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How Statistics Fool Juries

Peter Donnelly is a statistician with a sense of humor. He starts his talk with a classic statistician joke: "How do you tell an introverted statistician from an extroverted statistician? The extrovert is the one who looks at the other person's shoes." But he's not all fun and games. In a 2005 TED Talk, Donnelly explains a little about with it's like to be a professional statistician, then launches into a fascinating explanation of how statistics are misunderstood by typical audiences. He gives examples for the audience to examine (a few multiple-choice questions), to prove his point -- and I'll admit, he got me. I didn't get the examples right, though I was pretty confident in my answers.

This reminds me of when I was on a jury room in a personal injury case. While my experience wasn't related to statistics, it was an issue of science which seemed like we should have been able to prove the right answer one way or another -- but we failed. The jury had an hour-long argument about physics, trying to determine whether a driver's upper body in a car would be pushed forward or backward when the car was hit from behind. (There was a key question regarding whether a specific injury could have been caused by the impact, or was a pre-existing condition.) We even built a model, but that failed to convince anybody of what the real-world behavior would be. Everyone on the jury insisted that his or her own mental model was correct (which tended to align with their gut feeling about the guilt or innocence of the defendant), and neither demonstrating the physics of the situation, nor thinking through it with a shared mental model made any difference. It was an interesting day, to say the least. (See also: whiplash.)

Anyway, Donnelly's talk is a great example of how attorneys (or really anyone) can exploit general misunderstanding of statistics in order to make an invalid point -- and most of us won't notice that anything is wrong. There's even a term for one specific statistical misuse, the prosecutor's fallacy, and Donnelly explains how it was exploited in the Sally Clark case. Definitely worth a look!

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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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Name the Author Based on the Character
May 23, 2017
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