But the constant and surprising raft of new applications for AI gives the impression that the future of entertainment is hard to predict. My general opinion on AI-generated "photographs" has been that they're like early CGI in movies: impressive at a glance but only because we haven't learnt the telltale signs to look for yet. Yet every new version of deep-learning models such as Midjourney produces images with more natural-looking people and more believable surroundings, even if (for now) there's still a general Lynchian vibe, and regularly horrifying mistakes in the fingers and teeth, or objects that float or collide with each other in the wrong ways. The new version of the OpenAI's language model has only been out for a week, and already one user has discovered that it can read and interpret the source code of a video game, and repackage it as a sort of choose-your-own-adventure novel. Who's to say it won't soon be able to create its own games from scratch based on requests? Voice models of the most prominent US celebrities are so easily accessible that creators only need to provide a written script to have audio content of them saying anything.

A guy with no background in film or artificial intelligence is working on making an entire movie -- in a provocative attempt to demonstrate that generative AI art models can open ambitious levels of filmmaking to the masses. German tech entrepreneur Fabian Stelzer told PC Magazine that his 70s-style sci-fi film "Salt" will feature all artificial voices except his own, and that generative models will also create the film's footage and sound effects, too. From what Stelzer's been posting to Twitter to tease his progress, the film apears to be about space travelers who encounter a planet with an overgrowth of bizarre salt -- but beyond that basic premise, the neuroscientist is allowing his Twitter followers to determine which specific directions the film will go in a sort of "Choose Your Own Adventure" style for the AI age. "I definitely want to have a'Director's Cut' at some point, or a'Community's Cut,' but the real goal is to transcend the medium of film into something new," he said. "Like enable everyone in the community to eventually use a model that lets them write their own scenes."

Let's say for the sake of argument you're stuck at home for a long time watching too much of the stuff we euphemistically call "streaming content," by which I mean movies and TV. Come up with your own reason -- anything from being one of Japan's pathologically introverted hikikomori to, say, hiding out from some sort of potentially lethal respiratory virus. In any case, you will at some point sour on all the available programming options and scroll glumly through all the familiar title selection menus until you give up. Tiger King is more of a punch line than a TV show at this point, and, sure, you could plumb the depths of history's most creative auteurs over on the Criterion Channel, but that sounds hard, and if you are like me, you consider reading the morning news emotional labor. But what if there were a movie streaming service with no downsides?

Disney Research used deep learning methods to develop a new means of assessing complex audience reactions to movies via facial expressions and demonstrated that the new technique outperformed conventional methods. The new method, called factorized variational autoencoders or FVAEs demonstrated a surprising ability to reliably predict a viewer's facial expressions for the remainder of the movie after observing an audience member for only a few minutes. While the experimental results are still preliminary, this approach demonstrates tremendous promise to more accurately model group facial expressions in a wide range of applications. "The FVAEs were able to learn concepts such as smiling and laughing on their own," said Zhiwei Deng, a Ph.D. student at Simon Fraser University who served as a lab associate at Disney Research. "What's more, they were able to show how these facial expressions correlated with humorous scenes." The researchers will present their findings at the IEEE Conference on Computer Vision and Pattern Recognition on July 22 in Honolulu.