Andrej Karpathy

It's something like that.

Because the cortex is famously very plastic as well.

You can rewire, you know, parts of brains.

And there was the slightly gruesome experiments with rewiring, like, visual cortex to the auditory cortex, and this animal, like, learned fine, etc.

So I think that this is kind of like cortical tissue.

I think when we're doing reasoning and planning inside the neural networks, so basically doing reasoning traces for thinking models, that's kind of like the prefrontal cortex.

And then I think maybe those are like little check marks, but I still think there's many brain parts and nuclei that are not explored.

So maybe, for example, there's a basal ganglia doing a bit of reinforcement learning when we fine tune the models on reinforcement learning.

But, you know, whereas like the hippocampus, not obvious what that would be.

some parts are probably not important.

Maybe the cerebellum is, like, not important to cognition, it's thought, so maybe we can skip some of it.

But I still think there's, for example, the amygdala, all the emotions and instincts.

And there's probably, like, a bunch of other nuclei in the brain that are very ancient that I don't think we've, like, really replicated.

I don't actually know that we should be pursuing, you know, the building of an analog of human brain.

I'm, again, an engineer, mostly at heart.

But...

I still feel like maybe another way to answer the question is you're not going to hire this thing as an intern.

And it's missing a lot of it's because it comes with a lot of these cognitive deficits that we all intuitively feel when we talk to the models.

And so it's just like not fully there yet.

You can look at it as like not all the brain parts are checked off yet.