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

Tweets

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. Joscha Bach‏ @Plinz 19 Jun 2018
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      Joscha Bach Retweeted sol

      A continuous hypercomputer could solve the halting problem, which would lead to a paradox. That seems to imply that hypercomputation is not theoretically possible and the universe must therefore be discrete?https://twitter.com/fire__exit/status/1009256131624128512 …

      Joscha Bach added,

      sol @fire__exit
      dude what pic.twitter.com/8Bu60rmsUN
      Show this thread
      7 replies 1 retweet 20 likes
    2. Carlos E. Perez‏ @IntuitMachine 20 Jun 2018
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      Replying to @Plinz

      The current paradox is: Why can you evolve differential computing but not be able to do as well with discrete computing? Why does evolutionary methods not work as well as gradient descent in learning?

      1 reply 0 retweets 2 likes
    3. Joscha Bach‏ @Plinz 20 Jun 2018
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      Replying to @IntuitMachine

      Why is that a paradox? Directed search is going to be more efficient than undirected search. Large search spaces are often only tractable when you discover and exploit gradients.

      1 reply 0 retweets 0 likes
    4. Carlos E. Perez‏ @IntuitMachine 20 Jun 2018
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      Replying to @Plinz

      There aren't any gradients in a discrete system like the brain.

      1 reply 0 retweets 0 likes
    5. Joscha Bach‏ @Plinz 20 Jun 2018
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      Replying to @IntuitMachine

      That is not clear. The brain is not differentiable as a whole, but you are certainly able to converge very fast when you smell where the cookies are! There is a lot of (mostly speculative) research on how the brain could realize local gradient descent.

      1 reply 0 retweets 0 likes
    6. Carlos E. Perez‏ @IntuitMachine 20 Jun 2018
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      Replying to @Plinz

      Triangulation and trajectory tracking are skills that are independent from gradient descent learning. Are there not experiments that our measure of distance is not continuous?

      1 reply 0 retweets 0 likes
      Joscha Bach‏ @Plinz 20 Jun 2018
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      Replying to @IntuitMachine

      Distance is represented with cascades, but that is unrelated. You don't need to implement gradient descent on the level of individual neurons. (Our computers also don't implement it on individual transistors.)

      12:05 PM - 20 Jun 2018
      1 reply 0 retweets 0 likes
        1. New conversation
        2. Carlos E. Perez‏ @IntuitMachine 20 Jun 2018
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          Replying to @Plinz

          I can't disprove that gradient descent can't be be simulated by neurons. Good model for now, but I am not too comfortable with it yet.

          2 replies 0 retweets 0 likes
        3. Joscha Bach‏ @Plinz 20 Jun 2018
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          Replying to @IntuitMachine

          How would you feel if you saw a more general but less efficient paradigm that looks more natural to individual neurons but would allow them to learn to execute a gradient descent algorithm where needed?

          1 reply 0 retweets 0 likes
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