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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 29 Jan 2016
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      Semi-skeptical discussion of the Google Go-playing paper: https://medium.com/backchannel/has-deepmind-really-passed-go-adc85e256bec#.lar62he8k … (HT @concutere & others)

      1 reply 0 retweets 1 like
    2. David Chapman‏ @Meaningness 29 Jan 2016
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      Replying to @Meaningness

      One of MANY reasons the Google Go player is uninteresting is that Go is uninteresting: utterly unlike most problems humans encounter.

      1 reply 0 retweets 4 likes
    3. David Chapman‏ @Meaningness 29 Jan 2016
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      Replying to @Meaningness

      Boardgames like Go are devoid of nebulosity (http://meaningness.com/nebulosity ), which is the main source of difficulty in the real world.

      1 reply 0 retweets 7 likes
    4. David Chapman‏ @Meaningness 29 Jan 2016
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      Replying to @Meaningness

      The deep-learning image recognition results are genuinely interesting—unlike the Go ones—because recognition is a real, and nebulous, task.

      1 reply 0 retweets 3 likes
    5. David Chapman‏ @Meaningness 29 Jan 2016
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      Replying to @Meaningness

      Boardgame programs all have two parts: tree search to look ahead at future moves, and a static evaluator that says how good the board looks

      1 reply 0 retweets 1 like
    6. David Chapman‏ @Meaningness 29 Jan 2016
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      Replying to @Meaningness

      Computers win at chess by brute force: searching possible moves much further ahead than humans, evaluating millions of possibilities.

      1 reply 0 retweets 0 likes
    7. David Chapman‏ @Meaningness 29 Jan 2016
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      Replying to @Meaningness

      It has always been known that brute-force tree search won’t work for Go; there are too many possible moves. So, you need a better evaluator.

      1 reply 0 retweets 0 likes
    8. David Chapman‏ @Meaningness 29 Jan 2016
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      Replying to @Meaningness

      The standard analysis of Go has always been that grandmasters see regional patterns on the board that are good or bad. That’s evaluation.

      1 reply 0 retweets 1 like
    9. David Chapman‏ @Meaningness 29 Jan 2016
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      Replying to @Meaningness

      What Google did was spend incredible quantities of computer time playing vast numbers of games—noting which board configurations led to wins

      1 reply 0 retweets 1 like
    10. David Chapman‏ @Meaningness 29 Jan 2016
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      Replying to @Meaningness

      It is hard to see how this strategy could have not-worked.

      2 replies 0 retweets 0 likes
      David Chapman‏ @Meaningness 29 Jan 2016
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      Only question is whether the AlphaGo “neural” network did any non-obvious generalization. I don’t have access to the journal article, but >

      2:12 PM - 29 Jan 2016
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        2. David Chapman‏ @Meaningness 29 Jan 2016
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          Replying to @Meaningness

          > nothing I have read suggests it did. Generally, when analyzing “neural” learning results, I’ve found they didn’t do anything interesting.

          1 reply 0 retweets 1 like
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        2. Meredith L. Patterson‏Verified account @maradydd 2 Feb 2016
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          Replying to @Meaningness

          @Meaningness https://web.archive.org/web/20160128151110/https://storage.googleapis.com/deepmind-data/assets/papers/deepmind-mastering-go.pdf … but tbqh I wasn't too impressed with "look! you can mapreduce with hybrid [sup|unsup]ervised systems!"

          2 replies 0 retweets 1 like
        3. David Chapman‏ @Meaningness 2 Feb 2016
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          Replying to @maradydd

          @maradydd Thank you very much indeed! Will read (in copious spare time :-)

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