Deep learning AI "autoencodes" Blade Runner, recreates it so faithfully it gets a takedown notice

Artist and researcher Terence Broad is working on his master's at Goldsmith's computing department; his dissertation involved training neural networks to "autoencode" movies they've been fed. "Autoencoding" is a process that reduces complex information to a small subset that the neural net believes to be most significant; in Broad's dissertation, he reduced each frame of Ridley Scott's Blade Runner to a 200 digit number, then invoked the net to reconstruct the image just using that data. What happens when machine-learning systems begin to do the same with audiovisual works, in order to do things that are protected under statute (for example, adding realtime scene narration for people with visual impairments), or legitimate areas of scholarly research? On Medium, where he detailed the project, he wrote that he "was astonished at how well the model performed as soon as I started training it on Blade Runner," and that he would "certainly be doing more experiments training these models on more films in future to see what they produce." The potential for machines to accurately and easily "read" and recreate video footage opens up exciting possibilities both for artificial intelligence and video creation.