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[Computational Complexity] The World in Its Own Terms

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  • Lance
    The New York Times Magazine last Sunday focused on technology on education. Lots of good reads but what caught my eye was an article by Microsoft Research s
    Message 1 of 1 , Sep 21, 2010
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      The New York Times Magazine last Sunday focused on technology on education. Lots of good reads but what caught my eye was an article by Microsoft Research's Jaron Lanier, basically arguing that computational thinking ruins your enjoyment of life.
      A career in computer science makes you see the world in its terms. You start to see money as a form of information display instead of as a store of value. Money flows are the computational output of a lot of people planning, promising, evaluating, hedging and scheming, and those behaviors start to look like a set of algorithms. You start to see the weather as a computer processing bits tweaked by the sun, and gravity as a cosmic calculation that keeps events in time and space consistent.
      This way of seeing is becoming ever more common as people have experiences with computers. While it has its glorious moments, the computational perspective can at times be uniquely unromantic.
      Nothing kills music for me as much as having some algorithm calculate what music I will want to hear. That seems to miss the whole point. Inventing your musical taste is the point, isn’t it? Bringing computers into the middle of that is like paying someone to program a robot to have sex on your behalf so you don’t have to.
      And yet it seems we benefit from shining an objectifying digital light to disinfect our funky, lying selves once in a while. It’s heartless to have music chosen by digital algorithms. But at least there are fewer people held hostage to the tastes of bad radio D.J.’s than there once were. The trick is being ambidextrous, holding one hand to the heart while counting on the digits of the other.
      Lanier is mostly upset that a computer can predict his likes and dislikes so easily. Sorry Jaron, you are just not as complex as you thought you were.

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      Posted By Lance to Computational Complexity at 9/21/2010 06:24:00 AM
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