Innovators Under 35
I14. Ian Goodfellow, 31
Google
Brain Team
Invented a way for neural networks to
get better by working together.
A
few years ago, after some heated debate in a Montreal pub, Ian Goodfellow
dreamed up one of the most intriguing ideas in artificial intelligence. By
applying game theory, he devised a way for a machine-learning system to
effectively teach itself about how the world works. This ability could help
make computers smarter by sidestepping the need to feed them painstakingly
labeled training data.
Goodfellow
was studying how neural networks can learn without human supervision. Usually a
network needs labeled examples to learn effectively. While it’s also possible
to learn from unlabeled data, this had typically not worked very well.
Goodfellow, now a staff research scientist with the Google Brain team, wondered
if two neural networks could work in tandem. One network could learn about a
data set and generate examples; the second could try to tell whether they were
real or fake, allowing the first to tweak its parameters in an effort to
improve.
After
returning from the pub, Goodfellow coded the first example of what he named a
“generative adversarial network,” or GAN. The dueling-neural-network approach
has vastly improved learning from unlabeled data. GANs can already perform some
dazzling tricks. By internalizing the characteristics of a collection of
photos, for example, a GAN can improve the resolution of a pixelated image. It
can also dream up realistic fake photos, or apply a particular artistic style
to an image. “You can think of generative models as giving artificial intelligence
a form of imagination,” Goodfellow says.
—Will Knight
—Will Knight
MIT TECHNOLOGY REVIEW
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