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A Brief History of the ATM

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Today, we think nothing of walking out of our houses on a Friday night without a penny in our pocket. The reason is that there is a network of ATMs around the globe: In the UK and U.S. alone, there are around 150 ATMs per 100,000 people—plenty to go around. According to analysts RBR, 2.25 million machines dispensed cash automatically at the end of 2010, and that’s expected to grow beyond 3 million by 2016.

But although we use them without a second thought, precious few of us know how they came to sit on our high streets and in the walls of our banks.

Luther George Simjan’s Bankograph

City Bank of New York installed a machine called a Bankograph in 1961. This wasn’t an ATM as we know it, though: rather than dispensing cash, it acted as an automated way to deposit cash and checks. One thing that it did share with the machines we use today was its general look and design.

Transported back to the early 1960s for the six months the Bankograph was available (it was removed after it proved unpopular to account holders, probably because it was new and untried), a modern-day person would likely be able to recognize it as something similar to today's ATMs. Blocky and boxy, it cemented the design standards for companies that would follow.

John Shepherd-Barron’s chocolate dispenser

According to John Shepherd-Barron, the reason we have ATMs is his love of chocolate and him running late one Saturday. He managed to miss the midday closing time of his local bank on a Saturday in 1965, meaning he couldn’t take out any cash for the weekend. He got thinking that cash ought to be as easy to get as chocolate bars from a dispensing machine.

Shepherd-Barron’s inspiration struck in the bath, where he was relaxing after a long day working for De La Rue, a global currency printer. Switching out chocolate bars for cash, the laborer took his idea to his bosses, who in turn presented them to Barclays Bank. The company was keen, and on June 27, 1967, the Enfield High Street branch of Barclays began dispensing cash, £10 at a time. Users inserted a single-use paper voucher (which would be mailed back to the customer to prevent fraud) and keyed in a four-digit code that we know now as a PIN, and they were given their money.

Meanwhile, in Sweden…

Nine days after Barclays unveiled their Enfield cash machine, Nixdorf, a Swedish bank, installed their first ATM dispensing kronor. They called their machine the Bankomat, a name which lives on in many European languages (including the Italian bancomat) as the term for ATMs.

From that point on there was a flurry of machine unveilings: Westminster Bank in the UK allowed their customers to use their own-branded ATMs in 1968. Around the same time Japanese savers could withdraw yen from their own machines, and a year later the first US-based machines came to market in Rockville Centre, Long Island, New York. Chemical Bank, the owners of the new automated teller machine, declared that “our bank will open at 9:00 and never close again.

Networked ATMs

Attaching ATMs to an internet connection became paramount to enable bank balances to update automatically and dynamically. The added complication of this caused the market to narrow somewhat in the coming decades, with two companies, Diebold and NCR, becoming the front runners and providing most of the machines used. They were replaced by other, nimbler manufacturers with better-looking and performing machines, and today ATMs are everywhere, always on, and constantly being used.

There are machines on U.S. Navy frigates and at the remote McMurdo research center at the tip of Ross Island in the Antarctic. Children can buy toy versions of ATMs to play with, and though we often bank online through our web browsers, there’s still a need, early on a Sunday morning or late on a Friday night, for the glowing slot of the ATM.

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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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