XNOR.ai frees AI from the prison of the supercomputer

When someone talks about AI, or machine learning, or deep convolutional networks, what they're really talking about is -- as is the case for so many computing concepts -- a lot of carefully manicured math. At the heart of these versatile and powerful networks is a volume of calculation only achievable by the equivalent of supercomputers. More than anything else, this computational cost is what is holding back applying AI in devices of comparatively little brain: phones, embedded sensors, cameras. If that cost could be cut by a couple orders of magnitude, AI would be unfettered from its banks of parallel processors and free to inhabit practically any device -- which is exactly what a breakthrough at the Allen Institute for AI makes possible. XNOR.ai is, essentially, a bit of clever computer-native math that enables AI-like models for vision, speech recognition, to run practically anywhere.