In the new era of creative collaboration, can machines and nature co-create art and music? In the SonoSynthesis project, a team of researchers from the Ars Electronica Futurelab brings together AI, biology, and Deep Space technologies to develop an immersive audio and visual experience created through the interaction between artificial intelligence and biological entities.
In the project, various microorganisms receive and express the musical data provided by an AI-based music composition system in a range of petri dishes. The biological data points on the petri dish grow and change over time, creating new data points and beautiful microscopic landscapes. The new data points go back to the AI system for further music generation – thus creating a cycle of artistic interaction.
The research team, Ali Nikrang, Raphael Schaumburg-Lippe, and Yoko Shimizu, brings together diverse expertise in this interdisciplinary collaboration. Ali Nikrang is the creator of Ricercar, an AI-based collaborative music composition system. Yoko Shimizu is the developer of Bio Ink, a living ink that grows freely beyond the digital input by humans and machines. Raphael Schaumburg-Lippe is an expert in 3D visualization, capturing biological processes in immersive ways that can be experienced in Deep Space 8K.
The three researchers are now embarking on an endeavor to explore many questions that arise in the new era of co-creation. Can machines and nature interact with each other? What happens when machines and nature interact and co-create? Who is creating the artwork? Where does the creativity come from? Who owns the intellectual property (IP) of the non-human (biological) creations? During the Ars Electronica Festival, the project will be presented as an immersive audio and visual experience in the Deep Space 8K environment.
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Interested in similar projects? The following Ars Electronica Futurelab projects are related to the ideas and concepts presented here. An overview of all our productions, cooperations and projects can be found in our project archive.