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The Craziest Hall & Oates Video Ever

Sometimes there's a music video that changes everything -- a video that makes your bad day turn good, turns your frown upside down, and confuses the everliving hell out of you. Today, I bring you that video. In 1976 1973, Hall & Oates made a "promotional video" for their single "She's Gone." It's a spectacular testament to the power of the format: Hall and Oates slump in armchairs, scowling, lazily smoking and occasionally lip-syncing. There's so much going on here (and not going on here) that I think I'll defer to the experts, Stereogum, to explain:

Is Daryl Hall wearing high-heeled clogs and smoking a cigarette during the video? Yep. Are they literally “paying” the devil to replace the woman by throwing fake money in the air every time they sing the lyric? Of course. Is John Oates wearing a tuxedo shirt with neither arms, sides, nor back? You bet! And does the devil then help him into a tuxedo jacket, and does that tuxedo jacket have flippers, and does Oates then rip off a wicked fake solo while holding the guitar in his flipper-clad hands? Yes, yes, yes, yes!

You're welcome.

So, internet, you tell me: was this a contractual obligation, a bad trip at the public TV studio, or what? For comparison, check out their smooth performance of the song on Old Grey Whistle Test.

Update, 27 April 2012: Thanks to reader Redcoat for pointing us to a Losanjealous interview with John Oates, explaining the backstory. The most pertinent facts: it was actually 1973 when this art film music video was shot, and Oates himself leaked the video. Here's the relevant snippet:

[Oates:] Well, I'll give you a little background about what happened with that "She's Gone" thing. First of all, it was 1973. There was no MTV, there was no outlet for anything like this. You know, it might be one of the first music videos ever made. I really couldn't say, honestly, but it definitely would be a contender. What happened was, we were asked to lip sync "She's Gone" for a teenage TV dance show broadcast out of Atlantic City, New Jersey. And we really didn't want to do that; we didn't want to pretend to sing the song. It was supposed to be shot in a television studio in Philadelphia. So we thought, with the mindset that we were in at the time -- and I won't say more on that, either --

([Interviewer] Ryan is laughing again.)

[Oates:] We showed up at the television studio with a chair from our living room. The woman who's walking through the picture -- that's Sarah...

[Ryan:] Oh, wow.

[Oates:] And the devil who comes through was our road manager at the time. And we brought Monopoly money, and those weird instruments, and they thought we were nuts. They really thought that. My sister directed that video.

[Ryan:] You're kidding me.

[Oates:] They thought we were completely insane. They actually didn't air it; they wouldn't air it. But we had it this whole time, and eventually I leaked it out to the internet, 'cause I just thought the world should see it.

(John Oates is laughing. Ryan is laughing.)

(Via Kung Fu Grippe, via @SuzyWhatever.)

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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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