AI需要物理的严谨性，而非仅靠规模堆叠。
AI Needs Physics More Than Physics Needs AI
- 提出用物理理论约束AI模型，提升可解释性。
- 指出当前大模型存在参数冗余与缺乏科学规律建模。
- 适合关注AI可信性与科学建模的研究者阅读。
人工智能常被描绘为变革性技术，但十余年的炒作后，其实际影响仍局限于少数高调的科学与商业成果。2024年诺贝尔化学奖与物理学奖虽认可了AI潜力，但整体评估显示其影响更多是宣传性的而非技术性的。我们主张，尽管当前AI可能影响物理学，但物理学对这代AI的贡献远大于反向影响。现有架构——大型语言模型、推理模型与代理型AI——依赖数万亿无意义参数，存在分布偏移、缺乏不确定性量化、无法提供机制解释，甚至未能捕捉基本科学定律。本文综述这些局限，强调量子AI与模拟计算的机遇，并提出‘大智能’（Big AI）路线图：融合理论严谨性与机器学习灵活性。
原文摘要 · Abstract (English)
Artificial intelligence (AI) is commonly depicted as transformative. Yet, after more than a decade of hype, its measurable impact remains modest outside a few high-profile scientific and commercial successes. The 2024 Nobel Prizes in Chemistry and Physics recognized AI's potential, but broader assessments indicate the impact to date is often more promotional than technical. We argue that while current AI may influence physics, physics has significantly more to offer this generation of AI. Current architectures - large language models, reasoning models, and agentic AI - can depend on trillions of meaningless parameters, suffer from distributional bias, lack uncertainty quantification, provide no mechanistic insights, and fail to capture even elementary scientific laws. We review critiques of these limits, highlight opportunities in quantum AI and analogue computing, and lay down a roadmap for the adoption of 'Big AI': a synthesis of theory-based rigour with the flexibility of machine learning.
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