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Early Commercials for 10 Best-Selling Game Systems

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1. Atari 2600, 1977

This is probably the first Atari 2600 commercial to air in the U.S., just before Christmas 1977. Poor George leaves empty-handed, but we’re willing to bet he got his hands on an Atari eventually: 30 million other people did.

2. Nintendo Game & Watch, 1981

“Neentendo” illustrates an early version of the cinematic game trailer here; when your graphics don’t hold up, just hire animators. The Game & Watch kept 43 million kids occupied through the 80s and early 90s.

3. ColecoVision, 1982

Coleco released just over two million of the “most advanced video game system you can buy” into the world in the early 80s.

4. Nintendo Entertainment System, 1985

In these kids’ defense, Legend of Zelda was pretty rad. The NES sold just over 61 million units during its lifetime.

5. Sega Genesis, 1990

Sega’s marketing plan wasn’t subtle. The Genesis (or Mega Drive for everyone outside of the US) sold about 40 million units.

6. Super NES, 1990

Nintendo’s response was a bit more civilized. It paid off eventually: the SNES made its way into 49 million homes.

7. Game Boy, 1990

Nintendo one-upped itself by sending the Game & Watch packing to make room for Game Boy, which would release in color the next year. Nearly 120 million handheld systems flooded the market and school buses.

8. Game Gear, 1991

Sega’s plan to (obliquely) talk smack about Game Boy’s lack of color ended up being a bad idea. When the Game Boy Color released, Game Gear’s edge was gone. Regardless, it still ended up being one of the best-selling early portable systems; whether or not those 11 million kids actually wanted a Game Boy is debatable.

9. PlayStation, 1994

We’re not sure if humiliation was the driving force for sales or just a perk, but Sony’s PlayStation went gangbusters, totaling more than 102 million consoles sold.

10. Xbox, 2001

Right from the start, Xbox commercials were weird. But Microsoft did well for themselves, though: the original Xbox now resides in 24 million attics.

Can you out-fact the Facts Machine? Go to this post and leave a comment with your own amazing video game fact. If your fact is deemed sufficiently Amazing, you could win the mental_floss t-shirt of your choice.

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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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Name the Author Based on the Character
May 23, 2017
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