The Next Biennial Should Be Curated by a Machine

The Next Biennial Should be Curated by a Machine is a series of machine learning experiments exploring the relationship between curating and Artificial Intelligence (AI). Making reference to the e-flux project ‘The Next Documenta Should Be Curated by an Artist” (2003) –– which questioned the structures of the art world and the privileged position of curators within it –– the project extends this question to AI.  It asks how AI might offer new agential perspectives on curatorial practices. What would the next Biennial, or any large scale exhibition, look like if AI intervened in the curatorial process to make sense of the vast amount of art world data that far exceeds the capacity of the individual human curator alone? Under this overarching concept, a number of parallel experiments have been realised applying various machine learning techniques (a subset of AI) to work on (‘curate’) datasets derived from various biennial exhibitions. The experiments include: B³(NSCAM) – a collaboration with artists Ubermorgen;  AI-TNB – a collaboration with Eva Cetinić (experiment machine learning concept and implementation), MetaObjects (Ashley Lee Wong and Andrew Crowe) and Sui (web development and design), both released in the context of Liverpool Biennial 2021 and  the former commissioned with the Whitney Museum of American Art; and Newly Formed – a collaboration with artist Yehwan Song, Digital Visual Studies research project at the University of Zurich, and Helsinki Art Museum HAM, commissioned for Helsinki Biennial 2023.

Links to projects and more information can be found at

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