AI能否追随爱因斯坦的脚步？

中文摘要 AI 辅助

人工智能正在加速物理学发现，但或许偏离了爱因斯坦级别的理论构建。为理解这一差距，需注意一个显著趋势：尽管最受关注的AI在物理学发现中的贡献十分成功，却似乎与物理学的历史发展方向相反。人类物理学发现大致经历了从古代模式预测，到开普勒等的现象学定律，再到相对论、标准模型等基于原理的普适理论的过程；而AI在物理学发现中的突出贡献则呈相反方向：早期里程碑强调符号回归等显式方程发现方法，近年前沿成果则是AlphaFold、GraphCast等强大预测模型，精度极高却无法提供清晰理论理解。若该趋势持续，AI将在预测上极为出色，但可能始终难以提出量子引力或其他范式级理论的首个重要候选。本文综述AI用于物理学发现的当前格局，强调一项关键缺失技能：提出正确问题或发明正确原理以指导新理论构建及证伪测试的能力。自17世纪以来，这种发现模式推动了诸多最深刻进展，对称、简洁及新数学框架在实验测试前就指导理论构建。为AI系统配备此类技能，可使其从在已知框架内预测转向提出物理学的下一次范式级发现。

英文摘要

AI is accelerating physics discovery, but perhaps away from Einstein-level theory building. To understand this gap, we must recognize a striking trend: while being very successful, the most visible AI contributions to physics discovery appear to mirror the historical development of physics, but in reverse. Human discovery in physics progressed, in broad strokes, from ancient pattern prediction, through phenomenological laws such as Kepler's, to principle-based universal theories such as relativity and the Standard Model. On the AI side, prominent contributions to physics discovery point in the opposite direction: early milestones emphasized explicit equation-discovery methods, such as symbolic regression, whereas more recent frontier contributions are powerful predictors such as AlphaFold and GraphCast, which can be remarkably accurate yet do not provide clear theoretical understanding. If this trend continues, AI would become extraordinarily good at prediction but may struggle to ever propose its first serious contender to quantum gravity or other paradigm-level theories. We review the current landscape of AI for physics discovery and highlight a critical missing skill: the ability to pose the right questions or invent the right principles to guide the development of new theories and the tests to falsify them. This mode of discovery has driven many of the deepest advances since the 17th century, where symmetry, simplicity, and new mathematical frameworks guided theory construction before experimental tests. Equipping AI systems with such skills could move them from predicting within known frameworks to proposing the next paradigm-level discovery in physics.

发表机构

• Technion - Israel Institute of Technology（以色列理工学院）
• Massachusetts Institute of Technology（麻省理工学院）

机构由 AI 辅助整理，请以论文原文为准。