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The Faces Behind 31 Disney Villains

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Ever wonder if the actors who voice the villains are just as mean-looking as their on-screen counterparts? Wonder no more. For the most part, the answer is no—but there are definitely a few uncanny resemblances in the bunch.

1. Maleficent from Sleeping Beauty, Eleanor Audley

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2. Captain Hook from Peter Pan, Hans Conried

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3. Cruella de Vil from 101 Dalmatians, Betty Lou Gerson

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4. Ursula from The Little Mermaid, Pat Carroll

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5. Queen of Hearts from Alice in Wonderland, Verna Felton

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6. Queen Grimhilde from Snow White and the Seven Dwarfs, Lucille La Verne

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7. Gaston from Beauty and the Beast, Richard White


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8. Gothel from Tangled, Donna Murphy


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9. Madam Mim from Sword in the Stone, Martha Wentworth


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10. Jafar from Aladdin, Jonathan Freeman

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11. Scar from The Lion King, Jeremy Irons

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12. Ratigan from The Great Mouse Detective, Vincent Price

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13. Kaa from The Jungle Book, Sterling Holloway

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14. Prince John from Robin Hood, Peter Ustinov


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15. Si and Am from Lady and the Tramp, Peggy Lee


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16. Madame Medusa from The Rescuers, Geraldine Page


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17. Oogie Boogie from The Nightmare Before Christmas, Ken Page


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18. Yzma from The Emperor's New Groove, Eartha Kitt


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19. Sid Phillips from Toy Story, Erik von Detten


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20. Hades from Hercules, James Woods


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21. Shan Yu from Mulan, Miguel Ferrer


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22. Randall from Monsters, Inc., Steve Buscemi


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23. Syndrome from The Incredibles, Jason Lee


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24. Dr. Facilier from The Princess and the Frog, Keith David


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25. Lotso from Toy Story 3, Ned Beatty


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26. King Candy from Wreck-It Ralph, Alan Wray Tudyk


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27. Percival McLeach from The Rescuers Down Under, George C. Scott


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28. Judge Frollo from The Hunchback of Notre Dame, Tony Jay


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29. Governor Ratcliffe from Pocahontas, David Ogden Stiers


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30. Smee from Peter Pan, Bill Thompson


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31. Charles F. Muntz from Up, Christopher Plummer


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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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What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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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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