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The Quick 10: The Insurance Policies on 10 Famous Body Parts

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While most of us insure rather mundane things – lives, houses, cars – there’s a certain populace who make livings based on very specific body parts and need financial protection in case the ability to earn that living is ever taken away. Hey, you’d probably insure your hair too if you had the luscious locks of Troy Polamalu. Here’s what a few famous body parts will be (or would have been) worth if they were ever lost or incapacitated.

1. Ben Turpin’s eyes. Turpin was one of the biggest silent film stars of his day, largely thanks to his crossed eyes. While most people would be happy to have this ocular oddity fixed, Turpin certainly didn’t want to correct his cash cow. He took out a $25,000 Lloyd’s of London policy against his eyes going back to normal and kicked off the celebrity insurance trend.

2. Betty Grable’s legs. Grable’s gams were outrageously insured at $1 million each at the insistence of her then-employer, Century Fox.

3. Michael Flatley’s legs. They may not be as shapely as Betty Grable’s, but it’s safe to say that Flatley wouldn’t have headlined Riverdance and Lord of the Dance without them. That's why he made the Guinness Book of World Records for having the highest insurance premium placed on a dancer's legs - $40,000,000. He also made the book for being the highest paid dancer with an income of $1,600,000 weekly.

4. Marlene Dietrich’s voice. It was one in a million, which is why she insured it for that amount - $1,000,000.

5. Gene Simmons' tongue. Do you think his $1 million policy included a "no hot beverages" clause?

6. Bruce Springsteen’s voice. The Boss insured his talent to the tune of $6 million back in the ‘80s.

7. Troy Polamalu’s hair. Just last year, Head & Shoulders took out a $1 million insurance policy on the Steelers safety’s hair. It might be a good investment – on at least one occasion, Polamalu has been tackled because an opponent grabbed his three-foot-long mane.

8. Bette Davis’ svelte figure. Miss Davis took $28,000 out against weight gain. I suppose a chunk of change would offer a bit of solace after an ice cream binge.

9. Jimmy Durante’s nose. It was worth $50,000 – nothing to sniff at.

10. America Ferrera’s smile. She might be most famous for her Ugly role, but it’s her gorgeous grin that was insured for $10 million. Aquafresh made sure their investment was protected when she was the spokesperson for their White Trays product.

And here’s one that was flat out refused: Paul Oldfield, a “flatulist” who produces classics such as “Twinkle Twinkle Little Star” with his farts asked for coverage in case his talent should ever escape him – permanently. Lloyd’s said no.

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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]