Earlier quoted context omitted.
But, isn't AlphaGo a solution to kind of specific mathematical problem? And that it has passed with flying colors? What I mean is, yes, neural networks are stochastic and that seems to be why they're bad at logic; on the other hand it' not exactly hallucinating a game of Go, and that seems different to how neural networks are prone to hallucination and confabulation on natural language or X-ray imaging.
Sure, but people have already applied deep learning techniques to theorem proving. There are some impressive results (which the press doesn't seem at all interested in because it doesn't have ChatGPT in the title). It's really harder than one might imagine to develop a system which is good at higher order logic, premise selection, backtracking, algebraic manipulation, arithmetic, conjecturing, pattern recognition, vi…
But even there, can we say scientifically that LLMs cannot do math? Do we actually know that? And in my mind, that would imply LLMs cannot achieve AGI either. What do we actually know about the limitations of various approaches?
And couldn't people argue that it's not even necessary to think in terms of capabilities as if they were modules or pieces? Maybe just brute-force the whole thing, make a planetary scale computer. In principle.