Engineering and AI: Advancing the synergy

被引:0
|
作者
Chellappa, Ramalingam [1 ,2 ]
Madhavan, Guru [3 ]
Schlesinger, T. E. [1 ]
Anderson, John L. [3 ]
机构
[1] Johns Hopkins Univ, Whiting Sch Engn, Baltimore, MD 21218 USA
[2] Johns Hopkins Univ, Sch Med, Baltimore, MD 21218 USA
[3] Natl Acad Engn, Washington, DC 20001 USA
来源
PNAS NEXUS | 2025年 / 4卷 / 03期
关键词
artificial intelligence; autonomous systems; robotics; materials and manufacturing; ethics and responsibility;
D O I
10.1093/pnasnexus/pgaf030
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
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页数:9
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