PageRank

In the Plex: How Google Thinks, Works, and Shapes Our Lives
by Steven Levy
Published 12 Apr 2011

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 Google.com: 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.

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Cutts noticed that one nasty site used some clever methods to game Google’s blocking system and score high in search results. “It was an eye-opening moment,” says Cutts. “Page-Rank and link analysis may be spam-resistant, but nothing is spam-proof.” The problem went far beyond porn. Google had won its audience in part because it had been effective in eliminating search spam. But now that Google was the dominant means of finding things on the Internet, a high ranking for a given keyword could drive millions of dollars of business to a site. 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.

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But when he told his boss, Dow Jones reasserted itself and hired a lawyer to review the patent, which it refiled in February 1997. (Stanford University would not file its patent for Larry Page’s PageRank system until January 1998.) Nonetheless, Dow Jones did nothing with Li’s system. “I tried to convince them it was important, but their business had nothing to do with Internet search, so they didn’t care,” he says. Robin Li quit and joined the West Coast search company called Info-seek. In 1999, Disney bought the company and soon thereafter Li returned to China. It was there in Beijing that he would later meet—and compete with—Larry Page and Sergey Brin. Page and Brin had launched their project as a stepping-stone to possible dissertations.

Nine Algorithms That Changed the Future: The Ingenious Ideas That Drive Today's Computers
by John MacCormick and Chris Bishop
Published 27 Dec 2011

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.

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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 its title suggests, this paper does much more than describe PageRank. It is, in fact, a complete description of the Google system as it existed in 1998. But buried in the technical details of the system is a description of what may well be the first algorithmic gem to emerge in the 21st century: the PageRank algorithm.

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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.

The Googlization of Everything:
by Siva Vaidhyanathan
Published 1 Jan 2010

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.

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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.

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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.

Algorithms of Oppression: How Search Engines Reinforce Racism
by Safiya Umoja Noble
Published 8 Jan 2018

The lawsuit by Search King and PR Network against Google alleged that Google decreased the page rank of its clients in a direct effort to annihilate competition.13 Since Bob Massa, the president of Search King and PR Ad Network, issued a statement against Google’s biased ranking practices, Google’s business practices have been under increased scrutiny, both in the U.S. and globally. Why Public Policy Matters Given the controversies over commercial, cultural, and ethnic representations of information in PageRank, the question that the Federal Trade Commission might ask today, however, is whether search engines such as Google should be regulated over the values they assign to racial, gendered, and sexual identities, as evidenced by the types of results that are retrieved.

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See also Prodigy Flaherty, Colin, 104 Fleisher, Peter, 128 Ford, Thomas E., 89 Foskett, Anthony Charles, 136 Fouché, Rayvon, 108 France, “Charter of good practices on the right to be forgotten . . . ,” 121 FreePorn.com blog, 87 free speech and free speech protections, 46, 57, 172; corporate, 143 Fuchs, Christian, 162 Fujioka, Yuki, 89 Fuller, Matthew, 54 Furner, Jonathan, 135–36 Galloway, Alex, 148 Gandy, Oscar Jr., 85, 125 Gardner, Tracey A., 59 Gillespie, Tarleton, 26 Golash-Boza, Tanya, 80 Gold, Danny, 120 Goodman, Ellen P., 130 Google: apologies, 6; archives of non-public search results, 122, 129; competitors blocked, 56; critiques of, 28, 33, 36–37, 56, 163–64; data storage policies, 125–29; “diversity problems,” 64–65, 69, 163; mainstream corporate news conglomerates, 49; method of rebuilding index, 189n38; near-monopoly status, 34–36, 86, 156, 188n10, 198n32; privacy policy, 129–30; right to transparency of data removal, 130–31; surplus labor through free use, 162; underemployment of Black women, 69; unlawful page removal policy, 42; wage gap, 2 Google AdWords, 86–87, 106, 116; ‘black girls’ search results, 68, 86–87; cost per click (CPC), 46–47 Google bombing, 46–47, 189n47; George W. Bush and miserable failure, 48; Santorum, Rick, 47, 189n50 Google Books, 50, 86, 191n77; “fair use” ruling, 157 Google Glass: “Glassholes,” 164; neocolonial project, 164 Google Image Labeler, 188n27 Google Image Search, 6 Google Instant, X-rated front-page results, 155 Google Maps, search for “N*gger” yields White House, 6–8 Google PageRank, 11, 38–42, 46–47, 54, 158, 189n48 Google Search, 3–4, 86; algorithm control, 179; autocorrection to “himself,” 142; autosuggestions, 6, 11, 15, 20–21, 24; Black feminist perspective, 30–31; commercial environment influence, 24, 179; computer science for decision-making, 148–49; consumer protection, 188n10; disclaimer, 31, 42, 159; disclaimer for search for “Jew,” 44, 88n24, 143, 189n42; filters for advertisers, 45; front organizations for hate-based groups, 116–17; glitch tagging African Americans as “apes,” 6; image search, 6, 20–23, 191n73; prioritization of its properties, 162; priority ranking, 18, 32, 42, 63, 65, 118, 155, 158; public resource, 50; response to stereotypes, 82; sexism and discrimination, 15–16.

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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.

Understanding search engines: mathematical modeling and text retrieval
by Michael W. Berry and Murray Browne
Published 15 Jan 2005

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.

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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.

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If p — 0.85 is the fraction of time that the random walk (or Markov chain) follows an outlink and (1 — p) = 0.15 is the fraction of time that an arbitrary webpage is chosen (independent of the link structure of the web graph), then the derived PageRank vector x or normalized left-hand eigenvector of the modified stochastic (Google) matrix can be shown (with 4 significant decimal digits) to be Here, e? — (I 1 1 1 1) and x^ — e?/5. If the set of webpages judged relevant to a user's query is {1,2,3}, then webpage 3 would be ranked as the most relevant, followed by webpages 1 and 2, respectively. Figure 7.3: PageRank example using the 5-node graph from Figure 7.1. 7.2. PageRank Method 87 A rank-1 perturbation8 of the n x n stochastic matrix A defined by models random surfing of the Web in that not all webpages are accessed via outlinks from other pages.

Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy
by George Gilder
Published 16 Jul 2018

By every measure, the most widespread, immense, and influential of Markov chains today is Google’s foundational algorithm, PageRank, which encompasses the petabyte reaches of the entire World Wide Web. Treating the Web as a Markov chain enables Google’s search engine to gauge the probability that a particular Web page satisfies your search.5 To construct his uncanny search engine, Larry Page paradoxically began with the Markovian assumption that no one is actually searching for anything. His “random surfer” concept makes Markov central to the Google era. PageRank treats the Internet user as if he were taking a random walk across the Web, which we users know is not what we are doing.

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Indeed, in a sense omega is the crystalized, concentrated essence of mathematical creativity.” Chapter 3: Google’s Roots and Religions 1. http://citeseer.ist.psu.edu/stats/articles. I counted the Stanford and Google papers. 2. A lucid explanation of PageRank and search technology is John MacCormick, Nine Algorithms that Changed the Future: The Ingenious Ideas that Drive Today’s Computers (Princeton: Princeton University Press, 2012), 10–37. 3. Larry Page, hey, virtually all his quotes are accessible on Google! 4. David Gelernter, Mirror Worlds (New York: Oxford University Press, 1992). 5. Page, ibid. 6. If you prefer the text version beyond all the Google search resources on the saga of its inventors and founders, it is lavishly there in Steven Levy’s In the Plex: How Google Thinks, Works, and Shapes our Lives (New York: Simon & Schuster, 2011), or in the silken New Yorker prose of media-savvy Ken Auletta, Googled: The End of the World as We Know It (New York: Penguin Books, 2010).

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Although still based in New York, Blockstack chose the Computer Museum in Mountain View, minutes from the Google campus, for its coming-out party: the Blockstack Summit 2017. Its marketing chief, Patrick Stanley, asked me to speak on “Life after Google.” A little more than two weeks before, Ali’s doctoral committee at Princeton had finally approved his dissertation, “Trust-to-Trust Design of a New Internet.” Composed with Shea’s help, it was comparable in its scope and ambition to Larry Page’s “PageRank” thesis at Stanford. Ali makes the case for a new Internet architecture and then declares that, in prototype, it has already been in place for three years, requiring only 44,344 lines of Python software language code.

The Innovators: How a Group of Inventors, Hackers, Geniuses and Geeks Created the Digital Revolution
by Walter Isaacson
Published 6 Oct 2014

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.

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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.

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In addition to the sources cited below, this section is based on my interview and conversations with Larry Page; Larry Page commencement address at the University of Michigan, May 2, 2009; Larry Page and Sergey Brin interviews, Academy of Achievement, Oct. 28, 2000; “The Lost Google Tapes,” interviews by John Ince with Sergey Brin, Larry Page, and others, Jan. 2000, http://www.podtech.net/home/?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.

I'm Feeling Lucky: The Confessions of Google Employee Number 59
by Douglas Edwards
Published 11 Jul 2011

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.

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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. It was just the sort of geeky feature engineers loved, because it provided an objective data point from which to form an opinion. All happiness and joy. 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.

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"Once we have an index," Craig continued, "we assign a rank to each page based on its importance with our PageRank algorithm. PageRank is Google's secret sauce." "Secret sauce?" I leaned forward to learn what we had that was better than all the other search engines that our founders seemed so quick to dismiss. "PageRank looks at all the pages on the web and assigns a value to them based on who else links to them. The more credible the sites linking to them, the higher the PageRank. That's the first half of the recipe." I wrote "pageRank" under the Ben Franklin spectacles and drew an oval around it. It looked a little like a clown mouth, so I sketched a skull around it and added some Bozo hair on the sides.

The Filter Bubble: What the Internet Is Hiding From You
by Eli Pariser
Published 11 May 2011

But when they released the beta site into the wild, the traffic chart went vertical. Google worked—out of the box, it was the best search site on the Internet. 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.

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But at times, this attitude can verge on a “Guns don’t kill people, people do” mentality—a willful blindness to how their design decisions affect the daily lives of millions. That Facebook’s button is named Like prioritizes some kinds of information over others. That Google has moved from PageRank—which is designed to show the societal consensus result—to a mix of PageRank and personalization represents a shift in how Google understands relevance and meaning. This amorality would be par for the corporate course if it didn’t coincide with sweeping, world-changing rhetoric from the same people and entities. Google’s mission to organize the world’s information and make it accessible to everyone carries a clear moral and even political connotation—a democratic redistribution of knowledge from closed-door elites to the people.

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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. The open-source operating system Linux, for example, is actually more secure and harder to penetrate with a virus than closed ones like Microsoft’s Windows or Apple’s OS X.

Googled: The End of the World as We Know It
by Ken Auletta
Published 1 Jan 2009

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. Google offered to advertisers a program called AdWords, which allowed potential advertisers to bid to place small text ads next to the results for key search words.

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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.

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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.

Mining of Massive Datasets
by Jure Leskovec , Anand Rajaraman and Jeffrey David Ullman
Published 13 Nov 2014

Haveliwala, “Efficient computation of PageRank,” Stanford Univ. Dept. of Computer Science technical report, Sept., 1999. Available as http://infolab.stanford.edu/~taherh/papers/efficient-pr.pdf [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.”

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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.

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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.

The Art of SEO
by Eric Enge , Stephan Spencer , Jessie Stricchiola and Rand Fishkin
Published 7 Mar 2012

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 (http://infolab.stanford.edu/~backrub/google.html) 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.

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Your competitors are obviously ranking based on the strength of their links, so researching the sources of those links can provide insight into where they derive that value. PageRank of the domain Google does not publish a value for the PageRank of a domain (the PageRank values you can get from the Google Toolbar are for individual pages), but many believe that Google does calculate a Domain PageRank value. Most SEOs choose to use the PageRank of the website’s home page as an estimate of Domain PageRank. You can look at the PageRank of the home page to get an idea of whether the site is penalized and to get a crude sense of the potential value of a link from the site.

Programming Collective Intelligence
by Toby Segaran
Published 17 Dec 2008

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.

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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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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.

Artificial Unintelligence: How Computers Misunderstand the World
by Meredith Broussard
Published 19 Apr 2018

“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.

…

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. However, more and more people went online, content swelled, and Google began to make money based on selling advertising on web pages. The search-ranking model was taken from academic publishing; the advertising model was taken from print publishing. As people learned how to game the PageRank algorithm to elevate their position in search results, popularity became a kind of currency on the web. Google engineers had to add factors to search so that spammers wouldn’t game the system.

Our Final Invention: Artificial Intelligence and the End of the Human Era
by James Barrat
Published 30 Sep 2013

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. A score of 2 means twice the quality as score of 1, and so on. Many variables account for “quality.” Size is important—bigger Web sites are better, and so are older ones.

…

People always make the assumption: Memepunks, “Google A.I. a Twinkle in Larry Page’s Eye,” May 26, 2006, http://memepunks.blogspot.com/2006/05/google-ai-twinkle-in-larry-pages-eye.html (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, http://www.nytimes.com/2012/04/15/technology/google-is-fined-for-impeding-us-inquiry-on-data-collection.html (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.

…

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, http://ppcblog.com/how-google-works/ (accessed October 10, 2011). mankind’s primary tool: Schwartz, Evan, “The Mobile Device is Becoming Humankind’s Primary Tool,” Technology Review, November 29, 2010, http://www.technologyreview.com/news/421826/the-mobile-device-is-becoming-humankinds-primary-tool-infographics-feature/ (accessed December 4, 2011). you merely think of a question: Carr, Nicholas, “When Google Grows Up,” Forbes.com, January 11, 2008, http://www.forbes.com/2008/01/11/google-carr-computing-tech-enter-cx_ag_0111computing.html (accessed March 10, 2011).

What Algorithms Want: Imagination in the Age of Computing
by Ed Finn
Published 10 Mar 2017

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.

…

These systems present a limited space of public governance (e.g., allowing Facebook users to promote particular causes through “liking” them), but their seemingly democratic interfaces are facades for the much deeper edifice of algorithmic arbitrage. Facebook, Google, Netflix, and the rest do not often engage in overt censorship, but rather algorithmically curate the content they wish us to see, a process media scholar Ganaele Langlois terms “the management of degrees of meaningfulness and the attribution of cultural value.”54 Like the PageRank algorithm and the many interventions Google makes to prevent its exploitation by anyone other than Google, these systems for arbitrage mix user empowerment with strict informational control to encourage particular behaviors and hide the margins and rough edges away.

…

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.

Digital Wars: Apple, Google, Microsoft and the Battle for the Internet
by Charles Arthur
Published 3 Mar 2012

(i), (ii), (iii) see also Microsoft Bang & Olufsen (i) Bartz, Carol (i) Basillie, Jim (i) Battelle, John (i), (ii) Bauer, John (i) BBC iPlayer (i) Bechtolsheim, Andy (i), (ii) Beckham, David and Victoria (i) BenQ (i) Berg, Achim (i) Berkowitz, Steve (i), (ii) Best Buy (i), (ii), (iii), (iv) Bezos, Jeff (i) Bilton, Nick (i) BlackBerry (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix), (x), (xi) BlackBerry Messenger (i) BlackBerry Storm (i), (ii) Block, Ryan (i) Blodget, Henry (i) Bloomberg (i), (ii) BMG (i) Boeing (i) Boies, David (i), (ii) Bondcom (i) Bountii.com (i) Bowman, Douglas (i) Bracken, Mike (i) Brass, Richard (i) Brin, Sergey (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix) see also Google; Page, Larry Bronfman, Edgar (i) Brunner, Robert (i) Buffett, Warren (i) BusinessWeek (i), (ii), (iii), (iv), (v), (vi), (vii) Bylund, Anders (i) Carr, Nick (i) Chafkin, Max (i) Chambers, Mike (i) China (i), (ii), (iii) and Apple (i), (ii), (iii), (iv) and Google (i), (ii), (iii), (iv), (v), (vi), (vii) and Microsoft (i) mobile web browsing (i) China Mobile (i), (ii), (iii) China Unicom (i), (ii) Chou, Peter (i) CinemaNow (i) Cingular and the iPhone (i), (ii), (iii), (iv), (v) and the ROKR (i), (ii) Cisco Systems (i) ClearType (i) Cleary, Danika (i), (ii) CNET (i), (ii), (iii) Colligan, Ed (i), (ii), (iii) Compaq (i), (ii) ComScore (i), (ii), (iii) Cook, Tim (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix), (x) Creative Strategies (i) Creative Technologies (i), (ii), (iii), (iv), (v), (vi), (vii) Creative Labs (i), (ii) Cringely, Robert X (i), (ii), (iii), (iv) Crothall, Geoffrey (i) Daisey, Mike (i), (ii) Dalai Lama (i) Danyong, Sun (i) Daring Fireball (i), (ii) Deal, Tim (i) Dean, Jeff (i) DEC (i) Dediu, Horace (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix), (x), (xi), (xii), (xiii), (xiv) ’Dediu’s Law’ (i) Dell Computer (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix), (x), (xi), (xii), (xiii) Dell DJ (i), (ii), (iii) Dell, Michael (i), (ii), (iii), (iv) Design Crazy (i) Deutschman, Alan (i) Digital Equipment Corporation (i) Divine, Jamie (i) Dogfight (i), (ii) Dowd, Maureen (i) Drance, Matt (i), (ii), (iii), (iv) Drummond, David (i), (ii) Dunn, Jason (i) EarthLink (i), (ii) eBay (i) Edwards, Doug (i), (ii), (iii), (iv), (v), (vi), (vii), (viii) Eisen, Bruce (i) Electronic Arts (i) Elop, Stephen (i), (ii), (iii), (iv) EMI (i) eMusic (i) Engadget (i), (ii) Ericsson (i) European Patent Office (i) Evangelist, Mike (i) Evans, Benedict (i) Evslin, Tom (i) Facebook (i), (ii), (iii), (iv), (v), (vi) Fadell, Tony (i), (ii), (iii) Fester, Dave (i), (ii), (iii), (iv) FingerWorks (i), (ii) Fiorina, Carly (i), (ii) Flash (i), (ii), (iii), (iv), (v), (vi) Flowers, Melvyn (i), (ii) Foley, Mary Jo (i), (ii), (iii) Forrester Research (i), (ii) Forstall, Scott (i), (ii), (iii), (iv) Fortune (i), (ii), (iii), (iv), (v) Foundem (i) Foxconn Technology (i), (ii), (iii) Fried, Ina (i) Galaxy Tab (i), (ii) Galvin, Chris (i) Gartenberg, Michael (i), (ii), (iii), (iv) Gartner (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix), (x), (xi) Gates, Bill (i), (ii), (iii) SPOT watch (i) and Steve Jobs (i), (ii), (iii) see also Ballmer, Steve; Microsoft; Sculley, John Gateway (i), (ii) Gemmell, Matt (i) Ghemawat, Sanjay (i) Gibbons, Tom (i) Gilligan, Amy K (i) Gladwell, Malcolm (i), (ii), (iii) Glass, Ira (i) Glazer, Rob (i) Golvin, Charles (i), (ii) Google (i), (ii), (iii) ‘40 shades of blue’ (i), (ii) AdSense (i) and advertising (i), (ii), (iii) AdWords (i), (ii), (iii), (iv), (v), (vi) Android (i), (ii), (iii), (iv) 4G patent auction (i) China, manufacturing in (i), (ii) and Flash (i) and the iPhone (i), (ii) and Microsoft (i), (ii), (iii), (iv) Oracle patent dispute (i) origins of (i), (ii) and standardization (i) and tablets (i), (ii), (iii) antitrust investigation (i) and AOL (i) Bigtable (i), (ii) Buzz (i) Checkout (i) Chinese market (i), (ii), (iii), (iv) google.cn (i) ’Great Firewall’ (i) hacking (i) Chrome (i), (ii), (iii), (iv), (v) Compete (i) confrontation with Microsoft (i), (ii) data, importance of (i) Gmail (i), (ii) Goggles (i) Google Now (i) Google Play (i), (ii) Google+ (i), (ii) hiring policy (i), (ii) Instant (i) Maps (i), (ii), (iii), (iv) location data (i), (ii) users, loss of (i) vector vs raster images (i) market capitalization (i) Music All Access (i) Nest (i) and the New York terrorist attacks 2001 (i) Overture lawsuit (i) PageRank (i), (ii) and porn (i), (ii) profitability of (i) public offering (i) QuickOffice (i) and selling (i) Street View (i), (ii) and Yahoo (i), (ii), (iii), (iv) see also Brin, Sergey; Kordestani, Omid; Meyer, Marissa; Page, Larry; Schmidt, Eric; Silverstein, Craig Googled (i) GoTo.com (i), (ii), (iii) Gou, Terry (i) Grayson, Ian (i) Greene, Jay (i), (ii), (iii), (iv) Griffin, Paul (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix), (x) Griffin Technology (i), (ii) Grokster (i), (ii) Gross, Bill (i), (ii) Gruber, John (i), (ii), (iii), (iv), (v) Guardian (i), (ii), (iii) Gundotra, Vic (i), (ii) Hachamovitch, Dean (i) Handango (i) Handspring (i) Harlow, Jo (i) Hase, Koji (i), (ii), (iii) Hauser, Hermann (i) Hedlund, Marc (i) Heiner, Dave (i) Heins, Thorsten (i) Hewlett-Packard (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix), (x), (xi), (xii), (xiii), (xiv), (xv) Hitachi (i) Hockenberry, Craig (i) Hölzle, Urs (i), (ii), (iii), (iv) Hotmail (i) HTC (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix) Huawei (i) Hwang, Suk-Joo (i) I’m Feeling Lucky (i) IBM (i), (ii), (iii), (iv), (v), (vi) IDC (i), (ii), (iii), (iv) Idealab (i) i-mode (i) Inktomi (i), (ii), (iii) Instagram (i) Intel (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix) Intellectual Ventures (i) Iovine, Jimmy (i) iRiver (i), (ii), (iii) Ive, Jonathan (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix) Jackson, Thomas Penfield (i), (ii), (iii) Java (i), (ii) Jefcoate, Kevin (i) Jha, Sanjay (i) Jintao, Hu (i) Jobs, Steve (i), (ii), (iii) and Bill Gates (i), (ii) death (i) departure from Apple (i) see also Apple; Cook, Tim; Forstall, Scott; Ive, Jonathan; Schiller, Phil Johnson, Kevin (i) Johnson, Ron (i) Jones, Nick (i) Joswiak, Greg (i), (ii) Jupiter Research (i), (ii), (iii), (iv) Kallasvuo, Oli-Pekka (i), (ii), (iii), (iv), (v) Khan, Irene (i) Khan, Sabih (i) King, Brian (i), (ii) King, Shawn (i) Kingsoft (i) Kleinberg, Jonathan (i) Knook, Pieter (i), (ii) and competition from China (i) and Microsoft’s antitrust judgment (i) and Pink (i), (ii) and Steve Ballmer (i) and Windows Mobile (i), (ii), (iii), (iv), (v) and the Xbox (i) and Zune (i), (ii), (iii), (iv), (v) Komiyama, Hideki (i) Kordestani, Omid (i), (ii), (iii) Kornblum, Janet (i) Krellenstein, Marc (i) Laakmann, Gayle (i), (ii), (iii), (iv) Lawton, Chris (i) Lazaridis, Mike (i), (ii), (iii), (iv) Lees, Andy (i), (ii), (iii), (iv), (v), (vi), (vii), (viii) Lenovo (i), (ii) LG (i), (ii), (iii), (iv), (v), (vi) LimeWire (i), (ii) LinkExchange (i), (ii) Linux (i), (ii), (iii), (iv), (v), (vi), (vii) Lodsys (i), (ii) Lotus (i), (ii) Lucovsky, Marc (i) Lynn, Matthew (i) Ma, Bryan (i) MacroSolve (i), (ii) Madrigal, Alexis (i) Mapquest (i) Mayer, Marissa (i), (ii), (iii), (iv), (v) Media Metrix (i), (ii), (iii) MeeGo (i) Mehdi, Yusuf (i), (ii), (iii), (iv), (v), (vi), (vii) Meisel, Ted (i) Microsoft antitrust trial (i), (ii) APIs (i) company split (i) impact of (i) and Apple QuickTime (i) Azure (i) Bing Maps (i) ’Cashback’ (i) China, manufacturing in (i) Chinese market (i) censorship (i) pirating of software (i) confrontation with Google (i), (ii) Courier (i), (ii) Danger (i), (ii), (iii) acquisition by Microsoft (i), (ii), (iii) disintegration of the team (i), (ii), (iii) digital rights management (DRM) of music (i) DirectX (i) and Facebook (i) horizontal system (i) Internet Explorer (i), (ii) Janus (i), (ii), (iii), (iv) ’Keywords’ (i) market capitalization (i) and Netscape (i), (ii) and Nokia (i), (ii), (iii), (iv), (v), (vi), (vii) ’Pink’ (i), (ii) announcement (i) failure of (i) PlaysForSure (i), (ii), (iii) failure of (i) problems with (i), (ii), (iii) rebranding and end (i) and the Zune (i) Portable Media Center (PMC) (i) potential acquisition of Overture (i), (ii), (iii) ’roadmap’ (i) search (i), (ii), (iii), (iv), (v) and antitrust (i) Bing (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix), (x) launch and crash (i) and Office (i) page design (i) profitability of (i) Project Underdog (i), (ii), (iii), (iv) rebranding (i) Surface tablets (i), (ii), (iii) and tablets (i), (ii), (iii), (iv) Flash (i) Windows and ARM (i), (ii) WebTV (i) Windows (i), (ii) Windows Media Audio (i), (ii) Windows Media Player (i), (ii), (iii) Windows Mobile (i), (ii), (iii), (iv), (v), (vi) and Android (i), (ii) decline (i) peak (i) Windows Phone (i), (ii), (iii), (iv), (v) and tablets (i) Windows RT (i) Windows Server (i), (ii), (iii) Xbox (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix) Xbox 360 (ii) Xbox Live Music Marketplace (i) Xbox Music (i) and the Zune (i) and Yahoo search (i), (ii) Zune (i), (ii), (iii), (iv), (v) Christmas 2006 (i) demise of (i) failings of (i) market position (i), (ii) and music in the cloud (i), (ii) and the Xbox (i) Zune Music Store (i), (ii) see also Allard, J; Ballmer, Steve; Gates, Bill; Knook, Pieter; Sculley, John; Sinofsky, Steve; Spolsky, Joel Milanesi, Carolina (i), (ii), (iii) Miller, Trudy (i) Mobile World Congress (i) Morris, Doug (i), (ii) Moss, Ken (i), (ii) Mossberg, Walt (i) Motorola (i), (ii), (iii), (iv), (v), (vi) and Android (i) and the iPhone (i) and iTunes (i), (ii) Motorola Mobility (MMI) (i), (ii) Q phone (i) ROKR (i), (ii), (iii) Mozilla Firefox (i), (ii) Mudd, Dennis (i) Mundie, Craig (i) MusicMatch (i), (ii), (iii), (iv), (v), (vi), (vii) MusicNet (i), (ii) Myerson, Terry (i) Myhrvold, Nathan (i) Nadella, Satya (i) Namco (i) Napster (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix), (x), (xi) Narayen, Shantanu (i) Navteq (i), (ii) Netscape (i), (ii), (iii), (iv), (v), (vi), (vii) and Google (i), (ii), (iii), (iv) and Windows (i) (ii), (iii) New York Times (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix) New Yorker (i), (ii) NeXT Computer (i), (ii), (iii), (iv), (v) Nintendo (i), (ii), (iii) and 4G (i) Nokia (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix), (x), (xi), (xii) Apple patent dispute (i), (ii) Communicator (i) and the iPhone (i), (ii), (iii), (iv), (v), (vi), (vii), (viii) Lumia (i), (ii) and Microsoft (i), (ii), (iii), (iv), (v), (vi), (vii) N91 (i) and Navteq (i), (ii) and Steve Ballmer (i) and Symbian (i), (ii), (iii), (iv), (v), (vi) touchscreen development (i), (ii) Norlander, Rebecca (i) Norman, Don (i), (ii), (iii) Northern Light (i) Novell (i), (ii), (iii) NPD Group (i), (ii), (iii) O2 (i) Observer (i) Ohlweiler, Bob (i), (ii), (iii), (iv), (v), (vi), (vii) Ojanpera, Tero (i) Open Handset Alliance (OHA) (i) Oracle (i), (ii), (iii), (iv) Overture (i), (ii), (iii) acquisition by Yahoo (i) Google lawsuit (i), (ii) potential acquisition by Microsoft (i), (ii), (iii) Ozzie, Ray (i), (ii) PA Semi (i) Page, Larry (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix), (x), (xi), (xii) see also Brin, Sergey; Google Palm (i), (ii), (iii), (iv) acquisition by Hewlett-Packard (i) Pilot (i) Pre (i) profitability (i), (ii) Treo (i), (ii) Pandora (i) Partovi, Ali (i) Parvez, Shaun (i), (ii) Payne, Chris (i), (ii), (iii), (iv), (v), (vi) see also Microsoft PC World (i) PeopleSoft (i) Pepsi (i), (ii), (iii) Peterschmidt, David (i) Peterson, Matthew (i) ’phablets’ (i) Pixar (i), (ii), (iii) Placebase (i) PressPlay (i), (ii), (iii) Qualcomm (i) Quanta (i) Raff, Shivaun (i), (ii), (iii) Real Networks (i), (ii), (iii) Helix (i) Red Hat (i), (ii) Reindorp, Jason (i) Research In Motion (RIM) (i), (ii), (iii), (iv), (v), (vi), (vii), (viii) and Android (i), (ii), (iii) and Bing (i), (ii) and the iPhone (i), (ii), (iii), (iv) PlayBook (i), (ii), (iii), (iv) renaming to BlackBerry (i) Rockstar Bidco (i) writeoffs (i) see also BlackBerry Rockstar Bidco (i), (ii) Rosenberg, Scott (i) Rubin, Andy (i), (ii), (iii), (iv), (v) and Flash (i) and Google phones (i) and Motorola Mobility (i), (ii) and touch-based devices (i) see also Google; Microsoft Rubinstein, Jon (i), (ii), (iii), (iv), (v), (vi), (vii) Samsung (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix), (x), (xi), (xii), (xiii), (xiv), (xv), (xvi), (xvii) SanDisk (i) SAP (i) Sasse, Jonathan (i) Sasser, Cabel (i) Savander, Niklas (i) Schiller, Phil (i), (ii), (iii), (iv), and apps (i) and the iPhone (i) and iPod nano (i), (ii) and Wal-Mart (i) and 4G (i) Schmidt, Eric (i), (ii), (iii), (iv), (v), (vi), (vii) and Android (i), (ii) and AOL (i) and Google Goggles (i) and the iPhone (i), (ii), (iii), (iv) Schmitz, Rob (i) Schoeben, Rob (i) Schofield, Jack (i) Sculley, John (i), (ii), (iii), (iv), (v), (vi) Search (i) SEC (i), (ii) Second Coming of Steve Jobs, The (i) Sega (i) Shaw, Frank (i) Siemens (i) Sigman, Stan (i), (ii), (iii), (iv) Silverstein, Craig (i) Sinofsky, Steven (i), (ii), (iii), (iv), (v), (vi), (vii) see also ARM architecture; Microsoft Slashdot (i), (ii) Snapchat (i) Sony (i), (ii), (iii), (iv), (v), (vi) and digital rights management (DRM) (i) MiniDisc (i), (ii), (iii) PressPlay (i), (ii) Rockstar Bidco (i) Walkman (i), (ii), (iii) Sony Ericsson (i), (ii), (iii), (iv), (v), (vi), (vii) SoundJam (i) Spindler, Michael (i) Spolsky, Joel (i), (ii), (iii) Spotify (i) Sprint (i) Stac Electronics (i) standards-essential patents (SEPs) (i) Starbucks (i) StatCounter (i) Stephens, Mark (i), (ii) Stringer, Howard (i) Sullivan, Danny (i) Sun Microsystems (i), (ii), (iii), (iv), (v), (vi), (vii) Super Monkey Ball (i) Symbian (i), (ii), (iii), (iv), (v) apps (i), (ii) and Flash (i) licencing (i) loss of market share (i), (ii), (iii), (iv) Tao, Shi (i) Telefónica (i) Thompson, Rick (i) Time Warner (i), (ii) T-Mobile (i), (ii) TomTom (i) Topolsky, Joshua (i) Toshiba (i), (ii), (iii), (iv), (v), (vi) traffic acquisition costs (TACs) (i), (ii) Twitter (i), (ii), (iii), (iv) Universal (i), (ii), (iii) US Patent Office (i) Usenet (i) Vanjoki, Anssi (i) Varian, Hal (i) Verizon (i), (ii), (iii), (iv), (v) Virgin Electronics (i), (ii) Visa (i) Vodafone (i), (ii), (iii) Vogelstein, Fred (i), (ii), (iii), (iv), (v), (vi), (vii), (viii) Wall Street Journal (i), (ii) Wal-Mart (i), (ii), (iii), (iv) Wapner, Scott (i) Warner Music (i) Warren, Todd (i) Washington Post (i), (ii) Watsa, Prem (i) Waze (i) WebM (i), (ii) Wilcox, Joe (i), (ii) Wildstrom, Steve (i), (ii), (iii), (iv) Williamson, Richard (i) Windsor, Richard (i) Winfrey, Oprah (i), (ii) Wired (i), (ii), (iii), (iv), (v), (vi) Wojcicki, Susan (i) WordPerfect (i), (ii) Xiaomi (i) Yahoo (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix) Flickr (i) and Google (i), (ii), (iii), (iv) and GoTo (i) and Inktomi (i) and LinkExchange (i) localization (i) and Microsoft (i), (ii) and Overture (i), (ii) Tao, Shi (i) Yandex (i) Yang, Jerry (i), (ii), (iii), (iv) see also Yahoo YouTube (i), (ii), (iii), (iv), (v), (vi), (vii) Zander, Ed (i), (ii) ZTE (i), (ii), (iii) Zuckerberg, Mark (i), (ii) see also Facebook Publisher’s note Every possible effort has been made to ensure that the information contained in this book is accurate at the time of going to press, and the publishers and author cannot accept responsibility for any errors or omissions, however caused.

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This ebook published in 2014 by Kogan Page Limited 2nd Floor, 45 Gee Street London EC1V 3RS UK www.koganpage.com © 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!

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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.

Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots
by John Markoff
Published 24 Aug 2015

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. Google initially began by collecting and organizing human knowledge and then making it available to humans as part of a glorified Memex, the original global information retrieval system first proposed by Vannevar Bush in the Atlantic Monthly in 1945.11 As the company has evolved, however, it has started to push heavily toward systems that replace rather than extend humans. Google’s executives have obviously thought to some degree about the societal consequences of the systems they are creating.

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(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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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! for almost a half-billion dollars. By then, he was a very wealthy Silicon Valley technologist looking for interesting projects. In 2006 he backed both Ng and Salisbury and hired Salisbury’s students to join Willow Garage, a laboratory he’d already created to facilitate the next generation of robotics technology—like designing driverless cars.

The Internet Is Not the Answer
by Andrew Keen
Published 5 Jan 2015

It’s something that has only occurred in one or two places in the whole course of human history,”77 Moritz says in describing this personal revolution engineered by data factories like Google, Facebook, LinkedIn, Instagram, and Yelp. 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.

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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.

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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. It was processing around 40,000 search queries each second, which computes into 3.5 billion daily searches or 1.2 trillion annual searches.

Thinking Machines: The Inside Story of Artificial Intelligence and Our Race to Build the Future
by Luke Dormehl
Published 10 Aug 2016

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.

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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 Eterni.me 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!

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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. Instructed to maximise the elements found in each image, Deep Dream created trippy flights of fancy. Given an image and asked to classify it and then add more detail, the neural network became trapped in strange, fascinating feedback loops. Clouds were associated with birds, and Deep Dream sought to make them ever more ‘birdlike’. A photograph of a clear sky would rapidly be filled with Google’s idealised avians, as though the world’s most powerful search engine had suddenly decided to become a graffiti artist.

Bold: How to Go Big, Create Wealth and Impact the World
by Peter H. Diamandis and Steven Kotler
Published 3 Feb 2015

v=9pmPa_KxsAM. 40 Joann Muller, “No Hands, No Feet: My Unnerving Ride in Google’s Driverless Car,” Forbes, March 21, 2013, http://www.forbes.com/sites/joannmuller/2013/03/21/no-hands-no-feet-my-unnerving-ride-in-googles-driverless-car/. 41 Robert Hof, “10 Breakthrough Technologies 2013: Deep Learning,” MIT Technology Review, April 23, 2013, http://www.technologyreview.com/featuredstory/513696/deep-learning/. 42 Steven Levy, “Google’s Larry Page on Why Moon Shots Matter,” Wired, January 17, 2013, http://www.wired.com/2013/01/ff-qa-larry-page/all/. 43 Larry Page, “Beyond Today—Larry Page—Zeitgeist 2012.” 44 Larry Page, “Google+: Calico Announcement,” Google+, September 2013, https://plus.google.com/+LarryPage/posts/Lh8SKC6sED1. 45 Harry McCracken and Lev Grossman, “Google vs. Death,” Time, September 30, 2013, http://time.com/574/google-vs-death/. 46 Jason Calacanis, “#googlewinseverything (part 1),” Launch, October 30, 2013, http://blog.launch.co/blog/googlewinseverything-part-1.html. PART THREE: THE BOLD CROWD Chapter Seven: Crowdsourcing: Marketplace of the Rising Billion 1 Netcraft Web Server Survey, Netcraft, Accessed June 2014, http://news.netcraft.com/archives/category/web-server-survey/. 2 AI with Jake Nickell and Jacob DeHart. 3 Jeff Howe, “The Rise of Crowdfunding,” Wired, 2006, http://archive.wired.com/wired/archive/14.06/crowds_pr.html. 4 Rob Hof, “Second Life’s First Millionaire,” Bloomberg Businessweek, November 26, 2006, http://www.businessweek.com/the_thread/techbeat/archives/2006/11/second_lifes_fi.html. 5 Jeff Howe, “Crowdsourcing: A Definition,” Crowdsourcing, http://crowdsourcing.typepad.com/cs/2006/06/crowdsourcing_a.html. 6 “Statistics,” Kiva, http://www.kiva.org/about/stats. 7 Rob Walker, “The Trivialities and Transcendence of Kickstarter,” New York Times, August 5, 2011, http://www.nytimes.com/2011/08/07/magazine/the-trivialities-and-transcendence-of-kickstarter.html?

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v=G-0KJF3uLP8. 31 “About Blue Origin,” Blue Origin, July 2014, http://www.blueorigin.com/about/. 32 Alistair Barr, “Amazon testing delivery by drone, CEO Bezos Says,” USA Today, December 2, 2013, referencing a 60 Minutes interview with Jeff Bezos, http://www.usatoday.com/story/tech/2013/12/01/amazon-bezos-drone-delivery/3799021/. 33 Jay Yarow, “Jeff Bezos’ Shareholder Letter Is Out,” Business Insider, April 10, 2014, http://www.businessinsider.com/jeff-bezos-shareholder-letter-2014-4. 34 “Larry Page Biography,” Academy of Achievement, January 21, 2011, http://www.achievement.org/autodoc/page/pag0bio-1. 35 Marcus Wohlsen, “Google Without Larry Page Would Not Be Like Apple Without Steve Jobs,” Wired, October 18, 2013, http://www.wired.com/2013/10/google-without-page/. 36 Google Inc., 2012, Form 10-K 2012, retrieved from SEC Edgar website: http://www.sec.gov/Archives/edgar/data/1288776/000119312513028362/d452134d10k.htm. 37 Larry Page, “Beyond Today—Larry Page—Zeitgeist 2012,” Google Zeitgeist, Zeitgeist Minds, May 22, 2012, https://www.youtube.com/watch?

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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.

Surveillance Valley: The Rise of the Military-Digital Complex
by Yasha Levine
Published 6 Feb 2018

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, http://ilpubs.stanford.edu:8090/422/1/1999-66.pdf. 36.

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In the end, the rank of any given webpage would be the sum total of all the links and their values that pointed to it. Once the values of a few initial webpages entered the PageRank algorithm, new rankings propagated recursively through the whole web. “We converted the entire web into a big equation with several hundred million variables, which are the page ranks of all the web pages,” Brin explained not long after launching Google.31 It was a dynamic mathematical model of the Internet. If one value changed, then the whole thing would be recomputed.32 They folded it into an experimental search engine they called “BackRub” and put it up on Stanford’s internal network.

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Those people are free to ignore or even bad-mouth Gmail, but they shouldn’t try to stop Google from offering Gmail to the rest of us,” declared New York Times technology journalist David Pogue in May. “We know a good thing when we see it.”67 A few months later, on August 19, 2004, Google went public. When the bell rang that afternoon to close NASDAQ trading, Google was worth $23 billion.68 Sergey Brin and Larry Page attained oligarch status in the space of a single workday, while hundreds of their employees became instant multimillionaires, including the company cook. But concerns about Google’s business model would continue to haunt the company.

What Would Google Do?
by Jeff Jarvis
Published 15 Feb 2009

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.

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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. In essence, Google interprets a link from page A to page B as a vote, by page A, for page B. But, Google looks at considerably more than the sheer volume of votes, or links a page receives; for example, it also analyzes the page that casts the vote. Votes cast by pages that are themselves “important” weigh more heavily and help to make other pages “important.”

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There will always be flaming cat videos next to art online. But there is the opportunity to make more art now. 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.

The Digital Divide: Arguments for and Against Facebook, Google, Texting, and the Age of Social Netwo Rking
by Mark Bauerlein
Published 7 Sep 2011

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.

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, the first great Internet success story, was born as a catalog, or directory of links, an aggregation of the best work of thousands, then millions of Web users. While Yahoo! has since moved into the business of creating many types of content, its role as a portal to the collective work of the Net’s users remains the core of its value. • Google’s breakthrough in search, which quickly made it the undisputed search market leader, was PageRank, a method of using the link structure of the Web rather than just the characteristics of documents to provide better search results. • eBay’s product is the collective activity of all its users; like the Web itself, eBay grows organically in response to user activity, and the company’s role is as an enabler of a context in which that user activity can happen.

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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. “The ultimate search engine is something as smart as people—or smarter,” Page said in a speech a few years back. “For us, working on search is a way to work on artificial intelligence.”

Alpha Girls: The Women Upstarts Who Took on Silicon Valley's Male Culture and Made the Deals of a Lifetime
by Julian Guthrie
Published 15 Nov 2019

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. She knew that most other search engines, like Yahoo!, were still using humans to help build the ontologies. Before the Google guys began making the rounds looking for financing, they had followed a well-trodden path to the offices of attorney Larry Sonsini, who had helped incorporate, build, and take public just about every major tech company, from ROLM and Apple to Netscape, Pixar, and hundreds more.

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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.

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The Russian-born math whiz and Stanford computer science graduate had come to Accel Partners to talk about his start-up, Google. His co-founder, Larry Page, the more introverted of the two, watched from the sidelines. Theresia wondered if Larry wasn’t talking to her because she was an associate, not a partner. Theresia cooled it with the spin shot, and as much as it pained her, she curtailed her signature middle-rod pull shot. Her only job at the moment, as an associate, was to entertain the Google guys and develop a rapport with them. Everyone in the Valley wanted a piece of Google. In this case, playing to win meant making sure she lost. It was the spring of 1999.

Designing Great Data Products
by Jeremy Howard , Mike Loukides and Margit Zwemer
Published 23 Mar 2012

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.

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Back in 1997, AltaVista was king of the algorithmic search world. 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.

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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.

World Without Mind: The Existential Threat of Big Tech
by Franklin Foer
Published 31 Aug 2017

“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.

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Carl broke from his jovial form: Larry Page, Google I/O 2013 Keynote, May 15, 2013. “it’s AI complete”: Larry Page, “Envisioning the Future for Google: Always a Search Engine?” (lecture, Stanford University, Stanford, CA, May 1, 2002.) “directly attached to your brain”: Steven Levy, “All Eyes on Google,” Newsweek, April 11, 2004. “a little version of Google”: Vise and Malseed, 281. Horrified by his discovery, the captain dragged Descartes’s creation: Stephen Gaukroger, Descartes (Oxford University Press, 1995), 1. “an extended, non-thinking thing”: Steven Nadler, The Philosopher, the Priest, and the Painter (Princeton University Press, 2013), 106.

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That’s not to say that Schmidt was timid. Those years witnessed Google’s plot to upload every book on the planet and the creation of products that are now commonplace utilities, like Gmail, Google Docs, and Google Maps. But those ambitions never stretched quite far enough to satisfy Larry Page. In 2011, Page shifted himself back into the corner office, the CEO job he held at Google’s birth. And he redirected the company toward singularitarian goals. Over the years, he had befriended Kurzweil and worked with him on assorted projects. After he returned to his old job, Page hired Kurzweil and anointed him Google’s director of engineering.

Data Mining: Concepts, Models, Methods, and Algorithms
by Mehmed Kantardzić
Published 2 Jan 2003

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.

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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. Of course, the initial version of the PageRank algorithm, explained in a simplified form in this text, had numerous modifications to evolve into the current commercial version implemented in the Google search engine. 11.6 TEXT MINING Enormous amounts of knowledge reside today in text documents that are stored either within organizations or are freely available.

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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.

The Black Box Society: The Secret Algorithms That Control Money and Information
by Frank Pasquale
Published 17 Nov 2014

Mark Walters, “How Does Google Rank Websites?” SEOmark. Available at http://www.seomark.co.uk /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, http://feyla39.wordpress.com /page/2/. Google’s current mission statement is “to organize the world’s information and make it universally accessible and useful.”

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But commercial success has given the company almost inconceivable power, not least over what we find online.35 Google does not reveal the details of its ranking methods. It has explained their broad outlines, and the process sounds reassuringly straightforward. 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.

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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 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).

Don't Be Evil: How Big Tech Betrayed Its Founding Principles--And All of US
by Rana Foroohar
Published 5 Nov 2019

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).

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I can’t tell you how many technologists and venture capitalists I’ve spoken to in the past several years who say that they simply won’t invest in areas that Google or Facebook or Amazon or Apple are likely to play in, because of the difficulties inherent in protecting open-source technology, and/or defending patents against the big guys, who inevitably have more time and legal muscle on their side. As technologist Jaron Lanier has pointed out, the most profitable assets, like Google’s own PageRank algorithms, or the closed system of the iPhone, are almost always proprietary, rather than open. “While the open approach has been able to create lovely, polished copies, it hasn’t been so good at creating notable originals,” says Lanier,16 a fact that underscores the way in which Big Tech firms push open-source to the extent that it aids their ability to profit from others’ innovation, but rarely let competitors anywhere near the code that powers their own key technologies.

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Department of Justice took on the issue, claiming it had granted Google too many anticompetitive rights, and that the book-scanning and -selling project was a monopoly issue. Larry Page called the legal challenge a “travesty to humanity,” while Sergey Brin wrote a sanctimonious piece in The New York Times defending Google’s efforts. At court proceedings in 2010, Google’s attorney Daralyn J. Durie argued that “copyright infringement is evil to the extent that it is not compensated and that it harms the economic interests of rights holders.” It was a clever argument, because it shifted attention toward the fact that Google was, after all, facilitating book sales—and away from the fact that Google itself was becoming the major beneficiary of a huge amount of copyrighted content.

Valley of Genius: The Uncensored History of Silicon Valley (As Told by the Hackers, Founders, and Freaks Who Made It Boom)
by Adam Fisher
Published 9 Jul 2018

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.

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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.

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The check was made out to “Google Inc.,” which didn’t exist at the time, which was a big problem. Larry Page: We didn’t have a checking account, we didn’t have a company, we didn’t have anything. David Cheriton: Andy just handed them the money: “Let’s work out the details later; let’s get going.” Sergey Brin: We hadn’t really discussed valuations and stuff like that. He figured it would pay off, and he was right. We finalized all the details on the round after that. I guess we figured if we didn’t agree later, that it would be a loan. He liked us and he just wanted to sort of push us forward. Larry Page: It was pretty unreal.

The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism
by Nick Couldry and Ulises A. Mejias
Published 19 Aug 2019

The New “Social Theory” All models for information processing involve some grid by which they imagine the social world (think of an examination system and its categories of grades). But, as Bernhard Rieder shows in an important analysis of Google’s PageRank algorithm, practices of computation cast a particular type of shadow on the social world, imprinting it with their own “theory.” Because computers can only process “ideas that can be made computable,” a computer model’s selection of what it can count shapes how that computer “meet[s] the world.” It is socially significant that Google tweaked its PageRank algorithm early on so as to give more weight to remote links than might be given otherwise. The result, in Rieder’s view, is “a largely conservative vision of society” that interprets content based on accumulated total linkages, downplaying intense new patterns of linkage.127 The important point is not the details of Google’s constantly changing algorithm but that each software system—and every interface built on that software—embeds decisions that encapsulate a particular “theory of the social” on which that software’s functioning relies.128 The wider consequences of this new social theory need to be understood.

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Braverman writes that “every line Marx wrote on this subject makes it clear that he did not expect from capitalism or from science and machinery as used by capitalism, no matter how complex they become, any general increase in the technical scope, scientific knowledge, or broadening of the competence of the worker, and that he in fact expected the opposite” (Labor, 160). 141. Hardt and Negri, Assembly. 142. For example, our searches’ input to Google’s PageRank algorithm (Hardt and Negri, Assembly, 169). 143. As they put it, “Exploit yourself, capital tells productive subjectivities, and they respond, we want to valorize ourselves, govern the common that we produce” (Hardt and Negri, Assembly, 123). 144. Hardt and Negri, Assembly, 169. Chapter 2 1.

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See infrastructures of connection Neuhouser, Frederick, 257n82 neural networks, 142 neuroeconomics, 141 “new data relations,” 225n36 Nietzsche, Friedrich, 252–53n10 Nissenbaum, Helen, 177, 178 Noble, Safiya Umoja, 68 nodocentrism, 95 noopolitics, 250n157 norming, 119 North American Free Trade Agreement (NAFTA), 105 Northeastern University, 143 Nudge (Thaler, Sunstein), 139–40 Obama, Barack, 105, 177 “Of the Different Human Races” (Kant), 78 oil and gas sector: versus data as resource, 89–90; revenue of, 54 onboarding, social quantification by, 52 Oneself as Another (Ricoeur), 254n34 Open Graph (Facebook), 2 Oracle, 130 Organization for Economic Cooperation and Development, 89–90 O’Sullivan, David, 223n2 Other versus Self, 239n34 PageFair, 11 PageRank (Google), 137 Palantir, 145 Palihapitiya, Chamath, 221n16 panopticon, 99, 100 paranodality, 205–7, 214 Parks, Lisa, 45–46 Pasquale, Frank, 124 Paytm, 13–14, 54 Pearson Education, 175, 176 Pentland, Alex, 138–39 personality testing, 63 personalization: education and autonomy, 175–76; ideology of, 16–17, 61; outdoor retargeting with, 132.

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
by Pedro Domingos
Published 21 Sep 2015

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.

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“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.

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See also Cancer drugs Duhigg, Charles, 223 Dynamic programming, 220 Eastwood, Clint, 65 Echolocation, 26, 299 Eddington, Arthur, 75 Effect, law of, 218 eHarmony, 265 Eigenfaces, 215 80/20 rule, 43 Einstein, Albert, 75, 200 Eldredge, Niles, 127 Electronic circuits, genetic programming and, 133–134 Eliza (help desk), 198 EM (expectation maximization) algorithm, 209–210 Emotions, learning and, 218 Empathy-eliciting robots, 285 Empiricists, 57–58 Employment, effect of machine learning on, 276–279 Enlightenment, rationalism vs. empiricism, 58 Entropy, 87 Epinions, 231 Equations, 4, 50 Essay on Population (Malthus), 178, 235 Ethics, robot armies and, 280–281 Eugene Onegin (Pushkin), 153–154 “Explaining away” phenomenon, 163 Evaluation learning algorithms and, 283 Markov logic networks and, 249 Master Algorithm and, 239, 241, 243 Evolution, 28–29, 121–142 Baldwinian, 139 Darwin’s algorithm, 122–128 human-directed, 286–289, 311 Master Algorithm and, 28–29 of robots, 121–122, 137, 303 role of sex in, 134–137 technological, 136–137 See also Genetic algorithms Evolutionaries, 51, 52, 54 Alchemy and, 252–253 exploration-exploitation dilemma, 128–130, 221 further reading, 303–304 genetic programming and, 52 Holland and, 127 Master Algorithm and, 240–241 nature and, 137–139 Evolutionary computation, 121–142 Evolutionary robotics, 121–122, 303 Exclusive-OR function (XOR), 100–101, 112, 195 Exploration-exploitation dilemma, 128–130, 221 Exponential function, machine learning and, 73–74 The Extended Phenotype (Dawkins), 284 Facebook, 44, 291 data and, 14, 274 facial recognition technology, 179–180 machine learning and, 11 relational learning and, 230 sharing via, 271–272 Facial identification, 179–180, 182 False discovery rate, 77, 301 Farming, as analogy for machine learning, 6–7 Feature selection, 188–189 Feature template, 248 Feature weighting, 189 Ferret brain rewiring, 26, 299 Feynman, Richard, 4 Filter bubble, 270 Filtering spam, rule for, 125–127 First principal component of the data, 214 Fisher, Ronald, 122 Fitness Fisher on, 122 in genetic programming, 132 Master Algorithm and, 243 neural learning and, 138–139 sex and, 135 Fitness function, 123–124 Fitness maximum, genetic algorithms and, 127–128, 129 Fix, Evelyn, 178–179, 186 Fodor, Jerry, 38 Forecasting, S curves and, 106 Foundation Medicine, 41, 261 Foundation (Asimov), 232 Fractal geometry, 30, 300 Freakonomics (Dubner & Levitt), 275 Frequentist interpretation of probability, 149 Freund, Yoav, 238 Friedman, Milton, 151 Frontiers, 185, 187, 191, 196 “Funes the Memorious” (Borges), 71 Futility of bias-free learning, 64 FuturICT project, 258 Galileo, 14, 72 Galois, Évariste, 200 Game theory, machine learning and, 20 Gaming, reinforcement learning and, 222 Gates, Bill, 22, 55, 152 GECCO (Genetic and Evolutionary Computing Conference), 136 Gene expression microarrays, 84–85 Generalizations, choosing, 60, 61 Generative model, Bayesian network as, 159 Gene regulation, Bayesian networks and, 159 Genetic algorithms, 122–128 Alchemy and, 252 backpropagation vs., 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.

Big Data: A Revolution That Will Transform How We Live, Work, and Think
by Viktor Mayer-Schonberger and Kenneth Cukier
Published 5 Mar 2013

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

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Falls to Lowest Level Since 2008,” Bloomberg, August 13, 2012 (http://www.bloomberg.com/news/2012-08-13/stock-trading-in-u-s-hits-low 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 (http://sloanreview.mit.edu/themagazine/2012fall/54104/howbigdataisdifferent/). Facebook stats—Facebook IPO prospectus, “Form S-1 Registration Statement,” U.S. Securities and Exchange Commission, February 1, 2012 (http://sec.gov/Archives/edgar/data/1326801/000119312512034517/d287954ds1.htm). YouTube stats—Larry Page, “Update from the CEO,” Google, April 2012 (http://investor.google.com/corporate/2012/ceo-letter.html).

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Merchant ships desperately wanted to get hold of his charts; Maury insisted that in return they too hand over their logs (an early version of a viral social network). “Every ship that navigates the high seas,” he proclaimed, “may henceforth be regarded as a floating observatory, a temple of science.” To fine-tune the charts, he sought other data points (just as Google built upon the PageRank algorithm to include more signals). He got captains to throw bottles with notes indicating the day, position, wind, and prevailing current into the sea at regular intervals, and to retrieve any such bottles that they spotted. Many ships flew a special flag to show they were cooperating with the information exchange (presaging the link-sharing icons that appear on some web pages).

Filterworld: How Algorithms Flattened Culture
by Kyle Chayka
Published 15 Jan 2024

Algorithmic feeds are sometimes more formally and literally labeled “recommender systems,” for the simple act of choosing a piece of content. The first wholly mainstream Internet algorithm, one that almost every Internet user has encountered, was the Google Search algorithm. In 1996, while studying at Stanford University, Sergey Brin and Larry Page, the cofounders of Google, began work on what would become PageRank, a system for crawling the Internet (which at that point amounted to perhaps one hundred million documents in total) and identifying which sites and pages were more useful or informative than others. PageRank worked by measuring how many times a website was linked to by other sites, similar to the way academic papers cite key pieces of past research.

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Page and Brin’s prediction that their system would remain functional and scalable as the Internet grew were correct. Decades later, PageRank has become almost tyrannical, a system that dominates how and when websites are seen. It’s vital for a business or resource to make it to that first page of Google Search results by adapting to the PageRank algorithm. In the early 2000s, I perused many successive pages of Google results to find exactly what I was looking for. More recently, I hardly ever make it to the second page, in part because Google Search now frontloads text that it gauges will be relevant, pulling it from websites and displaying it directly to the user at the top of the search page, before the actual results.

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“We expect that advertising funded search engines will be inherently biased towards the advertisers and away from the needs of the consumers,” the entrepreneurs wrote in 1998. Yet, in 2000, they launched Google AdWords as the company’s pilot product for advertisers. It is amusing to read their critique today, as advertising now provides the vast majority of Google’s revenue—more than 80 percent in 2020. As PageRank attracted billions of users to Google Search, the company could also track what the users were searching for and could thus sell advertisers space on particular search queries. The ads a user sees were just as informed by the algorithm as the search results were. And advertising, built on the search algorithm, turned Google into a behemoth. By the early 2000s, algorithmic filtering was already dictating our digital experiences.

Genius Makers: The Mavericks Who Brought A. I. To Google, Facebook, and the World
by Cade Metz
Published 15 Mar 2021

After graduate school, where he studied computer science at its foundational levels—the “compilers” that turn software code into something the computer can understand—he joined a Silicon Valley research lab run by the Digital Equipment Corporation, and as the influence of this onetime giant of the computer industry waned, he was among the top DEC researchers who streamed into Google just as the company was taking off. Google’s early success is often attributed to PageRank, the search algorithm developed by Larry Page while he and his cofounder, Sergey Brin, were graduate students at Stanford. But the slim, square-jawed, classically handsome Dean, who spoke with a polite shyness and a slight lisp, was just as important to the company’s rapid rise—if not more so. He and a handful of other engineers built the sweeping software systems that underpinned the Google search engine, systems that ran across thousands of computer servers and multiple data centers, allowing PageRank to instantly serve millions of people with each passing second.

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As the controversy roiled, Suleyman urged Pichai and Walker to finalize ethical guidelines that would formally define what Google would and would not build. * * * — IN mid-May, a group of independent academics addressed an open letter to Larry Page, Sundar Pichai, Fei-Fei Li, and the head of the Google cloud business. “As scholars, academics, and researchers who study, teach about, and develop information technology, we write in solidarity with the 3100+ Google employees, joined by other technology workers, who oppose Google’s participation in Project Maven,” the letter read. “We wholeheartedly support their demand that Google terminate its contract with the DoD, and that Google and its parent company Alphabet commit not to develop military technologies and not to use the personal data that they collect for military purposes.”

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Demis Hassabis, Shane Legg, and Mustafa Suleyman found DeepMind. Stanford professor Andrew Ng pitches Project Marvin to Google chief executive Larry Page. 2011—University of Toronto researcher Navdeep Jaitly interns at Google in Montreal, building a new speech recognition system through deep learning. Andrew Ng, Jeff Dean, and Greg Corrado found Google Brain. Google deploys speech recognition service based on deep learning. 2012—Andrew Ng, Jeff Dean, and Greg Corrado publish the Cat Paper. Andrew Ng leaves Google. Geoff Hinton “interns” at Google Brain. Geoff Hinton, Ilya Sutskever, and Alex Krizhevsky publish the AlexNet paper.

The Stack: On Software and Sovereignty
by Benjamin H. Bratton
Published 19 Feb 2016

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. This computational meritocracy is in the service of a universalist mission to not only organize the world's information but to “make it accessible and useful.”

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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. Konrad Becker and Felix Stalder (Innsbruck: Studien Verlag, 2009). 69. 

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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.”

Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World With OKRs
by John Doerr
Published 23 Apr 2018

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.

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Thousand percent improvement requires rethinking problems, exploring what’s technically possible and having fun in the process. At Google, in line with Andy Grove’s old standard, aspirational OKRs are set at 60 to 70 percent attainment. In other words, performance is expected to fall short at least 30 percent of the time. And that’s considered success! Eric Schmidt, Larry Page, and Sergey Brin with Google’s first self-driving car, 2011—10x thinking in action! Google has had its share of colossal misfires, from Helpouts to Google Answers. Living in the 70 percent zone entails a liberal sprinkling of moonshots and a willingness to court failure.

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“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).

The Smartphone Society
by Nicole Aschoff

It’s a problem often associated with Google because Google is search. The story of Page and Brin’s extraordinarily clever invention is well known. In the midnineties, as graduate students at Stanford, they created an algorithm, called PageRank, that ranks webpages by how many other pages are linked to it and how many people have visited that page. It was fair and useful. Suddenly you could find stuff that you wanted to find on the internet. We all started using Google. Google became a verb, “to google.” We used Google to find everything, and soon everything could be found on Google. Early on, Google investors demanded that Brin and Page add advertising so that their search engine would be not only useful but also profitable.

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The top ten apps people kept on their smartphones in 2017 were the following: 1. Facebook 2. Gmail 3. Google Maps 4. Amazon 5. Facebook Messenger 6. YouTube 7. Google Search 8. Google Play Store 9. Instagram 10. Apple App Store Mark Zuckerberg, Sergei Brin, Larry Page, Jeff Bezos—these are the titans of today. Facebook is social media. Google is search. Amazon is e-commerce. Jeff Bezos, founder and CEO of Amazon.com, is worth $112 billion. Mark Zuckerberg, the CEO of Facebook, is worth $69 billion. Larry Page and Sergey Brin, the founders of Google, more than $50 billion each. But these men’s titanic profiles are based on more than their wealth.

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This is on top of Amazon’s wildly successful venture Amazon Web Services (AWS), which provides on-demand, pay-as-you-go cloud-computing platforms for individuals, small and large businesses, and even governments, and generates half of the company’s profits.8 AWS customers include Netflix, CapitalOne, Condé Nast, and the Central Intelligence Agency, which paid Amazon $600 million for cloud space. Google has developed an equally voracious appetite since its founding. Google owns YouTube and Android and, of course, Google Search. It is also the key piece of a sprawling conglomerate called Alphabet. Google founders Sergey Brin and Larry Page created Alphabet in 2015 to organize their growing pile of tech companies—a conglomerate that, in addition to Google, includes companies focused on biotech (Calico), cybersecurity (Chronicle), wind power (Makani), and the life sciences (Verily).

So You've Been Publicly Shamed
by Jon Ronson
Published 9 Mar 2015

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.

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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. But for Michael the problem with Google is that it is forever evolving - adjusting its algorithm in ways it keeps secret. ‘Google is a tricky beast and a moving target,’ Michael told me. ‘And so we try to decipher it, to reverse-engineer it.’ This was what Michael knew right now: ‘Google tends to like stuff that’s old. It seems to think old stuff has a certain authority. And Google tends to like stuff that’s new. With the intervening stuff, week six, week twelve, there’s a dip.’

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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.

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It)
by Salim Ismail and Yuri van Geest
Published 17 Oct 2014

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.

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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.

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* ( ) 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?

Automate This: How Algorithms Came to Rule Our World
by Christopher Steiner
Published 29 Aug 2012

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.

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W., 176 C (programming language), 12, 38 C++, 189 cadences, 82, 87 Caesars, 135 calculus, 58, 59–60, 136 calculus ratiocinator, 59 California, 215 California, University of, at Berkeley, 1, 139 California, University of, at San Francisco (UCSF), automated pharmacy at, 154–55 California, University of, at Santa Cruz, 89, 90, 92 call centers, 177–78, 181–83, 192–96 call options, 21–22, 29–30 overpriced, 33 underpriced, 33 Canada, parliament of, 178–79 Cantor Fitzgerald, 133–35 Capers, Hedges, 176–78, 181–82, 195 capital, stock market as way to raise, 51 capitalism, 120, 153 Peterffy’s childhood, 18 carbon dioxide, 166 Carnegie Hall, 91 Carnegie Mellon, 128, 131–32 carrier pigeons, 121–22 Cash, Johnny, 87 Catalan, 80 celiac disease, 157 Central Intelligence Agency (CIA), 70, 136–40 cervical cancer, 153–54 chaos theory, 71 Charlotte Bobcats, 142 Chase, Herbert, 162 Chemistry.com, 144 chess, 199 Deep Blue and computer, 126–27, 129, 133, 141 Chicago, Ill., 128, 130, 186, 190, 192, 198 algorithmic trading in, 40, 46, 49, 51 communication between markets in New York and, 42, 113–18, 123–24 options trading in, 27 Chicago, University of, 23, 140, 186, 191 Chicago Board Options Exchange, 27, 36, 38, 40, 114 Chicago Cubs, 142 Chicago Mercantile Exchange, 40, 51–52, 133 Chicago Research and Trading, 40, 46 Chicago Tribune, 8 chimpanzees, humans’ divergence from, 161 Cho, Rich, 142 Chopin, Frederic, 96, 98 chorales, 93 chords, musical, 82, 106–10 Cielo Networks, 124 Cincinnati Stock Exchange, 46 Citadel, 190 Citi Capital Markets, 200 Citigroup, 186, 192 Civil War, 122 classical music, algorithms and, 89–103 Clinton, Bill, 176 cloud computing, 120–21 Cloudera, 206, 216 Clue, 135 CNBC, Dow crash and, 2–3 CNN, 137 Codecademy, 9–10 cognitive science, 97 Cold War, 136, 168, 169 collateralized debt obligations (CDOs), 189, 209 Columbia Records, 87 Columbia University, 162 Combinet, 131 Come Away with Me, 82–83 Comes the Fiery Night (Cope), 100–101 commerce, personality types in, 163 commodities, golden mean and, 57 commodities options, 22 commodities trading, 20–25, 27, 51, 130 communication: human, 170–71 under stress, 145 voice, 195 communications networks, financial markets and, 120–25 communism, 136 competition, stock prices and, 27 computer code, 73 computer dating, algorithms for, 143–45 computer languages, 74 computers, 73 circuitry of, 74 early home, 28 early office use of, 19–20 handheld, 36–39, 41, 44–45 improvements in, 48 Peterffy’s early trading via, 12–16 computer science, 71, 157, 188, 200, 201, 213 Cope’s algorithmic music and, 91 congestive heart failure, 159 consumer data, 192–93 Conway, Kelly, 177, 180–83, 186–88, 190, 191–97, 198 coordinated algorithms, 5 Cope, David, 89–102 Emmy created by, 93–99 hostility toward the algorithmic music of, 90–91, 95, 96–99 on question of authorship, 95 Cornell University, 213 coronary bypass surgery, 158 correlative risk, 65 Correlator (algorithm), 42–45 Cosby, Bill, 34 cosines, 106 cowboy bets, 30 Cramer, Jim, 3, 4 creativity, by algorithms, 76–77 credit default swaps (CDSs), 65 Credit Suisse, 116, 186 Credit Suisse First Boston, 189 crude oil trades, 51 Cuba, 153 currency rates, fluctuations in, 54 customer service, 178 fraudulent calls by, 193 personality types in, 163, 164, 180–83, 195, 214 cytotechnologists, 153 Dalhousie University, 105 dark fiber, 114–20, 122 data: gathering of, 203–5 sifting of, 62, 206–7 data feeds, hacking of, 15, 17 data mines, 206 Da Vinci Code, The (Brown), 57 decision trees: algorithms as, 6 binary, 26, 171 declarative statements, 180 Deep Blue, 126–27, 129, 133, 141 Defense Department, U.S., 73 Office of Net Assessment at, 140 delta neutral trades, 33 Dennett, Daniel, 97 Deo, 81–82 derivatives, 60 values of, 41–42 Deutsche Bank, 190 deviations, 63 differential equations: in options trading, 22 partial, 23 digital files, 81 Disney studios, 76 disruptors, in music composition, 102–3 divisors, algorithm for, 55 “DJ Got Us Fallin’ in Love,” 89 DNA, 70, 159 algorithmic analysis of, 160–61 atomic structure of, 56 Dodge, Anne, 156–57 Donino, Tom, 4 dot-com crash, 188 Dow Jones, news service for trading bots by, 48 Dow Jones Industrial Average, 2–4, 191 driving, algorithms for, 214–16 Dropbox, 199 drought, 130 drugs: anesthetic, 160 in PDR, 146 Duke University, 189, 198 DuPont, 29–30 Durant, Kevin, 142 Eagles, the, 78 Eastern Europe, 193, 218 eBay, 188 economy: growth sectors in, 218–20 troubled recent, 189, 208, 210–11 Edison, Thomas, 123 education: in math and science, 218–19 personality types in, 195 in programming, 9–10 Education Department, New York City, 147–48 Egypt, 140 eHarmony, 144 Einhorn, David, 128 Eisen, Michael, 1 elections, of 1992, 176 electronic trading networks, 185 Elements (Euclid), 55 Elizabeth Wende Breast Clinic, 154 eLoyalty, 177, 180–83, 186–88, 191–97 e-mail, 195–96, 204 language patterns and social influence in, 212–14 EMI, 87 Emily Howell (algorithm), 99 Emmy (algorithm), 90, 94–99 recording contract for, 95–96 Emory University, 189 emotions-driven people, 172–73, 174, 175, 176, 180, 187, 194, 197 empathy, 176 engineering, financial, 209 engineers, 62 algorithms and, 6 career goals of, 189–90, 198, 200, 210–11, 218–20 at Facebook, 70 at Google, 47 in intelligence analysis, 139–40 music algorithms and, 78, 79 Peterffy as, 32, 48 in sports management, 142 on Wall Street, 13, 23, 24, 46, 47, 49, 119, 185, 202, 207, 211 England, 72 English-French translation software, 178–79 entrepreneurs, 208–11 online, 53 Epagogix, 75 Epstein, Theo, 142 equity exchanges, 38 Erasmus of Rotterdam, 69 Euclid, 55 Euclidean algorithm, 55 Euler, Leonhard, 64, 65, 68–71, 105, 111 Euler’s formula, 70–71 Euphrates Valley, 55 Europe: algorithmic trading in, 47, 49 pop charts in, 79 Evanston, Ill., 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!

…

Simpson jurors evaluated, 177 see also litigation Lawrence, Peter, 1–2 least squares method, 62–63 Le Corbusier, 56 Lee, Spike, 87 Lehman Brothers, 191, 192 Leibniz, Gottfried, 26, 57–61, 68, 72 binary language of, 57–58, 60–61, 71, 73 Leipzig, 58 Lennon, John, 104, 107–8 “In My Life” claimed by, 110–11 as math savant, 103 “Let It Be,” 103 Levchin, Max, 188 leverage, trading on margin with, 51 Lewis, Michael, 141, 202 Li, David X., 65 Liber Abaci (The Book of Calculation) (Fibonacci), 56–57 Library of Congress, 193 Lin, Jeremy, 142–43 linguistics, 187 liquidity crisis, potential, 51–52 Lisp, 12, 93, 94 lit fiber, 114, 120 lithium hydroxide, 166 Lithuania, 69 litigation: health insurers and, 181 stock prices and potential, 27 Walgreens and, 156 logic: algorithms and, 71 broken down into mechanical operations, 58–59 logic theory, 73 logic trees, 171 London, 59, 66–67, 68, 121, 198 Los Angeles International Airport, security algorithm at, 135 Los Angeles Lakers, 143 loudness, 93, 106 Lovelace, Ada, 73 Lovell, James, 165–67 Lulea, Sweden, 204 lunar module, 166 lung cancer, 154 McAfee, Andrew P., 217–18 McCartney, Paul, 104, 105, 107 “In My Life” claimed by, 110–11 as math savant, 103 McCready, Mike, 78–83, 85–89 McGuire, Terry, 145, 168–72, 174–76 machine-learning algorithms, 79, 100 Magnetar Capital, 3–4, 10 Mahler, Gustav, 98 Major Market Index, 40, 41 Making of a Fly, The (Lawrence), prices of, 1–2 Malyshev, Mikhail, 190 management consultants, 189 margin, trading with, 51 market cap, price swings and, 49 market makers: bids and offers by, 35–36 Peterffy as, 31, 35–36, 38, 51 market risk, 66 Maroon 5, 85 Marseille, 147, 149 Marshall, Andrew, 140 Martin, George, 108–10 Martin, Max (Martin Sandberg), 88–89 math: behind algorithms, 6, 53 education in, 218–20 mathematicians: algorithms and, 6, 71 online, 53 on Wall Street, 13, 23, 24, 27, 71, 179, 185, 201–3 Mattingly, Ken, 167 MBAs: eLoyalty’s experience with, 187 Peterffy’s refusal to hire, 47 MDCT scans, 154 measurement errors, distribution of, 63 medical algorithms, 54, 146 in diagnosis and testing, 151–56, 216 in organ sharing, 147–51 patient data and home monitoring in, 158–59 physicians’ practice and, 156–62 medical residencies, game theory and matching for, 147 medicine, evidence-based, 156 Mehta, Puneet, 200, 201 melodies, 82, 87, 93 Mercer, Robert, 178–80 Merrill Lynch, 191, 192, 200 Messiah, 68 metal: trading of, 27 volatility of, 22 MGM, 135 Miami University, 91 Michigan, 201 Michigan, University of, 136 Microsoft, 67, 124, 209 microwaves, 124 Midas (algorithm), 134 Miller, Andre, 143 mind-reading bots, 178, 181–83 Minneapolis, Minn., 192–93 minor-league statistics, baseball, 141 MIT, 24, 73, 128, 160, 179, 188, 217 Mocatta & Goldsmid, 20 Mocatta Group, 20, 21–25, 31 model building, predictive, 63 modifiers, 71 Boolean, 72–73 Mojo magazine, 110 Moneyball (Lewis), 141 money markets, 214 money streams, present value of future, 57 Montalenti, Andrew, 200–201 Morgan Stanley, 116, 128, 186, 191, 200–201, 204 mortgage-backed securities, 203 mortgages, 57 defaults on, 65 quantitative, 202 subprime, 65, 202, 216 Mosaic, 116 movies, algorithms and, 75–76 Mozart, Wolfgang Amadeus, 77, 89, 90, 91, 96 MP3 sharing, 83 M Resort Spa, sports betting at, 133–35 Mubarak, Hosni, 140 Muller, Peter, 128 music, 214 algorithms in creation of, 76–77, 89–103 decoding Beatles’, 70, 103–11 disruptors in, 102–3 homogenization or variety in, 88–89 outliers in, 102 predictive algorithms for success of, 77–89 Music X-Ray, 86–87 Musikalisches Würfelspiel, 91 mutual funds, 50 MyCityWay, 200 Najarian, John A., 119 Naples, 121 Napoleon I, emperor of France, 121 Napster, 81 Narrative Science, 218 NASA: Houston mission control of, 166, 175 predictive science at, 61, 164, 165–72, 174–77, 180, 194 Nasdaq, 177 algorithm dominance of, 49 Peterffy and, 11–17, 32, 42, 47–48, 185 terminals of, 14–17, 42 trading method at, 14 National Heart, Lung, and Blood Institute, 159 Nationsbank, Chicago Research and Trading Group bought by, 46 NBA, 142–43 Neanderthals, human crossbreeding with, 161 Nebraska, 79–80, 85 Netflix, 112, 207 Netherlands, 121 Netscape, 116, 188 Nevermind, 102 New England Patriots, 134 New Jersey, 115, 116 Newsweek, 126 Newton, Isaac, 57, 58, 59, 64, 65 New York, N.Y., 122, 130, 192, 201–2, 206 communication between markets in Chicago and, 42, 113–18, 123–24 financial markets in, 20, 198 high school matching algorithm in, 147–48 McCready’s move to, 85 Mocatta’s headquarters in, 26 Peterffy’s arrival in, 19 tech startups in, 210 New York Commodities Exchange (NYCE), 26 New Yorker, 156 New York Giants, 134 New York Knicks, 143 New York magazine, 34 New York State, health department of, 160 New York Stock Exchange (NYSE), 3, 38–40, 44–45, 49, 83, 123, 184–85 New York Times, 123, 158 New York University, 37, 132, 136, 201, 202 New Zealand, 77, 100, 191 Nietzsche, Friedrich, 69 Nirvana, 102 Nixon, Richard M., 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 Parse.ly, 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 Perfectmatch.com, 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.

Nothing but Net: 10 Timeless Stock-Picking Lessons From One of Wall Street’s Top Tech Analysts
by Mark Mahaney
Published 9 Nov 2021

And from almost the beginning, that created premium revenue growth opportunities for Google and helped drive the share price higher for years and years. The Brief Story of Google Google was founded by Page and Brin in 1998 while they were PhD students at Stanford University. Their bold goal was to instantly deliver relevant information on any topic for anybody anywhere in the world. They accomplished this—at least better than anyone else has been able to so far—by deploying a proprietary algorithm called PageRank that ranked all web pages based on the number and quality of links to that page.

…

After I got off the air, Cramer complimented me for having the chutzpah to show up and acknowledge my mistake. Boy, what a mistake. So that was the Big No in terms of how well I called GOOGL as a stock. The Big Yes is that I upgraded GOOGL shares to a Buy a few months later and consistently kept with that call for the next 16 years, with the exception of a small window around the Eric Schmidt–to–Larry Page CEO transition in 2011, when I temporarily switched to a Hold rating. A company with Google’s TAM, Google’s premium revenue growth track record, Google’s extremely high level of innovation, and Google’s profitability—you stick with that. UBER AND DASH—DAMS, SAMS, AND TAMS Here are two recently public companies that provide good examples of large TAMs—two companies that based solely on their TAMs should warrant at least a brief study by tech and growth investors.

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S&P, 281t, 282t and pricing power flywheel, 194–197, 195f product innovation, 119–123 and Qwikster, 206–208 revenue, 97–102 rise of, 5 sell-offs of, 41–45 share price, 42f, 97f, 271f and streaming service, 212–214 sub forecasts of, 76 Network effects, 170 New York Times, 187 Nike, 173 NILE (Blue Nile), 23–24 No earnings, companies with, 242–254, 243t Obama, Michelle, 128 One Up on Wall Street (Lynch), 1, 2, 5, 77 OpenTable, 31 Orbitz, 90 Other Bets segment, of Google, 208 Outliers (Gladwell), 23, 205 Overature, 80 Overstretching, by Groupon, 30–31 Ownership, of mistakes, 219, 221 Page, Larry: and Burning Man, 220t as CEO of Google, 9, 157 as company founder, 145, 146, 204t, 208 innovation by, 209 and Eric Schmidt, 219 PageRank algorithm, 147 Pandora, 132–133, 166, 210 Past performance, of management teams, 222–223 PayPal, 80, 83, 205 PCLN (see Priceline [PCLN]) Peloton: during Covid-19 pandemic, 17, 303 fundamentals at, 261t Pet Valu, 17 Pets.com (IPET), 67, 68 PetSmart, 68 Pinterest: fundamentals at, 261t market cap of, 247t marketing potential on, 137 profitability of, 248–250, 249t as tech stock, 3 Pitt, Brad, 135 Pittman, Bob, 7, 8 Plated, 20 Platform companies: Amazon as, 119 Uber as, 159 valuation of, 241–242 Podcasts, on Spotify, 128, 131 Postmates, 185f Precision trap, 254–255 Priceline (PCLN): acquisitions of, 80 as competition, 31 fundamentals of, 92t, 95t management teams at, 210 marketing by, 168 revenue, 90–96 reverse stock split of, 28 share price, 92f, 94f total addressable market, 164–165 (See also Booking.com [BKNG]) “Priceline Stock: Dominant, Growing, and Undervalued,” 91 Pricing power flywheel, 192–197 Product innovation, 113–141, 295–296 Amazon vs. eBay, 179 Amazon Web Services, 115–119 defining, 114 of Facebook, 268 Google, 155 importance of getting right, 63 Netflix, 119–123, 273 Spotify, 128–133 Stitch Fix, 124–128 Twitter, 133–139 Uber, 275 Profit, 111 Profitability logic tests, 248–254 Psychology, stock-picking and, 16–17 “Pulling a Google,” 153, 164, 264 Purple Carrot, 20 Quarters: challenges with forecasting, 56 trading around, 74 QVC, 24 Qwikster, 121, 206–208 Rallies in the Valley, 44 Randolph, Marc, 204t, 220t Rascoff, Spencer, 188, 191, 209 Redfin (RDFN), 250–251, 251t Regulation, 308–310 Relevance, of lessons, 305–308 Research, conducting your own, 140 Return on investment (ROI), 147–148 Revenue, 75–111, 294–295 Amazon Web Services, 117f during Covid-19 pandemic, 105–107, 304–305 deceleration of, 110 eBay, 83–86 Facebook, 268 and growth curve initiatives, 102–105 importance of, 77–81 in Internet sector, 81–82 Netflix, 97–102, 272–273 Priceline, 90–96 and stock prices, 63–64 20% revenue “rule,” 107–109 Twitter, 135 Uber, 275 and valuation, 78f Yahoo!

Machine, Platform, Crowd: Harnessing Our Digital Future
by Andrew McAfee and Erik Brynjolfsson
Published 26 Jun 2017

Page and Brin added a clever twist by weighting the importance of each link by the number of pages that in turn linked to each of the pages that originated the links, and so on, and so on. 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.

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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. This emergent structure changes and grows as the content itself does, and lets us smoothly and easily navigate all the content that the crowd comes up with. The second problem that inevitably comes with an uncontrolled crowd is that some of its members misbehave in hurtful ways. The core can evict bad actors—from the company, the library, or the payroll—but the web really can’t; it’s too easy to come in by employing another user name or IP address,‡ or to hide behind anonymity.

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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 Overstock.com, 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 Parse.ly, 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
Published 27 Sep 2018

search_query =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.

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(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.

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See also specific topics Neuroscience Research Program (NRP), 92 Neurovigil, 12, 13 Newell, Allen, 31–32, 91, 99 Newsome, William T., 293n16, 315n8 Newton, Isaac, 196 Nguyen, Anh, 139f Nilekani, Nandan, 173 Norman, Donald A., 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.

Work Rules!: Insights From Inside Google That Will Transform How You Live and Lead
by Laszlo Bock
Published 31 Mar 2015

Adam Lashinsky, “Larry Page: Google should be like a family,” Fortune, January 19, 2012, http://fortune.com/2012/01/19/larry-page-google-should-be-like-a-family/. 20. Larry Page’s University of Michigan Commencement Address, http://googlepress.blogspot.com/2009/05/larry-pages-university-of-michigan.html. 21. Mark Malseed, “The Story of Sergey Brin,” Moment, February–March 2007, http://www.momentmag.com/the-story-of-sergey-brin/. 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, http://www.wired.com/wired/archive/13.08/battelle.html.

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Pierce, “Mervin Joe Kelly, 1894–1971” (Washington, DC: National Academy of Sciences, 1975), http://www.nasonline.org/publications/biographical-memoirs/memoir-pdfs/kelly-mervin.pdf. 34. “Google Search Now Supports Cherokee,” Google (official blog), March 25, 2011, http://googleblog.blogspot.com/2011/03/google-search-now-supports-cherokee.html. 35. “Some Weekend Work That Will (Hopefully) Enable More Egyptians to Be Heard,” Google (official blog), January 31, 2011, http://googleblog.blogspot.com/2011/01/some-weekend-work-that-will-hopefully.html. 36. Lashinsky, “Larry Page: Google should be like a family.” 37. Edgar H. Schein, Organizational Culture and Leadership (San Francisco: Jossey-Bass, 2010). 38.

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But our operating assumption is that anything we’re doing, we can do better. The first Google search index in 1998 had twenty-six million unique Web pages. By 2000, it had one billion. 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.

The Economic Singularity: Artificial Intelligence and the Death of Capitalism
by Calum Chace
Published 17 Jul 2016

[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. 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?”

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[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. Like IBM Watson, this is an example of how AI systems are often agglomerations of numerous approaches. In October 2015, Google confirmed that it had added a new technique called RankBrain to its search offering. RankBrain is a machine learning technique, and it was already the third-most important component of the overall search service.

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[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.

Loonshots: How to Nurture the Crazy Ideas That Win Wars, Cure Diseases, and Transform Industries
by Safi Bahcall
Published 19 Mar 2019

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?

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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).

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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).

To Save Everything, Click Here: The Folly of Technological Solutionism
by Evgeny Morozov
Published 15 Nov 2013

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 Hunch.com 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.

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Google also likes to invoke noble terms like “democracy” to show that what its algorithms compute is not just objective but also just. 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.

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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 Personal.com 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 Politifact.com 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.

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
Published 27 Dec 2008

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.

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Arriving at the “Googleplex,” as the campus of office buildings is called, to attend a “Scifoo”—an “un-conference” science camp hosted by Google, the British scientific journal Nature, and the O’Reilly Media Group—I realized that I had parked on the wrong side of the complex. Not to worry. 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.

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When it came time for them to leave their mark, their philanthropy became associated with colossal buildings such as Rockefeller Center, the New York Public Library, and Carnegie Hall. Contrast that to the efforts of Bill Gates, Warren Buffett, and Michael Bloomberg, who have dedicated their wealth—made from “soft” industries—to addressing such issues as malaria and education. It is to this new world of work built by folks like Bloomberg and Google’s Sergey Brin and Larry Page that we now turn. 1∗Never mind that with global reserves dwindling, oil is now increasingly dirty and difficult to extract. 2∗Its main purpose at the time? Helping to build the hydrogen bomb—only the single most destructive invention in human history still to this day. 3∗Americans work an average of 25.1 hours per week (averaged across all working-age persons) in contrast to Germans, for instance, who average 18.6 hours.

Understanding Sponsored Search: Core Elements of Keyword Advertising
by Jim Jansen
Published 25 Jul 2011

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 Terms.com) (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.

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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.

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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 http://www.google.com/press/pressrel/pressrelease39.html [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. google.com/press/pressrel/select.html [13] Google. 2010. Google, Corporate Information, Our Philosophy. Retrieved July 13, 2010, from http://www.google.com/corporate/tenthings.html [14] Saracevic, T. 1975. “Relevance: A Review of and a Framework for the Thinking on the Notion in Information Science.”

Joel on Software
by Joel Spolsky
Published 1 Aug 2004

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 www.google.com/technology/index.html. 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.

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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.

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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 PayMyBills.com 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.

WTF?: What's the Future and Why It's Up to Us
by Tim O'Reilly
Published 9 Oct 2017

See “Hal Varian on How the Web Challenges Managers,” McKinsey & Company, January 2009, http://www.mckinsey.com/industries/high-tech/our-insights/hal-varian-on-how-the-web-challenges-managers. 157 “the right values for these parameters is something of a black art”: Sergey Brin and Larry Page, “The Anatomy of a Large-Scale Hypertextual Web Search Engine,” Stanford University, retrieved March 31, 2017, http://infolab.stanford. edu/~backrub/google.html. 158 as many as 50,000 subsignals: Danny Sullivan, “FAQ: All About the Google RankBrain Algorithm,” Search Engine Land, June 23, 2016, http://searchengine land.com/faq-all-about-the-new-google-rankbrain-algorithm-234440. 158 “new synapses for the global brain”: Tim O’Reilly, “Freebase Will Prove Addictive,” O’Reilly Radar, March 8, 2007, http://radar.oreilly.com/2007/03/free base-will-prove-addictive.html. 158 “10 experiments for every successful launch”: Matt McGee, “BusinessWeek Dives Deep into Google’s Search Quality,” Search Engine Land, October 6, 2009, http://searchengineland.com/businessweek-dives-deep-into-googles-search-quality-27317. 159 the manual that they provide: Search Quality Evaluator Guide, Google, March 14, 2017, http://static.googleusercontent.com/media/www.google.com/en//inside search/howsearchworks/assets/search qualityevaluatorguidelines.pdf. 160 “Another big difference”: Brin and Page, “The Anatomy of a Large-Scale Hypertextual Web Search Engine,” Section 3.2.

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Figuring out the right values for these parameters is something of a black art.” Google says that the number of signals used to calculate relevance has grown to over 200, and search engine marketing guru Danny Sullivan estimates that there may be as many as 50,000 subsignals. Each of these signals is measured and calculated by a complex of programs and algorithms, each with its own fitness function it is trying to optimize. 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.

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And as to congestion, while the current algorithm is optimized to create shorter wait times, there is no reason it couldn’t take into account other factors that improve customer satisfaction and lower cost, such as the impact of too many drivers on congestion and wait time. Algorithmic dispatch and routing is in its early stages; to think otherwise is to believe that the evolution of Google Search ended in 1998 with the invention of PageRank. For this multi-factor optimization to work, though, Uber and Lyft have to make a deep commitment to evolving their algorithms to take into account all of the stakeholders in their marketplace. It is not clear that they are doing so. Understanding the differences between means and ends is a good way to help untangle the regulatory disagreements between the TNCs (transportation network companies) and taxi and limousine regulators.

The Signal and the Noise: Why So Many Predictions Fail-But Some Don't
by Nate Silver
Published 31 Aug 2012

Bill Wyman, “The 100 Greatest Moments in Rock History,” Chicago Reader, September 28, 1995. http://www.chicagoreader.com/chicago/the-100-greatest-moments-in-rock-history/Content?oid=888578. 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. http://web.archive.org/web/20020506051802/www-diglib.stanford.edu/cgi-bin/WP/get/SIDL-WP-1997-0072?1. 46. “How Search Works,” by Google via YouTube, March 4, 2010. http://www.youtube.com/watch?v=BNHR6IQJGZs. 47. Per interview with Vasik Rajlich. 48. “Amateurs beat GMs in PAL / CSS Freestyle,” ChessBase News. http://www.chessbase.com/newsdetail.asp?

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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. Of course, this is not such an easy task—two hundred signals applied to an almost infinite array of potential search queries. This is why Google places so much emphasis on experimentation and testing. The product you know as Google search, as good as it is, will very probably be a little bit different tomorrow.

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David, 206 Mathis, Catherine, 25, 462 matrices, in weather forecasting, 114–18 Mauna Loa Observatory, 375, 401 Maunder Minimum, 392 Mayfield, Max, 109, 110, 138–41 measles, 214, 223–24, 225 Mechanical Turk, 262–64, 263, 265, 281, 282 media bias, 60 medical diagnoses, 448 meditation, 328 Medvedev, Dmitri, 48 Memphis, Tenn., 396 Mercury, 374 Merrill Lynch, 353 metacognition, 273 methane, 374, 375 Met Office (UK), 394, 408 Mexico, 210, 215–16 Mexico City, 144 middle class, 189 Middle East, 398 Midway Islands, 413 Milledge, Lastings, 89 Millikan, Arikia, 334 mind blindness, 419 minor league system, 92–93 Mississippi, 109, 123–24 MIT, 384 MMR shots, 224 modeling for insights, 229 models: agent-based, 226, 227–29, 230 bugs in, 285–86 of CDO defaults, 13, 22, 26, 27, 29, 42, 45 for chess, 267 of climate system, 371, 380, 384–85, 401–6, 402 crudeness of, 7 of elections, 15 foxlike approach of, 68 FRED, 226 fundamentals-based, 68 language as, 230 naïve trust in, 11 overfitting in, 163–71, 166, 168–71, 185, 191, 452n, 478 for predicting earthquakes, 158–61, 167 regression, 100 signal vs. noise in, 388–89 SIR, 220–21, 221, 223, 225, 389 thought experiments as, 488 use and abuse of, 230 as useful even in failure, 230–31 for weather forecasting, 114–18, 119, 120, 121, 122, 123–25, 225, 226, 388 Model T, 212 Mojave Desert, 159–60 Molina, Yadier, 101 moment magnitude scale, 142n momentum trading, 344–45, 345, 368 Moneyball (Lewis), 9, 10, 77, 86, 87, 92, 93–94, 95, 99, 101, 105, 107, 314, 446 Moneymaker, Chris, 294–95, 296, 327 Mongols, 145n Monroe Doctrine, 419 Moody’s, 19, 24–25, 43, 44, 45, 463 Morgan, Joe, 102 Morris, Dick, 55, 56, 61 mortgage-backed securities, 462 home sales vs., 34–35, 35, 39, 42, 43 nonlinearity of, 119 ratings of, 19, 20, 24, 68 shorting of, 355 mortgages, 24 defaults on, 27–29, 184 subprime, 27, 33, 464 Mount Pinatubo, 392, 399–400 Moussaoui, Zacarias, 422, 444 MRSA, 227, 228 MSM, 222, 222, 487 MSNBC, 51n Müller-Lyer illusion, 366, 367 multiplier effect, 42 mumps, 224 Murphy, Allan, 129 Murphy, Donald, 89 mutual funds, 339–40, 340, 356, 363–64, 498 Nadal, Rafael, 331, 357-58, 496 Naehring, Tim, 77 Nagasaki, Japan, 432 Nagin, Ray, 110, 140–41 Napoleon I, Emperor of France, 262 NASA, 174–75, 370, 379, 393–95 NASDAQ, 346, 346, 348, 365 Nash, John, 419 National Academy of Sciences, 384 National Basketball Association (NBA), 92, 234–40, 255n National Center for Atmospheric Research (NCAR), 110, 111, 118 National Collegiate Athletic Association (NCAA), 451n national debt, 189, 509 National Economic Council, 37 National Football League (NFL), 92, 185–86, 336, 480 National Hurricane Center, 108, 109–10, 126, 138–41 National Institute of Nuclear Physics, 143 National Journal, 57–58 National League, 79 National Oceanic and Atmospheric Administration (NOAA), 122, 393–95 National Park Service, 267 National Science Foundation, 473 National Weather Service (NWS), 21, 122–23, 125, 126, 127–28, 131, 135, 139, 178–79, 393–94 NATO, 428–29, 429, 430–31, 431, 437, 438, 439 Nature, 13, 254, 409 Nauru, 372 nearest neighbor analysis, 85 negative feedback, 38, 39 neighborhoods, 224–25, 226–27, 230 Netherlands, 31, 210 New Jersey, 391 New Madrid Fault, 154 New Orleans, La., 108–9, 138, 139–40, 387, 388 Newsweek, 399 Newton, Isaac, 112, 114, 118, 241, 249, 448 New York, N.Y., 219n, 391, 391, 396, 432, 474, 514 New Yorker, 103 New York Knicks, 119 New York Stock Exchange, 329, 363, 370 New York Times, 146, 205–6, 276, 281, 356, 433, 484 New York Yankees, 74 New Zealand, 210 9/11 Commission, 444, 445 9/11 Commission Report, 423 Ninety-Five Theses (Luther), 4 Ningirsu, 112 nitrous oxide, 375 Nixon, Richard, 400 No Free Lunch, 361–62 noise, 63, 250 in batting averages, 339 in climatology, 371–73 definitions of, 416 in financial markets, 362–64 increase in, 13 in predictive models, 388–89 signals vs., 8, 13, 17, 60, 81, 133, 145, 154, 162, 163, 173, 185, 196, 285–86, 295, 327, 340, 371–73, 388–89, 390–91, 404, 448, 451, 453 in stock market, 368 “Noise” (Black), 362 no-limit hold ’em, 300–308, 309–11, 315–16, 316, 318, 324n, 495 nonlinear systems, 29, 118–19, 120, 376–77 Nordhaus, William, 398 North American Aerospace Defense Command (NORAD), 423 Norway, 31 NRSROs, see ratings agencies Nuclear Cities Initiative, 512 nuclear weapons, 434, 436, 438 see also weapons of mass destruction null hypothesis, 260 see also statistical significance test Nunn, Sam, 434 Oakland Athletics, 87, 92, 99–100, 106, 471 Obama, Barack, 40, 49, 55, 59, 252, 358, 379, 444, 468, 473 obesity, 372, 373 objective truth, 14 objectivity, 14, 64, 72–73, 100, 252, 253, 255, 258-59, 288, 313, 403, 453 observer effect, 188, 472 Occam’s razor, 389 Odean, Terrance, 359 Oklahoma City bombing, 425, 427 Okun’s law, 189 Omaha, Nebr., 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 PokerKingBlog.com, 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 RealClimate.org, 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 rec.sport.baseball, 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.

The Secret War Between Downloading and Uploading: Tales of the Computer as Culture Machine
by Peter Lunenfeld
Published 31 Mar 2011

Raymond, The Cathedral and the Bazaar: Musings on Linux and Open Source by an Accidental Revolutionary (Cambridge, MA: O’Reilly, 1999), available at <http://www.catb.org/~esr/writings/cathedral-bazaar/>. 29 . In the corporation’s own words, from “Ten Things Google Has Found to Be True,” available at <http://www.google.com/intl/en/corporate/tenthings.html>: 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.

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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.

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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.

The Crux
by Richard Rumelt
Published 27 Apr 2022

When I had been talking with Bill Gross in Pasadena, Google founders Larry Page and Sergey Brin were receiving their first round of $25 million in venture capital. They were also trying to fix search and had invented a clever algorithm (PageRank) that became best in the industry. They had solved the search problem, but they also struggled with the how-to-make-money problem. They saw Gross’s GoTo but were dead set against ruining their PageRank search results with paid-for links. Their challenge was to provide accurate search results but also, somehow, to make money. Sometime in early 1999, Sal Kamangar, Google’s ninth employee, managed a team that defined and built Google’s AdWords system.

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Alphabet Acquisitions in 2016 Company Business Complement to BandPage Platform for musicians YouTube Pie Business communications Spaces Synergyse Interactive tutorials Google Docs Webpass Internet service provider Google Fiber Moodstocks Image recognition Google Photos Anvato Cloud-based video services Google Cloud Platform Kifi Link management Spaces LaunchKit Mobile tool maker Firebase Orbitera Cloud software Google Cloud Platform Apigee API mgmt and predictive analytics Google Cloud Platform Urban Engines Location-based analytics Google Maps API.AI Natural language processing Google Assistant FameBit Branded content YouTube Eyefluence Eye tracking, virtual reality Google VR LeapDroid Android emulator Android Qwiklabs Cloud-based training platform Google Cloud Platform Cronologics Smartwatches Android Wear Source: https://en.wikipedia.org/wiki/List_of_mergers_and_acquisitions_by_Alphabet reproduced via Creative Commons license https://creativecommons.org/licenses/by-sa/3.0 INGREDIENT 5: DON’T OVERPAY One reason so many research studies keep showing negative returns to acquiring firms is that acquirers are overpaying for what they get.

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In 2002 Overture filed a patent infringement suit against Google that was settled in 2003 for a payment of $350 million in Google shares. Overture’s basic claim was for “a method of generating a search result list … [and] ordering the identified search listings into a search result list in accordance with the value of the respective bid amounts.” Since Google did not use bids to order its search results, Overture’s patent may or may not have had relevance to Google. 15. With the rise of mobile search, Google moved paid ads to the top of the search results, somewhat fogging the issue. Today, in 2021, Google has unfortunately further blurred the line between organic search results and paid advertising with formatting changes that make it hard to tell the difference.

I Hate the Internet: A Novel
by Jarett Kobek
Published 3 Nov 2016

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. 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.

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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.

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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.

Little Bets: How Breakthrough Ideas Emerge From Small Discoveries
by Peter Sims
Published 18 Apr 2011

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.

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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.

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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.

Human Frontiers: The Future of Big Ideas in an Age of Small Thinking
by Michael Bhaskar
Published 2 Nov 2021

Business trumpets its capacity for innovation even as it channels resources into the most cautious forms of innovation. The economist Mariana Mazzucato argues that cheerleaders for innovation like Google, Apple and big pharma are in fact reliant on government-built technologies. The basic technologies behind the iPhone, like GPS, capacitive touchscreens, voice-enabled assistants and Internet connectivity all relied on government-funded support; Google's core PageRank algorithm even acknowledges it on the patent. Developing new treatments for disease requires huge grants from the National Institutes of Health or similar; only once foundational work is established do startups and corporates step in.

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Quite simply, the more you have in any given area, the more difficult further breakthroughs become – like wealth, there is a declining marginal utility to ideas. The first iPhone was a huge breakthrough, a new kind of device. A new smartphone is better, more widespread, but it's just another phone. The Google algorithm, like the iPhone, was a colossal breakthrough in consumer tech (and computer science); but today, with thousands of the world's finest minds working on it, PageRank improves at a relatively slower rate than its initial creation. Even over much longer timescales this effect plays out. Arguably human wisdom or morality has not improved for thousands of years – it's hard to say we have decisively gone beyond Buddhism or the Stoics in this regard, and perhaps we never will or can.54 And there is a further mechanism: the more we have, the more we may overlook or misunderstand potential breakthroughs.

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Perhaps we could redesign curricula around discovery and experiment; move away from ticking boxes, and towards imagination and the free play of ideas. Look at the successes of Finnish education, now an exemplar, and its principles based on teaching things like transversal thinking. Schools could aim to incorporate more insights from Montessori Schools, which educated entrepreneurs including Jeff Bezos, Larry Page, Sergey Brin and Jimmy Wales.38 Their success hints at the potential in self- and peer-directed learning. In India a research programme showed how effectively school children learned on their own, unaided, when using and programming a computer. But the power of peer learning applies at university level as well.

You Are Not a Gadget
by Jaron Lanier
Published 12 Jan 2010

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.

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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.

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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.

Choose Yourself!
by James Altucher
Published 14 Sep 2013

* * * 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.

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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. Right down my entire routine from beginning to end as detailed as possible and change one thing and make it better. Ten chapters for my next book. Ten ways I can save time. For instance, don’t watch TV, drink, have stupid business calls, don’t play chess during the day, don’t have dinner (I definitely will not starve), don’t go into the city to meet one person at a time for coffee, don’t waste time being angry at that person who did X, Y, and Z to you, and so on.

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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”.

Internet for the People: The Fight for Our Digital Future
by Ben Tarnoff
Published 13 Jun 2022

The method outlined in the paper, called PageRank, drew on a technique known as citation analysis; see Safiya Umoja Noble, Algorithms of Oppression: How Search Engines Reinforce Racism (New York: New York University Press, 2018), 38–41. 89, But as Google moved off-campus … More than 4 million searches a day: Sergey Brin, interview by Leslie Walker, WashingtonPost.com, November 4, 1999. 90, The company began using … Levy identifies Google engineer Amit Patel as the first one who “realized the value of Google’s logs”; see In the Plex, 45–49. This is seconded in Douglas Edwards, I’m Feeling Lucky: The Confessions of Google Employee Number 59 (Boston: Houghton Mifflin Harcourt, 2011), which states that Patel’s first major project at Google involved creating “a rudimentary system to make sense of the logs that recorded user interactions with our site,” though according to Edwards, it would take three more years of development before Google’s logs analytics tool, “Sawmill,” was “activated” in 2003; see 344–45.

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It was still a prototype—it ran on a set of scavenged and thrifted computers in a dorm room—but a prototype that had become so popular that, at peak times, it used half of the university’s internet bandwidth. The students called it Google. That year, they founded a company of the same name. The story of Google has been told many times before. It has been celebrated and emulated, critiqued and parodied. But what has receded in the telling and retelling of this story is the original problem that the young Larry Page and Sergey Brin were trying to solve. This is the problem of having too much data. Having too much data was one of many scenarios unforeseen by the internet’s architects.

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A note on my use of “Google”: since a corporate restructuring in 2015, Google’s parent company is called Alphabet, and Google is technically a subsidiary. But for simplicity’s sake I use “Google” to refer to both the parent company and its subsidiaries throughout the book. I do the same with Facebook, which in late 2021 rebranded as Meta. 29, Finding a more direct … The interconnection of small and medium-sized networks is known as “donut peering”; the “hole” in the donut consists of the larger networks that are being bypassed. Google Fiber: David Anders, “Whatever Happened to Google Fiber?,” CNET, March 5, 2021. 29, Content providers like Google … Own or lease more than half of undersea bandwidth: Adam Satariano, “How the Internet Travels Across Oceans,” New York Times, March 10, 2019.

Lurking: How a Person Became a User
by Joanne McNeil
Published 25 Feb 2020

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.

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Wishes, dreams, fears, wonderings—the glimmer of ordinary life—are specks in the sandbox that is its search box. 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.

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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. A modest scandal in his love life was reported in Vanity Fair. Meanwhile Page tends to let the company speak for him, an unusually subdued public profile in a region full of big personalities and eccentrics.

Super Founders: What Data Reveals About Billion-Dollar Startups
by Ali Tamaseb
Published 14 Sep 2021

Flagship Pioneering, a Boston-based venture-creation firm, has launched multiple large startups this way, including Moderna Therapeutics, which develops mRNA drugs and vaccines, and was one of the first companies to successfully develop a vaccine for COVID-19. Other ideas have sprung out of academic institutions. Google’s founders developed the PageRank algorithm while at Stanford and hosted Google on Stanford’s domain, as google.stanford.edu, until google.com was registered in 1997. Genentech, the biotechnology corporation, got its start from academic intellectual property. Before Robert Swanson launched the company, he was working as an associate at the recently formed venture capital firm Kleiner Perkins, where he learned about recombinant DNA technology through an investment the firm had made.

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It’s a myth that all billion-dollar companies are created by mission-driven founders solving their own personal problems. You can win as a missionary, and you can win as a mercenary. Elad Gil, one of the best angel investors in tech startups, pointed out to me that many approaches have worked in practice: Larry Page at Google was very mission driven in terms of organizing the world’s information and making it universally accessible and useful. At the same time, they were willing to sell Google for a million dollars very early on. So I think a lot of that belief in that mission sometimes comes immediately, but sometimes it comes later as the company is successful and people realize that they’re onto something and then it turns into their life mission.

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Founders of health and biotech billion-dollar startups were on average older, and founders of any age were successful at consumer and enterprise. ON SOLO FOUNDERS There’s another myth that founders will fail if they don’t have a partner alongside them. There are so many successful duos—Larry Page and Sergey Brin of Google, Steve Jobs and Steve Wozniak of Apple, Bill Hewlett and David Packard of HP—that it’s almost hard to imagine starting a company without a co-founder. In fact, most aspiring entrepreneurs are advised not to. This standard startup advice is so ingrained that some incubators and accelerator programs push founders away from solo entrepreneurship and encourage co-founder “dating” rituals as part of the program.

Adapt: Why Success Always Starts With Failure
by Tim Harford
Published 1 Jun 2011

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.

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Hamel comments that ‘like an organism favored by genetic good fortune, Google’s success owes much to serendipity’. That is true of many successful companies – John Mackey, the CEO of Whole Foods, calls himself ‘the accidental grocer’ – but Google have elevated it to a guiding principle. 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.

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Page had been trying to figure out whether it might be possible for Google to scan the world’s books into searchable form. 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.

Remix: Making Art and Commerce Thrive in the Hybrid Economy
by Lawrence Lessig
Published 2 Jan 2009

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 http://google.stanford.edu), 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.

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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.

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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.

The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do
by Erik J. Larson
Published 5 Apr 2021

Universities launched projects in natural language understanding and natural language processing. They found ways to, for example, extract names and other patterns from web pages (a capability called entity recognition); to disambiguate polysemous (multi-sense) words such as bank; to perform web-specific tasks like ranking and retrieving web pages (the famous example being Google’s PageRank, which Larry Page and Sergey Brin developed as Stanford graduate students in the 1990s); to classify news stories and other web pages by topic; to filter spam for email; and to serve up spontaneous product recommendations on commerce sites like Amazon. The list goes on and on. The shift away from linguistics and rule-based approaches to data-driven or “empirical” methods seemed to liberate AI from those early, cloudy days of work on machine translation, when seemingly endless problems with capturing meaning and context plagued engineering efforts.

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computer), 222–224 deep reinforcement learning, 125, 127 Dostoevsky, Fyodor, 64 Dreyfus, Hubert, 48, 74 earthquake prediction, 260–261 Eco, Umberto, 186 Edison, Thomas, 45 Einstein, Albert, 239, 276 ELIZA (computer program), 58–59, 192–193, 229 email, filtering spam in, 134–135 empirical constraint, 146–149, 173 Enigma (code making machine), 21, 23–24 entity recognition, 137 Etzioni, Oren, 129, 143–144 Eugene Goostman (computer program), 191–195, 214–216 evolutionary technology, 41–42 Ex Machina (film, Garland), 61, 78–80, 82, 84, 277 Facebook, 147, 229, 243 facts, data turned into, 291n12 Farecast (firm), 143–144 feature extraction, 146–147 Ferrucci, Dave, 222, 226 filter bubbles, 151 financial markets, 124 Fisch, Max H., 96–97 Fodor, Jerry, 53 formal systems, 284n6 Frankenstein (fictional character), 238 Frankenstein: Or, a Modern Prometheus (novel, Shelly), 238, 280 frequency assumptions, 150–154, 173 Fully Automated High-Quality Machine Translation, 48 functions, 139 Galileo, 160 gambler’s fallacy, 122 games, 125–126 Gardner, Dan, 69–70 Garland, Alex, 79, 80, 289n16 Gates, Bill, 75 general intelligence, 2, 31, 36; abduction in, 4; in machines, 38; nonexistance of, 27; possible theory of, 271 General Problem Solver (AI program), 51 Germany: Enigma machine of, 23–24; during World War II, 20–21 Go (game), 125, 131, 161–162 Gödel, Kurt, 11, 22, 239; incompleteness theorems of, 12–15; Turing on, 16–18 Golden, Rebecca, 250 Good, I. J. “Jack,” 3, 19; on computers, 46; on intelligence, 33–35, 37, 43, 62; Von Neumann on, 36 Google (firm), 220, 244 Google Brain (computer program), 296n4 Google Duplex, 227 Google Photos, 278–279 Google Talk to Books, 228 Google Translate (computer program), 56, 201, 202 Goostman, Eugene (computer program), 191–195, 214–216 gravimetrics, 157 gravity, 187 Great Britain, code breaking during World War II by, 20–24 Grice, Paul, 215 Grice’s Maxims, 215–216 guesses, 160, 183–184 Haugeland, John, 179, 294n17 Hawking, Stephen, 75 Hawkins, Jeff, 263, 264 Heisenberg, Werner, 72 hierarchical hidden Markov models, 265–266 Higgs, Peter, 254–255 Higgs boson, 254–255, 257–258 Hilbert, David, 14–16 Hill, Sean, 245, 246, 248 Hinton, Geoff, 75 hive mind: online collaboration as, 241–242; origins in Star Trek of, 240; science collaborations as, 245 Homo faber (man the builder), 65, 66 Horgan, John, 275–278 Hottois, Gilbert, 287n1 Human Brain Project, 245, 247–254, 256, 267–268 Human Genome Project, 252 human intelligence: artificial intelligence versus, 1–2; behaviorism on, 69; Data Brain projects and, 251; Good on, 33–35; infinite amount of knowledge in, 54; neocortical theories of, 263–268; as problem solving, 23; singularity as merging with machine intelligence, 47–48; social intelligence, 26–28; Stuart Russell’s definition of, 77, 83; thinking in, 184–186; Turing on, 23, 27–31, 30–32 human language.

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It might mean a writing instrument, or it might mean a small enclosure for animals. Using Google Translate, The box is in the pen translates to La boîte est dans le stylo, in French, where stylo means writing instrument, which isn’t the preferred interpretation (because boxes are typically larger than writing pens). In other words, “good enough” translation depends on one’s requirements. Google Translate might get less contextualized sentences most of the time, but there will be errors—and the errors might matter, depending on the person using the service. Services like Google Translate actually underscore the long-tail problem of statistical, or inductive, approaches that get worse on less likely examples or interpretations.

All the Money in the World
by Peter W. Bernstein
Published 17 Dec 2008

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

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But he only crossed the billion-dollar threshold 16 years later, when he was 43. He was worth $1.6 billion at age 45. 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.

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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.

Talk to Me: How Voice Computing Will Transform the Way We Live, Work, and Think
by James Vlahos
Published 1 Mar 2019

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.

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“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.

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Their artificially intelligent brains, in an overwhelming majority of cases, are made either by Amazon or Google. The name of Amazon’s AI is Alexa; her rival is the Google Assistant. The two tech giants are going about their business in very different ways at CES. Google is pulling out all of the promotional stops to declare that this is its trade show, its moment. All around Las Vegas, Google has made sure that a certain two words are ubiquitous. They are the ones that tell the Assistant to listen to users through any connected device: “Hey, Google.” The words are spelled out in giant letters on the monorail train that glides past the Strip: “Hey, Google.” On billboard-size video screens, murals, and walls: “Hey, Google.”

Coders: The Making of a New Tribe and the Remaking of the World
by Clive Thompson
Published 26 Mar 2019

Breisacher worked on Chrome for two years, and spent six in total at Google, an eternity in tech time. 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.

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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.

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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.

The Master Switch: The Rise and Fall of Information Empires
by Tim Wu
Published 2 Nov 2010

A company like Google, in contrast, succeeds by doing one (well-chosen) thing, but doing it better than anyone else. It’s the trait that makes Google the hedgehog to so many others’ fox. 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.

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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 http://google.stanford.edu/, 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.

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There are plenty of ways around Google: you can use domain names to navigate the Internet, or use one of Google’s competitors (Yahoo!, Bing, and the like), or for the truly hard-core, simply remember the IP addresses (e.g., 98.130.232.209), the way people once used to remember phone numbers. In fact, unlike AT&T, Google could be replaced at any time. And yet if by 2010 Google wasn’t the only game in town, it was clearly the most popular Internet switch; by its market share of the search business (over 65 percent) it clearly qualifies as a monopoly. In some ways, Google nevertheless enjoys a much broader control over switching than the old AT&T ever did.

Palo Alto: A History of California, Capitalism, and the World
by Malcolm Harris
Published 14 Feb 2023

Despite the host of exotic commercial fields Google has entered since, as well as the company’s reorganization under the Alphabet holding entity, Google advertising still, almost 20 years later, provides more than 80 percent of the conglomerate’s revenue.23 Accumulating information was the key to Google’s advantage. The PageRank search model—a useful scavenger for the ecosystem—was based on crawling and scraping the internet’s organic map of hyperlinks. As it scaled up, Google continued to make use of this efficient tool and the orientation behind it. After surviving the quick crash, it snapped up the online diary provider Blogger, which hadn’t been so lucky. It was expand or die, and the new CEO, Eric Schmidt, was all about growth. In 2004, Google took a very public shot at the web portal players Yahoo! and Microsoft.

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What Page was left with (and later, what he and Brin were left with) was more than a cool infographic; it was an index of citations. If one assumed that, as with scientific papers, the more frequently cited pages tended to be more useful, then the crawler’s map was instructional. This was the beginning of Larry’s PageRank algorithm, which was the beginning of Google. Hosted on the Stanford University Network and its Sun equipment, Google was a leap ahead of other search engines. Like Napster, the clever crawler went from cool project to essential internet tool in a matter of months. Page and Brin got the money they needed to leave Stanford from members of the informal comp sci fraternity: Sun founder Andy Bechtolsheim and his high-speed-Ethernet Granite Systems cofounder (and Stanford prof), David Cheriton, gave $100,000 each.19 They could afford it, recall, having recently sold the start-up to Cisco for a couple of hundred million dollars.20 It was a good investment.

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Smart founders figured out that though they could get a lot of users fast by sticking it to the man, their projects were better off in the medium term if they had symbiotic models and offered significant value to everyone involved. That’s not what the Stanford computer science students Larry Page and Sergey Brin were thinking about when they were building Google, but that’s what they found. Both were born in 1973, which made them young PhD students in the late ’90s. It had been almost a generation since Jobs and Gates, and to get to the top of the class it was no longer enough to be a curious kid with access to a computer.

Computer: A History of the Information Machine
by Martin Campbell-Kelly and Nathan Ensmenger
Published 29 Jul 2013

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 google.stanford.edu 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.

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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.

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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.

Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity
by Daron Acemoglu and Simon Johnson
Published 15 May 2023

Why not let human choices about linkages guide how search algorithms should prioritize relevant websites? This was at first a theoretical idea—the realization that this could be done. Then came the algorithmic solution of how to do it. This was the basis of their revolutionary PageRank algorithm (“Page” here reputedly refers both to Larry Page and the fact that pages are being ranked). Among the relevant pages, the idea was to prioritize those that received more links. So rather than using some ad hoc rules to decide which ones of the pages that have the word Neolithic should be suggested, the algorithm would rank these pages according to how many incoming links they received.

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Brin and Page’s 1998 paper, titled “The Anatomy of a Large-Scale Hypertextual Web Search Engine,” starts with this sentence: “In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems.” Page and Brin understood that this was a major breakthrough but did not have a clear plan for commercializing it. Larry Page is quoted as saying that “amazingly, I had no thought of building a search engine. The idea wasn’t even on the radar.”

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Of course, the problem is that many of these mentions are not that relevant, and only one or two websites would be the authoritative sources in which one can obtain the necessary information about the Neolithic Age and how, say, humans transitioned to settled life and permanent agriculture. Only a way of prioritizing the more important mentions would enable the relevant information to be quickly retrieved. But this is not what the early search engines were capable of doing. Enter two brash, smart young men, Larry Page and Sergey Brin. Page was a graduate student, working with the famous computer scientist Terry Winograd at Stanford, and Sergey Brin was his friend. Winograd, an early enthusiast for the currently dominant paradigm of AI, had by that point changed his mind and was working on problems in which human and machine knowledge could be combined, very much as Wiener, Licklider, and Engelbart had envisaged.

Facebook: The Inside Story
by Steven Levy
Published 25 Feb 2020

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.

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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.

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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. Holding 56 percent of the voting shares, Zuckerberg himself would have veto power over anything that other shareholders, or the board of directors, might order. Likewise, he mimicked the Google guys when he personally wrote a letter to shareholders in the S-1 prospectus that laid out the terms of the offering when it was announced on February 1, 2012.

The Information: A History, a Theory, a Flood
by James Gleick
Published 1 Mar 2011

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.

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.: 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.”

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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 http://ilpubs.stanford.edu:8090/422/1/1999-66.pdf. Pain, Stephanie. “Mr. Babbage and the Buskers.” New Scientist 179, no. 2408 (2003): 42. Paine, Albert Bigelow.

The Narrow Corridor: States, Societies, and the Fate of Liberty
by Daron Acemoglu and James A. Robinson
Published 23 Sep 2019

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. 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.

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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. Early success also brought more resources to invest in research and development and acquire companies that were developing technology that would be useful to Google’s further expansion. Winner-take-all effects were also at the root of the meteoric rise of Amazon, whose early growth as an online retailer and platform made it more attractive to sellers and users, and of Facebook, whose popularity as a social media platform critically depends on the users’ expectations that their friends are joining as well.

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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 Age of Surveillance Capitalism
by Shoshana Zuboff
Published 15 Jan 2019

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, http://www.khoslaventures.com/fireside-chat-with-google-co-founders-larry-page-and-sergey-brin. 7. Miguel Helft, “Fortune Exclusive: Larry Page on Google,” Fortune, December 11, 2012, http://fortune.com/2012/12/11/fortune-exclusive-larry-page-on-google. 8. Khosla, “Fireside Chat.” 9. Larry Page, “2013 Google I/O Keynote,” Google I/O, May 15, 2013, http://www.pcworld.com/article/2038841/hello-larry-googles-page-on-negativity-laws-and-competitors.html. 10. “Facebook’s (FB) CEO Mark Zuckerberg on Q4 2014 Results—Earnings Call Transcript,” Seeking Alpha, January 29, 2015, https://seekingalpha.com/article/2860966-facebooks-fb-ceo-mark-zuckerberg-on-q4-2014-results-earnings-call-transcript. 11.

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Eric Schmidt, “Alphabet’s Eric Schmidt: We Should Embrace Machine Learning—Not Fear It,” Newsweek, January 10, 2017, http://www.newsweek.com/2017/01/20/google-eric-schmidt-embrace-machine-learning-not-fear-it-540369.html. 4. Richard Waters, “FT Interview with Google Co-founder and CEO Larry Page,” Financial Times, October 31, 2014, http://www.ft.com/intl/cms/s/2/3173f19e-5fbc-11e4-8c27-00144feabdc0.html#axzz3JjXPNno5. 5. Marcus Wohlsen, “Larry Page Lays Out His Plan for Your Future,” Wired, March 2014, https://www.wired.com/2014/03/larry-page-using-google-build-future-well-living. 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, http://www.khoslaventures.com/fireside-chat-with-google-co-founders-larry-page-and-sergey-brin. 7.

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Vinod Khosla, “Fireside Chat with Google Co-Founders, Larry Page and Sergey Brin,” Khosla Ventures, July 3, 2014, http://www.khoslaventures.com/fireside-chat-with-google-co-founders-larry-page-and-sergey-brin. 24. Holman W. Jenkins, “Google and the Search for the Future,” Wall Street Journal, August 14, 2010, http://www.wsj.com/articles/SB10001424052748704901104575423294099527212. 25. See Lillian Cunningham, “Google’s Eric Schmidt Expounds on His Senate Testimony,” Washington Post, September 30, 2011, http://www.washingtonpost.com/national/on-leadership/googles-eric-schmidt-expounds-on-his-senate-testimony/2011/09/30/gIQAPyVgCL_story.html. 26.

Age of Context: Mobile, Sensors, Data and the Future of Privacy
by Robert Scoble and Shel Israel
Published 4 Sep 2013

This and many other nascent revolutionary applications of contextual software are right around the corner. 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.

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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. Unperturbed, Scoble spent far more time taking pictures than posing for them. In his first four months with Glass he took more than 6000 photos—along with dozens of videos—and he had only just begun. He says that’s about double the rate he was shooting on his smartphones.

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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. It could tell by your inquiry pattern that when you searched for “park in San Francisco” you wanted greenery and not some place to leave your car. Essentially, Google reversed the data equation. Instead of you learning to speak in a machine language, Google started to make machines recognize your natural language. This has made all the difference in the world.

The Numerati
by Stephen Baker
Published 11 Aug 2008

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.

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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.

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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.

Curation Nation
by Rosenbaum, Steven
Published 27 Jan 2011

(See also Advertising) Brin, Sergey Brinkley, Alan Britain’s Got Talent (TV program) BROADCAST: New York (TV program) Broadcast.com Brogan, Chris Broken, nature of Brooklyn Flea swap Burn Rate (Wolff) Business Insider buy.at Buzzmachine.com 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 chrisbrogan.com Citizens, of Curation Nation City Winery Civic leaders, of Curation Nation Clean, Steven Clinton, Bill CNBC CNN Cognitive Surplus (Shirky) Collins, Shawn Comcast ComcastMustDie.com 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 CreditCards.com 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) DailyFinance.com 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 Golfnow.com 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.

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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. That the concept of page and Larry’s last name were the same will go down as one of those great coincidences of history. While Sergey Brin, Google’s other cofounder, is no less brilliant, there’s no useful way to create a measure of quality called a Brin—though arguably Dewey did that very thing back in 1876.

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The sheer size and openness of the Web made human categorization impossible. 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).

The Creativity Code: How AI Is Learning to Write, Paint and Think
by Marcus Du Sautoy
Published 7 Mar 2019

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.

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By getting people to vote for a post on the site containing the words ‘idiot’ and an image of Trump, the connection between the two shot to the top of the Google ranking. The spike was smoothed out over time by the algorithm rather than by manual intervention. 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’.

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Although the basic engine is very public, there are parameters inside the algorithm that are kept secret and change over time, and which make the algorithm a little harder to hack. 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.

Everydata: The Misinformation Hidden in the Little Data You Consume Every Day
by John H. Johnson
Published 27 Apr 2016

Emily Oster, “Take Back Your Pregnancy,” Wall Street Journal website, August 9, 2013, http://www.wsj.com/news/articles/SB10001424127887323514404578652091268307904. 21. “The Value of Google Result Positioning,” Chitika website, June 7, 2013, https://chitika.com/google-positioning-value. 22. “Algorithms,” Google website, accessed April 20, 2015, http://www.google.com/insidesearch/howsearchworks/algorithms.html. 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, https://moz.com/search-ranking-factors/correlations. 24.

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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.

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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? Well, if you click over to Moz.com, you’ll see charts showing how more than 160 factors correlate to search engine rankings.23 It’s interesting stuff, and probably very useful if you’re looking for ways to increase your page ranking. But it’s not definitive, because it’s based largely on correlations. To its credit, Moz.com uses the word “correlation” 12 times on the page.24 In a separate blog post, it goes even further, explaining that “correlation data isn’t (necessarily) showing us ranking factors.”25 Sometimes, you simply can’t get your hands on the omitted variables.

Tools of Titans: The Tactics, Routines, and Habits of Billionaires, Icons, and World-Class Performers
by Timothy Ferriss
Published 6 Dec 2016

“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.”

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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.

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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?