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

@article{Borner2020MappingTC,
  title={Mapping the co-evolution of artificial intelligence, robotics, and the internet of things over 20 years (1998-2017)},
  author={Katy Borner and Olga Scrivner and Leonard E. Cross and Mike Gallant and Shutian Ma and Adam S. Martin and Elizabeth G. Record and Haici Yang and Jonathan M. Dilger},
  journal={PLoS ONE},
  year={2020},
  volume={15}
}
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… 
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References

SHOWING 1-10 OF 29 REFERENCES

A narrowing of AI research?

An analysis of the thematic diversity of AI research in arXiv, a widely used pre-prints site, suggests that diversity in AI research has stagnated in recent years, and that AI research involving private sector organisations tends to be less diverse than research in academia.

An indicator of technical emergence

A software script to generate a family of Emergence Indicators for a topic of interest is presented and results point to promising and actionable intelligence for R&D decision-makers.

What Is an Emerging Technology?

The definition of ‘emerging technologies’ is developed by combining a basic understanding of the term and in particular the concept of ’emergence’ with a review of key innovation studies dealing with definitional issues of technological emergence.

Design and Update of a Classification System: The UCSD Map of Science

The updated UCSD map of science and classification system is shown to have higher mapping accuracy, easier understandability as fewer journals are multiply classified, and higher usability for the generation of data overlays, among others.

Mixed-indicators model for identifying emerging research areas

A mixed model that combines different indicators to describe and predict key structural and dynamic features of emerging research areas shows that the indicators are indicative of emerging areas and they exhibit interesting temporal correlations.

Multi-disciplinarity breeds diversity: the influence of innovation project characteristics on diversity creation in nanotechnology

Nanotechnology is an emerging and promising field of research. Creating sufficient technological diversity among its alternatives is important for the long-term success of nanotechnologies, as well

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

The National Science Foundation

OIRM working group established in July 2014 to address challenges of relocation to the new NSF Headquarters in Alexandria, VA is focusing on developing “end state” business operations for all OIRM services areas identified.

Skill discrepancies between research, education, and jobs reveal the critical need to supply soft skills for the data economy

This study analyzes and visualize the dynamic skill (mis-)alignment between academic push, industry pull, and educational offerings, paying special attention to the rapidly emerging areas of data science and data engineering (DS/DE).