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motionbox.com presents HDYK? - {day 3}

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It's an all-new 5-day trivia hunt!

Co-puzzle Master Josh Halbur and I are happy to bring you the next How Did You Know? sponsored by the extraordinary video sharing site motionbox.com.

vado>> Prizes! Each month, motionbox.com will be giving out 3 prizes: a free premium subscription on their site; a Vado 8GB HD cam. Plus, the winner of the cam will get the opportunity to purchase a discounted premium membership on motionbox, as well.
>> How do you win? Rules: anyone who submits any kind of answers at all, right or wrong, is now eligible for a prize. So long as you submit, you'll be entered into a drawing to win the free, premium subscription to motionbox.com, worth $49.99. Next: the $199.99 Vado 8GB HD cam. Anyone who submits all the correct answers within 24 hours of the final puzzle post on day 5, will be eligible for a drawing for the this amazing little gadget that holds up to 2 hours in brilliant HD.

>> So what's in it for the person/team who finishes first with all the correct answers? Bragging rights, as always, with your photo/bio posted on our site. But also two chances to win the motionbox.com prizes, and, as is our tradition here at the _floss, your pick of any t-shirt from our store.

There you have it! This week, we're also thrilled to be collaborating with ringorang.com. One answer to one question on each days' Level 1 challenge can be found by answering a clue question over on the ringorang game, or by checking out their Twitter.com feed.

As always, if you're not a Fan of our Facebook page, be sure to add us so we can keep you updated, and you can get in on some additional nifty clues throughout the week. If you're new to our five-day hunt, you can read up on our new Rules page here. If you missed Day 1's challenge, that can be found right this way, Day 2 can be found down yonder. Okay, ready to get your Hunt on? Click on through.

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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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