Twilight star among three authors of paper explaining how ‘neural style transfer’ method was put to use in her directorial debut, the 17-minute short Come Swim 
 
 Twilight and Personal Shopper Kristen Stewart has co-authored a research paper on “neural style transfer”, an arcane technique that uses artificial intelligence to reconfigure an image in the style of another. 
 
 Written with Bhautik J Joshi, a research engineer at Adobe, and producer David Shapiro, Stewart’s paper is related to work done on her short film directing debut Come Swim, which received its world premiere at the Sundance film festival on Thursday. Called Bringing Impressionism to Life with Neural Style Transfer in Come Swim, the paper was submitted on Wednesday on Cornell University library’s open-access arXiv.org website, an online repository for scientific research papers. 
 
 Beyond Twilight: on the set of Personal Shopper with Kristen Stewart Read more 
 
 The paper describes the 17-minute Come Swim as “a poetic, impressionistic portrait of a heartbroken man underwater”, which is “grounded in a painting [by Stewart] of a man rousing from sleep” that “evokes the thoughts an individual has in the first moments of waking”. According to the paper, neural style transfer was then used in the opening and closing scenes to apply the style of the painting to the filmed images. 
 
 The methodology and process of this supposedly pioneering form of visual effects is described in detail over three pages, focussing on the difficulties in obtaining a high-quality moving image. 
 
 Stewart has demonstrated in the past she is not afraid to embrace unconventional creative techniques. In 2014 she revealed a cut-up style poem in Marie Claire called My Heart is a Wiffle Ball/Freedom Pole, whose first lines read: “I reared digital moonlight/You read its clock, scrawled neon/across that black” ||||| Hollywood and the academic artificial intelligence community have an interesting relationship, to say the least. Many people interested in the field, myself included, love science fiction, but regularly end up being the obnoxious commentator on family movie night pointing out that Johnny Depp’s Transcendence is more anti-intelligence than artificial intelligence. So it wasn’t all that surprising that researchers were a bit perplexed when actress and director Kristen Stewart appeared as an author on a machine learning paper. 
 
 To be someone in Hollywood, you've got to put your ML papers on Arxiv and you better use TensorFlow… https://t.co/2Rcg1ccJ36 — François Chollet (@fchollet) January 19, 2017 
 
 I used to tell Twilight jokes in my Intro to AI class. I take it all back. — Mark O. Riedl (@mark_riedl) January 19, 2017 
 
 I used to tell Twilight jokes in my Intro to AI class. I take it all back. — Mark O. Riedl (@mark_riedl) January 19, 2017 
 
 Stewart directed Come Swim, a short film about a man’s day told through both impressionism and realism. The film features the use of the so-called “style transfer” technique to build its story. 
 
 Style transfer is the fun technique that involves using convolutional neural networks to artistically alter video in real time. To do this, you basically show your algorithm an image (say a painting by van Gogh) and then teach it to apply the same painting technique to any regular photo. 
 
 The short, three-page paper, entitled Bringing Impressionism to Life with Neural Style Transfer in Come Swim, is really just a high-level case study about what it’s like to apply style-transfer techniques to cinema. 
 
 In addition to Stewart, Bhautik Joshi and David Shapiro of Adobe and Starlight Studios, respectively, speak to the challenges of trying to reign in style transfer for controlled use. The team’s approach comes off a bit like blindfolded dart-throwing, but, to be fair, the trial and error process and dealing with trade-offs is pretty much what machine learning research is like at all levels. 
 
 One would almost expect that artists and movie-makers would be more comfortable with improvisation than researchers, but this doesn’t seem to be the case. “In a production setting, however, a great deal of creative control is needed to tune the result, and a rigid set of algorithmic constraints run counter to the need for this creative exploration,” note the authors of the paper. ||||| Ever thought your selfies were as good as a Rembrandt? Or your party pics as apocalyptic as something by Hieronymus Bosch? Well, Facebook hears you, and it’s got something to help. This morning, the company announced that a number of AI-powered art filters would soon be arriving on its mobile app, allowing users to overlay photos, videos, and even live broadcasts with various artistic styles. 
 
 This type of AI feature is known as "style transfer" and has been around for a while now. Russian app Prisma popularized it earlier this year, and in October, Facebook showed off a prototype version working with live video. According to a report from Wired, the feature is now available in Facebook's main app in Ireland and is "due soon" in the US. You can see what sort of effects it produces below: 
 
 But as interesting as this is, purely as something fun to play with (and an example of Facebook putting more emphasis on the camera in its primary app), it's the technology that underpins it that's really significant. 
 
 Like many of the most interesting consumer AI applications out today, Facebook's art filters are built using deep neural networks. These are a type of computer program that's meant to mimic the actions of neurons in the brain, and that are trained using large amounts of data to recognize common patterns. Deep neural nets are used for everything from voice recognition in digital assistants to categorizing your holiday snaps in Google's Photos app. 
 
 The miniaturization of AI is a big step forward 
 
 Running these programs can be tricky, though, as they take up a lot of processing power. Often, programs that use this sort of AI need a connection to the internet to work. They send off your request to a server farm somewhere which does the actual computation, and then sends the results right back to you. With its new art filters, though, Facebook has managed to make these neural nets work locally on your phone. 
 
 To achieve this, Facebook has created an entire new deep learning architecture called Caffe2go. You can read about this technology in more technical detail in a Facebook blog post, but suffice to say, it's impressive work. The seminal paper on style transfer was published in 2015, and the resulting software worked only on images and had to run on a massive data center that still took seconds to deliver results. By comparison, Facebook's implementation processes 20 frames per second using just your smartphone's hardware. 
 
 This miniaturization of AI and Facebook's new Caffe2go infrastructure (which is available to developers) is going to enable lots of new features, says Facebook CTO Mike Schroepfer. "We can create gesture-based controls, where the computer can see where you're pointing and activate different styles or commands," writes Schroepfer. "We can recognize facial expressions and perform related actions, like putting a 'yay' filter over your selfie when you smile." 
 
 These examples probably don't sound like the grand future of AI you might have imagined, but they're just the beginning. Facebook already uses its AI to describe the content of images to visually impaired users, but imagine the same process happening offline with real-time video. In true 21st century style, better selfies are just the beginning.
– If she ever abandons acting, Kristen Stewart might have a gig in artificial intelligence. The Twilight actress has just published a research paper on a machine learning technique called "neural style transfer" and its use in film, reports Quartz. Style transfer is a "fun technique," according to Tech Crunch, though it's a bit complicated to explain. Essentially, an algorithm is used to apply the style of one thing to something else. In this case, Stewart—with help from Starlight Studios producer David Shapiro and Adobe research engineer Bhautik J Joshi—sought to transfer the look of one of her own paintings to scenes from her short film Come Swim. Using computer programs known as neural networks, the group was able to add "blocks of color and texture" to a test frame until it matched the look of the painting, which depicts "a man rousing from sleep," reports the Verge. Once they had achieved the look they wanted on the test frame, they applied the same technique to the opening and closing scenes of Come Swim, described as "a poetic, impressionistic portrait of a heartbroken man underwater," which premiered at the Sundance Film Festival on Thursday, per the Guardian. The Verge notes you might have used this same technique yourself: Facebook introduced a style transfer filter last year.