[danShumway]

Sometimes people use LLMs very broadly to talk about neural networks overall.

But to be clear, humans don't have emergent reasoning from language, we learn language as part of our overall reasoning. The short evidence for that being that children are capable of solving logic and spatial puzzles before they learn how to speak. Humans learn concepts like object permanence before we learn language complicated enough to describe that concept. And obviously people are capable of reasoning without learning how to write or interpret text tokens, there are plenty of illiterate people in the world who are nonetheless indisputably intelligent agents.

So ignoring other differences about how prediction works, humans are not similar to LLMs in the sense that LLMs are language models that when large enough either develop (or appear to develop depending on who you ask) reasoning capabilities. And that's not how humans work; we don't learn text tokens before we learn how to reason.

But very often when people make this claim they're trying to make a broader claim about neural networks or the role of prediction in learning in general. People might disagree or agree with the broader claim, I still think it oversimplifies how humans work, but the point is -- they're not actually saying something specific about LLMs, even though it sounds that way sometimes. It's just that the terminology gets conflated in people's heads.

We can have a debate about the similarities and differences between humans and neural networks, but I don't think anyone would seriously claim that GPT-4 in specific works the same way as a human does. I think people are using LLMs to refer to a broader category of AI research.

[FrustratedMonky]

Definitely, LLM's have gotten so much press, that many people arguing about 'AI', are thinking about LLM.

And, LLM's are not all that a human can do. Language is not everything about a human.

But there is an argument that there is part of the brain that produces language, and it has some LLM characteristics. It's just that the brain is bigger and does more than an LLM. So the brain is not an LLM.

The brain has many components. What happens when you take the problem solving of something like AlphaGo/AlphaStar, with the Vision processing in Cars or DaLLe, and the language processing in LLM. Add in hearing, touch.

It starts to look like the components of a brain.

[danShumway]

It's not that the brain is bigger than an LLM, it's that the way we learn written language (and spoken language too, tbh) is different from how LLMs learn language, and that the way we think about the world isn't derivative of language.

We don't learn to read or write by doing token prediction (if we did, subjects like spelling would be much easier). In fact, there was a movement in schools to teach reading by asking students to predict what words might be based on the context of the sentence, and it was a disaster and led to increased illiteracy rates and schools have started shifting back to phonics. Not only do we not learn that way, when we try to learn that way it leads to worse education outcomes.

The reason why brains are not like an LLM is not because we also have eyes and an LLM doesn't, it's because just isolating out our language "models", we are trained differently and interact with the rest of our brains differently.

If our language centers of our brain worked like an LLM, we would expect language skills to develop faster than reasoning capabilities within our writing/speaking. A primitive LLM like GPT-2 has very limited processing ability but is still able to imitate a wide range of styles and is still able to "speak" in a grammatically correct way. Humans are the opposite: we start out communicating complex ideas poorly and we start out using language poorly. We master language as a processing tool before we become competent at using language in general.