Deep learning gone wild, direct neural interface techniques, and hardware acceleration of neural networks.

Jun 05 2016

There is a graphic novel that is near and dear to my hearts by Warren Ellis called Planetary, the tagline of which is "It's a strange world. Let's keep it that way." This first article immediately made me go back and reread that graphic novel...

The field of deep learning has been around for just a short period of time insofar as computer science is concerned. To put it in a nutshell deep learning systems are software systems which attempt to model highly complex datasets in abstract ways using multiple layers of other machine learning and nonlinear processing algorithms stacked on top of one another, the output of one feeding the input of another. People are using them for all sorts of wild stuff these days, from sifting vast databases of unstructured data for novel patterns to new and creative ways to game the stock, bond, and currency markets. Or, if you're Terence Broad of London, accidentally get DMCA takedown requests.

Broad is working on his master's degree in Creative Computing, and as part of that work developed a deep learning system which he trained on lots of video footage to see if it became a more effective encoder by letting it teach itself how to watch video, in essence. It's not an obvious thing but representing video as data ("encoding") is a wild, hairy, scary field... there are dozens of algorithms for doing so and even more container formats for combining audio, video, and other kinds of data into a single file suitable for storage and playback. Broad built his deep learning construct to figure out more efficient ways of representing the same data in files all by itself, without human signal processing experts intervening. He then ran the movie Bladerunner through his construct, dumped its memory and uploaded it to video sharing site Vimeo. What happened shortly thereafter was that one of Warner Brothers' copyright infringement detection bots mistook the video output by Broad's deep learning construct by dumping its memory for a direct rip of the movie because the output of his deep learning system was so accurate and sent an automatic takedown request to the site because it couldn't tell the difference from the original. One of the videos in the article is a short side-by-side comparison of the original footage to the construct's memory. There are differences, to be sure - some of the sequences are flickering, rippling blotches of color that are recognizable if you look back at the original every few seconds, but other sequences are as a good a replica as I've ever seen. Some of the details are gone, some of the movement's gone, but a surprising amount of detail remains. If you grew up watching nth-generation bootlegs of the Fox edit of Bladerunner where the color's all messed up, you know what I'm talking about.