Systems infrastructure for deep learning software in flux

Deep learning appeared after a long gestation, or all of a sudden. You can take your pick, depending on where you were when mainstream media discovered a collection of statistical and artificial intelligence techniques that seemed to promise a new era of automated predictive analytics. The vote here is for a long gestation, although it's fair to say there is some suddenness about the way deep learning software is pushing a new class of analytics in which applications repeatedly churn through large sets of data, learning to predict likely outcomes as they go. A lengthy birthing process seems in play because, really, deep learning is an updated take on the machine learning process, which in turn was a new take on neural networks, an early form of artificial intelligence in which simulations mimic the human brain's neuron activity by weighting outputs and building connected sets of meaning. What marks deep learning software is use of multiple processing layers.