Artificial intelligence : A powerful paradigm for scientific research

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Xu , Y , Liu , X , Cao , X , Huang , C , Liu , E , Qian , S , Liu , X , Wu , Y , Dong , F , Qiu , C-W , Qiu , J , Hua , K , Su , W , Wu , J , Xu , H , Han , Y , Fu , C , Yin , Z , Liu , M , Roepman , R , Dietmann , S , Virta , M , Kengara , F , Zhang , Z , Zhang , L , Zhao , T , Dai , J , Yang , J , Lan , L , Luo , M , Liu , Z , An , T , Zhang , B , He , X , Cong , S , Liu , X , Zhang , W , Lewis , J P , Tiedje , J M , Wang , Q , An , Z , Wang , F , Zhang , L , Huang , T , Lu , C , Cai , Z , Wang , F & Zhang , J 2021 , ' Artificial intelligence : A powerful paradigm for scientific research ' , The innovation , vol. 2 , no. 4 , 100179 . https://doi.org/10.1016/j.xinn.2021.100179

Title: Artificial intelligence : A powerful paradigm for scientific research
Author: Xu, Yongjun; Liu, Xin; Cao, Xin; Huang, Changping; Liu, Enke; Qian, Sen; Liu, Xingchen; Wu, Yanjun; Dong, Fengliang; Qiu, Cheng-Wei; Qiu, Junjun; Hua, Keqin; Su, Wentao; Wu, Jian; Xu, Huiyu; Han, Yong; Fu, Chenguang; Yin, Zhigang; Liu, Miao; Roepman, Ronald; Dietmann, Sabine; Virta, Marko; Kengara, Fredrick; Zhang, Ze; Zhang, Lifu; Zhao, Taolan; Dai, Ji; Yang, Jialiang; Lan, Liang; Luo, Ming; Liu, Zhaofeng; An, Tao; Zhang, Bin; He, Xiao; Cong, Shan; Liu, Xiaohong; Zhang, Wei; Lewis, James P.; Tiedje, James M.; Wang, Qi; An, Zhulin; Wang, Fei; Zhang, Libo; Huang, Tao; Lu, Chuan; Cai, Zhipeng; Wang, Fang; Zhang, Jiabao
Contributor organization: Department of Microbiology
Date: 2021-11-28
Language: eng
Number of pages: 21
Belongs to series: The innovation
ISSN: 2666-6758
DOI: https://doi.org/10.1016/j.xinn.2021.100179
URI: http://hdl.handle.net/10138/340241
Abstract: Y Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.
Subject: artificial intelligence
machine learning
deep learning
information science
mathematics
medical science
materials science
geoscience
life science
physics
chemistry
NEURAL-NETWORKS
DEEP
IDENTIFICATION
OPTIMIZATION
NEUROSCIENCE
PREDICTION
ALGORITHM
SYMMETRY
CATALOG
MODELS
113 Computer and information sciences
Peer reviewed: Yes
Rights: cc_by_nc_nd
Usage restriction: openAccess
Self-archived version: publishedVersion


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