The AI-Enabled Scientific Frontier

2026-09-14

AI Summary

本文通过分析2000至2025年间2,507个AI与传统科学方法的对比案例，评估了AI在不同科学领域的表现及成本效益，指出AI是新科学前沿中一个有价值且不断进步的部分，但并非万能替代品。

Abstract

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.

Problem

• artificial intelligence
• scientific analysis
• traditional statistics
• scientific computing
• performance comparison

Innovation

• AI-enabled scientific frontier
• head-to-head comparisons
• computational cost
• scientific disciplines
• performance improvement