jan 29, 2012 - Deep Learning Takes Off

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In 2012, the British-born artificial-intelligence expert Geoffrey Hinton and a small team at the University of Toronto produced a stunning advance in AI by creating the most accurate visual-recognition system the world had yet seen. It was, and is, based on deep learning, an AI technique that enables a computer to recognize images through exposure to massive amounts of photographic data. The concept of training a neural network had existed for decades, but it had languished. Hinton had long suspected that what was needed was far more processing power, and many more images to train on, and by 2012 he was finally getting his wish, thanks to the huge number of digital images suddenly available thanks to smartphones and the internet. In the 2012 ImageNet competition, Hinton’s team created a system that could identify and sort more than a million images with an error rate of only 15.3 percent, 10 points better than the closest rival. Within months, AI companies were flocking to “deep learning,” and firms like Google were releasing open-source tools that let any tiny start-up easily train neural nets. Thanks to Hinton and his team, today even the smallest, start-ups can create robots that recognize everyday objects

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