人工通用智慧的定義

摘要

當前人工智慧通用智能（AGI）缺乏具體定義，導致現今專用型人工智慧與人類水平認知之間的差距難以釐清。本文提出一個可量化的框架來解決此問題，將AGI定義為需達到受過良好教育的成年人之認知廣度與熟練度。為實現此目標，我們以卡泰爾-霍恩-卡羅爾理論（目前實證最完備的人類認知模型）作為方法論基礎。該框架將通用智能分解為十大核心認知領域（包含推理、記憶與感知等），並改編成熟的人類心理計量測驗組來評估人工智慧系統。應用此框架分析發現，當代模型呈現高度「鋸齒狀」的認知剖面：雖然在知識密集型領域表現優異，現有人工智慧系統卻在基礎認知機制（特別是長期記憶儲存）存在關鍵缺陷。據此得出的AGI分數（如GPT-4達27%、GPT-5達58%）具體量化了當前技術的快速進展與實現AGI尚存的顯著差距。

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.

人工通用智慧的定義

A Definition of AGI

摘要

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