Noam Shazeer: evaluation

Transcript context

…I think of it as actually changing quite a bit in the last couple of decades. Two decades ago to one decade ago, it was awesome because you just wait, and like 18 months later, you get much faster hardware, and you don't have to do anything. And then more recently, I feel like the general-purpose CPU-based machine scaling has not been as good, like the fabrication process improvements are now taking three years instead of every two years. The architectural improvements in multi-core processors and so on are not giving you the same boost that we were getting 20 to 10 years ago. But I think at the same time, we're seeing much more specialized computational devices, like machine learning accelerators, TPUs, and very ML-focused GPUs, more recently, are making it so that we can actually get really high performance and good efficiency out of the more modern kinds of computations we want to run that are different than a twisty pile of C++ code trying to run Microsoft Office or something. It feels like the algorithms are following the hardware. Basically, what's happened is that at this point, arithmetic is very, very cheap, and moving data around is comparatively much more expensive. So pretty much all of deep learning has taken off roughly because of that. You can build it out of matrix multiplications that are N cubed operations and N squared bytes of data communication basically. Well, I would say that the pivot to hardware oriented around that was an important transition, because before that, we had CPUs and GPUs that were not especially well-suited for deep learning. And then we started to build TPUs at Google that were really just reduced-precision linear algebra machines, and then once you have that then you want to exploit it.…

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