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50 Shades of Gray from the First Comprehensive Guide to Color Naming

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Robert Ridgway was a famous ornithologist who wrote an eight-volume work on The Birds of North and Middle America. (Three more volumes were completed by a colleague after his death.) He saw a need for standardized color naming in ornithology and other sciences that had to classify large quantities of natural specimens, and published a system for identifying and naming 1115 colors in 1912.

Ridgway's Color Standards and Color Nomenclature was not the first attempt to standardize colors. Taxonomies of 100 to 400 color names had been published through the 19th century and more rigorous systems based on spectrum analysis or color-wheel placement had used symbols or numbers to represent exact combinations of color features (hue, tone, light, shade, etc.). Ridgway's, however, was the first to provide such a finely divided color categorization that also used words from natural language, which, he argued, despite their imprecision, were more useful to naturalists.

The book was printed with 1115 painstakingly produced color plates, including more than 100 shades of gray. The names for those grays include mellifluous terms like plumbeous (the color of lead), plumbago (a flower with lead-colored petals), glaucous (from the Latin/Greek for bluish-gray), vinaceous (wine-colored), cinerous (cinder-colored), and heliotrope (a flower with purplish petals). Varley is named for landscape painter John Varley and Payne after painter William Payne. After you read this list, you can proudly tell all your friends you were intellectually stimulated by reading 50 shades of gray.

1. Cadet Gray
2. Carbon Gray
3. Castor Gray
4. Cinereous
5. Clear Blue-Green Gray
6. Court Gray
7. Dawn Gray
8. Drab-Gray
9. French Gray
10. Glaucous-Gray
11. Dark Glaucous-Gray
12. Deep Glaucous-Gray
13. Gull Gray
14. Light Gull Gray
15. Hathi Gray
16. Heliotrope-Gray
17. Dark Heliotrope Slate
18. Iron Gray
19. Lavender-Gray
20. Lilac-Gray
21. Mineral Gray
22. Mouse Gray
23. Blackish Mouse Gray
24. Neutral Gray
25. Dusky Neutral Gray
26. Olive-Gray
27. Payne's Gray
28. Light Payne's Gray
29. Pale Payne's Gray
30. Pearl Gray
31. Plumbeous
32. Blackish Plumbeous
33. Plumbeous-Black
34. Dark Plumbeous
35. Plumbago Gray
36. Dark Plumbago Gray
37. Puritan Gray
38. Purplish Gray
39. Pallid Purplish Gray
40. Sky Gray
41. Slate Color
42. Slate-Gray
43. Slate-Black
44. Blackish Slate
45. Smoke Gray
46. Storm Gray
47. Varley's Gray
48. Vinaceous-Gray
49. Deep Vinaceous-Gray
50. Violet-Gray

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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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Nick Briggs/Comic Relief
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What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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Nick Briggs/Comic Relief

Fans of the romantic-comedy Love Actually recently got a bonus reunion in the form of Red Nose Day Actually, a short charity special that gave audiences a peek at where their favorite characters ended up almost 15 years later.

One of the most improbable pairings from the original film was between Jamie (Colin Firth) and Aurelia (Lúcia Moniz), who fell in love despite almost no shared vocabulary. Jamie is English, and Aurelia is Portuguese, and they know just enough of each other’s native tongues for Jamie to propose and Aurelia to accept.

A decade and a half on, they have both improved their knowledge of each other’s languages—if not perfectly, in Jamie’s case. But apparently, their love is much stronger than his grasp on Portuguese grammar, because they’ve got three bilingual kids and another on the way. (And still enjoy having important romantic moments in the car.)

In 2015, Love Actually script editor Emma Freud revealed via Twitter what happened between Karen and Harry (Emma Thompson and Alan Rickman, who passed away last year). Most of the other couples get happy endings in the short—even if Hugh Grant's character hasn't gotten any better at dancing.

[h/t TV Guide]

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