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Why Do Sign Language Interpreters Look So Animated?

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As New York City Mayor Bloomberg gave numerous televised addresses about the preparations the city was making for Hurricane Sandy, and then the storm’s aftermath, he was joined at the podium by a sign language interpreter, who immediately became a twitter darling. People watching the addresses tweeted that she was "amazing," "mesmerizing," "hypnotizing," and "AWESOME." Soon, her name was uncovered—Lydia Callis—and animated .gifs of her signing were posted. A couple of hours later, a tumblr was born. New York magazine called her "Hurricane Sandy's breakout star."

Callis was great, but not because she was so lively and animated. She was great because she was performing a seriously difficult mental task—simultaneously listening and translating on the spot—in a high-pressure, high-stakes situation. Sure, she was expressive, but that's because she was speaking a visual language. Signers are animated not because they are bubbly and energetic, but because sign language uses face and body movements as part of its grammar.

In American Sign Language, certain mouth and eye movements serve as adjectival or adverbial modifiers.

In this example, Bloomberg is explaining that things will get back to normal little by little. Callis is making the sign INCREASE, but her tight mouth and squinting eyes modify the verb to mean "increase in tiny increments." This facial expression can attach to various verbs to change their meaning to "a little bit."

Here, Bloomberg is urging people not to put out their garbage for collection because it will end up making a mess on the streets. Callis is making a sign for SPILL, while at the same time making what is known as the 'th' mouth adverbial. This mouth position modifies the verb to mean "sloppily done." If you attach it to WALK, WRITE, or DRIVE, it means "walk sloppily," "write messily," or "drive carelessly."

Movements of the head and eyebrows indicate sentence-level syntactic functions.


In this example, Bloomberg is warning people that the worst of the storm is coming. Callis signs WORST SOON HAPPEN. Her eyebrows are raised for WORST and SOON, then lowered for HAPPEN. This kind of eyebrow raise indicates topicalization, a common structure used by many languages. In topicalization, a component of a sentence is fronted, and then commented upon. A loose approximation of her sentence would be "Y'know the worst? Soon? It's gonna happen."


Bloomberg is urging people to use common sense and take the stairs instead of the elevator. Callis signs NEED GO-UP FLOOR USE STAIRS. During NEED GO-UP FLOOR her eyes are wide and her eyebrows raised. Then her eyebrows go down sharply and her eyes narrow for USE STAIRS. The wide-eyed eyebrow raise marks a conditional clause. It adds the sense of "if" to the portion it accompanies. The second clause is a serious command. She signs, "if you need to go up a floor, use the stairs."

Body position is used to indicate different discourse-level structures.

Here Bloomberg is urging people to check on road conditions before they go anywhere. He says, "The FDR may be open or closed." Callis signs OPEN while leaning to the left and CLOSED while leaning to the right. This shift in body position marks a contrastive structure. If Bloomberg were to continue making distinctions between the "open" and "closed" possibilities, she would use those same positions to maintain coherence while interpreting those other distinctions.


In this example, Bloomberg is saying that the worst will be over by tomorrow and that tomorrow when we look back "we'll certainly be on the other side of that curve." Callis signs DECREASE IMPROVE WEATHER POINT. On the first three signs she looks up and to her right. She turns back to the front on POINT.  Here her body shift marks the adoption of a role. She is being a hypothetical person saying "Ahhh, I see things are less intense, weather improving…" She then drops the role and turns forward to say (as Bloomberg does), "The point is, stay home."

Of course, some facial expressions in sign languages are just facial expressions.


Here, Bloomberg is responding to a reporter's question a little testily. Callis captures his bemused, impatient tone with her facial expression. In fact, Bloomberg captures it with his own facial expression. No one would call him animated, but he can also say a few things without words.

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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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