Paper short abstract:

Applications of machine learning to creative tasks produce surprisingly good results. The plausibility of outputs produced by machines in tasks such as painting pictures, writing poems, or composing music raises interesting questions about the nature of creativity. My contribution to the symposium will start from the FolkRNN project: a collaborative research-creation project exploring the use of machine learning as a creative tool. Together with my colleague Dr. Bob Sturm, we applied machine learning to a data set of traditional Irish music which we then used to generate new tunes in that traditional style (surreptitiously released as an album) but also to compose music that has little to do with the tradition.

Paper long abstract:

This project illustrates the very first steps in the use of new technology. The development is predicated on the capabilities of the technology (and lead by the technologists). But we are moving into the next stage where we need to ask deeper questions such as: what is our vision for future involvement of intelligent machines in creative activities? What does it mean to be musically (poetically, artistically) intelligent (and is it the same for humans and machines)? Should we examine what we value in creative pursuits (and how we pay for these)? Shifting the conversation in this direction requires a broader dialogue that integrates the knowledge and knowhow of artists-practitioners as well as scholars from multiple fields. Since January 2022 I am leading an international research network that aims to explore these issues in relation to music.