Some of the most fascinating research out there on machine learning as applied to art is being conducted by the enterprising researchers at Rutgers University’s Art and Artificial Intelligence Laboratory. Computer scientists there have previously developed algorithms to study artistic influence and to measure creativity in art history. Most recently, the lab’s team turned toward something a little different: it generated entirely new artworks using a new computational system that role plays as an artist, attempting to demonstrate creativity without any need for a human mind.
The results of this study were published last month in a paper penned by Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, and Marian Mazzone. To test their system, the researchers showed the generated artworks to a pool of 18 people to judge, mixed with 50 images of real paintings — half by famous Abstract Expressionists and half shown at Art Basel 2016, a fair that represents “the forefront of human creativity,” as Elgammal told Hyperallergic.
The results: participants largely preferred the machine-created artworks to those made by humans, and many even thought that the majority of works at Art Basel were generated by the programmed system. So, computers may be getting closer to autonomously producing their own art that people deem creative. Also, zombie formalism is real.
Although it creates its own images, the network, dubbed a Creative Adversarial Network (CAN), relies on creative human works during its learning process. Researchers programmed it to study 80,000 WikiArt images of Western paintings from the 15th to the 20th century so that it knew what kind of images have traditionally been aesthetically appealing. But the scientists didn’t want to devise a system that could merely emulate history paintings, genre…