Jen-Hsun Huang – NVIDIA Corp.
Three pillars of the cloud
“There are three major pillars of computing up in the cloud or in large data centers, hyperscale. One pillar is just internal use of computing systems, for developing, for training, for advancing artificial intelligence. That’s a high-performance computing problem.”
“the second pillar is inferencing. And inferencing as it turns out is far, far less complicated than training. It’s a trillion times less complicated, a billion times. It’s a trillion times less complicated. And so once the network is trained, it can be deployed. And there are thousands of networks that are going to be running inside these hyperscale data centers, thousands of different networks, not one, thousands of different types. And they’re detecting all kinds of different things. They’re inferring all kinds of different things, classifying, predicting, all kinds of different things, whether it’s photo or voice or videos or searches or whatnot.”
“And then the last pillar is cloud service providers, and that’s basically the outward public cloud provisioning a computing approach. It’s not about provisioning inferencing. It’s not about provisioning GPUs. It’s really provisioning a computing platform.”
*AI is going to eat software
“AI is going to infuse all of software. AI is going to eat software. Whereas Marc [Andreessen] said that software is going to eat the world, AI is going to eat software, and it’s going to be in every aspect of software. Every single software developer has to learn deep learning. Every single software developer has to apply machine learning. Every software developer will have to learn AI. Every single company will use AI. AI is the automation of automation, and it will likely be the transmission. We’re going to for the first time see the transmission of automation the way we’re seeing the transmission and wireless broadcast of information for the very first time. I’m going to be able to send you automation, send you a little automation by email.”
The impact of AI is huge and we’re just getting started
“And so the ability for AI to transform industry is well understood now. It’s really about automation of everything, and the implication of it is quite large. We’ve been using now deep learning – we’ve been in the area of deep learning for about six years. And the rest of the world has been focused on deep learning for about somewhere between one to two, and some of them are just learning about it.
And almost no companies today use AI in a large way. So on the one hand, we know now that the technology is of extreme value, and we’re getting a better understanding of how to apply it. On the other hand, no industry uses it at the moment. The automotive industry is in the process of being revolutionized because of it. The manufacturing industry will be. Everything in transport will be. Retail, e-tail, everything will be. And so I think the impact is going to be large, and we’re just getting started. We’re just getting started.”
The thing about tech is that it moves exponentially
“Now that’s kind of a first inning thing. The only trouble with a baseball analogy is that in the world of tech, things don’t – every inning is not the same. In the beginning the first inning feels like – it feels pretty casual and people are enjoying peanuts. The second inning for some reason is shorter and the third inning is shorter than that and the fourth inning is shorter than that. And the reason for that is because of exponential growth. Speed is accelerating.
And so from the bystanders who are on the outside looking in, by the time the third inning comes along, it’s going to feel like people are traveling at the speed of light next to you. If you happen to be on one of the photons, you’re going to be okay. But if you’re not on the deep learning train in a couple of two, three innings, it’s gone. And so that’s the challenge of that analogy because things aren’t moving in linear time. Things are moving exponentially.”
*Computing advances are no longer about transistors alone
“The easy way to think about that is that we can no longer rely – if we want to advance computing performance, we can no longer rely on transistor advances alone. That’s one of the reasons why NVIDIA has never been obsessed about having the latest transistors. We want the best transistors. There’s no question about it, but we don’t need it to advance. And the reason for that is because we advance computing on such a multitude of levels, all the way from architecture, this architecture we call GPU accelerated computing, to the software stacks on top, to the algorithms on top, to the applications that we work with. We tune it across the top, from top to bottom all the way from bottom to top. And so as a result, transistors is just one of the 10 things that we use. And like I said, it’s really, really important to us. And I want the best, and TSMC provides us the absolute best that we can get, and we push along with them as hard as we can. But in the final analysis, it’s one of the tools in the box.”
Colette M. Kress – NVIDIA Corp.
Data center revenue has tripled from a year ago
“Next, data center, record revenue of $409 million was nearly triple that of a year ago. The 38% rise from Q4 marked its seventh consecutive quarter of sequential improvement. Driving growth was demand from cloud service providers and enterprises building training clusters for web services, plus strong gains in high-performance computing, GRID graphics virtualization, and our DGX-1 AI super-computer.”
NVIDIA GPUs are at the center of AI
“AI has quickly emerged as the single most powerful force in technology, and at the center of AI are NVIDIA GPUs. All of the world’s major Internet and cloud service providers now use NVIDIA Tesla-based GPU accelerators, AWS, Facebook, Google, IBM, and Microsoft as well as Alibaba, Baidu, and Tencent. We also announced that Microsoft is bringing NVIDIA Tesla P100 and P40 GPUs to its Azure cloud.”