The field of machine learning is rapidly advancing, with a growing focus on developing models that can handle complex systems and uncertainty. Recent research has highlighted the importance of incorporating physical relationships and uncertainty quantification into machine learning models, enabling them to make more accurate and reliable predictions. This is particularly significant in applications such as weather forecasting and autonomous vehicles, where uncertainty can have a major impact on decision-making. Noteworthy papers in this area include the development of a Random-Set Neural Network (RS-NN) for uncertainty-aware image classification, and the introduction of a Common Task Framework (CTF) for evaluating scientific machine learning algorithms. The RS-NN has been shown to achieve state-of-the-art performance in uncertainty calibration, while the CTF provides a standardized framework for comparing the performance of different algorithms. Overall, these advances have the potential to significantly improve the accuracy and reliability of machine learning models in complex systems, and are likely to have a major impact on fields such as weather forecasting, autonomous vehicles, and education.