Google spits out about 4 million search results per minute (among many other duties), which consumes a lot of energy. According to a recent blog, it cut its electrical bills significantly by applying the same kind of machine learning used in speech recognition and other consumer applications. A data center engineer on a 20 percent project plotted environmental factors like outside air temperature, IT load and other server-related factors. He then developed a neural network that could see the “underlying story” in the data, predicting loads 99.6 percent of the time. With a bit more work, Mountain View managed to eke out significant savings by varying cooling and other factors. It also published a white paper to share the info with other data centers and prove once again that humans are redundant.