Artificial intelligence in paleontology

Yu，Congyu1,2,3; Qin，Fangbo4; Watanabe，Akinobu3,5,6; Yao，Weiqi7; Li，Ying4; Qin，Zichuan8,9; Liu，Yuming8; Wang，Haibing10; Jiangzuo，Qigao10,11; Hsiang，Allison Y.12; Ma，Chao1,2; Rayfield，Emily8; Benton，Michael J.8; Xu，Xing10,13,14

2024-05-01

10.1016/j.earscirev.2024.104765

Earth-Science Reviews   影响因子和分区

0012-8252

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 fast-growing 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.

Artificial intelligence Classification Deep learning Fossil Machine learning Paleontology Prediction Segmentation

SCI

英语

其他

GEOSCIENCES

2-s2.0-85189647782

Scopus

1.State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation & Institute of Sedimentary Geology,Chengdu University of Technology,Chengdu,610059,China
2.Key Laboratory of Deep-time Geography and Environment Reconstruction and Application of Ministry of Natural Resources,Chengdu University of Technology,Chengdu,610059,China
3.Division of Paleontology,American Museum of Natural History,New York,10024,United States
4.Institute of Automation,Chinese Academy of Sciences,Beijing,100190,China
5.Department of Anatomy,New York Institute of Technology College of Osteopathic Medicine,Old Westbury,11568,United States
6.Life Science Department,Natural History Museum,London,SW7 5BD,United Kingdom
7.Department of Ocean Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
8.Palaeobiology Research Group,School of Earth Sciences,University of Bristol,Bristol,BS8 1RJ,United Kingdom
9.School of Geography,Earth and Environmental Sciences,University of Birmingham,Birmingham,B15 2TT,United Kingdom
10.Key Laboratory of Vertebrate Evolution and Human Origins,Institute of Vertebrate Paleontology and Paleoanthropology,Chinese Academy of Sciences,Beijing,100044,China
11.School of Earth and Space Sciences,Peking University,Beijing,100087,China
12.Department of Geological Sciences,Stockholm University,Stockholm,Svante Arrhenius väg 8,10691,Sweden
13.Centre for Vertebrate Evolutionary Biology,Yunnan University,Kunming,650091,China
14.Paleontological Museum of Liaoning,Shenyang Normal University,Shenyang,253 North Huanghe Street, Liaoning Province,110034,China

Yu，Congyu,Qin，Fangbo,Watanabe，Akinobu,et al. Artificial intelligence in paleontology[J]. Earth-Science Reviews,2024,252.

Yu，Congyu.,Qin，Fangbo.,Watanabe，Akinobu.,Yao，Weiqi.,Li，Ying.,...&Xu，Xing.(2024).Artificial intelligence in paleontology.Earth-Science Reviews,252.

Yu，Congyu,et al."Artificial intelligence in paleontology".Earth-Science Reviews 252(2024).