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8 More Regular People Who Became Internet Memes

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Last month we introduced you to nine real people whose pictures have become famous on the internet. Now meet eight more!

1. Ridiculously Photogenic Guy

Zeddie Watkins Little is a good-looking guy. So good looking, in fact, that his face has been plastered over the Mona Lisa and Jesus. The shot above, taken during the 2012 Cooper River Bridge Run by computer programmer and self-taught photographer Will King, was a “total fluke,” Little says. After the photo stormed Reddit, Little chatted with Redditors about his newfound fame. He admits that he’s not hating the whole thing: “I have to say, I really enjoy being part of such a good joke.” He also showed up on Good Morning America with Will King to tell the story.

2. Skateboarding Professor

“Walking sucks, so I get there on my wheels.” That’s how Dr. Thomas Winter, a professor at the University of Nebraska Lincoln, explains why the skateboard is his preferred mode of transportation. While he says there’s nothing remarkable about the way he gets around campus, the internet begs to differ. After Redditor tr0llzor posted a picture of the 68-year-old rolling to class, a meme was born. Winter admits that he finds the image captions’ "contemporary slang" confusing, and is further confused by the idea of memes in general—in an interview with Mashable, the professor thought he was being asked about Richard Dawkins’ genetic memes.

3. Angry Hipster Girl

It started a couple of years ago with a Halloween costume: “Birkenstocks with wool socks, jean shorts, tie-dye crop top, braided band in my hair and a prescription pair of glasses... It seemed only appropriate to grab a quick and silly shot of my costume before going out to party. The next day I posted it on Facebook and Flickr, and then forgot about it.” Then a few months ago, Kate Killet’s friend forwarded a link to Quickmeme and asked if it was a picture of her. It was!

Since being branded Angry Hipster Girl, Kate says she’s been recognized by “lots of friends of friends and randoms I haven't talked to in years,” but hasn’t been approached by strangers about her memedom. Should you find yourself in the same situation, she has a bit of advice: “Enjoy your internet 5 minutes. Don't get mad or offended, the internet loves you. And turn off your Twitter and Facebook notifications, cus you'll get roughly a million.”

(This is Kate's official internet reveal. Thanks to _floss reader Jenny Serwylo, who is quite funny and also on Twitter, for outing her friend in the comments of my earlier post.)

4. Baby Godfather

The image that started the Baby Godfather meme, which is exactly what it sounds like, was taken at a wedding in 2010. Redditor timekeepsgoing posted the picture of his son with a request to Photoshop the intimidating little guy into scenes from The Godfather. Timekeepsgoing keeps a scrapbook of the best images for his son, who is now five years old.

5. Pepper-Spray Cop

The day Lt. John Pike nonchalantly pepper-sprayed a group of Occupy protesters at UC Davis in November 2011, the video and photos were spread across most major news outlets. Simultaneously, he started showing up in other places with his can of Defense Technology 56895 MK-9, casually spraying everyone from the Founding Fathers to Mister Rogers.

6. Friendzone Johnny

Just about everyone has felt the pang of unrequited love, but almost no one has to live through it while the internet laughs. Johnny Solis is the exception. In January 2012, he showed up at midnight to his friend Lizz’s house with flowers to wish Lizz a happy birthday. She took a picture, posted it to Facebook with the caption “I am so blessed to have such great friends. Thank you sososoo much Jonathon!” Within hours, it was shared on Reddit and Quickmeme. The next day, Johnny identified himself on Reddit and did a Q&A in a bodybuilding forum. Later, he and Lizz changed their Facebook statuses to “married to” each other, but Johnny revealed that this was just a tactic to “get people off [their] backs.”

Though Johnny and Lizz apparently never got together, he seems to be taking the ordeal in stride, saying that becoming a meme makes him happy because “I became famous and more girls are talking to me.”

7 & 8. Vancouver Riot Kiss

Canadians are famously well-mannered, but after the Canucks lost to the Bruins in last year’s Stanley Cup finals, our neighbors to the north took to the streets to wreak havoc. Caught in the mayhem, Alexandra Thomas was knocked to the ground by shield-wielding riot police. Her boyfriend, Scott Jones, swooped in to comfort her; the kiss was caught by a photographer and on video from several angles, most of which included fire and riot police in the frame. The smooching couple were subsequently Photoshopped into scenes of extreme danger or inappropriateness, or in the background of other famous kissing scenes.

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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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iStock
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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