Richard Sutton

Just the fact that you generalize is not necessarily good or bad.

You can generalize poorly, you can generalize well.

So generalization always will happen, but we need algorithms that will cause the generalization to be good rather than bad.

Well, large language models, so complex.

We don't really know what information they had prior.

We have to guess because they've been fed so much.

This is one reason why they're not a good way to do science.

It's just so uncontrolled, so unknown.

But if you come up with an entirely new... They're getting a bunch of things right, perhaps.

And so the question is why?

Well, it may be that they don't need to generalize to get them right because the only way to get some of them right is to form something which gets all of them right.

Right.

So if there's only one answer and you find it, that's not called generalization.

It's the only way to solve it, and so they find the only way to solve it.

Generalization is when it could be this way, it could be that way, and they do it the good way.

Well, there's nothing in them which will cause it to generalize well.

Creating dissent will cause them to find a solution to the problems they've seen.

And if there's only one way to solve them, they'll do that.

But there are many ways to solve it, some which generalize well, some which generalize poorly.

There's nothing in them, in the algorithms, that will cause them to generalize well.