In Japan, an A.I. system designed to distinguish croissants from bear claws has turned out to be capable of identifying cancer cells.
One morning in the spring of 2019, I entered a pastry shop in the Ueno train station, in Tokyo. The shop worked cafeteria-style. After taking a tray and tongs at the front, you browsed, plucking what you liked from heaps of baked goods. What first struck me was the selection, which seemed endless: there were croissants, turnovers, Danishes, pies, cakes, and open-faced sandwiches piled up everywhere, sometimes in dozens of varieties. But I was most surprised when I got to the register.
All this changed in 2012, when Alex Krizhevsky, a graduate student in computer science, released AlexNet, a program that approached image recognition using a technique called deep learning. AlexNet was a neural network, “deep” because its simulated neurons were arranged in many layers.
Hisashi Kambe, the man behind the pastry A.I., grew up in Nishiwaki City, a small town that sits at Japan’s geographic center. The city calls itself Japan’s navel; surrounded by mountains and rice fields, it’s best known for airy, yarn-dyed cotton fabrics woven in intricate patterns, which have been made there since the eighteenth century. As a teen-ager, Kambe planned to take over his father’s lumber business, which supplied wood to homes built in the traditional style.
A major development was the introduction of a backlight—the forerunner of the glowing rectangle I’d noticed in the Ueno store. It helped eliminate shadows, including the ones cast by a doughnut into a doughnut hole. At one point, when it became clear that baking times were never consistent, Kambe’s team made a study of the phenomenon. They came up with a mathematical model relating bakedness to color. In the end, they spent five years immersed in bread.
In the spring of 2018, Kambe was invited to speak about the A.I. identification of cancer cells at a conference in Sapporo. The other speakers had degrees from Harvard and Stanford. “High-class people,” he said. He felt out of place. But when he saw that they were all using deep-learning systems, he felt that he had something to contribute. At the conference, Kambe argued that there are tasks for which deep learning is still impractical.
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