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Making Money from YouTube

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Yesterday’s post about YouTube elicited some interesting comments and e-mails in my inbox... people wondering how one can make money by uploading videos to YouTube. As I wrote yesterday, more than 48 hours of video are uploaded to YouTube every minute. So your chances of cutting through the noise aren’t good at all. That said, I’m a firm believer in the old saying “Talent will always win out.” Talent, in the case of YouTube could be defined as anything adorable, wacky, shocking, hilarious or just plain different. If you look at the most viewed videos, they invariably fit into one of those categories.

So let’s say you’ve managed to capture a moment that fits into one of those categories. And let’s say that there are no copyright infringements, meaning: you aren’t using music you don’t own the rights to, or other copyrighted material that YouTube expressly prohibits in their Terms of Service. Assuming all that, how do you make money off your video?

Here’s the business model YouTube employs, which allows everyday folk like you and me to disrupt the traditional Hollywood models of production, distribution and monetization.

1. When a video on YouTube reaches a certain number of views, meaning it’s trending over a short period of time or accumulating views via the long tail (some vids explode years after they’ve been uploaded), YouTube will reach out to the channel owner (a channel on YouTube is just a unique user account associated with an e-mail address) and ask to partner with him/her.

2. At that point, the channel is now able to share in YouTube’s revenue stream. YouTube makes money by serving up ads. Some ads appear before the video plays. These are called pre-roll ads. Some of the pre-rolls can be skipped after 5 seconds, others you have to watch in their entirety before a video plays. When YouTube first launched, there were no ads. Over time, they’ve slowly introduced more and more pre-rolls and users don’t complain too much because, hey, they’re pretty short by comparison to TV ads and many can be skipped by clicking a button. There are also display ads that appear over the lower third of the video sometimes, toward the bottom of the viewer. These, too, can usually be closed with a click. And there are also ads on the actual channel page. Together, all these ads blend and add up to an average CPM of $2.

3. CP-what? CPM or cost-per-thousand. It’s actually cost per mille, the Latin for thousand. What this means is: Every time 1,000 people view a video, YouTube pays you $2. We won’t get too technical here and define what a “view” is because there are different rules for different videos, but basically when that view counter moves up a tick from 234 to 235, YouTube counts it as one view. So if a video is viewed 2,000 times, the channel is paid $4. Presumably, YouTube is making about the same off the video as the channel is, so the gross revenue from the video is probably more like $4 per 1,000 views.

4. This may not seem like much money, but when you consider that some videos are being viewed millions of times, it starts to add up. Well-known YouTube channels, like FreddieW, upload videos every week. At the moment, his channel has close to 354 million views (and he has more than one channel!). Doing the math then, 354 million divided by 1,000 = 354,000 x 2 = $708,000. So it’s through many videos and many channels that YouTubers are able to make a nice living off their work.
5. Okay, so you’re probably not going to be the next FreddieW. But still, a single video hit, like Charlie Bit My Finger, can also make a good deal of money. As I wrote yesterday, that video has been seen about 367 million times, which translates to about $700,000. Enough, perhaps, to put little Charlie and his brother through college.

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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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