Advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques present an opportunity to establish and develop a new paradigm for science and operations in the space weather community. Bridging the gap between the space science and the AI/ML community is crucial to working with the enormous datasets collected by space missions. Large, and freely available datasets of in-situ and remote observations collected over several decades of space missions allow for space weather to be an ideal application for contemporary AI/ML methods. We envision an all-encompassing session with presentations, including but not limited to, discussing the advances in space weather utilizing the nontraditional AI/ML approaches that consider the nonlinear and complex dynamics of space weather to improve classification, identification, and modeling and forecasting with uncertainty quantification and propagation. Studies that discuss evaluation metrics, uncertainty quantification, comparison of methods, reproducibility of results, and the use of novel techniques in the context of predictive space weather applications are encouraged. Finally, the application of machine learning for uncovering new scientific knowledge is also strongly encouraged.
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