Andrej Karpathy

And you have to do that by all the pre-training and all the LLM stuff.

So I kind of feel like maybe, loosely speaking, it was like people keep maybe trying to get the full thing too early a few times, where people really try to go after agents too early, I would say.

And that was Atari and Universe, and even my own experience.

And you actually have to do some things first before you sort of get to those agents.

And maybe now the agents are a lot more competent, but maybe we're still missing sort of some parts of that stack.

But I would say maybe those are like the three major buckets of what people were doing.

Training neural nets per tasks, trying to the first round of agents, and then maybe the LLMs and actually seeking the representation power of the neural networks before you tack on everything else on top.

And I think...

I mean, so Sutton was on your podcast, and I saw the podcast, and I had a write-up about that podcast almost that gets into a little bit of how I see things.

And I kind of feel like I'm very careful to make analogies to animals because they came about by a very different optimization process.

Animals are evolved, and they actually come with a huge amount of hardware that's built in.

And when, for example, my example in the post was the zebra.

A zebra gets born, and a few minutes later, it's running around and following its mother.

That's an extremely complicated thing to do.

That's not reinforcement learning.

That's something that's baked in.

And evolution obviously has some way of encoding the weights of our neural nets in ATCGs.

And I have no idea how that works, but it apparently works.

So I kind of feel like brains just came from a very different process.

And I'm very hesitant to take inspiration from it because we're not actually running that process.