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Who Exactly Is J.D. Power?

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It feels like every car commercial you see touts the vehicle’s sterling showing in a J.D. Power and Associates survey. The awards are bandied about as if their origins are common knowledge. But who exactly is J.D. Power, and who are his much-discussed associates? Let’s take a look at the man behind the car ads.

Who is this Power fellow?

To answer your first question, yes, the name J.D. Power is too terrific to be anything but real. James David Power earned his MBA from Wharton in the late 1950s and went on to work in finance for Ford before taking a job in advertising. On the inauspicious date of April 1, 1968, “Dave” Power and his wife/romantic associate, Julie, started a market research firm at their kitchen table.


Starting a market research company in your kitchen sounds like a longshot, but within a year Dave Power had convinced Toyota to buy his market research, and other large companies like Carnation soon followed. By 1981, the firm was conducting its U.S. Automotive Customer Satisfaction Index Study.

Super Bowl XVIII in 1984 turned out to be a big game for Power, and it wasn’t because he was a big fan of the victorious Los Angeles Raiders. Subaru ran an ad during the game that boasted of its cars’ impressive showing in the J.D. Power rankings. Although the honchos at Subaru probably didn’t realize it, the ad was a watershed moment for car advertising. The Subaru spot was the first car commercial to mention an automaker’s J.D. Power performance, and it started quite a trend. According to the J.D. Power website, over 350,000 television commercials have mentioned their ranking since that first Subaru ad.

What do the numbers mean?

One metric that constantly worms its way into car ads is J.D Power’s survey of “initial quality.” Aren’t all non-lemon cars high on initial quality? Perhaps not. Although the survey’s name doesn’t do much to tell you what it’s measuring, the actual methodology seems fairly sensible. The firm surveys new car owners after they’ve had their rides for 90 days to see what kind of problems they’ve had with their vehicles. The study provides a metric called PP100, or problems per 100 vehicles, and J.D. Power presents awards for the vehicles in each segment with the fewest problems.

J.D Power and Associates publishes four other major auto surveys each year, but the other one that seems to consistently come up in commercials is the group’s ranking of vehicle dependability. Like the initial quality survey, the Vehicle Dependability Study surveys a large group of car owners, but it doesn’t ask its questions until the cars are three years old. At that point, the survey asks what problems the car has had in the previous 12 months.

Where’s the money in these rankings?

It’s nice for consumers to know which cars are more likely to break down in their first 90 days on the road, but how does J.D. Power and Associates make any money? According to a 2004 U.S. News and World Report profile on the company, the auto rankings aren’t really all that lucrative. Automakers and other companies pay a licensing fee to use the awards in their ad campaigns, but the bulk of the firm’s revenue comes from selling its market research to curious companies. The survey rankings we hear about on TV are just a tiny portion of the data collected in the customer surveys, and the rest is placed in corporate hands.

This business model enabled J.D. Power himself to cash in for a nice chunk of money. In 2005 publisher McGraw-Hill acquired J.D. Power and Associates for an undisclosed sum. Although the name J.D. Power and Associates most frequently appears in car ads, the company tracks all sorts of industries, from healthcare to banking to telecommunications.

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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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Nick Briggs/Comic Relief
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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]

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