Things AIn't What They Used To Be

This performance work—titled in reference to Duke Ellington’s big band jazz classic, released over sixty years ago—offers a gentle provocation, contrasting traditional approaches to jazz improvisation with emerging paradigms in human–AI interaction. Combining real-time machine learning and deep learning tools, the piece stages a live collaboration between improvising human musicians and generative AI agents. Central to the work is a subversion of the established technique of the contrafact, whereby new melodies are composed over pre-existing chord progressions. Here, the process is inverted: AI agents are tasked with reharmonising composed melodic lines, thereby disrupting the expected harmonic framework. This indeterminacy both encourages and challenges the performers to find new musical responses.

Leveraging technologies such as Somax2, RAVE, Mosaïque, and Google MediaPipe within MaxMSP, the system enables algorithmic agents to act as both collaborative and disruptive partners in the performance loop. These agents generate unexpected musical gestures and offer novel, interactive modalities that stimulate and provoke the performers. The result is an evolving musical language that emerges from the entangled dynamics of this extended network of human and machine improvisers.