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Erin McCarthy

New App Makes Identifying Bird Species Easy

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Erin McCarthy

Last October, I was petsitting for a friend and, while out for a walk with the pup, came across a strange looking bird (above). It was sitting, shell shocked, on the hood of a car in Brooklyn. When it was still there an hour later, I decided I would take it and release it in a nearby park—but how do you rescue a bird if you don't even know what kind it is?

No one on Twitter or Instagram could help; a friend who is a birder guessed that it might be a woodcock. So, after reading how to best rescue those birds, I gently wrapped the bird in a towel, slipped it into a paper bag, and walked it down to Brooklyn Bridge Park, where I let it out in some vegetation. It ran away from me, flying in short bursts, as fast as it could.

If only I'd had Birdsnap. This electronic field guide, created by computer scientists at Columbia University and the University of Maryland, can identify 500 common North American birds with nothing but a cell phone photo. The process is simple: Take a photo or choose one from your phone's album; click on the eye and the tail; and wait for the potential matches to show up.

After I'd set the bird loose in the park, my birder friend texted again: another birder believed the bird was a juvenile Virginia Rail, a freshwater marsh bird that mostly keeps to itself. Birdsnap identified the bird correctly on the first try. (How it got on the hood of a car in Brooklyn will forever remain a mystery.)

Columbia Computer Science Professor Peter Belhumeur and University of Maryland Computer Science Professor David Jacobs came up with the idea for the app when they realized that the software and techniques they'd developed for facial recognition could also be used to identify species. Facial recognition algorithms work by finding the resemblance between comparable parts of faces, comparing a nose to other noses and an eye to other eyes, according to Columbia's Engineering Department. In Birdsnap, each species has 17 parts marked; the app detects the parts of the bird so it can compare them with what's in its database and discover species that are visually similar to the animal in an uploaded photo.

"What's really exciting about Birdsnap is that not only does it do well at identifying species, but it can also identify which parts of the bird the algorithm uses to identify each species," said Thomas Berg, a Columbia Engineering computer science PhD candidate. "Birdsnap then automatically annotates images of the bird to show these distinctive parts—birders call them 'field marks'—so the user can learn what to look for.”

And the app, available for the iPhone, does more than just identify birds: It also provides descriptions of the animals and their calls, shows their family trees and similar species, and includes range and sightings maps.

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iStock // Ekaterina Minaeva
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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
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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]

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