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From Here To Singularity

"The time from here to Singularity depends sensitively on the particulars of what we humans do during the next decade (and even the next few years)."

Stanford’s model helicopters teach themselves to fly

helicopter-stanford

Now this sounds to me like >narrow< ai, of a sort, but it is certainly generalizing, learning type behaviour. The helicopter monitors the activity of an expert helicopter pilot, and then, compensating for environmental differences (wind etc), performs the same maneuvers itself.

Stanford computer scientists have developed an artificial intelligence system that enables robotic helicopters to teach themselves to fly difficult stunts by watching other helicopters perform the same maneuvers.

The result is an autonomous helicopter than can perform a complete airshow of complex tricks on its own.

Stanford’s artificial intelligence system learned how to fly by “watching” the four-foot-long helicopters flown by expert radio control pilot Garett Oku. “Garett can pick up any helicopter, even ones he’s never seen, and go fly amazing aerobatics. So the question for us is always, why can’t computers do things like this?” Coates said.

Computers can, it turns out. On a recent morning in an empty field at the edge of campus, Abbeel and Coates sent up one of their helicopters to demonstrate autonomous flight. The aircraft, brightly painted Stanford red, is an off-the-shelf radio control helicopter, with instrumentation added by the researchers.

For five minutes, the chopper, on its own, ran through a dizzying series of stunts beyond the capabilities of a full-scale piloted helicopter and other autonomous remote control helicopters. The artificial-intelligence helicopter performed a smorgasbord of difficult maneuvers: traveling flips, rolls, loops with pirouettes, stall-turns with pirouettes, a knife-edge, an Immelmann, a slapper, an inverted tail slide and a hurricane, described as a “fast backward funnel.”

The pièce de résistance may have been the “tic toc,” in which the helicopter, while pointed straight up, hovers with a side-to-side motion as if it were the pendulum of an upside down clock.

“I think the range of maneuvers they can do is by far the largest” in the autonomous helicopter field, said Eric Feron, a Georgia Tech aeronautics and astronautics professor who worked on autonomous helicopters while at MIT. “But what’s more impressive is the technology that underlies this work. In a way, the machine teaches itself how to do this by watching an expert pilot fly. This is amazing.”

This post is useless without video. I’m going to do my best to find some.

To scientists, a helicopter in flight is an “unstable system” that comes unglued without constant input. Abbeel compares flying a helicopter to balancing a long pole in the palm of your hand: “If you don’t provide feedback, it will crash.”

Early on in their research, Abbeel and Coates attempted to write computer code that would specify the commands for the desired trajectory of a helicopter flying a specific maneuver. While this hand-coded approach succeeded with novice-level flips and rolls, it flopped with the complex tic-toc.”

It might seem that an autonomous helicopter could fly stunts by simply replaying the exact finger movements of an expert pilot using the joy sticks on the helicopter’s remote controller. That approach, however, is doomed to failure because of uncontrollable variables such as gusting winds.

So, narrow ai. No danger of it destroying the planet with nanotechnology to make helicopter stunts easier. But still pretty cool. Essentially, an expert system for helicopter stunts.

When the Stanford researchers decided their autonomous helicopter should be capable of flying airshow stunts, they realized that even defining their goal was difficult. What’s the formal specification for “flying well?” The answer, it turned out, was that “flying well” is whatever an expert radio control pilot does at an airshow.

I wonder, as AGI develops, how many developments will trickle down into the narrow-AI world. I suspect that, each year, narrow-AIs will become just a bit more and more general, gaining the ability to assimilate information from a wider and wider range of environmental variables and increasing complex situations. If we can develop expert systems to peform acrobatics, how soon until we replace the pilots, drivers and controls of all the world’s of vehicles and machinery with expert systems? After all, we are taking the first steps…

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