Outbreaks from the Grid
The Art + AI Research Group at UVM employs machine learning to investigate new approaches to artistic image production. More broadly, the project explores emerging artistic practices with Machine Learning and AI while referencing an artistic lineage to artists such as Wassily Kandinsky, Jonn Cage and Yoko Ono. These artists employed instructions and systems in their non-digital artworks. For example, Kandinsky developed a science of aesthetics with the basic elements of point, line and plane; Cage used the oracle ‘I Ching’ like a computer to inform his compositional decisions; Ono wrote poetic scores that turn her audience into active participants when they follow a series of imaginative instructions. Inspired by these artists and curious about the potential for human + machine collaboration, our work emerges through growth-mutation-evolution lifecycles; our human-machine alliances expose a dynamic relationship between creation and destruction.
Outbreaks from the Grid is the premier project of the Art + AI Research Group at the University of Vermont. In this project we cultivate artworks that evolve over generations. The resulting images emerge from both human and machine interference and express the peculiar human experience with architectures of order, power and chaos. The public is invited to participate in the project through generative art performances that take place in virtual and gallery settings.
Jenn Karson, lecturer Department of Art and Art History, University of Vermont
Kerime Toksu, Vermont Advanced Computing Core, University of Vermont
Syd Culbert. BA ’23
Anna Hulse, BA ’22
Ethan Davis, M.S. Data Science ‘21
Sarah Pell, M.S. Data Science ‘20
Fred Sanford, B.S. Mechanical Engineering ‘20
Yifeng Wei, B.A. Studio Art ’19, M.F.A Parsons School of Design ’21
CatCoders funding from Department of Computer Science, UVM Spring 2020
Northeast Cyberteam/NSF funding, Summer 2020
This work was supported in part by the National Science Foundation (NSF) under award No. OAC-1659377.
Computations were performed on the Vermont Advanced Computing Core supported in part by National Science Foundation (NSF) award No. OAC-1827314.