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1. An indicator of technical emergence

   Carley, S.F.; Newman, N.C.; Porter, A.L.; Garner, J. (April 2018, Scientometrics)

   Developing useful intelligence on scientific and technological emergence challenges those who would manage R&D portfolios, assess research programs, or manage innovation. Recently, the U.S. Intelligence Advanced Research Projects Activity Foresight and Understanding from Scientific Exposition Program has explored means to detect emergence via text analyses. We have been involved in positing conceptual bases for emergence, framing candidate indicators, and devising implementations. We now present a software script to generate a family of Emergence Indicators for a topic of interest. This paper offers some background, then discusses the development of this script through iterative rounds of testing, and then offers example findings. Results point to promising and actionable intelligence for R&D decision-makers. 

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2. Emergence scoring to identify frontier R&D topics and key players

   Porter, A.L.; Garner, J.; Carley, S.F.; Newman, N.C. (January 2018, Technological forecasting & social change)

   https://doi.org/10.1016/j.techfore.2018.04.016 Summary: Indicators of technological emergence promise valuable intelligence. We present an implemented algorithm to calculate emergence scores (EScores) for topical terms from abstract record sets. We offer a family of emergence indicators. Primary emergence indicators identify “frontier” terms based on their EScores. We then tally organizations, countries, or authors especially active in publishing (or patenting) on high EScore topics in a target R&D domain. We can score research fields on relative degree of emergence. This paper illustrates EScoring for Nano-Enabled Drug Delivery, Non-Linear Programming, Dye Sensitized Solar Cells, and Big Data. 

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3. Technological emergence indicators using emergence scoring

   Garner, J.; Carley, S.; Porter, A.L.; Newman, N.C. (January 2017, Proceedings of PICMET '17: Technology Management for Interconnected World)

   Indicators of technological emergence promise valuable intelligence to those determining R&D priorities. We present an implemented algorithm to calculate emergence scores for topical terms from abstract record sets. We offer a family of emergence indicators deriving from those scores. Primary emergence indicators identify “hot topic” terms, then we use those to generate secondary indicators that reflect organizations, countries, or authors especially active at research frontiers in a target domain. We also flag abstract records (papers or patents) rich in emergent technology content, and we score technological fields on relative degree of emergence. We show illustrative results for example topics -- Nano-Enabled Drug Delivery, Non-Linear Programming, Dye Sensitized Solar Cells, and Big Data. 

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4. Mapping the co-evolution of artificial intelligence, robotics, and the internet of things over 20 years (1998-2017)

   https://doi.org/10.1371/journal.pone.0242984  

   Börner, Katy; Scrivner, Olga; Cross, Leonard E.; Gallant, Michael; Ma, Shutian; Martin, Adam S.; Record, Lisel; Yang, Haici; Dilger, Jonathan M. (December 2020, PLOS ONE)

   Bouffanais, Roland (Ed.)

   Understanding the emergence, co-evolution, and convergence of science and technology (S&T) areas offers competitive intelligence for researchers, managers, policy makers, and others. 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 topical coverage (using the UCSD map of science), evolving co-author networks, and increasing convergence. The results support data-driven decision making when setting proper research and development (R&D) priorities; developing future S&T investment strategies; or performing effective research program assessment. 

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5. Artificial intelligence research strategy of the United States: critical assessment and policy recommendations

   https://doi.org/10.3389/fdata.2023.1206139  

   Gursoy, Furkan; Kakadiaris, Ioannis A. (August 2023, Frontiers in Big Data)

   The foundations of Artificial Intelligence (AI), a field whose applications are of great use and concern for society, can be traced back to the early years of the second half of the 20th century. Since then, the field has seen increased research output and funding cycles followed by setbacks. The new millennium has seen unprecedented interest in AI progress and expectations with significant financial investments from the public and private sectors. However, the continual acceleration of AI capabilities and real-world applications is not guaranteed. Mainly, accountability of AI systems in the context of the interplay between AI and the broader society is essential for adopting AI systems via the trust placed in them. Continual progress in AI research and development (R&D) can help tackle humanity's most significant challenges to improve social good. The authors of this paper suggest that the careful design of forward-looking research policies serves a crucial function in avoiding potential future setbacks in AI research, development, and use. The United States (US) has kept its leading role in R&D, mainly shaping the global trends in the field. Accordingly, this paper presents a critical assessment of the US National AI R&D Strategic Plan and prescribes six recommendations to improve future research strategies in the US and around the globe. 

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