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The language of office mates

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Right off the bat, this post is in no way piggybacking on all the obesity-your social circle debate. I've worked in a ton of different workspaces--part of that is the (desultory) nature of my business, part of that I attribute to an especially roiling tween & teendom. But everywhere I've worked, there were always a few people with whom I experienced a workaday yet still severe kind of infatuation--either out of desperation because the job was either scary or boring or actually dangerous, or sometimes because the job was too good to be true and so was everyone in the office. What grew out of these infatuations, was, inevitably, lingo. A shared language. Of course there are always inside jokes 100% endemic to your suite number, and conversations that pick up exactly where they left off at the next lunch or coffee or perhaps smoke break. But I'm talking about the lexicon that develops at a work place, and its staying power.

Now, of course this verbal appropriation happens in close friendships and romantic relationships, but I'm particularly interested in how our officemates shape our phraseology--mostly because office life and language is more functionally public, more sanctioned, and perhaps more in need of verbal ciphers.
At my current office, I find myself calling everyone "Mary Louise." It's not because this is the name of anyone I know or aspire to know (though I'd love the opportunity!)--it's just a saying one of my coworkers started, and it took over the entire office. Any proper pronoun is now predicated by "Mary Louise." And anytime someone needs to be corrected on a work-related issue, we firmly say: "Absolutely not." Often: "Mary Louise! Absolutely not." This habit has so inculcated my daily routine that I now find myself addressing cars as such: "Mary Louise! Absolutely not."

Via another office, I found myself saying (wince) "For sure!" to any request, and then just in place of "Got it," or "I understand," in place of all affirmations: "For sure"--though it eventually morphed into a single Frrsurr. In all my West Coast offices, I quickly learned that everything was "hateful" instead of horrid or rotten or anything else, and I was quick to conform. Hateful, hateful, hateful. But beyond the workplace, I'm not sure my friends took these developments in my vocabulary to heart, but maybe that's because I was too busy noticing the words and phrases they'd picked up. The offices where this kind of magical sparring was most prevalent were all busy offices, and I suppose all this talk was a shorthand I haven't even begun to psychoanalyze--any linguistic determinists out there who'd like to try? Every place I work seems to turn into its own Wayne's World. Otherwise, have you noticed/spearheaded anything like this in your workplace?

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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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