Geoffrey Hinton

I don't know that much physics, but I think it's like a plutonium reactor which generates its own fuel.

So if you think about something like AlphaGo that plays Go, initially it was trained, the early versions of Go playing programs with neural nets were trained to mimic the moves of experts.

And if you do that, you're never going to get that much better than the experts.

And also, you run out of data from experts.

But later on, they made it play against itself.

And when it played against itself, its neural nets could just keep on getting better, because they could generate more and more data about what was a good move.

And use up a large fraction of Google's computers playing games against itself.

Is this where we end up using the term deep learning?

No, all of this stuff I've been talking about is deep learning.

The deep in learning just means it's a neural net that has multiple layers.

You get diminished returns if you run out of data.

So it'll never run out of data.

And it's way, way better than a person will ever be.

Now, the question is, could that happen with language?

Well, if you take chess, it's true that a computer in the 90s beat Kasparov at chess, but it did it in a very boring way.

It did it by searching millions of positions.

It didn't have good intuitions.

It just used massive search.

If you take alpha zero, which is the chess equivalent to alpha go,

It's very different.