The considerations laid out in this paper highlight the complexity of formulating even such a simple task as learning the LS method for single black-white initial conditions and their mixed states for a NN. The prospect of applying ML to this and other exampled of cut-andshuffle mixing may require a more intentional formulation of the problem that more precisely specifies the abstract goal of mixing and depends on the ability to choose relevant data for training. Training for the abstract goal of mixing is a form of reinforcement learning (RL), which has many of the same considerations as the supervised learning approach used here but is still a developing field of research . The fact that a cut-and-shuffle mixing process does not have a unique end-state makes such a problem fundamentally different from many un-mixing problems like the solution of puzzles to which RL has been successfully applied . However, formulating the IET mixing problem as a puzzle where the "solved state" is a particular mixed state could allow a broader application of existing ideas and methods such as those used for solving a Rubik's cube using RL .