Mapping the co-evolution of artificial intelligence, robotics, and the internet of things over 20 years (1998-2017)
It provides data-driven decision support for researchers, managers, and policymakers to set R&D priorities and monitor trends in these strategic areas, but is incremental as it applies existing methods to new data.
This paper tackled the problem of understanding the co-evolution and convergence of AI, robotics, and IoT from 1998 to 2017 by analyzing 32,716 publications and 4,497 NSF awards, resulting in new metrics and visualizations that demonstrate how knowledge transition and concept emergence create potential for interdisciplinary research.
Understanding the emergence, co-evolution, and convergence of science and technology (S&T) areas offers competitive intelligence for researchers, managers, policy makers, and others. The resulting data-driven decision support helps set proper research and development (R&D) priorities; develop future S&T investment strategies; monitor key authors, organizations, or countries; perform effective research program assessment; and implement cutting-edge education/training efforts. This paper presents new funding, publication, and scholarly network metrics and visualizations that were validated via expert surveys. The metrics and visualizations exemplify the emergence and convergence of three areas of strategic interest: artificial intelligence (AI), robotics, and internet of things (IoT) over the last 20 years (1998-2017). For 32,716 publications and 4,497 NSF awards, we identify their conceptual space (using the UCSD map of science), geospatial network, and co-evolution landscape. The findings demonstrate how the transition of knowledge (through cross-discipline publications and citations) and the emergence of new concepts (through term bursting) create a tangible potential for interdisciplinary research and new disciplines.