Machine Learning, Kolmogorov Complexity, and Squishy Bunnies


We know that Machine Learning is an extremely powerful tool for tackling complex problems which we don't know how to solve by conventional means. Problems like image classification can be solved effectively by Machine Learning because at the end of the day, gathering data for that kind of task is much easier than coming up with hand-written rules for such a complex and difficult problem. But what about problems we already know how to solve? Is there any reason to apply Machine Learning to problems we already have working solutions for? Tasks such as physics simulation, where the rules and equations governing the task are already well known and explored?