Anecdotally, I've been using GPT4 to help me get overviews of techniques in fields which I'm working adjacent to. I've been pleasantly surprised at its ability

Anecdotally, I've been using GPT4 to help me get overviews of techniques in fields which I'm working adjacent to. I've been pleasantly surprised at its ability to quickly summarize not just ideas, but also write out the math. At the same time, there's been multiple occurrences where it has produced math mistakes, despite the fact that it can tell me that it knows how to do "the right thing" e.g. there's been multiple times where it will incorrectly reduce integrals with Dirac delta functions, or introduce subtle mistakes in analytically known formulas. The Dirac delta function example was funny to me -- it can tell me what to do when a delta function occurs in an integrand! Yet it makes a mistake reducing the same sort of integral in another context? So I'm not sure how to take these "GPTX is doing reasoning" claims. On the one hand, maybe? On the other hand, it's not perfect at it -- even when it appears to have knowledge that should ensure that it never gets that wrong. If I'm going to be using this system to help me do research, I'd love for it to be correct and not contradict itself. That's why I'm happy to have these GPTX layers shell out to something like Wolfram, or another symbolic system. That combination makes perfect sense to me. I'm also much more likely to trust that combination, instead of my current strategy - which is to basically re-process everything that GPTX gives me and assume that there are a few places where I'm likely to find mistakes.