衡量AI完成長任務的能力

摘要

尽管人工智能在基准测试上取得了快速进展，但基准性能在现实世界中的意义仍不明确。为了从人类能力的角度量化人工智能系统的能力，我们提出了一项新指标：50%任务完成时间跨度。这是指人类通常完成那些人工智能模型能以50%成功率完成的任务所需的时间。我们首先对具有相关领域专业知识的人类进行了计时，测试内容包括RE-Bench、HCAST以及66项新颖的较短任务。在这些任务上，当前的前沿AI模型（如Claude 3.7 Sonnet）的50%时间跨度约为50分钟。此外，自2019年以来，前沿AI的时间跨度大约每七个月翻一番，尽管这一趋势在2024年可能有所加速。AI模型时间跨度的增加似乎主要由更高的可靠性、适应错误的能力，以及更好的逻辑推理和工具使用能力所驱动。我们讨论了研究结果的局限性——包括其外部效度——以及自主性提升对危险能力的影响。如果这些结果能够推广到现实世界的软件任务中，根据这一趋势的推断预测，在五年内，AI系统将能够自动化许多目前人类需要一个月才能完成的软件任务。

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.

衡量AI完成長任務的能力

Measuring AI Ability to Complete Long Tasks

摘要

Summary

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