人艺智能：重新定义AI的本质与未来（Artificial Intelligence Redefined）

Artificial Intelligence Redefined

人艺智能：重新定义AI的本质与未来

钱 宏 Archer Hong Qian

“啊，人类，只有你才有艺术。”（席勒）

AI的未来不仅依赖于技术创新，更需要一种新的哲学视角来引导其发展。Amorsophia为我们提供了这一视角，它融合了智慧与爱的结合理念，强调AI必须在情感、伦理和智慧的平衡中发展。通过心（孞）联网（MindsWeb 或 MindsNetworking）的技术实现，AI将突破能耗、系统思维和情感伦理的瓶颈，进入一个真正与人类交互主体共生的时代。

为了实现这一愿景，全球亟需召集一次“新达特茅斯人工智慧模因会议”（NEW Dartmouth Research Project on AI-Amorsophia），集思广益制定出与AI哲学相匹配的技术框架和伦理标准。通过这一会议，制定出《AI-Amorsophia宪章》，并将其作为超主权、超地缘、超文明的《全球共生公约》的重要组成部分，引领人类进入一个“让任何统治全世界的帝国政治企图成为不可能”的共生新时代。 最终，通过“爱之智慧”，我们将不再仅仅面对AI作为工具或威胁的现实，而是打开一扇通往共生未来的大门，实现人类与AI真正的共生与繁荣。

——摘自《Amorsophia-MindsWeb：突破AI三大“瓶颈”之道》http://symbiosism.com.cn/8878.html

1956年，达特茅斯会议首次提出并确立了“Artificial Intelligence”这一概念与学科术语。这一选择是基于“Artificial”一词能够涵盖非机械式的智能表现，并突显其在虚拟性和创造性方面的潜力。相比之下，其他候选术语如“Simulated Intelligence”或“Anthropomorphic Intelligence”更多局限于模仿或拟人化的层面，而无法完全体现AI的技术广度与哲学深度。

“Artificial Intelligence”强调了人工构建的智能系统如何超越简单仿真，成为具备创造性、虚拟化属性的全新范畴。在达特茅斯讨论过程中，与会者们没有选择“Simulated Intelligence”（仿真智能）、“Counterfeit Intelligence”（仿造智能）、“Twin Intelligence”（孪生智能）、“Imitative Intelligence”（伪造智能）、“Model Intelligence”（模态智能）或“Anthropomorphic Intelligence”（拟人化智能），而是一致接受了麦卡锡提出的“Artificial Intelligence”。或许他们意识到了，这里的“Artificial”不仅意味着人工或人为，还蕴含着词头“Art”的深层意涵，即艺术性、虚构性与虚拟性。

因此，将“Artificial Intelligence”翻译为“人艺智能”，而非“人工智能”，可能更能体现AI的本质特性。这一创新翻译不仅在语言上更加贴切，还能够带来思维方式的深刻转变，为AI研发指引新的方向。

尽管将“Artificial Intelligence”重新翻译为“人艺智能”，可能引起争议与现实权衡，比如第一，传统认知惯性：目前“人工智能”已成为固定术语，改变翻译可能需要较大的推广和教育成本。第二，艺术的狭义理解：有些人可能将“人艺智能”误解为仅与艺术领域相关，而忽视其广义的技术与社会应用。

但是，我相孞，将“Artificial Intelligence”翻译为“人艺智能”，不仅更富有创意，而且也有“发生学”意义的根据。Artificial一词的词头“Art”，最早的意义与技术、技艺、技巧、技能相近，在希腊语中写作“τεχνη(techne)”，在拉丁语中则写作Ars。那个时候的艺术尚未分化，它与美、道德和实用都有关系。在相当长的历史时期里，在人们的观念中，艺术与技术几乎没有什么区别，艺术家与工匠（中文称“手艺人”）的社会地位，也大致差不多。18世纪法国思想家狄德罗(D. Diderot, 1713～1784)主编的《百科全书或科学、艺术、手工业详解辞典》中，“技术”条目就是用的“Art”一词，他把技术解释为“为了完成特定目标而协调动作的方法、手段和规则相结合的体系”，这是已知最早的技术的定义。

与“人工智能”相比，“人艺智能”在词义上更贴近“Artificial”一词的多重含义，同时也强调了AI技艺的本质特性。这种翻译的优胜性如下：

“人艺智能”的优越性

1. “Artificial”的多重含义

“Artificial”一词不仅指“人工的”或“人为的”，还包含“艺术性”（artistic）、“虚拟性”（virtual）、“创造性”（creative）等隐含意义。

• 词头“Art”来源于拉丁词根“ars”，意为“技巧、艺术或手法”。它并不限于机械性或劳动力驱动，而更多指向创造性和构建性。
• “人艺智能”捕捉了“人工”背后的艺术化和虚构性，精准传达了AI本质上的虚拟构建属性。具体而言，艺术化体现在生成式AI在文学、音乐、绘画等领域的原创性创作中，例如AI能够基于输入生成具有艺术审美的诗歌或绘画作品。虚构性则表现为AI通过算法模型构建虚拟环境或角色，例如虚拟助手和元宇宙中虚拟角色的交互，这些虚拟构建并非真实存在，但通过技术手段实现了逼真的模拟与功能化。

2. 更符合AI的核心特性

• 创造性（Creativity）：AI不仅是机械模拟，更以数据、算法和模型为基础构建新知识、新行为和新系统，这种“创造”的过程与艺术创作本质上具有相似性。
• 虚拟性（Virtuality）：AI的智能多表现在“虚拟环境”或“虚拟交互”中，其推理和行为通常通过模型的虚拟运算完成。
• 工具性（Instrumentality）：AI既是人类设计的工具，也是扩展人类智能的艺术性工具。

3. 突破“人工”一词的局限性

• “人工”更偏向劳动力替代，侧重机械性和物理操作，容易忽略AI在设计、创新和智能表达中的艺术化特征。
• 长期使用“人工智能”可能导致公众误解，认为AI仅是机械的延伸，而非创造性赋能的体现。例如，有些人将AI视为“工具型智能”，仅仅用于执行重复性任务，而忽略其在生成艺术、写作和创造新知识领域的潜力。例如，早期对ChatGPT的认知仅局限于问答工具，直到它在生成文学作品和创意内容上的表现才逐渐改变这一认知，这说明长期固化的术语可能限制公众对AI创造性特性的全面理解。

4. 哲学视角下的“人艺智能”

• “人艺智能”强调AI作为人类艺术与智能结合的产物，既体现了人类的创造力，又展示了AI在扩展这种创造力方面的潜力。
• 这一概念符合“交互主体共生”（Intersubjective Symbiosism）的理念，超越单纯对抗或替代关系，倡导共生合作的哲学愿景。

5. 更好的文化适应性与未来思考

• 从语言角度：中文中的“人艺”不仅涵盖技术层面，还包含精神和文化层面，具有广泛包容性。
• 从技术发展角度：AI未来发展方向将偏向艺术化与创造性应用（如生成艺术、情感计算等），“人艺智能”这一翻译更能体现其文化与技术演变。

看清与突破AI研发中的三大瓶颈

将“Artificial Intelligence”重新定义为“人艺智能”，不仅仅是语言上的调整，更能帮助看清、克服、突破目前AI研发中面临的三大瓶颈。

1. 高能耗与低能效的不匹配

当前主流AI模型（如深度学习）在训练和推理过程中需要消耗巨量能源，导致应用成本陡增，成为大规模普及的障碍。例如，GPT-3的训练据估算需要耗费超过1287兆瓦时的电力，相当于一辆普通燃油汽车连续行驶140万公里的碳排放量。这种高能耗直接限制了AI技术的普及与应用，尤其是在能源资源有限的情况下。

• 现状：大型语言模型的训练需要数万千瓦时的电力，其能耗与智能性形成反差。
• 突破路径：“人艺智能”强调系统设计的精巧性与效率，从自然和艺术中汲取灵感，开发低能耗、高能效的智能系统。
  • 低功耗神经网络：借鉴自然界高效的能量使用机制，开发轻量级、分布式神经网络，减少计算冗余。
  • 艺术化分布式架构：通过模拟艺术创作的分步构建方式，设计模块化AI系统，降低单点能耗。

2. 系统思维的局限性

现阶段的AI设计以系统性思维为核心，将信源（数据）、信道（算法）、信果（结果）分离处理。这种线性设计难以应对复杂动态系统，尤其在多元交互与不可预知环境中表现不足。

• 现状：系统模块割裂设计缺乏弹性与适应性。
• 突破路径：“人艺智能”倡导动态共生与艺术化思维，为复杂环境中的AI设计提供新的方向。具体操作方式包括：
  • 多模态协作：通过结合语言、视觉、声音等多模态信息，实现AI系统在复杂交互场景中的更高适应性，例如智慧城市中同时处理交通、气象和能源管理。
  • 动态反馈机制：借助实时数据输入和动态调整算法，使AI系统能够根据外部环境的变化即时优化自身行为，如智能制造中根据生产需求动态分配资源。
  • 艺术启发设计：借鉴艺术创作中的非线性思维，开发具有灵活性和创造力的算法框架，例如通过音乐和绘画的灵感优化神经网络的结构与功能。
  • 共生式设计：不再割裂信源、信道、信果，通过动态交互实现自适应优化。
  • 非线性竞合模型：借鉴艺术创作中的旋律、节奏、和声关系，为AI设计提供多层次动态调节的启示。

3. 数据+算法+算力+神经网络 ≠ 智慧

尽管AI在数据处理和预测能力上表现卓越，但真正的智慧包含创造力、伦理价值和情感理解，当前模型难以达到这一高度。

• 现状：现有AI模型以算力驱动，忽略智慧的多维度。
• 突破路径：“人艺智能”强调艺术性与哲学性的融入，推动AI从模拟智能向规范智慧迈进。
  • 艺术启发智慧：通过融入艺术创作的审美与创造力，开发原创性表达的AI系统。
  • 规范智慧框架：从哲学与伦理角度定义智慧内涵，使AI行为符合人类价值观。

未来展望：人艺智能的可能性

以“人艺智能”替代“人工智能”，不仅是术语上的更新，更是思维范式的革命性转变。这种新视角有助于：

1. 突破高能耗瓶颈：从自然与艺术中获取启发，构建低能耗高效能的智能系统。
2. 超越线性思维：通过艺术化的共生设计，增强AI在复杂环境中的适应力。
3. 规范智慧内涵：推动AI从模拟智能向真正智慧进化，同时融入人类价值观。

结论

将“Artificial Intelligence”翻译为“人艺智能”，是一种更贴合AI本质特性的创新尝试。相比于“人工智能”，“人艺智能”更能体现AI的虚拟性、艺术性和创造性，同时突出了其作为人类艺术与技术结合产物的本质。如果在学术、哲学和技术研发中逐步推广，这一翻译可能成为更加精准且富有文化意涵的表达，为AI的未来发展注入新的活力，消除人们（如马斯克、伊里亚、辛顿、赫拉利）对AI未来不确定性的疑虑，鼓励和规范人们（如奥特曼）对Ai的乐观精神！

2024年12月18日于温哥华

ChatGPT4o翻译如下：

Human-Artificial Intelligence: Redefining the Essence and Future of AI

“Ah, humanity, only you possess art.” (Schiller)

The future of AI relies not only on technological innovation but also on a new philosophical perspective to guide its development. Amorsophia offers this perspective by integrating the concept of love and wisdom, emphasizing that AI must develop in a balanced manner across emotional, ethical, and intellectual dimensions. Through the implementation of MindsWeb or MindsNetworking, AI can overcome the bottlenecks of energy consumption, systemic thinking, and emotional-ethical challenges, entering an era of true intersubjective symbiosis with humanity.

To realize this vision, the world urgently needs to convene a “New Dartmouth Research Project on AI-Amorsophia”, pooling global wisdom to create a technical framework and ethical standards aligned with AI’s philosophical underpinnings. This conference would establish the AI-Amorsophia Charter, making it a key component of a supranational, trans-geopolitical, and transcivilizational Global Symbiosis Pact, leading humanity into a “symbiotic new era where any imperial ambitions to rule the world become impossible.”

Ultimately, through “the wisdom of love”, humanity will no longer view AI solely as a tool or threat but will instead open a gateway to a symbiotic future, achieving genuine coexistence and prosperity between humans and AI.

Excerpted from Amorsophia-MindsWeb: Breaking Through the Three AI Bottlenecks. Read more at: symbiosism.com.cn/8878.html

In 1956, the Dartmouth Conference first proposed and established the concept and terminology of “Artificial Intelligence.” This choice was based on the term “Artificial,” which encompassed non-mechanical intelligent expressions and highlighted its potential for virtuality and creativity. In contrast, other proposed terms such as “Simulated Intelligence” or “Anthropomorphic Intelligence” were more confined to imitation or anthropomorphism, failing to fully capture the technological breadth and philosophical depth of AI. “Artificial Intelligence” emphasizes how artificially constructed intelligent systems transcend simple simulation to embody creativity and virtualized attributes. During the discussions, the conference did not choose “Simulated Intelligence,” “Counterfeit Intelligence,” “Twin Intelligence,” “Imitative Intelligence,” “Model Intelligence,” or “Anthropomorphic Intelligence,” but instead unanimously accepted McCarthy’s proposed term, “Artificial Intelligence.” Here, “Artificial” not only signified man-made or human-made but also carried the profound connotation of “Art,” implying artistry, fabrication, and virtuality.

However, I propose that translating “Artificial Intelligence” as “Human-Artificial Intelligence” is not only more creative but also has a solid foundation in “genetic epistemology.” The prefix “Art” in “Artificial” originally had meanings akin to technology, craftsmanship, skill, and technique. In Greek, it was written as τέχνη (techne), and in Latin, as ars. During that time, art had not yet diverged into separate disciplines; it was closely associated with aesthetics, morality, and practicality. For a significant part of history, art and technology were almost indistinguishable in people’s minds, and the social status of artists and craftsmen (commonly referred to as “artisans” in Chinese) was roughly equivalent.

In the 18th century, French thinker Denis Diderot (1713\u20131784) edited the Encyclopédie, in which the entry for “technology” was described using the term “Art.” Diderot defined it as “a system of methods, means, and rules combined to achieve specific objectives through coordinated actions.” This is known as one of the earliest definitions of technology.

Compared to “man-made intelligence,” “Human-Artificial Intelligence” aligns more closely with the multifaceted meaning of “Artificial” while also emphasizing the technical and artistic essence of AI. The advantages of this translation are as follows:

The Superiority of “Human-Artificial Intelligence”

1. The Multifaceted Meaning of “Artificial”

The term “Artificial” refers not only to “man-made” or “human-made” but also encompasses “artistic,” “virtual,” and “creative” dimensions.

• The prefix “Art” is derived from the Latin root “ars,” meaning “skill, art, or craft.” It is not limited to mechanical or labor-driven connotations but instead points toward creativity and construction.
• “Human-Artificial Intelligence” captures the artistic and fabricated aspects of “man-made,” accurately conveying the virtual constructive nature of AI. Specifically, artistry is reflected in generative AI’s original creations in literature, music, and painting, such as AI-generated poems or artwork based on inputs. Fabrication is manifested in AI’s construction of virtual environments or characters through algorithmic models, such as virtual assistants and metaverse avatars. These virtual constructs are not real but achieve realistic simulation and functionality through technological means.

2. Better Aligning with the Core Features of AI

• Creativity: AI is not merely mechanical simulation but builds new knowledge, behaviors, and systems based on data, algorithms, and models, akin to the process of artistic creation.
• Virtuality: AI intelligence is often expressed in “virtual environments” or “virtual interactions,” with reasoning and behavior accomplished through model-based virtual operations.
• Instrumentality: AI serves as a tool designed by humans and an artistic tool that extends human intelligence.

3. Overcoming the Limitations of “Man-Made”

• The term “man-made” leans towards labor substitution, focusing on mechanical and physical operations while overlooking AI’s artistic attributes in design, innovation, and intelligence expression.
• Prolonged use of “man-made intelligence” may lead to public misconceptions, viewing AI merely as an extension of machinery rather than as a tool for creative empowerment. For example, some perceive AI as “tool-based intelligence” solely for repetitive tasks, neglecting its potential in generative art, writing, and new knowledge creation.

4. Philosophical Perspective on “Human-Artificial Intelligence”

• “Human-Artificial Intelligence” emphasizes AI as a product of the combination of human art and intelligence, showcasing human creativity and AI’s potential to extend it.
• This concept aligns with the philosophy of “Intersubjective Symbiosism,” transcending simplistic confrontational or substitutionary relationships and advocating for cooperative coexistence.

5. Better Cultural Adaptation and Future Considerations

• From a linguistic perspective: The term “human-artificial” not only covers technical aspects but also includes spiritual and cultural dimensions, offering broad inclusivity.
• From a technological perspective: The future direction of AI development leans towards artistic and creative applications (e.g., generative art, emotional computing), and the translation “human-artificial intelligence” better reflects its cultural and technological evolution.

Understanding and Overcoming the Three Major Bottlenecks in AI Development

Redefining “Artificial Intelligence” as “Human-Artificial Intelligence” is not merely a linguistic adjustment; it helps identify and overcome the current three major bottlenecks in AI development.

1. Mismatch Between High Energy Consumption and Low Efficiency

Current mainstream AI models (e.g., deep learning) consume massive amounts of energy during training and inference, leading to soaring application costs and hindering widespread adoption. For example, GPT-3 training is estimated to consume over 1,287 megawatt-hours of electricity, equivalent to the carbon emissions of a conventional gasoline car driving 1.4 million kilometers. This high energy consumption directly limits the widespread adoption of AI technologies.

2. Limitations of Systemic Thinking

Present AI design relies on systemic thinking, separating signal sources (data), channels (algorithms), and results (outputs). This linear approach struggles to handle complex dynamic systems, particularly in multi-variable interactions and unpredictable environments.

3. Data + Algorithm + Computing Power + Neural Networks ≠ Intelligence

Although AI excels in data processing and prediction, true intelligence encompasses creativity, ethical values, and emotional understanding, dimensions that current models fail to achieve.

Future Prospects: The Possibility of “Human-Artificial Intelligence”

Replacing “man-made intelligence” with “human-artificial intelligence” is not only a terminological update but also a revolutionary paradigm shift. This new perspective can contribute to:

1. Overcoming High Energy Consumption Bottlenecks: Drawing inspiration from nature and art to design low-energy, high-efficiency intelligent systems.
2. Surpassing Linear Thinking: Enhancing AI adaptability in complex environments through artistic and symbiotic design.
3. Defining the Essence of Wisdom: Promoting AI evolution from simulated intelligence to genuine wisdom while integrating human values.

Conclusion

Translating “Artificial Intelligence” as “Human-Artificial Intelligence” is an innovative attempt that more accurately reflects the essence of AI. Compared to “man-made intelligence,” “human-artificial intelligence” better captures AI’s virtuality, artistry, and creativity while emphasizing its nature as a product of the fusion of human art and technology. Gradually introducing this term in academic, philosophical, and technological discussions could establish a more precise and culturally meaningful expression, providing new vitality to AI’s future development and addressing concerns about its uncertainties.