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Repetitive redundancies and other ridiculous nonsense

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The English language, as commonly spoken in this country, is full of excessive verbiage and unnecessary phraselogical redundancy -- in other words, tautology.

Taut "¢ ol "¢ o "¢ gy:
Needless repetition of an idea, esp. in words other than those of the immediate context, without imparting additional force or clearness, as in "widow woman."

Some tautological phrases are so ingrained in our popular language -- like "bits and pieces" or "first and foremost" -- that to simplify them would destroy their traditional impact. This is what interests me: when language abandons utilitarianism in favor of habit. Richard Kallan's book Armed Gunmen, True Facts and other Ridiculous Nonsense is a great collection of common and uncommon tautological phrases, and we thought we'd highlight some of our favorites here.

"¢ "False pretense"
If pretense is "the act of alleging falsely," as Dictionary.com asserts, then wouldn't a false pretense be ... true?

"¢ "Advance warning"
A warning delivered after the fact is known, I believe, as "Monday morning quarterbacking."

"¢ "Convicted felon"
If we're guilty until proven innocent, there shouldn't be too many convictionless felons running around.

"¢ "Surviving widow"
Kallan defines it thusly: "The last woman standing in an all-widow game of Russian roulette."

"¢ "Fall down"
Gravity tends to make this modifier unnecessary.

"¢ "All throughout"
More pervasive than occasionally throughout.

"¢ "Close proximity"
As opposed to a distant proximity?

"¢ "Sum total"
This really gets the point across ... and then sum.

"¢ "Shared dialogue"
When was the last time you heard a shared monologue?

"¢ "Mass exodus"
When everyone leaves church at the same time? And speaking of church ...

"¢ "Holy Bible"
I'm so tired of these unholy Bibles.

Repetitive redundancies are everywhere -- what are some of your favorites?

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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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May 23, 2017
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