Jensen Huang

The reason why you used it for Quake is because it is the first consumer supercomputer.

And so anyways, they made that breakthrough.

We were working on computer vision at the time.

It caught my attention.

And so we went to learn about it.

Simultaneously, this deep learning phenomenon was happening all over the country.

Universities after another recognized the importance of deep learning.

And all of this work was happening at Stanford, at Harvard, at Berkeley, just all over the place.

New York University, Yang LeCun, Andrew Yang at Stanford, so many different places.

And I see it cropping up everywhere.

And so my curiosity asked, what is so special about this form of machine learning?

And we've known about machine learning for a very long time.

We've known about AI for a very long time.

We've known about neural networks for a very long time.

What makes now the moment?

And so we realized that this architecture for deep neural networks back propagation, the way deep neural networks were created,

we could probably scale this problem, scale the solution to solve many problems that is essentially a universal function approximator.

Meaning back when you're in school, you have a box, inside of it is a function, you give it an input, it gives you an output.

And the reason why I call it a universal function approximator

is that this computer, instead of you describing the function, a function could be a Newton's equation, F equals ma.