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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.



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" The Hyperloop Movement is officially bigger than the man who started it. "
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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 XNOR.ai, a breakthrough at the Allen Institute for AI, makes possible.


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