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Greg Veis, YouTube Hunter: The NFL's most punitive punishers

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Included among the various oddities of a childhood spent in Pacific Palisades, California, are as follows: 1) Michael Keaton was my flag football coach; 2) I was big enough to start on the offensive line of said flag football team. At 75 pounds, 11 years old, I was hardly an imposing member of the line, nor was I so much a serviceable one -- fear of physical contact being a poor attribute in a lineman -- but the experience instilled in me a deep sympathy for the least appreciated position players in team sports. Here are these hulks of men, taking relentless punishment, recognizing that their hearts will give out years before they should -- and although they make far more money than they did 15 years ago (left tackles are only out-salaried by quarterbacks now, actually), they're barely respected for it. Do you know the names of all the starting o-linemen on your favorite team? Right. But over the past week, we have been reminded by excerpts of upcoming books and by the forced removal of Tampa Bay Buccaneers quarterback Chris Simms' spleen that linemen are, in fact, germane to football success. So next time you're watching Football Night in America, don't forget who's setting up those Manning-to-Harrison connections or creating those giant holes for Tomlinison to snake through.

Now that we've internalized that very, very important lesson, I would be doing a terrible disservice by screening YouTube footage of sweet offensive line play. (Why? Because it's boring, and Jon Gruden and Bill Belichick probably don't read a lot of So I'm going to show you what happens when blocking goes wrong. Or when a receiver gets led a little too far down the middle by a pass. Or when the demons of deepest hell come to inhabit a safety's corpus. A pancake block may be impressive, but in terms of sheer entertainment value, does it match this?

POW! It doesn't, huh? Don Beebe backflipping on his head, Earl Campbell bulldozing suckas... this stuff is YouTube gold. Here's another hard-hitting highlight video, this time with non-Semitic rappers earning the musical honors:

KABLAM! I am frothing at the mouth. I am not kidding. I have detected froth. Now watch Lawrence Taylor end Joe Theisman's career (wait for the reverse angle):

WHAMO! And to end this bloodfest, I bring you unabashed visual hagiographies of my two favorite defensive players: Taylor and Ronnie Lott. Key things to look LT's: Bill Parcell's full head of dark hair, the unbelievable one-handed sack at the 3:45 mark...and in Ronnie Lott's: everything; there's never been a nastier safety in the history of the game.

PS: Special shout-out to YouTube Hunter reader Simon who sent in this Japanese game show clip to the comments section two weeks ago...

That very footage was projected on the big screen at last night's Flaming Lips show, conferring everlasting coolness upon him and his family. Congratulations on being cool, Simon. Let us know how that works out for you.

In case you're wondering, the only thing I remember about Michael Keaton is that he always smelled good. A very fragrant man.

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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]