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The (New) New Einsteins: Roland Fryer

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In the current issue of mental_floss magazine, Erik Vance profiled nine "New Einsteins"—visionaries who are discovering how to grow organs, peer into black holes, levitate food, cure plagues, and let blind men see. This week, Mr. Vance will be anointing five additional New Einsteins here on, one per day. Today, it's Roland Fryer's turn.

Who He Is: Roland Fryer, professor of economics at Harvard

What He Did: Fryer is one of a small group of academics playing around with something called conditional cash transfer, which tries to tie welfare funds to specific performance. Take education. Fryer initiated an experiment program that paid third graders' families $10 per good test score, and $20 for seventh graders. Critics say he is bribing academic performance (not a new concept to many parents). But supporters say he is creating incentives. And those supporters are on both sides of the political spectrum. Conservatives like that he is using the free market to promote achievement and liberals like that he is bringing money to the schools.

Today, Fryer is one of the key architects of Opportunity NYC, which pays parents for things like maintaining health insurance, keeping a job, and taking their kid to the dentist "“ in addition to good test scores and grades. He is also associate director of the W.E.B. Du Bois Institute at Harvard (named after his personal hero) and the principle investigator for the American Inequity Lab.

Why You Should Start Idolizing Him Immediately:

Where do you start? Fryer's not some idealistic kid from the suburbs. As a child he didn't know his mother, his cousins sold crack, and at 13 he regularly carried a handgun. At 15 he had to bail his father (who would eventually get convicted of sexual assault) out of jail. In spite of this, he managed to go to the University of Texas on a sports scholarship and graduate magna cum laude after just two and a half years (while holding down a job to pay his dad's bail bondsman). Then he got a PhD four years later and by 27 he was a Harvard professor.

In his short academic career, Fryer has tackled some of society's most baffling questions. He has written on the effect of crack cocaine on urban communities, the stigmas around African American names and colleges in the workplace, and "acting white." He is also one of the few researchers who has looked at whether genetics might play a role in black underperformance in school. It's a dicey subject for anyone to broach (Fryer himself is African American), but it's one that he says needs to be considered along with all the other factors that might contribute to economic disparities. This is characteristic of Fryer's unapologetic style of data-focused economics. He told the New York Times that his goal is to figure out "where blacks went wrong" in an analytical way, looking for mechanisms for why things like test scores, pay rates, and life expectancy are so low in African American communities, in order to pull people away from the kind of emotional, anecdotal evidence that he feels frames the political debate on things like affirmative action.

Previous (New) New Einsteins: Marin Soljačić

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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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Opening Ceremony
These $425 Jeans Can Turn Into Jorts
May 19, 2017
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Opening Ceremony

Modular clothing used to consist of something simple, like a reversible jacket. Today, it’s a $425 pair of detachable jeans.

Apparel retailer Opening Ceremony recently debuted a pair of “2 in 1 Y/Project” trousers that look fairly peculiar. The legs are held to the crotch by a pair of loops, creating a disjointed C-3PO effect. Undo the loops and you can now remove the legs entirely, leaving a pair of jean shorts in their wake. The result goes from this:


Opening Ceremony

To this:


Opening Ceremony

The company also offers a slightly different cut with button tabs in black for $460. If these aren’t audacious enough for you, the Y/Project line includes jumpsuits with removable legs and garter-equipped jeans.

[h/t Mashable]