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May the tallest candidate win

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(... Or rather, the tallest candidate may win.) Speaking of the air up there, there are lots of factors people use when figuring out whom to vote for, but according to some political statisticians, one of the most influential (unconsciously, at least) may be height. The data on this sort of thing certainly isn't iron-clad, and there are some notable exceptions to the not-quite rule, such as Jimmy Carter's victory over two-inches-taller Gerald Ford in the 1976 presidential race. Here are some height-related factoids from electoral history, courtesy Cecil Adams:

"In Language on Vacation (1965), word and number buff Dmitri Borgmann claimed that in the 19 U.S. presidential elections between 1888 and 1960, the taller candidate won the popular vote all but once, when 6'2" Franklin Roosevelt beat 6'2-1/2" Wendell Willkie in 1940. In 1888 5'11" Grover Cleveland beat 5'6" Benjamin Harrison at the polls but was cuffed in the electoral college, and in 1896 and 1900 both candidates were the same height.

In his 1982 book Too Small, Too Tall psychologist John Gillis presents similar results: in the 21 presidential elections from 1904 to 1984, the taller candidate won 80 percent of the time. What's more, he says, in the whole history of the Republic, only two presidents -- Harrison and James Madison (5'4") -- were appreciably shorter than the average height in their day.

We glean further insight on this issue from a delightful book called The Height of Your Life (1980) by Ralph Keyes (5'7.62"). Keyes notes that a survey of the U.S. Senate in 1866 found the average height of the members to be 5'10-1/2", well above average for men at the time. Keyes's own survey of 27 senators found the average height had risen to 6'0.33", 3.33" taller than the average American male. A similar survey of 31 governors found the average height to be 6'0.46"."

This is kind of how you'd imagine cave men choosing their leaders. (Or in the case of Borat Sagdiyev's fictionalized nation of Kazakhstan, it's the "man who can suspend car battery from testes-satchel the longest.") Either way, I'm going to be watching the height (er, election) results closely tonight.


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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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