Jensen Huang

That was the big bang of modern AI.

We were lucky because we were inventing this technology, this computing approach.

We were lucky that they found it.

Turns out they were gamers and it was lucky they found it.

And it was lucky that we paid attention to that moment.

It was a little bit like, you know, that Star Trek, you know, first contact.

The Vulcans had to have seen the warp drive at that very moment.

If they didn't witness the warp drive, they would have never come to Earth and everything would have never happened.

It's a little bit like if I hadn't paid attention to that moment, that flash, and that flash didn't last long.

If I hadn't paid attention to that flash or our company didn't pay attention to it.

Who knows what would have happened?

But we saw that and we reasoned our way into this is a universal function approximator.

This is not just a computer vision approximator.

We could use this for all kinds of things if we could solve two problems.

The first problem is that we have to prove to ourselves it could scale.

The second problem we had to wait for, I guess, contribute to and wait for is

The world will never have enough data on input and output where we could supervise the AI to learn everything.

For example, if we have to supervise our children on everything they learn, the amount of information they can learn is limited.

We needed the AI, we needed the computer to have a method of learning without supervision.

And that's where we had to wait a few more years.