AI and Machine Learning have catapulted Python into the number one programming language. This same trend brought new frameworks, and new contributors which were not always aware of the existing systems and libraries. This has resulted in a divergence of fundamental array objects and diverging downstream functional stacks. I review some of the popular Machine Learning Frameworks as well as a brief history of NumPy and SciPy to provide context to a new proposed project of a general array interface to connect downstream computations with multiple backend implementations of logical arrays.