Kill Chaos

"Kill Chaos" broadly refers to research efforts aimed at understanding, mitigating, or harnessing chaotic behavior in diverse systems, from artificial neural networks to physical phenomena. Current research focuses on identifying "edges of chaos" – optimal complexity levels for intelligence emergence in AI and efficient exploration in robotics – and developing methods to detect and manage chaos in deep learning models (e.g., using modified vision transformers) and time series data (e.g., via transformer-based anomaly detection). This work has significant implications for improving the robustness, efficiency, and interpretability of AI systems, as well as for advancing our understanding and prediction of complex natural processes.