CLOSE
Original image

Calculating the Moby Quotient

Original image

If you spent your musically developmental and formative years in the basement of a college radio station, you know the satisfaction that comes from championing some unknown alterna-electro funk band from Portland. With this satisfaction comes the devastating emptiness when they inevitably sell their catchy debut single to Mitsubishi for use in a commercial for a sedan with above average gas mileage. The rise of illegal downloading, the shrinking of radio playlists, and the decline in CD sales have forced many artists to "sell out," often in television commercial form, to make ends meet.

The Washington Post recently enlisted an expert in hyperbolic geometry (!?!) to devise a formula that equates the precise degree of sell-out your favorite garage band has committed, bringing new meaning to the subgenre specific term math-rock. The mathematical result is represented by the Greek letter mu, here as "The Moby Quotient," named for the electronic artist that (in)famously sold every single last song on his 1999 album, Play, to varying commercial interests.

Being an expert in both barely relevant indie-rock minutiae and crippling sell-out related heartbreak, I've compiled a list of the most egregious offenders and punched their stats into the sell-out calculator.

Of Montreal for Outback Steakhouse

Song: "Wraith Pinned to the Mist (And Other Games)"
Not only did Of Montreal allow the steakhouse to use the song, they also changed the words from "Let's pretend we don't exist/Let's pretend we're in Antartica" to "Let's go Outback tonight/Life will still be there tomorrow." Nothing says 18 oz. rib eye medium-rare like Indie-pop!!

Mu =114.98

Devo for Dell Computers

Song: "Watch Us Work It"
Certainly tired of being portrayed as a chubby guy in a suit in Apple commercials, PC company Dell has turned to the next logical place "“ satirical social commentary disguised as angular new wave punk. The Devo track "Watch Us Work It" appears here hawking laptops.

Mu = 39.51

Nick Drake for Volkswagen

Song: "Pink Moon"
This Cabrio commercial not only sold lots and lots of cars. In a perfect stroke of synergy, it also sold lots and lots of Nick Drake albums. The relatively obscure English folk songwriter developed an American cult following based on the success of this advertisement featuring his song "Pink Moon". This commercial was also directed by Little Miss Sunshine duo Jonathan Dayton and Valerie Farris.

Mu = 29.46
(let's not be so hard on him, he'd been dead for over 25 years when the commercial aired)

Sting for Jaguar

Song: "Desert Rose"

Frustrated with regularly selling mere millions of albums, former Police frontman Sting granted British luxury carmakers Jaguar the rights to not only his track "Desert Rose" but also himself. Team Sting shot the video for the single with the intent of pitching it to Jaguar as a commercial. It worked, and Sting gave his song and his likeness to Jaguar for free, figuring it to be worth the asking price in free advertising. Ultimately, Brand New Day became Sting's best selling solo record to date.

Mu = 32.48 (plus an additional one million sell-out points for living in a castle"¦literally)

Band of Horses for Ford Edge

Song: "Funeral"
Northwestern indie-rock group Band of Horses appears in this commercial despite the obviously questionable choice of having a song titled "Funeral" to promote your recall-prone Ford brand. The alternative label Sub-Pop, once famous for anti-commercialism, has seen a handful of their roster promoting such products as M&Ms, McDonalds and Walmart recently. Movin' on up!

Mu = 145.61

Mangesh, Jason and Matthew for Enron

Say an editor or two from mental_floss joined an upstart writer like me (I'd play bass) and formed a dance-punk band, promptly selling our first song to Enron. We'd be so indie that nobody would have ever heard of us. Plus, we could quit these boring day jobs and focus on what really matters "“ the music, man.Mu = 170.44

Plug in your own bands and post your Moby Quotients!

Matthew Smith is an occasional contributor to mentalfloss.com

Original image
iStock // Ekaterina Minaeva
technology
arrow
Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
Original image
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!

Original image
iStock
Animals
arrow
Scientists Think They Know How Whales Got So Big
May 24, 2017
Original image
iStock

It can be difficult to understand how enormous the blue whale—the largest animal to ever exist—really is. The mammal can measure up to 105 feet long, have a tongue that can weigh as much as an elephant, and have a massive, golf cart–sized heart powering a 200-ton frame. But while the blue whale might currently be the Andre the Giant of the sea, it wasn’t always so imposing.

For the majority of the 30 million years that baleen whales (the blue whale is one) have occupied the Earth, the mammals usually topped off at roughly 30 feet in length. It wasn’t until about 3 million years ago that the clade of whales experienced an evolutionary growth spurt, tripling in size. And scientists haven’t had any concrete idea why, Wired reports.

A study published in the journal Proceedings of the Royal Society B might help change that. Researchers examined fossil records and studied phylogenetic models (evolutionary relationships) among baleen whales, and found some evidence that climate change may have been the catalyst for turning the large animals into behemoths.

As the ice ages wore on and oceans were receiving nutrient-rich runoff, the whales encountered an increasing number of krill—the small, shrimp-like creatures that provided a food source—resulting from upwelling waters. The more they ate, the more they grew, and their bodies adapted over time. Their mouths grew larger and their fat stores increased, helping them to fuel longer migrations to additional food-enriched areas. Today blue whales eat up to four tons of krill every day.

If climate change set the ancestors of the blue whale on the path to its enormous size today, the study invites the question of what it might do to them in the future. Changes in ocean currents or temperature could alter the amount of available nutrients to whales, cutting off their food supply. With demand for whale oil in the 1900s having already dented their numbers, scientists are hoping that further shifts in their oceanic ecosystem won’t relegate them to history.

[h/t Wired]

SECTIONS
BIG QUESTIONS
BIG QUESTIONS
WEATHER WATCH
BE THE CHANGE
JOB SECRETS
QUIZZES
WORLD WAR 1
SMART SHOPPING
STONES, BONES, & WRECKS
#TBT
THE PRESIDENTS
WORDS
RETROBITUARIES