Recent developments in artificial intelligence (AI) and machine learning (ML), driven by unprecedented data and computing capabilities, have transformed fields from computer vision to medicine, beginning to influence culture at large. These advances face key challenges: accuracy and trustworthiness issues, security vulnerabilities, algorithmic bias, lack of interpretability, and performance degradation when deployment conditions differ from training data. Fields lacking large datasets have yet to see similar impacts. This paper examines AI and ML's growing influence on engineering systems-from self-driving vehicles to materials discovery-while addressing safety and performance assurance. We analyze current progress and challenges to strengthen the engineering-AI synergy.