Internal State

"Internal state" research encompasses diverse fields, focusing on representing and utilizing internal representations of systems to improve prediction, decision-making, and control. Current research emphasizes developing robust methods for estimating and tracking internal states across various domains, employing techniques like Bayesian methods, neural networks (including recurrent and transformer architectures), and Gaussian processes. These advancements have significant implications for diverse applications, including improved battery management, more efficient power grid control, enhanced AI safety, and more accurate predictions in areas like weather forecasting and legal speech transcription.