Rutgers researchers'' AICAN system created art that sold for $16,000 at auction using a generative adversarial network

"What if a machine were programmed to create art on its own, with little to no human involvement? What if it were the primary creative force in the process? And if it were to create something novel, engaging, and moving, who should get credit for this work?"

"At Rutgers’ Art & AI Lab, we created AICAN, a program that could be thought of as a nearly autonomous artist that has learned existing styles and aesthetics and can generate innovative images of its own. People like AICAN’s work, and can’t distinguish it from that of human artists. Its pieces have been exhibited worldwide, and one even recently sold for $16,000 at an auction. When programming AICAN, we used an algorithm called the “creative adversarial network,” which compels AICAN to contend with two opposing forces. On one end, it tries to learn the aesthetics of existing works of art. On the other, it will be penalized if, when creating a work of its own, it too closely emulates an established style. Using our prior work on quantifying creativity, AICAN can judge how creative its individual pieces are. Since it has also learned the titles used by artists and art historians in the past, the algorithm can even give names to the works it generates. To test this, we showed subjects AICAN images and works created by human artists that were showcased at Art Basel, an annual fair that features cutting-edge contemporary art. We asked the participants whether each was made by a machine or an artist. We found that humans couldn’t tell the difference: 75% of the time, they thought the AICAN-generated images had been produced by a human artist."

"When programming AICAN, we used an algorithm called the “creative adversarial network,” which compels AICAN to contend with two opposing forces."

"We’ve fed the algorithm 80,000 images that represent the Western art canon over the previous five centuries."