AI has been dealing with uncertainty to have highly accurate results. This becomes even worse with reasonably small data sets or a variation in the data sets. This has far-reaching effects on decision-making, forecasting and learning mechanisms. This study seeks to unpack the nature of uncertainty that exists within AI by drawing ideas from established works, the latest developments and practical applications and provide a novel total uncertainty definition in AI. From inception theories up to current methodologies, this paper provides an integrated view of dealing with better total uncertainty as well as complexities of uncertainty in AI that help us understand its meaning and value across different domains.

翻译：人工智能一直在处理不确定性以获得高度精确的结果。当数据集规模较小或存在变异时，这一问题变得尤为严重。这对决策制定、预测和学习机制产生了深远影响。本研究旨在通过借鉴既有成果、最新进展与实际应用，揭示人工智能中不确定性的本质，并提出一种新颖的人工智能总体不确定性定义。从初始理论到当前方法，本文提供了处理更优总体不确定性以及人工智能中不确定性复杂性的综合视角，有助于我们理解其在不同领域中的意义与价值。