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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毛泽东思想对当前人工智能技术革命的指导意义1

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原始脚本

毛泽东思想对当前人工智能技术革命的指导意义第一部分，一，引言。
人工智能技术革命的本质与思想联结。
当前的人工智能技术革命并非单纯的技术升级或产业变革，而是一场牵动全球地缘政治格局、重塑经济逻辑、影响民生机理的无硝烟博弈。
其核心矛盾的复杂性，如技术自主与外部封锁、利益博弈的激烈性，如中美 AI 赛道竞争、发展阶段的长期性，如通用 AI 需数十年探索，与中国革命历程中抗日战争、社会主义建设初期的斗争场景高度相似。
毛泽东思想作为中国革命与建设的理论结晶，既包含实践、认识、实践的哲学方法论，也涵盖持久战、统一战线的战略智慧，更有群众路线的实践准则。
这些思想并非历史遗产，而是能为 AI 研发方向校准、阶段规划、博弈应对提供系统性指导的活的方法论。
可从哲学、实践、博弈三大维度构建现 AI 技术革命的指导框架。
二、哲学方法论，锚定 AI 研发的底层逻辑。
一 实践论破解 AI 技术迭代的认知闭环。
实践论1937年基于延安抗大讲课提纲整理而成，核心是实践、认识、实践的认识总公式，这一逻辑与 AI 迭代规律高度契合。
一、AI 训练的实践素材。
AI 模型的初始数据是人类社会实践的数字化记录，如医疗诊断数据、工业生产数据，如同革命时期从群众实践中收集经验。
例如大语言模型需依托海量真实文本，实践才能掌握语言逻辑与知识关联、认识。
二，AI 优化的在实践驱动。
模型训练后需投入实际场景检验，医疗 AI 在临床验证准确率，工业 AI 在生产线测试效率，反馈的错误与需求，新实践推动参数优化，深化认识。
正如实践论强调通过实践发展真理，AI 成熟正是在数据输入、训练、应用、优化的循环中实现的。
三，AI 价值的实践检验，脱离场景的 AI 如同纸上谈兵。
某些参数庞大的通用模型，若无法解决医疗教育痛点，价值便无法认可。
这正体现实践是检验真理的唯一标准，AI 优劣最终由应用效果判定。
二，矛盾论，化解 AI 发展的核心矛盾。
1937年的矛盾论深入论述矛盾普遍性与特殊性，主次矛盾关系，为分析 AI 问题提供工具。
一、通用与定制的平衡。
矛盾普遍性决定 AI 有数据驱动、算力支撑的通用规律。
特殊性则表现为场景差异。
医疗 AI 需符合诊断规范，工业 AI 需适配生产流程。
例如研发 AI 诊断系统，既要遵循图像识别通用算法普遍性，又要针对肺癌、糖尿病定制训练方案特殊性。
二、主次矛盾的优先解决。
A I 发展面临算力不足、数据质量、技术自主等矛盾。
当前技术自主是主要矛盾。
国外对高端 GPU 的封锁制约大模型研发，需集中资源攻克国产芯片制成工艺，而非在应用端追求参数竞赛，待短板补齐再解决次要矛盾。
三、矛盾转 转化的动态调整。
AI 初期技术可行性是主要方面，如提升语音识别准确率。
成熟后产业落地性成为核心，如集成到智能设备。
当前地缘博弈下，技术安全性更关键，需构建自主 AI 生态，如国产算法框架，避免被卡脖子。
毛泽东思想对当前人工智能技术革命的指导意义第二部分三，实践策略上，以实事求是规避 AI 发展误区。
一，反对本本主义，拒绝技术教条主义。
1930年毛泽东为反对为书本为教条，做反对本本主义，提出没有调查没有发言权，对 AI 发展极具针对性。
一、不盲目追随国外赛道。
全球 AI 热潮中，部分国家聚焦千亿参数通用模型，但我国制造业场景丰富，民生需求迫切，盲目跟跑会浪费资源。
应借鉴不为书只为实原则，优先布局工业 AI 如汽车焊接检测系统、民生 AI 如基层医疗诊断工具，形成差异化优势。
二、强化调查研究，把控源头，反对本本主义，强调调查就是解决问题， AI 研发需深入场景调研。
数据收集时走进医院，掌握病例加影像加病理报告的完整需求。
产品设计时走访中小企业，开发低成本易操作的系统，避免数据与需求脱节，功能与痛点错位。
二、湖南农民运动考察报告，客观认知 AI 发展。
1927年毛泽东实地考察湖南农民运动，批判糟得很与一切放任的极端观点，启示我们理性看待 AI 一、拒绝 AI 万能论。
部分观点认为 AI 可替代人类所有工作，但 AI 存在明显局限，医疗 AI 能辅助识别病灶，却无法替代医生综合诊断。
教育 AI 可推送学习资源，却缺乏教师的情感关怀。
需将 AI 定位为辅助工具，而非替代者。
二、反对 AI 威胁论，过度放大 AI 导致失业、引发伦理灾难的风险，会错失发展机遇。
 AI 主流是推动进步，工业 AI 提升效率、降低事故，医疗 AI 缓解资源短缺。
对风险需通过制度规范，如伦理准则、劳动保障解决，而非否定技术。
三、关心群众生活，注意工作方法，坚持 AI 民生导向。
1934年毛泽东提出革命战争依靠群众，强调关心群众衣食住行。
这一群众路线思想指引 AI 发展。
一、聚焦民生痛点研发。
我国基层医疗资源短缺、教育不均、老龄化护理压力大， AI 需瞄准这些痛点。
例如开发低成本 AI 诊断系统，解决乡镇医院诊断能力不足。
打造 AI 智慧教育平台，推送优质资源至农村。
研发养老护理机器人，弥补护理人员缺口。
二、通过民生应用凝聚共识。
若 AI 仅服务于商业利益，如平台精准营销，会让民众觉得与我无关。
去通过疫情防控中轨迹追溯、灾害中灾情预警等应用，让民众直观感受 AI 价值，主动支持技术发展，营造良好社会环境。

修正脚本

毛泽东思想对当前人工智能技术革命的指导意义第一部分，一，引言。
人工智能技术革命的本质与思想联结。
当前的人工智能技术革命并非单纯的技术升级或产业变革，而是一场牵动全球地缘政治格局、重塑经济逻辑、影响民生机理的无硝烟博弈。
其核心矛盾的复杂性，如技术自主与外部封锁、利益博弈的激烈性，如中美 AI 赛道竞争、发展阶段的长期性，如通用 AI 需数十年探索，与中国革命历程中抗日战争、社会主义建设初期的斗争场景高度相似。
毛泽东思想作为中国革命与建设的理论结晶，既包含实践、认识、实践的哲学方法论，也涵盖持久战、统一战线的战略智慧，更有群众路线的实践准则。
这些思想并非历史遗产，而是能为 AI 研发方向校准、阶段规划、博弈应对提供系统性指导的活的方法论。
可从哲学、实践、博弈三大维度构建现代AI 技术革命的指导框架。
二、哲学方法论，锚定 AI 研发的底层逻辑。
一 实践论破解 AI 技术迭代的认知闭环。
实践论1937年基于延安抗大讲课提纲整理而成，核心是实践、认识、实践的认识总公式，这一逻辑与 AI 迭代规律高度契合。
一、AI 训练的实践素材。
AI 模型的初始数据是人类社会实践的数字化记录，如医疗诊断数据、工业生产数据，如同革命时期从群众实践中收集经验。
例如大语言模型需依托海量真实文本，经实践获得认识，才能掌握语言逻辑与知识关联。
二，AI 优化的再实践驱动。
模型训练后需投入实际场景检验，医疗 AI 在临床验证准确率，工业 AI 在生产线测试效率，根据反馈的错误与需求，新实践推动参数优化，深化认识。
正如实践论强调通过实践发展真理，AI 成熟正是在数据输入、训练、应用、优化的循环中实现的。
三，AI 价值的实践检验，脱离场景的 AI 如同纸上谈兵。
某些参数庞大的通用模型，若无法解决医疗教育痛点，价值便不被认可。
这正体现实践是检验真理的唯一标准，AI 优劣最终由应用效果判定。
二，矛盾论，化解 AI 发展的核心矛盾。
1937年的矛盾论深入论述矛盾普遍性与特殊性，主次矛盾关系，为分析 AI 问题提供工具。
一、通用与定制的平衡。
矛盾普遍性决定 AI 有数据驱动、算力支撑的通用规律。
特殊性则表现为场景差异。
医疗 AI 需符合诊断规范，工业 AI 需适配生产流程。
例如研发 AI 诊断系统，既要遵循图像识别通用算法普遍性，又要针对肺癌、糖尿病定制训练方案特殊性。
二、主次矛盾的优先解决。
AI 发展面临算力不足、数据质量、技术自主等矛盾。
当前技术自主是主要矛盾。
国外对高端 GPU 的封锁制约大模型研发，需集中资源攻克国产芯片制程工艺，而非在应用端追求参数竞赛，待短板补齐再解决次要矛盾。
三、矛盾转化的动态调整。
AI 初期技术可行性是主要方面，如提升语音识别准确率。
成熟后产业落地性成为核心，如集成到智能设备。
当前地缘博弈下，技术安全性更关键，需构建自主 AI 生态，如国产算法框架，避免被卡脖子。
毛泽东思想对当前人工智能技术革命的指导意义第二部分三，实践策略上，以实事求是规避 AI 发展误区。
一，反对本本主义，拒绝技术教条主义。
1930年毛泽东为反对以书本为教条，作《反对本本主义》，提出没有调查没有发言权，对 AI 发展极具针对性。
一、不盲目追随国外赛道。
全球 AI 热潮中，部分国家聚焦千亿参数通用模型，但我国制造业场景丰富，民生需求迫切，盲目跟跑会浪费资源。
应借鉴不唯书只为实原则，优先布局工业 AI 如汽车焊接检测系统、民生 AI 如基层医疗诊断工具，形成差异化优势。
二、强化调查研究，把控源头，反对本本主义，强调调查就是解决问题， AI 研发需深入场景调研。
数据收集时走进医院，掌握病例加影像加病理报告的完整需求。
产品设计时走访中小企业，开发低成本易操作的系统，避免数据与需求脱节，功能与痛点错位。
二、湖南农民运动考察报告，客观认知 AI 发展。
1927年毛泽东实地考察湖南农民运动，批判糟得很与一概肯定的极端观点，启示我们理性看待 AI：一、拒绝 AI 万能论。
部分观点认为 AI 可替代人类所有工作，但 AI 存在明显局限，医疗 AI 能辅助识别病灶，却无法替代医生综合诊断。
教育 AI 可推送学习资源，却缺乏教师的情感关怀。
需将 AI 定位为辅助工具，而非替代者。
二、反对 AI 威胁论，过度放大 AI 导致失业、引发伦理灾难的风险，会错失发展机遇。
 AI 主流是推动进步，工业 AI 提升效率、降低事故，医疗 AI 缓解资源短缺。
对风险需通过制度规范，如伦理准则、劳动保障解决，而非否定技术。
三、关心群众生活，注意工作方法，坚持 AI 民生导向。
1934年毛泽东提出革命战争依靠群众，强调关心群众衣食住行。
这一群众路线思想指引 AI 发展。
一、聚焦民生痛点研发。
我国基层医疗资源短缺、教育不均、老龄化护理压力大， AI 需瞄准这些痛点。
例如开发低成本 AI 诊断系统，解决乡镇医院诊断能力不足。
打造 AI 智慧教育平台，推送优质资源至农村。
研发养老护理机器人，弥补护理人员缺口。
二、通过民生应用凝聚共识。
若 AI 仅服务于商业利益，如平台精准营销，会让民众觉得与我无关。
而通过疫情防控中轨迹追溯、灾害中灾情预警等应用，让民众直观感受 AI 价值，主动支持技术发展，营造良好社会环境。

英文翻译

The Guiding Significance of Mao Zedong Thought for the Current Artificial Intelligence Technology Revolution, Part I: Introduction.  
The Essence of the AI Technology Revolution and Its Connection to Thought.  
The current artificial intelligence technology revolution is not merely a technological upgrade or industrial transformation, but a smokeless struggle that reshapes global geopolitical patterns, realigns economic logic, and affects the mechanisms of people's livelihoods. The complexity of its core contradictions—such as technological autonomy versus external blockades, the intensity of interest conflicts (e.g., the Sino-US competition in the AI track), and the long-term nature of development stages (e.g., the decades-long exploration required for general AI)—shares high similarities with the struggles in the Chinese revolutionary process, such as the War of Resistance Against Japan and the early stages of socialist construction.  
Mao Zedong Thought, as the theoretical crystallization of China's revolution and construction, encompasses not only the philosophical methodology of "practice, knowledge, practice" but also the strategic wisdom of "protracted war" and "united front," as well as the practical principles of the "mass line." These ideas are not historical relics but a living methodology that can provide systematic guidance for calibrating AI development directions, stage planning, and response to strategic games. A guiding framework for the modern AI technology revolution can be constructed from three dimensions: philosophy, practice, and strategic gaming.

II. Philosophical Methodology: Anchoring the Underlying Logic of AI Development.  
1. "On Practice" Breaks the Cognitive Loop of AI Iteration.  
"On Practice," based on lecture notes from the Anti-Japanese Military and Political University in Yan'an in 1937, has at its core the general formula "practice, knowledge, practice," which aligns highly with the iterative patterns of AI.  
(1) Practical Material for AI Training.  
The initial data for AI models is a digital record of human social practice, such as medical diagnostic data and industrial production data, akin to gathering experience from the masses during the revolutionary period. For example, large language models rely on vast amounts of real-world text; only after acquiring knowledge through practice can they master linguistic logic and knowledge associations.  
(2) The Drive of Re-practice for AI Optimization.  
After training, models need to be tested in real-world scenarios—medical AI verified for accuracy in clinical settings, industrial AI tested for efficiency on production lines. Feedback on errors and needs drives parameter optimization through new practice, deepening knowledge. Just as "On Practice" emphasizes the development of truth through practice, AI maturation is achieved through the cycle of data input, training, application, and optimization.  
(3) Practical Testing of AI Value.  
An AI disconnected from real-world scenarios is akin to an armchair strategist. Some general models with enormous parameters, if they cannot address pain points in healthcare or education, will not have their value recognized. This reflects the principle that "practice is the sole criterion for testing truth"—AI's merits are ultimately judged by its application outcomes.

2. "On Contradiction" Resolves the Core Contradictions in AI Development.  
"On Contradiction" (1937) deeply discusses the universality and particularity of contradictions, as well as the relationship between primary and secondary contradictions, providing tools for analyzing AI issues.  
(1) Balancing Generality and Customization.  
The universality of contradictions determines that AI has general laws driven by data and supported by computing power. Particularity is reflected in scenario differences: medical AI must comply with diagnostic standards, while industrial AI must adapt to production processes. For example, developing an AI diagnostic system requires both following the general algorithms of image recognition (universality) and customizing training schemes for specific diseases like lung cancer or diabetes (particularity).  
(2) Prioritizing Primary and Secondary Contradictions.  
AI development faces contradictions such as insufficient computing power, data quality, and technological autonomy. Currently, technological autonomy is the primary contradiction. Foreign restrictions on high-end GPUs hinder the development of large models, so resources must be concentrated on overcoming bottlenecks in domestic chip manufacturing processes, rather than pursuing parameter competition on the application end. Only after addressing these shortcomings can secondary contradictions be resolved.  
(3) Dynamic Adjustment of Contradiction Transformation.  
In the early stages of AI, technical feasibility is the primary aspect—e.g., improving speech recognition accuracy. In mature stages, industrial implementation becomes core—e.g., integration into smart devices. Under the current geopolitical struggles, technological security is more critical, requiring the construction of a self-reliant AI ecosystem (e.g., domestic algorithm frameworks) to avoid being choked.

The Guiding Significance of Mao Zedong Thought for the Current Artificial Intelligence Technology Revolution, Part II:  
III. Practical Strategy: Avoiding AI Development Pitfalls with Seeking Truth from Facts.  
1. Oppose Book Worship and Reject Technological Dogmatism.  
In 1930, Mao Zedong wrote "Oppose Book Worship" to oppose taking books as dogma, proposing that "no investigation, no right to speak." This is highly relevant to AI development.  
(1) Do Not Blindly Follow Foreign Tracks.  
In the global AI craze, some countries focus on trillion-parameter general models, but China's rich manufacturing scenarios and urgent livelihood needs make blind imitation a waste of resources. The principle of "based on facts, not books" should be adopted, prioritizing the layout of industrial AI (e.g., automotive welding inspection systems) and livelihood AI (e.g., primary care diagnostic tools) to form differentiated advantages.  
(2) Strengthen Investigation and Research to Control the Source.  
Opposing book worship emphasizes that investigation is the solution to problems. AI development requires in-depth scenario research. When collecting data, go into hospitals to capture the complete needs of "cases + imaging + pathology reports." When designing products, visit small and medium-sized enterprises to develop low-cost, easy-to-use systems, avoiding disconnection between data and needs or misalignment between functions and pain points.

2. "Report on an Investigation of the Peasant Movement in Hunan" for Objective Understanding of AI Development.  
In 1927, Mao Zedong conducted an on-the-ground investigation of the Hunan peasant movement, criticizing extreme views such as "terrible" or "entirely good," inspiring us to view AI rationally:  
(1) Reject AI Omnipotence.  
Some argue that AI can replace all human work, but AI has clear limitations. Medical AI can assist in identifying lesions but cannot replace a doctor's comprehensive diagnosis. Educational AI can deliver learning resources but lacks a teacher's emotional care. AI should be positioned as an auxiliary tool, not a replacement.  
(2) Oppose AI Threat Theory.  
Exaggerating the risks of AI—such as unemployment or ethical disasters—can lead to missed development opportunities. The mainstream of AI is to drive progress: industrial AI improves efficiency and reduces accidents, medical AI alleviates resource shortages. Risks should be addressed through institutional norms (e.g., ethical guidelines, labor protections), not by denying technology itself.

3. "Pay Attention to the People's Livelihood" and "Pay Attention to Methods of Work" – Adhering to a People-Centered Approach in AI.  
In 1934, Mao Zedong stressed that revolutionary war relies on the masses, emphasizing care for their daily needs. This mass-line thinking guides AI development:  
(1) Focus R&D on People's Livelihood Pain Points.  
China faces shortages in primary healthcare resources, educational inequality, and high pressure from aging-related care demands. AI should target these pain points. For example, develop low-cost AI diagnostic systems to address insufficient diagnostic capabilities in township hospitals; create AI-powered smart education platforms to deliver quality resources to rural areas; and develop elderly care robots to fill the gap in nursing personnel.  
(2) Build Consensus Through Livelihood Applications.  
If AI serves only commercial interests—such as targeted marketing on platforms—the public will feel it is irrelevant to them. In contrast, applications like epidemic contact tracing or disaster early warning demonstrate AI's value to the public, encouraging active support for technological development and creating a favorable social environment.