AI‑Aided Ceramic Sculptures: Bridging Deep Learning with Materiality
Varvara Guljajeva and Mar Canet Sola (2023)
Abstract
With the advent of neural networks as powerful tools for generating various forms of media, so-called ‘Deep Learning’ (DL) has entered the sphere of art production. The concept of creative artificial intelligence (AI) has become part of popular discourse around 2D digital image-making, but can AI exceed the limitations of 2D media and be applied creatively in more tactile 3D media such as sculpture? In this paper, we describe what happens when AI is applied in a real-life production line, from concept to physical object. The article presents a case study that explore DL’s potential for creating a tactile sculpture guided only by text prompt and a 3D model. In the production process, we mix several methods, including neural, digital, and traditional, to achieve the final results. In terms of methodology, this is an artistic study that explores existing DL tools for 3D object generation and later manufacturing in 3D printed ceramics. In the study, we use practicebased research methods to explore what happens when modern technology meets traditional ways of production, such as pottery. Further, we discuss reference art projects that have utilised AI, lessons learned, and the potential use of DL tools in art production. The aim of the paper is to explore new meanings and to open new avenues for investigation that emerge by bringing together creative AI with materiality. Keywords: Ceramics · AI sculpture · 3D AI · 3D printing · interdisciplinary · physical AI · deep learning · AI art · creative AI · hybrid process · practice-based research · artistic research
Venue: In Artificial Intelligence in Music, Sound and Design. Springer Nature Switzerland.