alt Hacker News> I agree that it can be difficult to make sense of a model containing billions of parameters. Certainly a human can't understand such a model by inspecting the values of each parameter individually. But one can gain insight by examing (sic) the properties of the model—where it succeeds and fails, how well it learns as a function of data, etc.
Unfortunately, studying the behavior of a system doesn't necessarily provide insight into why it behaves that way; it may not even provide a good predictive model.
Norvig's textbook surely appears on the bookshelf of researchers including those building current top LLMs. So it's odd to say that such an approach "may not even provide a good predictive model". As of today, it is unquestionably the best known predictive model for natural language, by huge margin. I don't think that's for lack of trying, with billions of dollars or more at stake.
Whether that model provides "insight" (or a "cause"; I still don't know if that's supposed to mean something different) is a deeper question, and e.g. the topic of countless papers trying to make sense of LLM activations. I don't think the answer is obvious, but I found Norvig's discussion to be thoughtful. I'm surprised to see it viewed so negatively here, dismissed with no engagement with his specific arguments and examples.