人工知识如何转化为融通创新生态系统知识——基于超循环理论的研究

How to transform artificial knowledge into co-innovation ecosystem knowledge —— A hypercycle theory perspective

摘要:

人工智能在知识创造和创新驱动方面的潜力正不断被挖掘和得到认可，本文聚焦抓住人工智能带来的知识创造机会推动融通创新，对人工知识转化为融通创新生态系统知识的多层次过程、主要动力及固化机制等内容进行了研究。研究结果表明：（1）人工知识通过反应循环、催化循环和超循环逐级转化为融通创新主体职能部门的操作性知识、融通创新主体的战略性知识与融通创新生态系统的系统性知识；（2）人工知识的成功转化依赖于人机协同、职能部门协同和创新主体协同等多重动力因素；（3）人工知识转化流程的有效固化依赖于学习和忘却学习并行下的创造性学习机制。本文为人工知识转化为融通创新生态系统知识提供了一个理论框架，为人工智能赋能创新研究提供了新视角，也为利用人工智能的知识创造作用推动融通创新提供了管理启示。

Abstract:

The potential of artificial intelligence (AI) in knowledge creation and innovation is constantly being explored and recognized. To seize the knowledge creation opportunities brought by AI for promoting co-innovation, the multi-level process, main driving forces, and solidification mechanisms of transforming artificial knowledge into co-innovation ecosystem knowledge are studied. It is found that: (1) Artificial knowledge is gradually transformed into the operational knowledge of functional departments within innovation entities, the strategic knowledge of innovation entities, and the systemic knowledge of the innovation ecosystem through reaction cycles, catalytic cycles, and hypercycles respectively; (2) The successful transformation of artificial knowledge relies on multiple driving factors consisting of human-machine collaboration, functional department collaboration, and innovation subject collaboration; (3) The effective solidification of the process of artificial knowledge transformation relies on a creative learning mechanism that combines learning and unlearning. The findings provide a theoretical framework for understanding how artificial knowledge is transformed into co-innovation ecosystem knowledge, offer a new perspective for research on AI-enabled innovation, and provide managerial insights for utilizing the knowledge creation role of AI to promote co-innovation.