>I'm somewhat surprised at how poorly the cutting edge models do with being concise.
because they're not intelligent in the sense you're hinting at (conceptual integrity or generalization) but they are as the name suggests, large. Like comparing a forklift to a human. It's easier to bulldoze through a lot of things than tie your shoes.
If we weren't quite as impoverished conceptually and still had the vocabulary of the Catholics we'd recognize this as ratio (discursive knowledge) vs Intellectus (apprehending knowledge)
What an incredibly useless comment. You state a conclusion as fact without any supportive reasoning/evidence.
Prove that human intellect is different and that we solve problems using fundamentally different processes. I’m waiting.
"Prove"? Like, a formal proof, about intelligence?
yeah you know, that concept we've never been able to define using language, making heavy use of the human experience which can also not be captured in language (proof: how bad LLMs are at poetry)
the question is, when comparing a human and a large language model, whether the intellect (that cannot be captured in language) is different from anything the language model can actually do (e.g. language)
the answer to this seems quite obvious to me, and I would actually posit that the onus is on the other side, to prove they are even remotely similar
maybe people think that the voice in their heads is what is doing the thinking? is that the confusion here?
Do you think the LLM is the Chain of Thought? Did you also get confused by the name? Because, much like humans, the CoT is a tool to narrativize and maintain internal coherence. The actual thinking happens invisibly, in the forward pass. Just like...
>You state a conclusion as fact without any supportive reasoning/evidence.
No, it's the other way around, it's a reductive view on intelligence that mistakes its own methodology for ontology.
It's obvious to see that there's no intellect in an LLM as defined above because of how they work. LLMs put one token in front of the other, they don't work towards formal ends, there's no intentionality in them. They don't synthesize the information they process into a unified experience. Thinking an LLM can apprehend what it does because it can process large amounts of text is like thinking your TI-83 understands math because it can multiply large numbers.
That's also why the failure modes of LLMs are what they are. They can churn out tens of thousands of lines of code but also just as easily go in circles like a roomba. They can process an entire encyclopedia but not solve problems a 10 year old can solve.