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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. Carlos E. Perez‏ @IntuitMachine Jan 9
      • Report Tweet
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      Carlos E. Perez Retweeted Yann LeCun

      Yann LeCun makes a very clear and honest statement of why Deep Learning is not enough. It's not enough because there are functions that don't emerge intrinsically from the network.https://twitter.com/ylecun/status/1215286749477384192 …

      Carlos E. Perez added,

      Yann LeCun @ylecun
      Replying to @yudapearl
      DL is methodology: building a model by assembling parameterized modules into (possibly dynamic) graphs and optimizing it with gradient-based methods. In that sense it not "sufficient" because the architecture, the objective, and the optimization method must all be instantiated.
      3 replies 2 retweets 15 likes
    2. Joscha Bach‏ @Plinz Jan 9
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      Replying to @IntuitMachine

      This is not what he said here. Every stepwise constructive or evolutionary process is in a sense an optimization via a gradient based method, so DL is a sufficiently broad methodology, but you'll still need to set up the system performing the optimization or meta optimization.

      3 replies 0 retweets 9 likes
    3. Grady Booch‏Verified account @Grady_Booch Jan 10
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      Replying to @Plinz @IntuitMachine

      So, DL is an architecture AND a methodology AND the mechanism behind all of evolution? Next, you’ll tell me that is also serves to unify the theory of gravity with the standard model of physics! @GaryMarcus

      1 reply 0 retweets 4 likes
    4. Joscha Bach‏ @Plinz Jan 10
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      Replying to @Grady_Booch @IntuitMachine @GaryMarcus

      No, my understanding is that Yann LeCun thinks of DL as automating the optimization of a program for computing a complex function. Depending on the optimization method, the program or its generator function will often be differentiable.

      1 reply 0 retweets 2 likes
    5. This Tweet is unavailable.
      Joscha Bach‏ @Plinz Jan 10
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      Yes, DL is basically compositional function approximation. Before DL, ML was mostly limited to shallow models. For instance, end-to-end training for speech recognition or game playing was considered to be outside of the reach of ML.

      1:15 PM - 10 Jan 2020
      • 1 Retweet
      • 1 Like
      • Shyamal Suhana Chandra(श्यामल सुहाना चंद्रा)Ma🏠15
      2 replies 1 retweet 1 like
        1. New conversation
        2. Grady Booch‏Verified account @Grady_Booch Jan 10
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          Replying to @Plinz @IntuitMachine @GaryMarcus

          I accept that but now I have a provocative question. is deep learning possible outside the context of artificial neural networks?

          2 replies 0 retweets 0 likes
        3. Joscha Bach‏ @Plinz Jan 10
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          Replying to @Grady_Booch @IntuitMachine @GaryMarcus

          Yes, of course. CNNs are just the most popular area right now. I am wondering about a paradigm where each individual unit is an evolving reinforcement learner that utilizes an arbitrary shader program.

          2 replies 0 retweets 1 like
        4. 4 more replies
        1. This Tweet is unavailable.
        2. Joscha Bach‏ @Plinz Jan 10
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          There are many ways of doing that beyond regression, such as Monte Carlo, evolutionary algorithms, enumeration etc.

          0 replies 0 retweets 0 likes
        3. End of conversation
        1. Gary Marcus‏ @GaryMarcus Jan 10
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          Replying to @Plinz @Grady_Booch @IntuitMachine

          is Monte Carlo Tree Search compositional function approximation?

          0 replies 0 retweets 0 likes
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