Last summer, I spent a week at a conference dedicated to graphics-processing units—GPUs. It was presented by GPU big name Nvidia, a brand that is largely associated with gaming hardware.

At the conference, however, gaming was a sideshow. For that matter, graphics themselves (excluding VR) were a sideshow, despite being in the actual name.

In general, this was a machine learning conference, and, to most of the attendees, of course it was. With chipmaker AMD’s announcement this week at CES that the bleeding-edge of its GPU product line will be targeted at machine learning, at least initially, I thought it would be a good opportunity to take a step back and offer a bit of background on why GPUs and machine learning are so intimately connected in the first place.

It has to do with matrices. Of course, not all of those things are of equal importance when it comes to predicting the air temperature.

For example, what season it is might be 10 times as important as anything else, while air moisture might matter only a third as much as elevation. The point is that we can take a bunch of observations and then assign each of them a weight (or emphasis) indicating how important that observation is compared to the others. Read more from…

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