Machine Learning's Learning Opacity Rooted in Dynamical Complexity.

A new academic paper delves into the underexplored concept of "learning opacity" in machine learning, distinguishing it from the more commonly studied "prediction opacity." While prediction opacity concerns why a model makes a specific decision, learning opacity focuses on the lack of understanding regarding the dynamic evolution of model parameters, such as neural network weights, during the training process. The authors argue that this opacity stems from the inherent nature of ML training as a complex dynamical system. The research identifies three primary contributors to this complexity and subsequent opacity: the sensitivity of training outcomes to initial weight configurations, the intricate feedback loops present in gradient-based optimization algorithms, and the sensitivity of the learning process to the specific training data. The paper posits that because these properties are fundamental to how ML systems learn, attempting to eliminate or significantly dampen them would fundamentally alter the learning mechanism itself. Consequently, some aspects of learning opacity may be intrinsic and irreducible.