  Machine learning (ML) systems are rapidly increasing in size, are acquiring
new capabilities, and are increasingly deployed in high-stakes settings. As
with other powerful technologies, safety for ML should be a leading research
priority. In response 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. We present
four problems ready for research, namely withstanding hazards ("Robustness"),
identifying hazards ("Monitoring"), reducing inherent model hazards
("Alignment"), and reducing systemic hazards ("Systemic Safety"). Throughout,
we clarify each problem's motivation and provide concrete research directions.
