Artificial intelligence in paleontology
文献类型：期刊论文
| 作者 | Yu, Congyu12,13,14,15; Qin, Fangbo11; Watanabe, Akinobu9,10,12; Yao, Weiqi8; Li, Ying; Qin, Zichuan6,7; Liu, Yuming7; Wang, Haibing5; Qigao, Jiangzuo4,5; Hsiang, Allison Y.3 |
| 刊名 | EARTH-SCIENCE REVIEWS
 |
| 出版日期 | 2024-05-01 |
| 卷号 | 252页码:15 |
| 关键词 | Paleontology Fossil Artificial intelligence Machine learning Deep learning Classification Segmentation Prediction |
| ISSN号 | 0012-8252 |
| DOI | 10.1016/j.earscirev.2024.104765 |
| 通讯作者 | Yu, Congyu(congyuyu@cdut.edu.cn) |
| 英文摘要 | 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. |
| WOS关键词 | PHANEROZOIC TAXONOMIC DIVERSITY ; NEURAL-NETWORK ANALYSIS ; FOURIER SHAPE-ANALYSIS ; KINETIC-MODEL ; AUTOMATED IDENTIFICATION ; PLANKTIC FORAMINIFERA ; EXPERT-SYSTEMS ; DEEP ; RECOGNITION ; EVOLUTION |
| 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] |
| WOS研究方向 | Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:001227153700001 |
| 出版者 | ELSEVIER |
| 资助机构 | 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 |
| 源URL | [http://119.78.100.205/handle/311034/23681]  |
| 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.Shenyang Normal Univ, Paleontol Museum Liaoning, 253 North Huanghe St, Shenyang 110034, Liaoning, Peoples R China 2.Yunnan Univ, Ctr Vertebrate Evolutionary Biol, Kunming 650091, Peoples R China 3.Stockholm Univ, Dept Geol Sci, Svante Arrhenius vag 8, S-10691 Stockholm, Sweden 4.Peking Univ, Sch Earth & Space Sci, Beijing 100087, Peoples R China 5.Chinese Acad Sci, Inst Vertebrate Paleontol & Paleoanthropol, Key Lab Vertebrate Evolut & Human Origins, Beijing 100044, Peoples R China 6.Univ Birmingham, Sch Geog Earth & Environm Sci, Birmingham B15 2TT, England 7.Univ Bristol, Sch Earth Sci, Palaeobiol Res Grp, Bristol BS8 1RJ, England 8.Southern Univ Sci & Technol, Dept Ocean Sci & Engn, Shenzhen 518055, Peoples R China 9.Life Sci Dept, Nat Hist Museum, London SW7 5BD, England 10.New York Inst Technol, Dept Anat, Coll Osteopath Med, Old Westbury, NY 11568 USA |
| 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. |
入库方式： OAI收割
来源：古脊椎动物与古人类研究所
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