AI learn, AI do : En konstvetenskaplig studie om AI-modellers materialbetingade förmågor, aktörskap och deltagande inom konstnärliga processer

Sammanfattning: This master’s thesis investigates generative AI-art through the lens of actor network theory. By focusing on the role of images in datasets as a material that effects both AI-models and artworks, the decisively non-human agencies generative AI-models can be said to possess, and the traces and associations that generative AI-models imbue artworks with, this thesis aims to investigate art that has been created with GAN-models as well as contemporary text-to-image diffusion-models, by way of similar premises. Forgoing common discussions and questions regarding the status of AI-art as art that inundate many a reasoning regarding this topic, this thesis instead investigates the use of generative AI to make images and art with an understanding of it as a multifaceted practice that can be observed and experienced in a variety of ways.  General topics such as the way images are used to train AI-models, the blurry connections between trained images and generated images, the way AI-models can be used and interacted with by using prompts as well as different kinds of interfaces and AI-Image-generators, are investigated, followed by the analysis of a number of artworks for which generative AI has been used. Throughout this study generative AI-art emerges as a both novel and oftentimes contested artform that is defined by direct and indirect connection to other media, a varied understanding of what it is that the artificial intelligence appears to do, and a use of the AI-artwork as a means to comment the mediums emerging characteristics.