Congyu Yu , Fangbo Qin , Akinobu Watanabe , Weiqi Yao , Ying Li , Zichuan Qin , Yuming Liu , Haibing Wang , Qigao Jiangzuo , Allison Y. Hsiang , Chao Ma , Emily Rayfield , Michael J. Benton , Xing Xu
{"title":"古生物学中的人工智能","authors":"Congyu Yu , Fangbo Qin , Akinobu Watanabe , Weiqi Yao , Ying Li , Zichuan Qin , Yuming Liu , Haibing Wang , Qigao Jiangzuo , Allison Y. Hsiang , Chao Ma , Emily Rayfield , Michael J. Benton , Xing Xu","doi":"10.1016/j.earscirev.2024.104765","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":11483,"journal":{"name":"Earth-Science Reviews","volume":null,"pages":null},"PeriodicalIF":10.8000,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0012825224000928/pdfft?md5=10ea6f8a0dd667959c823da1d3650895&pid=1-s2.0-S0012825224000928-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence in paleontology\",\"authors\":\"Congyu Yu , Fangbo Qin , Akinobu Watanabe , Weiqi Yao , Ying Li , Zichuan Qin , Yuming Liu , Haibing Wang , Qigao Jiangzuo , Allison Y. Hsiang , Chao Ma , Emily Rayfield , Michael J. Benton , Xing Xu\",\"doi\":\"10.1016/j.earscirev.2024.104765\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":11483,\"journal\":{\"name\":\"Earth-Science Reviews\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":10.8000,\"publicationDate\":\"2024-04-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0012825224000928/pdfft?md5=10ea6f8a0dd667959c823da1d3650895&pid=1-s2.0-S0012825224000928-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Earth-Science Reviews\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0012825224000928\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth-Science Reviews","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0012825224000928","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
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.
期刊介绍:
Covering a much wider field than the usual specialist journals, Earth Science Reviews publishes review articles dealing with all aspects of Earth Sciences, and is an important vehicle for allowing readers to see their particular interest related to the Earth Sciences as a whole.