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The day I left home, 9/12/01

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Forgive me if today I blog about something a little more serious, and somewhat less _Flossy, than usual. It was exactly five years ago that I packed everything I owned into a station wagon and left home for good. This is a short piece remembering 9/11's less infamous but for me no less momentous neighbor, 9/12, accompanied by photographs I took along the way.

I had just graduated from college and was preparing to move from my childhood home in Florida all the way to Portland, Oregon. I had never been to Oregon. Its main attraction for me was sheer geographical distance: the route from Florida draws an impressive diagonal straight across this country's broad midsection. I had left home many times before "“ to go to school in Ohio for six months at a stretch, to go abroad for eight "“ but now my leaving meant more, and I wanted the move itself to symbolize that.

I was busy packing the station wagon, my mom fretting over small things, when we heard the news. It was raining fire in three states, and I sat slack-faced before the TV for the rest of the day. I left the next morning, as planned, but the trip had changed somehow; now it seemed like a journey across alien territory, from a home I didn't quite recognize to places uncharted. Was it even safe to travel through cities? It was only 9/12 -- no one was sure. Yet there was nothing I wanted more than just to drive, and feel a sense of forward motion; anything but the paralysis we had endured the day before.

ranch.jpgI took state roads so I could see the countryside. Ohio was a patchwork of little towns quilted together by cornfields, each flying a hundred flags, each with a church signboard exhorting its parishioners to pray. I put in three eighteen-hour days behind the wheel, so that when I stopped to sleep I dreamt only of driving. I felt there was safety in where I was going, but never in where I was, so I didn't stop for more than sleep until I got to Kenyon, my old college. Its bucolic campus had been a comfortable home for four years "“ but now a strange fog had settled. People seemed dizzy. There were kids whose parents were missing, who had driven to New York in the middle of the night, unsure of what they'd find; classes were cancelled, and had given way to vigils. I was a stranger in a community that had turned inward to lick its wounds, and drove away feeling like a vagabond.

I stopped next in Wyoming, to visit a friend who was working on his parents' 2,500 acre ranch in the magestic middle of nowhere. His father raised cattle and his mother pureblooded horses "“ or she had, until Leukemia claimed her life earlier that summer. I helped my friend and his father herd and groom the animals, and despite their reassurances, couldn't help feeling I had intruded on their grief. We talked about my friend's mother only once, walking on a rocky bluff that overlooked the ranch. Sometimes it was easy for him, he said, and sometimes it was really hard. So he had graduated and returned home to find his home gone too.
The next morning his dad siphoned gas into my tank and I continued on, through the strange deserts of Eastern Washington and Oregon, to Portland by nightfall. At one point while driving alongside Oregon's mile-wide Columbia River and its deep-cut banks of evergreen forest, I teared up, just happy that the most beautiful part of my drive was where I would be living. It had been a journey not only of extraordinary physical distance, but emotional distance as well: simultaneously my ideas of home and country had shifted, and everywhere along the way the people I met had been similarly knocked off-balance. I knew my track could not be retraced; the homes I left along its route would never be as I had known them.

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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]