Good categorization. However, the example of sound -> text uses a set of expected invariants for the problem. Invariants abound in sensory data related problems

Good categorization. However, the example of sound -> text uses a set of expected invariants for the problem. Invariants abound in sensory data related problems as they are inherently high dimensional as measured but our interest lies in a vastly reduced space. You could squint and look at these examples as PBT as well. Such powerful invariants have been hard (caveat: for me, personally, not done research) in general systems dev areas. Other examples more illustrative would be useful. Also, this is commonly done as "data augmentation" in ML ... which isn't surprising given the ML related examples linked in the post. When talking to software engineers without an ML exposure I often ask them to think of ML as "extreme TDD" and gave a basic level talk with that perspective [1]. [1]: http://sriku.org/blog/2019/03/07/talk-machine-learning-as-extreme-tdd/ http://sriku.org/blog/2019/03/07/talk-machine-learning-as-ex... edit: by "ML as extreme TDD", I don't quite mean the algorithms themselves as they're not central. I refer more to the data work involved.