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Facebook’s newest deep learning system makes samples of images that humans think are real 40% of the time

Researchers at Facebook have taken a step closer to a holy grail of artificial intelligence known as unsupervised learning. They’ve come up with a way to generate samples of real photographs that don’t look all that fake.

In fact, the computer-generated samples — of scenes featuring planes, cars, birds, and other objects — looked real to volunteers who took a look at them 40 percent of the time, according to a new paper on the research posted online yesterday. Facebook has submitted the paper for consideration in the upcoming Neural Information Processing Systems (NIPS) conference in Montreal. The research goes beyond the scope of supervised learning, which many startups and large companies, including Facebook, use for a wide variety of purposes.

Supervised deep learning traditionally involves training artificial neural networks on a large pile of data that come with labels — for instance, these 100 pictures show geese — and then throwing them a new piece of data, like a picture of a ostrich, to receive an educated guess, like whether or not the new picture depicts a goose.

With unsupervised learning, there are no labeled pictures to learn from. It’s sort of like the way people learn to identify things. Once you’ve seen one or two cell phones, you can recognize them immediately.