[ACCount37]

LLMs use the same type of "abstract thinking" process as humans. Which is why they can struggle with 6-digit multiplication (unlike computer code, very much like humans), but not with parsing out metaphors or describing what love is (unlike computer code, very much like humans). The capability profile of an LLM is amusingly humanlike.

Setting the bar for "AI" at "singularity" is a bit like setting requirements for "fusion" at "creating a star more powerful than the Sun". Very good for dismissing all existing fusion research, but not any good for actually understanding fusion.

If we had two humans, one with IQ 80 and another with IQ 120, we wouldn't say that one of them isn't "thinking". It's just that one of them is much worse at "thinking" than the other. Which is where a lot of LLMs are currently at. They are, for all intents and purposes, thinking. Are they any good at it though? Depends on what you want from them. Sometimes they're good enough, and sometimes they aren't.

[habinero]

> LLMs use the same type of "abstract thinking" process as humans

It's surprising you say that, considering we don't actually understand the mechanisms behind how humans think.

We do know that human brains are so good at patterns, they'll even see patterns and such that aren't actually there.

LLMs are a pile of statistics that can mimic human speech patterns if you don't tax them too hard. Anyone who thinks otherwise is just Clever Hans-ing themselves.

[ACCount37]

We understand the outcomes well enough. LLMs converge onto a similar process by being trained on human-made text. Is LLM reasoning a 1:1 replica of what the human brain does? No, but it does something very similar in function.

I see no reason to think that humans are anything more than "a pile of statistics that can mimic human speech patterns if you don't tax them too hard". Humans can get offended when you point it out though. It's too dismissive of their unique human gift of intelligence that a chatbot clearly doesn't have.

[habinero]

> We understand the outcomes well enough

We do not, in fact, "understand the outcomes well enough" lol.

I don't really care if you want to have an AI waifu or whatever. I'm pointing out that you're vastly underestimating the complexity behind human brains and cognition.

And that complex human brain of yours is attributing behaviors to a statistical model that the model does not, in fact, possess.

[jimbo808]

This is wrong on so many levels. I feel like this is what I would have said if I never took a neuroscience class, or actually used an LLM for any real work beyond just poking around ChatGPT from time to time between TED talks.

[adastra22]

There is no actual object-level argument in your reply, making it pretty useless. I’m left trying to infer what you might be talking about, and frankly it’s not obvious to me.

For example, what relevance is neuroscience here? Artificial neural nets and real brains are entirely different substrates. The “neural net” part is a misnomer. We shouldn’t expect them to work the same way.

What’s relevant is the psychology literature. Do artificial minds behave like real minds? In many ways they do — LLMs exhibit the same sorts fallacies and biases as human minds. Not exactly 1:1, but surprisingly close.

[jimbo808]

I didn't say brains and ANNs are the same, in fact I am making quite the opposite argument here.

LLMs exhibit these biases and fallacies because they regurgitate the biases and fallacies that were written by the humans that produced their training data.

[adastra22]

Maybe. That’s not an obvious conclusion in the strong sense that you mean it here. If you train a LLM on transcripts of multiplying very large numbers, machine generated and perfectly accurate transcripts, the LLM still exhibits the same sorts of mental math errors that people make.

Math, logical reasoning, etc. are cultural knowledge, not architecturally built-in. These biases and fallacies arise because of how we process higher order concepts via language-like mechanisms. It should not be surprising that LLMs, which mimic human-like natural language abilities (at the culture/learned level of abstraction, if not computation substrate) exhibit the same sorts of errors.