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Comment by jchw | Hacker News Reader

I don’t really see this; All of the currently popular machine learning frameworks either support GPU or are oriented around GPU based execution. For most developers working on AI it is still the defacto standard.

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I think.. both. Distributed computing (Hadoop) was why a lot of data got collected and made available(ish).

GPU's are the engines that made CNN's (in particular) tractable, and opened up a bunch of applications for many companies, and opened up a reasonable route to results for a generation of researchers.

Can anybody elaborate on why this is downvoted? This would be my guess as well, simd parallelism of GPUs solves only part of the challenges, you still need a general purpose data crunching machine to prepare and handle learning data.

For one GPU speedup over CPU isn’t that dramatic for small to medium sized problems, e.g. MNIST or CIFAR that one would try algorithm ideas on. So I think it’s a stretch to see GPU as essential to the new algorithms. On the other hand for large problems like the original Alpha Go you need to figure out the distributed computing to really scale.

This isn’t to say that GPUs aren’t nice. They do save time or for the same amount of time let you produce more polished results, which means in a competitive environment everyone would use them.

Exactly. GPUs are necessary now, but did not originally herald the deep learning revolution.

Except that's "currently". NNs can and were trained on CPUs originally.

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