Tamay Besiroglu

You're just learning to play it from scratch.

And I think at the time, it did impress a lot of people.

But then people try to apply it to math, they try to apply it to other domains, and it didn't work very well.

They weren't able to get competent agents at math.

So it's very possible that these models, at least the way we have them right now, you're going to try to do the same thing people did for reasoning, but for agency, it's not going to work very well, and then you're not going to make it.

Sure, but like they are not like they are not disconnected.

tasks.

That's like saying everything you do in the world is just like you just move parts of your body, and then you move your mouth and your tongue, and then you roll your head.

But that's a very, yeah, individually those things are simple.

Yeah, I mean, I would say that reasoning did seem... I mean, I think there's a reason to expect complex reasoning to not be as difficult as people might have thought, even in advance, because a lot of the tasks that AI solved very early on were tasks of...

Various kinds of complex reasoning.

So it wasn't the kind of reasoning that goes into when a human solves a math problem.

But if you look at the major AI milestones since 1950, a lot of them are for complex reasoning.

Like a chess is, you can say, a complex reasoning task.

Go is, you could say, a complex reasoning task.

So the problem in that case is that it's a very specific, narrow environment.

You can say that playing Go or playing chess, that also requires a certain amount of agency, and that's

That's true, but it's a very narrow task.

So that's like saying if you construct a software system that is able to react to very specific, very particular kind of images, or very specific video feeds or whatever, then you're getting close to general sensory motor skill automation.

But the general skill is something that's very different, and I think we're seeing that.