Innovation hubs have been traditionally located in certain areas, often in urban centers that are thought to promote the exchange of ideas. However, advancements in artificial intelligence (AI) may change the importance of geography in fostering innovation.
A recent study by well-known researcher Dr. Sijie Feng from New York University suggests that geographic proximity may not be as crucial for knowledge transfers as previously believed, and the use of unsupervised machine learning to measure the proximity of ideas has promising potential for understanding the localized effects of knowledge spillovers on regional economic growth.
Dr. Feng, an acclaimed researcher in her field, had earlier worked on measuring the effects of geographic proximity for innovation and local economies in her PLOS One paper “The proximity of ideas: An analysis of patent text using machine learning”, published in 2020.
The study challenges previous assumptions about the significance of geography in innovation hubs and could have far-reaching implications for the location of such hubs and policies aimed at promoting knowledge transfers. By using machine learning to measure the proximity of ideas, researchers can better understand how knowledge is shared across different regions and how it contributes to economic growth.
This is an exciting and novel development for the field because it means that innovation hubs can be located in areas that were previously overlooked or considered less desirable. For example, a low density area may have a high concentration of experts in a particular field, but because this knowledge is not widely known, it hasn’t contributed to regional economic growth. With the advancements in AI, it is now possible to measure the proximity of these ideas to other regions and to promote knowledge transfers to drive economic growth.
These findings further support the fact that the rise of AI is transforming the importance of innovation hubs. By challenging the assumptions that geography is essential for knowledge transfer, Dr. Feng’s study highlights the potential for machine learning to identify and measure knowledge spillovers. As a result, it offers new opportunities for the location of innovation hubs and policy-making that can facilitate the exchange of ideas and spur economic growth.
