AI在科学计算中易受微小扰动影响，结果可能大幅偏差。
Is AI Robust Enough for Scientific Research?
- 测试五类科学任务，发现神经网络对微小扰动敏感
- 扰动导致输出显著偏离，影响科学计算可靠性
- 提醒科研人员警惕AI系统隐性风险，适合关注AI安全者阅读
我们揭示了科学界长期忽视的现象：神经网络对微小扰动极为敏感，导致输出出现显著偏差。通过对气象预测、化学能量与力计算、流体动力学、量子色动力学及无线通信五个不同领域的分析，证明这种脆弱性是人工智能系统的普遍特征。这一发现暴露了在关键科学计算中依赖神经网络的潜在风险，呼吁进一步研究其可靠性和安全性。
原文摘要 · Abstract (English)
We uncover a phenomenon largely overlooked by the scientific community utilizing AI: neural networks exhibit high susceptibility to minute perturbations, resulting in significant deviations in their outputs. Through an analysis of five diverse application areas -- weather forecasting, chemical energy and force calculations, fluid dynamics, quantum chromodynamics, and wireless communication -- we demonstrate that this vulnerability is a broad and general characteristic of AI systems. This revelation exposes a hidden risk in relying on neural networks for essential scientific computations, calling further studies on their reliability and security.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。