Supervised learning algorithms can be understood not only as a set of techniques for building accurate models of data, but also as design tools that can enable rapid prototyping, iterative refinement, and embodied engagement— all activities that are crucial in the design of new musical instruments and other embodied interactions. Realising the creative potential of these algorithms requires a rethinking of the interfaces through which people provide data and build models, providing for tight interaction-feedback loops and efficient mechanisms for people to steer and explore algorithm behaviors.
I created the Wekinator software in 2009 to enable composers, game designers, and other creative practitioners to apply such an interactive approach to machine learning to their work. In this talk, I’ll discuss some of my findings from 6 years of observing this software in use in creative contexts, and my thoughts on the future of data and machine learning as design tools. I’ll also give a live demo of a new version of the software that will be released this summer.

Rebecca Fiebrink is a Lecturer at Goldsmiths, University of London. Her research lies at the intersection of computer science, human-computer interaction, and the digital arts. Fiebrink is the developer of the Wekinator system for real-time interactive machine learning (with a new version out this summer!), a co-creator of the Digital Fauvel platform for interactive musicology, and a Co-I on the Horizon 2020-funded RAPID-MIX project on real-time adaptive prototyping for industrial design of multimodal expressive technology. She was previously an Assistant Professor at Princeton University, where she co-directed the Princeton Laptop Orchestra. She has worked with companies including Microsoft Research, Sun Microsystems Research Labs, Imagine Research, and Smule, where she helped to build the #1 iTunes app “I am T-Pain.” She holds a PhD in Computer Science from Princeton University.