Jensen Huang: commitment

Transcript context

…There were those couple of years there where to a lot of the rest of the world, these looked like science projects. The technology companies here in Silicon Valley, particularly the social media companies, were just realizing huge economic value out of this—the Googles, the Facebooks, the Netflixes, et cetera. Obviously, that led to lots of things including OpenAI a couple of years later. But during those couple of years, when you saw just that huge economic value unlock here in Silicon Valley, how are you feeling during those times? The first thought was reasoning about how we should change our computing stack. The second thought is where can we find earliest possibilities of use? If we were to go build this computer, what would people use it to do? We were fortunate that working with the world’s universities and researchers was innate in our company. We were already working on CUDA, and CUDA’s early adopters were researchers because we democratized supercomputing. CUDA is not just used (as you know) for AI. CUDA is used for almost all fields of science. Everything from molecular dynamics to imaging, CT reconstruction to seismic processing, to weather simulations, quantum chemistry. The list goes on. The number of applications of CUDA in research was very high. When the time came and we realized that deep learning could be really interestingm it was natural for us to go back to the researchers, find every single AI researcher on the planet, and say how can we help you advance your work? That included Yann LeCun, Andrew Ng, and Jeff Hinton. That’s how I met all these people. I used to go to all the AI conferences, and that’s where I met Ilya Sutskever there for the first time. It was really about at that point, what are the systems that we can build and the software stacks that we can build to help you be more successful to advance the research? Because at the time, it looked like a toy. The first time I met Goodfellow, the GAN was like 32 by 32, and it was just a blurry image of a cat. But how far can it go? So we believed in it. We believed that you could scale deep learning because obviously it’s trained layer by layer. You could make the data sets larger and you could make the models larger. We believe that if you made that larger and larger, we get better and better. Kind of sensible. I think the discussions and the engagements with the researchers was the exact positive feedback system that we needed. I would go back to research. That’s where it all happened. When OpenAI was founded in 2015, that was such an important moment. That’s obvious today now, but at the time, I think most people, even people in tech were like, what is this? Were you involved in it at all? Because you were so connected to the researchers, to Ilya, taking that talent out of Google and Facebook, to be blunt, but reseeding the research community and opening it up was such an important moment. Were you involved in it at all?…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.