用人类完成50%任务的时间衡量AI编程能力，发现其进步迅速。
Measuring AI Ability to Complete Long Software Tasks
- 提出50%任务完成时间作为衡量AI编程能力的新指标
- 当前顶级AI完成任务需约50分钟，每7个月翻倍一次
- 适合关注AI自动化能力演进的研究者与开发者
尽管人工智能基准测试进展迅速，但其实际意义仍不明确。为量化AI系统在人类能力维度的表现，我们提出新指标：50%-任务完成时间窗口——即人类在相关领域专家水平下完成任务所需的时间，该任务正是AI模型能以50%成功率完成的。我们首先对具备相关领域专长的人类在RE-Bench、HCAST及66个新设计的短任务上进行了时间测量。结果显示，当前前沿AI模型如Claude 3.7 Sonnet的50%时间窗口约为50分钟。此外，自2019年以来，前沿AI的时间窗口每七个月左右翻倍，2024年趋势可能进一步加速。这一进步主要源于更高的可靠性、错误适应能力，以及更强的逻辑推理与工具使用能力。我们讨论了结果的局限性（包括外部效度）及其对危险自主能力的潜在影响。若该趋势适用于真实世界软件任务，外推表明：五年内，AI系统将能自动化目前需人类耗时一个月才能完成的多数软件任务。
原文摘要 · Abstract (English)
Despite rapid progress on AI benchmarks, the real-world meaning of benchmark performance remains unclear. To quantify the capabilities of AI systems in terms of human capabilities, we propose a new metric: 50%-task-completion time horizon. This is the time humans typically take to complete tasks that AI models can complete with 50% success rate. We first timed humans with relevant domain expertise on a combination of RE-Bench, HCAST, and 66 novel shorter tasks. On these tasks, current frontier AI models such as Claude 3.7 Sonnet have a 50% time horizon of around 50 minutes. Furthermore, frontier AI time horizon has been doubling approximately every seven months since 2019, though the trend may have accelerated in 2024. The increase in AI models' time horizons seems to be primarily driven by greater reliability and ability to adapt to mistakes, combined with better logical reasoning and tool use capabilities. We discuss the limitations of our results -- including their degree of external validity -- and the implications of increased autonomy for dangerous capabilities. If these results generalize to real-world software tasks, extrapolation of this trend predicts that within 5 years, AI systems will be capable of automating many software tasks that currently take humans a month.
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。