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Teacher Appreciation Week: NFL Edition

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"Socrates taught Plato, Plato taught Aristotle, Aristotle taught Alexander the Great"¦" That's how Mary kicked off mental_floss Teacher Appreciation Week.

A logical follow-up post would be one that extended this chain. A less logical one would jump ahead about 2300 years and trace the lineage of famous NFL teachers. Since I just read an excellent piece by Stephen Edelson on the latter, let's turn down that road.

You can trace many of today's successful teachers back to Cleveland Browns legend Paul Brown. After joining the NFL in 1950, Cleveland appeared in six straight NFL title games, winning three. The father of modern football was also a stubborn S.O.B. After being criticized for passing too much in a win against the Philadelphia Eagles, Brown refused to throw the ball once in their next meeting (another win), the only time that's ever happened since the advent of the forward pass. Brown went on to coach the expansion Cincinnati Bengals, where he begat Bill Walsh.

Bill Walsh spent seven seasons under Brown, and would eventually take over the lowly San Francisco 49ers. With Joe Montana, Walsh would win three Super Bowls, including two over his former team in Cincy. He retired after the 1988-89 season. Here's where the coaching tree branches out.

Walsh's successor in San Francisco, his defensive coordinator George Seifert, won the Super Bowl his first season. Mike Holmgren, the quarterbacks coach under Walsh and offensive coordinator under Seifert, went to two Super Bowls as coach of the Green Bay packers (winning one), and brought the Seattle Seahawks to Super Bowl XL last year. Seifert also begat Mike Shanahan, who won two titles with Denver, and Pete Carroll, who didn't find success with the Jets or Patriots, but has dominated college football, winning two national championships at USC.

Back to Holmgren -- it seems like his entire Packers staff went on to coach their own teams. His branch is a big one: Andy Reid, Jon Gruden, Ray Rhodes, Marty Mornhinweg, Mike Sherman, Steve Mariucci, and Dick Jauron.

Minnesota coach Brad Childress worked under Reid in Philly; Houston's Gary Kubiak comes from Shanahan's Broncos. And going back directly to Walsh, his assistant Dennis Green (Minnesota, then Arizona) begat Brian Billick (Baltimore).

That's a lot of mentoring. I could go into the Bill Parcells tree, or the Marty Schottenheimer one. But I'll save those for next Teacher Appreciation Week. Class dismissed.

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iStock // Ekaterina Minaeva
Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
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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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Why Your iPhone Doesn't Always Show You the 'Decline Call' Button
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When you get an incoming call to your iPhone, the options that light up your screen aren't always the same. Sometimes you have the option to decline a call, and sometimes you only see a slider that allows you to answer, without an option to send the caller straight to voicemail. Why the difference?

A while back, Business Insider tracked down the answer to this conundrum of modern communication, and the answer turns out to be fairly simple.

If you get a call while your phone is locked, you’ll see the "slide to answer" button. In order to decline the call, you have to double-tap the power button on the top of the phone.

If your phone is unlocked, however, the screen that appears during an incoming call is different. You’ll see the two buttons, "accept" or "decline."

Either way, you get the options to set a reminder to call that person back or to immediately send them a text message. ("Dad, stop calling me at work, it’s 9 a.m.!")

[h/t Business Insider]