Researchers at Google Brain have identified several concrete safety problems in artificial intelligence, including issues with misaligned objectives and unintended consequences of complex algorithms. These problems are explored in a new paper co-authored by researchers from Berkeley and Stanford. The authors aim to ensure that modern machine learning systems operate as intended, but acknowledge the challenges involved. They outline specific research areas, such as robustness and interpretability, where further work is needed. --- Why it matters: This matters because it highlights the need for more rigorous research into AI safety, which is critical for the development of trustworthy autonomous systems. Engineers working on AI will need to address these problems to ensure their systems don't cause unintended harm or consequences. Source: https://openai.com/index/concrete-ai-safety-problems