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4 Subtle Changes to English People Hardly Notice

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Everyone knows that language changes. It's easy to pick out words that have only been recently introduced (bromance, YOLO, derp) or sentence constructions that have gone out of style (How do you do? Have you a moment?), but we are constantly in the middle of language change that may not be noticeable for decades or even centuries. Some of the biggest and most lasting changes to language happen slowly and imperceptibly. The Great Vowel Shift, for example, was a series of pronunciation changes occurring over 350 years, and not really noticed for over 100 years after that. It resulted in an intelligibility gap between Modern and Middle English and created the annoying misalignment between English pronunciation and spelling. But it was impossible to see while it was going on.

These days, however, it is possible to spot subtle linguistic changes by analyzing large digital collections of text or transcribed speech, some of which cover long periods of time. Linguists can run the numbers on these large corpora to determine the direction of language use trends and whether they are statistically significant. Here are 4 rather subtle changes happening in English, as determined by looking at the numbers.

1. SHIFT FROM "THEY STARTED TO WALK" TO "THEY STARTED WALKING"

There are a number of verbs that can take a complement with another verb in either the "-ing" form or the "to" form: "They liked painting/to paint;" "We tried leaving/to leave;" "He didn't bother calling/to call." Both of these constructions are still used, and they have both been used for a long time. But there has been a steady shift over time from the "to" to the "-ing" complement. "Start" and "begin" saw a big increase in the "-ing" complement until leveling out in the 1940s, while emotion verbs like "like," "love," "hate," and "fear" saw their proportion of "-ing" complements start to rise in the 1950s and 60s. Not all verbs have participated in the shift: "stand," "intend," and "cease" went the "to" way.

2. GETTING MORE PROGRESSIVE

English has been getting more progressive over time—that is, the progressive form of the verb has steadily increased in use. (The progressive form is the –ing form that indicates something is continuous or ongoing: "They are speaking" vs. "They speak.") This change started hundreds of years ago, but in each subsequent era, the form has grown into parts of the grammar it hadn't had much to do with in previous eras. For example, at least in British English, its use in the passive ("It is being held" rather than "It is held") and with modal verbs like "should," "would," and "might" ("I should be going" rather than "I should go") has grown dramatically. There is also an increase of "be" in the progressive form with adjectives ("I'm being serious" vs. "I'm serious").

3. GOING TO, HAVE TO, NEED TO, WANT TO

It's pretty noticeable that words like "shall" and "ought" are on the way out, but "will," "should," and "can" are doing just fine. There are other members of this helping verb club though, and they have been on a steep climb this century. "Going to," "have to," "need to," and "want to" cover some of the same meaning territory as the other modal verbs. They first took hold in casual speech and have enjoyed a big increase in print in recent decades.

4. RISE OF THE "GET-PASSIVE"

The passive in English is usually formed with the verb "to be," yielding "they were fired" or "the tourist was robbed." But we also have the "get" passive, giving us "they got fired" and "the tourist got robbed." The get-passive goes back at least 300 years, but it has been on a rapid rise during the past 50 years. It is strongly associated with situations which are bad news for the subject—getting fired, getting robbed—but also situations that give some kind of benefit. (They got promoted. The tourist got paid.) However, the restrictions on its use may be relaxing over time and get-passives could get a whole lot bigger.

This article draws on work by Mark Davies, Geoffrey Leech, and Christian Mair.

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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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What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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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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