Art as Post-Rational Inquiry: Reimagining Creativity through AI-Generated Aesthetics

The “linguistic turn” in contemporary thought has significantly influenced our understanding of rationality, resulting in human experiences taking a back seat to the dominance of discourse, and the subordination of iconicity to semantics. Later shifts in philosophical method, referred to as “visual and interpretive turns”, acknowledged the importance of embodied experience and a pre-reflective, non-conceptual cognition in artistic understanding. This offers significant insights into the phenomenology of art as a creative practice and adopts the view of art as “constitutive, not merely illustrative, of basic mental operations, such as intuiting, inferring, associating, hallucinating, feeling arousal, and categorizing” (Stafford).

Artistic practices, ranging from surrealism and abstract expressionism to conceptualism, have reimagined preconceived boundaries of artistic expression beyond the purely rational. However, today, as another profound shift – the “generative turn” - is evolving and various artists are incorporating machine aesthetics into their practices, using machine learning models not only as a tool but also as a collaborator, the conventional notions of creativity and rationality are being challenged to a greater extent than ever before.

This paper brings forth questions such as how artistic practices that use “unsupervised learning” – the mode of machine learning that allows for unmediated processes – challenge conventional artistic paradigms and the traditional view of creativity as linked to human agency, how they defy traditional rational interpretations in their departure from prevalent artistic norms and conceptual frameworks, and what new and unconventional perspectives they introduce in the realm of artistic creation.