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Test Drive: 4 American Concept Cars

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By Bill DeMain

1. Lincoln Futura (1955)

Lincoln designer Bill Schmidt was scuba diving in the Bahamas when he had a close encounter with a shark. Inspired by the predator's sharp contours, Schmidt designed the Futura. The concept car, hand-built in Italy at a cost of $250,000, electrified the auto-show circuit. While most concept cars are quickly forgotten, the Futura found immortality when it was customized into TV's original Batmobile in 1966.

2. GM Sunraycer (1987)

In 1987, General Motors entered this alien-looking craft in the rigorous 1,950-mile World Solar Race in Australia. And it won! The one-seater was made of plastic and aluminum and weighed just 390 lbs. More impressively, it was fueled by a single magnet and 7,200 solar cells. Although the Sunraycer was soon retired, it did inspire the EV-1, the first electric car to make it onto America's highways in 1996.

3. Ford FX-Atmos (1954)

This space-age dream craft had a rocket body, bubble dome, and a radar screen on the dashboard. Unfortunately, no one took the FX seriously. No one, that is, except animators at Hanna-Barbera. Impressed by the futuristic look, they used the design as inspiration for the family car in The Jetsons.

4. Guy Negre's Air Car (2007)

Writer Jules Verne once envisioned a future in which cars would run on air. Now, inventor and environmentalist Guy Negre is hoping to make that dream a reality. His pod-like vehicle uses compressed air to power the cylinders of its piston engine. There's no combustion and no pollution. The drawbacks? It takes four hours to refill one tank, which can only carry the car 125 miles. Still, that hasn't stopped Tata Motors in India from promising to soon mass-produce this windbag on wheels.

This article originally appeared in the 'Scatterbrained' section of the March-April 2009 issue of mental_floss magazine.

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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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