Blending theatrical conventions, choreographed movement, hip-hop, and artistic experiments with machine learning, (Machine) Learning to Be is a participatory, devised, hybrid multimedia performance that engages with Artificial Intelligence (AI) systems and their societal impacts. The performance features an interactive choreographic interface that aims to engage AI as embodiment technologies and human and AI characters that aim to convey the multifaceted nature of AI, its dangers and possibilities for our communities. The performance is informed by a dataset created from our team and community members’ typed and whispered offerings. Rooted in visions of decolonial AI, (Machine) Learning to Be seeks to challenge established systems of control and imagine pluriversal future-presents with and through human and other-than-human forms of embodied intelligence.

Both online and in-person, (Machine) Learning to Be directly engages participants in the process of world-making. This work aims to spark a conversation around the possibilities and impacts of AI on the human body and society, using the hybrid performance model to expand access to the conversation for all who wish to engage.

The performance will have a developmental workshop with public sharing at Brown University in August 2024, produced in collaboration with Brown Arts Institute as part of the inaugural Brown Arts IGNITE Series, and will be presented in New England and Vancouver (Canada) in the Spring 2025.

Dataset creation

Submit your offering here.

Performance development intensive hosted by the Brown Arts Institute 
(Brown University, November 2023)

Workshop & Presentation at the Data Kinaesthetics Symposium 
(Harvard ArtLab, October 2023)

Ioana Jucan gave a talk titled “Performing with AI: Reflections on (Machine) Learning to Be” and Katherine Helen Fisher led a workshop on “Real-time Choreographic Interfaces.”