评估AI评估：风险与前景
Evaluating AI Evaluation: Perils and Prospects
AI总结：
本文指出当前AI评估方法存在根本缺陷，提出应借鉴认知科学的通用智能评估思路，分析了认知启发方法应用于通用AI的难点与新兴Evals领域，指明了推动AI评估走向严谨科学、助力安全AI发展的研究方向。
摘要：
随着AI系统展现出不断提升的能力与通用性，评估其真实潜力与安全性变得至关重要。本文认为当前针对这类系统的主流评估方法从根本上存在不足，加剧了与AI相关的风险与潜在危害。笔者提出，我们需要改革AI系统的评估方式，且应从认知科学领域汲取方法灵感——该学科在评估不同物种的通用智能方面有着悠久传统。我们将明确把认知启发式方法应用于通用AI系统时需要克服的若干难点，同时分析新兴的“Evals（评估体系）”领域。本文最后指出了若干有望完善AI评估的研究方向，推动该领域发展为一门严谨的科学学科，为安全AI系统的开发提供助力。
英文摘要：
As AI systems appear to exhibit ever-increasing capability and generality, assessing their true potential and safety becomes paramount. This paper contends that the prevalent evaluation methods for these systems are fundamentally inadequate, heightening the risks and potential hazards associated with AI. I argue that a reformation is required in the way we evaluate AI systems and that we should look towards cognitive sciences for inspiration in our approaches, which have a longstanding tradition of assessing general intelligence across diverse species. We will identify some of the difficulties that need to be overcome when applying cognitively-inspired approaches to general-purpose AI systems and also analyse the emerging area of "Evals". The paper concludes by identifying promising research pathways that could refine AI evaluation, advancing it towards a rigorous scientific domain that contributes to the development of safe AI systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
