Jensen Huang

So all of these people, all these scientists, and we had many too, working on computer vision algorithms, and these two kids, Ilya and Alex, under Jeff Hinton,

took a giant leap above it.

And it was based on this thing called AlexNet, this neural network.

And the way it ran, the way they made it work was literally buying two NVIDIA graphics cards.

Because NVIDIA's GPUs, we've been working on this new way of doing computing.

And our GPU's application is

And it's basically a supercomputing application back in 1984 in order to process computer games and what you have in your racing simulator.

That is called an image generator supercomputer.

And so NVIDIA started, our first application was computer graphics.

And we applied this new way of doing computing where we do things in parallel instead of sequentially.

A CPU does things sequentially.

Step one, step two, step three.

In our case, we break the problem down and we give it to thousands of processors.

And so our way of doing computation

is much more complicated.

But if you're able to formulate the problem in the way that we create it called CUDA, this is the invention of our company, if you could formulate it in that way, we could process everything simultaneously.

Now, in the case of computer graphics, it's easier to do because every single pixel on your screen is not related to every other pixel.

And so I could render multiple parts of the screen at the same time.

Not completely true because, you know, maybe the way lighting works or the way shadow works, there's a lot of dependency and such.

But computer graphics with all the pixels, I should be able to process everything simultaneously.