Robert B. Lisek PhD is an artist, mathematician and composer who focuses on systems, networks and processes (computational, biological, social). He is involved in a number of projects focused on media art, creative storytelling and interactive art. Drawing upon post-conceptual art, software art and meta-media, his work intentionally defies categorization. Lisek is a pioneer of art based on Artificial Intelligence and Machine Learning. Lisek is also a composer of contemporary music, author of many projects and scores on the intersection of spectral, stochastic, concret music, musica futurista and noise. Lisek is also a scientist who conducts research in the area of foundations of science (mathematics and computer science). His research interests are category theory and algebraic geometry. More: http://fundamental.art.pl/
Transfer-learning for music composing
How do you train a neural net so that if you transfer it into a new environment, it continues to work well or adapts quickly? It is important problem in artificial intelligence, especially in deep learning and it will have many application in future contemporary music composing.
Generalization is the ability humans have to generalize the concepts we know, so they can be combined in new ways that are unlike anything else we’ve seen. Transfer learning means that AI software deals with many new task and different classes of sound events.