The tradition of algorithmic music, which aims to design explicit and interpretable algorithms for generating music, has long been the primary provider of artificial music. But the rise of machine learning changes the game by making it possible to employ implicit but opaque algorithms—trained on datasets but whose details, by design, elude direct human understanding. It is this new tension that we aim to explore in this international conference. What changes does machine learning, and especially deep learning, bring to how musicians, listeners, and philosophers create, appreciate, and think about music?

Dates : November 3-5th 2026

Location : Nantes Université (France)

Invited speakers :

• Caterina Moruzzi, Edinburgh College of Art (philosophy, design informatics)

• Anna Huang, Massachusetts Institute of Technology, Boston (music, machine learning)

• Nemesio García-Carril Puy, Universidad Complutense de Madrid (philosophy of music)

Disciplines : philosophy, musicology, popular music studies, computer science

Language : English

Registration fees (provisional ; includes lunch) :

• Tenured researchers : 50 euros

• Postdoctoral researchers : 30 euros

• Students, doctoral candidates, independent scholars : free

Organizers : 

• Vincent Granata, Nantes Université, CAPHI (UR 7463)

• Pierre Saint-Germier, IRCAM, Paris (STMS Lab, UMR 9912)