This week, on the new-media platform Feral File, artist Refik Anadol presents Unsupervised, an exhibition of works created by training an artificial intelligence model with the public metadata of The Museum of Modern Art’s collection. Spanning more than 200 years of art, from paintings to photography to cars to video games, the Museum’s collection represents a unique data set for an artist who has worked with many different public archives. The AI-based abstract images and shapes in Unsupervised are interpretations of the Museum’s wide-ranging collection, weighted toward the exhibition of new artworks at MoMA this fall.
Starting with the exhibition opening on November 18, new artworks will be revealed and released over three days. Each work will be made available to collectors as nonfungible tokens, or NFTs.
MoMA curators Paola Antonelli and Michelle Kuo sat down with Anadol and Casey Reas, the artist-founder of Feral File, to talk about the ecology of mobile images, art in the age of mechanical learning, and the question: What if a machine tried to create “modern art”?
This conversation has been edited for length and clarity.
Refik Anadol Studio. Unsupervised — Data Universe — MoMA. 2021. Video. Courtesy the artist and Feral File
Paola Antonelli: Refik, how did you start thinking of your Machine Hallucinations series, of which Unsupervised is a part?
Refik Anadol: Five years ago, I was very fortunate to be one of the artists in residence at the Google Artists and Machine Intelligence program. This was the moment of DeepDream’s development, the very first time we were witnessing AI algorithms making an impact on the art and technology communities. I was purely a data artist, I guess, at the time. And I was truly blown away by how AI could profoundly change the thinking around producing art, and give us new tools. I wanted to explore several interrelated questions: Can a machine learn? Can it dream? Can it hallucinate?
Michelle Kuo: So can a machine hallucinate? Normally, machine learning is oriented toward achieving resemblance: How can the machine learn from vast amounts of data and then learn, identify, and create something that looks real, that looks like our world? But you are going in the opposite direction, away from resemblance and toward abstraction. How did you start to think about visual datasets and processing them differently?
RA: The first month of my residency at AMI, I found a wonderful open-source cultural archive in Istanbul, called SALT, with 1.7 million documents. Seeing these documents inspired …….