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The Late Movies: 7 Songs for 7 Continents

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While the debate rages on over the appropriate number of planets in our solar system (Revolve in Peace, Pluto), at least we are certain that there are seven continents on Earth, barring of course a reemergence of Atlantis.  For tonight's Late Movies, we're featuring seven songs: one for each land mass on this crazy spinning rock of ours.

North America: LCD Soundsystem - "North American Scum"
This is a live version of a track from their Sound Of Silver album. If you like this song, be sure and check out "All My Friends" from the same record. Fantastic.

South America: Shout Out Louds - "South America"
Yes, I saw it happen! It happened! Calm down...calm down!

Europe: Europe - "The Final Countdown"
As we move to across the Atlantic, Europe is represented by "The Final Countdown," a song that, in my mind, only reaches it full potential when accompanying a GOB Bluth magic show.

Africa: Toto - "Africa"
In the most inevitable song / continent selection ever, Africa is represented tonight by the classic Toto song that I'm sure will not become stuck in your head for the rest of the week.

Asia: Asia - "Heat Of The Moment"
You may not know this but there are actually two incarnations of this band, which has reunited in recent years: Asia and Asia Featuring John Payne. So there's a lot of Asia if you're interested. And I'm sure you are.

Australia: The Shins - "Australia"
A single off their 2008 album Wincing The Night Away, Australia is a catchy track from a great band. This just edges out Men At Work's "Down Under."

Antarctica: The Weakerthans - "Our Retired Explorer (Dines With Michel Foucault In Paris, 1961)"
A great Canadian band, The Weakerthans leave us tonight with a quick, upbeat tune about a penguin teaching French at the south pole. O Antarctica!

What about you guys? Any other songs to represent the continents you would suggest?


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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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