NEXT Machine Learning Paradigm: "DYNAMICAL" ML

Dynamical ML is machine learning that can adapt to variations over time; it requires "real-time recursive" learning algorithms and time-varying data models such as the ones described in the blog, Generalized Dynamical Machine Learning. In the process of DYNAMICAL machine learning (DML) applied to industrial IoT, the data model and the algorithms used (Generalized Dynamical Machine Learning) naturally generates what is called the "State-space" model of the machine. It may not *look* like the machine but it captures the dynamics in all its detail (there can be challenges in relating "states" to actual machine components though). I am a proponent of using the "State-space representation" that we get for FREE in Dynamical ML as the "digital twin". This is a topic of current exploration and advancement.