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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. Joscha Bach‏ @Plinz 29 Sep 2018
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      A model compresses a state space by capturing a set of invariances that predict the variance in the states. Its free parameters define the latent space of the model and should ideally fully correspond to the variability, the not-invariant (= unexplained) remainder of the state.

      1 reply 1 retweet 13 likes
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      Joscha Bach‏ @Plinz 29 Sep 2018
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      The proper representation of invariances (which include allowed set of values of the free parameters) is conditional on the reason for an invariance. The relationship to this reason is itself an invariance that needs to be made conditional on the foundations of its semantics.

      2:06 PM - 29 Sep 2018 from Manhattan, NY
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      • wilhelm Heiko Linke
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        2. Joscha Bach‏ @Plinz 29 Sep 2018
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          Most of our models are perceptual. Proofs in perceptual models are conducted by creating global coherence between all perceived features via propagating the state of the free parameters along their relationship functions to each other until a stable configuration is reached.

          1 reply 1 retweet 2 likes
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        3. Joscha Bach‏ @Plinz 29 Sep 2018
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          Reasoning is not just an alternative to perception. It is a tool that combines a toolbox of algorithms that are being used to repair perceptual models that don't achieve full coherence.

          2 replies 6 retweets 7 likes
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        4. End of conversation
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        2. mattgiammarino‏ @mattgiammarino 29 Sep 2018
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          Replying to @Plinz

          Ok, I'm not smart enough for this thread, but I want to understand! Could you give an example? Sorry if this is a dumb request.

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        3. Joscha Bach‏ @Plinz 29 Sep 2018
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          Replying to @mattgiammarino

          Discovered Invariance in the data = structure of the model (a set of variables with value ranges and a set of computational relationships between them) Discovered Variance in the data = the set of values of the variables that will explain most of the observations I am making

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        2. mattgiammarino‏ @mattgiammarino 1 Oct 2018
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          Replying to @Plinz

          2) Here's a probably-wrong paraphrase of the above: Models are proprer (truer?), when they reason about the invariances they use. This reasoning itself is a model, and so, is also depends on 'reasons' which are "the foundations of its semantics" - Um, but what are those? 🤯😉🙏

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        3. Joscha Bach‏ @Plinz 1 Oct 2018
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          Replying to @mattgiammarino

          It seems that universal computation and Bayes are at the root of modeling and learning.

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        2. mattgiammarino‏ @mattgiammarino 1 Oct 2018
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          Replying to @Plinz

          Ok, now I am stuck here! (No pressure to respond, I don't mean to use all your valuable thought cycles!). I think I have two issues. 1) What is an invariance? (is it a mathemaical description of a state space which stays the same for all possible input patterns?). #naivequestion

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        3. Joscha Bach‏ @Plinz 1 Oct 2018
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          Replying to @mattgiammarino

          An invarance is an aspect of the data that does not change. For instance, all even numbers have in common that their lowest bit is 0. All Russian words have in common that none contains an h. All cells contain carbon.

          1 reply 0 retweets 0 likes
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        1. mattgiammarino‏ @mattgiammarino 29 Sep 2018
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          Replying to @Plinz

          (like how the abstract terms like 'state space' or 'latent space' would apply to a particular model)

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