Italiano [English below]
Il mio intervento, intitolato “Transdisciplinary cooperations and autonomous creativities”, tenuto al Future Tense Symposium 3.0, presso il Beall Center for Art + Technology dell’Università della California a Irvine, è stato pubblicato su Noema. La conferenza chiudeva una serie di incontri organizzati per la mostra/progetto di ricerca “Future Tense: Art, Complexity, and Uncertainty”, prodotta dall’UCI Beall Center for Art + Technology come parte dell’iniziativa Art and Science Collide del Getty PST ART per il 2024. Il mio intervento ha discusso dell’evoluzione storica e della crescita di forme e operatività autonome e complesse, e della progressiva esternalizzazione al di fuori del corpo di funzioni e azioni umane, anche in ambito artistico. Di questo evento ho scritto anche qui.
Di seguito un estratto dal mio intervento pubblicato su Noema.
Pier Luigi Capucci, From The Anachronies series, 2024
My presentation, titled “Transdisciplinary cooperations and autonomous creativities”, given at the Future Tense Symposium 3.0 at the Beall Center for Art + Technology at the University of California, Irvine, has been published in Noema. The conference concluded a series of meetings organized for the exhibition/research project “Future Tense: Art, Complexity, and Uncertainty“, produced by the UCI Beall Center for Art + Technology as part of the Getty PST ART Art and Science Collide initiative for 2024. My presentation discussed the historical evolution and growth of autonomous and complex forms and operations, and the progressive externalization of human functions and actions outside the body, including in the artistic field. I have written on this event here.
Below is an excerpt from my presentation published in Noema.
Transdisciplinary cooperations and autonomous creativities
1. Autonomous Creativities
Autonomous creativity in the arts has been recurring since the dawn of human expression. In times of Generative Artificial Intelligence, and to the goals of this presentation, we can consider Generative Art as a precedent for Generative AI based art forms. According to Philip Galanter:
Generative art refers to any art practice where the artist uses a system, such as a set of natural language rules, a computer program, a machine, or other procedural invention, which is set into motion with some degree of autonomy, contributing to or resulting in a completed work of art. [1]
Some years later, Margaret Boden and Ernest Edmonds wrote that:
In Generative art, the artwork is generated, at least in part, by some process that is not under the artist’s direct control. [2]
In its broader meaning, Generative Art includes visual, textual, sonic, and audiovisual art forms generated through many kinds of more or less autonomous systems and processes: mechanical, electronic, computer-based, biological, natural, etc. Among the artists that are considered precursors are Frieder Nake, Georg Nees, Michael Noll, Hans Haacke, and Vera Molnár (who died in December 2023). An exhibition on this extraordinary artist was performed at the Beall Center in 2022.
Vera Molnár considered the computer to be deeply human. Once she said:
I think that there is nothing more human than a computer because it was invented by men. […] Thus, the most human art is made by computer, because every last bit of it is a human invention. [3]
Harold Cohen, one of the pioneers of Generative Art, highlighted the evolution of the computer’s role. In an interview, he said:
My whole history with information technology has had to do with a shift in the notion of the computer as an imitation of the human being, towards a recognition of the computer as an independent entity with its own capabilities, fundamentally different from those we have. [4]
With the advent of Generative AI, Generative Art experiences a second youth. Many exhibitions are open worldwide, like the retrospective dedicated to Harold Cohen that is running just now at the Whitney Museum.
2. Generative Artificial Intelligence and the relationship between text and picture
As we know, in generative AI a text, the prompt, can be transduced into a picture, a song, or a video. An instance of human culture, made of a huge amount of data, feeds the algorithms. The correspondence between the text and the picture depends on the dataset on which the generative AI is trained and on the release of the software, and the final results can be very different. The same text/prompt, even with the same algorithm and dataset, can originate different pictures, and this is more evident using different generative AI applications.
This process in AI making pictures is similar to what happens to humans: the same sentence is imagined in different ways by different people, depending on a large number of variables: context, culture, language, knowledge, ways of expression, the current condition of the people, etc. Secondly, in generative AI the process of picture creation is not under control. Painting, 2D digital drawing, and 3D modeling, are works in progress, with the artists having almost complete control of all stages leading to the final picture. In generative AI, even with the most descriptive and seemingly complete prompt, the final image is just one possible representation of the idea; the outcome can be unexpected, like a surprise. This control is similarly lacking when trying to create with generative AI a copy of an existing picture, unless it is a very famous one.
This happens because texts and images are very different ways to represent, with different purposes; therefore, the text-image match can’t be univocal. And this should bring to consideration in a different way the so called “prompt engineering.”
Notes
1) Philip Galanter, “What is Generative Art? Complexity Theory as a Context for Art Theory,” International Conference on Generative Art, 2003, online, https://www.philipgalanter.com/downloads/ga2003_paper.pdf (last access 15/09/23).
2) Margaret A. Boden, Ernest A. Edmonds, “What is generative art?”, Digital Creativity, vol. 20, n. 1-2, 2009, p.10, DOI: 10.1080/14626260902867915 (last access 10/02/24).
3) Vera Molnár in conversation with Hans Ulrich Obrist”, in Hans Ulrich Obrist, András Szánto (eds.), Bookmarks: Revisiting Hungarian Art of the 1960s and 1970s, Cologne/New York, Koenig Books, 2018, p. 80.
4) Harold Cohen, 2011, in Alex Estorick, “Quando il pittore ha imparato a programmare: AARON di Harold Cohen”, Flash Art, 26/02/18, online, https://flash—art.it/article/quando-il-pittore-ha-imparato-a-programmare-aaron-di-harold-cohen/ (last access 07/09/23).
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Read the complete text in Noema.
