AI时代的自催化知识动力学
Autocatalytic knowledge dynamics in the AI era
AI总结：
本研究构建知识宏观动力学模型，结合自催化作用与反作用力，分析AI对知识生产速率的影响，将模型应用于WIPO专利数据，揭示知识系统的相变规律及增长障碍。
AI中文摘要：
在AI时代，知识正以加速速率增长。本研究旨在构建知识的宏观动力学，并阐明AI在该过程中的作用。研究考察了一个自主知识生产系统，该系统发展的驱动力是外部或内部因素影响下其稳态的扰动。为解决新出现的问题，该系统会利用现有知识或生成新知识。该过程具有自催化性，即利用过去积累的知识创造新知识。知识生产受资源稀缺和环境扰动阻碍，这些因素会恶化系统状态；知识损失（尤其因知识过时导致）会减缓该过程；信息噪声也会抑制知识进步，导致能量耗散。旨在生成新知识的自催化作用，与资源稀缺、环境问题、知识损失及信息噪声引发的反作用力相结合，为构建知识动力学提供了基础。所得动力学方程表明，根据作用力的平衡情况，知识生产速率可发生以下变化：1）增长，确保系统发展；2）降至有限（非零）水平，系统退化但存续；3）降至零，系统崩溃。该模型被应用于WIPO专利申请数据，得到了参数值。AI的影响会增强自催化作用，但反作用力也会增大，作用力平衡的转变会引发知识动力学发生相变，系统演化可通过一系列相变推进。本研究还比较了AI发展的关键里程碑与知识动力学中所代表的作用力，并探讨了知识增长的障碍。
英文摘要：
In the AI era, knowledge is growing at an accelerated rate. The goal of this study is to construct the macrodynamics of knowledge and elucidate the role of AI in this process. An autonomous knowledge production system is examined. The driving force behind the system's development is the disturbance of its homeostasis under the impact of external or internal factors. To solve emerging problems, the system uses available or generates new knowledge. The process is autocatalytic in the sense that knowledge accumulated over the past is used to create new knowledge. Knowledge production is hampered by resource scarcity and environmental disturbances that worsen the system's state. The loss of knowledge, in particular due to its obsolescence, contributes to the process' slowdown. Information noise also inhibits the advancement of knowledge, leading to energy dissipation. The combination of autocatalysis, aimed at generating new knowledge, with counteracting forces caused by resource scarcity, environmental problems, knowledge loss, and information noise provides the basis for building knowledge dynamics. The resulting dynamic equation shows that, depending on the balance of the forces acting, the rate of knowledge production can change as follows: 1) increase, ensuring system's development; 2) decrease to a finite (non-zero) level: the system degrades but survives; and 3) decline to zero: the system collapses. The model was applied to WIPO patent filing data, yielding parameter values. The influence of AI enhances autocatalysis, but counteracting factors also increase. A shift in the balance of forces leads to a phase transition with a change in knowledge dynamics. The system's evolution can proceed through a series of phase transitions. Key milestones in AI development are compared with the forces represented in knowledge dynamics. Barriers to knowledge growth are discussed.
