LLMs are very useful, I use them every day as a software engineer to solve problems and search for information represented within the data available to them. But they are a specific type of intelligence, with many advantages and disadvantages vs human intelligence and it's not clear that just scaling or tweaking them without a theoretical, architectural change will make them more generally intelligent than humans (despite all US AI companies promising exactly that).

They are fundamentally based in language, and achieving deeper models of the world through language alone is deeply inefficient compared to the way humans model the world for years without any language at all. They do not learn at inference time. They don't have semantic understanding of the difference between their own output and other sources. etc etc.

That depth is the key for me. Of course they are capable of producing novel sentences that aren't in their training data, but the depth of that novelty is basically within the bounds of language itself. They are capable of more serious depth and more abstract reasoning than that, but I have experienced limits, which it then tries to surpass with tools to convert things it can't understand back into language (unit tests, LEAN) upon which it is trained.

Because I'm not an AI booster, my account is limited to 5 comments a day. So this is the last reply I'll be able to make today, if you want to continue the conversation we'll have to wait for tomorrow.