David Rosenthal: belief

Transcript context

…Right. So deep learning is generating a lot of buzz, a lot from this AlexNet competition. In 2013, Bryan Catanzaro, who's a research scientist at NVIDIA, published a paper with some other researchers at Stanford, which included Andrew Ng, where they were able to take this unsupervised learning approach that had been done inside the Google Brain team where the Google Brain team had published their work on this and it had a thousand nodes. This is a big part of the early neural network hype cycle of people trying cool stuff. This team was able to do it with just three nodes. Totally different model, super parallelized, lots of compute for a super short period of time, and a really high performance can puting way or HPC as it became known. This ends up being the very core of what becomes cuDNN, which is the library for deep neural networks that's actually baked into CUDA. That makes it easy for data scientists and research scientists everywhere who aren't hardware engineers or software engineers to just pretty easily write high performance deep neural networks on NVIDIA hardware. So this AlexNet thing, plus then Brian and Andrew Ng's paper, just collapses all these previously thought to be impossible lines to cross just makes it way easier, way more performant, and way less energy-intensive for other teams to do it in the future. Specifically to do deep learning. I think at this point, everybody knows that this is pretty important, but it's not that much of a leap to say, if you can train a computer to recognize images on its own, you can then train a computer to see on its own, to drive a car on its own, to play chess, to play go, to make your photos look really awesome when you take them on the latest iPhone, even if you don't have everything right. To eventually let you describe a scene and then have a transformer model paint that scene for you in a way that is unbelievable that a human didn't make it.…

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