Terence Tao

I mean, we should still have the deep, important problems

and humans should still be working on them.

But now we have this other way of doing science.

We can explore entire new fields of science by first getting these broad, moderately competent AI to sort of map it out and make all the easy observations and then identify certain islands of difficulty, which then human experts can come and work on.

So I see very much

a future of very complementary science.

Eventually, you would hope to get both breadth and depth and somehow get the best of both worlds.

But I think we need practice with the breadth side.

It's too new.

We don't even have the paradigms really to make full advantage of it.

But we will.

And then science will be unrecognizable after that.

MARK MANDELMANN- Right.

Yeah, so certainly in math, the process is often more important than the problem itself.

The problem is kind of a proxy for measuring your progress.

And I think even in software, there's different types of software tasks.

I mean, if you just kind of create a web page that does the same thing that 1,000 other web pages do,

there's sort of no skill to be learned.

Well, there's still some skill maybe that the individual programmer could pick up.

But, you know, for kind of a boilerplate type code, definitely, you know, it's something that you should definitely offload to AI.