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The Helicopter Controlled Entirely by the Human Brain

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By Harold Maass

Call it the quadcopter mind meld. A group of biomedical engineers at the University of Minnesota has developed a novel way to fly a robotic helicopter, using their own brains as the remote control. The team's research was published Tuesday in the Journal of Neural Engineering.

They're not the first to try such a trick. A Duke neuroscientist implanted electrodes into a monkey a few years ago to allow it to control a walking robot. But this research team managed its feat using an EEG cap laden with 64 electrodes, which can detect electric currents produced by neurons in the brain's motor cortex. That allows the wearer to control the aircraft by simply thinking about a series of hand gestures.

The subjects simply watched where the quadcopter was going on a computer screen, and clenched their fists to navigate it — left to go left, right to go right, both to rise. The commands were sent to the craft via WiFi, and the five subjects managed to pilot the helicopter to its target 66 percent of the time.

Predictably, tech-savvy reviewers found the idea of controlling a quadcopter by thought alone to be pretty cool. George Dvorsky at i09 said it was a truly remarkable accomplishment:

First, there's the order of complexity to consider. This quadcopter has to be navigated across three different dimensions... Incredibly, the copter can be seen zipping around the room as it flies through various sets of rings. It's wild to think that it’s being navigated by an external, human mind.

Second, the achievement offers yet another example of the potential for remote presence. Thought-controlled interfaces will not only allow people to move objects on a computer screen, or devices attached to themselves — but also external devices with capacities that significantly exceed our own. In this case, a flying toy. In future, we can expect to see remote presence technologies applied to even more powerful robotic devices, further blurring the boundary that separates our body from the environment. [i09]

Previous leaps forward with brain-computer interfaces have involved transmitting a command to a machine — say, a robotic arm — and triggering a pre-programmed task that would then be carried out to completion. Rachel Nuwer at Popular Mechanics said that explains why the researchers think their work could open new possibilities for those with physical limitations such as paralysis.

"This new system allows users to make asynchronous (real-time) decisions and change course in midstream rather than having to wait until the prior task is completed," Nuwer says. "In the real world, this would allow a person to start walking forward to the bathroom, for example, but then change his mind and head left into the kitchen."

The aim, said Leo Mirani at Quartz, is to help "the paralyzed to restore their 'autonomy of world exploration.' For healthy users, the possibilities are boundless."

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iStock // Ekaterina Minaeva
Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Nick Briggs/Comic Relief
What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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Nick Briggs/Comic Relief

Fans of the romantic-comedy Love Actually recently got a bonus reunion in the form of Red Nose Day Actually, a short charity special that gave audiences a peek at where their favorite characters ended up almost 15 years later.

One of the most improbable pairings from the original film was between Jamie (Colin Firth) and Aurelia (Lúcia Moniz), who fell in love despite almost no shared vocabulary. Jamie is English, and Aurelia is Portuguese, and they know just enough of each other’s native tongues for Jamie to propose and Aurelia to accept.

A decade and a half on, they have both improved their knowledge of each other’s languages—if not perfectly, in Jamie’s case. But apparently, their love is much stronger than his grasp on Portuguese grammar, because they’ve got three bilingual kids and another on the way. (And still enjoy having important romantic moments in the car.)

In 2015, Love Actually script editor Emma Freud revealed via Twitter what happened between Karen and Harry (Emma Thompson and Alan Rickman, who passed away last year). Most of the other couples get happy endings in the short—even if Hugh Grant's character hasn't gotten any better at dancing.

[h/t TV Guide]