Machine Learning and Artificial Intelligence Applied to Computational Arts, Music, and Games

This docent lecture presents an interdisciplinary research in which machine learning and artificial intelligence are developed through the lens of computational arts. Rather than following isolated disciplinary silos, technological and algorithmic choices in machine learning and AI are shaped by the real-world artistic context of each project in sound, music, audiovisual arts, computational creativity, and digital games. The presentation highlights work on deep learning for audio, multimodal interaction, audiovisual composition, and computational creativity in design of digital games, while also situating these contributions in broader cultural and societal relevance. Through artistic works and algorithmic advancements, the research examines how ML and AI can function as a material for composition, performance, and design inquiry, rather than an optimization tool or problem-solver. The lecture also addresses reproducibility, affordances and agency with highly autonomous AI algorithms, and the broader cultural and social relevance of AI in the arts and music. The research proposes a novel integrated approach in which technical AI innovation and artistic experimentation mutually shape new forms of knowledge and creativity.