The first episode of Sesame Street in 1969 included a segment called “One of These Things Is Not Like the Other.” Viewers were asked to consider a poster that displayed three 2s and one W, and to decide — while singing along to the game’s eponymous jingle — which symbol didn’t belong. Dozens of episodes of Sesame Street repeated the game, comparing everything from abstract patterns to plates of vegetables. Kids never had to relearn the rules. Understanding the distinction between “same” and “different” was enough.
Machines have a much harder time. One of the most powerful classes of artificial intelligence systems, known as convolutional neural networks or CNNs, can be trained to perform a range of sophisticated tasks better than humans can, from recognizing cancer in medical imagery to choosing moves in a game of Go. But recent research has shown that CNNs can tell if two simple visual patterns are identical or not only under very limited conditions. Vary those conditions even slightly, and the network’s performance plunges.
These results have caused debate among deep-learning researchers and cognitive scientists. Will better engineering produce CNNs that understand sameness and difference in the generalizable way that children do? Or are CNNs’ abstract-reasoning powers fundamentally limited, no matter how cleverly they’re built and trained? Whatever the case, most researchers seem to agree that understanding same-different relations is a crucial hallmark of intelligence, artificial or otherwise.
“Not only do you and I succeed at the same-different task, but a bunch of nonhuman animals do, too — including ducklings and bees,” said Chaz Firestone, who studies visual cognition at Johns Hopkins University. [Continue reading…]