Comment by littlestymaar

> There are many industries for which the vast majority of work done is closer to what I think you mean by "smart retrieval" than what I think you mean by "reasoning." Adult primary care and pediatrics, finance, law, veterinary medicine, software engineering, etc. At least half, if not upwards of 80% of the work in each of these fields is effectively pattern matching to a known set of protocols. They absolutely deal in novel problems as well, but it's not the majority of their work.

I wholeheartedly agree with that.

I'm in fact pretty bullish on LLMs, as tools with near infinite industrial use cases, but I really dislike the “AGI soon” narrative (which sets expectations way too high).

IMHO the biggest issue with LLMs isn't that they aren't good enough at solving math problem, but that there's no easy way to add information to a model after its training, which is a significant problem for a “smart information retrieval” system. RAG is used as a hack around this issue, but its performance can vary a ton with tasks. LORAs are another options, but they require significant work to make a dataset, and you can only cross your fingers the model keeps its abilities.