Warner Bros. DMCAs Insanely Awesome Recreation Of Blade Runner By Artificial Intelligence

from the oh-the-irony dept

I'm going to dispense with any introduction here, because the meat of this story is amazing and interesting in many different ways, so we'll jump right in. Blade Runner, the film based off of Philip K. Dick's classic novel, Do Androids Dream Of Electric Sheep, is a film classic in every last sense of the word. If you haven't seen it, you absolutely should. Also, if you indeed haven't seen the movie, you've watched at least one less film than an amazing artificial intelligence software developed by Terrance Broad, a London-based researcher working on his advanced degree in creative computing.

His dissertation, "Autoencoding Video Frames," sounds straightforwardly boring, until you realize that it's the key to the weird tangle of remix culture, internet copyright issues, and artificial intelligence that led Warner Bros. to file its takedown notice in the first place. Broad's goal was to apply "deep learning" — a fundamental piece of artificial intelligence that uses algorithmic machine learning — to video; he wanted to discover what kinds of creations a rudimentary form of AI might be able to generate when it was "taught" to understand real video data.

The practical application of Broad's research was to instruct the artificial neural network, an AI that is something of a simulacrum of the human brain or thought process, to watch Blade Runner several times and attempt to reconstruct its impression of what it had seen. In other words, the original film is the interpretation of the film through human eyes, while Broad's AI reconstructed what is essentially what the film looks like through the eyes of an artificial intelligence. And if that hasn't gotten your heartrate up a bit, then you and I live on entirely different planets.

The AI first had to learn to discern footage from Blade Runner from other footage. Once it had done that, Broad has the AI "watch" numerical representations of frames from the film and then attempt to reconstruct them into a visual medium.

Once it had taught itself to recognize the Blade Runner data, the encoder reduced each frame of the film to a 200-digit representation of itself and reconstructed those 200 digits into a new frame intended to match the original. (Broad chose a small file size, which contributes to the blurriness of the reconstruction in the images and videos I've included in this story.) Finally, Broad had the encoder resequence the reconstructed frames to match the order of the original film.

Broad repeated the "learning" process a total of six times for both films, each time tweaking the algorithm he used to help the machine get smarter about deciding how to read the assembled data. Here's what selected frames from Blade Runner looked like to the encoder after the sixth training. Below we see two columns of before/after shots. On the left is the original frame; on the right is the encoder's interpretation of the frame.

Below is video of the original film and the reconstruction side by side.


The blur and image issues are due in part to the compression of what the AI was asked to learn from and its response in reconstructing it. Regardless, the output product is amazingly accurate. The irony of having this AI learn to do this via Blade Runner specifically was intentional, of course. The irony of one unintended response to this project was not.

Last week, Warner Bros. issued a DMCA takedown notice to the video streaming website Vimeo. The notice concerned a pretty standard list of illegally uploaded files from media properties Warner owns the copyright to — including episodes of Friends and Pretty Little Liars, as well as two uploads featuring footage from the Ridley Scott movie Blade Runner.

Just a routine example of copyright infringement, right? Not exactly. Warner Bros. had just made a fascinating mistake. Some of the Blade Runner footage — which Warner has since reinstated — wasn't actually Blade Runner footage. Or, rather, it was, but not in any form the world had ever seen.

Yes, Warner Bros. DMCA'd the video of this project. To its credit, it later rescinded the DMCA request, but this project has fascinating implications for the copyright process and its collision with this kind of work. For instance, if the automatic crawlers looking for film footage snagged this automatically, is that essentially punishing Broad's AI for doing its task so accurately that its interpretation of the film so closely matched the original? And, at a more basic level, is the output of the AI even a reproduction copy of the original film, subjecting it to the DMCA process, or is it some kind of new "work" entirely? As the Vox post notes:

In other words: Warner had just DMCA'd an artificial reconstruction of a film about artificial intelligence being indistinguishable from humans, because it couldn't distinguish between the simulation and the real thing.

Other comments have made the point that if the video is simply the visual interpretation of the "thoughts" of an artificial intelligence, then how is that copyrightable? One can't copyright thoughts, after all, only the expression of those thoughts. If these are the thoughts of an AI, are they subject to copyright by virtue of the AI not being "human?" And I'm just going to totally leave alone the obvious subsequent question as to how we're going to define human, because, hell, that's the entire point of Dick's original work.

Broad noted to Vox that the way he used Blade Runner in his AI research doesn't exactly constitute a cut-and-dried legal case: "No one has ever made a video like this before, so I guess there is no precedent for this and no legal definition of whether these reconstructed videos are an infringement of copyright."

It's an as yet unanswered question, but one which will need to be tackled. Video encoding and delivery, like many other currently human tasks, is ripe for the employment of AI of the kind that Broad is trying to develop. The closer that software gets to becoming wetware, questions of copyright will have to be answered, lest they get in the way of progress.

Filed Under: ai, automation, blade runner, dmca, simulation, terrance broad
Companies: warner bros.


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  1. identicon
    quaquaquaqua, 11 Oct 2016 @ 6:20pm

    Re: Re: Re:

    People have called autoencoders "the most expensive times 1" because, when they work well it is as if you haven't done anything at all.

    The key though is in the neural networks architecture. Say you want to encode an color image that is 100 by 100 pixels. That means the input layer of the network would have 100 x 100 x 3 input neurons (the number of pixels times the three color channels). The output layer will also have 100 x 100 x 3 neurons. If the thing works there will be no difference between the input and output neurons -- or the difference will be minimized.

    Why is this different than copying? The difference is that somewhere in the middle, between the input and output layer the network has a bottleneck, where the number of neurons is reduced to only a few. After training the network you can view those few neurons as a small handful of variables that can optimally represent the image. Instead of needing 100 x 100 x 3 variables to represent the image you could use something as small as 12 variables (for example).

    One application is of course compression. But you can use the same principle for other purposes such as:
    denoising
    upscaling: https://github.com/nagadomi/waifu2x
    generative models (by slightly modifying the variables).

    non technical explanation at 43 minutes:
    http://ml4a.github.io/classes/itp-S16/06/#

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