To some neuroscientists, it seems as if artificial networks are beginning to reveal some of the actual methods our human brains use to learn. Via QuantaMagazine
whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.
“We are raising a generation of algorithms that are like undergrads [who] didn’t come to class the whole semester and then the night before the final, they’re cramming,” said, a computer scientist at the University of California, Berkeley. “They don’t really learn the material, but they do well on the test.”
Now some computational neuroscientists have begun to explore neural networks that have been trained with little or no human-labeled data. These “self-supervised learning” algorithms have proved enormously successful atand, more recently, image recognition. In recent work, computational models of the mammalian visual and auditory systems built using self-supervised learning models have shown a closer correspondence to brain function than their supervised-learning counterparts.
But as the field progressed, researchers realized the limitations of supervised training. For instance, in 2017, Leon Gatys, a computer scientist then at the University of Tübingen in Germany, and his colleagues took an image of a Ford Model T, then overlaid a leopard skin pattern across the photo, generating a bizarre but easily recognizable image. A leading artificial neural network correctly classified the original image as a Model T, but considered the modified image a leopard.
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