让机器学会识别自身无知，提升面对未知时的可靠性。
Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'
- 引入二阶不确定性数学框架，让模型认知自身知识边界。
- 相比传统方法，显著增强对陌生或对抗性数据的鲁棒性。
- 适合追求可信AI、高安全场景应用的研究者与开发者。
尽管人工智能在生成模型和大语言模型方面取得显著进展，但其在处理不确定性和泛化能力上仍存在明显短板。面对陌生或对抗性数据时，现有模型预测常失效。传统机器学习过度关注数据拟合，而现有不确定性量化方法也存在严重局限。本文主张转向认识论人工智能（epistemic AI），强调模型需在掌握知识的同时，明确意识到自身的未知，利用二阶不确定性度量的数学工具实现这一目标。该方法借助二阶不确定性表达的强表征能力，高效管理不确定性，有效提升系统的韧性与鲁棒性，使其更适应不可预测的真实世界环境。
原文摘要 · Abstract (English)
Despite AI's impressive achievements, including recent advances in generative and large language models, there remains a significant gap in the ability of AI systems to handle uncertainty and generalize beyond their training data. AI models consistently fail to make robust enough predictions when facing unfamiliar or adversarial data. Traditional machine learning approaches struggle to address this issue, due to an overemphasis on data fitting, while current uncertainty quantification approaches suffer from serious limitations. This position paper posits a paradigm shift towards epistemic artificial intelligence, emphasizing the need for models to learn from what they know while at the same time acknowledging their ignorance, using the mathematics of second-order uncertainty measures. This approach, which leverages the expressive power of such measures to efficiently manage uncertainty, offers an effective way to improve the resilience and robustness of AI systems, allowing them to better handle unpredictable real-world environments.
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