转型时期我国人工智能技术追赶的机会识别及适应性治理研究

The new round AI development is showing a complex situation of "triple transformation", which includes drives transformation, status transformation and mode transformation. The concurrent emergence of new models, new statuses and new drivers has brought a new window of opportunity for AI technology catching-up with high complexity, strong penetration and great creativity, and at the same time, it has also brought new all-round challenges for AI technology catching-up from theory to practice. How to scientifically subdivide the mode of technology catching-up? How to dynamically identify emerging opportunities of technology catching-up? How to adaptively govern the ecology of industrial technology catching-up? Scientific answers to these questions will determine the speed, depth and height of the new round AI development. In this study, case study, patent measurement, social network analysis and other methods are used comprehensively. Our study constructs a bivariate model of dividing technology catching-up model based on "technology innovation-market application", and completes the dynamic matching between different technology catching-up modes and AI enabling industries; deeply explores the differential influence of technology convergence, knowledge flow and embeddedness of innovative network on the technology catching-up performance of different modes, and realizes the dynamic identification of AI’s technology catching-up opportunities under different modes; and according to the different technology catching-up modes, the emerging opportunities and the dynamic roles, our research construct a new adaptive governance framework of AI’s technology catching-up ecology in China. This provides scientific theoretical basis and decision-making reference for precise design, professional planning and systematic layout of AI strategic planning.

新一轮人工智能发展正呈现出驱动转型、业态转型与模式转型 “三重转型”的复杂态势，新模式、新业态、新驱动的并发出现为复杂度高、渗透性强、创造力大的AI技术追赶带来了全新的机会窗口，与此同时也带来了从理论到实践的全方位挑战。如何科学地细分技术追赶模式？如何动态地识别技术追赶新兴机会？如何适应性地治理技术追赶生态？这些问题将决定着新一轮AI发展的速度、深度和高度。本研究综合运用案例深描法、专利计量法、社会网络分析法等多种研究方法，构建基于“技术创新-市场应用”的技术追赶模式细分双元模型，完成不同技术追赶模式与AI使能行业的动态匹配；探讨技术会聚、知识流动与嵌入性对不同模式技术追赶绩效的差异化影响，实现AI技术追赶机会的动态识别；根据不同技术追赶模式、新兴机会与动态作用构建我国新一轮AI技术追赶生态的适应性治理架构，为精准化设计、专业化统筹、系统化布局人工智能战略规划提供科学的理论依据和决策参考。

当今世界，人工智能作为主导新一轮颠覆性产业变革的中坚力量开始在多领域间蔓延式加速推进，新一代人工智能技术赋能正呈现出驱动转型、业态转型与模式转型“三重转型”的快速发展态势，人工智能技术赋能的推动力量由以往要素驱动转变为创新驱动，牵引力量由以往一元业态转变为双元业态，支撑力量由以往单一模式转变为复杂模式。新模式、新业态、新驱动的深度转变为复杂度高、渗透性强、突破力大的人工智能技术创新带来了全新的机会窗口。基于此，本研究主要开展研究如下：一是基于“核心驱动-样态变革-环境适配”构建了人工智能赋能产业创新生态转型的理论框架，分别从主体、机理、环境等多个方面阐释AI价值分配重构、生产制造升级、商业模式适变、组织决策变革的全面转型，探析人工智能技术追赶的内涵逻辑与机会窗口。二是基于“政策维度-技术维度-市场维度”构建了多维视角下人工智能技术追赶协同体系的理论框架，动态刻画了全球AI技术追赶的现状布局和竞争态势，探析规制性政策、颠覆性技术、通用型市场多维交织下的技术追赶协同联动体系。三是基于“技术创新-市场应用”构建了人工智能技术追赶模式划分的双元模型，在已有技术追赶模式划分理论中引入“市场应用”维度，将技术创新划分为渐进式创新和颠覆式创新两种，将市场应用划分为专用型场景和通用型场景两种，以此细分AI技术追赶的具体场域特征。四是基于“技术会聚网络-知识流动网络-技术共生网络”构建了人工智能技术追赶机会识别过程，运用多元回归分析等计量学方法系统评估技术会聚、知识流动以及网络嵌入性对AI技术追赶的影响效应。五是基于“治理主体-治理边界-治理对象-治理工具”构建了我国新一代人工智能技术追赶创新生态优化的适应性治理架构，从治理主体多元化、治理边界模糊化、治理对象复杂化以及治理工具多样化系统阐释“跨尺度”适应性治理体系的重要性，为精准化设计、专业化统筹、系统化布局AI战略规划提供科学的理论依据和决策参考。

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