Artificial intelligence: A powerful paradigm for scientific research
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-th...
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Elsevier
2021
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oai:doaj.org-article:f012a0d028854bffb505c4676fd1987e2021-11-28T04:39:21ZArtificial intelligence: A powerful paradigm for scientific research2666-675810.1016/j.xinn.2021.100179https://doaj.org/article/f012a0d028854bffb505c4676fd1987e2021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666675821001041https://doaj.org/toc/2666-6758Artificial 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. Public summary: • “Can machines think?” The goal of artificial intelligence (AI) is to enable machines to mimic human thoughts and behaviors, including learning, reasoning, predicting, and so on. • “Can AI do fundamental research?” AI coupled with machine learning techniques is impacting a wide range of fundamental sciences, including mathematics, medical science, physics, etc. • “How does AI accelerate fundamental research?” New research and applications are emerging rapidly with the support by AI infrastructure, including data storage, computing power, AI algorithms, and frameworks.Yongjun XuXin LiuXin CaoChangping HuangEnke LiuSen QianXingchen LiuYanjun WuFengliang DongCheng-Wei QiuJunjun QiuKeqin HuaWentao SuJian WuHuiyu XuYong HanChenguang FuZhigang YinMiao LiuRonald RoepmanSabine DietmannMarko VirtaFredrick KengaraZe ZhangLifu ZhangTaolan ZhaoJi DaiJialiang YangLiang LanMing LuoZhaofeng LiuTao AnBin ZhangXiao HeShan CongXiaohong LiuWei ZhangJames P. LewisJames M. TiedjeQi WangZhulin AnFei WangLibo ZhangTao HuangChuan LuZhipeng CaiFang WangJiabao ZhangElsevierarticleartificial intelligencemachine learningdeep learninginformation sciencemathematicsmedical scienceScience (General)Q1-390ENThe Innovation, Vol 2, Iss 4, Pp 100179- (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
artificial intelligence machine learning deep learning information science mathematics medical science Science (General) Q1-390 |
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artificial intelligence machine learning deep learning information science mathematics medical science Science (General) Q1-390 Yongjun Xu Xin Liu Xin Cao Changping Huang Enke Liu Sen Qian Xingchen Liu Yanjun Wu Fengliang Dong Cheng-Wei Qiu Junjun Qiu Keqin Hua Wentao Su Jian Wu Huiyu Xu Yong Han Chenguang Fu Zhigang Yin Miao Liu Ronald Roepman Sabine Dietmann Marko Virta Fredrick Kengara Ze Zhang Lifu Zhang Taolan Zhao Ji Dai Jialiang Yang Liang Lan Ming Luo Zhaofeng Liu Tao An Bin Zhang Xiao He Shan Cong Xiaohong Liu Wei Zhang James P. Lewis James M. Tiedje Qi Wang Zhulin An Fei Wang Libo Zhang Tao Huang Chuan Lu Zhipeng Cai Fang Wang Jiabao Zhang Artificial intelligence: A powerful paradigm for scientific research |
| description |
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. Public summary: • “Can machines think?” The goal of artificial intelligence (AI) is to enable machines to mimic human thoughts and behaviors, including learning, reasoning, predicting, and so on. • “Can AI do fundamental research?” AI coupled with machine learning techniques is impacting a wide range of fundamental sciences, including mathematics, medical science, physics, etc. • “How does AI accelerate fundamental research?” New research and applications are emerging rapidly with the support by AI infrastructure, including data storage, computing power, AI algorithms, and frameworks. |
| format |
article |
| author |
Yongjun Xu Xin Liu Xin Cao Changping Huang Enke Liu Sen Qian Xingchen Liu Yanjun Wu Fengliang Dong Cheng-Wei Qiu Junjun Qiu Keqin Hua Wentao Su Jian Wu Huiyu Xu Yong Han Chenguang Fu Zhigang Yin Miao Liu Ronald Roepman Sabine Dietmann Marko Virta Fredrick Kengara Ze Zhang Lifu Zhang Taolan Zhao Ji Dai Jialiang Yang Liang Lan Ming Luo Zhaofeng Liu Tao An Bin Zhang Xiao He Shan Cong Xiaohong Liu Wei Zhang James P. Lewis James M. Tiedje Qi Wang Zhulin An Fei Wang Libo Zhang Tao Huang Chuan Lu Zhipeng Cai Fang Wang Jiabao Zhang |
| author_facet |
Yongjun Xu Xin Liu Xin Cao Changping Huang Enke Liu Sen Qian Xingchen Liu Yanjun Wu Fengliang Dong Cheng-Wei Qiu Junjun Qiu Keqin Hua Wentao Su Jian Wu Huiyu Xu Yong Han Chenguang Fu Zhigang Yin Miao Liu Ronald Roepman Sabine Dietmann Marko Virta Fredrick Kengara Ze Zhang Lifu Zhang Taolan Zhao Ji Dai Jialiang Yang Liang Lan Ming Luo Zhaofeng Liu Tao An Bin Zhang Xiao He Shan Cong Xiaohong Liu Wei Zhang James P. Lewis James M. Tiedje Qi Wang Zhulin An Fei Wang Libo Zhang Tao Huang Chuan Lu Zhipeng Cai Fang Wang Jiabao Zhang |
| author_sort |
Yongjun Xu |
| title |
Artificial intelligence: A powerful paradigm for scientific research |
| title_short |
Artificial intelligence: A powerful paradigm for scientific research |
| title_full |
Artificial intelligence: A powerful paradigm for scientific research |
| title_fullStr |
Artificial intelligence: A powerful paradigm for scientific research |
| title_full_unstemmed |
Artificial intelligence: A powerful paradigm for scientific research |
| title_sort |
artificial intelligence: a powerful paradigm for scientific research |
| publisher |
Elsevier |
| publishDate |
2021 |
| url |
https://doaj.org/article/f012a0d028854bffb505c4676fd1987e |
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