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RECENT READING Tom Peters/11 July 2013

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  1. RECENT READING Tom Peters/11 July 2013

  2. FILTER BUBBLE

  3. The Filter Bubble: How the New, Personalized Web Is Changing What We Read and How We Think—Eli Pariser

  4. “bonding capital” vs. “bridging capital” —Eli Pariser, The Filter Bubble: How the New, Personalized Web Is Changing What We Read and How We Think

  5. “If you’re not paying for something, you are the product being sold.”—Andrew Lewis, MetaFilter.com (from Eli Pariser, The Filter Bubble: How the New, Personalized Web Is Changing What We Read and How We Think)

  6. “How much time you take between the moment you enter your query and the moment you click on a result sheds light [for Google] on your personality.”—Eli Pariser, The Filter Bubble: How the New, Personalized Web Is Changing What We Read and How We Think

  7. “It is hardly possible to overrate the value of placing human beings in contact with persons dis-similar to themselves, and with modes of thought and action unlike those with which they are familiar. Such communication has always been, and is peculiarly in the present age, one of the primary sources of progress.”—John Stuart Mill (1806-1873)

  8. “I believe this is the quest for what a personal computer really is. It is to capture one’s entire life.”—Gordon Bell

  9. “Psychologists have a name for this fallacy: fundamental attribution error. We tend to attribute peoples’ behavior to their inner traits and personality rather than to the situations in which they’re placed.”—Eli Pariser, The Filter Bubble: How the New, Personalized Web Is Changing What We Read and How We Think

  10. “Some people rush for a deal, others think that the deal means the merchandise is subpar. Just by eliminating the persuasion styles that rub people the wrong way [as deduced from prior Web behavior patterns], [the marketer] found he could increase the effectiveness of marketing materials from 30 to 40 percent.” —Eli Pariser, The Filter Bubble: How the New, Personalized Web Is Changing What We Read and How We Think

  11. “With new forms of ‘sentiment analysis’ it’s now possible to guess what mood one’s in. People use substantially more positive words when they’re up …” —Eli Pariser, The Filter Bubble: How the New, Personalized Web Is Changing What We Read and How We Think

  12. “LinkedIn offers a career trajectory prediction by comparing your resume to other peoples’ who are in your field but further along. LinkedIn can forecast where you’ll be in five years. … As a service to customers, it’s pretty useful. But imagine if LinkedIn offered the data to corporate clients to weed out people who are forecast to be losers. … It seems unfair for banks to discriminate against you because your high school buddy is bad at paying his bills or because you like something that a lot of loan defaulters also like. And that points to a basic problem with induction, the logical method by which algorithms use data to make predictions.”—Eli Pariser, The Filter Bubble: How the New, Personalized Web Is Changing What We Read and How We Think

  13. “Technodeterminism is alluring and convenient for newly powerful entrepreneurs because it absolves them of responsibility for what they do.” —Eli Pariser, The Filter Bubble: How the New, Personalized Web Is Changing What We Read and How We Think

  14. ROBOT FUTURES

  15. Robot Futures —Illah Reza Nourbakhsh, Professor of Robotics, Carnegie Mellon

  16. “Analytics can yield literally hundreds of millions of data points—far too many for human intuition to make any sense of the data. So in conjunction with the ability to store very big data about online behavior, researchers have developed strong tools for data mining, statistically evaluating correlations between many types and sources of data to expose hidden patterns and connections. The patterns predict human behavior—and even hidden human motivations.” —Illah Reza Nourbakhsh, Professor of Robotics, Carnegie Mellon,Robot Futures

  17. “[Very successful websites send 99% of their traffic to tried-and-true designs, but risk 1% of their traffic on new variations to discover ever better conversion rates from visits to dollars. When Google was choosing the right shade of blue for a navigation bar, the company famously performed A/B split testing across 41 shades of blue. … When numbers are large and hundreds of millions of people are in play, the tiniest improvements translate into breathtaking levels of profit improvement.”—Illah Reza Nourbakhsh, Professor of Robotics, Carnegie Mellon,Robot Futures

  18. “Robotics will drive this very innovation. Landing page tuning will bust out of the Internet and become ‘interaction tuning.’ Companies will apply their analytics engines to all interaction opportunities with people everywhere: online, in the car, in a supermarket aisle, on the sidewalk, and of course in your home.” —Illah Reza Nourbakhsh, Professor of Robotics, Carnegie Mellon, Robot Futures

  19. “Human level capability has not turned out to be a special stopping point from an engineering perspective. ….” Source: Illah Reza Nourbakhsh, Professor of Robotics, Carnegie Mellon, Robot Futures

  20. BIG DATA

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

  22. “As humans, we have been conditioned to look for causes, even though searching for causality is often difficult and may lead us down the wrong paths. In a big data world, by contrast, we won’t have to be fixated on causality; instead, we can discover patterns and correlations in the data that offer us novel and invaluable insights. The correlations may not tell us precisely why something is happening, but they alert us that it is happening. And in many situations, this is good enough. If millions of electronic medical records reveal that cancer sufferers who take a certain combination of aspirin and orange juice see their disease go into remission, then the exact cause for the remission in health may be less important than the fact that they lived.” Source: Big Data: A Revolution That Will Transform How We Live, Work, and Think, by Viktor Mayer-Schonberger and Kenneth Cukier

  23. “Correlations let us analyze a phenomenon not by shedding light on its inner workings, but by identifying a useful proxy for it.” Source: Big Data: A Revolution That Will Transform How We Live, Work, and Think, by Viktor Mayer-Schonberger and Kenneth Cukier

  24. “Predictions based on correlations lie at the heart of big data.” Source: Big Data: A Revolution That Will Transform How We Live, Work, and Think, by Viktor Mayer-Schonberger and Kenneth Cukier

  25. “There is a philosophical debate going back centuries over whether causality even exists.” Source: Big Data: A Revolution That Will Transform How We Live, Work, and Think, by Viktor Mayer-Schonberger and Kenneth Cukier

  26. “Unfortunately, Kahneman argues [Nobel laureate Daniel Kahneman’s masterpiece Thinking, Fast and Slow], very often our brain is too lazy to think slowly and methodically. Instead, we let the fast way of thinking take over. As a consequence, we often ‘see’ imaginary causalities, and thus fundamentally misunderstand the world.” Source: Big Data: A Revolution That Will Transform How We Live, Work, and Think, by Viktor Mayer-Schonberger and Kenneth Cukier

  27. Walmart: “[Using big data], the company noticed that prior to a hurricane, not only did sales of flashlights increase, but so did sales of Pop-Tarts. … Walmart stocked boxes of Pop-Tarts at the front of the store [and dramatically boosted sales].” Source: Big Data: A Revolution That Will Transform How We Live, Work, and Think, by Viktor Mayer-Schonberger and Kenneth Cukier

  28. “Aviva, a large insurance firm, has studied the idea of using credit reports and consumer-marketing data as proxies for the analysis of blood and urine samples for certain applicants. The intent is to identify those who may be at higher risk of illnesses like high blood pressure, diabetes, or depression. The method uses lifestyle data that includes hundreds of variables such as hobbies, the websites people visit, and the amount of television they watch, as well as estimates of their income. Aviva’s predictive model, developed by Deloitte Consulting, was considered successful at identifying health risks.” Source: Big Data: A Revolution That Will Transform How We Live, Work, and Think, by Viktor Mayer-Schonberger and Kenneth Cukier

  29. Editor-in-chief Chris Anderson authored a Wired cover story titled “The Petabyte Age.” The use of “big data” (more or less everything, not a sample) and the attendant primacy of correlation over causation as the basis for discovery was described thusly: “The data deluge makes the scientific method obsolete.” He also called the phenomenon “the end of theory.” Source: Big Data: A Revolution That Will Transform How We Live, Work, and Think, by Viktor Mayer-Schonberger and Kenneth Cukier

  30. AUTOMATE THIS: HOW ALGORITHMS CAME TO RULE THE WORLD

  31. Automate This: How Algorithms Came to Rule Our World—Christopher Steiner

  32. April 2011. Prof Michael Eisen goes to Amazon to buy book The Making of a Fly. Expects price to be $35-$40. Follows bid war for 3 days: Price hits $23,698,655.93. Culprit: “Unsupervised [pricing] algorithm.” (Parallels 5/6/10 Wall Street flash crash: Market dropped 1K points in about 5 minutes.) From: Christopher Steiner, Automate This: How Algorithms Came to Rule Our World

  33. “Algorithms have already written symphonies as moving as those composed by Beethoven, picked through legalese with the deftness of a senior law partner, diagnosed patients with more accuracy than a doctor, written news articles with the smooth hand of a seasonedreporter, and driven vehicles on urban highways with far better control than a human driver.” —Christopher Steiner,Automate This: How Algorithms Came to Rule Our World

  34. “When you ask [Cloudera founder Jeffrey] Hammerbacher what he sees as the most promising field that could be hacked by people like himself, he responds with two words: ‘Medical diagnostics.’ And clearly doctors should be watching their backs, but they should be extra vigilant knowing that the smartest guys of our generation—people like Hammerbacher---are gunning for them. The targets on their backs will only grow larger as their complication rates, their test results and their practicesare scrutinized by the unyielding eyeof algorithms built by smart engineers. Doctors aren’t going away, but those who want to ensure their employment in the future should find ways to be exceptional. Bots can handle the grunt work, the work that falls to our average practitioners.” —Christopher Steiner, Automate This: How Algorithms Came to Rule Our World

  35. Shades of Ned Ludd … “When Emmy [algorithm] produced orchestral pieces so impressive that some music scholars failed to identify them as the work of a machine, [Prof. David] Cope instantly created legions of enemies. … At an academic conference in Germany, one of his peers walked up to him and whacked him on the nose. …” —Christopher Steiner, Automate This: How Algorithms Came to Rule Our World

  36. “ … The audience then voted on the identity of each composition.* [Music theory professor and contest organizer] Larson’s pride took a ding when his piece was fingered as that belonging to the computer. When the crowd decided that [algorithm] Emmy’s piece was the true product of the late musician [Bach], Larson winced.” —Christopher Steiner, Automate This: How Algorithms Came to Rule Our World *There were three: Bach/Larson/Emmy-the-algorithm.

  37. “ … Which haiku are human writing and which are from a group of bits? Sampling centuries of haiku, devising rules, spotting patterns, and inventing ways to inject originality, Annie [algorithm] took to the short Japanese sets of prose the same way all of [Prof David] Cope’s. algorithms tackled classical music. ‘In the end, it’s just layers and layers of binary math, he says. … Cope says Annie’spenchantfortastefuloriginality could push her past most human composers who simply build on work of the past., which, in turn, was built on older works. …” —Christopher Steiner, Automate This: How Algorithms Came to Rule Our World

  38. Legal industry/Pattern Recognition/Discovery (e-discovery algorithms): 500 lawyers to … ONE Source: Race AGAINST the Machine, Erik Brynjolfsson and Andrew McAfee

  39. Lionbridge/IBM: GeoFluent Evaluated as successful in customer-service transactions; medical diagnosis Medical knowledge from labs, descriptions, via pattern recognition/intuition Watson/IBM: Beats human Jeopardy players w/ puns, other idiosyncratic word play Source: Race AGAINST the Machine, Erik Brynjolfsson and Andrew McAfee

  40. StatsMonkey: Sports writing (Readers cannot tell difference) Source: Race AGAINST the Machine, Erik Brynjolfsson and Andrew McAfee

  41. REALITY IS BROKEN: WHY GAMES MAKE US BETTER AND HOW THEY CAN CHANGE THE WORLD

  42. Reality Is Broken: Why Games Make Us Better and How They Can Change the World —Jane McGonigal

  43. MMORPG/Massively Multiplayer Online Role-Playing Game Source: Jane McGonigal, Reality Is Broken: Why Games Make Us Better and How They Can Change the World

  44. “Why exactly are we competing with each other to do the dirty work? We’re playing a free online game called Chore Wars —and it just so happens that ridding our real-world kingdom of toilet stains is worth more experience points, or XP, than any other chore in our apartment. … A mom in Texas describes a typical Chore Wars experience: ‘We have three kids, ages 9, 8, and 7. I sat down with the kids, showed them their characters and the adventures, and they literally jumped up and ran off to complete their chosen task. I’ve never seen my 8-year-old son make his bed. I nearly fainted when my husband cleaned out the toaster oven.’ …” —Jane McGonigal, Reality Is Broken: Why Games Make Us Better and How They Can Change the World

  45. “You get a sense of the scale and intricacy of the task by considering the sound effects alone: The game contains 54,000 pieces of audio and 40,000 lines of dialogue. There are 2,700 different noises for footsteps alone depending on whose foot is stepping on what.” —Sam Leithon Halo 3, from Jane McGonigal, Reality Is Broken: Why Games Make Us Better and How They Can Change the World

  46. “The popularity of an unwinnable game like Tetris completely upends the stereotype that gamers are highly competitive people who care more about winning than anything else. Competition and winning are not defining traits of games—nor are they defining interests of the people who love to play them. Many gamers would rather keep playing than win. In high-feedback games, the state of being intensely engaged may ultimately be more pleasurable than the satisfaction of winning.” —Jane McGonigal, Reality Is Broken: Why Games Make Us Better and How They Can Change the World

  47. “When we are playing a well-designed game, failure doesn’t disappoint us. It makes us happy in a very peculiar way: excited, interested, and most of all optimistic.”—Studies from M.I.N.D. Lab, Helsinki, in Jane McGonigal, Reality Is Broken: Why Games Make Us Better and How They Can Change the World

  48. “It may have once been true that computer games encouraged us to act more with machines than with each other. But if you still think of gamers as loners, then you’re not playing games.”—Jane McGonigal, Reality Is Broken: Why Games Make Us Better and How They Can Change the World

  49. “World of Warcraft is the singlemost powerful IV drip of productivity ever created.”—Brian, friend, in Jane McGonigal, Reality Is Broken: Why Games Make Us Better and How They Can Change the World

  50. 3-D PRINTING/ FAB LABS