If I am a musical instrument builder using ML to build a mapping, one obvious source of training data is me. I can simply record demonstrations of the way I might want to manipulate or move with my instrument, and pair these with examples of the sound synthesis or processing parameters I would like to be associated with those actions, and these can be come my training data. Or, if I am building an instrument for someone else to play, I could record examples of that person moving or manipulating the instrument.

This ability for a ML system builder to generate suitable training examples is unusual in the landscape of ML applications, where typically a person may use ML because they do not fully understand the phenomenon they are modeling (e.g., whether a medical patient is likely to respond to a treatment, or whether the price of a stock is likely to go up or down).