通用人工智能的定义

摘要

当前对通用人工智能（AGI）缺乏明确定义，导致人们难以看清当今专用人工智能与人类水平认知之间的差距。本文提出一种可量化的评估框架，将AGI定义为达到受过良好教育的成年人的认知广度与熟练度。为实现这一目标，我们将方法论建立在卡特尔-霍恩-卡罗尔理论——这一经过最广泛实证验证的人类认知模型之上。该框架将通用智能分解为十大核心认知领域（包括推理、记忆和感知），并采用成熟的人类心理计量测试工具来评估人工智能系统。应用该框架发现，当代模型呈现出高度"锯齿状"的认知能力分布：虽然现有AI系统在知识密集型领域表现优异，但在基础认知机制（尤其是长期记忆存储）方面存在严重缺陷。由此得出的AGI评分（如GPT-4为27%，GPT-5为58%）既量化了技术的飞速进步，也清晰标定了当前与AGI之间的实质性差距。

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 58%) concretely quantify both rapid progress and the substantial gap remaining before AGI.

通用人工智能的定义

A Definition of AGI

摘要

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