02/05/2021, 21:05 The Next Biennial Should be Curated by a Machine - A Research Proposition | Liverpool Biennial of Contemporary Art Page 1 of 4 https://www.biennial.com/journal/issue-9/the-next-biennial-should-be-curated-by-a-machine-a-research-proposition- Download PDF Published by Liverpool Biennial in partnership with DATA browser series. ISSN: 2399-9675 Editor: Joasia Krysa, Manuela Moscoso Editorial Assistant: Abi Mitchell Copyeditor: Melissa Larner Web Design: Mark El-Khatib Cover Design: Manuela Moscoso (artwork), Joasia Krysa (words), Helena Geilinger (graphic design) The Next Biennial Should be Curated by a Machine - A Research Proposition Joasia Krysa and Leonardo Impett The Next Biennial Should be Curated by a Machine The Next Biennial Should be Curated by a Machine is a research proposition - an inquiry into the relationship between curating and Artificial Intelligence (AI), and the possibility of developing an experimental system [1] capable of curating, based on human-machine learning principles.[2] Making reference to the e-flux 2013 project ‘The Next Documenta Should Be Curated by an Artist’[3] which questioned the structures of the art world and the position of curators within, this project extends the question to machines.[4] It asks how the counterpoint of automata might offer alien perspectives on conventional curatorial practices and curatorial knowledge? What would the next Biennial be like if machines intervened in the curatorial process, and helped to make sense of vast amounts of art world data that far exceeds the productive capacity of the human curator alone? The project takes the form of a series of research and artistic experiments that explore the application of machine learning algorithms (a subset of AI) to curation of large scale periodic contemporary art exhibitions, such as biennials, to reimagine curating as a self-learning human-machine system. [5] Under this overarching concept, two parallel experiments are developed in the framework of Liverpool Biennial: B³(NSCAM) and AI-TNB. B³(NSCAM) is developed as a collaboration with artists Ubermorgen, co-commissioned with The Whitney Museum of American Art for its online platform artport , curated by Christiane Paul. [6] It uses archival text material and datasets from both commissioning institutions and processes them through a group of machine learning algorithms, collectively named B³(NSCAM). [Fig. 5] Processing datasets (including curatorial texts) linguistically and semiotically, the AI system ‘learns’ their style and content, breaking and mixing them together. The generated texts are then presented to the user, with a degree of interactivity and ‘branching’, iteratively rewriting small parts of its own text at random. A parallel experiment, AI-TNB is commissioned as part of UKRI/AHRC Strategic Fund: Towards a National Collection to explore machine curation and visitor interaction in large scale exhibitions, taking Liverpool Biennial 2021 as a case study [7] [Fig.1] In this experiment, the biennial exhibition curated by Manuela Moscoso across multiple venues in Liverpool in the spring 2021, is interpreted as a parallel machine-curated online version.[8] The resulting ‘curatorial AI system’, or an AI Biennial, is an excercise in interaction through large datasets, using computer vision and natural language processing techniques with a focus on human- machine co-authorship.[9] [Fig. 2, 3, 4] Our relationship to computers is rapidly changing and so are developments in automation (AI), and so is our understanding of creative practices, including curatorial practice. The overall project takes machine learning algorithms beyond the ‘search engine’ paradigm in which they have been mostly used to date, and instead considers them to be curatorial agents, working alongside human curators.[10, 11] There are a number of issues arising from this, such as the degree to which creativity is compromised by the ‘intelligent’ machines we use, as well as how biases become reinforced.[12] Algorithms are biased because certain elements of a dataset are more heavily weighted, and once a system is trained on this data, further errors follow that broadly reflect inherent human biases in society. Can something similar be said of the art world, where one might imagine there to be a shared ‘dataset’ of artists and curators that reflect biases inherent to the art world? If this seems far too simplistic, it becomes more interesting once these two operating systems are correlated, and when they become entangled, and to speculate on what each might learn from the other. It is not just a case of identifying concerns – such as around inclusion of marginalised communities or worries about the forms of creativity produced through AI – but also an opportunity to think about the transformation of human-machine relations and curatorial practices. STAGES – ISSUE 9 Stages 9 Editorial: Curating, Biennials, and Artificial Intelligence Joasia Krysa and Manuela Moscoso Towards a Poetics Of Artificial Superintelligence: How Symbolic Language Can Help Us Grasp The Nature and Power of What is Coming Nora N. Khan MI3 (Machine Intelligence 3) Suzanne Treister A Visual Introduction To AI Elvia Vasconcelos Excavating AI: The Politics of Images in Machine Learning Training Sets Kate Crawford and Trevor Paglen Notes On A (Dis)continuous Surface Murad Khan Irresolvable Contradictions in Algorithmic Thought Leonardo Impett Creative AI Lab: The Back-End Environments of Art-Making Eva Jäger Creative AI Database Serpentine R&D Platform & Kings College London Research & Development at the Art Institution Victoria Ivanova and Ben Vickers Future Art Ecosystems (FAE): Strategies for an Art-Industrial Revolution Serpentine R&D Platform & Rival Strategy Curating Data: Infrastructures of Control and Affect … and Possible Beyonds Magda Tyzlik-Carver The Next Biennial Should be Curated by a Machine - A Research Proposition Joasia Krysa and Leonardo Impett Glossary LB2021 Projects Events Shop Support Education About Search