AI Safety

AI safety research focuses on mitigating the risks associated with increasingly powerful and autonomous AI systems, aiming to ensure these systems behave reliably and align with human values. Current research emphasizes developing robust safety benchmarks and evaluation tools for large language models (LLMs) and other foundation models, exploring techniques like adversarial training and unlearning to enhance model robustness and mitigate vulnerabilities such as jailbreaks. This field is crucial for responsible AI development and deployment, impacting both the scientific understanding of AI capabilities and the safe integration of AI into various applications. A key challenge is the need for holistic approaches that consider both technical and societal aspects of AI risk.