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Plinz's profile
Joscha Bach
Joscha Bach
Joscha Bach
@Plinz

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Joscha Bach

@Plinz

FOLLOWS YOU. Artificial Intelligence, Cognitive Architectures, Computation. The goal is integrity, not conformity.

San Francisco, CA
bach.ai
Joined April 2009

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    1. Gary Marcus‏ @GaryMarcus 27 Feb 2018
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      Gary Marcus Retweeted Joscha Bach

      Of course, evolution is statistical. But as I wrote in AlphaGo critique: blurring distinction between evolution & learning blurs causal mechanisms. May as well call it all change and lump rock formation with classical conditioning; key question is whether you need strong priors.https://twitter.com/plinz/status/968490295968063488 …

      Gary Marcus added,

      Joscha Bach @Plinz
      @GaryMarcus' nativist argument against statistical learning AI seems to rest on the premise that evolution is fundamentally different from a statistical learning paradigm. This is false (evolution IS a form of statistical learning), but I am grateful that Gary makes that point.
      8 replies 6 retweets 20 likes
    2. Joscha Bach‏ @Plinz 27 Feb 2018
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      Replying to @GaryMarcus

      As you can see in the comments you are confusing the audience. But to your argument: why do you think that the priors can be found by organic evolution but not by any other statistical search method?

      1 reply 0 retweets 2 likes
    3. Dean S Horak‏ @DeanSHorak 27 Feb 2018
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      Replying to @Plinz @GaryMarcus

      I tried to make a similar point to Gary a while back. I don't quite see the applicability of the idea of innateness in an artificial system. In biology it's simple developmental features are innate. There's no development in AI though - it's all computational/statistical.

      1 reply 0 retweets 0 likes
    4. Joscha Bach‏ @Plinz 27 Feb 2018
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      Replying to @DeanSHorak @GaryMarcus

      How many bits of innate knowledge do you think were accumulated in our evolutionary lineage, and what amount of data about our world would be required to extract the same amount of knowledge with principled statistical methods?

      1 reply 0 retweets 0 likes
    5. Nicholas Guttenberg‏ @ngutten 27 Feb 2018
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      Replying to @Plinz @DeanSHorak @GaryMarcus

      Not sure this kind of accounting is informative. The problem is that one way to construct a prior is to assemble a distribution, but I could also just change the support. Specifying the change in support is low bit cost, but incurs infinite KL divergence.

      1 reply 0 retweets 1 like
    6. Nicholas Guttenberg‏ @ngutten 27 Feb 2018
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      Replying to @ngutten @Plinz and

      For example, in physics we assume 'physics does not change with time' - that excludes an infinite subset of possibilities from consideration (so its 'worth' infinite bits compared to uniform prior), but it doesn't take infinite bits to specify or learn.

      2 replies 0 retweets 0 likes
    7. Joscha Bach‏ @Plinz 28 Feb 2018
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      Replying to @ngutten @DeanSHorak @GaryMarcus

      We don't have to make such an assumption a priori. The unchangeability of physics is a hypothesis that has to be supported by observations.

      1 reply 0 retweets 0 likes
    8. Nicholas Guttenberg‏ @ngutten 28 Feb 2018
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      Replying to @Plinz @DeanSHorak @GaryMarcus

      We need far fewer observations in order to attempt the assumption than we would need to render it certain. E.g. there's an infinity of hypotheses that look like 'physics is constant... until 3 seconds from now' that we can't directly disprove by observation but we discard anyhow.

      2 replies 0 retweets 1 like
      Joscha Bach‏ @Plinz 28 Feb 2018
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      Replying to @ngutten @DeanSHorak @GaryMarcus

      We already observe that physics is changing. The universe expands etc. When we observe change we can of course always construct an underlying regularity. We don’t need to discard any possibility.

      7:11 PM - 28 Feb 2018
      1 reply 0 retweets 0 likes
        1. New conversation
        2. Nicholas Guttenberg‏ @ngutten 28 Feb 2018
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          Replying to @Plinz @DeanSHorak @GaryMarcus

          In practice we do discard possibilities in order to actually get stuff done. Then, after the fact, if we find what we've built is sufficiently incompatible with observation, we discard and start rebuilding.

          1 reply 0 retweets 0 likes
        3. Nicholas Guttenberg‏ @ngutten 28 Feb 2018
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          Replying to @ngutten @Plinz and

          It's the 'getting stuff done' metric that regularizes the hypothesis space in this case compared to e.g. updating priors based on evidence. Hypotheses that are non-actionable or impossible to distinguish between can be merged or discarded without loss of performance.

          2 replies 0 retweets 0 likes
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