One of the most important and repeated lessons I learned through this work was that using ML to build creative interactions, such as new musical instruments and other performance interfaces, is fundamentally different from more conventional uses of ML, and the tools that we use to support this work must therefore look and behave differently as well.

Three key areas of difference, as I discuss below, pertain to the training data, the method for evaluating a model, and the methods for improving or changing a model.