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Meaningness's profile
David Chapman
David Chapman
David Chapman
@Meaningness

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David Chapman

@Meaningness

Better ways of thinking, feeling, and acting—around problems of meaning and meaninglessness; self and society; ethics, purpose, and value.

meaningness.com/about-my-sites
Joined September 2010

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    1. David Chapman‏ @Meaningness 23 Jun 2018
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      Hmm! Think I’ve probably figured out something that has been puzzling me since about 1981. Eurisko, one of the most interesting AI programs ever, dramatically won a naval simulation game with the heuristic “choose nearly-extreme parameter values.” Why did that work?pic.twitter.com/Zbm50jlK6l

      6 replies 28 retweets 131 likes
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    2. David Chapman‏ @Meaningness 23 Jun 2018
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      Likely explanation: this is a manifestation of the counter-intuitive fact that essentially all of the volume of a high-dimensional solid is within ε of the surface. Here Richard Hammond points out the implications for design (h/t @devonzuegel):pic.twitter.com/FYH6GmJkOz

      4 replies 12 retweets 92 likes
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    3. David Chapman‏ @Meaningness 23 Jun 2018
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      The human players Eurisko competed against had the wrong intuition that medium values are likely optimal—often true in a low-D space, but the game was >100-dimensional. This understanding of high-D spaces is now commonplace, but we didn’t know it in 1980s AI research.

      2 replies 2 retweets 53 likes
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    4. St. Rev  ☯️ 🏴 😻‏ @St_Rev 23 Jun 2018
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      Replying to @Meaningness @devonzuegel

      I think this is very subtly misframed; it's not that choosing near-extreme parameters is a good strategy (if n = 100, then [1,0,0,...,0] is on the surface, though 99% of its parameters are 'small'; also 'near the surface' is a very weak restriction!)

      4 replies 0 retweets 3 likes
    5. David Chapman‏ @Meaningness 23 Jun 2018
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      Replying to @St_Rev @devonzuegel

      It’s quite possible I’ve entirely misunderstood this, since concentration of measure is something I’ve only learned about recently and tangentially! My understanding of Hammond (maybe wrong) is that he’s assuming equal probability of an optimum per volume, and in high dimensions>

      1 reply 0 retweets 0 likes
    6. David Chapman‏ @Meaningness 23 Jun 2018
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      Replying to @Meaningness @St_Rev @devonzuegel

      nearly all the volume is near the surface, so most of your dimensions will be nearly extreme. Have I got this wrong? (Thinking about it: probably yes! You are near the surface even if most of your parameters are non-extreme, because you can’t avoid the surface?)

      1 reply 0 retweets 0 likes
    7. St. Rev  ☯️ 🏴 😻‏ @St_Rev 23 Jun 2018
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      Replying to @Meaningness @devonzuegel

      Yes. Consider the cube with corners at the origin and [1,1, ... ,1]. Using a uniform distribution on [0,1], pick a random element [x1, x2, ... ,x100]. Then the element is within min( min(x_i), min(1 - x_i)) of the surface!

      2 replies 0 retweets 1 like
    8. David Chapman‏ @Meaningness 23 Jun 2018
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      Replying to @St_Rev @devonzuegel

      Right. So, what Hammond says was that concentration of measure implies that “one or more parameters” should be extreme… and setting a few to near-extremes seems to have been adequate in the Traveller game (which was not what the human players expected).

      2 replies 0 retweets 0 likes
    9. St. Rev  ☯️ 🏴 😻‏ @St_Rev 23 Jun 2018
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      Replying to @Meaningness @devonzuegel

      The "nearly but not quite extreme" heuristic sounds confused, TBH. When the parameters take integer values, I would expect "actually extreme" to pop up a lot. I'd be curious to see the actual data here.

      1 reply 0 retweets 0 likes
      David Chapman‏ @Meaningness 23 Jun 2018
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      Replying to @St_Rev @devonzuegel

      David Chapman Retweeted Stephen Pimentel

      Yeah, everyone was pretty puzzled at the time; lots of things didn’t make sense.https://twitter.com/StephenPiment/status/1010738472666259456 …

      David Chapman added,

      Stephen Pimentel @StephenPiment
      Replying to @aaronchall @Meaningness @devonzuegel
      It's not hard to find the *papers* (e.g., http://www.aaai.org/Papers/AAAI/1983/AAAI83-059.pdf …). However, the project became rather controversial, in that the code was never released, and others had difficulty reproducing the results from the papers.
      9:52 PM - 23 Jun 2018
      1 reply 0 retweets 0 likes
        1. David Chapman‏ @Meaningness 23 Jun 2018
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          Replying to @Meaningness @St_Rev @devonzuegel

          David Chapman Retweeted We're A Plague Ship Not A Coffin

          This may explain the not-quite-extreme optimality though:https://twitter.com/TristanSevers/status/1010681280743084032 …

          David Chapman added,

          We're A Plague Ship Not A Coffin @TristanSevers
          Replying to @Meaningness
          Simulation designers prioritize checking their boundary conditions for brokenness, and therefore walled-out minmaxing is nearly always suboptimal.
          0 replies 0 retweets 1 like
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