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The Mystery of the Missing Money

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The Federal Reserve tells us that more than $1.4 trillion worth of U.S. currency is in circulation. But we only know where roughly 15 percent of that money is—in banks or in regular, everyday circulation in the United States. The other 85 percent of the United States currency supply is simply missing. No one knows for sure where it is or what it's doing. "We call this the currency enigma," said Edgar Feige, an economics professor emeritus, in an interview with American Public Media’s Marketplace. "It’s hard to figure out where this currency is and why so much of it is out there."

There are some good guesses, but no certainties. And those possibilities tell us something about how our economy—and the world's—really works.

The shadow economy

A chunk of the money is probably used in illegal transactions. This shadow economy is enabled by cash, which is generally the most anonymous method of payment. Economists like Feige put the size of the shadow economy—which includes drugs, prostitution and various other misdeeds—at hundreds of billions of dollars. At certain points, that has accounted for more than 20 percent of the country’s adjusted gross income.

But this doesn’t mean that all of the missing money goes into the shadow economy—after all, currency can be used again and again as it passes from person to person. That leads some economists to theorize that a relatively small percentage of the missing currency (less than 10 percent) is part of the black market.

The overseas equation

So where’s the rest of it? Much—if not most—of the money is probably overseas. U.S. bills are still seen around the world as some of the most stable and reliable currency available. So vast quantities of cash are hidden away for a rainy day (some $80 billion in Russia alone).

That’s not necessarily a bad thing. Think of it this way: If someone holds onto U.S. currency, they’re essentially giving the Federal Reserve free money. This concept is called seigniorage, and it’s a bit complicated to explain. Here’s the basic idea: The Fed creates money by buying government bonds from banks. As people demand more dollars—and hold onto them—the Fed buys more bonds to increase the supply. But those bonds earn interest, which means our central bank pockets billions of dollars in pure profit each year.

The Fed scrambles

All of this means that the Federal Reserve has a balancing act on its hands. It has to bring new money into circulation (since that earns it sweet, sweet moolah), but the bank also wants to to keep U.S. currency from being mainly used by gamblers and drug smugglers. In the 1960s, we stopped printing $500 and $1000 bills, as they were almost exclusively used illegally. These days, there's been criticism of the $100 bill for just the same reason. Believe it or not, there are more $100 bills in circulation than $20 bills!

The Fed also wants to make sure that the bills are as secure as possible—the demand makes the $100 an attractive target for counterfeiters. That’s why the $100 was redesigned in 2013.

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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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Name the Author Based on the Character
May 23, 2017
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