Comment by codebolt
1 year ago
> they’re made for handling language, not playing games with strict rules and strategies
Here's the opposite theory: Language encodes objective reasoning (or at least, it does some of the time). A sufficiently large ANN trained on sufficiently large amounts of text will develop internal mechanisms of reasoning that can be applied to domains outside of language.
Based on what we are currently seeing LLMs do, I'm becoming more and more convinced that this is the correct picture.
I share this idea but from the different perspective. It doesn’t develop these mechanisms, but casts a high-dimensional-enough shadow of their effect on itself. This vaguely explains why the more deep Gell-Mann-wise you are the less sharp that shadow is, because specificity cuts off “reasoning” hyperplanes.
It’s hard to explain emerging mechanisms because of the nature of generation, which is one-pass sequential matrix reduction. I say this while waving my hands, but listen. Reasoning is similar to Turing complete algorithms, and what LLMs can become through training is similar to limited pushdown automata at best. I think this is a good conceptual handle for it.
“Line of thought” is an interesting way to loop the process back, but it doesn’t show that much improvement, afaiu, and still is finite.
Otoh, a chess player takes as much time and “loops” as they need to get the result (ignoring competitive time limits).