Richard Sutton

And you agree that large language models don't have goals.

I think they have a goal.

What's the goal?

Next second prediction.

That's not a goal.

It doesn't change the world.

You know, tokens come at you, and if you predict them, you don't influence them.

Yeah, it's not a goal.

It's not a substantive goal.

You can't look at a system and say, oh, it has a goal if it's just sitting there predicting and being happy with itself that it's predicting accurately.

Well, the math problems are different.

Making a model of the physical world and carrying out the consequences of mathematical assumptions or operations, those are very different things.

The empirical world has to be learned.

You have to learn the consequences.

Whereas the math is more just computational.

It's more like standard planning.

So there they can have a goal to find the proof.

And they are in some way given that goal to find the proof.

It's an interesting question whether large language models are a case of the bitter lesson.

Because they are clearly a way of using massive computation, things that will scale with computation up to the limits of the internet.