Wired - 1.16.14 by Daniela Hernandez
‘I get very excited when we discover a way of making neural networks better — and when that’s closely related to how the brain works.’ — Geoffrey Hinton
To create one of those 3-D holographic images, you record how countless beams of light bounce off an object and then you store these little bits of information across a vast database. While still in high school, back in 1960s Britain, Hinton was fascinated by the idea that the brain stores memories in much the same way. Rather than keeping them in a single location, it spreads them across its enormous network of neurons.
This may seem like a small revelation, but it was a key moment for Hinton — “I got very excited about that idea,” he remembers. “That was the first time I got really into how the brain might work” — and it would have enormous consequences. Inspired by that high school conversation, Hinton went on to explore neural networks at Cambridge and the University of Edinburgh in Scotland, and by the early ’80s, he helped launch a wildly ambitious crusade to mimic the brain using computer hardware and software, to create a purer form of artificial intelligence we now call “deep learning.”
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