Just because the tools needed to make an autonomous weapon were widely available didn’t tell me how easy or hard it would be for someone to actually do it. What I wanted to understand was how widespread the technological know-how was to build a homemade robot that could harness state-of-the-art techniques in deep learning computer vision. Was this within reach of a DIY drone hobbyist or did these techniques require a PhD in computer science?
There is a burgeoning world of robot competitions among high school students, and this seemed like a great place to get a sense of what an amateur robot enthusiast could do. The FIRST Robotics Competition is one such competition that includes 75,000 students organized in over 3,000 teams across twenty-four countries. To get a handle on what these kids might be able to do, I headed to my local high school.
Less than a mile from my house is Thomas Jefferson High School for Science and Technology—“TJ,” for short. TJ is a math and science magnet school; kids have to apply to get in, and they are afforded opportunities above and beyond what most high school students have access to. But they’re still high school students—not world-class hackers or DARPA whizzes.
In the Automation and Robotics Lab at TJ, students get hands-on experience building and programming robots. When I visited, two dozen students sat at workbenches hunched over circuit boards or silently tapping away at computers. Behind them on the edges of the workshop lay discarded pieces of robots, like archeological relics of students’ projects from semesters prior. On a shelf sat “Roby Feliks,” the Rubik’s Cube solving robot. Nearby, a Raspberry Pi processor sat atop a plastic musical recorder, wires running from the circuit board to the instrument like some musical cyborg. Somewhat randomly in the center of the floor sat a half- disassembled robot, the remnants of TJ’s admission to the FIRST competition that year. Charles Dela Cuesta, the teacher in charge of the lab,
apologized for the mess, but it was exactly what I imagined a robot lab should look like.
Dela Cuesta came across as the kind of teacher you pray your own children have. Laid back and approachable, he seemed more like a lovable assistant coach than an aloof disciplinarian. The robotics lab had the feel of a place where students learn by doing, rather than sitting and copying down equations from a whiteboard.
Which isn’t to say that there wasn’t a whiteboard. There was. It sat in a corner amid a pile of other robotic projects, with circuit boards and wires draped over it. Students were designing an automatic whiteboard with a robot arm that could zip across the surface and sketch out designs from a computer. On the whiteboard were a series of inhumanly straight lines sketched out by the robot. It was at this point that I wanted to quit my job and sign up for a robotics class at TJ.
Dela Cuesta explained that all students at TJ must complete a robotics project in their freshmen year as part of their required coursework. “Every student in the building has had to design a small robot that is capable of navigating a maze and performing some sort of obstacle avoidance,” he said. Students are given a schematic of what the maze looks like so they get to choose how to solve the problem, whether to preprogram the robot’s moves or take the harder path of designing an autonomous robot that can figure it out on its own. After this required class, TJ offers two additional semesters of robotics electives, which can be complemented with up to five computer science courses in which students learn Java, C++, and Python.
These are vital programming tools for using robot control systems, like the Raspberry Pi processor, which runs on Linux and takes commands in Python. Dela Cuesta explained that even though most students come into TJ with no programming experience, many learn fast and some even take computer science courses over the summer to get ahead. “They can pretty much program in anything—Java, Python. . . . They’re just all over the place,” he said. Their senior year, all students at TJ must complete a senior project in an area of their choosing. Some of the most impressive robotics projects are those done by seniors who choose to make robotics their area of focus. Next to the whiteboard stood a bicycle propped up on its kickstand.
A large blue box sat inside the frame, wires snaking out of it to the gear shifters. Dela Cuesta explained it was an automatic gear shifter for the bike.
The box senses when it is time to shift and does so automatically, like an automatic transmission on a car.
The students’ projects have been getting better over the years, Dela Cuesta explained, as they are able to harness more advanced open-source components and software. A few years ago, a class project to create a robot tour guide for the school took two years to complete. Now, the timeline has been shortened to nine weeks. “The stuff that was impressive to me five, six years ago we could accomplish in a quarter of the time now. It just blows my mind,” he said. Still, Dela Cuesta pushes students to build things custom themselves rather than use existing components. “I like to have the students, as much as possible, build from scratch.” Partly, this is because it’s often easier to fit custom-built hardware into a robot, an approach that is possible because of the impressive array of tools Dela Cuesta has in his shop. Along a back wall were five 3-D printers, two laser cutters to make custom parts, and a mill to etch custom circuit boards. An even more important reason to have students do things themselves is they learn more that way. “Custom is where I want to go,” Dela Cuesta said. “They learn a lot more from it. It’s not just kind of this black box magic thing they plug in and it works. They have to really understand what they’re doing in order to make these things work.”
Across the hall in the computer systems lab, I saw the same ethos on display. The teachers emphasized having students do things themselves so they were learning the fundamental concepts, even if that meant re-solving problems that have already been solved. Repackaging open-source software isn’t what the teachers are after. That isn’t to say that students aren’t learning from the explosion in open-source neural network software. On one teacher’s desk sat a copy of Jeff Heaton’s Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks. (This title begs the uncomfortable question whether there is a parallel course of study, Artificial Intelligence for Machines, where machines learn to program other machines. The answer, I suppose, is “Not yet.”) Students are learning how to work with neural networks, but they’re doing so from the bottom up. A junior explained to me how he trained a neural network to play tic-tac-toe—
a problem that was solved over fifteen years ago, but remains a seminal coding problem. Next year, TJ will offer a course in computer vision that will cover convolutional neural networks.
Maybe it’s a cliché to say that the projects students were working on are mind-blowing, but I was floored by the things I saw TJ students doing. One student was disassembling a Keurig machine and turning it into a net- enabled coffeemaker so it could join the Internet of Things. Wires snaked through it as though the internet was physically infiltrating the coffeemaker, like Star Trek’s Borg. Another student was tinkering with something that looked like a cross between a 1980s Nintendo Power Glove and an Apple smartwatch. He explained it was a “gauntlet,” like that used by Iron Man.
When I stared at him blankly, he explained (in that patient explaining-to-an- old-person voice that young people use) that a gauntlet is the name for the wrist-mounted control that Iron Man uses to fly his suit. “Oh, yeah. That’s cool,” I said, clearly not getting it. I don’t feel like I need the full functionality of my smartphone mounted on my wrist, but then again I wouldn’t have thought ten years ago that I needed a touchscreen smartphone on my person at all times in the first place. Technology has a way of surprising us. Today’s technology landscape is a democratized one, where game-changing innovations don’t just come out of tech giants like Google and Apple but can come from anyone, even high-school students.
The AI revolution isn’t something that is happening out there, only in top- tier research labs. It’s happening everywhere.