Artificial intelligence is getting some better perspective. Like a person who can read someone else’s penmanship without studying lots of handwriting samples, next-gen image recognition AI can more easily identify familiar sights in new situations.
Made from a new type of virtual building block called capsules, these programs may cut down the enormous amount of data needed to train current image-identifying AI. And that could boost such technology as machine-made medical diagnoses, where example images may be scarce, or the responsiveness of self-driving cars, where the view is constantly shifting. Researchers with Google will present this new version of an artificial neural network at the Neural Information Processing Systems conference in Long Beach, Calif., on December 5.
Neural networks are webs of individual virtual nerve cells, or neurons, that learn to pick out objects in pictures by studying labeled example images. These networks largely classify pictures based on whether they contain certain features. For instance, a program trained on a series of head shots might conclude that a face has two eyes, a nose and a mouth. Show that program a face in profile with only one eye visible, though, and it may not recognize the photo as a face, explains Roland Memisevic, a computer scientist at the University of Montreal who was not involved in the work.
To overcome that limitation, researchers can train a neural network on millions of photos from myriad angles, and the program memorizes all the different ways a face might look. Compared with the human brain, which doesn’t need anywhere near a million examples to know what a face looks like, this system is wildly inefficient. “It’s a disaster,” Memisevic says. “Capsules try to fix that.”
Made from a new type of virtual building block called capsules, these programs may cut down the enormous amount of data needed to train current image-identifying AI. And that could boost such technology as machine-made medical diagnoses, where example images may be scarce, or the responsiveness of self-driving cars, where the view is constantly shifting. Researchers with Google will present this new version of an artificial neural network at the Neural Information Processing Systems conference in Long Beach, Calif., on December 5.
Neural networks are webs of individual virtual nerve cells, or neurons, that learn to pick out objects in pictures by studying labeled example images. These networks largely classify pictures based on whether they contain certain features. For instance, a program trained on a series of head shots might conclude that a face has two eyes, a nose and a mouth. Show that program a face in profile with only one eye visible, though, and it may not recognize the photo as a face, explains Roland Memisevic, a computer scientist at the University of Montreal who was not involved in the work.
To overcome that limitation, researchers can train a neural network on millions of photos from myriad angles, and the program memorizes all the different ways a face might look. Compared with the human brain, which doesn’t need anywhere near a million examples to know what a face looks like, this system is wildly inefficient. “It’s a disaster,” Memisevic says. “Capsules try to fix that.”
