Jensen Huang: evaluation

Transcript context

…And that is true if there was a large market of machine learning practitioners who would eventually show up and want to do all this great scientific computing and accelerated computing. But at the time when you were starting to invest what is now something like 10,000 person years in building that platform, did you ever feel like, oh man, we might’ve invested ahead of the demand for machine learning, since we’re a decade before the whole world is realizing it? I guess yes and no. When we saw deep learning, when we saw AlexNet and realized its incredible effectiveness and computer vision, we had the good sense, if you will, to go back to first principles and ask, what is it about this thing that made it so successful? When a new software technology or a new algorithm comes along and somehow leapfrogs 30 years of computer vision work, you have to take a step back and ask yourself, but why? Fundamentally, is it scalable? And if it’s scalable, what other problems can it solve? There were several observations that we made. The first observation is that if you have a whole lot of example data, you could teach this function to make predictions. What we’ve basically done is discovered a universal function approximator, because the dimensionality could be as high as you wanted to be. Because each layer is trained one layer at a time, there’s no reason why you can’t make very, very deep neural networks. Okay, now you just reason your way through. Now I go back to 12 years ago. You could just imagine the reasoning I’m going through in my head that we’ve discovered a universal function approximator. In fact, we might have discovered with a couple of more technologies, a universal computer that you can— Have you’re paying attention to the ImageNet competition every year leading up to this?…

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