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But instead of learning about the objects themselves, the computer learns the neuron stimulation pattern for each type of object. (Essentially, it's learning patterns of patterns: the patterns of neural reactions not just to pixels but to groupings of pixels.) Later, when it sees a new image of a tree, it will see how closely the resulting neuron pattern matches the ones produced by other tree images. Poggio says this is similar to the way a baby's brain gets imprinted with visual information and learns about the world around it.
The researchers applied standard tests to the system and found that it can detect people and cars in a street scene about 95 to 98 percent of the time, Bileschi says. The system doesn't just identify objects; it can view stills or video and recognize an action. It might recognize running, for instance, based on how a leg is bent or how quickly a person shifts position from one frame to the next.