Unsupervised — Machine Hallucinations — MoMA — Study 1

Unsupervised NFT Collection emerges from such an aesthetic/scientific vision and processes 138,151 pieces of metadata from the vast collection of The Museum of Modern Art in the mind of a machine. Using StyleGAN2 ADA to capture the machine’s transformative “hallucinations” of modern art in a multi-dimensional space, Anadol trains a unique AI model with subsets of the archive of MoMA’s collection of artworks, creating embeddings in 1024 dimensions. The sorted image datasets are then clustered into thematic categories to better understand the semantic context of data. This expanding data universe not only represents the interpolation of data as synthesis, but also becomes a latent cosmos in which hallucinatory potential arises from a novel form of artistic creativity interpreting MoMA’s unparalleled collection of modern and contemporary art. Spanning more than 200 years of art, from paintings to photography to cars to video games, the MoMA collection is an extraordinary data set—within which works from artistic movements such as Surrealism, not unlike Unsupervised, explored automatism, chance, and systems to generate unprecedented new art forms.

This is work that resonates aesthetically and also pushes machine-learning research into new territories. Anadol is in dialogue with scholars such as Jaakko Lehtinen, Distinguished Research Scientist at NVIDIA Research, who are inventing the technologies that the artist is using. On seeing this new body of work, Lehtinen remarked:

This is the beauty of fundamental research: to see the progress we’ve made on a hard, technical machine learning problem being unexpectedly channeled to serve such astounding creativity is extremely satisfying. We’re thrilled to witness the deepening interplay between art and AI research, and eagerly looking forward to seeing what we can do together in the future. Edition of 1.

Release date: 18 November 2021

Video (color, sound) 2160 ×  3840 pixels, 30 fps 3 minutes