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DeepArt.io is a website that uses deep neural networks to identify and combine stylistic elements of two separate images, a technique known as style transfer. But it’s not traditional “artificial intelligence”: no coding experience is required.
The program relies on a neural algorithm, developed by Leon Gatys and colleagues at the University of Tübingen in 2015. This has been used in photo filters on Facebook and Prisma, as well as on moving image. Kristen Stewart used style transfer in her directorial short film debut Come Swim to redraw a brief dream sequence.
In recent years, these kinds of programs have proliferated, using different techniques to create AI-assisted works which are both sophisticated and beautiful. In fact, a study says that AI-generated art now looks more convincingly human than work at Art Basel.
For good or bad, the consequences could transform mainstream art production, consumption, and artists.
First, a little history. The earliest known generative computergraphik, created around 1960 by Georg Nees, the German “father of computer art”, consisted mainly of black-and-white drawings of shapes. The first computer-generated music piece, Lejaren Hiller and Leonard Isaacson’s Illiac Suite for String Quartet, came in 1957. Both experiments were aimed at academic audiences, and not very “artistic.”
We’ve come a long way since then. Deepjazz, created by Princeton University Ph.D. student Ji-Sung Kim, used neural networks to detect jazz musical patterns and generate new songs.
Nvidia recently published a paper documenting how researchers, with incredibly convincing results, generate life-like images. The algorithm takes images of a winter street and predicts what it would look like during summer. Gene Kogan, a generative artist and author of Machine Learning for Artists has used similar methods to make realistic place images.
Cornell University and Adobe researchers have also been working on a sophisticated version of style transfer for photos. The process they’re developing can even use the sunset lighting of one photo and apply it to a daytime photo of another location. Google, too, has been working on “supercharging style transfer.” Researchers developed a way to combine multiple styles, mixing them like paints.
These may change how we value artists, too. We’re likely to see a proliferation of algorithmic art in mainstream culture. These tools will take some of the burden off of artists, but may lead to fewer job opportunities in the digital economy.
Taobao, a Chinese shopping website, created banner ads for its mega-shopping Singles’ Holiday by training algorithms on design patterns of successful ads Airbnb also showed off a tool which uses algorithmic art techniques to convert sketches into fully designed and functional prototypes.
The vibrant world of artistic potential that’s opened up by algorithms will be darkened by the potential for artists to lose control.