7 ms·

Wait I had the "It's just a stochastic parrot" parroted at me ad nauseum. Was this FUD all along?

nyrikki 3y ago
No, this paper doesn't change that claim at all. This paper is about learnability and not generalization of what is learned. From the papers summary, note that they are referencing learning and not some implications that it gets us closer to a universal quantifier. "We have provided evidence that LLMs learn linear representations of space and time that are unified across entity types and fairly robust to prompting, and that there exists individual neurons that are highly sensitive to these features. The corollary is that next token prediction alone is sufficient for learning a literal map of the world give sufficient model and data size."
- ImHereToVote 3y ago
  I mean parrots have neurons. I didn't realize the line referred to the actual birds "parrot". Parrots are wicked smart.
  - nyrikki 3y ago
    Parrots, in the context of stochastic parrots is in reference to repeating or imitation mechanically without understanding. The 'common sense' problem in AI is the important part, not the as of yet undefined abstract concept of intelligence. LLMs are still pattern matching and finding with some stochastic aspects. But artificial nurons are inspired by biological nurons, they are not equivalent in respect to computability. But artificial nural networks are useful and powerful for some problems that traditionally weren't solvable by other means. Biological nurons have to embrace non-linear stochastic processes, which is a different problem set. While not expressed in popsci this has been known for decades.
    - ImHereToVote 3y ago
      Great so biological neurons can perform feats of intuition, that simulated neurons simply can't. Even theoretically. Gotcha. Thank you very much for the explanation.
      - famouswaffles 3y ago
        Yeah that's not the case lol. ann's can model any function even non linear ones.
        nyrikki 3y ago
        Pairwise linear in respect to inputs, for feed forward networks like those that use attention is the restriction related to linear or linearizable. Those words are important. But we know that ML cannot model all turning machines in the limit. As to more reasonable models that gets more complex. Rice's theorem in respect to functions doesn't fit with you claim: For any non-trivial property of partial functions, no general and effective method can decide whether an algorithm computes a partial function with that property. The generalization problem being the important part. But if you want to claim that ANNs solve all of the undecidable problems too, write the paper and become famous.
        deleted 3y ago
        [deleted]
        famouswaffles 3y ago
        >But we know that ML cannot model all turning machines in the limit. No computer is truly turing without infinite memory, including humans. >Rice's theorem in respect to functions doesn't fit with you claim: For any non-trivial property of partial functions, no general and effective method can decide whether an algorithm computes a partial function with that property. The generalization problem being the important part. This is...irrelevant. The theorem makes the claim for complete generalization (which humans do not demonstrate) as you say. It doesn't actually matter if the algorithm can't make the claim of property for all processes. Just like it doesn't matter that computers or humans are not really turing complete. We don't need a computer that is exactly a human. We need a computer that works.
cjbprime 3y ago
The paper it originates from predates ChatGPT (let alone GPT-4). So I think a charitable interpretation is that it was partly wrong (and definitely unimaginative) at the time, and partly it's the case that very few people were expecting "let's just throw more parameters at it" to work and result in emergent general capabilities, which it appears to.

No, this paper doesn't change that claim at all. This paper is about learnability and not generalization of what is learned. From the papers summary, note that they are referencing learning and not some implications that it gets us closer to a universal quantifier. "We have provided evidence that LLMs learn linear representations of space and time that are unified across entity types and fairly robust to prompting, and that there exists individual neurons that are highly sensitive to these features. The corollary is that next token prediction alone is sufficient for learning a literal map of the world give sufficient model and data size."

ImHereToVote 3y ago
I mean parrots have neurons. I didn't realize the line referred to the actual birds "parrot". Parrots are wicked smart.
- nyrikki 3y ago
  Parrots, in the context of stochastic parrots is in reference to repeating or imitation mechanically without understanding. The 'common sense' problem in AI is the important part, not the as of yet undefined abstract concept of intelligence. LLMs are still pattern matching and finding with some stochastic aspects. But artificial nurons are inspired by biological nurons, they are not equivalent in respect to computability. But artificial nural networks are useful and powerful for some problems that traditionally weren't solvable by other means. Biological nurons have to embrace non-linear stochastic processes, which is a different problem set. While not expressed in popsci this has been known for decades.
  - ImHereToVote 3y ago
    Great so biological neurons can perform feats of intuition, that simulated neurons simply can't. Even theoretically. Gotcha. Thank you very much for the explanation.
    - famouswaffles 3y ago
      Yeah that's not the case lol. ann's can model any function even non linear ones.
      - nyrikki 3y ago
        Pairwise linear in respect to inputs, for feed forward networks like those that use attention is the restriction related to linear or linearizable. Those words are important. But we know that ML cannot model all turning machines in the limit. As to more reasonable models that gets more complex. Rice's theorem in respect to functions doesn't fit with you claim: For any non-trivial property of partial functions, no general and effective method can decide whether an algorithm computes a partial function with that property. The generalization problem being the important part. But if you want to claim that ANNs solve all of the undecidable problems too, write the paper and become famous.
        deleted 3y ago
        [deleted]
        famouswaffles 3y ago
        >But we know that ML cannot model all turning machines in the limit. No computer is truly turing without infinite memory, including humans. >Rice's theorem in respect to functions doesn't fit with you claim: For any non-trivial property of partial functions, no general and effective method can decide whether an algorithm computes a partial function with that property. The generalization problem being the important part. This is...irrelevant. The theorem makes the claim for complete generalization (which humans do not demonstrate) as you say. It doesn't actually matter if the algorithm can't make the claim of property for all processes. Just like it doesn't matter that computers or humans are not really turing complete. We don't need a computer that is exactly a human. We need a computer that works.

cjbprime 3y ago

The paper it originates from predates ChatGPT (let alone GPT-4). So I think a charitable interpretation is that it was partly wrong (and definitely unimaginative) at the time, and partly it's the case that very few people were expecting "let's just throw more parameters at it" to work and result in emergent general capabilities, which it appears to.