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Whatever Happened to Play?

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When browsing Bill Simmons’ new long-form sports site, Grantland, I found myself wondering two things. First, what is a “Grantland”? And second, didn’t the New York Times try this once?

Although it only lasted two years, the TimesPlay magazine was an early attempt at a print version of Grantland. It combined long-form sports stories with fun graphics about everything from stretching properly to Super Bowl betting.

The Times launched Play in 2006 as part of a scheme to publish several high-end magazines that would attract luxury products (the others were Key, a real estate magazine, and T Styles, a fashion magazine). The idea for Play was cooked up by Times editor Gerald Marzorati after he decided that ESPN: The Magazine and Sports Illustrated were directed towards teenagers and saw an opening for mature sports writing.

Marzorati recruited writers like Michael Lewis and David Foster Wallace to give Play a higher pedigree and allowed writers to pontificate on everything from Pete Carroll to offshore sailing. Play housed Foster Wallace’s must-read essay on Roger Federer (apparently the opportunity to write it was what drew him to the magazine) and even got the scoop on the creation of the UFL, an NFL knockoff that is still around today.

The magazine even took time to look at sports on the fringe, profiling juggler Vova Galchenko and his training regime that usually ended with him throwing clubs at a wall, or the America's Cup yacht race. Columnist Bryan Curtis even got in a profile of an 85-year-old softball player in New Mexico.

The magazine was especially noted for its graphics. Check out this graph of Super Bowl teams and how they performed against the spread (reprinted on or this analysis of how Tiger Woods wins a major (this was back when Tiger Woods actually won majors).

Despite widespread praise and a nomination for an American Society of Magazine Editors award, Play folded just two years later. Editorial director Marzorati later told the New York Observer that the Times killed the magazine because it was losing hundreds of thousands of dollars a year. In announcing that the magazine was closing, the Times said publicly that it had “more or less broken even,” although they attributed most of that success to an Olympics issue that sold out its all of its ad space.

“When it started, it was a quarterly and we wanted to get it to a monthly, but each year we kept saying ‘This isn’t the year. This isn’t the year,’” Marzorati said. “And the economy kept going in the wrong direction.’ We couldn’t see how we could possibly expand it.”

Lucky for us, the Times has the entirety of the Play archives on their website.

Oh, and as for my first question, it's a reference to sportswriter Grantland Rice, who was so prolific that he also had a college football bowl game named after him. Have any of you checked out yet? What do you think?

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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]