A Game of Dice: Machine Learning and the Question Concerning Art

TL;DR

This paper explores the philosophical and practical implications of machine learning in AI art, emphasizing manifold approximation as a key contribution and questioning its impact on artistic creation and authorship.

Contribution

It highlights manifold approximation as the core technical advancement in AI art and discusses its philosophical implications for creativity and authorship.

Findings

Manifold approximation is central to AI art's novelty.

AI art raises questions about authorship and originality.

The use of machine learning in art prompts a reevaluation of creative processes.

Abstract

We review some practical and philosophical questions raised by the use of machine learning in creative practice. Beyond the obvious problems regarding plagiarism and authorship, we argue that the novelty in AI Art relies mostly on a narrow machine learning contribution : manifold approximation. Nevertheless, this contribution creates a radical shift in the way we have to consider this movement. Is this omnipotent tool a blessing or a curse for the artists?