Since 2023, computer scientists have warned against model collapse—the contamination of training sets with AI-generated outputs that progressively degrade model performance. Exemplifying a positive-feedback-driven failure, it produces effects such as word repetition or pixel noise, ultimately leading to a loss of meaning and coherence—from an engineering standpoint.
However, from a creative perspective, collapse is not merely a breakdown: it also functions as a recursive mirror recalling early analog video feedback experiments, raising the question of what happens when a system turns inward and sees itself. In such cases, machine vision no longer transmits the world (as in television) but increasingly generates worlds from within.
Drawing on media archaeology through case studies of historical video synthesis techniques and contemporary artistic uses of machine learning, this paper examines what recursive training reveals about the dependent nature of AI-generated data. It argues that the potential effects of collapse challenge transhumanist ideals while inviting an aesthetic perspective, positioning noise and recursion as key concepts for understanding both art-making and the AI ecosystem.
Distributing agency across scales and networks, the latter currently remains reliant on new human-produced content, particularly within foundation models trained on massive datasets.
Blogger's Review: This article delves into the phenomenon of model collapse, revealing the complex interdependence between AI-generated content and its training data. This phenomenon not only impacts technical performance but also provokes profound reflections on creativity and artistry, especially regarding the roles of recursion and noise in the realm of machine vision.