Facebook is enabling our machine masters

According to an article in WIRED;


Facebook is enabling our machine masters;


YANN LECUN IS among those bringing a new level of artificial intelligence to popular internet services from the likes of Facebook, Google, and Microsoft.


As the head of AI research at Facebook, LeCun oversees the creation of vast “neural networks” that can recognize photos and respond to everyday human language. And similar work is driving speech recognition on Google’s Android phones, instant language translation on Microsoft’s Skype service, and so many other online tools that can “learn” over time. Using vast networks of computer processors, these systems approximate the networks of neurons inside the human brain, and in some ways, they can outperform humans themselves.

This week in the scientific journal Nature, LeCun—also a professor of computer science at New York University—details the current state of this “deep learning” technology in a paper penned alongside the two other academics most responsible for this movement: University of Toronto professor Geoff Hinton, who’s now at Google, and the University of Montreal’s Yoshua Bengio. The paper details the widespread progress of deep learning in recent years, showing the wider scientific community how this technology is reshaping our internet services—and how it will continue to reshape them in the years to come.

But as LeCun tells WIRED, deep learning will also extend beyond the internet, pushing into devices that can operate here in the physical world—things like robots and self-driving cars. Just last week, researchers at the University of California at Berkeley revealed a robotic system that uses deep learning tech to teach itself how to screw a cap onto a bottle. Early this year, big-name chip maker Nvidia and an Israeli company called Mobileye revealed that they were developing deep learning systems that can help power self-driving cars.

LeCun has been exploring similar types of “robotic perception” for over a decade, publishing his first paper on the subject in 2003. The idea was to use deep learning algorithms as a way for robots to identify and avoid obstacles as they moved through the world—something not unlike what’s needed with self-driving cars. “It’s now a very hot topic,” he says.