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Computing advances are slowing, we're reaching the limit of current methods - as Moore's Law, Dennard scaling and von Neumann's architecture all show their age. With the demands of AI workloads proving too much for these conventional approaches, there are those trying something different. In Issue 33, we look at an ambitious effort to use phase-change memory to usher in a new age of deep learning.