Artificial intelligence in paleontology

doi:10.1016/j.earscirev.2024.104765

IVPP-IR

	Artificial intelligence in paleontology
	Yu, Congyu 1,2,3,4; Qin, Fangbo 5; Watanabe, Akinobu 4,6,7; Yao, Weiqi 8; Li, Ying ; Qin, Zichuan 9,10; Liu, Yuming 9; Wang, Haibing 11; Qigao, Jiangzuo 11,12; Hsiang, Allison Y.13; Ma, Chao 1,2,3; Rayfield, Emily 9; Benton, Michael J.9; Xu, Xing14,15
	2024-05-01
发表期刊	EARTH-SCIENCE REVIEWS
ISSN	0012-8252
卷号	252 页码:15
通讯作者	Yu, Congyu([email protected])
摘要	The accumulation of large datasets and increasing data availability have led to the emergence of data-driven paleontological studies, which reveal an unprecedented picture of evolutionary history. However, the fastgrowing quantity and complication of data modalities make data processing laborious and inconsistent, while also lacking clear benchmarks to evaluate data collection and generation, and the performances of different methods on similar tasks. Recently, artificial intelligence (AI) has become widely practiced across scientific disciplines, but not so much to date in paleontology where traditionally manual workflows have been more usual. In this study, we review >70 paleontological AI studies since the 1980s, covering major tasks including micro- and macrofossil classification, image segmentation, and prediction. These studies feature a wide range of techniques such as Knowledge-Based Systems (KBS), neural networks, transfer learning, and many other machine learning methods to automate a variety of paleontological research workflows. Here, we discuss their methods, datasets, and performance and compare them with more conventional AI studies. We attribute the recent increase in paleontological AI studies most to the lowering of the entry bar in training and deployment of AI models rather than innovations in fossil data compilation and methods. We also present recently developed AI implementations such as diffusion model content generation and Large Language Models (LLMs) that may interface with paleontological research in the future. Even though AI has not yet been a significant part of the paleontologist's toolkit, successful implementation of AI is growing and shows promise for paradigm-transformative effects on paleontological research in the years to come.
关键词	Paleontology Fossil Artificial intelligence Machine learning Deep learning Classification Segmentation Prediction
DOI	10.1016/j.earscirev.2024.104765
关键词[WOS]	PHANEROZOIC TAXONOMIC DIVERSITY ; NEURAL-NETWORK ANALYSIS ; FOURIER SHAPE-ANALYSIS ; KINETIC-MODEL ; AUTOMATED IDENTIFICATION ; PLANKTIC FORAMINIFERA ; EXPERT-SYSTEMS ; DEEP ; RECOGNITION ; EVOLUTION
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[42288201] ; National Natural Science Foundation of China[42272017] ; Youth Innovation Promotion Association, CAS[2021068] ; Yunnan Revitalization Talent Support Program[202305AB350006] ; Swedish Research Council (Vetenskapsradet) Starting Researcher Grant[AR-NT 2020-03515] ; Chengdu University of Technology Zhufeng Starting Grant[10912-KYQD2023-09966]
项目资助者	National Natural Science Foundation of China ; Youth Innovation Promotion Association, CAS ; Yunnan Revitalization Talent Support Program ; Swedish Research Council (Vetenskapsradet) Starting Researcher Grant ; Chengdu University of Technology Zhufeng Starting Grant
WOS研究方向	Geology
WOS类目	Geosciences, Multidisciplinary
WOS记录号	WOS:001227153700001
出版者	ELSEVIER
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.205/handle/311034/23682
专题	中国科学院古脊椎动物与古人类研究所
通讯作者	Yu, Congyu
作者单位	1.Chengdu Univ Technol, State Key Lab Oil & Gas Reservoir Geol & Exploitat, Chengdu 610059, Peoples R China 2.Chengdu Univ Technol, Inst Sedimentary Geol, Chengdu 610059, Peoples R China 3.Chengdu Univ Technol, Key Lab Deep Time Geog & Environm Reconstruct & Ap, Minist Nat Resources, Chengdu 610059, Peoples R China 4.Amer Museum Nat Hist, Div Paleontol, New York, NY 10024 USA 5.Inst Automation, Inst Automat, Beijing 100190, Peoples R China 6.New York Inst Technol, Dept Anat, Coll Osteopath Med, Old Westbury, NY 11568 USA 7.Life Sci Dept, Nat Hist Museum, London SW7 5BD, England 8.Southern Univ Sci & Technol, Dept Ocean Sci & Engn, Shenzhen 518055, Peoples R China 9.Univ Bristol, Sch Earth Sci, Palaeobiol Res Grp, Bristol BS8 1RJ, England 10.Univ Birmingham, Sch Geog Earth & Environm Sci, Birmingham B15 2TT, England 11.Chinese Acad Sci, Inst Vertebrate Paleontol & Paleoanthropol, Key Lab Vertebrate Evolut & Human Origins, Beijing 100044, Peoples R China 12.Peking Univ, Sch Earth & Space Sci, Beijing 100087, Peoples R China 13.Stockholm Univ, Dept Geol Sci, Svante Arrhenius vag 8, S-10691 Stockholm, Sweden 14.Yunnan Univ, Ctr Vertebrate Evolutionary Biol, Kunming 650091, Peoples R China 15.Shenyang Normal Univ, Paleontol Museum Liaoning, 253 North Huanghe St, Shenyang 110034, Liaoning, Peoples R China
推荐引用方式 GB/T 7714	Yu, Congyu,Qin, Fangbo,Watanabe, Akinobu,et al. Artificial intelligence in paleontology[J]. EARTH-SCIENCE REVIEWS,2024,252:15.
APA	Yu, Congyu.,Qin, Fangbo.,Watanabe, Akinobu.,Yao, Weiqi.,Li, Ying.,...&Xu, Xing.(2024).Artificial intelligence in paleontology.EARTH-SCIENCE REVIEWS,252,15.
MLA	Yu, Congyu,et al."Artificial intelligence in paleontology".EARTH-SCIENCE REVIEWS 252(2024):15.