Advancing Control in the Era of ML and AI

How might the current and expected future advances in machine learning and artificial intelligence lead to new opportunities for the systems and control community? I have been motivated by this timely question to write this brief essay. Right at the start, I would like to state that the discussion below is meant to be comprehensive. Rather, my purpose is to present a perspective and some initial thinking on how the systems and control community could engage in this emerging future more fully. Machine Learning and Artificial Intelligence Context: Over the last decade, great advances have been made in machine learning (ML) and artificial intelligence (AI). Specifically, deep learning architectures, algorithms and techniques have created powerful tools to learn representations of large volumes of data in multiple layers of representation [1]. These tools appear to be very powerful and versatile in learning complex functions and discovering intricate structures in high-dimensional data. They have shown superior performance in image and speech recognition and are being applied in a wide variety of problems: drug discovery, particle physics, astronomy, and biomedicine. More broadly, confluence of insights and techniques from neuroscience, cognitive science, reinforcement learning (RL), and deep learning (DL) has led to very impressive progress in AI with amazing achievements in championship games and demonstration of human level control by an artificial agent [2, 3]. Increasing computational power (thanks to Moore’s Law progress) and availability of large amounts of data have been critical to many of these successes and will be increasingly even more important to continuing progress [4, 5]. More importantly, there is a flood of interest, and corresponding investments, from the academic, industrial and government sectors in ML and AI [6]. Control Systems: Control systems have a deep and broad base of foundational knowledge developed over the last 60 years. Dynamic systems modeling, structural properties, identification, stability, feedback, optimality, robustness, fault tolerance, and architecture have been among the central concerns on the theoretical side. These issues have been explored in a wide variety of settings: linear, nonlinear, stochastic, hybrid, distributed, supervisory, and others. Applications have been wide ranging: aerospace, automotive, manufacturing, chemical process, energy, power, transportation, etc. While there is a very rich history, the future is just as promising as there are a multitude of directions for future theoretical and applications research [7]. Future I believe that we are at a truly opportune moment to develop a forward looking vision that can inspire talented researchers for the next decade or more. On the one hand, a major goal of AI is to build machines that can learn and think for themselves [8], including having imagination, reasoning, planning, etc. On the other hand, we have a rich body of knowledge in control systems. The field of control can both benefit from and influence the ongoing revolutionary advances in ML and AI. These advances in ML and AI are going to be driven by the huge increases in computation and data, intense interest, and large investments in these fields across academic, industrial and government entities. By leveraging these ongoing and fortuitous advances in ML/AI, we can aim to have significantly more powerful and versatile control systems. For this, we would need to define specific goals that are currently unachievable with existing control techniques but could potentially be achieved by leveraging ML/AI advances. Such goals would likely be driven by major application areas for control. They would have implications and opportunities for theoretical developments in control. On the other side, we can identify ideas, tools and techniques from control systems that have the potential to advance AI in its quest of building machines that learn and think for themselves.