Data Fluencies Theater Project
We are an ensemble of performers, artists, musicians, and creative technologists, co-creating this performance as part of a Mellon-funded artistic research project called The Data Fluencies Theatre Project (DFTP). Through our interaction design and performative approach, we use the mediums of interactive arts and performance to bring together the community and provoke dialogues among them. Both online and in-person, (Machine) Learning to Be directly engages the community members 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.
Here are the bios of the Data Fluencies Theater Project team (directed by Ioana Jucan): https://websites.emerson.edu/data-fluencies/people/
(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 technology and an AI character that aims to convey the multifaceted nature of AI, its dangers, and possibilities for our communities. Animated by visions of decolonial AI and blending theatrical conventions, choreographed movement, hip-hop, and artistic experiments with machine learning, (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.
WeA hybrid, devised performance piece investigating our place in a data-filled world and the possibility of more equitable futures alongside AI, co-created by the Data Fluencies Theatre Project team.
*Video created by Evan Haacke
