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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...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8633405/ https://www.ncbi.nlm.nih.gov/pubmed/34877560 http://dx.doi.org/10.1016/j.xinn.2021.100179 |
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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 |
author_facet | 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 |
author_sort | Xu, Yongjun |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-8633405 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-86334052021-12-06 Artificial intelligence: A powerful paradigm for scientific research 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 Innovation (Camb) Review 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. Elsevier 2021-10-28 /pmc/articles/PMC8633405/ /pubmed/34877560 http://dx.doi.org/10.1016/j.xinn.2021.100179 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Review 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 Artificial intelligence: A powerful paradigm for scientific research |
title | 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_short | Artificial intelligence: A powerful paradigm for scientific research |
title_sort | artificial intelligence: a powerful paradigm for scientific research |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8633405/ https://www.ncbi.nlm.nih.gov/pubmed/34877560 http://dx.doi.org/10.1016/j.xinn.2021.100179 |
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