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michael_nielsen's profile
michael_nielsen
michael_nielsen
michael_nielsen
@michael_nielsen

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michael_nielsen

@michael_nielsen

Searching for the numinous. Co-purveyor of https://quantum.country/ 

San Francisco, CA
michaelnielsen.org
Joined July 2008

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    1. michael_nielsen‏ @michael_nielsen Mar 14
      • Report Tweet

      michael_nielsen Retweeted Greg Brockman

      Many people sharing this essay arguing that "computational scale beats clever new ideas". It takes for granted backprop, better activation functions, better learning methods, conv nets, better regularization techniques, etc etc. In other words, it seems to ignore the clever ideashttps://twitter.com/gdb/status/1106329741785653248 …

      michael_nielsen added,

      Greg BrockmanVerified account @gdb
      From Richard Sutton (http://incompleteideas.net/ ), an essay on the repeated historical finding that computational scale has always beaten cleverness in AI (and some commentary on why this is such a hard-to-accept fact): http://www.incompleteideas.net/IncIdeas/BitterLesson.html …
      18 replies 66 retweets 307 likes
      Show this thread
    2. michael_nielsen‏ @michael_nielsen Mar 14
      • Report Tweet

      An example is this argument that Deep Blue was just massive search. It wasn't: it used more than 8,000 hand-engineered features that incorporated a huge amount of chess knowledge. Contrast Sutton's essay on the left with the paper about Deep Blue on the right:pic.twitter.com/xyFQJH7LL9

      3 replies 7 retweets 44 likes
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    3. michael_nielsen‏ @michael_nielsen Mar 14
      • Report Tweet

      Of course, I don't disagree at all with the essay that computational scale buys you an incredible amount, and it's easy to under-estimate. But GANs & levering pawns (etc) weren't discovered with computational scale.

      2 replies 0 retweets 26 likes
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    4. michael_nielsen‏ @michael_nielsen Mar 14
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      michael_nielsen Retweeted Greg Brockman

      Nice followup from @gdb, making the point that it's a false dichotomy (scale versus cleverness):https://twitter.com/gdb/status/1106386231338762240 …

      michael_nielsen added,

      Greg BrockmanVerified account @gdb
      Replying to @etzioni
      Both are important! Look at GPT-2 for instance — that's a general-purpose architectural improvement (i.e. the Transformer) run at massive scale. One interesting point from the essay is that scale gets a bad rap — doing the reverse isn't a good way of fixing the problem!
      2 replies 0 retweets 19 likes
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      michael_nielsen‏ @michael_nielsen Mar 15
      • Report Tweet

      A better essay collecting some (correct!) examples in the same general direction is this paper by Banko & Brill: http://www.aclweb.org/anthology/P01-1005 … See eg this great graph, showing performance as a function of training data size. In this example: more data >> smarter algorithmpic.twitter.com/zwEmQSGMcP

      9:00 AM - 15 Mar 2019
      • 24 Likes
      • Margaretta Colangelo Akshay Chalana Carlos E. Perez 🧢 Giuseppe Carleo Kiru Gabriel Fernández F. Mayank Prakash Jain
      1 reply 0 retweets 24 likes
        1. michael_nielsen‏ @michael_nielsen Mar 15
          • Report Tweet

          There is, IMO, a good paper to be written following this up, carefully understanding the relationship between scale and clever ideas.

          2 replies 2 retweets 22 likes
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