(419a) Reframing Computing and Artificial Intelligence: Based on Principles from Statistical/Quantum Physics, Chemistry, and Information Theory

The current era of computing is mainly driven by mostly general-purpose computing architectures (e.g., von Neumann architectures) and deep neural networks (DNN) for wider applications for Machine Intelligence in driverless cars (Level 3 & 4), natural language processing (chatGPT), and scientific applications (High Performance Computing). However, these are limiting in many cases given the requirements of large sets of data for every application and as illustrated in previous work were specific guiding principles for use of AI/ML methods in chemistry and materials. We will address these inefficiencies and whether even more advanced architectures like nature-inspired and quantum computing can be solutions to address all applications. To address the limitations of these computing solutions in addressing all of the realistic complex problems associated with scientific and engineering computations from atomic-design to weather predictions, we propose a new logical framework for characterizing all information processing systems for a given application, linking principles from physics, chemistry, and information theory. This framework classifies all aspects of information processing, as it points to possibilities of many forms of computing that are yet to be discovered beyond the more traditional Boolean algebra and digital computing platforms using the lessons from nature. Building on the framework, we will illustrate specific examples of newer forms of computing and AI methods.