The way humans generalize is quite different. They'll generalize widely at first, and then sculpt the generalization down to relevant areas, strengthening those.

What AI does is kind of just the first step. So it never really gets the 'these things are related' end point. Not strongly enough, and not narrowly enough. Like an LLM will be one step closer to the hitler latent space if you mention vegetarianism. There's been studies on this.

There's a lot more too though, than that too, because we live in 4d timespace, and LLMs don't. we have built in hardware for theory of mind, 3d modelling, abstraction (dedicated wetware, mirror neurons etc). When we learn things like world modelling, theory of mind, abstraction, most of the scaffolding is already there. hundreds of thousands of years of evolution in the most complex dataset there is, life. With LLMs we are just throwing it a sea of words at what initially is random undifferentiated noise, and that data also don't contain a fair amount of any of this information. Nobody is writing down, say 'you need a car at the car wash', or all the million other things like it, because this is too obvious to be considered worth communicating, to a human.

It's miraculous we get as much as we do from AI.

Yeah, no, LLMs are not similar to children in this regard. Most stuff in humans is nuture and nature. Like with walking. Humans instinctively pedal their feet, have sensory data on their feet, in their ear canal. Evolution pushing us specifically toward walking. Most stuff humans do is like that. We are not blank slates. Consider for example, emotions. Specific emotions reward and punish specific areas of the brain. Embarrassment tunes social responses for example, but not say, a survival response. We have hundreds of these specific reward/punishment pairs so that our learning is sculpted in specific ways. And then we have a fine tuned extension process where not only does learning fade, but its sustained or strengthened in other ways at the same time. We also have salience detection. What to pay attention to, what to remember. What's important, what's relevant to a task.

LLMs just have complete this word or good/bad answer (flat reward), and a weight decay, basically.

Their learning is very simple. It's only really good at all, because we curate their datasets carefully ourselves, and feed it massive amounts of data, more than a human would ever read. But unlike evolution, it's not picking up different ways to learn better or different ways to filter or understand that data from this.