AI for Science的高质量发展对于加快实现高水平科技自立自强、建设世界科技强国具有重要意义。从AI、Science、for三个核心概念出发,探讨如何理解和发展AI for Science。在人机协作形态的历史演进中理解AI,即在感知性协作、肢体性协作...AI for Science的高质量发展对于加快实现高水平科技自立自强、建设世界科技强国具有重要意义。从AI、Science、for三个核心概念出发,探讨如何理解和发展AI for Science。在人机协作形态的历史演进中理解AI,即在感知性协作、肢体性协作、计算性协作到认知性协作的脉络中考察AI在科学研究中的角色。AI不再只是从外部为科研赋能,而是逐步介入科学家的思维过程,在认知层面与科学家建立深度协作。从全过程科研视角分析Science,AI不仅赋能科学知识生产,也推动科技传播的民主化与精准化,助力科研管理与评价的智能化。从技术—社会系统的视角来看,为了更好地释放AI for Science的潜力,应着眼于面向AI for Science的科研生态系统整体建设。未来应从人才培养、科研组织、数据资源、伦理治理等行动领域出发,提升科研人员的AI for Science胜任力、推动跨领域合作、促进高质量数据流通与共享以及加强科技伦理治理。展开更多
The use of artificial intelligence(AI)has increased since the middle of the 20th century,as evidenced by its applications to a wide range of engineering and science problems.Air traffic management(ATM)is becoming incr...The use of artificial intelligence(AI)has increased since the middle of the 20th century,as evidenced by its applications to a wide range of engineering and science problems.Air traffic management(ATM)is becoming increasingly automated and autonomous,making it lucrative for AI applications.This paper presents a systematic review of studies that employ AI techniques for improving ATM capability.A brief account of the history,structure,and advantages of these methods is provided,followed by the description of their applications to several representative ATM tasks,such as air traffic services(ATS),airspace management(AM),air traffic flow management(ATFM),and flight operations(FO).The major contribution of the current review is the professional survey of the AI application to ATM alongside with the description of their specific advantages:(i)these methods provide alternative approaches to conventional physical modeling techniques,(ii)these methods do not require knowing relevant internal system parameters,(iii)these methods are computationally more efficient,and(iv)these methods offer compact solutions to multivariable problems.In addition,this review offers a fresh outlook on future research.One is providing a clear rationale for the model type and structure selection for a given ATM mission.Another is to understand what makes a specific architecture or algorithm effective for a given ATM mission.These are among the most important issues that will continue to attract the attention of the AI research community and ATM work teams in the future.展开更多
文摘AI for Science的高质量发展对于加快实现高水平科技自立自强、建设世界科技强国具有重要意义。从AI、Science、for三个核心概念出发,探讨如何理解和发展AI for Science。在人机协作形态的历史演进中理解AI,即在感知性协作、肢体性协作、计算性协作到认知性协作的脉络中考察AI在科学研究中的角色。AI不再只是从外部为科研赋能,而是逐步介入科学家的思维过程,在认知层面与科学家建立深度协作。从全过程科研视角分析Science,AI不仅赋能科学知识生产,也推动科技传播的民主化与精准化,助力科研管理与评价的智能化。从技术—社会系统的视角来看,为了更好地释放AI for Science的潜力,应着眼于面向AI for Science的科研生态系统整体建设。未来应从人才培养、科研组织、数据资源、伦理治理等行动领域出发,提升科研人员的AI for Science胜任力、推动跨领域合作、促进高质量数据流通与共享以及加强科技伦理治理。
基金supported by the National Natural Science Foundation of China(62073330)the Natural Science Foundation of Hunan Province(2020JJ4339)the Scientific Research Fund of Hunan Province Education Department(20B272).
文摘The use of artificial intelligence(AI)has increased since the middle of the 20th century,as evidenced by its applications to a wide range of engineering and science problems.Air traffic management(ATM)is becoming increasingly automated and autonomous,making it lucrative for AI applications.This paper presents a systematic review of studies that employ AI techniques for improving ATM capability.A brief account of the history,structure,and advantages of these methods is provided,followed by the description of their applications to several representative ATM tasks,such as air traffic services(ATS),airspace management(AM),air traffic flow management(ATFM),and flight operations(FO).The major contribution of the current review is the professional survey of the AI application to ATM alongside with the description of their specific advantages:(i)these methods provide alternative approaches to conventional physical modeling techniques,(ii)these methods do not require knowing relevant internal system parameters,(iii)these methods are computationally more efficient,and(iv)these methods offer compact solutions to multivariable problems.In addition,this review offers a fresh outlook on future research.One is providing a clear rationale for the model type and structure selection for a given ATM mission.Another is to understand what makes a specific architecture or algorithm effective for a given ATM mission.These are among the most important issues that will continue to attract the attention of the AI research community and ATM work teams in the future.
