Terence Tao

Yeah, yeah, yeah.

There's a big hump to overcome right now.

I mean, it's like self-driving cars.

The safety margin has to be really high for it to be feasible.

So yeah, so there's a last mile problem with a lot of AI applications that they can develop tools that work 20%, 80% of the time, but it's still not good enough.

And in fact, even worse than good in some ways.

Right, right.

Yeah, this decade, I can see it like making a conjecture between two unrelated, two things that people thought was unrelated.

Oh, interesting.

Yeah, and actually has a real chance of being correct and meaningful.

No, that would be truly amazing.

Current models struggle a lot.

I mean, so a version of this is, I mean, the physicists have a dream of getting the AIs to discover new laws of physics.

Right.

The dream is you just feed it all this data, and here is a new patent that we didn't see before.

But the current state of the art even struggles to discover old laws of physics from the data.

Or if it does, there's a big concern of contamination, that it did it only because somewhere in its training data it already somehow knew Boyle's law or whatever law you're trying to reconstruct.

Part of it is that we don't have the right type of training data for this.

For the laws of physics, we don't have a million different universes with a million different laws of nature.

A lot of what we're missing in math is actually the negative space.