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Learn New Skills With Superhuman Speed

Wearable computers could provide the muscle memory to learn guitar chords or dance steps

3 min read
Photo of a hand with wearable computer.
Good Vibrations: By activating tiny vibration motors in its fingertips, the Mobile Music Touch glove speeds up the process of learning to play a piano melody.
Photo: Georgia Tech

The glove looks humdrum, like a garment you might pick up at a sporting-goods store. It’s made of soft black leather and fingerless, like a cyclist’s or weightlifter’s glove. The similarity is, however, deceiving.

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“I have a glove that can teach you how to play a piano melody,” Thad Starner declares when I call to chat about the future of wearable computing. Now a professor at the Georgia Institute of Technology and the technical lead of Google Glass, he helped pioneer the field in the 1990s as a student at MIT. “During this conversation, you could have learned ‘Amazing Grace.’ ”

“Really?” I say. “While we’re talking?”

“Sure,” he says and invites me to Atlanta to see for myself.

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Caitlyn Seim, a Ph.D student, slips the glove onto my hand. Inside each of the five finger holes she has sewn a flat vibration motor. The five tiny vibrators, which perch atop my digits like gemstones on rings, are wired to a microcontroller on the back of my hand. Seim has programmed it to fire the motors in the same sequence that my fingers would strike keys on a piano.

But she doesn’t tell me which tune I’ll be learning. “You’ll just feel a little buzzing,” she says, flipping on the electronics. Then Starner whisks me away to show off his lab’s myriad other projects: a language-translation app for Google Glass, a magnetic tongue implant for voicing silent commands to a computer, a smart vest to help divers communicate with dolphins, smart chew toys to help police dogs communicate with handlers, and all manner of other wonderfully wacky wearables.

Once every minute for the next 2 hours, the motors in the glove vibrate across my fingers. I try to figure out the pattern: buzz…middle finger...buzz…ring fin…buzz…buzz…ger...buzz…uh…buzz…buzz…crap. “IMPOSSIBLE,” I write in my notebook.

At last, Starner escorts me to a keyboard. He plays the first passage of a song—15 notes that the glove has supposedly taught me. I recognize the tune. It’s Beethoven’s “Ode to Joy.” I take off the glove.

“Start here,” Starner says, hitting the first note. I lay my fingers on the keys. Middle fingermiddle fingerring finger… “I don’t know,” I say, embarrassed.

“Don’t think about it,” Starner says.

I start again. Middle…middle…ring…pinky…pinky…ring…middle…pointer…. “This is crazy!” I say, still playing. And I don’t stop. I finish the first passage, then play the second, and start into the third.

“Now, hold on!” Starner interjects. “Have you played this before?”

“Never,” I say. It’s true—I never took piano lessons. Befuddled, he inspects the glove and discovers it’s been programmed to vibrate all four phrases of the song—61 notes, not 15. Typically, he explains, he and his students teach only one phrase at a time. I approach the keyboard again. I fumble a few tries—I’m learning, after all—but within minutes, I can play the melody perfectly. I feel giddy, like I’ve just discovered an innate talent I never knew I had.

“You just know what to do—it’s insane,” Seim notes. She recently taught herself to play “Ode to Joy” by wearing the glove while writing an application for a research grant. “It’s almost like watching a phantom hand.”

Starner and his colleagues believe that the repeated buzzing from the glove creates a muscle memory that enables a wearer to learn to play a song with far less practice than it would take without haptic stimulation. They have also studied the glove’s effect on people with spinal cord injuries and found that it can help them regain some sensation and dexterity in their hands. The researchers are now beginning experiments to test whether haptic gloves can teach braille typing and stenography—evidence that the technology could impart not just patterns but also language.

“We don’t know the limits,” Starner says. “Can we put these sorts of vibration motors on people’s legs and teach them how to dance? Can we teach people how to throw a better baseball?” He mentions a scene from the sci-fi thriller The Matrix in which the film’s heroes, Neo and Trinity, hijack a helicopter: “Can you fly that thing?” Neo asks his right-hand woman. “Not yet,” she says. The film cuts to Trinity’s eyelids flickering as the knowledge pours through a data port at the back of her skull. Seconds later they’re in the air.

“Of course you can’t do that,” I say.

Starner grins. “Not yet.”

For more on the future of wearable computers see “Wearable Computers Will Transform Language.”

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Meta Aims to Build the World’s Fastest AI Supercomputer

The AI Research SuperCluster could help the company develop real-time voice translations

3 min read
A brightly lit, high-ceilinged room with rows of silvery-black cabinets and yellow pipes near the ceiling.

Meta’s new AI supercomputer.

Meta

Meta, parent company of Facebook, says it has built a research supercomputer that is among the fastest on the planet. By the middle of this year, when an expansion of the system is complete, it will be the fastest, Meta researchers Kevin Lee and Shubho Sengupta write in a blog post today. The AI Research SuperCluster (RSC) will one day work with neural networks with trillions of parameters, they write. The number of parameters in neural network models have been rapidly growing. The natural language processor GPT-3, for example, has 175 billion parameters, and such sophisticated AIs are only expected to grow.

RSC is meant to address a critical limit to this growth, the time it takes to train a neural network. Generally, training involves testing a neural network against a large data set, measuring how far it is from doing its job accurately, using that error signal to tweak the network’s parameters, and repeating the cycle until the neural network reaches the needed level of accuracy. It can take weeks of computing for large networks, limiting how many new networks can be trialed in a given year. Several well-funded startups, such as Cerebras and SambaNova, were launched in part to address training times.

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Rooftop Drones for Autonomous Pigeon Harassment

Have invasive flying rats met their match?

3 min read
A pigeon on a poop covered wooden plank.
iStock photo

Feral pigeons are responsible for over a billion dollars of economic losses here in the United States every year. They’re especially annoying because the species isn’t native to this country—they were brought over from Europe (where they’re known as rock doves and are still quite annoying) because you can eat them, but enough of the birds escaped and liked it here that there are now stable populations all over the country, being gross.

In addition to carrying diseases (some of which can occasionally infect humans), pigeons are prolific and inconvenient urban poopers, deploying their acidic droppings in places that are exceptionally difficult to clean. Rooftops, as well as ledges and overhangs on building facades, are full of cozy nooks and crannies, and despite some attempts to brute-force the problem by putting metal or plastic spikes on every horizontal surface, there are usually more surfaces (and pigeons) than can be reasonably bespiked.

Researchers at EPFL in Switzerland believe that besting an aerial adversary requires an aerial approach, and so they’ve deployed an autonomous system that can identify roof-invading pigeons and then send a drone over to chase them away.

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Design with EMI in Mind Test Early Test Often

Solving EMI problems early in the design cycle is more cost-effective than solving them later. This is an iterative process of employing best practices and checking. Of course, it is a trade-off – and needs to be balanced against functionality and scheduling. But solving problems and verifying early helps reduce costs. This poster helps you to design with EMI in mind.