In consonance with the perceived need for interactive computer music to combine sonorous and sensuous experiences with critical spaces for considering the nature of human interaction, in the last few years an important marker of the growth of these technological practices has been the Live Algorithms for Music (LAM) research network,10 an initiative created in 2004 by computer scientist Tim Blackwell and composer Michael Young of Goldsmiths College in London. According to Young and Blackwell, LAM is conceived as “an inter-disciplinary community of musicians, software engineers and cognitive scientists,” sharing and furthering the goal of investigating “autonomous computers in music.” A series of Live Algorithms conferences have included research papers and performance contributions from musicians (electronic and instrumental), composers, artists, software engineers and researchers in computer science, cognitive science, robotics and mathematicians.
According to Young and Blackwell, LAM’s vision foregrounds “the development of an artificial music collaborator. This machine partner would take part in musical performance just as a human might; adapting sensitively to change, making creative contributions, and developing musical ideas suggested by others. Such a system would be running what we call a ‘live algorithm’.”
Blackwell and Young define a “live algorithm” by its features:
- a live algorithm can collaborate actively with human performers in real-time performance without a human operator
- a live algorithm can make apt and creative contributions to the musical dimensions of sound, time and structure
- live algorithms can contain a parametric representation of the aural environment which changes to reflect interaction between machine and environment.11
To be sure, the musical implications of “machine intelligence” animated many early forays into interactive music making. As discourses surrounding AI began to diffuse in the early 1990s, however, the emphasis shifted toward a complex set of aesthetic, philosophical, social, historical, and culturally oriented questions, situated at the crossroads of computer science, the arts, and the humanities. Thus, work on live algorithms for music has implications for evolutionary computation and artificial life, swarm intelligence, chaotic dynamics, cellular automata, neural networks, and the area of machine consciousness more generally.
Accompanying this interdisciplinary orientation has been a renewed theorization of the practice of improvisation. But why study improvisation? Among the many findings of the residency on improvisation I co-led at the University of California’s Humanities Research Institute in 2002 were these:
In a globalized environment, improvisation functions as a key element in emerging postcolonial forms of aesthetics and cultural production. In addition, improvisation mediates cross-cultural, transnational and cyberspatial (inter)artistic exchanges that produce new conceptions of identity, history and the body, as well as fostering socialization, enculturation, cultural formation and community development. Finally, the improvisative production of meaning and knowledge provides models for new forms of social mobilization that foreground agency, personality and difference, and that engage history, memory, agency, and self-determination.
