David Rosenthal: belief

Transcript context

…This is like someone breaking the four-minute mile. Actually, in some ways, it's more impressive than the four-minute mile thing because they just didn't brute force their way all the way there. They tried a completely different approach. Then boom, showed that we could get way more accurate than anyone else ever thought. What was that approach? Well, they called the team, which was composed of Alex Krizhevsky, the primary lead of the team. He was a Ph.D. student and collaborated with Ilya Sutskever and Geoff Hinton. Geoff Hinton was the Ph.D. advisor of Alex. They call it AlexNet. It is a convolutional neural network, which is a branch of artificial intelligence called deep learning. Deep learning is new for this use case, but Ben, you weren't exactly right. It had been around for a long time, a very long time. Deep learning neural networks, this was not a new idea. The algorithms had existed for many decades, I think, but they were really, really, really computationally intensive. They're required to train the models to do a deep neural network. You need a lot of computation on the order of grains of sand that exist on Earth. It was completely impossible with a traditional computer architecture that you could make these work in any practical applications. People were forecasting too. When with Moore's law will we be able to do this? It still seemed like the far future because not only did Moore's Law need to happen, but you also needed the NVIDIA approach of massively parallelizable architecture where suddenly, you could get all these incredible performance gains, not just because you're putting more transistors in a given space, but because you're able to run programs in parallel now.…

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