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Desert Monet: Emily Kngwarreye

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Last Friday marked the 14th anniversary of the death of Emily Kame Kngwarreye (1910-1996), an Australian Aboriginal artist. She has been described as “one of the most prominent and successful artists in the history of contemporary indigenous Australian art,” “one of the world's great painters,” and the “Desert Monet.” Akira Tatehata, the director of the National Museum of Art in Osaka, summed it up by saying, “...there can be only one way to describe her. She was just a genius.” So today we present some fun facts about Australia's most popular aboriginal artist... perhaps their most popular artist period.

1. Only beginning her painting career at age 78, Emily Kngwarreye went on to become a highly prolific artist and “one-woman industry,” producing more than 3,000 paintings by the time she died at age 86. That averages out to about one painting a day for those eight years. (She finished her last painting, “Yam Awelye—Blue” just four days before her death.) Her high output was due in part to the dependence of others on her income: more than once she postponed retirement in order to continue providing funds for her community. Despite having no children of her own, she was responsible for as many as 80 kinspeople. The money she earned—estimated to be as much as A$500,000 a year—was spent not on herself, but on purchasing necessities and gifts for others, including supplying a car each week to the community.

2. Kngwarreye, who never studied art, developed her own unique methods for painting. She would spread her canvases on the ground and paint while sitting on or next to them. Over the years, she began using larger brushes, and eventually began trimming down the hairs around the edge of the brush, leaving the middle hairs longer. This styling of her brushes produced unique effects in her paintings, like dots with strong centers and softer edges. Kngwarreye could use both her hands to paint and would often switch from hand to hand, sometimes employing a brush in each hand to paint simultaneously. She was reportedly strongest with her left hand, though.

3. “Earth's Creation,” the sister painting to “Earth's Creation II” (shown above), is considered by some to be “Australia's most important painting.” The 6.3 meter by 2.7 meter work, sewn together from 4 smaller pieces of linen, was sold in 2007 for $1.056 million, setting a record for the sale of indigenous art. But it wasn't just the highest price paid for an indigenous work of art – at the time, it was the highest price ever paid in Australia for a work of art by a female artist, too.

4. When asked to describe the meaning of her paintings, Kngwarreye answered, “Whole lot, that's all, whole lot, awelye, arlatyeye, ankerrthe, ntange, dingo, ankerre, intekwe, anthwerle and kame. That's what I paint: whole lot.” (Or, in English, “...whole lot, my dreaming, pencil yam, mountain devil lizard, grass seed, dingo, emu, small plant emu food, green bean and yam seed. That's what I paint...”) Many of her paintings center around yams, an important source of food for the aboriginal people and especially dear to Kngwarreye, whose middle name, Kame, refers to the yam's yellow flower.

5. Although she didn't begin her painting career until she was almost 80, Kngwarreye had been creating batiks for several years before that. She was a founding member of the Utopia Women's Batik Group in 1977, but gave up batik when she began painting on canvas. Kngwarreye cited several reasons for her switch, including that she “got a bit lazy... it was too much hard work,” that making batik uses up all the soap powder, and that her “eyesight deteriorated.”

6. Kngwarreye's community was fairly isolated, so it wasn't until the age of 9 that Kngwarreye saw a white man or a horse. Then, one day, she was out in a dry riverbed digging for yams when a white policeman passed through on a horse, leading an aboriginal prisoner in chains. Viewing this strange sight, Kngwarreye believed the white man to be a devil-spirit.

Larger versions of Kngwarreye's "Earth's Creation II," shown above top, and "Yam Dreaming," shown just above, are available.

Fans should check out the collections of Kngwarreye's work at MBANTUA and Songlines Aboriginal Art; the retrospective exhibition at DACOU Melbourne and the National Museum of Australia's "Utopia: the genius of Emily Kame Kngwarreye" exhibition; the Message Stick episode on Kngwarreye; this AIAM100 video on Kngwarreye; this Japanese video about Kngwarreye; and DACOU's collection of articles and stories about Kngwarreye.

"Feel Art Again" usually appears three times a week. Looking for a particular artist? Visit our archive for a complete listing of all 250+ artists that have been featured. You can e-mail us at with details of current exhibitions, for sources or further reading, or to suggest artists. Or you can head to our Facebook page, where you can do everything in one place.

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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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Scientists Think They Know How Whales Got So Big
May 24, 2017
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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]