Terence Tao

This is one thing that humans have.

And there's a metaphorical mathematical smell that it's not clear how to get the AI to duplicate that.

Eventually, I mean, so the way...

AlphaZero and so forth, they make progress on Go and chess and so forth.

In some sense, they have developed a sense of smell for Go and chess positions, that this position is good for white, it's good for black.

They can't enunciate why, but just having that sense of smell lets them strategize.

So if AIs gain that ability to sort of, a sense of viability of certain proof strategies, so you can say,

I'm going to try to break up this problem into two small subtasks, and they can say, oh, this looks good.

The two tasks look like they're simpler tasks than your main task, and they've still got a good chance of being true.

So this is good to try.

Or, no, you've made the problem worse because each of the two subproblems is actually harder than your original problem, which is actually what normally happens if you try a random thing to try.

Normally, it's very easy to transform a problem into an even harder problem.

Very rarely do you transform it into a simpler problem.

Yeah, so if they can pick up a sense of smell, then they could maybe start competing with human-level mathematicians.

I think all of the above.

A lot of it is we don't know how to use these tools because it's a paradigm that it's not...

We have not had, in the past, systems that are competent enough to understand complex instructions, that can work at massive scale, but are also unreliable.

It's a bit unreliable in subtle ways, whilst providing sufficiently good output.

It's an interesting combination.

You have graduate students that you work with who are kind of like this, but not at scale.