If we are evaluating LLM's purely on outcome alone, it seems only fair to compare them to humans that don't have fully functioning cognitive capabilities. We wouldn't discredit a schizophrenic person that hallucinates, we know that this person can also see reality for what it is, most of the time. I just don't think that finding absurd examples, usually after careful prompting, is a slam dunk against LLM's at all.
They can, it's just that Chomsky's dismissal of "weight adaptations" as insufficient is not actually justified by any evidence. The Transformer model augmented with memory is Turing complete, so it can in principle learn to compute anything using only weight adaptations.
* On the Computational Power of Transformers and its Implications in Sequence Modeling, https://arxiv.org/abs/2006.09286
Our brains are much more complex than an artificial NN, though. So the mere thought that the two somehow share functionality after a wildly different learning process is somewhat far-fetched.
Well sure, because CFGs can have unbounded depth, and all real brains are finite. But they can learn bounded CFGs:
https://arxiv.org/abs/2112.09174
> Our brains are much more complex than an artificial NN, though.
Yes, brains have a few orders of magnitude more parameters, and lots of evolutionary baggage. It's not clear how many parameters would be needed without that evolutionary baggage. Time will tell.
> So the mere thought that the two somehow share functionality after a wildly different learning process is somewhat far-fetched.
I don't know what precisely you mean by "share functionality", but it doesn't seem like a stretch to suggest they are in some sense isomorphic given Turing equivalence. Rule 110 doesn't look like it should share much with Turing machines or the lambda calculus or SKI combinators, but these are all equivalent in a formal sense.
> in some sense isomorphic given Turing equivalence
Then you're brain is just like any CPU, which is very far from what such a statement tries to express in the context of NNs.
Yes, the brain is like a CPU + a specific program running on that CPU. The properties of that program are where the isomorphism comes in. So to come full circle back to your original point, we don't yet know that "weight adaptations" are not a sufficient operational model for that program.
In contrast, the world is too complicated to learn through exposure. Something has to structure our learning process. Language is (in principle) not learnable, only when you make enough assumptions. Those assumptions must be coded somewhere in your brain and probably genes.
Tabula rasa sounds nice, but it is untenable and utterly lacks evidence.