Computer vision is now a part of everyday life. Facebook recognizes faces in the photos you post to the popular social network. The Google Photos app can find images buried in your collection, identifying everything from dogs to birthday parties to gravestones. Twitter can pinpoint pornographic images without help from human curators.
All of this “seeing” stems from a remarkably effective breed of artificial intelligence called deep learning. But as far as this much-hyped technology has come in recent years, a new experiment from Microsoft Research shows it’s only getting started. Deep learning can go so much deeper.
This revolution in computer vision was a long time coming. A key turning point came in 2012, when artificial intelligence researchers from the University of Toronto won a competition called ImageNet. ImageNet pits machines against each other in an image recognition contest—which computer can identify cats or cars or clouds more accurately?—and that year, the Toronto team, including researcher Alex Krizhevsky and professor Geoff Hinton, topped the contest using deep neural nets, a technology that learns to identify images by examining enormous numbers of them, rather than identifying images according to rules diligently hand-coded by humans.
Toronto’s win provided a roadmap for the future of deep learning. In the years since, the biggest names on the ‘net—including Facebook, Google, Twitter, and Microsoft—have used similar tech to build computer vision systems that can match and even surpass humans. “We can’t claim that our system ‘sees’ like a person does,” says Peter Lee, the head of research at Microsoft. “But what we can say is that for very specific, narrowly defined tasks, we can learn to be as good as humans.”
Roughly speaking, neural nets use hardware and software to approximate the web of neurons in the human brain. This idea dates to the 1980s, but in 2012, Krizhevsky and Hinton advanced the technology by running their neural nets atop graphics processing units, or GPUs. These specialized chips were originally designed to render images for games and other highly graphical software, but as it turns out, they’re also suited to the kind of math that drives neural nets. Google, Facebook, Twitter, Microsoft, and so many others now use GPU-powered-AI to handle image recognition and so many others tasks, from Internet search to security. Krizhevsky and Hinton joined the staff at Google.
Now, the latest ImageNet winner is pointing to what could be another step in the evolution of computer vision—and the wider field of artificial intelligence. Last month, a team of Microsoft researchers took the ImageNet crown using a new approach they call a deep residual network. The name doesn’t quite describe it. They’ve designed a neural net that’s significantly more complex than typical designs—one that spans 152 layers of mathematical operations, compared to the typical six or seven. It shows that, in the years to come, companies like Microsoft will be able to use vast clusters of GPUs and other specialized chips to significantly improve not only image recognition but other AI services, including systems that recognize speech and even understand language as we humans naturally speak it.
In other words, deep learning is nowhere close to reaching its potential. “We’re staring at a huge design space,” Lee says, “trying to figure out where to go next.”