IBM's resistive computing could massively accelerate AI -- and get us closer to Asimov's Positronic Brain ExtremeTech

With the recent rapid advances in machine learning has come a renaissance for neural networks -- computer software that solves problems a little bit like a human brain, by employing a complex process of pattern-matching distributed across many virtual nodes, or "neurons." Modern compute power has enabled neural networks to recognize images, speech, and faces, as well as to pilot self-driving cars, and win at Go and Jeopardy. Most computer scientists think that is only the beginning of what will ultimately be possible. Unfortunately, the hardware we use to train and run neural networks looks almost nothing like their architecture. That means it can take days or even weeks to train a neural network to solve a problem -- even on a compute cluster -- and then require a large amount of power to solve the problem once they're trained.