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The Late Movies: Films by Jem Cohen

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Jem Cohen is one of the pioneers of "experimental" documentary -- or "punk rock" doc, if you prefer -- and has spent his filmmaking career solidly outside the mainstream, working on shoestring budgets, making these very edgy, personal, lyrical films that don't feel quite like anything else out there. Coming up in the 70s and early 80s, one of the reasons he's called "punk rock" so often is that he claims that ethos as an inspiration, but also because he's been associated with a great many musicians and bands, and two of his feature-length films, Instrument and Benjamin Smoke, are portraits of musicians. (Also, each one took ten years to make.)

Given the insanity that's going on politically in our country right now, Cohen's film "Little Flags" might be the most appropriate way to kick things off:

This is a ten-minute excerpt from "Lost Book Found," my favorite film by Jem Cohen, and one I consider something of a personal inspiration. It's so mysterious and mythical and atmospheric, it makes me want to go out and shoot a film right now just thinking about it.

This is an excerpt from Benjamin Smoke, a film ten years in the making, about a radical, gay rock 'n' roller who lived fast and died young, and whose voice has been compared to singers like Tom Waits and Nick Cave and Lou Reed. I dig this song, and I like the ambiance of Smoke's town that Cohen captures.

Speaking of dead rockers, "Lucky Three" is a film of Elliot Smith playing three songs. It's really quite nice.

This is another clip from Benjamin Smoke. Smoke's looking a little rough around the old edges here. He passed away from Hep C in 1999 -- I can't imagine this was shot too many years before he succumbed. Strange, sometimes a-tonal, sometimes beautiful.

This is another Benjamin Smoke song. It's not part of any movie, but I think it's amazing, so ... yeah.

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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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