Despite the impressive achievements of AI, including advancements in generative models and large language models, there remains a significant gap in the ability of AI to handle uncertainty and generalize beyond the training data. We argue that AI models, especially in autonomous systems, fail to make robust predictions when faced with unfamiliar or adversarial data, as evidenced by incidents with autonomous vehicles. Traditional machine learning approaches struggle to address these issues due to an overemphasis on data fitting and domain adaptation. This position paper posits a paradigm shift towards epistemic artificial intelligence, emphasizing the need for models to learn not only from what they know but also from their ignorance. This approach, which focuses on recognizing and managing uncertainty, offers a potential solution to improve the resilience and robustness of AI systems, ensuring that they can better handle unpredictable real-world environments.

本研究解决了人工智能在处理不确定性和超越训练数据方面的能力不足的问题，特别是在自主系统中的表现。提出了一个以认识性人工智能为核心的新方法，强调模型不仅要从已知中学习，还要认识到自身的无知。研究发现这一方法能显著提高AI系统在复杂和不可预测环境中的鲁棒性。

位置：认识性人工智能对机器学习模型了解自己何时无知至关重要