TORONTO — In 2012, Geoffrey Hinton changed the way machines see the world.
Along with two graduate students at the University of Toronto, Mr. Hinton, a professor there, built a system that could analyze thousands of photos and teach itself to identify common objects like flowers and cars with an accuracy that didn’t seem possible.
He and his students soon moved to Google, and the mathematical technique that drove their system — called a neural network — spread across the tech world. This is how autonomous cars recognize things like street signs and pedestrians.
But as Mr. Hinton himself points out, his idea has had its limits. If a neural network is trained on images that show a coffee cup only from a side, for example, it is unlikely to recognize a coffee cup turned upside down.
Now Mr. Hinton and Sara Sabour, a young Google researcher, are exploring an alternative mathematical technique that he calls a capsule network.
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