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.

中文翻译：

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古生物学中的人工智能

大型数据集的积累和数据可用性的增加导致了数据驱动的古生物学研究的出现，揭示了前所未有的进化历史图景。然而，数据模态数量的快速增长和复杂性使得数据处理变得繁琐且不一致，同时也缺乏明确的基准来评估数据收集和生成以及不同方法在类似任务上的性能。最近，人工智能 (AI) 已在各个科学学科中得到广泛应用，但在古生物学领域迄今为止还没有如此广泛应用，因为传统的手动工作流程更为常见。在这项研究中，我们回顾了 20 世纪 80 年代以来超过 70 项古生物学 AI 研究，涵盖了微观和宏观化石分类、图像分割和预测等主要任务。这些研究采用了广泛的技术，例如基于知识的系统（KBS）、神经网络、迁移学习和许多其他机器学习方法，以实现各种古生物学研究工作流程的自动化。在这里，我们讨论他们的方法、数据集和性能，并将它们与更传统的人工智能研究进行比较。我们将近期古生物学人工智能研究的增长主要归因于人工智能模型训练和部署门槛的降低，而不是化石数据编译和方法的创新。我们还介绍了最近开发的人工智能实现，例如扩散模型内容生成和大型语言模型（LLM），它们可能在未来与古生物学研究相结合。尽管人工智能尚未成为古生物学家工具包的重要组成部分，但人工智能的成功实施正在不断增长，并有望在未来几年对古生物学研究产生范式变革的影响。