INSIDE FACEBOOK'S NEW ROBOTICS LAB, WHERE AI AND MACHINES FRIEND ONE ANOTHER

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INSIDE FACEBOOK'S NEW ROBOTICS LAB, WHERE AI AND MACHINES FRIEND ONE ANOTHER

Facebook’s nascent robotic platform looks a bit … chaotic. In a new lab in its palatial Silicon Valley HQ, a red and black Sawyer robot arm (from the recently defunct company Rethink Robotics) is waving all over the place with a mechanical whine. It’s supposed to casually move its hand to a spot in space to its right, but it goes up, up, up and way off course, then resets to its starting position. Then the arm goes right and gets pretty close to its destination. But then, agh!, it resets again before—maddeningly for those of us rooting for it—veering wildly off course again.Industrial Robotic Arm

But, like a hare zigzagging back and forth to avoid a falcon, this robot’s seeming madness is in fact a special brand of cleverness, one that Facebook thinks holds the key not only for better robots, but for developing better artificial intelligence. This robot, you see, is teaching itself to explore the world. And that, Facebook says, could one day lead to intelligent machines like telepresence robots.
AT THE MOMENT robots are very dumb—generally you have to spell everything out in code for them: This is how you roll forward, this is how you move your arm. We humans are much smarter in how we learn. Even babies understand that an object that moves out of view hasn’t vanished from the physical universe. They learn they can roll a ball, but not a couch. It’s fine to fall off a couch, but not a cliff.

All of that experimentation builds a model of the world in your brain, which is why later on you can learn to drive a car without crashing it immediately. “We know in advance that if we're driving near a cliff and we turn the wheel to the right, the car is going to run off a cliff and nothing good is going to happen,” says Yann LeCun, chief AI scientist at Facebook. We have a self-learned model in our head that keeps us from doing dumb things. Facebook is trying to give that kind of model to the machines too. Systems that learn “models of the world are in my opinion the next challenge to really make significant progress in AI,” LeCun adds.