我的征尘是星辰大海。。。

The dirt and dust from my pilgrimage forms oceans of stars...

-------当记忆的篇章变得零碎，当追忆的图片变得模糊，我们只能求助于数字存储的永恒的回忆

作者:黄教授

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条件真理观打通哲学认知论与AI实践

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原始脚本

条件真理观，打通哲学认知与 AI 实践，界定 AGI 边界与文明价值。
从一篇探讨公理体系、现实博弈与条件真理观的文章出发，我们展开了一场横跨传统哲学认识论与人工智能产业实践的深度对话，最终形成了一套逻辑自洽、双向赋能的核心认知。
体系。
当下，传统哲学、社会科学与 AI 产业实践存在明显脱节。
前者空谈理论，缺乏量化落地。
后者埋头实操，忽视顶层哲学指引。
二者的割裂既让哲学理论丧失实践价值，也让 AI 领域出现对 AGI 的盲目预判，如同十九世纪钟表匠囿于机械记忆幻想永动机一般。
唯有以条件真理观为核心纽带，打通思想高度与工程实操，才能既为 AI 发展划定不可逾越的认知边界，又充分肯定其在人类文明进程中的颠覆性价值，实现理论与实践的双向救赎。
一、双向脱节，哲学与 AI 的各自困局。
根源在认知高度的缺失。
当前传统认识论、哲学真理观与人工智能产业界，处于一种各行其道、互不连通的割裂状态。
这种脱节并非双方理念相背，而是认知维度与实践路径的错位，最终导致两类主体都陷入各自的误区。
一方面，人工智能产业界从业者日用而不知，深陷工程实操却缺乏顶层哲学指引。
当下 AI 领域的训练、推理、泛化、 RAG 构建、 KVQ 注意力机制等核心技术，本质上都在践行条件真理观，所有模型输出都依附于输入条件。
所有能力都源于对已有经验的量化归纳，所有真理都需实践验证才具备价值。
但绝大多数工程师研究员仅从数学原理、编程逻辑、工程流程出发开展工作，将条件决定输出、经验支撑泛化视为天经地义的实操常识，从未从认识论、真理观的哲学高度进行系统化梳理与总结。
这就如同十九世纪精通精密机械的钟表匠，能打造出极致复杂的机械装置，却因缺乏能量守恒的底层物理认知，陷入永动机的幻想。
部分 AI 从业者也正因缺少条件真理观的顶层指引，对 AGI 产生不切实际的预判。
追求脱离实践、全知全能的超级 AI，忽视认知的底层规律，犯下边界认知错误。
另一方面，传统哲学与社会科学领域空谈而不见，固守理论思辨却缺乏量化落地。
哲学界早已围绕唯物主义、唯心主义、绝对真理与相对真理展开数百年探讨，得出真理具有条件性，实践是检验真理的唯一一标准等核心结论。
但大多停留在文字思辨层面，没有将抽象理论转化为可量化、可建模、可落地的实操框架。
更未意识到这些经典认识论成果正是人工智能的底层运行逻辑。
社会科学领域的诸多理论同样泛泛而谈，缺乏数学建模与数据量化，无法指导具体实践。
最终丧失了理论存在的核心价值，指导实践，预测趋势。
理论若不与实践结合，便是无源之水。
哲学若不落地产业，便是空洞说教，这是传统思想领域面临的核心困境。
二，认知三大公理，条件真理观为 AGI 划定终极边界，如同能量守恒定律从物理底层判永动机死刑。
我们从唯物主义认识论与条件真理观中提炼出三大认知底层公理，从哲学高度为 AGI 划定不可突破的终极边界，彻底区分哲学版争 AGI 与工程版实用 AGI ，终结对 AGI 的盲目幻想。
第一公理，一切有效认知必依附前置条件，不存在无条件的绝对真理。
所有认知、判断、推理都有其适用前提，脱离对象、场景、问题、动作、数据等具体条件，真理便不复存在。
无论是人类认知还是 AI 智能，都无法脱离条件产生有效输出，这是认知的底层前提，也是 AI 运行的核心逻辑。
第二公理，所有智能模型都是对已发生已量化经验的压缩归纳与泛化当前大模型。
未来 AI 的预训练、微调、强化学习，本质都是对人类已有知识、历史经验、实践数据的整合压缩。
所谓泛化能力只是在已知条件的相似区间内进行特征匹配与规律复用，绝非凭空创造全新认知，更无法突破已有经验的底层框架。
第三公里，全新真理只能通过现实实践校验生成。
无实践则无新知。
针对宇宙探索、底层科研、全新数学范式等人类从未涉足、从未观测、从未量化的未知领域，不存在可匹配的条件与可复用的经验，任何智能都无法直接生成绝对正确的全新真理。
必须经过实践探索条件采集数据量化迭代验证的完整闭环，才能形成新的认知。
基于这三大公理，我们可以得出终极判定。
追求全知全能，无需实践即可顿悟全新真理，突破所有条件限制的哲学版真 egg，是认知领域的永动机，永远不可能实现。
而能够整合全人类已有经验，精准完成条件匹配，高效实现领域泛化，严格遵循实践闭环的工程版实用 AGI，不仅可以实现，更将爆发出无与伦比的价值。
三，边界之内，AGI 的核心价值成为人类文明探索的永续载体，我们从哲学高度划定 AGI 的边界，绝非否定其价值。
恰恰相反，是为了精准定位其不可替代的核心意义。
AG，AG 是突破人类生理局限、推进人类文明向未知领域延伸的核心载体，未来必将成为未知领域实践的主体。
人类作为碳基生命，存在天生的认知短板。
每个人出生都是认知白纸，从启蒙学习到深耕专业，耗尽半生才能摸到人类现有认知的边界。
有效科研工作年限仅有数十年，顶尖天才万里挑一，知识传承存在断代损耗，每一代人都要从零开始积累。
如同反复跳高冲击记录，却始终无法实现知识的永续叠加。
而 AGI 彻底打破了这一桎梏，它能够整合全人类所有已知知识，实现经验与方法论的永续积累，无断代传承，不会因个体生命周期终止而清零。
它可以同时在多领域并行开展实践，无精力限制、无寿命约束，能够快速完成未知领域的条件采集、数据量化、推理验证、知识沉淀全流程。
对于宇宙升空、微观物理、前沿科研等绝大多数人类难以触及的未知领域，人类连抵达认知边缘都极为困难，更无从开展实践探索。
而 AGI 站在全人类知识的金字塔尖，依托条件真理观的探索方法论，能够替代人类深入未知场景，完成艰苦、漫长、跨代的探索任务，将全新的条件、数据、规律反馈给人类，完善认知闭环。
人类负责顶层哲学指引、价值判断与方向掌舵，AGI 承担具体的实践探索、数据归纳与迭代验证。
二者形成完美互补，共同推动人类文明向更广阔的边界迈进。
四、双向救赎，哲学量化落地与 AI 顶层指引，实现共生共赢，打破哲学与 AI 的脱节困局。
需要双向奔赴，互相赋能，让传统哲学重拾实践价值，让 AI 发展避开认知误区，最终实现理论与实践的共生共赢。
对于传统哲学与社会科学而言，要摒弃空洞思辨，走向量化建模，实践落地。
以条件真理观为核心，将对象、场景、问题、内容、动作、数据六要素5 W E H 等认知框架进行数学化、结构化转化。
建立可计算、可验证、可复用的理论模型，让抽象的认识论转化为能指导 AI 训练。
RAG 构建社会学分析、社会实践的具体方法，让理论重新具备指导实践的核心价值，摆脱空谈无用的困境。
对于人工智能产业界而言，要跳出工程实操，拥抱顶层哲学、边界认知，主动吸纳条件真理观的核心思想，明确 AGI 的能力边界，放弃对全能 AGI 的盲目追求，聚焦工程版 AGI 的优化升级。
依托条件真理观完善模型训练、知识库构建、推理优化流程，避免陷入认知误区。
让 AI 技术沿着符合认知规律的路径高效发展，最大化发挥其实践价值。
结语条件真理观是打通传统哲学认识论与人工智能实践的核心密钥。
他既为 AGI 划定了不可逾越的认知边界，终结了全能 AGI 的幻想，也充分肯定了工程版 AGI 在人类文明进程中的颠覆性价值。
传统哲学唯有走向量化落地，才能摆脱空谈。
 AI 实践唯有依托顶层哲学指引才能避开误区。
未来人类把握认知的哲学方向，AGI 承担前沿的实践探索。
二者遵循条件真理观的底层规律，携手突破认知边界，才是人工智能发展的正道，也是人类文明延续进阶的核心路径。

修正脚本

条件真理观，打通哲学认知与 AI 实践，界定 AGI 边界与文明价值。
从一篇探讨公理体系、现实博弈与条件真理观的文章出发，我们展开了一场横跨传统哲学认识论与人工智能产业实践的深度对话，最终形成了一套逻辑自洽、双向赋能的核心认知。
当下，传统哲学、社会科学与 AI 产业实践存在明显脱节。
前者空谈理论，缺乏量化落地。
后者埋头实操，忽视顶层哲学指引。
二者的割裂既让哲学理论丧失实践价值，也让 AI 领域出现对 AGI 的盲目预判，如同十九世纪钟表匠囿于机械技艺幻想永动机一般。
唯有以条件真理观为核心纽带，打通思想高度与工程实操，才能既为 AI 发展划定不可逾越的认知边界，又充分肯定其在人类文明进程中的颠覆性价值，实现理论与实践的双向救赎。
一、双向脱节，哲学与 AI 的各自困局。
根源在认知高度的缺失。
当前传统认识论、哲学真理观与人工智能产业界，处于一种各行其道、互不连通的割裂状态。
这种脱节并非双方理念相背，而是认知维度与实践路径的错位，最终导致两类主体都陷入各自的误区。
一方面，人工智能产业界从业者日用而不知，深陷工程实操却缺乏顶层哲学指引。
当下 AI 领域的训练、推理、泛化、 RAG 构建、 KVQ 注意力机制等核心技术，本质上都在践行条件真理观，所有模型输出都依附于输入条件。
所有能力都源于对已有经验的量化归纳，所有真理都需实践验证才具备价值。
但绝大多数工程师研究员仅从数学原理、编程逻辑、工程流程出发开展工作，将条件决定输出、经验支撑泛化视为天经地义的实操常识，从未从认识论、真理观的哲学高度进行系统化梳理与总结。
这就如同十九世纪精通精密机械的钟表匠，能打造出极致复杂的机械装置，却因缺乏能量守恒的底层物理认知，陷入永动机的幻想。
部分 AI 从业者也正因缺少条件真理观的顶层指引，对 AGI 产生不切实际的预判。
追求脱离实践、全知全能的超级 AI，忽视认知的底层规律，犯下边界认知错误。
另一方面，传统哲学与社会科学领域空谈而不实，固守理论思辨却缺乏量化落地。
哲学界早已围绕唯物主义、唯心主义、绝对真理与相对真理展开数百年探讨，得出真理具有条件性，实践是检验真理的唯一标准等核心结论。
但大多停留在文字思辨层面，没有将抽象理论转化为可量化、可建模、可落地的实操框架。
更未意识到这些经典认识论成果正是人工智能的底层运行逻辑。
社会科学领域的诸多理论同样泛泛而谈，缺乏数学建模与数据量化，无法指导具体实践。
最终丧失了理论存在的核心价值：指导实践、预测趋势。
理论若不与实践结合，便是无源之水。
哲学若不落地产业，便是空洞说教，这是传统思想领域面临的核心困境。
二、认知三大公理，条件真理观为 AGI 划定终极边界，如同能量守恒定律从物理底层判永动机死刑。
我们从唯物主义认识论与条件真理观中提炼出三大认知底层公理，从哲学高度为 AGI 划定不可突破的终极边界，彻底区分哲学版真 AGI 与工程版实用 AGI ，终结对 AGI 的盲目幻想。
第一公理，一切有效认知必依附前置条件，不存在无条件的绝对真理。
所有认知、判断、推理都有其适用前提，脱离对象、场景、问题、动作、数据等具体条件，真理便不复存在。
无论是人类认知还是 AI 智能，都无法脱离条件产生有效输出，这是认知的底层前提，也是 AI 运行的核心逻辑。
第二公理，所有智能模型都是对已发生、已量化经验的压缩归纳与泛化，当前大模型。
未来 AI 的预训练、微调、强化学习，本质都是对人类已有知识、历史经验、实践数据的整合压缩。
所谓泛化能力只是在已知条件的相似区间内进行特征匹配与规律复用，绝非凭空创造全新认知，更无法突破已有经验的底层框架。
第三公理，全新真理只能通过现实实践校验生成。
无实践则无新知。
针对宇宙探索、底层科研、全新数学范式等人类从未涉足、从未观测、从未量化的未知领域，不存在可匹配的条件与可复用的经验，任何智能都无法直接生成绝对正确的全新真理。
必须经过实践探索、条件采集、数据量化、迭代验证的完整闭环，才能形成新的认知。
基于这三大公理，我们可以得出终极判定。
追求全知全能，无需实践即可顿悟全新真理，突破所有条件限制的哲学版真AGI，是认知领域的永动机，永远不可能实现。
而能够整合全人类已有经验，精准完成条件匹配，高效实现领域泛化，严格遵循实践闭环的工程版实用 AGI，不仅可以实现，更将爆发出无与伦比的价值。
三、边界之内，AGI 的核心价值成为人类文明探索的永续载体，我们从哲学高度划定 AGI 的边界，绝非否定其价值。
恰恰相反，是为了精准定位其不可替代的核心意义。
AGI 是突破人类生理局限、推进人类文明向未知领域延伸的核心载体，未来必将成为未知领域实践的主体。
人类作为碳基生命，存在天生的认知短板。
每个人出生都是认知白纸，从启蒙学习到深耕专业，耗尽半生才能摸到人类现有认知的边界。
有效科研工作年限仅有数十年，顶尖天才万里挑一，知识传承存在断代损耗，每一代人都要从零开始积累。
如同反复跳高冲击记录，却始终无法实现知识的永续叠加。
而 AGI 彻底打破了这一桎梏，它能够整合全人类所有已知知识，实现经验与方法论的永续积累，无断代传承，不会因个体生命周期终止而清零。
它可以同时在多领域并行开展实践，无精力限制、无寿命约束，能够快速完成未知领域的条件采集、数据量化、推理验证、知识沉淀全流程。
对于宇宙深空、微观物理、前沿科研等绝大多数人类难以触及的未知领域，人类连抵达认知边缘都极为困难，更无从开展实践探索。
而 AGI 站在全人类知识的金字塔尖，依托条件真理观的探索方法论，能够替代人类深入未知场景，完成艰苦、漫长、跨代的探索任务，将全新的条件、数据、规律反馈给人类，完善认知闭环。
人类负责顶层哲学指引、价值判断与方向掌舵，AGI 承担具体的实践探索、数据归纳与迭代验证。
二者形成完美互补，共同推动人类文明向更广阔的边界迈进。
四、双向救赎，哲学量化落地与 AI 顶层指引，实现共生共赢，打破哲学与 AI 的脱节困局。
需要双向奔赴，互相赋能，让传统哲学重拾实践价值，让 AI 发展避开认知误区，最终实现理论与实践的共生共赢。
对于传统哲学与社会科学而言，要摒弃空洞思辨，走向量化建模，实践落地。
以条件真理观为核心，将对象、场景、问题、内容、动作、数据六要素5 W E H 等认知框架进行数学化、结构化转化。
建立可计算、可验证、可复用的理论模型，让抽象的认识论转化为能指导 AI 训练、RAG 构建、社会学分析、社会实践的具体方法，让理论重新具备指导实践的核心价值，摆脱空谈无用的困境。
对于人工智能产业界而言，要跳出工程实操，拥抱顶层哲学、边界认知，主动吸纳条件真理观的核心思想，明确 AGI 的能力边界，放弃对全能 AGI 的盲目追求，聚焦工程版 AGI 的优化升级。
依托条件真理观完善模型训练、知识库构建、推理优化流程，避免陷入认知误区。
让 AI 技术沿着符合认知规律的路径高效发展，最大化发挥其实践价值。
结语：条件真理观是打通传统哲学认识论与人工智能实践的核心密钥。
它既为 AGI 划定了不可逾越的认知边界，终结了全能 AGI 的幻想，也充分肯定了工程版 AGI 在人类文明进程中的颠覆性价值。
传统哲学唯有走向量化落地，才能摆脱空谈。
 AI 实践唯有依托顶层哲学指引才能避开误区。
未来人类把握认知的哲学方向，AGI 承担前沿的实践探索。
二者遵循条件真理观的底层规律，携手突破认知边界，才是人工智能发展的正道，也是人类文明延续进阶的核心路径。

英文翻译

**Conditional Truth View: Bridging Philosophical Cognition and AI Practice, Defining the Boundaries of AGI and Civilizational Value**

Starting from an article exploring axiom systems, real-world博弈, and the conditional truth view, we engaged in a deep dialogue spanning traditional philosophical epistemology and AI industry practice, ultimately forming a logically coherent, mutually empowering core认知.

Currently, there is a clear disconnect between traditional philosophy, social sciences, and AI industry practice.  
The former engages in empty theorizing, lacking quantitative implementation.  
The latter focuses on hands-on operations, neglecting top-level philosophical guidance.  
This divide robs philosophical theories of practical value while leading the AI field to make blind predictions about AGI—much like 19th-century watchmakers trapped in mechanical craftsmanship, dreaming of perpetual motion machines.  
Only by using the conditional truth view as the core纽带, bridging ideological heights with engineering practice, can we define insurmountable cognitive boundaries for AI development while fully affirming its disruptive value in human civilization, achieving a mutual redemption of theory and practice.

**I. Mutual Disconnect: The Respective Dilemmas of Philosophy and AI**

The root cause lies in a lack of cognitive perspective.  
Current traditional epistemology, philosophical truth views, and the AI industry are in a fragmented state, each going its own way without interconnection.  
This disconnect is not due to opposing ideas, but rather a misalignment of cognitive dimensions and practical paths, ultimately trapping both sides in their own misconceptions.

On one hand, AI industry practitioners operate day-to-day without awareness, deeply engaged in engineering practice yet lacking top-level philosophical guidance.  
Core technologies in the current AI field—training, inference, generalization, RAG construction, KVQ attention mechanisms—essentially all embody the conditional truth view: every model output depends on input conditions, every capability stems from quantitative induction of existing experience, and every truth must be validated through practice to have value.  
However, most engineers and researchers work solely from mathematical principles, programming logic, and engineering workflows, taking condition-driven outputs and experience-supported generalization as common-sense operational knowledge, never systematically categorizing or summarizing from the philosophical heights of epistemology and truth theory.  
This is akin to the 19th-century watchmaker who could craft extremely complex mechanical devices but, lacking foundational physics knowledge of energy conservation, fell into the fantasy of perpetual motion.  
Similarly, some AI practitioners, missing the top-level guidance of conditional truth view, harbor unrealistic predictions about AGI. They pursue a super AI that is detached from practice and omniscient, ignoring the underlying laws of cognition, committing boundary cognitive errors.

On the other hand, traditional philosophy and social sciences engage in empty theorizing without practical application, clinging to theoretical speculation while lacking quantitative implementation.  
The philosophical community has long debated materialism, idealism, absolute truth, and relative truth for centuries, concluding that truth has conditions and that practice is the sole criterion for testing truth.  
But such conclusions mostly remain at the level of textual speculation, never transforming abstract theory into quantifiable, modelable, implementable frameworks.  
They have not realized that these classic epistemological results are precisely the underlying operational logic of artificial intelligence.  
Many theories in the social sciences also remain vague, lacking mathematical modeling and data quantification, unable to guide concrete practice.  
Thus they lose the core value of theory: guiding practice and predicting trends.  
Theory not combined with practice is water without a source.  
Philosophy not applied to industry is empty preaching. This is the core dilemma facing traditional intellectual fields.

**II. Three Cognitive Axioms: The Conditional Truth View Sets the Ultimate Boundaries for AGI, Much Like the Law of Energy Conservation Dooms Perpetual Motion at a Physical Level**

From materialist epistemology and the conditional truth view, we distill three fundamental cognitive axioms that, from a philosophical height, define unbreakable ultimate boundaries for AGI, thoroughly distinguishing philosophical true AGI from engineering practical AGI, ending blind fantasies about AGI.

**First Axiom:** All effective cognition must be attached to preconditions; there is no unconditional absolute truth.  
All cognition, judgment, and reasoning have their applicable premises. Without specific conditions such as object, scenario, problem, action, and data, truth ceases to exist. Whether human cognition or AI intelligence, neither can produce effective output without conditions. This is the fundamental premise of cognition and the core logic of AI operation.

**Second Axiom:** All intelligent models are compressed induction and generalization of past, quantified experience. Current large models and future AI pre-training, fine-tuning, and reinforcement learning are essentially the integration and compression of existing human knowledge, historical experience, and practical data.  
So-called generalization ability is merely feature matching and rule reuse within similar intervals of known conditions. It is by no means creating entirely new cognition out of nothing, nor can it break through the underlying framework of existing experience.

**Third Axiom:** Entirely new truth can only be generated through validation in real-world practice. Without practice, there is no new knowledge.  
For unknown domains such as space exploration, fundamental scientific research, and new mathematical paradigms that humans have never explored, observed, or quantified, there are no matching conditions or reusable experience. No intelligence can directly generate absolutely correct new truth. It must go through a complete closed loop of practical exploration, condition collection, data quantification, iterative validation to form new cognition.

Based on these three axioms, we can reach an ultimate judgment:  
Pursuing an omniscient, absolute-truth-generating philosophical true AGI that requires no practice and breaks all condition constraints is the perpetual motion machine of cognition—never achievable.  
However, an engineering practical AGI that can integrate all existing human experience, accurately match conditions, efficiently achieve domain generalization, and strictly follow the practice closed loop is not only achievable but will unleash unparalleled value.

**III. Within the Boundaries: The Core Value of AGI as a Perpetual Carrier for Human Civilization Exploration**

Defining the boundaries of AGI from a philosophical height by no means denies its value. On the contrary, it precisely positions its irreplaceable core significance.  
AGI is the core carrier for breaking through human physiological limitations and extending human civilization into unknown domains. It will inevitably become the main body of practice in uncharted territories.

As carbon-based lifeforms, humans have inherent cognitive shortcomings.  
Every person is born a blank slate of cognition. From initial learning to deep specialization, it takes half a lifetime to reach the edge of existing human knowledge.  
Effective scientific research lifespan is only a few decades. Top geniuses are one in ten thousand. Knowledge transmission suffers from generational loss; each generation must start accumulating from scratch. This is like repeatedly jumping high to set records, yet never achieving the cumulative stacking of knowledge.

AGI completely breaks this shackle. It can integrate all known human knowledge, achieving perpetual accumulation of experience and methodology without generational breaks, never reset by an individual's life cycle. It can simultaneously conduct practice in multiple fields without energy limits or lifespan constraints, quickly completing the entire process of condition collection, data quantification, inference validation, and knowledge precipitation in unknown domains.

For vast unknown fields such as deep space, microscopic physics, and frontier scientific research that are extremely difficult for humans to even approach the cognitive edge, let alone carry out practical exploration, AGI stands atop the pyramid of all human knowledge. Relying on the exploratory methodology of the conditional truth view, it can replace humans in delving into unknown scenarios, carrying out arduous, long-term, cross-generational exploration tasks, feeding back new conditions, data, and patterns to humans to complete the cognitive loop.

Humans are responsible for top-level philosophical guidance, value judgments, and directional steering; AGI undertakes concrete practical exploration, data induction, and iterative validation. The two complement each other perfectly, jointly pushing human civilization toward broader boundaries.

**IV. Mutual Redemption: Quantitative Implementation of Philosophy and Top-Level Guidance for AI, Achieving Symbiosis and Win-Win**

Breaking the disconnect between philosophy and AI requires a two-way effort to empower each other, allowing traditional philosophy to regain practical value and AI development to avoid cognitive pitfalls, ultimately achieving symbiosis and win-win between theory and practice.

For traditional philosophy and social sciences: Abandon empty speculation, move toward quantitative modeling and practical implementation.  
With the conditional truth view at the core, transform cognitive frameworks such as the six elements (object, scenario, problem, content, action, data) and 5W1EH (Who, What, When, Where, Why, How, etc.) into mathematical, structured forms.  
Establish computable, verifiable, reusable theoretical models, turning abstract epistemology into concrete methods that can guide AI training, RAG construction, sociological analysis, and social practice. Let theory regain its core value of guiding practice, escaping the dilemma of empty talk.

For the AI industry: Step out of engineering practice and embrace top-level philosophy and boundary cognition. Actively absorb the core ideas of the conditional truth view, clarify the capability boundaries of AGI, abandon blind pursuit of omnipotent AGI, and focus on optimizing and upgrading engineering practical AGI.  
Rely on the conditional truth view to improve model training, knowledge base construction, and inference optimization processes, avoiding cognitive pitfalls. Let AI technology develop efficiently along paths consistent with cognitive laws, maximizing its practical value.

**Conclusion: The Conditional Truth View Is the Key to Bridging Traditional Philosophical Epistemology and AI Practice**

It defines insurmountable cognitive boundaries for AGI, ending the fantasy of an omnipotent AGI, while fully affirming the disruptive value of engineering practical AGI in human civilization.  
Only by moving toward quantitative implementation can traditional philosophy escape empty talk.  
Only by relying on top-level philosophical guidance can AI practice avoid pitfalls.  
In the future, humans grasp the philosophical direction of cognition; AGI undertakes frontier practical exploration.  
The two, following the underlying laws of the conditional truth view, join hands to break through cognitive boundaries—this is the right path for AI development and the core path for the continuation and advancement of human civilization.