Artistic Strategies to Guide Neural Networks

TL;DR

This paper examines how artists utilize neural networks in creative practices, highlighting the potentials, limits, and impact of AI technology on art, emphasizing the collaborative and interpretative nature of AI-assisted art creation.

Contribution

It introduces a practice-based approach to explore AI's role in art, emphasizing translation models and the importance of artistic understanding and critique in AI-driven creativity.

Findings

AI enables new artistic forms like high-res images and 3D objects.

Translation models facilitate cross-media artistic expression.

Artworks influence AI development and vice versa.

Abstract

Artificial Intelligence is present in the generation and distribution of culture. How do artists exploit neural networks? What impact do these algorithms have on artistic practice? Through a practice-based research methodology, this paper explores the potentials and limits of current AI technology, more precisely deep neural networks, in the context of image, text, form and translation of semiotic spaces. In a relatively short time, the generation of high-resolution images and 3D objects has been achieved. There are models, like CLIP and text2mesh, that do not need the same kind of media input as the output; we call them translation models. Such a twist contributes toward creativity arousal, which manifests itself in art practice and feeds back to the developers' pipeline. Yet again, we see how artworks act as catalysts for technology development. Those creative scenarios and processes are enabled not solely by AI models, but by the hard work behind implementing these new technologies. AI does not create a 'push-a-button' masterpiece but requires a deep understanding of the technology behind it, and a creative and critical mindset. Thus, AI opens new avenues for inspiration and offers novel tool sets, and yet again the question of authorship is asked.