"What should or shouldn’t be a wh-island" is literally a statement of "what words might come after some other words"! An LLM encodes billions of such statements, just unfortunately in a quantity and form that makes them incomprehensible to an unaided human. That part is strictly worse; but the LLM's statements model language well enough to generate it, and that part is strictly better.
As I read Norvig's essay, it's about that tradeoff, of whether a simple and comprehensible but inaccurate model shows more promise than a model that's incomprehensible except in statistical terms with the aid of a computer, but far more accurate. I understand there's a large group of people who think Norvig is wrong or incoherent; but when those people have no accomplishments except within the framework they themselves have constructed, what am I supposed to think?
Beyond that, if I have a model that tells me whether a sentence is valid, then I can always try different words until I find one that makes it valid. Any sufficiently good model is thus capable of generation. Chomsky never proposed anything capable of that; but that just means his models were bad, not that he was working on a different task.
As to the relationship between signals from biological neurons and ANN activations, I mean something like the paper linked below, whose authors write:
> Thus, even though the goal of contemporary AI is to improve model performance and not necessarily to build models of brain processing, this endeavor appears to be rapidly converging on architectures that might capture key aspects of language processing in the human mind and brain.
https://www.biorxiv.org/content/10.1101/2020.06.26.174482v3....
I emphasize again that I believe these results have been oversold in the popular press, but the idea that an ANN trained on brain output (including written language) might provide insight into the physical, causal structure of the brain is pretty mainstream now.
