Comment by HarHarVeryFunny

2 days ago

Animal/human brains and LLMs have fundamentally different goals (or loss functions, if you prefer), even though both are based around prediction.

LLMs are trained to auto-regressively predict text continuations. They are not concerned with the external world and any objective experimentally verifiable facts - they are just self-predicting "this is what I'm going to say next", having learnt that from the training data (i.e. "what would the training data say next").

Humans/animals are embodied, living in the real world, whose design has been honed by a "loss function" favoring survival. Animals are "designed" to learn facts about the real world, and react to those facts in a way that helps them survive.

What humans/animals are predicting is not some auto-regressive "what will I do next", but rather what will HAPPEN next, based largely on outward-looking sensory inputs, but also internal inputs.

Animals are predicting something EXTERNAL (facts) vs LLMs predicting something INTERNAL (what will I say next).

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HarHarVeryFunny

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coldtea  2 days ago

>Humans/animals are embodied, living in the real world, whose design has been honed by a "loss function" favoring survival. Animals are "designed" to learn facts about the real world, and react to those facts in a way that helps them survive.

Yes - but LLMs also get this "embodied knowledge" passed down from human-generated training data. We are their sensory inputs in a way (which includes their training images, audio, and video too).

They do learn in a batch manner, and we learn many things not from books but from a more interactive direct being in the world. But after we distill our direct experiences and throughts derived from them as text, we pass them down to the LLMs.

Hey, there's even some kind of "loss function" in the LLM case - from the thumbs up/down feedback we are asked to give to their answers in Chat UIs, to $5/hour "mechanical turks" in Africa or something tasked with scoring their output, to rounds of optimization and pruning during training.

>Animals are predicting something EXTERNAL (facts) vs LLMs predicting something INTERNAL (what will I say next).

I don't think that matters much, in both cases it's information in, information out.

Human animals predict "what they will say/do next" all the time, just like they also predict what they will encounter next ("my house is round that corner", "that car is going to make a turn").

Our prompt to an LLM serves the same role as sensory input from the external world plays to our predictions.

  • HarHarVeryFunny  2 days ago

    > Yes - but LLMs also get this "embodied knowledge" passed down from human-generated training data.

    It's not the same though. It's the difference between reading about something and, maybe having read the book and/or watched the video, learning to DO it yourself, acting based on the content of your own mind.

    The LLM learns 2nd hand heresay, with no idea of what's true or false, what generalizations are valid, or what would be hallucinatory, etc, etc.

    The human learns verifiable facts, uses curiosity to explore and fill the gaps, be creative etc.

    I think it's pretty obvious why LLMs have all the limitations and deficiencies that they do.

    If 2nd hand heresay (from 1000's of conflicting sources) really was good as 1st hand experience and real-world prediction, then we'd not be having this discussion - we'd be bowing to our AGI overlords (well, at least once the AI also got real-time incremental learning, internal memory, looping, some type of (virtual?) embodiment, autonomy ...).