Thinking Process:

  1. Understanding the Core Issue: The document explores the limitations of AI in avoiding infinite loops (such as framework problems) and the critical role of consciousness in human intelligence. It needs to clarify why AI cannot escape loops like humans, with the core issue being the lack of consciousness and survival motivation.
  2. Analyzing Technical Methods: The document mentions four AI technologies (simulated computers, human-like computing, neuromorphic computing, dynamic system methods), requiring an analysis of each method’s principles, advantages, and shortcomings, such as the precision issues of simulated computers or the lack of emotional effects in neuromorphic computing.
  3. Differentiating Intelligence and Consciousness: It is essential to emphasize the distinction between intelligence (algorithmic capability) and consciousness (perception at the level of existence), pointing out that AI’s “unconscious intelligence” cannot handle scenarios requiring meaning judgments.
  4. Theoretical Framework of Embodied Consciousness: Integrate Anil Seth’s concept of “embodied consciousness,” explaining that consciousness depends on the interaction of the body, environment, and time, rather than merely algorithmic or structural simulation.
  5. Refuting Common Misconceptions: Clarify the misconception that “AI will have consciousness if it is intelligent enough,” emphasizing that consciousness must be rooted in survival motivation and the instinct to combat entropy, not just technical implementation.
  6. Summarizing the Conclusion: The fundamental reason AI struggles with infinite loops is the “problem of existence,” not a technical flaw; human consciousness’s uniqueness stems from its deep connection to time and entropy.

Answer:
The fundamental reason AI struggles to escape infinite loops lies in its lack of consciousness and survival motivation. Although existing technologies (such as simulated computers, neuromorphic computing) attempt to simulate human temporal or structural aspects, none have recreated the core traits of consciousness—perception of meaning and the instinct to combat entropy.

Key Point Analysis:

  1. Technical Limitations:
    • Simulated Computers: While they closely resemble human continuous time perception, they lack precision and are easily disrupted by environmental factors.
    • Neuromorphic Computing: Simulates brain structure and signal transmission but fails to integrate emotions (such as hunger, pain) or survival motivation, making it unable to actively avoid loops.
    • Dynamic System Methods: Focuses on state changes rather than algorithms, yet lacks an intrinsic motivation to “maintain order.”
  2. Irreplaceability of Consciousness:
    • Consciousness is not a software detached from the body but a product of the interaction between the body, environment, and time (embodied consciousness). For example, humans can judge (e.g., “this cup of water can quench my thirst”) by integrating memory, current state, and environment, while AI can only execute predefined rules and cannot perceive the meaning of actions.
    • Intelligence (such as algorithms) can simulate behavior, but consciousness (such as self-awareness) requires survival motivation (such as the instinct to combat entropy), which AI cannot replicate.
  3. Differentiating Consciousness and Intelligence:
    • Intelligence is “action capability,” achievable through algorithms (e.g., AI writing articles); consciousness is “existence capability,” rooted in the experience of time and entropy (e.g., humans’ perception of “being alive”). AI’s “unconscious intelligence” exposes limitations when encountering scenarios requiring meaning judgments (e.g., avoiding loops).
  4. Conclusion and Outlook:
    • The essence of AI’s infinite loops is the “problem of existence,” not a technical flaw. Human consciousness’s uniqueness stems from its deep connection to time and entropy, which AI cannot simulate. For AI to achieve true “open-ended intelligence” (AGI), it must transcend technical levels and confront the essence of consciousness—this might still require prolonged exploration.

Recommended Further Reading: Anil Seth’s The Consciousness Machine: Becoming Yourself further explores the theory of embodied consciousness; suggested for deeper understanding of the relationship between consciousness and intelligence.

Translation

思考过程：

  1. 理解核心问题：用户提供的文档探讨了AI在避免无限循环（如框架问题）中的局限性，以及意识在人类智能中的关键作用。需要明确AI为何无法像人类一样摆脱循环，核心在于缺乏意识与生存驱动。
  2. 梳理技术方法：文档提到四种AI技术（模拟计算机、凡人计算、神经形态计算、动力系统方法），需分析每种方法的原理、优势及不足，例如模拟计算机的精度问题、神经形态计算缺乏情感效应等。
  3. 意识与智能的区分：需强调智能（算法能力）与意识（存在层面的感知）的区别，指出AI的“无意识智能”无法应对需要意义判断的场景。
  4. 具身意识的理论：需整合阿尼尔·塞斯的“具身意识”概念，说明意识依赖身体、环境与时间的互动，而非单纯算法或结构模拟。
  5. 反驳常见误区：澄清“AI足够智能就会有意识”的错误认知，强调意识需植根于生存驱动与熵对抗的本能，而非技术实现。
  6. 总结结论：AI的无限循环本质是“存在问题”，而非技术缺陷；人类意识的独特性源于其与时间、熵的深度绑定。

回答：
AI难以摆脱无限循环的根本原因在于其缺乏意识与生存驱动。尽管现有技术（如模拟计算机、神经形态计算）尝试模拟人类的时序或结构，但均未能复现意识的核心特质——对“意义”的感知与对抗熵的本能。

关键点解析：

  1. 技术局限：
    • 模拟计算机：虽接近人类对时间的连续感知，但精度低且易受环境干扰。
    • 神经形态计算：模拟大脑结构与信号传递，却未整合情感（如饥饿、疼痛）或生存驱动，无法主动避免循环。
    • 动力系统方法：关注状态变化而非算法，但缺乏“维持有序”的内在动机。
  2. 意识的不可替代性：
    • 意识并非脱离身体的软件，而是身体、环境与时间互动的产物（具身意识）。例如，人类能通过整合记忆、当前状态与环境判断（如“这杯水能解渴”），而AI仅能执行预设规则，无法感知行动的意义。
    • 智能（如算法）可模拟行为，但意识（如自我感知）需依赖生存驱动（如对抗熵增的本能），这是AI无法复制的。
  3. 意识与智能的区分：
    • 智能是“行动能力”，可通过算法实现（如AI写文章）；意识是“存在能力”，需植根于时间与熵的体验（如人类对“活着”的感知）。AI的“无意识智能”在遇到需判断意义的场景（如避免循环）时会暴露局限。
  4. 结论与展望：
    • AI的无限循环本质是“存在问题”，而非技术缺陷。人类意识的独特性源于其与时间、熵的深度绑定，这是AI无法模拟的。未来若AI要实现真正的“开放式智能”（AGI），需超越技术层面，直面意识的本质——这或许仍需漫长探索。

推荐延伸阅读：阿尼尔·塞斯的《意识机器：成为你自己》进一步探讨了具身意识的理论，建议深入阅读以理解意识与智能的深层关联。

Reference:

https://bigthink.com/neuropsych/anil-seth-consciousness-time-perception/

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