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Here’s How Many Books You Can Expect to Read Before You Die

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Life is too short to suffer through a book you just don't like. For proof of that, Literary Hub has done some (slightly morbid) calculations regarding how many books you'll be able to squeeze in during your remaining years on Earth.  

The table below breaks down the number of books you'll have time for if you maintain your current reading habits. Twenty-five-year-old women, for example, have 61 years left to live according to the Social Security Life Expectancy Calculator. Assuming they live that long, average readers in that group have 732 more books to read in their lifetimes. "Average" in this case means people who read 12 books per year. Lit Hub also crunched the numbers for voracious readers (50 books per year) and super readers (80 books per year). After pinpointing your maximum capacity, you may want to edit down your reading list to only include books you're genuinely excited about.

25 and female (61 years left)
Average reader: 732
Voracious reader: 3050
Super reader: 4880

25 and male (57 years left)
Average reader: 684
Voracious reader: 2850
Super reader: 4560

30 and female (56 years left)
Average reader: 672
Voracious reader: 2800
Super reader: 4480

30 and male (52 years left)
Average reader: 624
Voracious reader: 2600
Super reader: 4160

35 and female (51 years left)
Average reader: 612
Voracious reader: 2550
Super reader: 4080

35 and male (47 years left)
Average reader: 564
Voracious reader: 2350
Super reader: 3670

40 and female (45.5 years left)
Average reader: 546
Voracious reader: 2275
Super reader: 3640

40 and male (42 years left)
Average reader: 504
Voracious reader: 2100
Super reader: 3260

45 and female (40.5 years left)
Average reader: 486
Voracious reader: 2025
Super reader: 3240

45 and male (37 years left)
Average reader: 444
Voracious reader: 1850
Super reader: 2960

50 and female (35.5 years left)
Average reader: 426
Voracious reader: 1775
Super reader: 2840

50 and male (32 years left)
Average reader: 384
Voracious reader: 1600
Super reader: 2560

55 and female (31 years left)
Average reader: 372
Voracious reader: 1550
Super reader: 2480

55 and male (28 years left)
Average reader: 336
Voracious reader: 1400
Super reader: 2240

60 and female: 86 (26 years left)
Average reader: 312
Voracious reader: 1300
Super reader: 2080

60 and male (23 years left)
Average reader: 276
Voracious reader: 1150
Super reader: 1840

65 and female (22 years left)
Average reader: 264
Voracious reader: 1100
Super reader: 1760

65 and male (19 years left)
Average reader: 228
Voracious reader: 950
Super reader: 1520

70 and female (17.5 years left)
Average reader: 210
Voracious reader: 875
Super reader: 1400

70 and male (15 years left)
Average reader: 180
Voracious reader: 750
Super reader: 1200

75 and female (14 years left)
Average reader: 168
Voracious reader: 700
Super reader: 1120

75 and male (12 years left)
Average reader: 144
Voracious reader: 600
Super reader: 960

80 and female (10 years left)
Average reader: 120
Voracious reader: 500
Super reader: 800

80 and male (9 years left)
Average reader: 108
Voracious reader: 450
Super reader: 720

[h/t Literary Hub]

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iStock // Ekaterina Minaeva
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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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Nick Briggs/Comic Relief
entertainment
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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]

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