AI赋能科学前沿

中文摘要 AI 辅助

随着人工智能能力的提升，它日益被视为一种通用的科学方法。但这些说法有多真实？AI是否在所有技术上都表现更优，还是仅在某些方面，并且这种情况如何变化？为了评估这些说法，我们汇编了一个包含2,507项AI与其他科学分析技术之间头对头比较的语料库，这些比较覆盖了27个科学学科，来源于2000年至2025年初发表的论文。我们发现了一个深刻的二分法。相对于传统统计学，AI通常表现更优，但计算成本显著更高。然而，也有近四分之一的案例中，AI既更昂贵又比传统统计技术表现更差，且这一比例十年来保持稳定。相对于科学计算，AI通常表现不佳，但计算成本较低。这种情况已开始改变：自2020年以来，AI相对于科学计算的性能显著增强，目前在一半以上的比较中表现更优。这些模式表明，AI并非现有方法的普遍替代品，而是新的AI赋能科学前沿中一个有价值且不断改进的组成部分。

英文摘要

As artificial intelligence's capabilities improve, it is increasingly viewed as a general scientific method. But how true are these claims? Does AI outperform all techniques, or only some, and how is this changing? To assess the claims, we assemble a corpus of 2,507 head-to-head comparisons between AI and other scientific analysis techniques across 27 scientific disciplines from papers published between 2000 and early 2025. We find a profound dichotomy. Relative to traditional statistics, AI often outperforms, but at a significantly higher computational cost. But there are also nearly a quarter of cases where AI is both more expensive and performs worse than traditional statistical techniques and this fraction has been stable for a decade. Relative to scientific computing, AI often underperforms, but at lower computational cost. This has begun to change: since 2020, AI's performance against scientific computing has notably strengthened and it now outperforms on more than half of comparisons. These patterns suggest that AI is therefore not a universal replacement for existing methods, but rather a valuable -- and improving -- part of a new AI-enabled scientific frontier.

发表机构

• MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）
• MIT FutureTech（麻省理工学院未来科技研究所）

机构由 AI 辅助整理，请以论文原文为准。