Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems, particularly in high-risk domains such as healthcare, autonomous systems, and financial technology, where decision-making processes must account for uncertainty. This review explores the evolution of uncertainty quantification techniques in AI, distinguishing between aleatoric and epistemic uncertainties, and discusses the mathematical foundations and methods used to quantify these uncertainties. We provide an overview of advanced techniques, including probabilistic methods, ensemble learning, sampling-based approaches, and generative models, while also highlighting hybrid approaches that integrate domain-specific knowledge. Furthermore, we examine the diverse applications of UQ across various fields, emphasizing its impact on decision-making, predictive accuracy, and system robustness. The review also addresses key challenges such as scalability, efficiency, and integration with explainable AI, and outlines future directions for research in this rapidly developing area. Through this comprehensive survey, we aim to provide a deeper understanding of UQ's role in enhancing the reliability, safety, and trustworthiness of AI systems.

本研究针对人工智能系统中的不确定性量化（UQ）问题进行了深入探讨，尤其在医疗、自动化系统及金融科技等高风险领域的决策过程中，如何应对不确定性。文章总结了不确定性量化技术的发展，提出了包括概率方法、集成学习和生成模型等先进技术，并探讨了未来研究的方向和面临的挑战，旨在提升人工智能系统的可靠性和安全性。

从偶然性到认知性：探索人工智能中的不确定性量化技术