Many of Google's advances in machine learning really do owe their existence to just throwing computers at the problem. Jeff Dean et al realized several years b

Many of Google's advances in machine learning really do owe their existence to just throwing computers at the problem. Jeff Dean et al realized several years back that there were techniques in the literature that didn't seem to work all that well in the small, but they might work better with a crapton of parameters, if you trained the model with unprecedented amounts of CPU time. They were right: throwing megawatts at the problem is effective. The line on this forum is that Google's product is its customers' data, but that's never been right. Google's product is, and has always been, dirt-cheap computing. They have a really large amount of computers, they are building more right now, and they want you to use them. The surplus of computer power within Google is what makes Googlers sit around thinking "sure, that was more CPU time than anyone has ever used for anything before, but what if we used 100x that much?"