‘If you consider the whole process, then what you have is something more like conceptual art than traditional painting’ - Ahmed Elgammal, director of the Art and Artificial Intelligence Lab at Rutgers University One way to do that, surely, is to conduct a kind of visual Turing test, to show the output of the algorithms to human evaluators, flesh-and-blood discriminators, and ask if they can tell the difference. They are still addressing the fundamental question of whether the images produced by their networks can be called art at all. No AI researchers are claiming that much just yet. This raises the intriguing notion that AI algorithms do not merely make pictures, they also tend to model the course of art history - as if art’s long progression from figuration to abstraction were part of a program that has been running in the collective unconscious for half a millennium, and the whole story of our visual culture were a mathematical inevitability. The network has learned that it finds more solutions when it tends toward abstraction: that is where there is the space for novelty.’ If it wants to make something novel, then it cannot go back and produce figurative works as existed before the 20th century. ‘An interesting question is: why is so much of the CAN’s art abstract? I think it is because the algorithm has grasped that art progresses in a certain trajectory. ‘I am surprised by the output every time I run it,’ says Elgammal. The basic binary hokey-cokey is the same - maker and judge, artist and critic - but CAN is specifically programmed to produce novelty, something different from what it sees in the data set, which in this case consists of all manner of paintings from the 14th century on. Ahmed Elgammal, director of the Art and Artificial Intelligence Lab at Rutgers University in New Jersey, is working with a system that he calls CAN - a ‘creative’ rather a ‘generative’ network. But we found that portraits provided the best way to illustrate our point, which is that algorithms are able to emulate creativity.’Įlsewhere in the AI world, researchers are playing other art-historical games. ‘We did some work with nudes and landscapes, and we also tried feeding the algorithm sets of works by famous painters. It turns out that the difficulty was part of the collective’s thinking. It must also surely be the case that portraiture is an extremely tough genre for AI to take on, since humans are highly attuned to the curves and complexities of a face in a way that a machine cannot be. ‘The Discriminator is looking for the features of the image - a face, shoulders - and for now it is more easily fooled than a human eye.’ ‘It is an attribute of the model that there is distortion,’ says Caselles-Dupré. There is something weirdly contemporary about him: he looks unnervingly like one of Glenn Brown’s art-historical appropriations. ‘We found that portraits provided the best way to illustrate our point, which is that algorithms are able to emulate creativity’ - Hugo Caselles-Dupré of Obviousīut one of the beguiling things about the depiction of Edmond Belamy is that it departs from a human idea of an 18th-century portrait. The aim is to fool the Discriminator into thinking that the new images are real-life portraits.
#Will your next meeting it obvious generator#
The Generator makes a new image based on the set, then the Discriminator tries to spot the difference between a human-made image and one created by the Generator.
We fed the system with a data set of 15,000 portraits painted between the 14th century to the 20th. ‘On one side is the Generator, on the other the Discriminator. ‘The algorithm is composed of two parts,’ says Caselles-Dupré. They are engaged in exploring the interface between art and artificial intelligence, and their method goes by the acronym GAN, which stands for ‘generative adversarial network’. The painting, if that is the right term, is one of a group of portraits of the fictional Belamy family created by Obvious, a Paris-based collective consisting of Hugo Caselles-Dupré, Pierre Fautrel and Gauthier Vernier.
0 kommentar(er)
