Comment by kazinator
7 days ago
The problem with your reasoning is that some humans cannot solve the problem even without the irrelevant info about cats.
We can easily cherry pick our humans to fit any hypothesis about humans, because there are dumb humans.
The issue is that AI models which, on the surface, appear to be similar to the smarter quantile of humans in solving certain problems, become confused in ways that humans in that problem-solving class would not be.
That's obviously because the language model is not generally intelligent it's just retrieving tokens from a high-dimensional statistically fit function. The extra info injects noise into the calculation which confounds it.
> We can easily cherry pick our humans to fit any hypothesis about humans, because there are dumb humans.
Nah. You would take a large number of humans, make half of them take the test with distractions and half without distracting statements and then you would compare their results statistically. Yes there would be some dumb ones, but as long as you test on enough people they would show up in both samples rougly at the same rate.
> become confused in ways that humans in that problem-solving class would not be.
You just state the same thing others are disputing. Do you think it will suddenly become convincing if you write it down a few more times?
That's obviously because the brain is not generally intelligent it's just retrieving concepts from a high-dimensional statistically fit function. The extra info injects noise into the calculation which confounds it.
The problem with your low-effort retort is that, for example, the brain can wield language without having to scan anywhere near hundreds of terabytes of text. People acquire language from vastly fewer examples, and are able to infer/postulate rules, and articulate the rules.
We don't know how.
While there may be activity going on in the brain interpretable as high-dimensional functions mapping inputs to outputs, you are not doing everything with just one fixed function evaluating static weights from a feed-forward network.
If it is like neural nets, it might be something like numerous models of different types, dynamically evolving and interacting.
The problem with your answer is that you make affirmations using logical fallacies. We both don't know how LLMs, and brains works to produce output. Any affirmation toward that without proof is affirming things without any basis.
For example in this response: > the brain can wield language without having to scan anywhere near hundreds of terabytes of text.
The amount of text we need to train an LLM only goes down, even 2 years ago it was showed you need less than a few millions words: https://tallinzen.net/media/papers/mueller_linzen_2023_acl.p... , in order to "acquire" english.
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Yes, how... obvious?
I don't know, do we even know how the brain works? Like, definitively? Because I'm pretty sure we don't.
Yeah we don't, that's one of the point of my reply, we don't know how LLMs works either.
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