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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 16 Feb 2018
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      David Chapman Retweeted Stephanie M. Lee

      AI doesn’t replicate. Having worked in the field, I can usually see why a paper’s result is nonsense, but the public can’t, and many researchers can’t.https://twitter.com/stephaniemlee/status/964612382650646529 …

      David Chapman added,

      Stephanie M. LeeVerified account @stephaniemlee
      the replication crisis is hitting artificial intelligence: of 400 papers, "only 6% shared the algorithm's code. only a third shared the data they tested their algorithms on" http://www.sciencemag.org/news/2018/02/missing-data-hinder-replication-artificial-intelligence-studies …
      2 replies 11 retweets 40 likes
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    2. David Chapman‏ @Meaningness 16 Feb 2018
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      I’ve been skeptical about DL results because 25 years ago I reran the key experiments that were hyped as showing backprop (the underlying tech) was incredible. In each case I found that the researchers were fooling themselves. Not deliberate fraud, but sloppy work.

      3 replies 2 retweets 18 likes
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    3. Kaj Sotala‏ @xuenay 17 Feb 2018
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      Replying to @Meaningness

      Would love to hear details about this!

      1 reply 0 retweets 0 likes
    4. David Chapman‏ @Meaningness 17 Feb 2018
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      Replying to @xuenay

      The only one I wrote up was this one. The then-most-hyped version of RL+backprop turned out to work less well than RL+perceptron. https://www.ijcai.org/Proceedings/91-2/Papers/018.pdf …

      1 reply 0 retweets 2 likes
    5. David Chapman‏ @Meaningness 17 Feb 2018
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      Replying to @Meaningness @xuenay

      The one that got me really annoyed was XOR. The narrative was that Minsky&Papert unfairly killed perceptrons with that, and you could learn XOR if you added hidden layers. 1/

      1 reply 0 retweets 1 like
      David Chapman‏ @Meaningness 17 Feb 2018
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      Replying to @Meaningness @xuenay

      This turned out not to be true in any interesting sense. You can compute XOR with a feedforward network, but backprop won’t learn it reliably, nor in a reasonable length of time. It has to get lucky. 2/

      6:08 PM - 17 Feb 2018
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      • Kaj Sotala
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        1. New conversation
        2. David Chapman‏ @Meaningness 17 Feb 2018
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          Replying to @Meaningness @xuenay

          My recollection is that other people figured this out a few years later and that mostly killed off backprop research until ~2012. My memory of the details is vague however. There were a few others, but XOR and RL were the ones that seemed most significant. 3/3

          1 reply 0 retweets 1 like
        3. David Chapman‏ @Meaningness 17 Feb 2018
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          Replying to @Meaningness @xuenay

          There’s a new paper that came out just a couple days ago that’s making the rounds called “Deep Reinforcement Learning Doesn’t Work” or something like that. I haven’t had time to look at it, but it didn’t work in 1992 so I’m not surprised.

          1 reply 1 retweet 2 likes
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        2. Kaj Sotala‏ @xuenay 18 Feb 2018
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          Replying to @Meaningness

          OH. And I thought that I was doing something wrong back when I was trying to get an intuition for backprop and was simulating it by hand in a spreadsheet, but couldn't get it to converge for XOR.

          1 reply 0 retweets 0 likes
        3. David Chapman‏ @Meaningness 18 Feb 2018
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          Replying to @xuenay

          I did this 25 years ago, but my recollection is that backprop can only find the solution by accident. There’s no guiding gradient. You have to set the hyperparameters to force a random walk over the whole space, and hope it falls into the golf hole eventually.

          1 reply 0 retweets 1 like
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