[aprilfoo]

"AI" is too much of a broad umbrella term of competing ideas, from symbolic logic (FOL, expert systems) to statistical operations (NNs). It's clear today that the latter has won the race, but ignoring this history doesn't seem to be a very smart move.

I'm in no way an expert but I feel that today's LLMs lack some concepts well known in the research of logical reasoning. Something like: semantic.

[ACCount37]

AI is a broad field because intelligence is broad field.

And what's remarkable about LLMs is exactly that: they don't reason like machines. They don't use the kind of hard machine logic you see in an if-else chain. They reason using the same type of associative abstract thinking as humans do.

[aprilfoo]

Surely "intelligence" is a broad field... i might not be so that great at it, but i hope that's ok.

"[LLMs] reason using the same type of associative abstract thinking as humans do": do you have a reference for this bold statement?

I entered "associative abstract thinking llm" in a good old search engine. The results point to papers rather hinting that they're not so good at it (yet?), for example: https://articles.emp0.com/abstract-reasoning-in-llms/.

[ACCount37]

I don't have a single reference that says outright "LLMs are doing the same kind of abstract thinking as humans do". Rather, this is something that's scattered across a thousand articles and evaluations - in which LLMs prove over and over again that they excel at the cognitive skills that were once exclusive to humans - or fail at them in ways that are amusingly humanlike.

But the closest thing is probably Anthropic's famous interpretability papers:

https://transformer-circuits.pub/2024/scaling-monosemanticit...

https://transformer-circuits.pub/2025/attribution-graphs/bio...

In which Anthropic finds circuits in an LLM that correspond to high level abstracts an LLM can recognize and use, and traces down the way they can be connected. Which forms the foundation of associative abstract thinking.