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4 Cormac McCarthy Villians That Make Our Skin Crawl

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Even if you didn't see it, you probably heard about last year's Best Picture winner, No Country for Old Men, and its bowl-haired, cattlegun-wielding antagonist, Chigurh, played to a chilling tee by Javier Bardem. But Bardem's Oscar-winning performance as cinema's strangest new villain only reinforced what fans of author Cormac McCarthy's novels already knew: his baddies are unforgettable. McCarthy writes lean-but-contemplative western-ish noir thrillers (and the occasional post-apocalyptic thriller) that eschew the easy black-hat/white-hat good guys and bad guys dichotomy. But even in the potent worlds of moral ambiguity he creates, there's always one character who stands out as particularly unsavory. Here are a few who gave us nightmares.

1. The Child from Child of God

The reader spends the first 80 pages of this novel thinking that the amoral, short-tempered necrophiliac we've come to regard as the protagonist is the titular "child," until he comes across an even more despicable character, and regards him thusly:

A hugeheaded bald and slobbering primate that inhabited the lower reaches of the house, familiar of the warped floorboards and the holes tacked up with foodtins hammered flat, a consort of roaches and great hairy spiders in their season, perennially benastied and afflicted with a nameless crud.

The Child is given a small bird to play with, and quickly sets about chewing its legs off, leaving it to "flutter about on the floor, small red nubs working in the soft down." Yeeeesh.

2. The Kid from Blood Meridian

The nameless "Kid" in McCarthy's expansive, bloody, Melvillian masterpiece is sort of a victim of circumstance. Almost apologizing for the death he will bring in the coming pages, McCarthy describes a boy born to kill:

See the child. The mother dead these fourteen years did incubate in her own bosom the creature who would carry her off. The father never speaks her name, the child does not know it. He has a sister in this world that he will not see again. He watches, pale and unwashed. He can neither read nor write and in him broods already a taste for mindless violence. All history present in that visage, the child the father of the man.

Wow. If I could write like that, I'd ... uh ... do stuff.

3. The Cannibal Gangs in The Road

cover190.jpgMcCarthy's latest novel is a Pulitzer-winning post-apocalyptic vision of the world after a holy war, and possibly Oprah's unlikeliest Book Club pick ever. After No Country proved a big hit in theaters, the film rights to The Road were snapped up double-quick, and a film version starring Viggo Mortenson should be hitting theaters late this year or early next. A departure from McCarthy's norm, the novel isn't a western -- it's set in the south, or the burning wreckage of what's left of it, and concerns the seemingly hopeless journey of one man and his young son south along a smoking highway toward the sea, where maybe things will be better. On their journey they come across all manner of horror wrought by human desperation (everyone is starving; good and evil have long since gone out the window), the main perpetrators of which are roving gangs of armed cannibals, who will enslave and slowly devour any living souls they happen upon. As McCarthy describes them, they're a perfect vision of sci-fi horror:

They came shuffling through the ash casting their hooded heads from side to side. Some of them wearing canister masks. One in a biohazard suit. Stained and filthy. Slouching along with clubs in their hands, lengths of pipe. Coughing.

Soon, we get a close-up view of one of them:

Eyes collared in cups of grime and deeply sunk. Like an animal inside a skull looking out the eyeholes. He wore a beard that had been cut square across the bottom with shears and he had a tattoo of a bird on his neck done by someone with an illformed notion of their appearance. He was lean, wiry, rachitic.

Of course I had to look up "rachitic," which Merriam-Webster says comes from the Latin rachitis, an inflammation of the spine, adopted into English for its similarity to "rickets."

4. Anton Chigurh from No Country for Old Men

nocountryforoldmen.jpgThe Coen Brothers dressed him in a jean jacket and a bowl haircut, but in the novel, McCarthy hardly describes his most famous villain at all. The few mentions we get come from other characters who encounter him, usually right before they get an airgun shot to the forehead. In more than 300 pages, this is just about all we get: "The man looked at Chigurh's eyes for the first time. Blue as lapis. At once glistening and totally opaque. Like wet stones." Then later: "Blue eyes. Serene. Dark hair. Something about him seemed faintly exotic. Beyond Moss' experience." And one mention of his attitude: "He seemed oddly untroubled. As if this were all part of his day." Like he's an alien, or some force of nature. Very nice, McCarthy. Very nice.

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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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Nick Briggs/Comic Relief
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]