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YouTube Rolls Out Auto-Captioning

TechCrunch reports that YouTube has launched auto-captions for English-language videos. In short, they're using machine translation to provide subtitles (also known as "closed captioning") on many of their videos. Not every video can be auto-captioned, and the captions won't be perfect -- in fact, in many cases, I expect the captions will be quite bad. But it's a big step, and an exciting start. Video publishers can fine-tune the captions themselves, which should lead to a huge increase the amount (and quality) of closed-captioned content available online.

Here are some clarifications from Google's engineering team about the auto-captions and their limitations:

• While we plan to broaden the feature to include more languages in the months to come, currently, auto-captioning is only for videos where English is spoken.

• Just like any speech recognition application, auto-captions require a clearly spoken audio track. Videos with background noise or a muffled voice can't be auto-captioned. President Obama's speech on the recent Chilean Earthquake is a good example of the kind of audio that works for auto-captions.

• Auto-captions aren't perfect and just like any other transcription, the owner of the video needs to check to make sure they're accurate. In other cases, the audio file may not be good enough to generate auto-captions. But please be patient -- our speech recognition technology gets better every day.

• Auto-captions should be available to everyone who's interested in using them. We're also working to provide auto-captions for all past user uploads that fit the above mentioned requirements. If you're having trouble enabling them for your video, please visit our Help Center: this article is for uploaders and this article is for viewers.

Here's a video by students at the California School for the Deaf in Fremont, using the auto-captioning feature, explaining how the feature helps the deaf -- and those who are learning English:

After the jump, check out a regular video and note how the captions aren't always perfect. But...better than nothing.

Here's John Green, former _floss writer and blogger. You may need to hit the little "CC" button to turn on captions, depending on your platform. Also, if you're using the HTML5 beta (in other words, if you're an extreme nerd like me), captions don't work -- until you opt out of the beta. Now, I'll be honest -- the auto-captioning kinda sucks here. Then again, it's a whole lot better than nothing, and Green is using a bunch of self-coined terms like "Nerdfighter" and "Nerdfighteria."

I'm wondering if anyone can find a good example of the auto-captioning working well? Post links in the comments. (Note that Google mentions Obama's recent speech on the Chilean earthquake as an example of the captions working well. It looks to me like those captions have been hand-tweaked, as another copy of that same speech here shows lots of mistakes. Not awful mistakes, but certainly not perfect.)

Original image
iStock // Ekaterina Minaeva
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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
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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