给通用人工智能下量化定义，用人类认知模型评估AI能力差距。
A Definition of AGI
- 基于人类认知理论，将通用智能拆解为10个核心能力域。
- 当前AI在知识类任务表现强，但长期记忆等基础能力严重不足。
- 首次给出可量化的AGI得分，如GPT-4仅27%，适合评估研究者参考。
当前人工智能缺乏对通用智能（AGI）的明确界定，导致与人类认知水平之间的差距模糊不清。本文提出一个可量化的框架，将AGI定义为达到受过良好教育成人的认知多样性和熟练度。该方法以卡特尔-霍恩-卡罗尔（Cattell-Horn-Carroll）认知理论为基础，将通用智能分解为十个核心认知领域，包括推理、记忆和感知，并采用经验证的人类心理测量工具评估AI系统。应用该框架发现，现有模型的认知能力呈现高度“锯齿状”分布：在知识密集型任务中表现优异，但在基础认知机制上存在关键缺陷，尤其是长期记忆存储能力。由此得出的AGI得分（如GPT-4为27%，GPT-5为57%）清晰量化了进展速度与尚未跨越的巨大鸿沟。
原文摘要 · Abstract (English)
The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quantifiable framework to address this, defining AGI as matching the cognitive versatility and proficiency of a well-educated adult. To operationalize this, we ground our methodology in Cattell-Horn-Carroll theory, the most empirically validated model of human cognition. The framework dissects general intelligence into ten core cognitive domains-including reasoning, memory, and perception-and adapts established human psychometric batteries to evaluate AI systems. Application of this framework reveals a highly "jagged" cognitive profile in contemporary models. While proficient in knowledge-intensive domains, current AI systems have critical deficits in foundational cognitive machinery, particularly long-term memory storage. The resulting AGI scores (e.g., GPT-4 at 27%, GPT-5 at 57%) concretely quantify both rapid progress and the substantial gap remaining before AGI.
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