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Daily links 02/26/2012

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  • Author: Adriana
  • Published: Feb 23rd, 2012
  • Category: Stuff
  • Comments: 1

Daily links 02/23/2012

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  • tags: companies secrets datamining big data data data analysis privacy nytimes

    • In other words, if Target piggybacked on existing habits — the same cues and rewards they already knew got customers to buy cleaning supplies or socks — then they could insert a new routine: buying baby products, as well. There’s a cue (“Oh, a coupon for something I need!”) a routine (“Buy! Buy! Buy!”) and a reward (“I can take that off my list”).
    • As long as Target camouflaged how much it knew, as long as the habit felt familiar, the new behavior took hold.
  • tags: companies secrets datamining big data data data analysis privacy nytimes

    • Deciphering cues is hard, however. Our lives often contain too much information to figure out what is triggering a particular behavior. Do you eat breakfast at a certain time because you’re hungry? Or because the morning news is on? Or because your kids have started eating? Experiments have shown that most cues fit into one of five categories: location, time, emotional state, other people or the immediately preceding action.
    • To shift the routine — to socialize, rather than eat a cookie — I needed to piggyback on an existing habit. So now, every day around 3:30, I stand up, look around the newsroom for someone to talk to, spend 10 minutes gossiping, then go back to my desk. The cue and reward have stayed the same. Only the routine has shifted. It doesn’t feel like a decision, any more than the M.I.T. rats made a decision to run through the maze. It’s now a habit.
  • tags: companies secrets datamining big data data data analysis privacy nytimes

    • At which point someone asked an important question: How are women going to react when they figure out how much Target knows?
  • tags: companies secrets datamining big data data data analysis privacy nytimes

  • tags: companies secrets datamining big data data data analysis privacy nytimes

    • The reason Febreze wasn’t selling, the marketers realized, was that people couldn’t detect most of the bad smells in their lives. If you live with nine cats, you become desensitized to their scents. If you smoke cigarettes, eventually you don’t smell smoke anymore. Even the strongest odors fade with constant exposure. That’s why Febreze was a failure. The product’s cue — the bad smells that were supposed to trigger daily use — was hidden from the people who needed it the most. And Febreze’s reward (an odorless home) was meaningless to someone who couldn’t smell offensive scents in the first place.
    • The marketers needed to position Febreze as something that came at the end of the cleaning ritual, the reward, rather than as a whole new cleaning routine.
  • tags: companies secrets datamining big data data data analysis privacy nytimes

  • tags: companies secrets datamining big data data data analysis privacy

    • Left to its own devices, the brain will try to make almost any repeated behavior into a habit, because habits allow our minds to conserve effort. But conserving mental energy is tricky, because if our brains power down at the wrong moment, we might fail to notice something important, like a child riding her bike down the sidewalk or a speeding car coming down the street. So we’ve devised a clever system to determine when to let a habit take over. It’s something that happens whenever a chunk of behavior starts or ends — and it helps to explain why habits are so difficult to change once they’re formed, despite our best intentions.
    • Over time, this loop — cue, routine, reward; cue, routine, reward — becomes more and more automatic. The cue and reward become neurologically intertwined until a sense of craving emerges. What’s unique about cues and rewards, however, is how subtle they can be. Neurological studies like the ones in Graybiel’s lab have revealed that some cues span just milliseconds. And rewards can range from the obvious (like the sugar rush that a morning doughnut habit provides) to the infinitesimal (like the barely noticeable — but measurable — sense of relief the brain experiences after successfully navigating the driveway).
    • But it’s also true that once the loop is established and a habit emerges, your brain stops fully participating in decision-making. So unless you deliberately fight a habit — unless you find new cues and rewards — the old pattern will unfold automatically.
    • The reason Target can snoop on our shopping habits is that, over the past two decades, the science of habit formation has become a major field of research in neurology and psychology departments at hundreds of major medical centers and universities, as well as inside extremely well financed corporate labs.
    • As the ability to analyze data has grown more and more fine-grained, the push to understand how daily habits influence our decisions has become one of the most exciting topics in clinical research, even though most of us are hardly aware those patterns exist. One study from Duke University estimated that habits, rather than conscious decision-making, shape 45 percent of the choices we make every day, and recent discoveries have begun to change everything from the way we think about dieting to how doctors conceive treatments for anxiety, depression and addictions.
    • For companies like Target, the exhaustive rendering of our conscious and unconscious patterns into data sets and algorithms has revolutionized what they know about us and, therefore, how precisely they can sell.
    • This process, in which the brain converts a sequence of actions into an automatic routine, is called “chunking.” There are dozens, if not hundreds, of behavioral chunks we rely on every day. Some are simple: you automatically put toothpaste on your toothbrush before sticking it in your mouth. Some, like making the kids’ lunch, are a little more complex. Still others are so complicated that it’s remarkable to realize that a habit could have emerged at all.

Posted from Diigo. The rest of my favorite links are here.

Daily links 02/18/2012

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Posted from Diigo. The rest of my favorite links are here.

Daily links 02/17/2012

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Daily links 02/14/2012

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  • misunderstands the value of data as opposed to price for data. Also, direct market in personal data unlikely, currently personal data is an externality to some other transaction or exchange of value.

    tags: personaldata nytimes data business VRM mine project privacy

    • Many of the new ideas center on a concept known as the personal data locker. People keep a single account with information about themselves. Businesses would pay for this data because it allows them to offer personalized products and advertising. And because people retain control over the data in their lockers, they can demand something of value in return. Maybe a discounted vacation, or a cash payment.
    • A challenge for the company will be whether it can offer enough money to persuade people to use the system. Consumer information is worth billions in aggregate, but individually, the bits of data are worth practically nothing. A study by JPMorgan Chase last year showed that a unique user was worth $4 to Facebook and $24 to Google. Others looked at Facebook’s recent filings with the Securities and Exchange Commission and placed the value of a user as high as $120.

    • Instead, he says people will create data lockers and share their contents because they will receive compelling services by doing so. This idea has already been successful with Mint.com, which has shown that people will share confidential financial information in exchange for money-management advice.
    • The final barrier is that people may find creating detailed databases about themselves too onerous to justify the potential rewards. In order to create a real market for data, enough people need to see an immediate, tangible benefit in filling up their lockers, said Mr. Green of Personal.

Posted from Diigo. The rest of my favorite links are here.

Daily links 02/13/2012

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  • worth watching

    tags: medical device science science fiction technology self-hacking

    • The company is vying to be the first to create a device that can scan a patient’s body and return a medical diagnosis. The device, known in medical design lore as the “tricorder,” is one of the elusive holy grails of health engineering.
    • While numbers for the forward thinking market are hard to pin, the medical imaging market is worth almost $6 billion, according to analysts Frost & Sullivan.
    • The first iteration of the product is expected in 2013 and will be basic, providing a thermometer, suggestions from medical websites, and GPS functions that can lead you to a nearby hospital. A neck patch worn by users gives the device information on vital signs. The product works with “hyperspectral camera” technology, meaning it organizes visual data by color. For example, says de Brouwer, the scans of people with lung cancer might show up the same color, and the device essentially analyzes the patterns.
    • De Brouwer sees an entirely new medical infrastructure around the device in the future. He says it will look a bit like the OnStar service, a system from General Motors (GM) that helps drivers during road disasters. The device will collect internal information from a user who is ill, and, like OnStar, will send the user to a tracking agent, who can then connect to a doctor for the diagnosis. While this preserves the role of the doctor, it will eliminate the need for doctor’s offices, says de Brouwer.

Posted from Diigo. The rest of my favorite links are here.

Daily links 02/12/2012

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    • As an (Human) Intelligence Technology, Prof. Levy places the invention of the computer in a long lineage of other such technologies, starting with hieroglyphs, then the alphabet, much later the movable type. For Prof Levy, while computer theory will likely not change very much – computers will only get much more powerful – there is however a huge theoretical work to be done on the next evolution of Intelligence Technologies that they will finally enable.
    • The IEML dictionary (or, I should say, IEML dictionaries, because you can theoretically have many of them), is a network of interrelated concepts where every node or word or sentence receives its meaning from the nodes it is related to. Now, and here is where things get interesting, all these relationships are mathematically computable. What it means is that IEML makes it possible to make computations on meaning.
    • when we become aware of our behavior, we are unlocking our power to act on it; that is the single most compelling incentive for users to annotate their data on a daily basis – and IEML can do nothing for you if you don’t annotate something with it

Posted from Diigo. The rest of my favorite links are here.

Daily links 02/10/2012

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Daily links 02/07/2012

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Posted from Diigo. The rest of my favorite links are here.

Daily links 02/05/2012

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Daily links 02/02/2012

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Posted from Diigo. The rest of my favorite links are here.

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