Summary:
This article discusses the debate in the field of artificial intelligence regarding general artificial intelligence (AGI), focusing on the controversy over whether large language models (such as GPT-4 and GPT-5) can achieve AGI. Industry leaders (such as OpenAI and Google) believe large language models have potential, while the academic community (such as Sutton, Marcus, and LeCun) points out their core flaws. The article cites a new AGI definition framework proposed by Yoshua Bengio’s team, based on CHC theory (cognitive ability classification), which evaluates current models’ capabilities through quantifiable metrics. The study highlights that although GPT-5 performs well on some tasks, it still has issues such as long-term memory loss, inadequate reasoning ability, and defects in multimodal understanding. Additionally, the article introduces the concept of “capability distortion,” where models mask fundamental flaws through technical means (such as working memory expansion or external search), creating a false illusion of general intelligence. Ultimately, this research provides measurable evaluation criteria for AGI development but also acknowledges its limitations (such as cultural bias).

Key Points:

  • Industry vs. Academic Controversy:
    Industry supporters argue that large language models have the potential to achieve AGI, while academics (such as Sutton, Marcus, LeCun) criticize their core flaws (e.g., long-term memory loss, rigid reasoning).
  • New AGI Definition Framework:
    Proposed by Yoshua Bengio’s team, this framework is based on CHC theory (cognitive ability classification), breaking down AGI capabilities into ten dimensions (e.g., long-term memory, immediate reasoning, visual processing) and quantitatively assessing model performance.
  • Evaluation Results of GPT-4 and GPT-5:
    GPT-4 scores only 27% in AGI metrics, while GPT-5 improves to 58%, yet significant shortcomings remain:
    • Long-term Memory Deficiency: Unable to accumulate experience or correct errors, requiring cold starts for each conversation.
    • Inadequate Reasoning Ability: Unable to adapt to rule changes (e.g., Wisconsin Card Sorting Test), lacking metacognitive capabilities.
    • Multimodal Understanding Defects: Unable to perform complex visual reasoning (e.g., spatial scanning) or in-depth auditory analysis (e.g., phoneme coding).
  • Capability Distortion Phenomenon:
    Models mask fundamental flaws through technical means (e.g., working memory expansion, external search), creating an illusion of “seemingly general” intelligence, but lacking underlying universal intelligence.
  • Research Significance and Limitations:
    This framework provides measurable evaluation criteria for AGI development but requires further optimization (e.g., cultural weight adjustments) and acknowledges that current tests are based on English and Western cultural systems.

References:

  1. CHC Theory: A cognitive ability classification framework used to quantify ten dimensions of AGI (e.g., long-term memory, visual reasoning).
  2. Bengio Team’s Paper: First systematic proposal of AGI evaluation standards, based on CHC theory to decompose capability dimensions.
  3. Retrieval-Augmented Generation (RAG) Technology: A technique using external search to mask model memory deficiencies, cited as a typical example of “capability distortion” by the study.

Translation

总结：
本文讨论了人工智能领域关于通用人工智能（AGI）的争议，重点分析了大型语言模型（如GPT-4和GPT-5）是否能实现AGI的争论。行业领袖（如OpenAI和Google）认为大型语言模型具备潜力，而学术界（如Sutton、Marcus和LeCun）则指出其核心缺陷。文章引用了Yoshua Bengio团队提出的新AGI定义框架，基于CHC理论（认知能力的分类），通过量化指标评估当前模型的能力短板。研究指出，尽管GPT-5在部分任务上表现优异，但存在长期记忆缺失、推理能力不足、多模态理解缺陷等问题。此外，文章提出“能力扭曲”概念，即模型通过技术手段（如工作记忆扩展或外部搜索）掩盖根本缺陷，制造出虚假的通用智能假象。最终，该研究为AGI发展提供了可衡量的评估标准，但也承认其局限性（如文化偏见）。

关键点：

  • 行业与学术的争议：
    行业支持者认为大型语言模型具备实现AGI的潜力，而学术界（如Sutton、Marcus、LeCun）批评其核心缺陷（如长期记忆缺失、推理僵化）。
  • 新AGI定义框架：
    由Yoshua Bengio团队提出，基于CHC理论（认知能力的分类），将AGI能力拆分为十大维度（如长期记忆、即时推理、视觉处理等），并量化评估模型表现。
  • GPT-4与GPT-5的评估结果：
    GPT-4在AGI指标中仅得27%，GPT-5提升至58%，但仍存在显著短板：
    • 长期记忆缺失：无法积累经验或修正错误，每次对话均需冷启动。
    • 推理能力不足：无法适应规则变化（如威斯康星卡片分类测验），缺乏元认知能力。
    • 多模态理解缺陷：无法进行复杂视觉推理（如空间扫描）或深入听觉分析（如音素编码）。
  • 能力扭曲现象：
    模型通过技术手段（如工作记忆扩展、外部搜索）掩盖根本缺陷，制造出“看似通用”的假象，但实际缺乏底层通用智能。
  • 研究意义与局限性：
    该框架为AGI发展提供可衡量的评估标准，但需进一步优化（如文化权重调整），并承认当前测试基于英语和西方文化体系。

参考文献：

  1. CHC理论：认知能力分类框架，用于量化AGI的十大维度（如长期记忆、视觉推理等）。
  2. Bengio团队论文：首次系统性提出AGI评估标准，基于CHC理论拆分能力维度。
  3. 检索增强生成（RAG）技术：通过外部搜索掩盖模型记忆缺陷的手段，被研究指出为“能力扭曲”的典型例证。

Reference:

https://www.arxiv.org/pdf/2510.18212

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