The field is moving towards developing more sophisticated methods for detecting anomalies and improving system reliability. Researchers are exploring new approaches to identify potential threats and failures in complex systems, such as multi-processor system-on-chips and cloud infrastructure. The use of machine learning and data-driven techniques is becoming increasingly popular in this area. Notably, innovative methods for real-time anomaly detection and memory failure prediction are being proposed, which have the potential to significantly improve system reliability and performance. Some noteworthy papers in this area include: M$^2$-MFP, which proposes a multi-scale and multi-level memory failure prediction framework that outperforms existing state-of-the-art methods. Real-Time Decorrelation-Based Anomaly Detection for Multivariate Time Series, which introduces a novel real-time decorrelation-based anomaly detection method that achieves superior performance across diverse anomaly types.