Polarization and Integration in Global AI Research

The AI race amplifies security risks and international tensions. While the US restricts mobility and knowledge flows, challenges regulatory efforts to protect its advantage, China leads initiatives of global governance. Both strategies depend on cross-country relationships in AI innovation; yet, how this system evolves is unclear. Here, we measure the processes of polarization and integration in the global AI research over three decades by using large-scale data of scientific publications. Comparing cross-country collaboration and citation links to their random realizations, we find that the US and China have long diverged in both dimensions, forming two poles around which global AI research increasingly revolves. While the United Kingdom and Germany have integrated exclusively with the US, many European countries have converged with both poles. Developing and further developed countries, however, only integrate with China, signaling its expanding influence over the international AI research landscape. Our results inform national science policies and efforts toward global AI regulations.

Mining urban lifestyles: urban computing, human behavior and recommender systems

In the last decade, the digital age has sharply redefined the way we study human behavior. With the advancement of data storage and sensing technologies, electronic records now encompass a diverse spectrum of human activity, ranging from location data, phone and email communication to Twitter activity and open-source contributions on Wikipedia and OpenStreetMap. In particular, the study of the shopping and mobility patterns of individual consumers has the potential to give deeper insight into the lifestyles and infrastructure of the region. Credit card records (CCRs) provide detailed insight into purchase behavior and have been found to have inherent regularity in consumer shopping patterns; call detail records (CDRs) present new opportunities to understand human mobility, analyze wealth, and model social network dynamics. In this chapter, we jointly model the lifestyles of individuals, a more challenging problem with higher variability when compared to the aggregated behavior of city regions. Using collective matrix factorization, we propose a unified dual view of lifestyles. Understanding these lifestyles will not only inform commercial opportunities, but also help policymakers and nonprofit organizations understand the characteristics and needs of the entire region, as well as of the individuals within that region. The applications of this range from targeted advertisements and promotions to the diffusion of digital financial services among low-income groups.