Generative Diffusion of Artificial Intelligence Innovation: An Innovation Ecological Approach

人工智能（AI）可以说是最重要的技术创新之一，因为它重塑了我们对工作、组织和竞争的看法。随着人工智能在组织和创新方面的作用不断扩大，我们必须了解人工智能技术如何随着它在不同行业的不断扩散而发展。在本研究中，研究者将人工智能创新定义为人工智能创新生态中由异质参与者独立设计和开发的组成技术组件之间持续和动态相互作用的动态涌现结果。通过这样做，研究者探索了人工智能创新的意义是如何随着人工智能创新生态的边界不断变化和扩展而演变的。通过对当代人工智能创新的全面历史分析，该项目帮助我们准确了解人工智能是如何演变的，以及未来可能会如何演变。该项目确定了人工智能创新生态中潜在的薄弱环节，以继续发展人工智能创新，指导未来的投资和研究工作。研究者正在使用两种不同但相互关联的研究活动的多方法。首先，研究者正在对人工智能创新的新兴演变进行定性分析，利用有关当前人工智能创新发展的档案数据，这些数据来自有关人工智能的新闻文章、相关的使能技术组件及其应用。其次，研究者正在进行计算分析，通过分析(a)公开可用的文件，包括主流新闻、学术研究出版物，以及(b) GitHub平台上使用开源人工智能框架的开源项目，了解如何理解人工智能创新随着时间的推移在不同领域的生成扩散。具体来说，研究者正在利用最新的计算工具，即关系图卷积网络方法，来研究创新生态的演变。这些分析旨在确定人工智能创新发展和发展的动态模式。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Artificial Intelligence (AI) is arguably one of the most significant technological innovations as it reshapes the way we think about work, organizing, and competitions. As AI's role in organizing and innovations continues to expand, we must understand how AI technology evolves as it continues to get diffused throughout different industries. In this study, the investigator conceptualizes AI innovation as a dynamic emergent outcome of ongoing and dynamic interactions among constituent technological components independently designed and developed by heterogeneous actors in the AI innovation ecology. By doing so, the investigator explores how the meaning of AI innovation evolves as the boundary of AI's innovation ecology continues to shift and expand. Through a comprehensive historical analysis of contemporary AI innovation, the project helps us understand exactly how AI has evolved and will likely evolve in the future. The project identifies potential weak links in the AI innovation ecology for continuing development of AI innovation to direct future investments and research efforts.The investigator is using a multi-method with two distinct but interrelated research activities. First, the investigator is conducting a qualitative analysis of the AI innovation's emergent evolution, leveraging archival data about the development of current AI innovation from news articles on AI, related enabling technology components, and its applications. Second, the investigator is conducting computational analyses to understand how to understand the generative diffusion of AI innovations over time through different fields by analyzing (a) publicly available documents, including mainstream news, academic research publications, and (b) open-source projects in GitHub platform that use open-source AI frameworks. Specifically, the investigator is leveraging recent computational tools, namely the Relational Graph Convolutional Networks method, in studying the evolution of innovation ecology. These analyses aim to identify the dynamic patterns by which AI innovation is evolving and moving through.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.