[TeMPOraL]

> I think the more realistic argument is that the model can generalize, but only by learning shortcuts (e.g. how to pattern match a problem to a likely answer) and simple algorithms (e.g. how to propagate carries in a multiplication).

This is exactly what humans do too. Anything more and we need to use tools to externalize state and algorithms. Pen and paper are tools too.

[sigotirandolas]

My thought is that we humans are bad (by computer standards) at arithmetic and memorization because those are not evolutionarily useful on their own.

On the other hand general problem solving is, and so far any attempt to replicate it using computer algorithms has more or less failed. So it must be more complex than just some simple heuristics.

Perhaps the answer is just "more compute" but the argument that "because LLMs somewhat resemble human reasoning, we must be really close!" (instead of 25+ years away) seems wishful thinking, when:

(1) LLMs leverage a much bigger knowledge base than any human can memorize, yet

(2) LLMs fail spectacularly at certain problems and behaviours humans find easy

[thomasahle]

> On the other hand general problem solving is, and so far any attempt to replicate it using computer algorithms has more or less failed.

Well, this is what the whole debate is about isn't it? Can LRMs do "general problem solving"? Can humans? What exactly does it mean?

[sigotirandolas]

A lot of it is being able to make reasonable decisions under novel and incomplete information and being able to reflect and refine on their outcome.

LLMs's huge knowledge base covers for their incapacity to reason under incomplete information, but when you find a gap in their knowledge, they are terrible at recovering from it.