Nature

, том 620 , издание 7972 , страницы 47-60

Scientific discovery in the age of artificial intelligence

Hanchen Wang ^{1, 2, 3, 4}

Tianfan Fu ⁵

Yuanqi Du ⁶

Wenhao Gao ⁷

Kexin Huang ⁴

Ziming Liu ⁸

Payal Chandak ⁹

Shengchao Liu ^{10, 11}

Peter Van Katwyk ^{12, 13}

Andreea Deac ^{10, 11}

Anima Anandkumar ^{2, 14}

Karianne Bergen ^{12, 13}

Carla P. Gomes ⁶

Shirley Ho ^{15, 16, 17, 18}

Pushmeet Kohli ¹⁹

Joan Lasenby ¹

Jure Leskovec ⁴

Tie-Yan Liu ²⁰

Arjun Manrai ²¹

Debora S. Marks ^{22, 23}

Bharath Ramsundar ²⁴

Le Song ^{25, 26}

Jimeng Sun ²⁷

Jian Tang ^{10, 28, 29}

Petar VeliČković ^{19, 30}

Max Welling ^{31, 32}

Linfeng Zhang ^{33, 34}

Connor W. Coley ^{7, 35}

Yoshua Bengio ^{10, 11}

Marinka Zitnik ^{21, 23, 36, 37}

Скрыть аффилиации авторов Показать аффилиации авторов: 37 аффилиаций

Department Of Engineering, University Of Cambridge, Cambridge, UK |

Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, USA |

Department of Research and Early Development, Genentech Inc, South San Francisco, USA |

⁴

Department of Computer Science, Stanford University, Stanford, USA |

⁵

Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, USA |

⁶

Department of Computer Science, Cornell University, Ithaca, USA |

⁷

Department of Chemical Engineering, Massachusetts Institute Of Technology, Cambridge, USA |

⁸

Department of Physics, Massachusetts Institute of Technology, Cambridge, USA |

⁹

Harvard-MIT Program in Health Sciences and Technology, Cambridge, USA |

¹⁰

Mila – Quebec AI institute, Montreal, Canada |

¹¹

Université de Montréal, Montreal, Canada |

¹²

Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, USA |

¹³

Data Science Institute, Brown University, Providence, USA |

¹⁴

Nvidia, Santa Clara, USA |

¹⁵

Center for Computational Astrophysics, Flatiron Institute, New York, USA |

¹⁶

Department of Astrophysical Sciences, Princeton University, Princeton, USA |

¹⁷

Department of Physics, Carnegie Mellon University, Pittsburgh, USA |

¹⁸

Department of Physics and Center for Data Science, New York University, New York, USA |

¹⁹

Google DeepMind, London, UK |

²⁰

Microsoft Research, Beijing, China |

²¹

Department of BIomedical Informatics, Harvard Medical School, Boston, USA |

²²

Department of Systems Biology, Harvard Medical School, Boston, USA |

²³

Broad Institute of MIT and Harvard, Cambridge, USA |

²⁴

Deep Forest Sciences, Palo Alto, USA |

²⁵

BioMap, Beijing, China |

²⁶

Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates |

²⁷

University of Illinois At Urbana-Champaign, Champaign, USA |

²⁸

Hec Montréal, Montreal, Canada |

²⁹

CIFAR AI Chair, Toronto, Canada |

³⁰

Department of Computer Science and Technology, University of Cambridge, Cambridge, UK |

³¹

University of amsterdam, Amsterdam, Netherlands |

³²

Microsoft Research Amsterdam, Amsterdam, Netherlands |

³³

DP Technology, Beijing, China |

³⁴

AI for Science Institute, Beijing, China |

³⁵

Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, USA |

³⁶

Harvard Data Science Initiative, Cambridge, USA |

³⁷

Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, USA |

Тип публикации: Journal Article

Дата публикации: 2023-08-02

Springer Nature

Nature

scimago Q1

wos Q1

БС1

SJR: 18.288

CiteScore: 78.1

Impact factor: 48.5

ISSN: 00280836, 14764687

DOI: 10.1038/s41586-023-06221-2

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PubMed ID: 37532811

Multidisciplinary

Краткое описание

Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI tools need a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation. The advances in artificial intelligence over the past decade are examined, with a discussion on how artificial intelligence systems can aid the scientific process and the central issues that remain despite advances.

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д.х.н., проф., проф., Российская академия наук

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Сколковский институт науки и технологий

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Wang H. et al. Scientific discovery in the age of artificial intelligence // Nature. 2023. Vol. 620. No. 7972. pp. 47-60.

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Wang H. et al. Scientific discovery in the age of artificial intelligence // Nature. 2023. Vol. 620. No. 7972. pp. 47-60.

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TY - JOUR

DO - 10.1038/s41586-023-06221-2

UR - https://doi.org/10.1038/s41586-023-06221-2

TI - Scientific discovery in the age of artificial intelligence

T2 - Nature

AU - Wang, Hanchen

AU - Fu, Tianfan

AU - Du, Yuanqi

AU - Gao, Wenhao

AU - Huang, Kexin

AU - Liu, Ziming

AU - Chandak, Payal

AU - Liu, Shengchao

AU - Van Katwyk, Peter

AU - Deac, Andreea

AU - Anandkumar, Anima

AU - Bergen, Karianne

AU - Gomes, Carla P.

AU - Ho, Shirley

AU - Kohli, Pushmeet

AU - Lasenby, Joan

AU - Leskovec, Jure

AU - Liu, Tie-Yan

AU - Manrai, Arjun

AU - Marks, Debora S.

AU - Ramsundar, Bharath

AU - Song, Le

AU - Sun, Jimeng

AU - Tang, Jian

AU - VeliČković, Petar

AU - Welling, Max

AU - Zhang, Linfeng

AU - Coley, Connor W.

AU - Bengio, Yoshua

AU - Zitnik, Marinka

PY - 2023

DA - 2023/08/02

PB - Springer Nature

SP - 47-60

IS - 7972

VL - 620

PMID - 37532811

SN - 0028-0836

SN - 1476-4687

ER -

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@article{2023_Wang,

author = {Hanchen Wang and Tianfan Fu and Yuanqi Du and Wenhao Gao and Kexin Huang and Ziming Liu and Payal Chandak and Shengchao Liu and Peter Van Katwyk and Andreea Deac and Anima Anandkumar and Karianne Bergen and Carla P. Gomes and Shirley Ho and Pushmeet Kohli and Joan Lasenby and Jure Leskovec and Tie-Yan Liu and Arjun Manrai and Debora S. Marks and Bharath Ramsundar and Le Song and Jimeng Sun and Jian Tang and Petar VeliČković and Max Welling and Linfeng Zhang and Connor W. Coley and Yoshua Bengio and Marinka Zitnik and others},

title = {Scientific discovery in the age of artificial intelligence},

journal = {Nature},

year = {2023},

volume = {620},

publisher = {Springer Nature},

month = {aug},

url = {https://doi.org/10.1038/s41586-023-06221-2},

number = {7972},

pages = {47--60},

doi = {10.1038/s41586-023-06221-2}

}

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Wang, Hanchen, et al. “Scientific discovery in the age of artificial intelligence.” Nature, vol. 620, no. 7972, Aug. 2023, pp. 47-60. https://doi.org/10.1038/s41586-023-06221-2.

Издатель

Springer Nature

Журнал

Nature

scimago Q1

wos Q1

БС1

SJR

18.288

CiteScore

78.1

Impact factor

48.5

ISSN

00280836 (Print)

14764687 (Electronic)

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