Computers, for all of their computational muscle, do not hold a candle to humans in the ability to recognize patterns or images. This basic quandary in computational theory – why can computers crunch numbers but cannot efficiently process images – has stumped scientists for many years.
Now, researchers at Arizona State University have come up with a model that could help unlock some of the secrets of how humans process patterns and possibly lead to smarter robots. The advance concerns oscillatory associative memory networks, basically the ability to see a pattern, store it and then retrieve that pattern when needed. A good example is how humans can recognize faces.
"It is still a really big mystery as to how human beings can remember so many faces, but that it is extremely difficult for a computer to do," said Ying-Cheng Lai, an ASU professor of mathematics and a professor of electrical engineering in the Ira A. Fulton School of Engineering.
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