Dwarkesh Patel

Now, maybe the lifetime learning is not analogous to RL, to your point.

Is that compatible with the thing you were saying, or would you disagree with that?

Just to steel man the other perspective, because after doing this in an interview and thinking about it a bit, he has an important point here.

Evolution does not give us the knowledge, really, right?

It gives us the algorithm to find the knowledge.

And that seems different from pre-training.

So if perhaps the perspective is that pre-training helps build the kind of entity which can learn better, it teaches meta-learning.

and therefore it is similar to like finding an algorithm.

But if it's like evolution gives us knowledge and pre-training gives us knowledge, that analogy seems to break down.

There's so much interesting stuff there.

Okay, so let's start with in-context learning.

This is an obvious point, but I think it's worth just like saying it explicitly and meditating on it.

The situation in which these models seem the most intelligent, in which they are like, I talk to them and I'm like, wow, there's really something on the other end that's responding to me thinking about things.

If it like makes a mistake, it's like, oh, wait, that's actually the wrong way to think about it.

I'm backing up.

All that is happening in context.

That's where I feel like the real intelligence you can like visibly see.

And that in context learning process is developed by gradient descent on pre-training, right?

Like it spontaneously meta learns in context learning.

But the in context learning itself is not gradient descent in the same way that our lifetime intelligence as humans to be able to do things is conditioned by evolution.