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fchollet's profile
François Chollet
François Chollet
François Chollet
Verified account
@fchollet

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François CholletVerified account

@fchollet

Deep learning @google. Creator of Keras. Author of 'Deep Learning with Python'. Opinions are my own.

United States
fchollet.com
Joined August 2009

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    1. François Chollet‏Verified account @fchollet 1 Jul 2018

      Central planning is unbeatable as a resource allocation mechanism for small systems. As you attempt to scale it up, it becomes terribly ineffective, to such an extent that decentralized control algorithms wastly outperform it, despite being inherently wasteful

      13 replies 97 retweets 357 likes
      Show this thread
    2. François Chollet‏Verified account @fchollet 1 Jul 2018

      You don't need to look at country-scale economies to observe this effect (obviously free markets >> central planning), it applies even to large companies. Past a certain scale you need teams that compete against each other with overlapping products. But why?

      5 replies 9 retweets 75 likes
      Show this thread
    3. François Chollet‏Verified account @fchollet 1 Jul 2018

      I think it's a technology problem, not an intrinsic issue. Better cybernetics will enable us to scale efficient central control (while taking into account uncertainty and exploration) to increasingly large systems in the future

      9 replies 9 retweets 47 likes
      Show this thread
    4. François Chollet‏Verified account @fchollet 1 Jul 2018

      This is tightly related to AI as well: it's the question of local vs. global optimization in a non-differentiable system, open-ended goal-setting, objective propagation from one module to its neighbors, parent, and internal submodules...

      1 reply 5 retweets 65 likes
      Show this thread
    5. François Chollet‏Verified account @fchollet 1 Jul 2018

      Backprop is a centralized answer, that only applies to differentiable functions where "control" means adjusting some parameters and where the optimization objective is already known. Its range of applicability is minuscule

      6 replies 10 retweets 85 likes
      Show this thread
    6. Steven Hansen‏ @Zergylord 1 Jul 2018
      Replying to @fchollet

      The objective function isn't always known e.g. GANs. RL problems also involve objectives that can only be sampled from. I get the local vs global argument, but it's unclear what you mean about backprop being limited by objectives.

      1 reply 0 retweets 0 likes
    7. François Chollet‏Verified account @fchollet 1 Jul 2018
      Replying to @Zergylord

      "known" objective doesn't mean "can be expressed analytically as a func of the data", it means you know what you're optimizing for from the start. Your GAN implementation itself is an expression of a known optimization objective. "Unknown objective" refers to open-ended problems.

      2 replies 0 retweets 4 likes
      François Chollet‏Verified account @fchollet 1 Jul 2018
      Replying to @fchollet @Zergylord

      As for sampling -- that can only work if your objective space has a structure that can be easily embedded in a continuous space. Basically why deep RL doesn't work

      3:55 AM - 1 Jul 2018
      • 2 Likes
      • M فيصل السالم
      1 reply 0 retweets 2 likes
        1. Steven Hansen‏ @Zergylord 1 Jul 2018
          Replying to @fchollet

          Saying "deep RL doesn't work" is an excessively broad claim. The fact that the value function of Go can be embedded in this way makes me question the significant of this difficulty.

          0 replies 0 retweets 2 likes
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