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Alton Brown's Green Bean Casserole

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Our resident food expert dives into the relatively recent birth of green bean casserole.

Unlike many Thanksgiving stalwarts, the green bean casserole (GBC) can’t trace its roots to culinary tradition. It was developed in 1955 by Dorcas Reilly, an economist for the Campbell Soup Company. That’s right—like so many American classics, the casserole was developed as a sales tool.

Around the 1930s, big food companies like Campbell’s learned that in order to sell newly developed and processed foods, it was best to pump out recipes that featured them. The GBC was a “jiffy” casserole, requiring few ingredients and little time.

Reilly’s original recipe contains only six ingredients: cream of mushroom soup, milk, soy sauce (exotic for the time), black pepper, green beans (cooked or canned), and canned French-fried onions.

Its economy and flavor—largely thanks to gobs of fat and salt—earned the GBC near instant popularity. But a Thanksgiving feature story published by the Associated Press propelled it to immortality.

Campbell’s cream of mushroom soup, which was invented in 1934, had become a pantry staple by ’55. Meanwhile, canned French-fried onions were developed one year earlier, in 1933, but languished until Reilly’s culinary breakthrough.

The bad news: A mere half cup of canned cream of mushroom soup can contain 6 g of fat and a whopping 870 mg of sodium. Canned green beans are ugly and mushy, while canned French-fried onions are downright creepy. The good news: Fresh green bean casserole is a great dish that can be whipped up from scratch with just a little more fuss.

For the topping:
2 medium onions, thinly sliced
1/4 cup all-purpose flour
2 tablespoons panko breadcrumbs
1 teaspoon kosher salt
Non-stick spray

For beans and sauce:
2 tablespoons plus 1 teaspoon kosher salt, divided
1 pound fresh green beans, rinsed, trimmed and halved
2 tablespoons unsalted butter
12 ounces mushrooms, trimmed and cut into 1/2- inch pieces
1/2 teaspoon freshly ground black pepper
2 cloves garlic, minced
1/4 teaspoon freshly ground nutmeg
2 tablespoons all-purpose flour
1 cup chicken broth
1 cup half and half

• Preheat the oven to 475 degrees F.

• Combine the onions, flour, panko and salt in a large mixing bowl and toss to combine. Coat a sheet pan with non-stick spray and evenly spread the onions on the pan. Bake in the oven until golden brown, tossing every 10 minutes, for approximately 30 minutes. Once done, remove from the oven and set aside until ready to use. Turn the oven down to 400 degrees F.

• While the onions are cooking, prepare the beans. Bring a gallon of water and 2 tablespoons of salt to a boil in an 8-quart saucepan. Add the beans and blanch for 5 minutes. Drain in a colander and immediately plunge the beans into a large bowl of ice water to stop the cooking. Drain and set aside.

• Melt the butter in a 10-inch cast iron skillet set over medium-high heat. Add the mushrooms, 1 teaspoon salt and pepper and cook, stirring occasionally, until the mushrooms begin to give up some of their liquid, approximately 4 to 5 minutes. Add the garlic and nutmeg and continue to cook for another 1 to 2 minutes. Sprinkle the flour over the mixture and stir to combine. Cook for 1 minute. Add the broth and simmer for 1 minute. Decrease the heat to medium-low and add the half and half. Cook until the mixture thickens, stirring occasionally, approximately 6 to 8 minutes.

• Remove from the heat and stir in 1/4 of the onions and all of the green beans. Top with the remaining onions. Place into the oven and bake until bubbly, approximately 15 minutes. Remove and serve immediately.

Yield: 4 to 6 servings

Want more good news? You can try out more of my recipes by heading to mentalfloss.com/alton.

This story originally appeared in mental_floss magazine. Now go download our new iPad app! Or get a free issue of mental_floss magazine via mail.

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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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Name the Author Based on the Character
May 23, 2017
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