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Black Sunday: The Storm That Gave Us the Dust Bowl

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It seemed like an ordinary day at first. Like any other day, folks on the Great Plains were struggling to get by. People walked to church, swept up from the dust storm that had blown through the week before, perhaps discussed the Congressional hearings that had brought the plight of the region, which had been ravaged by drought and the economic effects of the Great Depression, to the attention of the rest of the nation.

But Black Sunday—April 14, 1935—was no ordinary day. 

That afternoon, a gigantic cloud swept across the Great Plains. It was 1000 miles long and blew at speeds up to 100 miles per hour. It was made of 300,000 tons of dust whipped from the ground of northern farmlands, where poor soil conservation techniques had led to widespread erosion made worse by the unending drought.

Great Plains residents were used to dust, but they had never seen anything like this. One observer compared it to “the Red Sea closing in on the Israel children … it got so dark that you couldn’t see your hand before your face, you couldn’t see anybody in the room.”

“You couldn’t see the street lights,” recalled Jim Williams, who watched the storm from his home in Dodge City, Kansas. “It rolled over and over and over and over and over when it came in,” another witness remembered, “and it was coal black; it was coal black, and it was terrible that afternoon. It was hot and dry.”

Humans weren’t the only ones terrified by the storm. Birds fled ahead of the cloud. Confused by the dark, chickens started to go inside to roost. Cows ran in circles. 

Once the storm subsided, a simple spring day had become the worst day in recent memory. The “black blizzard” that swept across the plains states left a trail of devastation in its wake—leveled fields, crashed cars, reports of people who had been blinded or given pneumonia by the storm. Everything was covered in dust, which choked wells and killed cattle. “Black Sunday,” as the storm became known, was the death knell for the poor farmers of Oklahoma and Texas. Demoralized and impoverished, thousands of so-called “Okies” cut their losses and began the long migration to more favorable locations like California.

In Boise City, Oklahoma, an Associated Press reporter named Robert E. Geiger had weathered the storm with photographer Harry G. Eisenhard. “Three little words achingly familiar on a Western farmer’s tongue,” he wrote after the storm, “rule life in the dust bowl of the continent—if it rains.” Some speculate that Geiger meant to say “dust belt,” a term he used to refer to the devastated region before and after Black Sunday.

Inadvertent or no, the term was picked up almost immediately. Geiger had given name to a phenomenon that would come to define the economic and social impacts of the Great Depression. But though Black Sunday and the Dust Bowl it helped name drew attention to the plight of the plains and turned soil conservation into a national priority, its effects were best summed up by a folk singer, not a reporter or politician. These are some of the lyrics to Woody Guthrie’s “Dust Storm Disaster,” which tells the story of the “deathlike black” cloud that enveloped America that day in 1935:

It covered up our fences, it covered up our barns,
It covered up our tractors in this wild and dusty storm.
We loaded our jalopies and piled our families in,
We rattled down that highway to never come back again.

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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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