Visual Literacy and the Aesthetics of AI: Revisiting the Legacy of Information Aesthetics

This project investigates the historical and theoretical foundations of AI-generated art by revisiting the tradition of Information Aesthetics developed in the 1950s and 1960s by Max Bense and Abraham Moles, with particular attention to the theoretical contributions of the Stuttgart Schule and its role in the emergence of computational aesthetics. Rather than approaching generative AI as a radical break with previous artistic practices, the project situates it within a broader genealogy of algorithmic and computational aesthetics, highlighting both continuities and transformations in the relationship between information, visual culture, and artistic production.

The research combines archival investigation, theoretical analysis, and close readings of historical and contemporary artworks. It focuses on concepts such as entropy, aesthetic measure, noise, and improbability, investigating their relevance for understanding AI-generated images and contemporary visual literacy. Particular attention is given to the aesthetic value of the improbable and to the historical shift from the archive as a site of representation and memory to the archive as a predictive infrastructure that enables statistical image generation.

By reconnecting historical theories of computational art with current debates on generative AI, the project aims to develop an updated conceptual framework for interpreting AI-generated images. More broadly, it contributes to a critical genealogy of generative aesthetics and offers new perspectives on the cultural and epistemological implications of artificial intelligence in visual art.