smatovic wrote: ↑Sat Sep 28, 2024 12:30 pmThis is based on the assumption of supervised learning, meanwhile big tech is running out of human generated data, and meanwhile human data is generative AI polluted, so the next step in line is some kind of reinforcement learning for generative AI (we already had such a transition in Go and Chess). IMO this is the part where funny things can start to happen....when the machine starts to teach itself.
I agree. A similar question is whether LLMs have "emergent properties" (link) that act as a force multiplier to their intelligence. For me, the answer is "yes": I believe LLMs to be more intelligent than they would be if they did not have them. They sometimes hallucinate, but they also, IMO, give good answers to questions which are not in their training data more often than they would do if no emergence had happened.
Quickly looking for evidence, I found this section of a wiki article - link. In that section, I found:
* on the positive side, good patterns containing generalised models of the world do appear in LLMs
* on the negative side, it is shown that sometimes, what looks like highly intelligent work, turns out to have been caused by reasoning shortcuts
Regarding "reasoning shortcuts": in the early days of chatbots (starting with Eliza in 1966), people were more impressed than they ought to have been because "...human judges are so ready to give the benefit of the doubt when conversational responses are capable of being interpreted as intelligent" (from link).
A quick chess thought (my opinion): the metrics measured by clauses of code in an HCE (hand coded evaluation) can fairly be described as "reasoning shortcuts", because they run very quickly, and they give some guidance as to the quality of white's position, but they don't genuinely measure that quality in a way that a sophisticated understanding of chess would, and they are known to not be useful in many positions.