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4 Million Bananas: Wal-Mart by the Numbers

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Earlier this week, the Supreme Court threw out a colossal class-action suit against Wal-Mart on behalf of about 1.5 million female workers, who claimed the retail behemoth had discriminated against them in both promotions and wages. The court didn’t say whether or not Wal-Mart had indeed discriminated against the ladies—only that the suit could not continue as a class-action suit.

The court’s decision has shined a spotlight on other large class-action cases, the future of anti-discrimination suits, and, of course, Wal-Mart itself. So, with that, here’s a list of facts and figures about Wal-Mart, and how it compares to the rest of the world.

Number of Wal-Mart stores worldwide: 9,198

Number of Target stores worldwide: 1,750
Number of people employed by Wal-Mart, in millions: 2.1

Population of Houston, Texas, in millions: 2.1
Number of bananas, in millions, a single Wal-Mart grocery distribution center can store: 4

Number of bananas the National Zoo in Washington D.C. buys annually to feed its animals: 7,692

Number of years it would take the animals at the National Zoo to eat the number of bananas in a single Wal-Mart grocery distribution center: 520

Number of Wal-Mart grocery distribution centers: 38

Amount, in billions, Wal-Mart made in sales during a twelve-month period last year: $416

Amount, in billions, the second-largest company in the world, Royal Dutch Shell, made in sales during the same time period last year: $368

Amount consumers spend on average at Wal-Mart every hour: $47,457,000
Square footage of the Wal-Mart Super Center outside Albany, New York: 260,000

Number of professional football fields that could fit inside that store: 4.5
Percentage of American women who shop at Wal-Mart at least once a week: 20

Year Pew Research Center coined the term “Wal-Mart Mom” to describe an influential new voting bloc: 1999

Percentage of Wal-Mart shoppers who voted for George W. Bush in 2004: 85
Number of days conservative Christian groups boycotted Wal-Mart in 2005 after employees were instructed to say "Happy Holidays" instead of "Merry Christmas": 3
Year a shirt with the slogan “Someday a woman will be president!” was temporarily banned from a Miami-area Wal-Mart because of customer concerns over ”family values”: 1995
The price of a black Barbie doll on sale at a Louisiana Wal-Mart in 2010: $3.00

The price of a while Barbie doll on sale at Louisiana Wal-Mart in 2010: $5.93
Number of current and former female Wal-Mart employees, in millions, who are plaintiffs in the class action suit, Dukes v. Wal-Mart Stores: 1.5

Average wages lost per year claimed by each of the plaintiffs: $1,100
Year German courts ruled Wal-Mart could not ban workplace romances (or “sexually meaningful communication of any type”): 2005

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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]