The old -- Garbage In, Garbage Out -- GIGO principle originated during the early days of computing, but it may be even more applicable today. With the explosion of data available that can be collected, there's a temptation to assume that analyses and meta-analyses can make sense of all that data and produce incredible insights. However, we should probably have some skepticism before we jump into the deep end of data and expect miraculous results.
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.
It's a source of wonder and excitement for some, panic and concern for others, and a whole lot of cutting edge work for the people actually making it happen: artificial intelligence, the end-game for computing (and, as some would have you believe, humanity). But when you set aside the sci-fi predictions, doomsday warnings and hypothetical extremes, AI is a real thing happening all around us right now — and achieving some pretty impressive feats:
The accomplishments of artificial intelligence are making it a popular topic in the news again, both for its wins and its (apparent) failures. General artificial intelligence hasn't quite lived up to its full potential yet, but more open source AI projects could help speed up development. Here are just a few reminders that open source AI projects are making progress -- hopefully towards a more 'John Henry' type of AI and less of a scary Skynet program.
In case you missed it, humanity has been dealt a decisive intellectual blow by a go-playing computer program called AlphaGo. We mentioned AlphaGo back in January when Google announced that it had defeated European Go champion Fan Hui and was challenging Lee Sedol next. So now that the results are in, AlphaGo has shown the world that artificial intelligence can best the best of humanity at our most difficult games. We've seen this already with chess, and if you don't remember, people tried to make a variant of chess called Arimaa that humans could hold up as a game people could win over computers (ahem, that didn't work). We still have Calvinball, Diplomacy and certain forms of poker....
The rise of fantasy sports and realistic video games for every major sport has expanded the audience and engagement incredibly. Even if you can't throw a spiral, you can still manage a fantasy football team. Sabermetrics changed baseball, and deep learning algorithms are about to change how a lot of other sports are played. Computers aren't just going to beat people at chess and Go. They might become better talent scouts and strategists for every major sport.
Despite our supposed intelligence, humans don't actually know how our own brains work. But even in our ignorance, we're still developing algorithms and machines that might catch on to how we learn and think. Google's autonomous vehicle project has a pretty good driving record, except that the world is messy, and predicting how human drivers will react isn't always certain -- especially when they drive buses. Our relationship with robots is going to be more and more complex in the next few years. We'll need to recognize when robots are faulty, and that might get harder and harder to do.
Human intelligence is about to be bested by computers playing the game of Go, and software already soundly defeats people at games like chess and specific variations of poker. If we're trying to keep our smug superiority, people are still better than AI at MMORPGs and a few other skills... but it might not be too long before we hand over the controls of monetary policy to algorithms.
Artificially intelligent assistants are everywhere in science fiction, and they're slowly creeping into reality. However, most of the time, these things are limited to answering simple questions like "what's the weather?" or "who won the Super Bowl?" And even then, simple questions don't always return the desired simple answer. Still, we must press on because technology will only get better. Here are just a few more digital assistants that are going to be fighting for your attention someday.
Artificial intelligence projects have gotten more attention over the past few years as some major milestones have been achieved and point to a promising future for AI (or cognitive computing or whatever we want to call it now). Software is getting better and better at recognizing what we're writing and saying... and now it's getting better at seeing what we're up to. Check out a few of these projects where computers are identifying visual images and correctly identifying a wide variety of things.