Machine Learning Safety: Unsolved Problems - KDnuggets

Along with researchers from Google Brain and OpenAI, we are releasing a paper on Unsolved Problems in ML Safety. Due to emerging safety challenges in ML, such as those introduced by recent large-scale models, we provide a new roadmap for ML Safety and refine the technical problems that the field needs to address. As a preview of the paper, in this post, we consider a subset of the paper's directions, namely withstanding hazards ("Robustness"), identifying hazards ("Monitoring"), and steering ML systems ("Alignment"). Robustness research aims to build systems that are less vulnerable to extreme hazards and adversarial threats. Two problems in robustness are robustness to long tails and robustness to adversarial examples.