Now the critical question is: How much faster is it without AI, just because of the specialized dedicated processing chips?
Otherwise, they might be comparing a single virtualized CPU core against a high-end GPU for things like matrix multiplication ... and then the result that GPU > slow CPU isn't really that impressive.
The paper answers this question:
> While the simulations presented typically run in minutes to days, the DENSE emulators can process multiple sets of input parameters in milliseconds to a few seconds with one CPU core, or even faster when using a Titan X GPU card. For the GCM simulation which takes about 1150 CPU-hours to run, the emulator speedup is a factor of 110 million on a like-for-like basis, and over 2 billion with a GPU card. The speed up achieved by DENSE emulators for each test case is shown in Figure 2(h)
Based on similar work we are doing at the startup I work for, this isn't just GPU magic. ML is a heuristic alternative to simulations which already operate on specialized GPUs and TPUs. This modeling acceleration is one of the many ways in which ML is poised to change everything.
The same way that a human can, for instance, approximately draw iso-temperature lines around a candle flame, without having to perform simulations...except the neural net is some 99%+ as accurate and detailed as a full simulation. That's exactly why neural nets excel - they learn complex heuristics much like humans do, but with the added power of digitized computation and memory.
However, I think the degree to which neural networks can extrapolate is still an open question, so it is important to thoroughly sample your problem space during training. That's a bit of an artform given that these spaces are typically high-dimensional.