Instruments like Spring Spyre would arguably be impossible to make without the use of ML; the mappings involved are just too complex for a human programmer to manage. Other instruments, perhaps like those in From the Waters, might feasibly be created with some painstaking programming without ML; however, the question arises whether an instrument builder would commit the time and effort needed to build the desired mapping, or whether they might just decide to build something simpler. Undoubtedly, IML enables people to make instruments and sounds they would not make otherwise.

Furthermore, using IML can arguably change one’s creative process, compared to writing mappings by hand. I’ve already discussed above how designing from demonstrated examples can allow instrument makers to take a more embodied approach to design, and focusing on movement and sound rather than programming can make the creation process more enjoyable. But using IML can also introduce fruitful surprises into the design process, particularly when using regression: because regression models are capable of producing output values that have not been present in the training set, giving these models inputs that are unlike those in the training set can lead to unexpected behaviours. In instrument building, these unexpected behaviours usually arise in the form of new sounds—some of which may be undesired, but some of which may compelling, and which an instrument builder may want to reinforce in their instrument through adding new training examples that include those sounds. This is very different from the “surprises” one usually encounters during programming (e.g., compiler errors, no sound happening, things just not working).