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The European American: John Singer Sargent

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On this date 84 years ago, John Singer Sargent (1856-1925) passed away. The American expat—who never actually set foot in America until he was 21 years old—is regarded as the "most successful portrait painter of his era." Over the course of his career, Sargent produced at least 900 oil paintings—including "Carnation, Lily, Lily, Rose" (left) and "Lady Agnew" (right)—and 2,000 watercolors, as well as "countless" charcoal sketches and pencil drawings.

1. With "nomadic expat" parents, John Singer Sargent had a less than typical upbringing. He was born in Florence, Italy, with American citizenship and spent his youth travelling Europe, particularly Italy, France, Austria, and Germany. A few attempts were made to send Sargent for formally schooling, but none were successful. Despite the lack of formal schooling, Sargent grew up well-accomplished: he was fluent in French, Italian, German, and English and was a skilled pianist and painter.

2. At the height of his career, Sargent was bringing in about $5,000 a portrait, equivalent to about $130,000 per portrait today. Sargent conducted all commission negotiations himself. After the details were settled, he would visit the client's home to see where the portrait would be hung and to review the client's wardrobe. The client would usually come to Sargent's studio for 8 to 10 sittings, during which time Sargent would chat with his clients while painting directly onto the canvas; he rarely sketched first. On occasion, Sargent would even play the piano for his clients.

3. Sargent once remarked, "Every time I paint a portrait I lose a friend." With the Wertheimer family, though, he gained friends. Sargent was commissioned in 1898 to paint portraits of Asher Wertheimer and his wife for their silver anniversary; the two portraits evolved into 12 family portraits over the course of 10 years, Sargent's largest commission from a single patron. The commission occupied all of Sargent's time, to the extent that he claimed to be in a state of "chronic Wertheimerism." He became close with several of the family members during those 10 years.

4. Although Sargent is known for his portraits, he apparently didn't enjoy painting them very much. Sargent complained of portraiture, "What a nuisance having to entertain the sitter and to look happy when one feels wretched." After closing his studio in 1907, at the age of 51, Sargent exclaimed, "I hate to paint portraits! I hope never to paint another portrait in my life"¦ I have had enough of it."

5. Sargent was extremely private about his personal life, but he maintained a very active social life. A fellow American painter described Sargent's studio as "always a sociable place." Sargent's circle of friends included Henry James, Claude Monet, and August Rodin, all of whom he painted. Robert Louis Stevenson, Theodore Roosevelt, and Woodrow Wilson also sat for him. While Sargent never married, his sex life "was notorious in Paris, and in Venice, positively scandalous," according to another painter.

6. In 1897, Sargent received three international honors. He was elected an academician at the National Academy of Design in New York and at the Academy of Art in London, and he was made a member of France's Legion of Honor. Ten years later, he was also offered a knighthood, but he declined the honor, choosing instead to remain an American citizen.

Larger versions of "Carnation, Lily, Lily, Rose" (1885) and "Lady Agnew" (1892) are available.

Fans should check out the John Singer Sargent Virtual Gallery; the collections of Sargent's work at the ARC, the Met, artnet, and NYU's Grey Art Gallery; Sargent's sketchbook for "The Triumph of Religion"; his letters in the Archives of American Art; Sargent's Harvard murals; the Sargent education guides from the Seattle Art Museum and NEH; Sarah Choate Sears' portrait of Sargent; and this photograph of Sargent at work in his studio.

"Feel Art Again" appears every Tuesday, Thursday, and Saturday. You can e-mail us at feelartagain@gmail.com with details of current exhibitions, for sources or further reading, or to suggest artists.

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iStock // Ekaterina Minaeva
technology
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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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Animals
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Scientists Think They Know How Whales Got So Big
May 24, 2017
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iStock

It can be difficult to understand how enormous the blue whale—the largest animal to ever exist—really is. The mammal can measure up to 105 feet long, have a tongue that can weigh as much as an elephant, and have a massive, golf cart–sized heart powering a 200-ton frame. But while the blue whale might currently be the Andre the Giant of the sea, it wasn’t always so imposing.

For the majority of the 30 million years that baleen whales (the blue whale is one) have occupied the Earth, the mammals usually topped off at roughly 30 feet in length. It wasn’t until about 3 million years ago that the clade of whales experienced an evolutionary growth spurt, tripling in size. And scientists haven’t had any concrete idea why, Wired reports.

A study published in the journal Proceedings of the Royal Society B might help change that. Researchers examined fossil records and studied phylogenetic models (evolutionary relationships) among baleen whales, and found some evidence that climate change may have been the catalyst for turning the large animals into behemoths.

As the ice ages wore on and oceans were receiving nutrient-rich runoff, the whales encountered an increasing number of krill—the small, shrimp-like creatures that provided a food source—resulting from upwelling waters. The more they ate, the more they grew, and their bodies adapted over time. Their mouths grew larger and their fat stores increased, helping them to fuel longer migrations to additional food-enriched areas. Today blue whales eat up to four tons of krill every day.

If climate change set the ancestors of the blue whale on the path to its enormous size today, the study invites the question of what it might do to them in the future. Changes in ocean currents or temperature could alter the amount of available nutrients to whales, cutting off their food supply. With demand for whale oil in the 1900s having already dented their numbers, scientists are hoping that further shifts in their oceanic ecosystem won’t relegate them to history.

[h/t Wired]

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