He wore them when he went to Europe, making presentations at tech conferences and letting hundreds of people give his Glass device a quick try. After two weeks, he posted his first review to Google+, the default social network for Google Glass users, declaring “I’m never going to live another day without a wearable computer on my face.” To illustrate his point, his wife Maryam photographed him in the shower wearing his Glass. Some scorned the stunt. “If Google Glass fails, it is Robert Scoble’s fault,” bemoaned author-speaker Peter Shankman in a blog post. Larry Page, Google’s CEO, told Scoble in front of a large audience that he “did not appreciate” the shower photo.

From a contextual perspective, we hold Google in particularly high regard, but the real game-changing development is the gadget Scoble is wearing on our back cover—Google Glass. Chapter 2 Through the Glass, Looking Right now, most of us look at the people with Google Glass like the dudes who first walked around with the big brick phones. Amber Naslund, SideraWorks The first of them went to Sergey Brin, Larry Page, and Eric Schmidt. Brin, who runs Project Glass, the company’s much-touted digital eyewear program, has rarely been seen in public again without them. Before anyone outside the company could actually touch the device, or see the world through its perspective, the hoopla had begun and has not stopped. Neither has the controversy. Google Glass is the flagship contextual device.

So next-generation companies like Google started building networks of gigantic data centers that employed millions of computers to host all the data being produced. Storing this data was the smaller of two challenges. The bigger one was figuring out how everyday people could extract the little spoonfuls they wanted from inside the new unstructured big data mountains. Google again led the way. Until 2012, the essence of its data search engine was Page Rank, which used complex mathematical equations, or algorithms, to understand connections between web pages and then rank them by relevance in search results. Before Google, we got back haystacks when we searched for needles. Then we had to sift through pages and pages of possible answers to find the one right for us. Page Rank started to understand the rudimentary context of a search.

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The Numerati by Stephen Baker

Berlin Wall, Black Swan, business process, call centre, correlation does not imply causation, Drosophila, full employment, illegal immigration, index card, Isaac Newton, job automation, job satisfaction, McMansion, Myron Scholes, natural language processing, PageRank, personalized medicine, recommendation engine, RFID, Silicon Valley, Skype, statistical model, Watson beat the top human players on Jeopardy!

The Numerati are in control. They'll have their way with us. Wrong. Even the greatest and most powerful of the Numerati only master certain domains. Everywhere else, they'll be just like the rest of us: objects of study. Larry Page, for example, is a cofounder of Google and a titan in the world of the Numerati. His scientists are building machines to crunch hundreds of billions of our search queries and clicks, and to sell us, in neatly organized buckets, to advertisers. But when Josh Gotbaum's political program pours through consumer data and classifies millions of California voters, it plunks Larry Page into a bucket of Still Waters or Right Clicks. Whether they're patients with a genetic predisposition for blindness or supermarket shoppers with a sky-high tendency to throw a candy bar in the cart, the Numerati are sitting in the databases with the rest of us.

His point is that mathematicians model misunderstandings of the world, often using the data at hand instead of chasing down the hidden facts. He tells the story of a drunk looking for his keys on a dark night under a streetlight. He's looking for them under that lamp not necessarily because he dropped them there but because it's the only place with light. Later that afternoon, I'm sitting at an outdoor patio with Craig Silverstein, Google's chief technologist. He was the number-one employee at Google. The founders, Larry Page and Sergey Brin, hired him because neither one of them, for all their brilliant ideas, knew much about search engines. It's sunny and the wind is blowing the pages of my notebook, and I tell Silverstein the story about the drunk looking for his keys. He smiles. He's heard it many times before. He recalls a science fair in junior high, where his project featured lots of good data he'd come up with.

Spam blogs, or splogs, they called them. The purpose of splogs was to use the immense power of Google to cash in on the fast-growing field of blog advertising. Google offered a service called Adsense. If you signed up for it, Google would automatically place relevant advertisements onto your blog or Web page. If you wrote about weddings, the system would detect this and drop in ad banners, say, for flowers, gowns, and tuxedos. If a reader clicked the banner, the advertiser would pay Google a few cents, and Google would share the take with the blogger. For bloggers, it looked like a great way to bring in advertising revenue with absolutely no sales staff. Just click the box, blog energetically, and wait for the check from Google. But when I surveyed bloggers that spring and asked how they were faring, most of them complained.

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Curation Nation by Rosenbaum, Steven

Amazon Mechanical Turk, Andrew Keen, barriers to entry, citizen journalism, cognitive dissonance, commoditize, creative destruction, crowdsourcing, disintermediation,, future of journalism, Jason Scott:, means of production, PageRank, pattern recognition, post-work, postindustrial economy, pre–internet, Sand Hill Road, Silicon Valley, Skype, social graph, social web, Steve Jobs, Tony Hsieh, Yogi Berra

Today Google runs over one million servers in data centers around the world and processes over one billion search requests and 20 petabytes of user-generated data every day. Dewey was a human system, with a rigid digital classification. Google replaced human classification with digital discovery and a “black box” formula that ranked pages based on a complex and changing algorithm that let Google determine a page of data’s relative value for a particular search term. The concept of page rank was powerful, and it resulted in a taxonomy that created an entire industry of consultants and advisors who helped Web-content makers increase search engine optimization (SEO). That Larry Page, one of the Google cofounders, understood that the unit of measure for Web content was pages rather than domains, URLs, articles, authors, sources, or any other dimension helped to shape the Web for almost 10 years.

(See also Advertising) Brin, Sergey Brinkley, Alan Britain’s Got Talent (TV program) BROADCAST: New York (TV program) Brogan, Chris Broken, nature of Brooklyn Flea swap Burn Rate (Wolff) Business Insider Cable television Cablevision Calacanis, Jason CameraPlanet 9/11 Archive Carnegie, Andrew Carolla, Adam Carr, Paul Cars Direct CBS News CBS Radio CD-ROMs Chaos Scenarios, The (Garfield) Chen, Steve Citizens, of Curation Nation City Winery Civic leaders, of Curation Nation Clean, Steven Clinton, Bill CNBC CNN Cognitive Surplus (Shirky) Collins, Shawn Comcast Commerce, nature of Commission Junction Community antenna television (CATV) Community information Compete Consistency Consumer conversations Content creation by brands content entrepreneurs in machines versus humans in magazines and Content farms Content Generation Content strategy brands and cupcake analogy for curation in curation mix and emergence of nature of publishing and social media and stakeholders in Content Strategy (Halvorson) Contests Cooper, Frank Corradina, Linda Cost per acquisition (CPA) Cost per click (CPC) Cost per sale (CPS) Craigslist Creative artists Creative Commons Credit card information Crenshaw, Marshall Crowd Fusion Cruise ships Cuban, Mark Cult of the Amateur, The (Keen) Curated networks BlogHer Glam Media human repeaters in SB Nation Curation accidental as adding value aggregation versus applications of of consumer conversations content entrepreneurs and critics of curation economy curation manifesto defined history of human element of impact of legal issues in low-value moral issues in nature of need for origins of in shift from industrial to information age trend toward varieties of Curation nation CurationStation Curiosity Curse of the Mogul, The (Seave) Data mining Davola, Joe Daylife Dell Dell, Michael Demand Media Demby, Eric Democratization trend Denton, Nick Des Jardins, Jory Dewey, Melvil Dewey Decimal System DEWmocracy Diesel Digg Digital Millennium Copyright Act (DMCA) Digital natives Diller, Barry DJs Domain names Donohue, Joe Döpfner, Mathias Dorsey, Jack DoubleClick Drudge, Matt Drudge Report Dvorkin, Lewis DVR Dyson, Esther Earned media eBay Edelman PR Worldwide Editorial calendars Eliason, Frank Engadget Engage (Solis) Entertainment Weekly Entrepreneur magazine Etsy Facebook data mining and Facebook Connect Facebook Places Like button Open Graph origins of Fair use Fast Company Fett, Boba Film critics Finance First-person publishing Flickr Flipboard Flip cams Food critics Forbes magazine Ford, Henry Forry, Clinton Foursquare FOX News Frankfurt Kurnit Klein & Selz, PC Free (Anderson) Free content curation Friend-curated information F*cked Company Future of Privacy Forum Garfield, Bob Gartner, Gideon Gartner Group Gates, Bill Gawker Gelman, Lauren General Mills Generation C Gilt Group Giuliani, Rudy Gizmodo Glam Media Global Business Network Godin, Seth Google Google Ad Sense Google Affiliate Network Google Images Google Index Google Maps Google News Google Reader keyword tool page rank algorithm Gowalla Grub Street Hadden, Briton Hall, Colby Halvorson, Kristina Hampton University Ham radio Hansell, Saul Harvard University Here Comes Everybody (Shirky) Hewitt, Perry Heywood, Jamie Hileman, Kristen Hippeau, Eric Hirschhorn, Jason Hitwise Holt, Courtney Huffington, Arianna Huffington Post HUGE design Hulu Hurley, Chad Internet, launch of commercial iPad iPhone iTunes iVillage Jarvis, Jeff Jobs, Steve Journalism bionic financial machines versus humans in SB Nation and Joystiq Kaboodle Kaplan, Dina Kaplan, Philip Kashi Kasprzak, Michelle Kawaja, Terrence Keen, Andrew Keyword search terms Kinsley, Michael Kissinger, Henry Kurnit, Rick Kurnit, Scott Law of unintended consequences Lego Libraries Lincoln Center Library for the Performing Arts (New York City) Lindzon, Howard Linked economy LinkedIn Linked stories LinkShare Listenomics Livingston, Troy M.

Kurnit explains it this way: “What Congress was doing with the Digital Millennium Copyright Act is admitting that they can’t establish regulations. If Congress tried to sit down and write a copyright law for this medium, it would be out of date before it got enacted, and they essentially recognized that. So one could posit that Google’s response was, ‘We just want to proliferate content because we’re the curator; we don’t even care about being paid for content, we give away our content.’” Google is, after all, free. It gives away links to content that it aggregates via Web crawlers and curates using its page rank algorithm. Free content curation is at the core of Google’s business model. And by all accounts, it’s doing pretty well. Viacom is now appealing the judge’s ruling. But, in the industry broadly, the claim that aggregation is stealing seems to be fading into history. There are plenty of skirmishes about where the lines should be drawn, with folks like Nick Denton claiming that Huffington is stealing from Gawker, or the Newser versus The Wrap kerfuffle that I wrote about in chapter 3.

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

3D printing, Ada Lovelace, Albert Einstein, Alvin Roth, Andrew Wiles, Automated Insights, Benoit Mandelbrot, Claude Shannon: information theory, computer vision, correlation does not imply causation, crowdsourcing, data is the new oil, Donald Trump, double helix, Douglas Hofstadter, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, Flash crash, Gödel, Escher, Bach, Henri Poincaré, Jacquard loom, John Conway, Kickstarter, Loebner Prize, mandelbrot fractal, Minecraft, music of the spheres, Narrative Science, natural language processing, Netflix Prize, PageRank, pattern recognition, Paul Erdős, Peter Thiel, random walk, Ray Kurzweil, recommendation engine, Rubik’s Cube, Second Machine Age, Silicon Valley, speech recognition, Turing test, Watson beat the top human players on Jeopardy!, wikimedia commons

When her grandson Ben opened her laptop and found ‘Please translate these roman numerals mcmxcviii thank you’, he couldn’t resist tweeting the world about his nan’s misconception. He got a shock when someone at Google replied with the following tweet: Dearest Ben’s Nan. Hope you’re well. In a world of billions of Searches, yours made us smile. Oh, and it’s 1998. Thank YOU Ben’s Nan brought out the human in Google on this occasion, but there is no way any company could respond personally to the million searches Google receives every fifteen seconds. So if it isn’t magic Google elves scouring the internet, how does Google succeed in so spectacularly locating the answers you want? It all comes down to the power and beauty of the algorithm Larry Page and Sergey Brin cooked up in their dorm rooms at Stanford in 1996. They originally wanted to call their new algorithm ‘Backrub’, but eventually settled instead on ‘Google’, inspired by the mathematical number for one followed by 100 zeros, which is known as a googol.

But the fascinating thing is the robustness of the Google algorithm and its imperviousness to being gamed. It is very difficult for a website to do anything on its own site that will increase its rank. It must rely on others to boost its position. If you look at the websites that Google’s page rank algorithm scores highly, you will see a lot of major news sources and university websites like Oxford and Harvard. This is because many outside websites will link to findings and opinions on university websites, because the research we do is valued by many people across the world. Interestingly this means that when anyone with a website within the Oxford network links to an external site, the link will cause a boost to the external website’s page rank, as Oxford is sharing a bit of its huge prestige (or cache of balls) with that website.

Google does not like to play God but trusts in the long run in the power of its mathematics. The internet is of course a dynamic beast, with new websites emerging every nanosecond and new links being made as existing sites are shut down or updated. This means that page ranks need to change dynamically. In order for Google to keep pace with the constant evolution of the internet, it must regularly trawl through the network and update its count of the links between sites using what it rather endearingly calls ‘Google spiders’. Tech junkies and sports coaches have discovered that this way of evaluating the nodes in a network can also be applied to other networks. One of the most intriguing external applications has been in the realm of football (of the European kind, which Americans think of as soccer). When sizing up the opposition, it can be important to identify a key player who will control the way the team plays or be the hub through which all play seems to pass.

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Everydata: The Misinformation Hidden in the Little Data You Consume Every Day by John H. Johnson

Affordable Care Act / Obamacare, Black Swan, business intelligence, Carmen Reinhart, cognitive bias, correlation does not imply causation, Daniel Kahneman / Amos Tversky, Donald Trump,, Kenneth Rogoff, labor-force participation, lake wobegon effect, Long Term Capital Management, Mercator projection, Mercator projection distort size, especially Greenland and Africa, meta analysis, meta-analysis, Nate Silver, obamacare, p-value, PageRank, pattern recognition, publication bias, QR code, randomized controlled trial, risk-adjusted returns, Ronald Reagan, selection bias, statistical model, The Signal and the Noise by Nate Silver, Thomas Bayes, Tim Cook: Apple, wikimedia commons, Yogi Berra

Washington Post website, April 22, 2015, 20. Emily Oster, “Take Back Your Pregnancy,” Wall Street Journal website, August 9, 2013, 21. “The Value of Google Result Positioning,” Chitika website, June 7, 2013, 22. “Algorithms,” Google website, accessed April 20, 2015, Here, you’ll also find a link to “The Anatomy of a Large-Scale Hypertextual Web Search Engine,” in which Sergey Brin and Larry Page presented Google. 23. “Search Engine Ranking Factors 2015,” Moz website, accessed September 1, 2015, 24. “Search Engine Ranking Factors 2015, Expert Survey and Correlation Data,” Moz website, accessed September 1, 2015, 25.

What if you’re truly unable to determine what the omitted variables are? Here’s an example. If you run a business, you would probably love to nearly double the traffic to your company’s website. After all, the number-one spot on Google search results gets almost twice the traffic that the number-two spot does.21 Depending on your business, moving up just one spot in Google rankings could bring millions of additional visitors. So how do you improve your ranking? According to Google, the engine determines search results using algorithms that rely on “more than 200 unique signals or ‘clues’ that make it possible to guess what you might really be looking for.”22 The problem is that Google doesn’t give you details about what those 200-plus signals are—perhaps because it doesn’t want to give away its competitive advantage. How do you deal with more than 200 omitted variables?

But the way in which you interpret this data could be taking a big bite out of your budget.34 NEST EGG (ON THEIR FACE) When Google announced that it was buying Nest—the thermostat company—some people thought they could make a few dollars by buying stock in the company that trades as NEST. In just one day (January 14, 2014), NEST stock went up 1900 percent. Unfortunately for the get-rich-quick crowd, NEST is not the ticker name for Nest—it’s the name for Nestor, a company that sells traffic enforcement systems. (Nest, the thermostat company, was not a publicly traded company, although as of 2015 it is owned by Alphabet, the holding company created by Google.) Nestor had gone into receivership in 2009, and had no assets. The data was accurate. The news about Google buying Nest was true. But investors didn’t check their facts and ended up buying a penny stock instead of the latest Google acquisition. (Nestor’s share price did drop—although not as quickly as it rose.

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Tools of Titans: The Tactics, Routines, and Habits of Billionaires, Icons, and World-Class Performers by Timothy Ferriss

Airbnb, Alexander Shulgin, artificial general intelligence, asset allocation, Atul Gawande, augmented reality, back-to-the-land, Ben Horowitz, Bernie Madoff, Bertrand Russell: In Praise of Idleness, Black Swan, blue-collar work, Boris Johnson, Buckminster Fuller, business process, Cal Newport, call centre, Charles Lindbergh, Checklist Manifesto, cognitive bias, cognitive dissonance, Colonization of Mars, Columbine, commoditize, correlation does not imply causation, David Brooks, David Graeber, diversification, diversified portfolio, Donald Trump, effective altruism, Elon Musk, fault tolerance, fear of failure, Firefox, follow your passion, future of work, Google X / Alphabet X, Howard Zinn, Hugh Fearnley-Whittingstall, Jeff Bezos, job satisfaction, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Kickstarter, Lao Tzu, lateral thinking, life extension, lifelogging, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Mason jar, Menlo Park, Mikhail Gorbachev, MITM: man-in-the-middle, Nelson Mandela, Nicholas Carr, optical character recognition, PageRank, passive income, pattern recognition, Paul Graham, peer-to-peer, Peter H. Diamandis: Planetary Resources, Peter Singer: altruism, Peter Thiel, phenotype, PIHKAL and TIHKAL, post scarcity, post-work, premature optimization, QWERTY keyboard, Ralph Waldo Emerson, Ray Kurzweil, recommendation engine, rent-seeking, Richard Feynman, risk tolerance, Ronald Reagan, selection bias, sharing economy, side project, Silicon Valley, skunkworks, Skype, Snapchat, social graph, software as a service, software is eating the world, stem cell, Stephen Hawking, Steve Jobs, Stewart Brand, superintelligent machines, Tesla Model S, The Wisdom of Crowds, Thomas L Friedman, Wall-E, Washington Consensus, Whole Earth Catalog, Y Combinator, zero-sum game

“When 99% of people doubt you, you’re either gravely wrong or about to make history.” “I saw this the other day, and this comes from Scott Belsky [page 359], who was a founder of Behance.” “The best way to become a billionaire is to help a billion people.” Peter co-founded Singularity University with Ray Kurzweil. In 2008, at their founding conference at NASA Ames Research Center in Mountain View, California, Google co-founder Larry Page spoke. Among other things, he underscored how he assesses projects: “I now have a very simple metric I use: Are you working on something that can change the world? Yes or no? The answer for 99.99999% of people is ‘no.’ I think we need to be training people on how to change the world.” Origins of the XPRIZE and “SuperCredibility” “The fact of the matter is I read this book, The Spirit of St.

Here are my three primary responses to online criticism: Starve it of oxygen (ignore it)—90% Pour gasoline on it (promote it)—8% Engage with trolls after too much wine (and really regret it)—2% I’m not going to cover option number three, but the first two are worth explaining. The reason that you would want to starve 90% of oxygen is because doing otherwise gives your haters extra Google juice. In other words, if you reply publicly—worst-case scenario, you put something on another site with high page rank and link to the critic—all you’re going to do is gift them powerful inbound links, increase traffic, and ensure the persistence and prominence of the piece. In some cases, I’ve had to bite my tongue for months at a time to wait for something (infuriating BS that I could easily refute) to drop off the front page or even the second page of Google results. It’s very, very hard to stay silent, and it’s very, very important to have that self-control. Rewatch the “Hoooold! Hooooooold!” scene from Braveheart. But what about pouring gasoline on 8% of the negative?

That you are working on a unique problem that people are not solving elsewhere. “When Elon Musk started SpaceX, they set out the mission to go to Mars. You may agree or disagree with that as a mission statement, but it was a problem that was not going to be solved outside of SpaceX. All of the people working there knew that, and it motivated them tremendously.” TF: Peter has written elsewhere, “The next Bill Gates will not build an operating system. The next Larry Page or Sergey Brin won’t make a search engine. And the next Mark Zuckerberg won’t create a social network. If you are copying these guys, you aren’t learning from them.” ✸ How would you reply to someone who says that your position on college and higher education is hypocritical since you, yourself, went to Stanford for both undergraduate and law school? [Context: Many people see Peter as “anti-college” due to his Thiel Fellowship, which “gives $100,000 to young people who want to build new things instead of sitting in a classroom.”]