Astro Teller knows how to draw attention. As the director of X, aka the “moonshoot factory,” he famously navigates the Google campus on rollerblades, even indoors. He was wearing his rollerblades on Thursday when he glided into a roomful of reporters to announce that Project Loon—Alphabet’s wacky-sounding plan to deliver the internet to the world’s farthest-flung places via giant balloons—is even closer to reality than the company previously thought. It was a made-for-the-press moment, but Teller buried the lede. It’s cool that these balloons may soon start broadcasting internet signals from the stratosphere. But the bigger deal here is that machine learning is moving beyond its digital origins into the real world.
This past summer, the X lab launched an internet balloon into the stratosphere over Peru, where it stayed for nearly 100 days. Originally, the company thought Project Loon would require hundreds of balloons drifting more or less aimlessly across the globe. But the balloons over Peru came equipped with navigational systems built around on machine-learning techniques able to detect subtle patterns in atmospheric conditions—patterns humans alone could not discern. The system reliably kept balloons in the same general area, even amid all the uncertainty of the weather up in the stratosphere. That means Project Loon can bring the internet to unserved areas using far fewer balloons.
“We can now run an experiment and try to give service in a particular place in the world with 10 or 20 or 30 balloons, not with 200 or 300 or 400 balloons,” Teller said. In the process, Project Loon becomes not just logistically simpler but also cheaper. “The service has a much better chance of ultimately being profitable.”
In recent months, machine learning has reinvented image and speech recognition, language translation and ad targeting. It has cracked the ancient game of Go. But these are just the earliest developments in what researchers see as a vast movement toward a wide range of systems that can learn to perform takes on par with or even better than we humans. For now, most of what machine learning can accomplish takes place in the purely digital realm.
