Networked creativity emerged from the intersection of my long-standing interest in the creative process, a curiosity (and confusion) about machine learning, and a fascination with Actor-Network Theory, which I discovered during my master’s. This project formed the basis of my thesis in Digital Sociology.
Abstract
Recent developments in artificial intelligence (AI) have given rise to technologies like Generative Adversarial Networks (GANs), capturing the interest of artists for their potential to transform artistic practices. Using ethnographic approaches, this thesis explores how machine learning is shaping the process of making visual art. Drawing on contemporary sociologists of art and Actor-Network Theory, I examine the interactions between humans and non-humans to illuminate the intertwined processes of working with neural networks. Investigating complex algorithms within creative contexts not only reveals the inner workings of artistic production but also offers a different perspective on the broader “AI” narrative. Art and artistic practices, I argue, emerge through a dynamic assemblage of human and non-human agents.