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Wikimedia Commons

8 Cool Natural Earth Illusions

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Wikimedia Commons

A phenomenon called pareidolia is what makes us interpret random stimuli as something meaningful—for example, believing a grilled cheese sandwich resembles the Virgin Mary and is suddenly worth $28,000. In less extreme versions, the phenomenon simply makes us recognize faces and familiar shapes in random shapes. But even if you know that the resulting illusion carries no deeper meaning, they're still fun to look at. Here are 8 fantastic examples of the phenomenon in nature.

Special thanks to Moillusions.com, which features one of the best illusion collections on the net.

1. The Sleeping Indian

Sheep Mountain in Wyoming (above) goes by the far more descriptive name of “The Sleeping Indian” when viewed from the nearby Jackson Hole valley. The mountain looks like an Indian chief with a full head dress lying on his back.

2. The Dinosaur Lake

This brachiosaurus-shaped lake can be found in Zagreb, Croatia. If you want to find it for yourself in Google Maps, use the latitude and longitude of 45.78231 N, 16.024332 E.

3. The Dragon of Alberta

If you happen to be visiting a farm in Medicine Hat, Alberta, be sure to check your location on Google Earth. Who knows, you could be standing right in the mouth of this gorgeous plot of land naturally shaped like a dragon. Find it for yourself on Google at 50°01'45.29 N, 110°13'20.59 W.

4. The Badlands Guardian

One of the most famous Google Earth illusions, the Badlands Guardian was discovered in 2006 by Lynn Hickox at 50°0'38.20"N 110°6'48.32"W. While the chief and his headdress are all natural, humans have added one fitting touch to his appearance—the line that looks like an earbud attached to his ear is actually a road to an oil well. Interestingly, although the image appears to be a small mountain range when viewed on Google, it is actually a valley.

5. The Old Man of the Mountain

This is the only rock formation on this list that you can no longer go see, as the rocks that made up the face of the “Old Man” collapsed in 2003. The illusion, located on Cannon Mountain in New Hampshire, was first noted in 1805 and became the state emblem in 1945. Fans of the rock formation are working to create a memorial monument at the base of the mountain.

6. The Apache Head in the Rocks

Those who regret not getting to see the Old Man of the Mountain while it was still standing can console themselves by seeing one of the many similar rock formations located around the globe. The Apache Head in the Rocks located in Ebihens, France, is always a great alternative.

7. The Alien in the Desert

This one isn’t as clear as many of the others, but with its massive head and eyes paired with a tiny mouth and chin, this face shape in the desert looks a lot like the stereotypical description of alien visitors. Fittingly, this alien head illusion can be found just outside of Area 51 in Nevada, giving conspiracy theorists even more evidence that “they” are among us—even if only in the sand.

You can find this one on Google maps at 37°13'31.37 N, 115°53'27.06 W, but be warned—the face is upside down on the map.

8. Mother Nature Crying

It’s easy to imagine Mother Nature crying after all the pain she’s suffered throughout the years, which is why Michael Nolan’s gorgeous image of a weeping face in a glacier immediately makes people think of Mother Nature. In the photographer’s own words, “This is how one would imagine Mother Nature would express her sentiments about our inability to reduce global warming. It seemed an obvious place for her to appear, on a retreating ice shelf, crying.”

We’ve all seen animals and faces in clouds and mountains, but do any of you know of more striking examples of natural illusions like the ones seen here?

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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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