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.

翻译：人工智能常被描绘为具有变革性。然而，经过十多年的炒作，除了少数备受瞩目的科学和商业成功案例外，其可衡量的影响仍然有限。2024年诺贝尔化学奖和物理学奖认可了人工智能的潜力，但更广泛的评估表明，迄今为止其影响往往更多是宣传性的而非技术性的。我们认为，虽然当前的人工智能可能影响物理学，但物理学对这一代人工智能的贡献要大得多。当前的架构——大语言模型、推理模型和智能体人工智能——可能依赖于数万亿个无意义的参数，存在分布偏差，缺乏不确定性量化，无法提供机制性见解，甚至无法捕捉基本的科学定律。我们回顾了对这些局限性的批评，强调了量子人工智能和模拟计算中的机遇，并为采用“大人工智能”制定了路线图：将基于理论的严谨性与机器学习的灵活性相结合。