Since the late 2010s, Artificial Intelligence (AI) including machine learning, boosted through deep learning, has boomed as a vital tool to leverage computer vision, natural language processing and speech recognition in revolutionizing zoological research. This review provides an overview of the primary tasks, core models, datasets, and applications of AI in zoological research, including animal classification, resource conservation, behavior, development, genetics and evolution, breeding and health, disease models, and paleontology. Additionally, we explore the challenges and future directions of integrating AI into this field. Based on numerous case studies, this review outlines various avenues for incorporating AI into zoological research and underscores its potential to enhance our understanding of the intricate relationships that exist within the animal kingdom. As we build a bridge between beast and byte realms, this review serves as a resource for envisioning novel AI applications in zoological research that have not yet been explored.
2010年以来，包括机器学习在内的人工智能，在深度学习的推动下成为了一种重要的工具，用于提升计算机视觉、自然语言处理和语音识别的准确率，并在变革动物学研究方面发挥了重要作用。该文概述了人工智能在动物学研究中的主要任务、核心模型、数据集和其在动物分类和资源保护、行为、发育、遗传和进化、繁殖和健康、疾病模型以及古生物学中的应用。此外，我们还探讨了将人工智能整合到这一领域中的挑战和未来方向。通过大量的案例研究，我们发现人工智能在动物研究中有着巨大的潜力，可以帮助我们更好地理解动物世界内部复杂关系。当我们在动物学和人工智能领域之间架起一座桥梁时，这篇综述为我们设想尚未探索的动物学研究中的新型人工智能应用提供了资源。.
Keywords: Animal science; Behavior analysis; Biomolecular sequences analysis; Classification model; Data extraction.