Fool the Discriminator
Last week Christie’s sold at auction a portrait ‘created by anartificial intelligence’ for $432,500. The canvas from the art collective Obvious was described as a portrait of the fictional ‘Edmond Belamy’, and signed with an equation:
It expresses the concept underlying the class of machine-learning algorithms known as Generative Adversarial Networks (GANs), which were used to produce the portrait. GANs were first outlined by researchers led by Ian Goodfellow in 2014. The name ‘Belamy’ is an affectionate homage, ‘bel ami’ being a loose translation of ‘good fellow’.
Hugo Caselles-Dupré, one of the members of Obvious and a computer science PhD student, explained the idea behind GANs as a competition between two algorithms, a Generator and a Discriminator:
We fed the system with a data set of 15,000 portraits painted between the 14th century to the 20th. 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. The aim is to fool the Discriminator into thinking that the new images are real-life portraits.
The Generator will eventually produce images close enough to the real-life portraits for the Discriminator to have only a 50:50 chance of distinguishing them. Viewed as an optimisation algorithm to a data distribution, it isn’t very compelling, but as an analogy for the relationship between artist and critic it’s hard to resist, which is surely part of the reason for Obvious’s success.
The computer hardware required to run these networks is expensive. Graphics Processing Units (GPUs) can be optimised for the intensive calculations required, but they don’t come cheap. Portrait of Edmond Belamy is low-resolution because that’s about as far as a bunch of amateurs can stretch. As Caselles-Dupré complained in an interview:
With the big GAN papers, it's like, ‘OK, it requires like 512 GPU cores,’ something that we don't have, we don't have the budget for this … So yeah, we want to do this innovative stuff, but we've got to start somewhere to get some financing and continue working, having some credibility, having opportunities to get to access to more computational power.
Whatever Obvious’s aims, Christie’s proclaimed the sale as the birth of ‘a new medium’ that raised questions about authorship: human or algorithm? But it soon became apparent that Obvious had produced the portrait using code written by other AI artists, and other, more prosaic questions of authorship arose.
Robbie Barrat has written and made available several repositories of code to produce art from GANs, including most of the code used by Obvious. ‘Am I crazy for thinking they really just used my network and are selling the results?’ he asked. Others saw the sale as a betrayal of the open-source ethos of the AI art community; Caselles-Dupré had repeatedly asked Barrat for assistance and modifications to the code in late 2017 without saying that they planned to sell the results.
‘I wonder why they missed the opportunity to declare their work as an AI-readymade and bring us the first digital Duchamp,’ Mario Klingemanncommented. Unlike Duchamp, however, it seems that Obvious weren’t setting out to be provocative. Unprepared for their success, they have since been hastily trying to clarify that they greatly admire those whose code they used.
The Obvious debacle is a curious reflection of the Fountain episode. Duchamp submitted his readymade urinal, signed ‘R. Mutt’, to the Society of Independent Artists to test the rule that all who paid their fee could exhibit. Rather than rejecting it outright, the Society quietly removed it from the show. Christie’s reaction to the Portrait of Edmond Belamy couldn’t have been more different. The auction house exhibited and sold a low-resolution inkjet print produced from open-source code freely available online, seizing the opportunity to proclaim it a historic first and open a lucrative new market.
Having playfully transferred authorship to the algorithm to attract attention, Obvious are now experiencing the loss of authorship that comes once a work of art is public. ‘We totally understand that the whole community around AI and art took this as a really bad thing,’ Caselles-Dupré has said, ‘and now we agree with them, but we can't really convey this message because we are being a bit misrepresented.’ Duchamp wouldn’t have been surprised. ‘Obviously any work of art or literature, in the public domain,’ he wrote in 1956, ‘is automatically the subject or the victim of such transformations.’