Deep Else

A Critical Framework for AI Art

View the Project on GitHub dejangrba/deep-else

Deep Else

A Critical Framework for AI Art

Abstract

From a small community of pioneering artists who experimented with artificial intelligence (AI) in the 1970s, AI art has expanded, gained visibility, and attained socio-cultural relevance since the second half of the 2010s. Its topics, methodologies, presentational formats, and implications are closely related to a range of disciplines engaged in the research and application of AI. In this paper, I present a comprehensive framework for the critical exploration of AI art. It comprises the context of AI art, its prominent poetic features, major issues, and possible directions. I address the poetic, expressive, and ethical layers of AI art practices within the context of contemporary art, AI research, and related disciplines. I focus on the works that exemplify poetic complexity and manifest the epistemic or political ambiguities indicative of a broader milieu of contemporary culture, AI science, technology, economy, and society. By comparing, acknowledging, and contextualizing both their accomplishments and shortcomings, I outline the prospective strategies to advance the field. The aim of this framework is to expand the existing critical discourse of AI art with new perspectives which can be used to examine the creative attributes of emerging practices and to assess their cultural significance and socio-political impact. It contributes to rethinking and redefining the art/science/technology critique in the age when the arts, together with science and technology, are becoming increasingly responsible for changing ecologies, shaping cultural values, and political normalization.

Keywords: AI Art, Anthropomorphism, Artificial Intelligence, Creativity, Deep Learning, Digital Art, Generative Art, Machine Learning, Mainstream Contemporary Art, New Media Art.

Full Text

https://www.mdpi.com/2673-6470/2/1/1/pdf.

Publication

MDPI Digital 2022, (2), 1: 1-32. ISSN 2673-6470. https://doi.org/10.3390/digital2010001.

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