AI发展快但缺根基，本文用三重严谨性框架剖析其得失。
The Role of Rigor in Artificial Intelligence
- 提出概念、认知、操作三重严谨性框架，系统审视AI发展路径。
- 指出当前深度学习依赖实证迭代，理论滞后导致可靠性隐患。
- 适合关注AI本质、可信部署与未来范式转型的研究者阅读。
人工智能虽取得非凡成就，却缺乏成熟学科应有的理论基础。与传统科学不同，现代AI的发展主要依赖性能驱动的迭代和‘炼金术式’实验。这种矛盾促使我们从严谨性角度系统分析AI。本文提出一个包含概念严谨性（澄清基础概念）、认知严谨性（建立科学理解）和操作严谨性（确保可靠性能与部署）的三部分框架。利用该框架，分析了智能与理解的不同定义、深度学习经验方法的优劣、基准测试的优势与陷阱，以及现代AI系统对理论发展的阻碍。文章认为，AI独特的发展轨迹源于各范式间严谨性的互动，导致操作严谨性在现代深度学习中占据主导地位。这一视角既解释了AI的快速进步，也揭示了其持续不确定性，同时阐明了将AI转化为成熟科学与可靠技术所面临的挑战。
原文摘要 · Abstract (English)
Artificial intelligence (AI) has achieved extraordinary capabilities despite lacking many of the conceptual and scientific foundations associated with mature disciplines. Unlike traditional sciences, where reliable technology typically emerges from theoretical understanding, modern AI has progressed largely through performance-driven iteration and "alchemical" experimentation. This tension motivates a systematic analysis of AI through the lens of rigor. We introduce a three-part framework consisting of conceptual rigor (clarifying foundational concepts), epistemic rigor (establishing scientific understanding), and operational rigor (ensuring reliable performance and deployment). Using this framework, we analyze competing conceptions of intelligence and understanding, the strengths and limitations of the empirical approach to deep learning, the power and pitfalls of benchmarks, and the obstacles to theory development posed by modern AI systems. We argue that the distinctive trajectory of AI arises from how forms of rigor interact across paradigms, resulting in the primacy of operational rigor in modern deep learning. This perspective helps explain both AI's rapid advances and its persistent uncertainties, while clarifying the challenges involved in transforming AI into a mature science and reliable technology.
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