人艺智能:重新定义AI的本质与未来 |
送交者: 孞烎Archer 2024年12月19日00:06:05 于 [竞技沙龙] 发送悄悄话 |
Artificial Intelligence Redefined
人艺智能:重新定义AI的本质与未来
钱 宏 Archer Hong Qian
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”,是一个富有创意的建议,值得深入探讨。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的本质特性。这种翻译的优胜性如下:
“人艺智能”的优越性
“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。
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。
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。
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。
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:
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
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. Therefore, translating "Artificial Intelligence" as "Human-Artificial Intelligence" rather than "Man-Made Intelligence" may better reflect the essence of AI. This innovative translation not only aligns more closely with the language but also brings a profound shift in thought, guiding new directions for AI development. The Superiority of "Human-Artificial Intelligence"
The term "Artificial" refers not only to "man-made" or "human-made" but also encompasses "artistic," "virtual," and "creative" dimensions.
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.
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.
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.
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:
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. |
|
|
|
实用资讯 | |