Robots recklessly driving cheap electric kiddie cars. Autonomous machines shining lasers at ants — and spraying water at bewildered cats — for the amusement of cackling grandchildren.
Hobbyists are just getting started with deep-learning technologies that give them cheap, off-the-shelf capabilities that put Ronald Reagan’s Star Wars program to shame.
In the latest edition of the AI Podcast, NVIDIA engineer Bob Bond and Make: Magazine Executive Editor Mike Senese explain to host Michael Copeland how they’ve taken the once esoteric technology of deep learning and put it to work on offbeat projects that can be tackled on budgets of a few hundred bucks.
“One of the big things that’s happening — and it’s happening in real time right now — is the technology is finally hitting a point where we, as consumers, have access to this type of capability,” Senese says.
Making Cats Scat
Bond, a veteran engineer, is no technical novice. But he’s not a deep learning researcher either. He explains how he used off-the-shelf hobbyist tools — and NVIDIA’s Jetson TX1 system-on-a-chip — to recognize cats, and turn the sprinklers on to shoo them from his lawn.
He’s also built a contraption out of mirrors, servo motors and two lasers to harass the ants that scuttle across his kitchen floor, much to the amusement of his grandchildren.
“One of my friends said I was the first person to arm an AI,” Bond says.
He isn’t alone. Senese said deep-learning hardware and software are cheap enough — and user-friendly enough — to enable hobbyists to build AI into a huge range of gizmos. He and his colleagues, for example, used deep learning to create a gadget that could recognize cats, then turn on a laser pointer and wave it around for their amusement.
Kiddie Cars, Grownup Careers
There’s even an autonomous division of the Power Racing League, which sends grown men and women around tracks at high speed on souped up children’s ride-on cars.
The cars may cost less than a grand to assemble, but the race has become a proving ground for autonomous technology talent. “Some of these guys who were racing power wheels last year are now at Tesla and other big corporations,” Senese adds.
And if you missed our podcast last week, it’s worth a listen: Bryan Catanzaro, NVIDIA’s head of applied deep learning research, talks about what’s next for deep learning.