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How Web Ads Work

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You visit this blog every day (and if you don't, why the heck not?!). Sometimes you notice the ads on the page, sometimes you don't. Regardless, ever wonder how they help offset the costs of running a Web site/blog? Ever wonder how they get on the site? Well, it's time for a little primer. Web Ads 101, if you will.

There are two basic kinds of ads, display ads and text ads. Text ads are slowly being phased out, though our friends over at Neatorama still use them on post pages. The one pictured to the side for GoPro Cam is a good example. These ads are dynamically fed into the page by Google AdWords, who figure out what kinds of ads to place on the page based on what kinds of keywords they see in the post itself. The title of the post can often influence the Google placement bots, which is how you sometimes get those funny juxtapositions; a post about the benefits of nuclear power can trigger a text ad to sign a petition against nuclear power. A Web site makes money when you click the text and go off to the petition Web site. How much money? It depends on the size of the Web site, the placement of the ad, and a bunch of other factors. But it's not much money at all.

Much more common are display ads. These come in various sizes and shapes known in the industry as leaderboards, skyscrapers, blocks, and in rare cases, page skins where the sponsor takes over a page and wraps the whole site with their campaign. These ads are very rarely click-based, meaning they don't need to be clicked for the Web site to make money. The bulk of display ads usually earn between 25 cents and $1 per every thousand impressions, or views. Some campaigns on popular Web sites earn a lot more, but the traditional "run-of-network" ad (sometimes called "backfill") is generally under $1 per 1000 impressions. With display ads, the advertiser is looking to get the brand out there in front of you and is willing to pay whether you click through or not. On average, .02% of people will click through on any given ad—so it's really more about raising company/product/brand awareness.

Publishers, like mentalfloss.com and other sites, generally work with multiple ad networks that serve up these display ads. If one can't fill the space, they'll pass the code for that ad block on to another network and so forth down the line until something shows up. It's all quite complex and determined by how many times you've visited the Web site in one session, how many impressions the ad network is contracted to serve up overall, how many impressions the ad network has promised to supply to a particular Web site, what geographic location you're in, what the Web site's click-through-rate is (okay, sure, sometimes those clicks do add up to something) and many other factors too detailed to go into here.

Of course, there are lots of different kinds of ads making their way onto pages. Pop-ups, ads that expand when you roll over them, video ads, lightbox ads that need to be closed before you can view a page, gates that prevent you from seeing the content until you watch a few seconds of flash video, and others. But display ads are still far and away the most common because they're the easiest to work into site design and the least obtrusive.

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iStock // Ekaterina Minaeva
technology
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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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iStock
Animals
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Scientists Think They Know How Whales Got So Big
May 24, 2017
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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]

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