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Bank Robbery Ain't What it Used to Be

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When we think of bank robbers, we think of the Golden Age of the Public Enemy, the John Dillingers and Bonnies and Clydes of the 1930s and 40s, when hardscrabble times seemed to call for desperate measures, and the robbers seemed more like gun-toting Robin Hoods than vicious criminals. More recently, the bank robber in popular culture has morphed into the Dangerous Gentleman; think George Clooney in Out of Sight or Cary Grant in To Catch a Thief. Even Al Pacino's angry, strange, likeable character in Dog Day Afternoon. But how the times have changed. These days, the internet is a much more profitable place to do your thieving -- in 2003 and 2004, an estimated $2 billion was stolen from online checking accounts -- leaving real-world stick-up-jobs the province of the desperate, the dumb, and more often than not, the drug-addicted. Recently, weaponless "note-jobs" have largely replaced the loud, aggressive handgun stickups portrayed so often on film, thanks to the offer-no-resistance safety policies of most banks. But the hauls aren't what they used to be -- in the 1970s, robbers could expect to make almost $20,000 from the average haul; these days the average is half that. So what lunk-heads out there are still pulling these low-profit, high-risk crimes? Let's take a gander.

Rogue's Gallery

robbery.jpgWith the recent downturn in the economy dovetailing with an upswing in bank robberies, the Houston branch of the FBI has had trouble keeping the public interested in (and looking out for) the robbers it catches on tape. To combat this problem (and spice things up a bit), it's taken to giving whimsical nicknames to all its suspects. For instance: The "Visine Villain," who robbed a Houston-area bank sporting some seriously bloodshot eyes. The "Bathroom Bandit," who used the loo before looting the bank. The "Hard-Hatted Handyman," who, you guessed it, robbed a bank wearing a hard-hat. Hardly the stuff of legend, these folks.

A few of recent vintage have been more entertaining:
"¢ the "Barbie Bandits," a pair of blonde 19-year-old women who sported ponytails and movie-star sunglasses as they robbed a bank in Atlanta last September.
"¢ the "Grandpa Bandit," a 91-year-old Texan man who tried to hold up his local bank in 2004.
"¢ the "Tie Rob Bandit," a well-dressed East Providence bank robber who passed demand notes that were both curt and courteous: "Give me $3000. I have a gun. Have a nice day."

They're dumber these days

Uncreative disguises aside, bank robbers seem to be getting dumber, as their more-adept criminal brethren shift their efforts to the internet. A few examples from New England's Boston Phoenix:

When it comes to bank robbing, Rhode Island has seen its share of on-the-job buffoonery. Last year, one benighted bank robber in Providence presented the teller with a note demanding "$50s, $30s [sic] and $20s."

Another, in Swansea, Massachusetts, upon being told that the teller had no cash, promptly passed out in shock. (He was still unconscious when the police arrived.) In Cranston, one robber was quickly apprehended after holding up a local bank, wearing a mechanic's shirt with his own name embroidered on the breast pocket.

Meanwhile, in Providence, one armored-car robber's attempt to make a hasty getaway was foiled when he found that his loot was four bags of money containing $3200 — in pennies.

The internet is full of dumb-bank-robber stories, like this one about a guy who robbed a bank while yakking on his cellphone the whole time, or this video of a thwarted robber who couldn't get out of the bank because he was pushing on a door that read "pull." So what's the moral of our story? According to statistics, more than three quarters of all bank robbers get caught -- sometimes as many as 95%, depending on the city -- and given those odds, you're better off in Atlantic City. Bank robbery sure ain't what it used to be.

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iStock // Ekaterina Minaeva
Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
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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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Here's How to Change Your Name on Facebook
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Whether you want to change your legal name, adopt a new nickname, or simply reinvent your online persona, it's helpful to know the process of resetting your name on Facebook. The social media site isn't a fan of fake accounts, and as a result changing your name is a little more complicated than updating your profile picture or relationship status. Luckily, Daily Dot laid out the steps.

Start by going to the blue bar at the top of the page in desktop view and clicking the down arrow to the far right. From here, go to Settings. This should take you to the General Account Settings page. Find your name as it appears on your profile and click the Edit link to the right of it. Now, you can input your preferred first and last name, and if you’d like, your middle name.

The steps are similar in Facebook mobile. To find Settings, tap the More option in the bottom right corner. Go to Account Settings, then General, then hit your name to change it.

Whatever you type should adhere to Facebook's guidelines, which prohibit symbols, numbers, unusual capitalization, and honorifics like Mr., Ms., and Dr. Before landing on a name, make sure you’re ready to commit to it: Facebook won’t let you update it again for 60 days. If you aren’t happy with these restrictions, adding a secondary name or a name pronunciation might better suit your needs. You can do this by going to the Details About You heading under the About page of your profile.

[h/t Daily Dot]