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Netflix Rental Patterns, or, Minnesotans Love to Rent "The Bucket List"

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A bit of nerdy interactive fun for you today: the New York Times has an Interactive Map of Netflix Queues covering 100 "frequently rented" titles from 2009, including very heavily-rented titles like The Curious Case of Benjamin Button and less-rented titles like Happy-Go-Lucky and Adventureland.

The map is interactive, allowing you to slide or step through various movie titles and see how popular each title is in twelve metro areas in the US: New York City, Boston, Chicago, Washington, the Bay Area (aka San Francisco, Oakland, etc.), Los Angeles, Seattle, Minneapolis, Denver, Atlanta, Dallas, and Miami. What's better, you get a geographic breakdown by ZIP code within each metro area, so you can see if a title is popular in the city center or the 'burbs. For example, Milk (a biopic about Harvey Milk, who lived and worked in San Francisco) is the #1 rented title in many ZIP codes in the city center of San Francisco.

While the Times notes that distinct patterns are visible with the titles Mad Men (popular in city centers), Obsessed (popular in predominantly black neighborhoods), and Last Chance Harvey (popular everywhere but city centers), I found the most interesting pattern overall in The Bucket List: popular almost nowhere except in Minneapolis, where it stains the landscape like a bizarre, vaguely urine-colored invader (seen above). Check out the map for yourself and see what you can figure out! (See also: the comment thread for the infographic, which is full of privacy concerns, political flames, and requests for more info on the raw data and methodology. One excellent comment from "DCR" in Arlington, Virginia: "We don't just live in red, blue and purple zip codes; we live in Milk, Tyler Perry and Slumdog Millionaire zip codes.")

For what it's worth, you can get similar data yourself directly from Netflix if you're a member. Scroll to the bottom of the Netflix site, click Friends, scroll down to "Unique in [Your City] and click "See What's popular in Other Locations," then note the box in the upper right portion of the page -- you can get statistics for any ZIP code . Here's a direct link that may or may not work for you.

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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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Name the Author Based on the Character
May 23, 2017
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