Comment on LLMs and Humans are Cognitive Cousins – Sean Carroll podcast w/Dr. Chandra Sripada

• halm@leminal.space ⁨1⁩ ⁨day⁩ ago

  If i could prove it, it would be proof that we do. Our brains aren’t logic machines, they’re a wet mess where cognition emerges irrationally.

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  • communist@lemmy.frozeninferno.xyz ⁨23⁩ ⁨hours⁩ ago

    So you’re going by the vibes then? Why even bother with your comment? What basis in neuroscience is this founded in?

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    • halm@leminal.space ⁨22⁩ ⁨hours⁩ ago

      It’s “stochastic”, if you want to be pedantic, and I get the feeling that’s all you’re here for. “In fact”.

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      • communist@lemmy.frozeninferno.xyz ⁨21⁩ ⁨hours⁩ ago

        The fact that all you can do is reply to a typo is exactly the issue, there’s nothing of substance here

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      • Sina@beehaw.org ⁨21⁩ ⁨hours⁩ ago

        stochastic

        Humans are largely too & tbh it’s a rather weak argument these days, but it’s also reinforcing the opposite view, because you are being really stochastic with this comment itself.

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• TehPers@beehaw.org ⁨21⁩ ⁨hours⁩ ago

  What a dumb question. “Cognition” is an abstract concept that just leads to debate over its definition, except that it’s commonly understood that humans have cognition.

  This entire interview also seems predicated on the belief that neural networks work fundamentally the same as human brains, which is an oversimplification of brains. To begin with, neural networks cannot train themselves at inference time (context windows aren’t training). Also, humanity has barely a concept of how the brain works and regularly learns new things about it. What we do know is that they are more complex than a bunch of connected neurons, and that we have no way of modelling something we don’t yet fully understand.

  So do neural networks have cognition? If they did, then we’ve had cognitive AIs for longer than I’ve been alive.

  The rest of the interview is imaginative fiction based on what they think the model is doing predicated on what Anthropic likes to advertise their models do.

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  As basic evidence, if I tell you that the word “strawberry” has 3 "r"s, you can remember that. You can recall that no matter how many conversations we have in between when I told you that and when I asked you again.

  If I teach you how to count how many "r"s there are in the word “strawberry”, you learn a skill. In the future, if I ask how many "r"s there are in the word “strawberry”, you can count them using your new skill.

  If I teach a LLM how many there are or how to count the letters, it can only use that skill or knowlege if it can effectively search for it. Its ability to execute skills (which, to begin with, isn’t it executing the skills but it asking something else to do them) is diminished the more skills it gains access to, and its “knowlege” (aka long-term storage) becomes diluted when it has access to more of it.

  This is blatantly untrue of humans, who can process knowlege and skills they learn, efficiently and quickly search them, and actually get faster and more accurate as they develop related skills and learn related knowlege.

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  • communist@lemmy.frozeninferno.xyz ⁨21⁩ ⁨hours⁩ ago

    Your example falls flat on two counts

    Llm’s that only have one glyph per token can easily count this, and yeah we train in bursts because it’s computationally cheaper, this hardly means anything important.

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    • TehPers@beehaw.org ⁨20⁩ ⁨hours⁩ ago

      Llm’s that only have one glyph per token can easily count this

      [Citation needed]

      There’s no difference between counting tokens containing the letter “r” and tokens solely representing the letter “r”. To begin with, this assumes the way we encode words in our brain is by tokenizing each letter, which we pretty much know to be false (we can process words without seeing evry ltr of th wrd and even think about them without it being mentioned).

      yeah we train in bursts because it’s computationally cheaper, this hardly means anything important.

      No, this is not how neural networks work at all.

      We train in bursts because we need a ground truth/scoring function to guide the learning, which means we need a way to guide the model in the right direction. Even with unsupervised learning, the model has a way to determine whether it’s going in the right direction, like with GANs.

      Neural networks are also incapable of synthesizing completely new skills from its own weights. Humans have been doing that for the entirety of human history.

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