ML Safety Challenges
Summary
Machine Learning (ML) safety challenges encompass a wide range of critical issues that need to be addressed as AI systems become more powerful and widely deployed. These challenges include robustness to various hazards, effective monitoring and identification of potential risks, alignment of AI systems with human values and intentions, and mitigation of systemic risks. Researchers have developed frameworks and environments to test and evaluate AI safety properties, such as safe interruptibility, avoiding side effects, and robustness to distributional shifts. Additionally, the concept of “prepotence” has been introduced to help delineate potential existential risks from AI. As the field progresses, it is crucial to consider both the benefits and potential negative side effects of various research directions in AI safety, ensuring that developments are implemented with adequate forethought and oversight to safeguard humanity’s long-term prospects.