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A Baby Step for Computer Learning

Marissa Cevallos writes in Science:

Infants still have a chubby leg up on the best supercomputers when it comes to picking up languages, but now researchers have created a program that can teach itself to distinguish vowel sounds like those in “train” and “bed.” The software, the first to figure out vowel categories from human sounds, lends credence to the notion that language is more learned than innate.

Psychologists have long been baffled by how children catch on to language’s nuances. For decades, researchers debated whether language is acquired through experience or whether humans are wired for it. The dispute motivated computer scientists to explore the question electronically, using so-called neural networks, computer programs in which layers of virtual neurons send messages back and forth. Just as the brain strengthens its own neural connections by repetition, the neural network learns by reinforcing the same output from similar inputs. For example, if the computer has just sorted “play” into the “long a” vowel category, it’s more likely “hay” would end up in the same category on a future iteration. But mothers don’t conveniently sort out vowel sounds, like “ay,” “ee,” “eye,” “oh,” and “ewe,” into neat categories for their infants. Somehow, the child figures out how many vowel categories are supposed to exist while sorting through sounds.

James “Jay” McClelland, a cognitive neuroscientist at Stanford University in Palo Alto, California, wanted his computer to do the same.

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