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When Did the FBI Start Using “Wanted” Posters?

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On a December night in 1919, a 23-year-old soldier named William N. Bishop managed to slip out of the stockade at Virginia’s Camp A.A. Humphreys and escape into the surrounding woods. Unable to locate Bishop, the army requested the assistance of the U.S. Department of Justice’s Division of Investigation, the precursor to the Federal Bureau of Investigation.

The Division’s assistant director, Frank Burke, compiled all the information he could get on Bishop – a complete physical description (including a note on a mole in his right armpit), names and addresses of people he might visit or stay with, a photostat (a type of early photocopy) of his most recent portrait, and more – and sent a copy, dated December 15, 1919, and labeled “Identification Order No. 1,” to all the Division’s agents and other law enforcement in the search area.

The memo was, essentially, the first FBI wanted poster, before it was even called the FBI. The IO, as it quickly came to be called by law enforcement, helped immensely in the manhunt for Bishop, who was captured and returned to the army less than five months after his escape. Over the next decade, IOs became a staple of fugitive-hunting for federal, state and municipal law enforcement agencies, and were soon adopted in Canada and Europe, too.

By the 1930s, the IO had evolved into a standard format used across the country. They were made as 8x8 flyers, and displayed the fugitive’s photos, criminal record and background information, as well as images of their fingerprints pulled from the FBI’s growing repository. Around this time, the Bureau also started sending the IOs to more than just the agents and officers directly involved in a case by making them available to police stations and post offices around the country.

Since the hunt for Bishop in 1919, the FBI has issued almost 6,000 Identification Orders. You can see ones for some famous fugitives – including “Pretty Boy” Floyd (IO No. 1194), “Machine Gun” Kelly (No. 1203), John Dillinger (No. 1217), “Baby Face” Nelson (No. 1223) and Bonnie and Clyde (No. 1227) – at the Bureau’s website.

A Different Kind of Top 10

In the 1950s, the Bureau took IOs in a new direction and created the “Ten Most Wanted Fugitives” list. Ninety-four percent of the fugitives featured on the list since its first release have been captured.

Whenever one of these “Ten Most Wanted” fugitives is caught, the Bureau holds something of a fugitive casting call and asks each of its field offices for suggestions for a new listee. Armed with nominations, Special Agents go over the fugitives’ files and add a new one to the list, largely based on two considerations: whether or not the fugitive is a danger to society, and whether adding them to the list would significantly increase the likelihood that they would be caught.

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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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May 23, 2017
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