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Vladimir Nabokov Talks Synesthesia

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GingkoPress

You’ve probably heard of synesthesia, the condition that makes people involuntarily associate something with a sense that wouldn’t normally be associated otherwise. For instance, some synesthetes report that the word “blue” tastes inky, or find that an E sharp is always chartreuse.

Lolita author Vladimir Nabokov had grapheme-color synesthesia, which is when people see specific letters in specific colors—and we’re not just talking when they’re reading a Richard Scarry book. His way with words is legendary, of course, so it’s no surprise that his description of how he saw letters was rather fascinating.

“It's called color hearing,” Nabokov told the BBC in 1962. “Perhaps one in a thousand has that. But I'm told by psychologists that most children have it, that later they lose that aptitude when they are told by stupid parents that it's all nonsense, an A isn't black, a B isn't brown—now don't be absurd.”

When the interviewer asked what color his own initials were, Nabokov replied that the “V is a kind of pale, transparent pink: I think it's called, technically, quartz pink: this is one of the closest colors that I can connect with the V. And the N, on the other hand, is a greyish-yellowish oatmeal color.”

What’s more is that Nabokov “heard” different colors in different languages:

“The long "a" of the English alphabet has for me the tint of weathered wood, but a French "a" evokes polished ebony. This black group also includes hard "g" (vulcanized rubber) and "r" (a sooty rag being ripped). Oatmeal "n", noodle-limp "l", and the ivory-backed hand mirror of an "o" take care of the whites. I am puzzled by my French "on" which I see as the brimming tension-surface of alcohol in a small glass.”

Oddly—though maybe not that odd, as it’s been estimated that 1 in 23 people have some form of the condition—his wife, Véra, also had synesthesia. So did their son, which is how they discovered this interesting tidbit:

“My wife has this gift of seeing letters in color, too, but her colors are completely different. There are, perhaps, two or three letters where we coincide, but otherwise the colors are quite different. It turned out, we discovered one day, that my son, who was a little boy at the time—I think he was ten or eleven—sees letters in colors, too. Quite naturally he would say, "Oh, this isn't that color, this is this color," and so on. Then we asked him to list his colors and we discovered that in one case, one letter which he sees as purple, or perhaps mauve, is pink to me and blue to my wife. This is the letter M. So the combination of pink and blue makes lilac in his case. Which is as if genes were painting in aquarelle.”

No doubt the way he saw letters colored the way he wrote—quite beautifully, obviously.

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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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