social graph

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pages: 541 words: 109,698

Mining the Social Web: Finding Needles in the Social Haystack by Matthew A. Russell


Climategate, cloud computing, crowdsourcing,, fault tolerance, Firefox, full text search, Georg Cantor, Google Earth, information retrieval, Mark Zuckerberg, natural language processing, NP-complete, profit motive, Saturday Night Live, semantic web, Silicon Valley, slashdot, social graph, social web, statistical model, Steve Jobs, supply-chain management, text mining, traveling salesman, Turing test, web application

For example, if Matthew is referenced in one hyperlink with the URL but as in another URL, those two nodes will remain distinct in the graph even though they most likely point to the same resource on the Web. Fortunately, XFN defines a special rel="me" value that can be used for identity consolidation. Google’s Social Graph API takes this very approach to connect a user’s various profiles, and there exist many examples of services that use rel="me" to allow users to connect profiles across multiple external sites. Another (much lesser) issue in resolving URLs is the use or omission of a trailing slash at the end. Most well-designed sites will automatically redirect one to the other, so this detail is mostly a nonissue. Fortunately, others have also recognized these kinds of problems and decided to do something about them. SocialGraph Node Mapper is an interesting open source project that standardizes URLs relative to trailing slashes, the presence of “www”, etc., but it also recognizes that various social networking sites might expose different URLs that all link back to the same person.

visualizing similarity with graphs, Clustering Posts with Cosine Similarity similarity, calculating by computing common friends/followers, Calculating Similarity by Computing Common Friends and Followers SIMILE Timeline, Visualizing Mail “Events” with SIMILE Timeline, Analyzing Your Own Mail Data, Visualizing Mail “Events” with SIMILE Timeline, Visualizing Mail “Events” with SIMILE Timeline expected data format, Visualizing Mail “Events” with SIMILE Timeline online demonstrations, Visualizing Mail “Events” with SIMILE Timeline social graph APIs (Twitter), online documentation, A Lean, Mean Data-Collecting Machine social web, An Evolutionary Revolution? SocialGraph Node Mapper, Brief analysis of breadth-first techniques sorting, Sensible Sorting, Sorting Documents by Value documents by value, Sorting Documents by Value documents in CouchDB, Sensible Sorting split method, using to tokenize text, Data Hacking with NLTK, Before You Go Off and Try to Build a Search Engine… spreadsheets, visualizing Facebook network data, Visualizing with spreadsheets (the old-fashioned way) statistical models processing natural language, Quality of Analytics stemming verbs, Querying Buzz Data with TF-IDF stopwords, Data Hacking with NLTK, Analysis of Luhn’s Summarization Algorithm downloading NLTK stopword data, Data Hacking with NLTK filtering out before document summarization, Analysis of Luhn’s Summarization Algorithm streaming API (Twitter), Analyzing Tweets (One Entity at a Time) Strong Links API, The Infochimps “Strong Links” API, Interactive 3D Graph Visualization student’s t-score, How the Collocation Sausage Is Made: Contingency Tables and Scoring Functions subject-verb-object triples, Entity-Centric Analysis: A Deeper Understanding of the Data, Man Cannot Live on Facts Alone summarizing documents, Summarizing Documents, Analysis of Luhn’s Summarization Algorithm, Summarizing Documents, Analysis of Luhn’s Summarization Algorithm analysis of Luhn’s algorithm, Analysis of Luhn’s Summarization Algorithm Tim O’Reilly Radar blog post (example), Summarizing Documents summingReducer function, Frequency by date/time range, What entities are in Tim’s tweets?

Let’s whip up a simple script for harvesting XFN data similar to the service offered by rubhub, a social search engine that crawls and indexes a large number of websites using XFN. You might also want to check out one of the many online XFN tools if you want to explore the full specification before moving on to the next section. A Breadth-First Crawl of XFN Data Let’s get social by mining some XFN data and building out a social graph from it. Given that XFN can be embedded into any conceivable web page, the bad news is that we’re about to do some web scraping. The good news, however, is that it’s probably the most trivial web scraping you’ll ever do, and the BeautifulSoup package absolutely minimizes the burden. The code in Example 2-2 uses Ajaxian, a popular blog about modern-day web development, as the basis of the graph.

pages: 274 words: 75,846

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


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

The term stereotyping (which in this sense comes from Walter Lippmann, incidentally) is often used to refer to malicious xenophobic patterns that aren’t true—“people of this skin color are less intelligent” is a classic example. But stereotypes and the negative consequences that flow from them aren’t fair to specific people even if they’re generally pretty accurate. Marketers are already exploring the gray area between what can be predicted and what predictions are fair. According to Charlie Stryker, an old hand in the behavioral targeting industry who spoke at the Social Graph Symposium, the U.S. Army has had terrific success using social-graph data to recruit for the military—after all, if six of your Facebook buddies have enlisted, it’s likely that you would consider doing so too. Drawing inferences based on what people like you or people linked to you do is pretty good business. And it’s not just the army. Banks are beginning to use social data to decide to whom to offer loans: If your friends don’t pay on time, it’s likely that you’ll be a deadbeat too.

.: How We Got from the Company Man, Family Dinners, and the Affluent Society to the Home Office, BlackBerry Moms, and Economic Anxiety (New York: Pantheon, 2008), 164. 130 “Model-T version of what’s possible”: Geoff Duncan, “Netflix Offers $1Mln for Good Movie Picks,” Digital Trends, Oct. 2, 2006, accessed Dec. 15, 2010, 130 “a PC and some great insight”: Katie Hafner, “And If You Liked the Movie, a Netflix Contest May Reward You Handsomely,” New York Times, Oct. 2, 2006, accessed Dec. 15, 2010, 131 success using social-graph data: Charlie Stryler, Marketing Panel at 2010 Social Graph Symposium, Microsoft Campus, Mountain View, CA, May 21, 2010. 132 “the creditworthiness of your friends”: Julia Angwin, “Web’s New Gold Mine,” Wall Street Journal, July 30, 2010, accessed on Feb. 7, 2011, 133 reality doesn’t work that way: David Hume, An Enquiry Concerning Human Understanding, Harvard Classics, volume 37, Section VII, Part I, online edition, (P.

PayPal PeekYou persuasion profiling Phantom Public, The (Lippmann) Philby, Kim Phorm Piaget, Jean Picasa Picasso, Pablo PK List Management Plato politics electoral districts and partisans and programmers and voting Popper, Karl postmaterialism predictions present bias priming effect privacy Facebook and facial recognition and genetic Procter & Gamble product recommendations Proulx, Travis Pulitzer, Joseph push technology and pull technology Putnam, Robert Qiang, Xiao Rapleaf Rather, Dan Raz, Guy reality augmented Reality Hunger (Shields) Reddit Rendon, John (Sunstein) retargeting RFID chips robots Rodriguez de Montalvo, Garci Rolling Stone Roombas Rotenberg, Marc Rothstein, Mark Rove, Karl Royal Caribbean Rubel, Steve Rubicon Project Rumsfeld, Donald Rushkoff, Douglas Salam, Reihan Sandberg, Sheryl schemata Schmidt, Eric Schudson, Michael Schulz, Kathryn science Scientific American Scorpion sentiment analysis Sentry serendipity Shields, David Shirky, Clay Siegel, Lee signals click Simonton, Dean Singhal, Amit Sleepwalkers, The (Koestler) smart devices Smith, J. Walker social capital social graph Social Graph Symposium Social Network, The Solove, Daniel solution horizon Startup School Steitz, Mark stereotyping Stewart, Neal Stryker, Charlie Sullivan, Danny Sunstein, Cass systematization Taleb, Nassim Nicholas Tapestry TargusInfo Taylor, Bret technodeterminism technology television advertising on mean world syndrome and Tetlock, Philip Thiel, Peter This American Life Thompson, Clive Time Tocqueville, Alexis de Torvalds, Linus town hall meetings traffic transparency Trotsky, Leon Turner, Fred Twitter Facebook compared with Últimas Noticias Unabomber uncanny valley Upshot Vaidhyanathan, Siva video games Wales, Jimmy Wall Street Journal Walmart Washington Post Web site morphing Westen, Drew Where Good Ideas Come From (Johnson) Whole Earth Catalog WikiLeaks Wikipedia Winer, Dave Winner, Langdon Winograd, Terry Wired Wiseman, Richard Woolworth, Andy Wright, David Wu, Tim Yahoo News Upshot Y Combinator Yeager, Sam Yelp You Tube LeanBack Zittrain, Jonathan Zuckerberg, Mark Table of Contents Title Page Copyright Page Dedication Introduction Chapter 1 - The Race for Relevance Chapter 2 - The User Is the Content Chapter 3 - The Adderall Society Chapter 4 - The You Loop Chapter 5 - The Public Is Irrelevant Chapter 6 - Hello, World!

pages: 302 words: 73,581

Platform Scale: How an Emerging Business Model Helps Startups Build Large Empires With Minimum Investment by Sangeet Paul Choudary


3D printing, Airbnb, Amazon Web Services, barriers to entry, bitcoin, blockchain, business process, Clayton Christensen, collaborative economy, crowdsourcing, cryptocurrency, data acquisition, frictionless, game design, hive mind, Internet of things, invisible hand, Kickstarter, Lean Startup, Lyft, M-Pesa, Mark Zuckerberg, means of production, multi-sided market, Network effects, new economy, Paul Graham, recommendation engine, ride hailing / ride sharing, shareholder value, sharing economy, Silicon Valley, Skype, Snapchat, social graph, social software, software as a service, software is eating the world, Spread Networks laid a new fibre optics cable between New York and Chicago, TaskRabbit, the payments system, too big to fail, transport as a service, two-sided market, Uber and Lyft, Uber for X, Wave and Pay

However, these multihoming costs are not as strong as they used to be. INSUFFICIENT NETWORK EFFECT Two shifts have brought about a rapid decline in multihoming costs. First, the rise of the social graph allows users to port their personal networks between different platforms. A new platform, like Instagram, can leverage the single sign-on enabled by the social graph to build an alternate network of users rapidly. Second, mobile-based access allows users to switch easily and rapidly between different apps multiple times a day. This allows multihoming at a scale that was once unimaginable. Drivers today use Uber, Lyft, and a host of other apps simultaneously and switch between them several times a day. The convenience of the social graph, coupled with the ease of switching between platforms, has eroded the lock-in that once kept users bound to a network. THINKING BEYOND THE NETWORK EFFECT The network effect isn’t quite as effective at retaining producers and consumers as it once was.

Amazon’s “People who purchased this product also purchased this product” feature is based on a collaborative filter. Many recommendation platforms allow users to filter results based on a “people like you” parameter. This, again, is a collaborative filter. The most important innovation in recent times that has led to the spread of collaborative filters is the implementation of Facebook’s social graph. Through the social graph, third-party platforms like TripAdvisor serve reviews based on a collaborative filter of people who are close to you on the graph. Finally, it is important to note that the network itself is a filter. Who you follow determines what you consume. On Twitter, who you follow is the critical filter. Relevance is almost entirely dictated by it. On Facebook, who you are connected to and how often you interact with them strengthen the newsfeed filter.

The 7c’s of trust – Confirmed Identity, Centralized Moderation, Community Feedback, Codified Behavior, Culture, Completeness, Cover – explored in this chapter, are a set of themes that platform creators may use to build trust on platforms. CONFIRMED IDENTITY Identity can be used to help build trust. The rise of Facebook’s social graph helped create real identity on the Internet, at least compared to the anonymity that was involved in much Internet participation prior to that. Today, Lyft riders link their accounts to their Facebook profiles. Tinder and a whole range of other social platforms require users to sign up through Facebook Connect. The social graph isn’t foolproof, and confirmation of identity may require more for different types of interactions. Airbnb confirms a listing by sending out photographers to a specific apartment. Sittercity babysitters must go through a stringent vetting process before being allowed onboard.

pages: 527 words: 147,690

Terms of Service: Social Media and the Price of Constant Connection by Jacob Silverman


23andMe, 4chan, A Declaration of the Independence of Cyberspace, Airbnb, airport security, Amazon Mechanical Turk, augmented reality, Brian Krebs, California gold rush, call centre, cloud computing, cognitive dissonance, correlation does not imply causation, Credit Default Swap, crowdsourcing, don't be evil, Edward Snowden, feminist movement, Filter Bubble, Firefox, Flash crash, game design, global village, Google Chrome, Google Glasses, hive mind, income inequality, informal economy, information retrieval, Internet of things, Jaron Lanier, jimmy wales, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, late capitalism, license plate recognition, life extension, Lyft, Mark Zuckerberg, Mars Rover, Marshall McLuhan, meta analysis, meta-analysis, Minecraft, move fast and break things, national security letter, Network effects, new economy, Nicholas Carr, Occupy movement, optical character recognition, payday loans, Peter Thiel, postindustrial economy, prediction markets, pre–internet, price discrimination, price stability, profit motive, quantitative hedge fund, race to the bottom, Ray Kurzweil, recommendation engine, rent control, RFID, ride hailing / ride sharing, self-driving car, sentiment analysis, shareholder value, sharing economy, Silicon Valley, Silicon Valley ideology, Snapchat, social graph, social web, sorting algorithm, Steve Ballmer, Steve Jobs, Steven Levy, TaskRabbit, technoutopianism, telemarketer, transportation-network company, Turing test, Uber and Lyft, Uber for X, universal basic income, unpaid internship, women in the workforce, Y Combinator, Zipcar

When social networks like Facebook and Twitter offer to “authenticate” users (such as by the blue check mark that appears next to verified Twitter accounts), they are not speaking in terms of users being able to fulfill their own sense of authenticity, their own ideas about what it means to be true to one’s self. Rather, they are authenticating, or verifying, users for the network’s purposes. THE SOCIAL GRAPH AND FRICTIONLESS SHARING The “social graph” is a term popularized by Facebook, and it has spread throughout the industry, coming to stand in for the complex web of relationships we create and maintain, both online and off. You are probably connected to your parents online but also to friends, family, coworkers, strangers you talk to on Twitter, people you buy from on eBay, anonymous interlocutors on message boards, your college friends on Instagram, and so on. The social graph is who you talk to, how often, and what these relationships might say about you. And in the hands of Google, Facebook, and Twitter, it’s a potential gold mine of data that advertisers love.

And in the hands of Google, Facebook, and Twitter, it’s a potential gold mine of data that advertisers love. It’s human life as a clutch of data points, every feeling and expression and relationship recorded, mined, algorithmized. The social graph, however, is only as useful as the data to which it’s connected. And it’s pretty limited if you don’t share very often or if Facebook or Google or Twitter can’t learn what you do when you’re not on their sites. That’s why these companies have led the way in socializing—or surveilling—the entire Web. Each social widget, each Like or +1 button on a Web page, of which there are millions now, acts as a tracking beacon, feeding information back to the company that owns it. The practice has its roots in the advertising and tracking industry, in which third-party companies have long used online advertisements to plant cookies—small files containing information about the user—on user’s machines.

Upon learning this news, Katherine Losse followed up on Twitter, citing her own remarks about Zuckerberg wanting to turn us all into cells into a single organism, one that Facebook naturally would control. For Facebook, she wrote, “the privacy of thought is a problem for tech to overcome.” That’s why Facebook’s status bar doesn’t ask you what’s new; it asks, “What’s on your mind?” That is both a prompt and an explicit statement of intent: to know, whenever possible, what we are thinking and doing. THE SPREAD OF GOOGLE+ Facebook’s promotion of the social graph—and its attendant features, such as Like buttons, third-party apps, and universal log-ins that allow you to use your social-media identity across services—has some competition in Google and its Google+ social network. The two companies are also ideological fellow travelers, with Google+ representing the search company’s own effort to apply a social layer over the Internet and to capture and filter all user behavior through its own social network.

pages: 455 words: 133,322

The Facebook Effect by David Kirkpatrick


Andy Kessler, Burning Man, delayed gratification, demand response, don't be evil, global village, happiness index / gross national happiness, Howard Rheingold, Jeff Bezos, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Network effects, Peter Thiel, rolodex, Sand Hill Road, sharing economy, Silicon Valley, Silicon Valley startup, Skype, social graph, social software, social web, Startup school, Steve Ballmer, Steve Jobs, Stewart Brand, the payments system, The Wealth of Nations by Adam Smith, Whole Earth Review, winner-take-all economy, Y Combinator

Zuckerberg was beginning to talk about what he would come to label the “social graph,” meaning the web of relationships articulated inside Facebook as the result of users connecting with their friends. With Facebook photos, your friends—your social graph—provided more information, context, and a sense of companionship. But it only worked because the photos were tagged with people’s names and Facebook alerted people when they were tagged. The tags determined how the photos were distributed through the service. “Watching the growth of tagging,” says Cohler, “was the first ‘aha’ for us about how the social graph could be used as a distribution system. The mechanism of distribution was the relationships between people.” Perhaps applying the social graph to other online activities would make them more interesting and useful, too.

By “distribution” he meant that by connecting with your friends on Facebook you had assembled a network, this so-called social graph, and it could be employed to distribute any sort of information. If you added a photo, it told your friends. Ditto if you changed your relationship status, or announced that you were heading to Mexico for the weekend. But it could also tell your friends about any action you took using any software to which your social graph was connected. So far, though, the only applications that took advantage of this distribution capability were photos, events, and a few others created by Facebook itself. Most software companies, were they to conclude that they had such an ability to create uniquely powerful applications, would create more of them. They might make shopping applications on top of their social graph, or games, or applications for businesses.

It was garnering more usage than, which had been for years the leading website for invitations. “So why were photos and events so good?” he asked. “It was because despite all their shortcomings they had one thing no one else had. And that was integration with the social graph.” This was Facebook’s own conceptual breakthrough, and Zuckerberg was proud of the term he used to describe it. “We did some thinking and we decided that the core value of Facebook is in the set of friend connections,” he continued. “We call that the social graph, in the mathematical sense of a series of nodes and connections. The nodes are the individuals and the connections are the friendships.” Then his enthusiasm veered, it seemed at the time, toward overstatement: “We have the most powerful distribution mechanism that’s been created in a generation.”

pages: 201 words: 63,192

Graph Databases by Ian Robinson, Jim Webber, Emil Eifrem


Amazon Web Services, anti-pattern, bioinformatics, corporate governance, create, read, update, delete, data acquisition,, fault tolerance, linked data, loose coupling, Network effects, recommendation engine, semantic web, sentiment analysis, social graph, software as a service, SPARQL, web application

For an excellent introduction to how graphs provide insight into complex events and behaviors, see David Easley and Jon Kleinberg, Networks, Crowds, and Markets: Reasoning about a Highly Connected World (Cambridge University Press, 2010) 2 | Chapter 1: Introduction the construction of a space rocket, to a system of roads, and from the supply-chain or provenance of foodstuff, to medical history for populations, and beyond. For example, Twitter’s data is easily represented as a graph. In Figure 1-1 we see a small network of followers. The relationships are key here in establishing the semantic context: namely, that Billy follows Harry, and that Harry, in turn, follows Billy. Ruth and Harry likewise follow each other, but sadly, while Ruth follows Billy, Billy hasn’t (yet) recip‐ rocated. Figure 1-1. A small social graph Of course, Twitter’s real graph is hundreds of millions of times larger than the example in Figure 1-1, but it works on precisely the same principles. In Figure 1-2 we’ve expanded the graph to include the messages published by Ruth. What is a Graph? | 3 Figure 1-2. Publishing messages Though simple, Figure 1-2 shows the expressive power of the graph model. It’s easy to see that Ruth has published a string of messages.

In their book Connec‐ ted, social scientists Nicholas Christakis and James Fowler show how, despite knowing nothing about an individual, we can better predict that person’s behavior by under‐ standing who they are connected to, than we can by accumulating facts about them.1 Social applications allows organizations to gain competitive and operational advantage by leveraging information about the connections between people, together with discrete information about individuals, to facilitate collaboration and flow of information, and predict behavior. 1. See Nicholas Christakis and James Fowler, Connected: The Amazing Power of Social Networks and How They Shape Our Lives (HarperPress, 2011) 94 | Chapter 5: Graphs in the Real World As Facebook’s use of the term social graph implies, graph data model and graph databases are a natural fit for this overtly relationship-centerd domain. Social networks help us identify the direct and indirect relationships between people, groups and the things with which they interact, allowing users to rate, review and discover each other and the things they care about. By understanding who interacts with whom, how people are connected, and what representatives within a group are likely to do or choose based on the aggregate behaviour of the group, we generate tremendous insight into the unseen forces that influence individual behaviours.

In practice this means that data like phone numbers and zip codes can be inlined in the property store file directly, rather than being pushed out to the dynamic stores. This results in reduced I/O operations and improved throughput, since only a single file access is required. In addition to inlining certain compatible property values, Neo4j also maintains space discipline on property names. For example in a social graph, there will likely be many nodes with properties like first_name and last_name. It would be wasteful if each property name was written out to disk verbatim, and so instead property names are indirectly referenced from the property store through the property index file. The prop‐ erty index allows all properties with the same name to share a single record, and thus for repetitive graphs—a very common use case-- Neo4j achieves considerable space and I/O savings.

pages: 260 words: 76,223

Ctrl Alt Delete: Reboot Your Business. Reboot Your Life. Your Future Depends on It. by Mitch Joel


3D printing, Amazon Web Services, augmented reality, call centre, clockwatching, cloud computing, Firefox, future of work, ghettoisation, Google Chrome, Google Glasses, Google Hangouts, Khan Academy, Kickstarter, Kodak vs Instagram, Lean Startup, Mark Zuckerberg, Network effects, new economy, Occupy movement, place-making, prediction markets, pre–internet, recommendation engine, Richard Florida, risk tolerance, self-driving car, Silicon Valley, Silicon Valley startup, Skype, social graph, social web, Steve Jobs, Steve Wozniak, Thomas L Friedman, Tim Cook: Apple, Tony Hsieh, WikiLeaks

Lesson #4—Create a mutually beneficial world. In the case of Beats By Dre and Target, it’s not healthy to be going after each individual for the “like” on Facebook. The true opportunity is to figure out how to create a mutually beneficial world, instead of one where you are now competing with your own partners. Lesson #5—True fans. The majority of people do not want to friend or like your brand. They use their social graphs for friends, family, and those they made fun of in high school. The intrusion of brands is simply that: an intrusion. Your business will never get everyone to like it. So instead, turn to the fanatical. Find and nurture your true fans. Your heavy users. As that relationship delivers, they will become evangelists for you and you will begin to experience the network effect. IT’S NOT (PERFECTLY) CLEAR.

This means that big data is coming to marketing, and the insights that we will soon have available to us—at the business level—will make what we’re spending on computers, servers, and capital infrastructure pale in comparison. This will finally give us true knowledge of what it takes to acquire customers and keep them. Consumers are already demonstrating their desires in this area by using their smartphones to do everything from scanning QR codes to sharing their experiences with their peers on Facebook and Twitter. When you combine their usage (the linear data) with the circular data (what they’re doing in their social graph), and with all of this new big data trending information, it’s easy to see how much this will affect everything we know about connecting to our consumers. BE ACCOUNTABLE TO YOUR BRAND. Imagine a day when you could have all of the data and analytics you have ever wanted. Imagine being able to track and analyze the journey of your consumers. Imagine being able to be a fly on the wall for all of their conversations with family and friends about what they love and hate about your brand, the competitors, and the other brands that impact their lives.

For social to be social, it has to be something that people can both easily find and share. The goal to being social is to make everything that you are doing as sharable and as findable as possible. When you do this in a one-screen world, people find the brand, share it, and engage with it. When people engage with it, they are (hopefully) engaging with you as well. As they engage with something that resonates with them, they tend to share it throughout their social graphs. This makes it increasingly more findable for others. Yes, there are a few brands that are able to leverage this and have actual conversations with consumers, but those brands are few and far between. Plus, in a world of 140 characters, text messages, and +1s, is a conversation all that it’s cracked up to be? If we can simply make consumers’ lives better by providing them with what they want when they want it, is that not delivering more than the brand had initially promised?

pages: 304 words: 82,395

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


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

Besides, the company is still adjusting its business model (and privacy policy) for the amount and type of data collection it wants to do. Hence much more of the criticism it has faced centers on what information it is capable of collecting than on what it has actually done with that data. Facebook had around one billion users in 2012, who were interconnected through over 100 billion friendships. The resulting social graph represents more than 10 percent of the total world population, datafied and available to a single company. The potential uses are extraordinary. A number of startups have looked into adapting the social graph to use as signals for establishing credit scores. The idea is that birds of a feather flock together: prudent people befriend like-minded types, while the profligate hang out among themselves. If it pans out, Facebook could be the next FICO, the credit-scoring agency. The rich datasets from social media firms may well form the basis of new businesses that go far beyond the superficial sharing of photos, status updates, and “likes.”

Before big data, our analysis was usually limited to testing a small number of hypotheses that we defined well before we even collected the data. When we let the data speak, we can make connections that we had never thought existed. Hence, some hedge funds parse Twitter to predict the performance of the stock market. Amazon and Netflix base their product recommendations on a myriad of user interactions on their sites. Twitter, LinkedIn, and Facebook all map users’ “social graph” of relationships to learn their preferences. Of course, humans have been analyzing data for millennia. Writing was developed in ancient Mesopotamia because bureaucrats wanted an efficient tool to record and keep track of information. Since biblical times governments have held censuses to gather huge datasets on their citizenry, and for two hundred years actuaries have similarly collected large troves of data concerning the risks they hope to understand—or at least avoid.

The idea of datafication is the backbone of many of the Web’s social media companies. Social networking platforms don’t simply offer us a way to find and stay in touch with friends and colleagues, they take intangible elements of our everyday life and transform them into data that can be used to do new things. Facebook datafied relationships; they always existed and constituted information, but they were never formally defined as data until Facebook’s “social graph.” Twitter enabled the datafication of sentiment by creating an easy way for people to record and share their stray thoughts, which had previously been lost to the winds of time. LinkedIn datafied our long-past professional experiences, just as Maury transformed old logbooks, turning that information into predictions about our present and future: whom we may know, or a job we may want. Such uses of the data are still embryonic.

pages: 286 words: 82,065

Curation Nation by Rosenbaum, Steven


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

“For a media brand, the concept of curating content becomes an important component of your strategy for connecting with an audience,” says Dan McCarthy, CEO of NCI, one of the United States’ largest local media companies serving the housing market. “People have a large appetite for content; the increase in the proportion of content that they are accessing through trusted connections suggests that people are looking into their social graph to ensure a good content experience. When a consumer includes a media brand in their social graph, they are inviting that brand to help guide their exploration of good content. It doesn’t all have to be original. It does all have to be useful and relevant to the brand experience.” So you can see that the PR folks and the advertising folks don’t look at the world the same way. Maheu points to campaigns like the Old Spice Man, which was done by a competitive agency, as evidence that broadcast television still drives buzz and starts consumer conversations.

THE FUTURE OF CONTENT For guys like Miller and Kurnit, who started their careers in the television business but were already looking past it to what would come next, curation isn’t a buzzword or a trend; it’s the future of content and nothing less. Says Miller, “I believe that there has to be curation, which by the way can run the gamut from traditional media like magazines and newspapers to bloggers like Michael Arrington. The social systems will do it with friends and your social graph. And the volume of content will continue to explode, which will create more content than advertising can support. So advertisers will need curation. In the end specialists win, the greater the specialization, the greater the win.” And Kurnit’s take is clearly in the same spirit: “There’s no question the crowd is more efficient than directed individuals. So it’s interesting that holds core to its 750 guides one guide per topic area, and I think for About, it continues to do very well, that’s a very good idea.

No longer do you have to grab a line, bring it to your Facebook page, and share the link. You simple click “Like” on any piece of content, blog post, photo, or brand or e-commerce offering. That expression of your support is now linked to your profile and shared with your friends. From a content consumer’s perspective, it allows them to view aggregated recommendations of all their friends within their social graph. For a look at what that might look like, start here: Social media is both the source of much of the increased volume of data and increasingly the tools to empower curation, both accidental and purposeful. Simply put, we’re each making more data and recommending more things. The data we make comes from our posts, pictures, location data, and recommendations; the data we curate comes from what we link to, recommend, and endorse.

pages: 39 words: 4,665

Data Source Handbook by Pete Warden

Amazon:, Menlo Park, openstreetmap, phenotype, social graph

ISBN: 978-1-449-30314-3 [LSI] 1295970672 Table of Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Data Source Handbook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Websites WHOIS Blekko Compete Delicious BackType PagePeeker People by Email WebFinger Flickr Gravatar Amazon AIM FriendFeed Google Social Graph MySpace Github Rapleaf Jigsaw People by Name WhitePages LinkedIn GenderFromName People by Account Klout Qwerly Search Terms BOSS 1 1 2 3 3 4 5 5 5 6 6 6 7 7 8 8 9 10 10 11 11 11 11 11 12 12 12 12 13 v Blekko Bing Google Custom Search Wikipedia Google Suggest Wolfram Alpha Locations SimpleGeo Yahoo! Google Geocoding API CityGrid Geodict GeoNames US Census Zillow Neighborhoods Natural Earth US National Weather Service OpenStreetMap MaxMind Companies CrunchBase ZoomInfo Hoover’s Yahoo!

,"id":"twitter"}, {"username":"tadghin","name":"YouTube","url":"", "profileUrl":"", "iconUrl":"...","id":"youtube"}, {"url":"","iconUrl":"...","id":"facebook", "profileUrl":"", "name":"Facebook"}], "nickname":"timoreilly","id":"d85e8470-25c5-11dd-9ea1-003048343a40"} Google Social Graph Though it’s an early experiment that’s largely been superseded by Webfinger, this Google API can still be useful for the rich connection information it exposes for signedup users. Unfortunately, it’s not as well-populated as you might expect. It doesn’t require any developer keys to access: 8 | Data Source Handbook curl "\" { "canonical_mapping": { "": "sgn://mailto/?

pages: 247 words: 81,135

The Great Fragmentation: And Why the Future of All Business Is Small by Steve Sammartino


3D printing, additive manufacturing, Airbnb, augmented reality, barriers to entry, Bill Gates: Altair 8800, bitcoin, BRICs, Buckminster Fuller, citizen journalism, collaborative consumption, cryptocurrency, Elon Musk, fiat currency, Frederick Winslow Taylor, game design, Google X / Alphabet X, haute couture, helicopter parent, illegal immigration, index fund, Jeff Bezos, jimmy wales, Kickstarter, knowledge economy, Law of Accelerating Returns, market design, Metcalfe's law, Minecraft, minimum viable product, Network effects, new economy, post scarcity, prediction markets, pre–internet, profit motive, race to the bottom, random walk, Ray Kurzweil, recommendation engine, remote working, RFID, self-driving car, sharing economy, side project, Silicon Valley, Silicon Valley startup, skunkworks, Skype, social graph, social web, software is eating the world, Steve Jobs, too big to fail, web application

How does business go about doing business if we throw out the old methods of going to the market and marketing to people? (Mind you, marketing isn’t evil. It’s beautiful and powerful, it’s just that it needs to be used in a more human way.) If marketers embrace this philosophy, we’ll all end up better off after the interaction. The way we replace demographics is with social and interest graphs. Social graphs The social graph is the network that results from relationships that are digitally facilitated and maintained through virtual connections, which can now be spread more quickly using social-media tools. These connections are, theoretically, easier to make and easier to maintain than when our connection methods were all physical in nature. Interest graphs The interest graph is the online representation of the stuff we really care about.

INDEX 3D printing access and accessibility see also barriers; communication; digital; social media — factors of production — knowledge adoption rates advertising see also marketing; mass media; promotion; television Airbnb Alibaba Amazon antifragility Apple artisanal production creativity audience see also crowd — connecting with — vs target Away from Keyboard (AFK) banking see also crowdfunding; currencies barriers Beck (musician) big as a disadvantage bioengineering biomimicry biotechnology bitcoins blogs borrowed interest brand business strategies change see disruption and disruptive change Cluetrain Manifesto co-creation coffee culture Cold War collaboration collaborative consumption collective sentience commerce, future see also retail and retailers communication see also advertising; promotion; social media; social relationships — channels — tools community vs target competition and competitors component retail computers see also connecting and connection; internet; networks; smartphones; social media; software; technology era; 3D printing; web connecting and connection see also social media; social relationships — home/world — machines — people — things consumerism consumption silos content, delivery of coopetition corporations see also industrial era; retail and retailers; technology era costs see also finance; price co-working space creativity crowd, contribution by the crowdfunding cryptocurrencies culture — hacking — startup currencies see also banking deflation demographics device convergence digital see also computers; internet; music; smartphone; retail and retailers, online; social media; social relationships; technology; web; work — cohorts — era — footprint — revolution — skills — strategy — tools — world disruption and disruptive change DNA as an operating system drones Dunbar's number e-commerce see retail and retailers, online economic development, changing education employment, lifetime see also labour; work ephermalization Facebook see also social media finance, peer to peer see also banking; crowdfunding; currencies Ford, Henry 4Ps Foursquare fragmentation — of cities — industrial — Lego car example gadgets see also computers; smartphone; tools games and gaming behaviour gamification geo-location glass cockpit Global Financial Crisis (GFC) globalisation Google hacking hourglass strategy IFTTT (If this then that) industrialists (capital class) industry, redefining industrial era see also consumerism; marketing; retail and retailers — hacking — life in influencers information-based work infrastructure — changing — declining importance of — legacy innovation intention interest-based groups see also niches interest graphs internet see also access and accessibility; connecting and connection; social media; social relationships; web In Real Life (IRL) isolation iTunes see also music Jumpstart Our Business Startups (JOBS) Act (USA) keyboards knowledge economy lab vs factory labour see also work — low-cost language layering legacy — industries — infrastructure — media Lego car project life — in boxes — in gaming future — hack living standards see also life location see place, work making see also artisanal production; retail and retailers; 3D printing malleable marketplace manufacturing see also artisanal production; industrial era; making; product; 3D printing; tools — desktop marketing see also advertising; consumerism; 4Ps; mass media; promotion; retail and retailers — demographics, use in — industrial era — language — mass — metrics — new — post-industrial — predictive — research — target — traditional mass media ; see also advertising; marketing; media; promotion; television — after materialism media see also communication; legacy; mass media; newspapers; niches; television — consumption — hacking — platform vs content — subscription Metcalfe's law MOOC (Massive Open Online Course) Moore's law music Napster Netflix netizens networks see also connecting and connection; media; social media; social relationships newspapers see also media niches nodes nondustrial company Oaida, Raul oDesk office, end of the omniconnection era open source parasocial interaction payment systems Pebble phones, number of mobile see also smartphones photography Pinterest piracy place — of work platforms pop culture power-generating technologies price see also costs privacy see also social media; social relationships product — unfinished production see also industrial era; product; 3D printing — mass projecteer Project October Sky promotion see also advertising; marketing; mass media; media quantified self Racovitsa, Vasilii remote controls RepRap 3D printer retail cold spot retail and retailers — changing — digital — direct — hacking — mass — online — price — small — strategies — traditional rewards robots Sans nation state economy scientific management search engines self-hacking self-publishing self-storage sensors sharing see also social media; social relationships smartphones smartwatch social graphs social media (digitally enhanced conversation) see also Facebook; social relationships; Twitter; YouTube social relationships see also social graphs; social media — digital software speed subcultures Super Awesome Micro Project see Lego car project Super Bowl mentality target tastemakers technology see also computers; digital; open source; social media; smartphones; social relationships; software; 3D printing; work — deflation — era — free — revolution — speed — stack teenagers, marketing to television Tesla Motors thingernet thinking and technology times tools see also artisanal production; communication; computers; digital; making; smartphones; social media; 3D printing — changing — old trust Twitter Uber unlearning usability gap user experience volumetric mindset wages — growth — low — minimum web see also connecting and connection; digital; internet; retail and retailers, online; social media; social relationships — three phases of — tools Wikipedia work — digital era — industrial era — location of — options words see language Yahoo YouTube Learn more with practical advice from our experts WILEY END USER LICENSE AGREEMENT Go to to access Wiley’s ebook EULA.

It’s based on the real values we have and the things we do and support, hence forming a more genuine identity. The interest graph matters because it doesn’t just track the activity undertaken by people, but also what they hope to do — where they want to go, what they want to buy, who they want to follow and meet, and what they want to change. Social + interests = intention It gets interesting where these two ideas intersect. The overlaying of the social graph and the interest graph tells us much about a person’s intentions. When people develop relationships based on a connection of interests facilitated by social-media connections we can see the true predictive persona. It’s actually how the best and most enduring relationships have always been formed; it’s just that now we can form them more quickly, develop larger cohorts and there’s less luck involved in finding similar souls.

pages: 270 words: 79,180

The Middleman Economy: How Brokers, Agents, Dealers, and Everyday Matchmakers Create Value and Profit by Marina Krakovsky


Affordable Care Act / Obamacare, Airbnb, Al Roth, Black Swan, buy low sell high, Credit Default Swap, cross-subsidies, crowdsourcing, disintermediation, diversified portfolio, experimental economics, George Akerlof, Goldman Sachs: Vampire Squid, income inequality, index fund, Jean Tirole, Lean Startup, Lyft, Mark Zuckerberg, market microstructure, Martin Wolf, McMansion, Menlo Park, moral hazard, multi-sided market, Network effects, patent troll, Paul Graham, Peter Thiel, pez dispenser, ride hailing / ride sharing, Sand Hill Road, sharing economy, Silicon Valley, social graph, supply-chain management, TaskRabbit, The Market for Lemons, too big to fail, trade route, transaction costs, two-sided market, Uber for X, ultimatum game, Y Combinator

Nozad might balk at this abstract, overly mathematical depiction of the ties between people, but it’s a common way to look at human connections, especially in our Web 2.0 era. When Mark Zuckerberg or Jeff Weiner talk about the “social graph,” this is what they mean, except they’re referring to users of Facebook or LinkedIn. The points, or nodes, represent individual people, while the lines or links represent the social ties between the individuals.12 Our social graphs from the online world are often a crude replica of our actual social networks. Just think of the people you may be close to who don’t use social media. (Some of your close relatives, whom you talk to by phone, may not be on Facebook at all, and your own kids might be following half their classmates on Instagram, but not you.) Conversely, think of all your LinkedIn connections whom you last saw two jobs ago, if at all. Your social graphs online certainly overlap with your real-world network, but they aren’t the same thing.

For example, graph theory in computer science uses the term “edge” for what social scientists call a “tie.” 13.For example, see Nathan Eagle, Sandy Pentland, and David Lazer, “Inferring Friendship Network Structure by Using Mobile Phone Data,” Proceedings of the National Academy of Sciences 106, no. 36 (2009): 15274–78. 14.Sociologists also use the term “sociogram,” whereas computer scientists favor “social graph.” Both refer to a network diagram. 15.See, for example, Anatol Rapoport and William J. Horvath, “A Study of a Large Sociogram,” Behavioral Science 6, no. 4 (October 1961): 279–91, and Carlo Morselli, Inside Criminal Networks (New York: Springer, 2009). 16.This is the principle of homophily: birds of a feather flock together. 17.Mark Granovetter, Getting A Job: A Study of Contacts and Careers (Cambridge, MA: Harvard University Press, 1974) and “The Strength of Weak Ties,” American Journal of Sociology 78, no. 6 (May 1973): 1360–1380. 18.Ronald Burt, Structural Holes: The Social Structure of Competition (Harvard University Press, 1992). 19.Sociologists most often use the term “network broker” or simply “broker” to describe this person.

., 15–17, 203 Reinhard Model and Talent Agency, 105, 107–8 repairman problem, 141–2 reputation brands and, 67 Certifiers and, 47, 51–2, 61–7, 72 Concierges and, 164, 166 Enforcers and, 81–6, 88–9, 92–3, 96, 101–3, 107 game theory and, 13 helpfulness and, 22 Insulators and, 179, 182–6 management of, 89 Risk Bearers and, 124, 126, 132–3, 142–3 value of, 62–5 watchdogs and, 85–6 risk Certifiers and, 51–2, 72 diversification and, 138 embracing external risk, 125–8 Enforcers and, 77, 85–6, 96, 108–9 heroes and, 133 Insulators and, 178, 183 internal vs. external, 123 investment and, 10, 13, 124–5 loyalty and, 77 online, 134–6 pooling of, 137–42 reducing, 113–20 sharing, 120–3 shifting, 111–13 supply-side vs. demand-side, 137–8 see also Risk Bearers Risk Bearers art world and, 113–17 benefiting from power laws, 128–9 contrarianism and, 132–3 explained, 7 external risk and, 125–8 fish market and, 117–20 humility and, 130–2 micro-VCs, 133–6 overview, 111–13 promise and perils of sharing risk, 120–3 role, 111 unpredictability and, 136–43 VCs and, 124–5 Robboy, Howard, 54–6, 66, 68, 70, 72 Robin Hood effect, 192 Rocky Mountain Home Staging, 193–4 Rosenhaus, Drew, 173–5, 182–3, 185–6, 190 Roth, Al, 176, 180, 194 Rysman, Marc, 38 San Francisco 49ers, 179 scale economies of, 142, 167 pooling and, 139 returns to, 43 Scheibehenne, Benjamin, 155 Schwartz, Barry, 155 Scott, Jeff, 186–90, 194 scouting, 51–7 Sears, 140 Sequoia Capital, 18–21, 125 Setai hotel, 148–9 Shamon, Carol, 67, 190 Shapiro, Carl, 63 Shapiro, Ron, 186 Shark Never Sleeps, A (Rosenhaus), 174 Shark Tank (TV series), 11 Shirky, Clay, 134 Shropshire, Kenneth, 186 Simon, Herbert, 154–6 simplicity, 168–70, 172 SitterCity, 36–46, 80, 130, 164 smuggling, 174 social graph, 23 Society of Actuaries, 122 Sports Illustrated, 173 Spulber, Daniel, 5 Steiner, Robert L., 9 stereotypes, 10 Stoxstill-Diggs, LaJuan, 30–5, 44–6, 64–5 structural holes, 25–7, 29, 44 Taibbi, Matt, 10 TaskRabbit, 5–6, 36, 38, 91, 124, 132 Templeton, Chuck, 79, 81, 83, 108, 133 Thiel, Peter, 127, 129 Thiers, Genevieve, 36–7, 39–44, 130, 164 Toys ‘R’ Us, 137 Travel + Leisure, 146 Travelocity, 145 Trident Media Group, 69 TripAdvisor, 158 Trulia, 4 Tsukiji, Japan, 117–19 Twitter, 4, 25, 124, 127–9, 134 two-sided markets balancing, 198–9 Bridges as, 38–46 economic theory and, 13 Enforcers and, 80, 84, 109 Uber, 5, 36, 38, 91, 126, 136–7, 140–1, 198, 203 Uganda, 75 Ultimatum Game, 183 unpredictability, 50, 68, 112, 116, 138–42 see also risk US Bureau of Labor Statistics, 2 venture capitalists (VCs) as Bridges, 20, 26–7, 29, 43 contrarianism and, 132–3 entrepreneurs and, 20, 29, 132–3 founders and, 26 humility and, 130–2 as middlement, 6, 13 micro-VCs, 133–6, 197 power laws and, 128–9 as Risk Bearers, 124–33, 138, 90–2 W.

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


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

Likewise, the other biologic omes are the proteome, all of your proteins; the metabolome, your metabolites; the microbiome, representing the microbes that coinhabit you; and the epigenome, comprised of the side chains of DNA and how it is packaged. Finally, there’s the exposome, referring to your environment, all that you are exposed to. Collectively, I have coined the term “panoromic” adopted from the word panoramic, meaning lots of information and covering many topics.5 A panoromic view of each individual provides a comprehensive sweep across all the omes relevant to health and medicine. Social Graph and the Phenome The term “social graph” encompasses a dense package of information, including demographics, location, family and friends, friends of friends, interests, likes, education, pets, pictures, videos, and much more. This is precisely the sort of information stored on sites like Facebook, a fact that hasn’t escaped researchers. The prominent mathematician, Stephen Wolfram, the founder of a computation knowledge engine known as Wolfram Alpha, developed a consumer software product known as “personal analytics for Facebook” that within a minute provides a remarkable set of data and graphics about oneself and one’s social network—what Wolfram calls “a dashboard for life.”6,7 If you are a Facebook registrant and haven’t seen this, I encourage you to take a look at yours for free:

We essentially get a phenome from this information—“the composite of an individual’s observable characteristics and traits.”9 Noteworthy is the point that for any given individual, particularly as we age, there is unlikely to be just one phenotype; instead, multiple conditions are likely to be present, which makes one’s phenome not as straightforward as it might appear. For example, blood pressure tends to rise with age, while visual acuity declines. Ideally, someday, we will have all of this data comprehensively collected as the phenome for each individual—the social graph plus the traditional medical record information—and continually updated. While the social graph is subsidiary to the phenome, there’s no question that one’s social network plays an important role in health. Sensors and the Physiome Perhaps the biggest advance in tracking an individual’s information in recent years is the outpouring of an extraordinary number of biosensors. There are now wearable wireless sensors, either commercially available or in clinical development, to capture physiologic data on a smartphone.

privacy and security concerns, 228–230 real-time test results, 121 social networking, 42–44 Smeeth, Liam, 227 Smith, Adam, 42 Snapchat, 221 Snowden, Edward, 219, 225 Snyder, Michael, 88 Social graph of the individual, 81–83 Social media clinical trials through, 212–214 data collection through, 220–221, 223 global spread increasing global autonomy, 47–48 identifying genetic commonalities in disease, 9 importance of online health communities, 10–12 machine learning, 245 managed competition, 51 open-source software, 197 predictive analysis at the individual level, 243–245 social graphs, 82–83 Social networks, 42–44 Soon-Shion, Patrick, 203–204 Spatiotemporal applications, 79–80 Spinal fusion, 214–215 Splinter, Mike, 175 Spontaneous coronary artery dissection (SCAD), 211–212 The Sports Gene (Epstein), 94 Sports injuries, 94–95 Stanford University, 112 Star Trek (television program), 286 Statins, 31–33 Stephens, Richard, 226–227 Stethoscope, 96, 119–120, 175–176, 253–256, 276, 289 Stone, Neil, 33 Sudden infant death syndrome (SIDS), 92, 103 Supreme Court, US, 74–76 Surgery Center of Oklahoma, 152–153 Surveillance, 219–224 Take Care clinics, 163 Target, 224, 239 Targeted marketing, 221–225, 243 Tay-Sachs disease, 89 TechFreedom, 69 Technion Institute, Haifa, Israel, 110 Technology adoption, 7(fig.)

pages: 903 words: 235,753

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


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

None of the other digital currency projects is built on the core currency over which Facebook still has privileged position of access: the microeconomies of recognized social debt from which the value of money is primordially derived (at least for humans; HST algorithms are a different story).55 But to date, Facebook's furtive and ill-conceived experiments at the monetization of that capital are based on strategies of rent more than mediation, such as reciprocal likes, selling post promotion, charging users to message each other, and so essentially taxing the graph's own growth. This may be a doubtful recipe for the conversion of public obligation into private money and back again. The magical ontology of money requires a trust so trusting that it requires no deliberation, and while social graph-based platforms may be where new currencies will be sustained in the long run, Facebook may have soiled its own punch, and so perhaps we'll see banks becoming social graph platforms before we see graph platforms becoming banks. Still Facebook is the most widely engaged social media site with well over a billion active users and so its potential for structuring human communication according to its own logics of platform sovereignty remains profound. 30.  Apple Apple has assumed a mantle from Disney for preeminence in mass-scale, closed-loop experience design.56 By comparison with the extractive micromanagement style of Facebook, Apple's closed world is ruled with luxury carrots more than with behaviorist sticks.

Today's political geographic conflicts are often defined as exceptions to that normal model, and many are driven, enabled, or enforced in significant measure by planetary computation: byzantine international and subnational bodies, a proliferation of enclaves and exclaves, noncontiguous states, diasporic nationalisms, global brand affiliations, wide-scale demographic mobilization and containment, free trade corridors and special economic zones, massive file-sharing networks both legal and illegal, material and manufacturing logistical vectors, polar and subpolar resource appropriations, panoptic satellite platforms, alternative currencies, atavistic and irredentist religious imaginaries, cloud data and social-graph identity platforms, big data biopolitics of population medicine, equities markets held in place by an algorithmic arms race of supercomputational trading, deep cold wars over data aggregation across state and party lines, and so on. In relation to the incommensurate demands of diverse protocols, these rewrite and redivide the spaces of geopolitics in ways that are inclusive of aerial volumes, atmospheric envelopes, and oceanic depths.

The Stack discussed in the following chapters is a vast software/hardware formation, a proto-megastructure built of crisscrossed oceans, layered concrete and fiber optics, urban metal and fleshy fingers, abstract identities and the fortified skins of oversubscribed national sovereignty. It is a machine literally circumscribing the planet, which not only pierces and distorts Westphalian models of state territory but also produces new spaces in its own image: clouds, networks, zones, social graphs, ecologies, megacities, formal and informal violence, weird theologies, all superimposed one on the other. This aggregate machine becomes a systematic technology according to the properties and limitations of that very spatial order. The layers of The Stack, some continental in scale and others microscopic, work in specific relation to the layer above and below it. As I have suggested, the fragile complementarity between the layers composing The Stack is discussed both as an idealized model for how platforms may be designed and as a description of some of the ways that they already work now.

pages: 299 words: 91,839

What Would Google Do? by Jeff Jarvis


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

Scott Heiferman, founder of Meetup, also brought historical perspective to the discussion, writing a brief manifesto for change in the coming decade, chock full of hip blog references (the “social graph” to which he refers is what Mark Zuckerberg calls the architecture of personal connections on Facebook): Historically, when people are free to assemble & associate, they self-organize insurance, cooperatively. Later it became the centralized, professionalized industry we know today. I predict there’ll be some kind of massive craigslistification of insurance by April 27, 2018. It’s about de-institutionalization—not from the government borg (social security), not from the corporate borg (AIG). The New Social [graph] Security. Decentralized, self-organized. Not just DIY, but DIO (Do It Ourselves). That is the big theme for everything now. There is the great promise and power of the Google age: DIO.

Let that sip of rhetorical cabernet roll around on the palate for a minute. Elegant organization. When you think about it, that is precisely what Zuckerberg brought to Harvard—then other universities, then the rest of the world—with his social platform. Harvard’s community had been doing what it wanted to do for more than three centuries before Zuckerberg came along. He just helped them do it better. Facebook enabled people to organize their social networks—the social graph, he calls it: who they are, what they do, who they know, and, not unimportantly, what they look like. It was an instant hit because it met a need. It organized social life at Harvard. At this Davos meeting (which was off the record, but Zuckerberg gave me permission to blog it), he told the story of his Harvard art course. Zuckerberg didn’t have time to attend a single class or to study. After all, he was busy founding a $15 billion company.

See FARC The Revolution Will Not Be Televised (Trippi), 238 Rheingold, Howard, 106 Richardson, Will, 211 Rose, Kevin, 4, 132, 134 Rosenberg, Jonathan, 217 Rosen, Jay, 134–35 Roussel, Edward, 123 Rubel, Steve, 223 Rusbridger, Alan, 126 Rushkoff, Douglas, 226 Ryanair, 79 Ryan, Pat, 64, 62 Sandberg, Sheryl, 94 SANS, 180 scale, 54–57 Schmidt, Eric on Carr, 235 on Gmail, 6 on home-page sponsors, 36 on mistakes, 94 on mobile market, 79 Scion, 174 Scoble, Robert, 150 search-engine optimization (SEO), 41–42 rules, 44–45 search engines, 5, 20 Searls, Doc, 3, 82, 96–97, 149, 170 VRM and, 201–2 secrets, 97 Seed Camp, 193, 142 Segal, Rick, 15, 95 self-publishing, 73 self-searches, 20 Semel, Terry, 81 SEO. See search-engine optimization Sequoia Capital, 189 Shardanand, Upendra, 35 Shirky, Clay, 50, 60, 151, 191–92, 197, 235–36, 237 Silverman, Dwight, 13 simplicity, 114–16, 236, 39 Sirius Satellite Radio, 131 Skype, 31, 50 Smart Mobs (Rheingold), 106 Smith, Quincy, 38 Smolan, Rick, 140 social business, 158 social graph, 49 socialization, 211–12 social-media, 172–73 social responsibility, 47 social web, 51 Sorrell, Martin, 42, 100 specialization, 26–27, 154 speed, 103–4, 105–6 Spitzer, Eliot, 96 splogs, 43 Starbucks, 60–62 Stern, Howard, 95, 131–32 Stewart, Jon, 95–96 StudieVZ, 50 Supreme Court, 225 Surowiecki, James, 88 talent, 146, 240 Tapscott, Don, 113, 151, 225 targeting, 151, 179–80 teaching, 193, 214–15 teamwork, 217 TechCrunch, 107, 192 Technorati, 15, 20 TechTV, 132 telecommunications, 165–71 Telegraph Media Group, 123 television, 84 cable, 167 decline of, 65–66 listings, 109–10 networks, 135 Television Without Pity, 135 Tesco, 179 Tesla Motors, 175 testing, 214 Threadbanger, 180 Threadless, 57 TimesSelect, 78 Time Warner, 80–81 Tobaccowala, Rishad, 114, 121–22, 145–48, 151, 177 on Apple, 228 toilet paper, 180–81, 31 Toto, 181 Toyota, 174–75 transparency, 83, 97–98 journalism and, 92 PR and, 223 Tribune Company, 129 Trippi, Joe, 238 trust, 74, 170 control v., 82–83 in customers, 83–84 Tumblr, 192 Turner, Ted, 134 TV Guide, 109–10 20 percent rule, 111, 114 23andMe, 205 Twitter, 20, 126 Dell and, 46 mobs and, 107 real time and, 105–6 Tyndall, Andrew, 220 Union Square Ventures, 30 University of Phoenix, 217 Updike, John, 138 The Vanishing Newspaper (Meyer), 125 Vardi, Yossi, 31–32 Vaynerchuk, Gary, 107, 157–61 VC.

pages: 366 words: 94,209

Throwing Rocks at the Google Bus: How Growth Became the Enemy of Prosperity by Douglas Rushkoff


3D printing, Airbnb, algorithmic trading, Amazon Mechanical Turk, Andrew Keen, bank run, banking crisis, barriers to entry, bitcoin, blockchain, Burning Man, business process, buy low sell high, California gold rush, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, centralized clearinghouse, citizen journalism, clean water, cloud computing, collaborative economy, collective bargaining, colonial exploitation, Community Supported Agriculture, corporate personhood, crowdsourcing, cryptocurrency, disintermediation, diversified portfolio, Elon Musk, Erik Brynjolfsson, ethereum blockchain, fiat currency, Firefox, Flash crash, full employment, future of work, gig economy, Gini coefficient, global supply chain, global village, Google bus, Howard Rheingold, IBM and the Holocaust, impulse control, income inequality, index fund, iterative process, Jaron Lanier, Jeff Bezos, jimmy wales, job automation, Joseph Schumpeter, Kickstarter, loss aversion, Lyft, Mark Zuckerberg, market bubble, market fundamentalism, Marshall McLuhan, means of production, medical bankruptcy, minimum viable product, Naomi Klein, Network effects, new economy, Norbert Wiener, Oculus Rift, passive investing, payday loans, peer-to-peer lending, Peter Thiel, post-industrial society, profit motive, quantitative easing, race to the bottom, recommendation engine, reserve currency, RFID, Richard Stallman, ride hailing / ride sharing, Ronald Reagan, Satoshi Nakamoto, Second Machine Age, shareholder value, sharing economy, Silicon Valley, Snapchat, social graph, software patent, Steve Jobs, TaskRabbit, trade route, transportation-network company, Turing test, Uber and Lyft, Uber for X, unpaid internship, Y Combinator, young professional, Zipcar

Digital networks simulate the very same human social dynamics fueling the communities of artists like Palmer in order to generate goodwill and mass excitement for their corporate clients. It’s a one-sided, highly controlled relationship in which, invariably, the platforms and companies with which we engage learn more about us than we ever learn about them. Social marketing creates the illusion of a natural, nonmarketed groundswell of interest and, more importantly, provides marketers with a map of social connections and influences. These social graphs, as they’re called in the industry, are the fundamental building blocks of big data companies’ analyses. Big data is worth more than the sum of its parts. It is the technology for solving everything from terrorism to tuberculosis, as well as the purported payoff for otherwise unprofitable tech businesses, from smartphones to video games. Like pop stars, these health, entertainment, and content “plays” will make no money on their own—but the data they can glean from their users will be gold to marketers.

., 65 austerity, 136–37 auto attendants, 14 Bandcamp, 29–30 Barber, Brad, 177 Barnes & Noble, 83, 87 barter, 127 barter exchanges, 159 Basecamp, 59–60 BASF, 107 Battle-Bro, 121 Bauwens, Michael, 221 bazaars, 16–18 money and, 127 obsolescence of, caused by corporations, 70–71 Bell, Daniel, 53 Belloc, Hilaire, 229 benefit corporations, 119 Ben & Jerry’s, 80, 205 Benna, Ted, 171 BerkShare, 154–55 Best Buy, 90 Bezos, Jeff, 90, 92–93 Biewald, Lukas, 49–50 big data, 39–44 data point collection and comparisons of, 41–42 game changing product invention reduced by reliance on, 43 predicting future choices, as means of, 41, 42–43 reduction in spontaneity of customers and, 43 social graphs and, 40 suspicion of, as increasing value of data already being sold, 43–44 traditional market research, distinguished, 41 Big Shift, 76 biopiracy, 218 biotech crash of 1987, 6 Bitcoin, 143–49, 150–51, 152, 219, 222 BitTorrent, 142–43, 219 Blackboard, 95–96 Blackstone Group, 115 black swans, 183 blockchain, 144–51, 222 Bitcoin, 144, 145, 146, 147, 149, 222 decentralized autonomous corporations (DACs) and, 149–50 Blogger, 8, 31 Bloomberg, 182 Bodie, Zvie, 174 Borders, 83, 87 bot programs, 37 bounded investing, 210–15 Bovino, Beth Ann, 81–82 Brand, Russell, 36 branding, 20 social, and “likes” economy, 35–37 Branson, Richard, 121 Brin, Sergey, 92–93 Bristol Pounds, 156 British East India Company, 71–72 Brixton Pounds, 156 brokered barter system, 127 Brynjolfsson, Erik, 23, 53 Buffett, Warren, 168, 209 burn rate, 190 Bush, Jeb, 227–28 Calacanis, Jason, 201 Calvert, 209–10 Campbell Soup Company, 119 capital.

Bean, 80 local currencies, 154–65 cooperative community currencies, 160–65 free money theory, currencies based on, 156–59 local multiplier effect, 155 Long Tail theory, 26, 33 low-profit limited liability company (L3C), 120–21 Luckett, Oliver, 35–36 Lyft, 45, 47, 87 Machine Learning lab, 90–91 McAfee, Andrew, 23, 53 McCluhan, Marshall, 229 McKenna, Terence, 234 McLuhan, Marshall, 69 Magic Eraser Duo, 107 Maker’s Row, 30 malware, 37 marketing. See also advertising big data and, 42 branding and, 20, 35–37 “likes” economy and, 35–37 mass, 19–20 social graphs generated by, 40 market makers, 178–79 market money, 127–28, 130 Marx, Karl, 83, 138 mass media, 20–21 maturity, 98 Mecklenburg, George, 159 medical debt, 153 Meetup, 196–97 microfinancing platforms, 202–4 Microsoft, 83 Microventures, 202–3 Mill, John Stuart, 135 mining, of bitcoins, 145, 147 MIT Technology Review,53 Mondragon Corporation, 220, 222 money basket of commodities approach to backing of, 139 blockchains and, 144–51, 222 central currency system and (See central currency system) cooperative currencies, 160–65 debt and, 152–54 digital transaction networks and, 140–51 extractive purpose of, 128–31 free money theory, currencies based on, 156–59 gold standard and, 139 grain receipts, 128 history of, 126–31 local currencies, 154–65 manipulating human financial behavior to serve, 151–52 market, 127–28, 130 operating system nature of centrally-issued, 125–26 outlawing of local currencies and replacement with coin of the realm, 128–29 precious metals and, 128 reprogramming of, 138–51 traditional bank’s role in serving communities, 165–67 traditional purpose of, 126 as unbound, 212–13 velocity of, 140–41 monopolies chartered, 18, 56, 70, 101, 125, 131 platform, 82–93, 101 power-law dynamics and, 27–28 Monsanto, 218 Morgan Stanley, 195 Mozilla Corporation, 122–23 Mozilla Foundation, 122–23 Mr.

pages: 224 words: 64,156

You Are Not a Gadget by Jaron Lanier


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

The whole artifice, the whole idea of fake friendship, is just bait laid by the lords of the clouds to lure hypothetical advertisers—we might call them messianic advertisers—who could someday show up. The hope of a thousand Silicon Valley start-ups is that firms like Face-book are capturing extremely valuable information called the “social graph.” Using this information, an advertiser might hypothetically be able to target all the members of a peer group just as they are forming their opinions about brands, habits, and so on. Peer pressure is the great power behind adolescent behavior, goes the reasoning, and adolescent choices become life choices. So if someone could crack the mystery of how to make perfect ads using the social graph, an advertiser would be able to design peer pressure biases in a population of real people who would then be primed to buy whatever the advertiser is selling for their whole lives. The situation with social networks is layered with multiple absurdities.

The advertising idea hasn’t made any money so far, because ad dollars appear to be better spent on searches and in web pages. If the revenue never appears, then a weird imposition of a database-as-reality ideology will have colored generations of teen peer group and romantic experiences for no business or other purpose. If, on the other hand, the revenue does appear, evidence suggests that its impact will be truly negative. When Facebook has attempted to turn the social graph into a profit center in the past, it has created ethical disasters. A famous example was 2007’s Beacon. This was a suddenly imposed feature that was hard to opt out of. When a Facebook user made a purchase anywhere on the internet, the event was broadcast to all the so-called friends in that person’s network. The motivation was to find a way to package peer pressure as a service that could be sold to advertisers.

pages: 407 words: 103,501

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


Amazon Mechanical Turk, Andrew Keen, centre right, citizen journalism, collaborative editing, computer age, computer vision, corporate governance, crowdsourcing, David Brooks, disintermediation, Frederick Winslow Taylor, Howard Rheingold, invention of movable type, invention of the steam engine, invention of the telephone, Jaron Lanier, Jeff Bezos, jimmy wales, Kevin Kelly, knowledge worker, late fees, Mark Zuckerberg, Marshall McLuhan, means of production, meta analysis, meta-analysis, Network effects, new economy, Nicholas Carr, PageRank,, Results Only Work Environment, Saturday Night Live, search engine result page, semantic web, Silicon Valley, slashdot, social graph, social web, software as a service, speech recognition, Steve Jobs, Stewart Brand, technology bubble, Ted Nelson, The Wisdom of Crowds, Thorstein Veblen, web application

But others have already taken on the characteristic of fundamental system services. Take for example the domain registries of the DNS, which are a backbone service of the Internet. Or consider CDDB, used by virtually every music application to look up the metadata for songs and albums. Mapping data from providers like Navteq and TeleAtlas is used by virtually all online mapping applications. There is a race on right now to own the social graph. But we must ask whether this service is so fundamental that it needs to be open to all. It’s easy to forget that only fifteen years ago, e-mail was as fragmented as social networking is today, with hundreds of incompatible e-mail systems joined by fragile and congested gateways. One of those systems—Internet RFC 822 e-mail—became the gold standard for interchange. We expect to see similar standardization in key Internet utilities and subsystems.

Ti Kan, Steve Scherf, and Graham Toal, the creators of CDDB, realized that the sequence of track lengths on a CD formed a unique signature that could be correlated with artist, album, and song names. Larry Page and Sergey Brin realized that a link is a vote. Marc Hedlund at Wesabe realized that every credit card swipe is also a vote, that there is hidden meaning in repeated visits to the same merchant. Mark Zuckerberg at Facebook realized that friend relationships online actually constitute a generalized social graph. They thus turn what at first appeared to be unstructured into structured data. And all of them used both machines and humans to do it. . . . >>> the rise of real time: a collective mind As it becomes more conversational, search has also gotten faster. Blogging added tens of millions of sites that needed to be crawled daily or even hourly, but microblogging requires instantaneous update—which means a significant shift in both infrastructure and approach.

Seinfeld (television series) Self-portraits Self-publishing Self-realization Self-sufficiency Semantic priming Semiotic democracy Sensory deprivation September 11, 2001 terrorist attacks Serialization SERP. See Search Engine Results Page Sesame Street Shakesville (blog) Shirky, Clay Shoutcast Simulations Six Degrees: The Science of a Connected Age (Watts) Skrenta, Rich “Skyful of Lies” and Black Swans (Gowing) Slashdot Slatalla, Michelle Slate (magazine) Sleeper Curve Slingbox SLVR phone Small world experiment Social currency Social graph Social media Social mind The Social Network (film) Social networking sites. See also specific sites activism and advertising on amount of users on development of identity setup on learning and marketing on politicians on privacy dangers and self-exposure through self-portraits on spam on weak ties on Social rules and norms Social saturation Social skills Socrates Solitude Sony Soundscape, cell phones and South Korea Spamming, on social network sites Speech recognition Speed, Net Geners and Spengler, Oswald Splash screens Spotlight (blog) Squarciafico, Hieronimo Standage, Tom Starbucks Starkweather, Gary Star Trek (television series) Star Wars Stone, Linda Street Fighter II (video game) “The Strength of Weak Ties” (Granovetter) Suburbanization Sundance Resort Suriowecki, James Survival of the fittest Survivor (television series) Swados, Harvey Swarm intelligence Switch costs Tagging TakingITGlobal Task management Task switching Taylor, Frederick Winslow Teachout, Zephyr Techgnosis Technics and Civilization (Mumford) Techno-brain burnout Technology Education and Design (TED) Technomadicity Technorati TED.

pages: 184 words: 53,625

Future Perfect: The Case for Progress in a Networked Age by Steven Johnson


airport security, algorithmic trading, banking crisis, barriers to entry, Bernie Sanders, call centre, Captain Sullenberger Hudson, Cass Sunstein, cognitive dissonance, credit crunch, crowdsourcing, dark matter, Dava Sobel, David Brooks, future of journalism, hive mind, Howard Rheingold, HyperCard, Jane Jacobs, John Gruber, John Harrison: Longitude, Kevin Kelly, Kickstarter, lone genius, Mark Zuckerberg, meta analysis, meta-analysis, Naomi Klein, Nate Silver, Occupy movement, packet switching, Peter Thiel, planetary scale, pre–internet, RAND corporation, risk tolerance, shareholder value, Silicon Valley, Silicon Valley startup, social graph, Steve Jobs, Steven Pinker, Stewart Brand, The Death and Life of Great American Cities, Tim Cook: Apple, urban planning, WikiLeaks, working poor, X Prize

The Facebook mission can be boiled down to the old E. M. Forster slogan: “Only connect.” The company wants to strengthen the social ties that allow humans around the planet to connect, organize, converse, and share. At one point, Zuckerberg writes: By helping people form these connections, we hope to rewire the way people spread and consume information. We think the world’s information infrastructure should resemble the social graph—a network built from the bottom up or peer-to-peer, rather than the monolithic, top-down structure that has existed to date. In other words, the Facebook platform is a continuation of the Web and Internet platforms that lie beneath it: it is a Baran Web, not a Legrand Star. And it considers the cultivation and proliferation of Baran Webs to be its defining mission. Zuckerberg clearly believes that the peer-network structure can and should take hold in countless industries, in both the private and public sectors.

On the one hand, the conviction that peer networks can be a transformative force for good in the world is perhaps the core belief of the peer-progressive worldview. So when you hear one of the richest and most influential young men in the world delivering that sermon—in an S-1 filing, no less—it’s hard to hold back from shouting out a few hallelujahs. But there’s a difference here, one that makes all the difference. The platforms of the Web and the Internet were pure peer networks, owned by everyone. Facebook is a private corporation; the social graph that Zuckerberg celebrates is a proprietary technology, an asset owned by the shareholders of Facebook itself. And as far as corporations go, Facebook is astonishingly top-heavy: the S-1 revealed that Zuckerberg personally controls 57 percent of Facebook’s voting stock, giving him control over the company’s destiny that far exceeds anything Bill Gates or Steve Jobs ever had. The cognitive dissonance could drown out a Sonic Youth concert: Facebook believes in peer-to-peer networks for the world, but within its own walls, the company prefers top-down control centralized in a charismatic leader.

pages: 91 words: 18,831

Getting Started With OAuth 2.0 by Ryan Boyd


social graph, web application

Connecting users with their data results in improved day-to-day efficiency by eliminating data silos and also allows developers to differentiate their applications from the competition. OAuth provides the ability for these applications to access a user’s data securely, without requiring the user to take the scary step of handing over an account password. Types of functionality provided by OAuth-enabled APIs include the following: Getting access to a user’s social graph—their Facebook friends, people they’re following on Twitter, or their Google Contacts Sharing information about a user’s activities on your site by posting to their Facebook wall or Twitter stream Accessing a user’s Google Docs or Dropbox account to store data in their online filesystem of choice Integrating business applications with one another to drive smarter decisions by mashing up multiple data sources such as a Salesforce CRM and TripIt travel plan In order to access or update private data via each of these APIs, an application needs to be delegated access by the owner of the data.

pages: 285 words: 81,743

Start-Up Nation: The Story of Israel's Economic Miracle by Dan Senor; Saul Singer


agricultural Revolution, Albert Einstein, back-to-the-land, banking crisis, Boycotts of Israel, call centre, Celtic Tiger, cleantech, Dissolution of the Soviet Union, friendly fire, immigration reform, labor-force participation, new economy, pez dispenser, post scarcity, profit motive, Silicon Valley, smart grid, social graph, sovereign wealth fund, Steve Ballmer, web application, women in the workforce, Yom Kippur War

Alex Vieux, CEO of Red Herring magazine, told us that he has been to “a million high-tech conferences, on multiple continents. I see Israelis like Medved give presentations all the time, alongside their peers from other countries. The others are always making a pitch for their specific company. The Israelis are always making a pitch for Israel.”9 CHAPTER 4 Harvard, Princeton, and Yale The social graph is very simple here. Everybody knows everybody. —YOSSI VARDI DAVID AMIR MET US AT HIS JERUSALEM HOME in his pilot’s uniform, but there was nothing Top Gun about him. Soft-spoken, thoughtful, and self-deprecating, he looked, even in uniform, more like an American liberal arts student than the typical pilot with crisp military bearing. Yet as he explained with pride how the Israeli Air Force trained some of the best pilots in the world—according to numerous international competitions as well as their record in battle—it became easy to see how he fit in.1 While students in other countries are preoccupied with deciding which college to attend, Israelis are weighing the merits of different military units.

It nourishes an entirely different kind of lifelong bond.”6 Indeed, relationships developed during military service form another network in what is already a very small and interconnected country. “The whole country is one degree of separation,” says Yossi Vardi, the godfather of dozens of Internet start-ups and one of the champion networkers in the wired world. Like Jon Medved, Vardi is one of Israel’s legendary business ambassadors. Vardi says he knows of Israeli companies that have stopped using help-wanted ads: “It’s now all word of mouth. . . . The social graph is very simple here. Everybody knows everybody; everybody was serving in the army with the brother of everybody; the mother of everybody was the teacher in their school; the uncle was the commander of somebody else’s unit. Nobody can hide. If you don’t behave, you cannot disappear to Wyoming or California. There is a very high degree of transparency.”7 The benefits of this kind of interconnectedness are not limited to Israel, although in Israel they are unusually intense and widespread.

pages: 369 words: 80,355

Too Big to Know: Rethinking Knowledge Now That the Facts Aren't the Facts, Experts Are Everywhere, and the Smartest Person in the Room Is the Room by David Weinberger


airport security, Alfred Russel Wallace, Amazon Mechanical Turk, Berlin Wall, Black Swan, book scanning, Cass Sunstein, corporate social responsibility, crowdsourcing, Danny Hillis, David Brooks, Debian, double entry bookkeeping, double helix,, Exxon Valdez, Fall of the Berlin Wall, future of journalism, Galaxy Zoo, Hacker Ethic, Haight Ashbury, hive mind, Howard Rheingold, invention of the telegraph, jimmy wales, John Harrison: Longitude, Kevin Kelly, linked data, Netflix Prize, New Journalism, Nicholas Carr, Norbert Wiener, openstreetmap, P = NP, Pluto: dwarf planet, profit motive, Ralph Waldo Emerson, RAND corporation, Ray Kurzweil, Republic of Letters, RFID, Richard Feynman, Richard Feynman, Ronald Reagan, semantic web, slashdot, social graph, Steven Pinker, Stewart Brand, technological singularity, Ted Nelson, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, Whole Earth Catalog, X Prize

Disclosure: I am a member of the Digital Public Library of America’s “technical workstream,” and the library lab that I co-direct will have entered the DPLA’s call for project ideas before this book is printed. 4 Kevin Kelly, What Technology Wants (Penguin, 2010). 5 James Aitken Wylie, The History of Protestantism with Five Hundred and Fifty Illustrations by the Best Artist, Vol. 1 (Cassell, 1899), p. 113, 6 See Ethan Zuckerman’s excellent post “Shortcuts in the Social Graph,” October 14, 2010, 7 During the 2008 presidential campaign, Sarah Palin was accused of pressuring a local librarian to censor some books. See Rindi White, “Palin Pressured Wasilla Librarian,” Anchorage Daily News, September 4, 2008, 8 Tim Berners-Lee, “Linked Data,” July 27, 2006, 9 This was the price quoted at Fisher Scientific on June 11, 2011.

pages: 268 words: 75,850

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


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

As per the promotional literature supplied by the team: In a crowded room you don’t even have to bother working out who takes your fancy. The phone does all that. If it spots another phone with a good match—male or female—the two handsets beep and exchange information using Bluetooth radio technology. The rest is up to you.25 Apps like Serendipity are part of a new trend in technology called social discovery, which has grown out of social networking. Where social networking is about connecting with people already on your social graph, social discovery is all about meeting new people. There are few better examples in this book of The Formula in action than MIT’s Serendipity system. Here is a problem (“chance”) and a task (“making it more efficient”). Executed correctly, the computer might provide an answer to the question asked by Humphrey Bogart’s character in Casablanca. “Of all the gin joints in all the towns in all the world, why did such-and-such a person walk in to yours?”

Words like “relevant” and “newsworthy” are loaded terms that encourage (but often fail to answer) the seemingly obvious follow-up question: “relevant” and “newsworthy” to whom? In the case of a company like Google, the answer is simple: to the company’s shareholders, of course. Facebook’s algorithms can similarly be viewed as a formula for maintaining and building your friendship circle—but of course the reality is that Facebook’s purpose isn’t to make you friends, but rather to monetize your social graph through advertising.41 Hopefully, this questioning process is starting to happen. A number of researchers working with recommender systems have told me how user expectations have changed in recent years. Where five or ten years ago, people would be happy with any recommendations, today an increasing number want to know why these recommendations have been made for them. With asking why we are expected to take things at “interface value” will come the ability to critique the continued algorithmization of everything.

pages: 538 words: 141,822

The Net Delusion: The Dark Side of Internet Freedom by Evgeny Morozov


A Declaration of the Independence of Cyberspace, Ayatollah Khomeini, Berlin Wall, borderless world, Buckminster Fuller, Cass Sunstein, citizen journalism, cloud computing, cognitive dissonance, Columbine, computer age, conceptual framework, crowdsourcing, Dissolution of the Soviet Union, don't be evil, failed state, Fall of the Berlin Wall, Francis Fukuyama: the end of history, global village, Google Earth, illegal immigration, invention of radio, invention of the printing press, invisible hand, John von Neumann, Marshall McLuhan, Naomi Klein, Network effects, new economy, New Urbanism, pirate software, pre–internet, Productivity paradox, RAND corporation, Ronald Reagan, Ronald Reagan: Tear down this wall, Silicon Valley, Silicon Valley startup, Sinatra Doctrine, Skype, Slavoj Žižek, social graph, Steve Jobs, technoutopianism, The Wisdom of Crowds, urban planning, Washington Consensus, WikiLeaks, women in the workforce

The belief that the Internet is too big to censor is dangerously naïve. As the Web becomes even more social, nothing prevents governments—or any other interested players—from building censorship engines powered by recommendation technology similar to that of Amazon and Netflix. The only difference, however, would be that instead of being prompted to check out the “recommended” pages, we’d be denied access to them. The “social graph”—a collection of all our connections across different sites (think of a graph that shows everyone you are connected to on different sites across the Web, from Facebook to Twitter to YouTube)—a concept so much beloved by the “digerati,” could encircle all of us. The main reason why censorship methods have not yet become more social is because much of our Internet browsing is still done anonymously.

Breaking the firewalls to discover that the content one seeks has been deleted by a zealous intermediary or taken down through a cyber-attack is going to be disappointing. There are plenty of things to be done to protect against this new, more aggressive kind of censorship. One is to search for ways to provide mirrors of websites that are under DDoS attacks or to train their administrators, many of whom are self-taught and may not be managing the crisis properly, to do so. Another is to find ways to disrupt, mute, or even intentionally pollute our “social graph,” rendering it useless to those who would like to restrict access to information based on user demographics. We may even want to figure out how everyone online can pretend to be an investment banker seeking to read Financial Times! One could also make it harder to hijack and delete various groups from Facebook and other social networking sites. Or one could design a way to profit from methods like “crowdsourcing” in fighting, not just facilitating, Internet censorship; surely if a group of government royalists troll the Web to find new censorship targets, another group could also be searching for websites in need of extra protection?

F., and D. G. Johnson. “Data Retention and the Panoptic Society: The Social Benefits of Forgetfulness.” Information Society 18, no. 1 (2002): 33-45. “Bloggery Soobwajut, Chto FSB Prosit Udaljat’ Posty na Temu Akcij Protesta.” Rambler-Novosti, December 24, 2008. . Bonneau, J., J. Anderson, R. Anderson, and F. Stajano. “Eight Friends Are Enough: Social Graph Approximation via Public Listings.” In Proceedings of the Second ACM EuroSys Workshop on Social Network Systems, 13-18. 2009. Bunyan, T. “Just over the Horizon: The Surveillance Society and the State in the EU.” Race & Class 51, no. 3 (2010): 1. “Cambodia Shuts Off SMS Ahead of Elections.” Associated Press, April 2, 2007. Carver, G. A., Jr. “Intelligence in the Age of Glasnost.” Foreign Affairs 69 (1989): 147.

pages: 606 words: 157,120

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


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

Amazon didn’t have an army of bored freelancers who could do virtually any job as long as they received their few pennies per hour. (And even those human freelancers might become unnecessary once automated image-recognition software gets better.) Most importantly, there was no way for all our friends to see the contents of our trash bins; fifteen years ago, even our personal websites wouldn’t get the same level of attention from our acquaintances—our entire “social graph,” as the geeks would put it—that our trash bins might receive from our Facebook friends today. Now that we are all using the same platform—Facebook—it becomes possible to steer our behavior with the help of social games and competitions; we no longer have to save the environment at our own pace using our own unique tools. There is power in standardization! These two innovations—that more and more of our life is now mediated through smart sensor-powered technologies and that our friends and acquaintances can now follow us anywhere, making it possible to create new types of incentives—will profoundly change the work of social engineers, policymakers, and many other do-gooders.

All will be tempted to exploit the power of these new techniques, either individually or in combination, to solve a particular problem, be it obesity, climate change, or congestion. Today we already have smart mirrors that, thanks to complex sensors, can track and display our pulse rates based on slight variations in the brightness of our faces; soon, we’ll have mirrors that, thanks to their ability to tap into our “social graph,” will nudge us to lose weight because we look pudgier than most of our Facebook friends. Or consider a prototype teapot built by British designer-cum-activist Chris Adams. The teapot comes with a small orb that can either glow green (making tea is okay) or red (perhaps you should wait). What determines the coloring? Well, the orb, with the help of some easily available open-source hardware and software, is connected to a site called Can I Turn It On?

Had Sigmund Freud lived long enough, he would have probably been replaced by a pedometer: in this brave new world, who needs psychoanalysis—the obsolete practice of narrative imagination—to “take stock of ourselves,” when the algorithmic option looks so tempting? If the Quantified Self movement allows us to establish our authenticity with numbers, social networking allows us to accomplish that in subtler, seemingly more creative ways: by curating the timeline of our life, by uploading our favorite photos, by using the coolest apps on the block, by maintaining a unique social graph (Facebook speak for a set of human connections that each user has). If only one looks closely enough, one can discern how the themes of fakeness and authenticity shape Facebook’s own self-presentation. So Mark Zuckerberg claims that “the social web can’t exist until you are your real self online.” Peter Thiel, the first private investor in Facebook, contrasts the authenticity offered by Facebook—where no pseudonyms are allowed—with that of its former rival, MySpace, where everything goes.

pages: 464 words: 127,283

Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia by Anthony M. Townsend


1960s counterculture, 4chan, A Pattern Language, Airbnb, Amazon Web Services, anti-communist, Apple II, Bay Area Rapid Transit, Burning Man, business process, call centre, carbon footprint, charter city, chief data officer, clean water, cleantech, cloud computing, computer age, congestion charging, connected car, crack epidemic, crowdsourcing, DARPA: Urban Challenge, data acquisition, Deng Xiaoping, East Village, Edward Glaeser, game design, garden city movement, Geoffrey West, Santa Fe Institute, George Gilder, ghettoisation, global supply chain, Grace Hopper, Haight Ashbury, Hedy Lamarr / George Antheil, hive mind, Howard Rheingold, interchangeable parts, Internet Archive, Internet of things, Jacquard loom, Jacquard loom, Jane Jacobs, jitney, John Snow's cholera map, Khan Academy, Kibera, knowledge worker, load shedding, M-Pesa, Mark Zuckerberg, megacity, mobile money, mutually assured destruction, new economy, New Urbanism, Norbert Wiener, Occupy movement, openstreetmap, packet switching, patent troll, place-making, planetary scale, popular electronics, RFC: Request For Comment, RFID, ride hailing / ride sharing, Robert Gordon, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart grid, smart meter, social graph, social software, social web, special economic zone, Steve Jobs, Steve Wozniak, Stuxnet, supply-chain management, technoutopianism, Ted Kaczynski, telepresence, The Death and Life of Great American Cities, too big to fail, trade route, Tyler Cowen: Great Stagnation, Upton Sinclair, uranium enrichment, urban decay, urban planning, urban renewal, Vannevar Bush, working poor, working-age population, X Prize, Y2K, zero day, Zipcar

In her most influential book, The Death and Life of Great American Cities, the acclaimed urbanist Jane Jacobs argued that “cities have the capability of providing something for everybody, only because, and only when, they are created by everybody.”39 Yet over fifty years later, as we set out to create the smart cities of the twenty-first century, we seem to have again forgotten this hard-learned truth. But there is hope that a new civic order will arise in smart cities, and pull every last one of us into the effort to make them better places. Cities used to be full of strangers and chance encounters. Today we can mine the social graph in an instant by simply taking a photo. Algorithms churn in the cloud, telling the little things in our pocket where we should eat and whom we should date. It’s a jarring transformation. But even as old norms fade into the past, we’re learning new ways to thrive on mass connectedness. A sharing economy has mushroomed overnight, as people swap everything from spare bedrooms to cars, in a synergistic exploitation of new technology and more earth-friendly consumption.

Dodgeball spread virally and Crowley and Rainert spun it out of the university as a for-profit venture. From the three hundred or so students and friends who used the service during their grad school days, membership expanded to a thousand at the new startup’s launch. Within a year, over thirty thousand people had logins.21 As Dodgeball became a virtual dashboard for a certain slice of Manhattan’s digerati, its social graph—the web of friendships recorded in its database, and the flow of check-ins its users created—formed a new kind of urban media that Crowley and Rainert eagerly employed to design new experiences. One tweak tried to help you make new friends. Normally you only saw the check-ins of your direct friends, but if a friend of a friend checked in nearby, you’d get an alert urging you to go say hi. Another experiment turned Dodgeball into a romantic matchmaking machine, letting you declare a “crush” on another user and alerting him or her when you checked in nearby, to give you a shot at a hookup.

If it does truly create a new global trade in smart city solutions, local officials may be under more pressure than ever to make sure their dollars go to local firms that could themselves use CityMart for a real shot at larger success. My phone buzzed with directions to my next appointment. That evening I was using Barcelona’s cafés and bars as a kind of virtual conference center, all coordinated through my Foursquare social graph. Haselmayer offered his cynical view of the smart-cities industry, which had gathered in Barcelona for one of its biggest global trade shows to date. “The debate on smart cities has become all about [technical] architecture, where IBM says a smart city is nothing else but a corporation, and you need a good kind of architecture and then everything happens. That is an unrealistic view of how a city works, and it’s a monolithic approach.

pages: 323 words: 95,939

Present Shock: When Everything Happens Now by Douglas Rushkoff


algorithmic trading, Andrew Keen, bank run, Benoit Mandelbrot, big-box store, Black Swan, British Empire, Buckminster Fuller, cashless society, citizen journalism, clockwork universe, cognitive dissonance, Credit Default Swap, crowdsourcing, Danny Hillis, disintermediation, Donald Trump, double helix, East Village, Elliott wave, European colonialism, Extropian, facts on the ground, Flash crash, game design, global supply chain, global village, Howard Rheingold, hypertext link, Inbox Zero, invention of agriculture, invention of hypertext, invisible hand, iterative process, John Nash: game theory, Kevin Kelly, laissez-faire capitalism, Law of Accelerating Returns, loss aversion, mandelbrot fractal, Marshall McLuhan, Merlin Mann, Milgram experiment, mutually assured destruction, Network effects, New Urbanism, Nicholas Carr, Norbert Wiener, Occupy movement, passive investing, pattern recognition, peak oil, price mechanism, prisoner's dilemma, Ralph Nelson Elliott, RAND corporation, Ray Kurzweil, recommendation engine, Silicon Valley, Skype, social graph, South Sea Bubble, Steve Jobs, Steve Wozniak, Steven Pinker, Stewart Brand, supply-chain management, the medium is the message, The Wisdom of Crowds, theory of mind, Turing test, upwardly mobile, Whole Earth Catalog, WikiLeaks, Y2K

It’s as if all the Facebook updates, Twitter streams, email messages, and live-streamed video could combine to create a total picture of our true personal status, or that of our business, at any given moment. And there are plenty of companies out there churning all this data in real time in order to present us with metrics and graphs claiming to represent the essence of this reality for us. And even when they work, they are mere snapshots of a moment ago. Our Facebook profile and the social graph that can be derived from it, however intricate, is still just a moment locked in time, a static picture. This quest for digital omniscience, though understandable, is self-defeating. Most of the information we get at lightning speed is so temporal as to be stale by the time it reaches us. We scramble over the buttons of the car radio in an effort to get to the right station at the right minute-after-the-hour for the traffic report.

Everything is recorded, yet almost none of it feels truly accessible. A change in file format renders decades of stored files unusable, while a silly, forgotten Facebook comment we wrote when drunk can resurface at a job interview. In the digital universe, our personal history and its sense of narrative is succeeded by our social networking profile—a snapshot of the current moment. The information itself—our social graph of friends and likes—is a product being sold to market researchers in order to better predict and guide our futures. Using past data to steer the future, however, ends up negating the present. The futile quest for omniscience we looked at earlier in this chapter encourages us, particularly businesses, to seek ever more fresh and up-to-the-minute samples, as if this will render the present coherent to us.

pages: 532 words: 139,706

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


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

But his long pauses when asked about Google, and the way he shifted uncomfortably in his chair, suggest the tension between the two companies. He was somewhat less circumspect about MySpace, his main competitor among social networking sites: “What they’re doing is very much different from us. On a fundamental level, what they’re doing is not mapping out real connections. They’re helping people meet new people. Rather than using the social graph and the connections people have in order to facilitate decentralized communication, they’re using it as a platform to pump and push media out to people. They call themselves a next-generation media company. We don’t even think we’re a media company. We’re a technology company.” Facebook is not a content company, he said, just as a telephone company is not. In fact, in some ways Facebook is like a telephone conversation, with all your friends on the same call.

Kwan Lee is not alone in thinking that Google is mistaken to treat search as an engineering problem. John Borthwick, who created one of the first city Web sites, sold it to AOL in 1997, and later became senior vice president of technology and alliances for Time Warner, thinks Google “lacks a social gene.” (Borthwick has since founded and now runs Betaworks, which seeds money for social media.) Information, he said, “needs a social context. You need to incorporate the social graph [the connections among people] into search. Twitter becomes a platform for search. People put out Tweets—‘I’m thinking about buying a camera. What does anyone think of this camera?’” It’s the wisdom of crowds—your crowd of friends. “Google is just focused on CPU—central processing computers—and ignores the processing of the human brain.” He believes this makes its search vulnerable. Google obviously has come to share this concern for a senior Google executive confirms that they tried—and failed—to acquire Twitter.

pages: 598 words: 134,339

Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World by Bruce Schneier


23andMe, Airbnb, airport security, AltaVista, Anne Wojcicki, augmented reality, Benjamin Mako Hill, Black Swan, Brewster Kahle, Brian Krebs, call centre, Cass Sunstein, Chelsea Manning, citizen journalism, cloud computing, congestion charging, disintermediation, Edward Snowden, experimental subject, failed state, fault tolerance, Ferguson, Missouri, Filter Bubble, Firefox, friendly fire, Google Chrome, Google Glasses, hindsight bias, informal economy, Internet Archive, Internet of things, Jacob Appelbaum, Jaron Lanier, Julian Assange, Kevin Kelly, license plate recognition, linked data, Lyft, Mark Zuckerberg, Nash equilibrium, Nate Silver, national security letter, Network effects, Occupy movement, payday loans, pre–internet, price discrimination, profit motive, race to the bottom, RAND corporation, recommendation engine, RFID, self-driving car, Silicon Valley, Skype, smart cities, smart grid, Snapchat, social graph, software as a service, South China Sea, stealth mode startup, Steven Levy, Stuxnet, TaskRabbit, telemarketer, Tim Cook: Apple, transaction costs, Uber and Lyft, urban planning, WikiLeaks, zero day

Amit Agarwal (2013), “Sleeping Time,” Digital Inspiration, Your buddy lists and address books: Two studies of Facebook social graphs show how easy it is to predict these and other personal traits. Carter Jernigan and Behram R. T. Mistree (5 Oct 2009), “Gaydar: Facebook friendships expose sexual orientation,” First Monday 14, Michal Kosinski, David Stillwell, and Thore Graepel (11 Mar 2013), “Private traits and attributes are predictable from digital records of human behavior,” Proceedings of the National Academy of Sciences of the United States of America (Early Edition), Your e-mail headers reveal: The MIT Media Lab tool Immersion builds a social graph from your e-mail metadata. MIT Media Lab (2013), “Immersion: A people-centric view of your email life,”

pages: 468 words: 124,573

How to Build a Billion Dollar App: Discover the Secrets of the Most Successful Entrepreneurs of Our Time by George Berkowski


Airbnb, Amazon Web Services, barriers to entry, Black Swan, business intelligence, call centre, crowdsourcing,, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, iterative process, Jeff Bezos, Jony Ive, Kickstarter, knowledge worker, Lean Startup, loose coupling, Mark Zuckerberg, minimum viable product, move fast and break things, Network effects, Oculus Rift, Paul Graham, self-driving car, Silicon Valley, Silicon Valley startup, Skype, Snapchat, social graph, software as a service, software is eating the world, Steve Jobs, Steven Levy, Y Combinator

The startup world is very social, and most events involve pizza and beer and everyone is interested in meeting everyone else. Here are some great events that will get you talking to the right people. DEVELOPER MEETUPS. Tech companies host all kinds of events to showcase new technologies or features to developers. Facebook has one called Developer Garage. Each month the company invites developers together to talk about the latest features, such as Social Graph, Facebook Search or Facebook advertising. Companies such as Google, Yahoo!, LinkedIn and numerous other big software companies organise similar events all the time. Even though they are targeted at software developers, don’t be scared to attend if you’re not technical. Go along, pretend to be a developer (to get in), enjoy the pizza and beer, and then start talking to anyone and everyone. Not only will you get a flavour for what’s hot in terms of technology, but you’ll also start to understand how software developers think and communicate.

You can see where I am going with this. Blurring Business Models Flipboard has a very simple mission: to let people discover and share online content in beautiful, simple and meaningful ways. About 90 million people regularly use the app and it’s one of my favourite apps, on both the iPad and the iPhone. The first wave of Flipboard’s growth was fuelled by automatically creating ‘personal magazines’ directly from the social graph of its users. Flipboard pulls in stories from your friends’ tweets, Facebook posts and Google+ accounts, and then cleverly curates them into a highly readable format. Its second wave of growth was giving users (including advertisers) the power to create – and distribute – their own magazines. Within months of launching the new magazine-publishing feature, users had created some 3.5 million of them.1 The stats are impressive, with the average user spending 15 minutes browsing the app per session.

pages: 202 words: 59,883

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


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

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. When Facebook rapidly evolved into the world’s biggest site, it made a series of forward leaps related to searching. First, it came up with the social graph, which examines relationships between people instead of data. It extrapolated relevant data by examining graphical representations rather than strings of text. Next, Facebook created a Graph API (Application Programming Interface) that enabled third-party developers to connect and share data with the Facebook platform using common verbs such as “read,” “listen to,” “like,” “comment on” and so forth.

pages: 272 words: 64,626

Eat People: And Other Unapologetic Rules for Game-Changing Entrepreneurs by Andy Kessler


23andMe, Andy Kessler, bank run, barriers to entry, Berlin Wall, British Empire, business process, California gold rush, carbon footprint, Cass Sunstein, cloud computing, collateralized debt obligation, collective bargaining, computer age, disintermediation, Eugene Fama: efficient market hypothesis, fiat currency, Firefox, Fractional reserve banking, George Gilder, Gordon Gekko, greed is good, income inequality, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, Joseph Schumpeter, knowledge economy, knowledge worker, libertarian paternalism, low skilled workers, Mark Zuckerberg, McMansion, Netflix Prize, packet switching, personalized medicine,, prediction markets, pre–internet, profit motive, race to the bottom, Richard Thaler, risk tolerance, risk-adjusted returns, Silicon Valley, six sigma, Skype, social graph, Steve Jobs, The Wealth of Nations by Adam Smith, transcontinental railway, transfer pricing, Yogi Berra

No matter.) Gruesome, the metaphor anyway, but real. It just happens slowly enough that it’s more like Chinese water torture than a flood. But how to prepare for all this change? College, when it’s not learning you how to learn, prepares students for the very jobs that Free Radicals are busy getting rid of! A Free Radical had best prepare for a world of networks and mobility and attention mining and social graphs or whatever nom du jour the social commentators come up with, and the best way is to find a set of Servers, and then eat them by rendering them obsolete. RULE #8 Markets Make Better Decisions Than Managers YOU WOULD THINK THAT, AFTER THE CREDIT CRISIS OF 2008, no one would trust markets ever again. But you should. Free Radicals embrace markets and learn to trust them more than their own instincts.

pages: 266 words: 80,018

The Snowden Files: The Inside Story of the World's Most Wanted Man by Luke Harding


affirmative action, airport security, Anton Chekhov, Apple's 1984 Super Bowl advert, Berlin Wall, Chelsea Manning, don't be evil, Edward Snowden, Etonian, Firefox, Google Earth, Jacob Appelbaum, job-hopping, Julian Assange, Khan Academy, kremlinology, Mark Zuckerberg, Maui Hawaii, national security letter, pre–internet, Ralph Waldo Emerson, rolodex, Silicon Valley, Skype, social graph, Steve Jobs, web application, WikiLeaks

The idea was to perform something called ‘contact chaining’ on the records of communications, or metadata, it received. Contact chaining is a process of establishing connections between senders and recipients and their contacts. Done rigorously, it establishes a map of connections between people that doesn’t involve actually listening to their phone calls or reading the contents of their emails. Long before Facebook ever existed, the NSA was toying with what the social network would later unveil as a ‘social graph’. But there was a problem. The Justice Department’s intelligence policy branch determined in 1999 that metadata was covered under FISA’s definition of electronic surveillance. That meant that contact chaining was kosher for non-American communications, but if it ensnared Americans, the NSA would be breaking the law. Adding complexity, the transmission of electronic communications even between foreigners overseas could transit through the US, since the data splits apart into digital ‘packets’ rather than travelling from point to point over a telephone line.

pages: 284 words: 79,265

The Half-Life of Facts: Why Everything We Know Has an Expiration Date by Samuel Arbesman


Albert Einstein, Alfred Russel Wallace, Amazon Mechanical Turk, Andrew Wiles, bioinformatics, British Empire, Chelsea Manning, Clayton Christensen, cognitive bias, cognitive dissonance, conceptual framework, David Brooks, demographic transition, double entry bookkeeping, double helix, Galaxy Zoo, guest worker program, Gödel, Escher, Bach, Ignaz Semmelweis: hand washing, index fund, invention of movable type, Isaac Newton, John Harrison: Longitude, Kevin Kelly, life extension, meta analysis, meta-analysis, Milgram experiment, Nicholas Carr, p-value, Paul Erdős, Pluto: dwarf planet, randomized controlled trial, Richard Feynman, Richard Feynman, Rodney Brooks, social graph, social web, text mining, the scientific method, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, Tyler Cowen: Great Stagnation

Available online: 205 we are up to the tenth revision: The American version even has tens of thousands more classifications than the international version. 205 Just as being exposed: Johnson, Steven. Everything Bad Is Good for You. New York: Riverhead Books, 2005. 205 This is about the number of soldiers: Christakis, Nicholas A., and James H. Fowler. Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives. New York, New York, USA: Little Brown, 2009. 206 and is about 190, as of 2011: Ugander, Johan et al. “The Anatomy of the Facebook Social Graph”; 206 we increase the number of people we are close to: O’Malley, A. James, et al. “Egocentric Social Network Structure, Health, and Pro-Social Behaviors in a National Panel Study of Americans.” PLoS ONE. 7(5): e36250. 206 Sherlock Holmes argued this very point: Doyle, Arthur Conan. A Study in Scarlet, 1887. First published by Ward Lock & Co. in Beeton’s Christmas Annual, London.

Big Data at Work: Dispelling the Myths, Uncovering the Opportunities by Thomas H. Davenport


Automated Insights, autonomous vehicles, bioinformatics, business intelligence, business process, call centre, chief data officer, cloud computing, data acquisition, Edward Snowden, Erik Brynjolfsson, intermodal, Internet of things, Jeff Bezos, knowledge worker, Mark Zuckerberg, move fast and break things, Narrative Science, natural language processing, Netflix Prize, New Journalism, recommendation engine, RFID, self-driving car, sentiment analysis, Silicon Valley, smart grid, smart meter, social graph, sorting algorithm, statistical model, Tesla Model S, text mining

As a group of researchers wrote about LinkedIn: LinkedIn contributes to the Voldemort distributed ­storage ­system and more than 10 more open-source projects. “We ­contribute, Chapter_07.indd 160 03/12/13 12:42 PM What You Can Learn from Start-Ups and Online Firms   161 they contribute and the code moves ­forward,” says David Henke, senior vice president of operations at LinkedIn.2 Another LinkedIn data scientist told me: We are working on some database enhancements to a social graph database. They will be open source when we’re done. There are some IP [intellectual property] considerations, but overall LinkedIn believes in building on the open-source ­framework since we benefit from it. This lesson could be taken too far, of course. All of the c­ ompanies I’ve mentioned keep some big data assets to themselves. However, given all the benefits that every firm has received from open-source software, virtually every firm should try to give something back.

Raw Data Is an Oxymoron by Lisa Gitelman


collateralized debt obligation, computer age, continuous integration, crowdsourcing, Drosophila, Edmond Halley, Filter Bubble, Firefox, Google Earth, Howard Rheingold, index card, informal economy, Isaac Newton, Johann Wolfgang von Goethe, knowledge worker, Louis Daguerre, Menlo Park, optical character recognition, RFID, Richard Thaler, Silicon Valley, social graph, software studies, statistical model, Stephen Hawking, Steven Pinker, text mining, time value of money, trade route, Turing machine, urban renewal, Vannevar Bush

Google’s incorporation of DoubleClick, one of the largest behavioral targeting companies, as well as its partnership with Verizon, would likely be the closest approximation of this single database fantasy, but there is as yet no one entity legally (and technologically) capable of aggregating the entirety of “our” data, which would include not only all governmental and financial records but also our entire search and purchase history, along with our relationship to the social graph. (The value at present is in the aggregating of just a few of these data components.) It is the more general sense that data storage is permanent Dataveillance and Countervailance that leads Viktor Mayer-Schönberger to claim that we have been produced as Borgesian figures, like Funes, who have lost the capacity to forget and thereby lost the capacity to structure a temporal narrative.35 More concretely, the consequence of total storage is that the much-heralded second act of American lives—the mythology of reinvention— cannot be possible if all of the data from the first act is easily accessible.

pages: 666 words: 181,495

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


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

Meanwhile, Google would develop its own location-based service, Latitude. By then, there were a number of location-based start-ups, all of which owed something to Dodgeball. One of the hottest was called Foursquare. Its cofounder was Dennis Crowley. Google had a built-in disadvantage in the social networking sweepstakes. It was happy to gather information about the intricate web of personal and professional connections known as the “social graph” (a term favored by Facebook’s Mark Zuckerberg) and integrate that data as signals in its search engine. But the basic premise of social networking—that a personal recommendation from a friend was more valuable than all of human wisdom, as represented by Google Search—was viewed with horror at Google. Page and Brin had started Google on the premise that the algorithm would provide the only answer.

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

pages: 933 words: 205,691

Hadoop: The Definitive Guide by Tom White


Amazon Web Services, bioinformatics, business intelligence, combinatorial explosion, database schema, Debian, domain-specific language,, fault tolerance, full text search, Grace Hopper, information retrieval, Internet Archive, linked data, loose coupling, openstreetmap, recommendation engine, RFID, SETI@home, social graph, web application

For example, we’ve used variants of the same algorithm[148] to do each of: Rank the most important pages in the Wikipedia linked-document collection. Google uses a vastly more refined version of this approach to identify top search hits. Identify celebrities and experts in the Twitter social graph. Users who have many more followers than their “trstrank” would imply are often spammers. Predict a school’s impact on student education, using millions of anonymized exam scores gathered over five years. Measuring Community The most interesting network in the Infochimps collection is a massive crawl of the Twitter social graph. With more than 90 million nodes, 2 billion edges, it is a marvelous instrument for understanding what people talk about and how they relate to each other. Here is an exploration, using the subgraph of “People who talk about Infochimps or Hadoop,”[149] of three ways to characterize a user’s community: Who are the people they converse with (the @reply graph)?

pages: 924 words: 241,081

The Art of Community by Jono Bacon


barriers to entry, collaborative editing, crowdsourcing, Debian, DevOps,, Firefox, game design, Johann Wolfgang von Goethe, Jono Bacon, Kickstarter, Mark Zuckerberg, openstreetmap, Richard Stallman, side project, Silicon Valley, Skype, slashdot, social graph, software as a service, telemarketer, union organizing, VA Linux, web application

Don’t get dragged into that; many of these folks do this to prove their abilities, knowledge, and intellect…but the real proof is not in the academia that can be cited; rather, it is in the community that you build. In this chapter we are going to focus on the substance of how social media can help you grow and build your community. orm-interview-snippet: The Community Case Book I’m not convinced that the current generation of social media sites have helped communities to prosper in the way that earlier technologies like mailing lists or even Usenet did. They create a social graph centered on the individual rather than the community. The development of tools to support communities is still an untapped opportunity. —Tim O’Reilly, on Social Media Read the full interview in Chapter 14. Being Social One of the challenges of talking about technology in books is that technology changes so rapidly that the content can quickly become outdated. This is a particular challenge for a topic such as social media, as we need to talk about specific tools to really understand how social media works.

The Arab Spring is obviously the canonical example, but #ows (Occupy Wall Street) is happening right now as an example of the interplay between social media and real-world disruption. How do you feel that social media has opened up opportunities for communities to prosper? I’m not convinced that the current generation of social media sites have helped communities to prosper in the way that earlier technologies like mailing lists or even Usenet did. They create a social graph centered on the individual rather than the community. The development of tools to support communities is still an untapped opportunity. Today social media seems to largely involve the exchange of messages. How do you think social media can evolve to further empower collaborative community beyond that of exchanging messages? If you consider Twitter, Facebook, and Google+ to be the apogee of social media, yes, it is mainly about the exchange of messages.

pages: 713 words: 93,944

Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement by Eric Redmond, Jim Wilson, Jim R. Wilson


Amazon Web Services, create, read, update, delete, data is the new oil, database schema, Debian, domain-specific language,, fault tolerance, full text search, general-purpose programming language, linked data, MVC pattern, natural language processing, node package manager, random walk, recommendation engine, Skype, social graph, web application

We sit upon a vast ocean of data, yet until it’s refined into information, it’s unusable (and with a more crude comparison, there’s a lot of money in data these days). The ease of collecting and ultimately storing, mining, and refining the data out there starts with the database you choose. Deciding which database to choose is often more complex than merely considering which genre maps best to a given domain’s data. Though a social graph may seem to clearly function best with a graph database, if you’re Facebook, you simply have far too much data to choose one. You are more likely going to choose a “Big Data” implementation, such as HBase or Riak. This will force your hand into choosing a columnar or key-value store. In other cases, though you may believe a relational database is clearly the best option for bank transactions, it’s worth knowing that Neo4j also supports ACID transactions, expanding your options.

pages: 379 words: 109,612

Is the Internet Changing the Way You Think?: The Net's Impact on Our Minds and Future by John Brockman


A Declaration of the Independence of Cyberspace, Albert Einstein, AltaVista, Amazon Mechanical Turk, Asperger Syndrome, availability heuristic, Benoit Mandelbrot, biofilm, Black Swan, British Empire, conceptual framework, corporate governance, Danny Hillis, Douglas Engelbart, Emanuel Derman, epigenetics, Flynn Effect, Frank Gehry, Google Earth, hive mind, Howard Rheingold, index card, information retrieval, Internet Archive, invention of writing, Jane Jacobs, Jaron Lanier, Kevin Kelly, lone genius, loss aversion, mandelbrot fractal, Marshall McLuhan, Menlo Park, meta analysis, meta-analysis, New Journalism, Nicholas Carr, out of africa, Ponzi scheme, pre–internet, Richard Feynman, Richard Feynman, Rodney Brooks, Ronald Reagan, Schrödinger's Cat, Search for Extraterrestrial Intelligence, SETI@home, Silicon Valley, Skype, slashdot, smart grid, social graph, social software, social web, Stephen Hawking, Steve Wozniak, Steven Pinker, Stewart Brand, Ted Nelson, telepresence, the medium is the message, the scientific method, The Wealth of Nations by Adam Smith, theory of mind, trade route, upwardly mobile, Vernor Vinge, Whole Earth Catalog, X Prize

Now we all have blogs tethered to our mobile phones, even if they are micro in nature, with Facebook and Twitter accounts. We shouldn’t wait for facts; we should be speculating and testing assumptions as news and knowledge unfold. Facts are, of course, valuable, but speculation gets me further and builds better webs in my mind. We’ve moved from being jurors to being investigators, and the audience is onstage. Support thought bombs and the people who throw them into your social graph. It’s messy but essential. Study the reactions on either side of the aisle, because reactions can be more telling than the facts sometimes. That’s how the Internet has changed my thinking: Trust nothing, debate everything. Harmful One-Liners, an Ocean of Facts, and Rewired Minds Haim Harari Physicist, former president, Weizmann Institute of Science; author, A View from the Eye of the Storm: Terror and Reason in the Middle East It is entirely possible that the Internet is changing our way of thinking in more ways than I am willing to admit, but there are three clear changes that are palpable.

pages: 540 words: 103,101

Building Microservices by Sam Newman


airport security, Amazon Web Services, anti-pattern, business process, call centre, continuous integration, create, read, update, delete, defense in depth, Edward Snowden, fault tolerance, index card, information retrieval, Infrastructure as a Service, inventory management, job automation, load shedding, loose coupling, platform as a service, premature optimization, pull request, recommendation engine, social graph, software as a service, the built environment, web application, WebSocket, x509 certificate

If one part of our system needs to improve its performance, we might decide to use a different technology stack that is better able to achieve the performance levels required. We may also decide that how we store our data needs to change for different parts of our system. For example, for a social network, we might store our users’ interactions in a graph-oriented database to reflect the highly interconnected nature of a social graph, but perhaps the posts the users make could be stored in a document-oriented data store, giving rise to a heterogeneous architecture like the one shown in Figure 1-1. Figure 1-1. Microservices can allow you to more easily embrace different technologies With microservices, we are also able to adopt technology more quickly, and understand how new advancements may help us. One of the biggest barriers to trying out and adopting new technology is the risks associated with it.

pages: 391 words: 105,382

Utopia Is Creepy: And Other Provocations by Nicholas Carr


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

Marketing is conversational, says Zuckerberg, and advertising is social. There is no intimacy that is not a branding opportunity, no friendship that can’t be monetized, no kiss that doesn’t carry an exchange of value. “Facebook’s ad system,” goes a company press release, “serves Social Ads that combine social actions from your friends—such as a purchase of a product or review of a restaurant—with an advertiser’s message.” What Zuckerberg calls the social graph is, it turns out, a platform for social graft. The Fortune 500 is lining up for the new Facebook service. Coke’s in, big time: The Coca-Cola Company will feature its Sprite brand on a new Facebook Page and will invite users to add an application to their account called “Sprite Sips.” People will be able to create, configure and interact with an animated Sprite Sips character. For consumers in the United States, the experience can be enhanced by entering a PIN code found under the cap of every 20 oz. bottle of Sprite to unlock special features and accessories.

pages: 364 words: 99,897

The Industries of the Future by Alec Ross


23andMe, 3D printing, Airbnb, algorithmic trading, AltaVista, Anne Wojcicki, autonomous vehicles, banking crisis, barriers to entry, Bernie Madoff, bioinformatics, bitcoin, blockchain, Brian Krebs, British Empire, business intelligence, call centre, carbon footprint, cloud computing, collaborative consumption, connected car, corporate governance, Credit Default Swap, cryptocurrency, David Brooks, disintermediation, Dissolution of the Soviet Union, distributed ledger, Edward Glaeser, Edward Snowden,, Erik Brynjolfsson, fiat currency, future of work, global supply chain, Google X / Alphabet X, industrial robot, Internet of things, invention of the printing press, Jaron Lanier, Jeff Bezos, job automation, knowledge economy, knowledge worker, litecoin, M-Pesa, Mark Zuckerberg, Mikhail Gorbachev, mobile money, money: store of value / unit of account / medium of exchange, new economy, offshore financial centre, open economy, peer-to-peer lending, personalized medicine, Peter Thiel, precision agriculture, pre–internet, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Satoshi Nakamoto, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, social graph, software as a service, special economic zone, supply-chain management, supply-chain management software, technoutopianism, underbanked, Vernor Vinge, Watson beat the top human players on Jeopardy!, women in the workforce, Y Combinator, young professional

Because big data often relies on historical data or at least the status quo, it can easily reproduce discrimination against disadvantaged racial and ethnic minorities. The propensity models used in many algorithms can bake in a bias against someone who lived in the zip code of a low-income neighborhood at any point in his or her life. If an algorithm used by human resources companies queries your social graph and positively weighs candidates with the most existing connections to a workforce, it makes it more difficult to break in in the first place. In effect, these algorithms can hide bias behind a curtain of code. Big data is, by its nature, soulless and uncreative. It nudges us this way and that for reasons we are not meant to understand. It strips us of our privacy and puts our mistakes, secrets, and scandals on public display.

pages: 371 words: 108,317

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


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

Books, including fiction, will become a web of names and a community of ideas. (You can, of course, suppress links—and their connections—if you don’t want to see them, as you might while reading a novel. But novels are a tiny subset of everything that is written.) Over the next three decades, scholars and fans, aided by computational algorithms, will knit together the books of the world into a single networked literature. A reader will be able to generate a social graph of an idea, or a timeline of a concept, or a networked map of influence for any notion in the library. We’ll come to understand that no work, no idea stands alone, but that all good, true, and beautiful things are ecosystems of intertwined parts and related entities, past and present. Even when the central core of a text is authored by a lone author (as is likely for many fictional books), the auxiliary networked references, discussions, critiques, bibliography, and hyperlinks surrounding a book will probably be a collaboration.

pages: 283 words: 85,824

The People's Platform: Taking Back Power and Culture in the Digital Age by Astra Taylor


A Declaration of the Independence of Cyberspace, Andrew Keen, barriers to entry, Berlin Wall, big-box store, Brewster Kahle, citizen journalism, cloud computing, collateralized debt obligation, Community Supported Agriculture, conceptual framework, corporate social responsibility, cross-subsidies, crowdsourcing, David Brooks, digital Maoism, disintermediation, don't be evil, Donald Trump, Edward Snowden, Fall of the Berlin Wall, Filter Bubble, future of journalism, George Gilder, Google Chrome, Google Glasses, hive mind, income inequality, informal economy, Internet Archive, Internet of things, invisible hand, Jane Jacobs, Jaron Lanier, Jeff Bezos, job automation, Julian Assange, Kevin Kelly, Kickstarter, knowledge worker, Mark Zuckerberg, means of production, Naomi Klein, Narrative Science, Network effects, new economy, New Journalism, New Urbanism, Nicholas Carr, oil rush, Peter Thiel, Plutocrats, plutocrats, pre–internet, profit motive, recommendation engine, Richard Florida, Richard Stallman, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley ideology, slashdot, Slavoj Žižek, Snapchat, social graph, Steve Jobs, Stewart Brand, technoutopianism, trade route, Whole Earth Catalog, WikiLeaks, winner-take-all economy, Works Progress Administration, young professional

Fred Turner, From Counterculture to Cyberculture: Stewart Brand, the Whole Earth Network, and the Rise of Digital Utopianism (Chicago: University of Chicago Press, 2006), 238 and 247. 16. For a good discussion of this history, see Evgeny Morozov’s profile of Tim O’Reilly, supporter of the open source movement and founder of O’Reilly Media. Evgeny Morozov, “The Meme Hustler,” Baffler, no. 22 (2013). 17. Openness is the “key to success,” says Jeff Jarvis in What Would Google Do? (New York: HarperBusiness, 2009), 4. 18. Rob Horning, “Social Graph vs. Social Class,” New Inquiry, March 23, 2012. 19. Lawrence Lessig, “The Architecture of Innovation,” Duke Law Journal 51, no. 1783 (2002). Related arguments about the limitations of the framework of left versus right and state versus market are made by Steven Johnson in Future Perfect: The Case for Progress in a Networked Age (New York: Riverhead Books, 2012) and his op-ed “Peer Power, from Potholes to Patents,” Wall Street Journal, September 21, 2012, as well as by Yochai Benkler in The Penguin and the Leviathan: The Triumph of Cooperation over Self-Interest (New York: Crown Business, 2011). 20.

pages: 597 words: 119,204

Website Optimization by Andrew B. King


AltaVista, bounce rate, don't be evil,, Firefox, In Cold Blood by Truman Capote, information retrieval, iterative process, medical malpractice, Network effects, performance metric, search engine result page, second-price auction, second-price sealed-bid, semantic web, Silicon Valley, slashdot, social graph, Steve Jobs, web application

Metadata generally means machine-readable "data about data," which can take many forms. Perhaps the simplest form falls under the classification of microformats, [35] which can be as simple as a single attribute value such as nofollow, described in more detail in "Step 10: Build Inbound Links with Online Promotion," earlier in this chapter. Another popular single-attribute microformat is XFN, [36] which allows individual links to be labeled as connections on a social graph, with values such as acquaintance, co-worker, spouse, or even sweetheart. A special value of me indicates that a link points to another resource from the same author, as in the following example: <a href="" rel="me">Homepage</a> Some microformats expose more structure, particularly to represent people and events and to review information, all of which can help make sites more presentable in semantic search engines.

pages: 574 words: 164,509

Superintelligence: Paths, Dangers, Strategies by Nick Bostrom


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

Even so, it would not be too difficult to identify most capable individuals with a serious long-standing interest in artificial general intelligence research. Such individuals usually leave visible trails. They may have published academic papers, presented at conferences, posted on Internet forums, or earned degrees from leading computer science departments. They may also have had communications with other AI researchers, allowing them to be identified by mapping the social graph. Projects designed from the outset to be secret could be more difficult to detect. An ordinary software development project could serve as a front.26 Only careful analysis of the code being produced would reveal the true nature of what the project was trying to accomplish. Such analysis would require a lot of (highly skilled) manpower, whence only a small number of suspect projects could be scrutinized at this level.

pages: 525 words: 116,295

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


3D printing, access to a mobile phone, additive manufacturing, airport security, Amazon Mechanical Turk, Amazon Web Services, anti-communist, augmented reality, Ayatollah Khomeini, barriers to entry, bitcoin, borderless world, call centre, Chelsea Manning, citizen journalism, clean water, cloud computing, crowdsourcing, data acquisition, Dean Kamen, Elon Musk, failed state, fear of failure, Filter Bubble, Google Earth, Google Glasses, hive mind, income inequality, information trail, invention of the printing press, job automation, Julian Assange, Khan Academy, Kickstarter, knowledge economy, Law of Accelerating Returns, market fundamentalism, means of production, mobile money, mutually assured destruction, Naomi Klein, offshore financial centre, peer-to-peer lending, personalized medicine, Peter Singer: altruism, Ray Kurzweil, RFID, self-driving car, sentiment analysis, Silicon Valley, Skype, Snapchat, social graph, speech recognition, Steve Jobs, Steven Pinker, Stewart Brand, Stuxnet, The Wisdom of Crowds, upwardly mobile, Whole Earth Catalog, WikiLeaks, young professional, zero day

We had the opportunity to tour the command center for Plataforma México, Mexico’s impressive national crime database and perhaps the best model of an integrated data system operating today. Housed in an underground bunker in the Secretariat of Public Security compound in Mexico City, this large database integrates intelligence, crime reports and real-time data from surveillance cameras and other inputs from agencies and states across the country. Specialized algorithms can extract patterns, project social graphs and monitor restive areas for violence and crime as well as for natural disasters and other civilian emergencies. The level of surveillance and technological sophistication of Plataforma México that we saw is extraordinary—but then, so are the security challenges that Mexican authorities face. Therein lies the challenge looking ahead: Mexico is the ideal location for a pilot project like this because of its entrenched security problems, but once the model has been proven, what is to stop other states with less justifiable motivations from building something similar?

pages: 559 words: 155,372

Chaos Monkeys: Obscene Fortune and Random Failure in Silicon Valley by Antonio Garcia Martinez


Airbnb, airport security, Amazon Web Services, Burning Man, Celtic Tiger, centralized clearinghouse, cognitive dissonance, collective bargaining, corporate governance, Credit Default Swap, crowdsourcing, death of newspapers, El Camino Real, Elon Musk, Emanuel Derman, financial independence, global supply chain, Goldman Sachs: Vampire Squid, hive mind, income inequality, interest rate swap, intermodal, Jeff Bezos, Malcom McLean invented shipping containers, Mark Zuckerberg, Maui Hawaii, means of production, Menlo Park, minimum viable product, move fast and break things, Network effects, Paul Graham, performance metric, Peter Thiel, Ponzi scheme, pre–internet, Ralph Waldo Emerson, random walk, Sand Hill Road, Scientific racism, second-price auction, self-driving car, Silicon Valley, Silicon Valley startup, Skype, Snapchat, social graph, social web, Socratic dialogue, Steve Jobs, telemarketer, urban renewal, Y Combinator, éminence grise

I never heard the use of any bundling term like “buck,” but sums of millions of users were splashed around between products and test groups like chips at a one- or two-dollar poker table. What would have been an important user milestone for any consumer startup became the most minimal unit of account inside Facebook. As a geographic tangent: New Zealand was commonly used as a test bed for new user-facing products. It was perfect due to its English-language usage, its relative isolation in terms of the social graph (i.e., most friend links were internal to the country), and, frankly, its lack of newsworthiness, so any gossip or reporting of new Facebook features ran a low risk of leaking back to the real target markets of the United States and Europe. Aotearoa is the original Maori word for New Zealand, which roughly translated means “Facebook test set.” Thus does that verdant island nation, graced with stunning fjords and clear alpine lakes, sample whatever random product twiddle a twenty-three-year-old Facebook engineer in Menlo Park dreams up.

pages: 515 words: 126,820

Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World by Don Tapscott, Alex Tapscott


Airbnb, altcoin, asset-backed security, autonomous vehicles, barriers to entry, bitcoin, blockchain, Bretton Woods, business process, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, clean water, cloud computing, cognitive dissonance, corporate governance, corporate social responsibility, Credit Default Swap, crowdsourcing, cryptocurrency, disintermediation, distributed ledger, Donald Trump, double entry bookkeeping, Edward Snowden, Elon Musk, Erik Brynjolfsson, ethereum blockchain, failed state, fiat currency, financial innovation, Firefox, first square of the chessboard, first square of the chessboard / second half of the chessboard, future of work, Galaxy Zoo, George Gilder, glass ceiling, Google bus, Hernando de Soto, income inequality, informal economy, interest rate swap, Internet of things, Jeff Bezos, jimmy wales, Kickstarter, knowledge worker, Kodak vs Instagram, Lean Startup, litecoin, Lyft, M-Pesa, Mark Zuckerberg, Marshall McLuhan, means of production, microcredit, mobile money, Network effects, new economy, Oculus Rift, pattern recognition, peer-to-peer lending, performance metric, Peter Thiel, planetary scale, Ponzi scheme, prediction markets, price mechanism, Productivity paradox, quantitative easing, ransomware, Ray Kurzweil, renewable energy credits, rent-seeking, ride hailing / ride sharing, Ronald Coase, Ronald Reagan, Satoshi Nakamoto, Second Machine Age, seigniorage, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, smart grid, social graph, social software, Stephen Hawking, Steve Jobs, Steve Wozniak, Stewart Brand, supply-chain management, TaskRabbit, The Fortune at the Bottom of the Pyramid, The Nature of the Firm, The Wisdom of Crowds, transaction costs, Turing complete, Turing test, Uber and Lyft, unbanked and underbanked, underbanked, unorthodox policies, X Prize, Y2K, Zipcar

Privacy is enhanced in other manners too. For example, spy agencies can’t conduct traffic analysis because they are unable to discern the source or destination of messages. There would also be a nifty mechanism for finding people and feeds that you might care about. In addition, distributed tools aggregate and present interesting new people or information for you to follow or friend, possibly using Facebook’s social graph to help out. Lubin calls this “bootstrapping the decentralized Web using the pillars of the centralized Web.”42 Experience shows that value ultimately wins out in the digital age. The benefits of this distributed model are huge—at least to the users and companies. The huge resources of social media companies notwithstanding, there is no end to the richness and functionality that we can develop in such an open source environment.

pages: 552 words: 168,518

MacroWikinomics: Rebooting Business and the World by Don Tapscott, Anthony D. Williams


accounting loophole / creative accounting, airport security, Andrew Keen, augmented reality, Ayatollah Khomeini, barriers to entry, bioinformatics, Bretton Woods, business climate, business process, car-free, carbon footprint, citizen journalism, Clayton Christensen, clean water, Climategate, Climatic Research Unit, cloud computing, collaborative editing, collapse of Lehman Brothers, collateralized debt obligation, colonial rule, corporate governance, corporate social responsibility, crowdsourcing, death of newspapers, demographic transition, distributed generation, don't be evil,, energy security, energy transition, Exxon Valdez, failed state, fault tolerance, financial innovation, Galaxy Zoo, game design, global village, Google Earth, Hans Rosling, hive mind, Home mortgage interest deduction, interchangeable parts, Internet of things, invention of movable type, Isaac Newton, James Watt: steam engine, Jaron Lanier, jimmy wales, Joseph Schumpeter, Julian Assange, Kevin Kelly, knowledge economy, knowledge worker, Marshall McLuhan, medical bankruptcy, megacity, mortgage tax deduction, Netflix Prize, new economy, Nicholas Carr, oil shock, online collectivism, open borders, open economy, pattern recognition, peer-to-peer lending, personalized medicine, Ray Kurzweil, RFID, ride hailing / ride sharing, Ronald Reagan, scientific mainstream, shareholder value, Silicon Valley, Skype, smart grid, smart meter, social graph, social web, software patent, Steve Jobs, text mining, the scientific method, The Wisdom of Crowds, transaction costs, transfer pricing, University of East Anglia, urban sprawl, value at risk, WikiLeaks, X Prize, young professional, Zipcar

“The network works a bit like Facebook,” says Reinicke. “If you had a good ride with someone you can add them to your network and the next time you need a ride their profile is searched first.” The network extends to two degrees to increase the chances of a match. A host of other start-ups are also exploring the space of “social commuting” by developing communities around ridesharing. Some, such as GoLoco or Zimride, rely on the social graph to create groups of friends who carpool. Others such as PickupPal or Carticipate use geopositioning—either mobile or computer based—to match people departing from the same location. One such start-up called Wikit proposes a form of transportation marketplace, where drivers could advertise their daily routes using their incar GPS device and would-be passengers could publish their current location, desired destinations, and the amount they are willing to pay to get there, from the convenience of their GPS-enabled phone.

pages: 677 words: 206,548

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


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

Sexual orientation, relationship status, schools attended, family tree, lists of friends, age, gender, e-mail addresses, place of birth, news interests, work history, catalogs of favorite things, religion, political affiliation, purchases, photographs, and videos—Facebook is a marketer’s dream. Advertisers know every last intimate detail about a Facebook user’s life and can thus market to him or her with extreme precision based upon the social graph Facebook has generated. Moreover, Facebook created a variety of innovations that allow it to track users across the entirety of the Web, including via its omnipresent Like button. You’ve been trained to click on the cute little blue thumbs-up button to express your support for a particular idea, status update, or photograph; after all, it’s the polite thing to do. Your friends see that you support their message, but what neither of you see is what happens with the data generated with each and every Like—data that are captured, dissected, and sold to marketers and data brokers around the world.

pages: 669 words: 210,153

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


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

This gives fans a deeper connection with people they admire but do not know. [TF: Twitter also used a “Top 100” most-followed list early on to pour gasoline on competition.] Syndication of content. Emotional reaction: Users are beginning to use the nomenclature “RT” to indicate a “retweet” (this was common practice before the official retweet feature was developed). This ad hoc feature allows users to syndicate messages beyond their social graph, giving a user’s message increased visibility. The real-time nature of Twitter allows news stories to break faster than traditional media (even at the time, my startup, Digg). In allowing myself to feel these features through the eyes of the users, I can get a sense of the excitement around them. * * * This type of thinking can also be applied to larger industry trends. My colleague and friend David Prager was one of the first owners of the Tesla Model S.

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Free Speech: Ten Principles for a Connected World by Timothy Garton Ash


A Declaration of the Independence of Cyberspace, Affordable Care Act / Obamacare, Andrew Keen, Apple II, Ayatollah Khomeini, battle of ideas, Berlin Wall, bitcoin, British Empire, Cass Sunstein, Chelsea Manning, citizen journalism, Clapham omnibus, colonial rule, crowdsourcing, David Attenborough, don't be evil, Edward Snowden, Etonian, European colonialism, eurozone crisis, failed state, Fall of the Berlin Wall, Ferguson, Missouri, Filter Bubble, financial independence, Firefox, Galaxy Zoo, global village, index card, Internet Archive, invention of movable type, invention of writing, Jaron Lanier, jimmy wales, Julian Assange, Mark Zuckerberg, Marshall McLuhan, megacity, mutually assured destruction, national security letter, Netflix Prize, Nicholas Carr, obamacare, Peace of Westphalia, Peter Thiel, pre–internet, profit motive, RAND corporation, Ray Kurzweil, Ronald Reagan, semantic web, Silicon Valley, Simon Singh, Snapchat, social graph, Stephen Hawking, Steve Jobs, Steve Wozniak, The Death and Life of Great American Cities, The Wisdom of Crowds, Turing test, We are Anonymous. We are Legion, WikiLeaks, World Values Survey, Yom Kippur War

Another reason for the imbalance of power between state and citizen is the development of those very technologies that have given us an unprecedented increase in our ability to communicate with others. Peter Swire, a member of the panel charged by President Obama with preparing what became The NSA Report, argues that the early twenty-first century is a ‘golden age of surveillance’ for security services. He ascribes this to three technological developments in particular: the minutely detailed location data provided by mobile phones, the ‘social graph’ of contacts that we all produce, even if we are not active on social media, and the array of ‘big data’ that has created digital dossiers on us all.16 We should add to Swire’s list of technologies the phenomenon of P2, since private companies actually collect most of the information into which states tap, licitly or illicitly. Commercial surveillance, for the twin purposes of better customer service and maximising profit, feeds state surveillance, justified in the name of security.