Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions

Artificial intelligence (AI) is a set of rapidly expanding disruptive technologies that are radically transforming various aspects related to people, business, society, and the environment. With the proliferation of digital computing devices and the emergence of big data, AI is increasingly offering significant opportunities for society and business organizations. The growing interest of scholars and practitioners in AI has resulted in the diversity of research topics explored in bulks of scholarly literature published in leading research outlets. This study aims to map the intellectual structure and evolution of the conceptual structure of overall AI research published in Technological Forecasting and Social Change (TF&SC). This study uses machine learning-based structural topic modeling (STM) to extract, report, and visualize the latent topics from the AI research literature. Further, the disciplinary patterns in the intellectual structure of AI research are examined with the additional objective of assessing the disciplinary impact of AI. The results of the topic modeling reveal eight key topics, out of which the topics concerning healthcare, circular economy and sustainable supply chain, adoption of AI by consumers, and AI for decision-making are showing a rising trend over the years. AI research has a significant influence on disciplines such as business, management, and accounting, social science, engineering, computer science, and mathematics. The study provides an insightful agenda for the future based on evidence-based research directions that would benefit future AI scholars to identify contemporary research issues and develop impactful research to solve complex societal problems

Tracing the evolution of service robotics : Insights from a topic modeling approach

Acord transformatiu CRUE-CSICAltres ajuts: Helmholtz Association (HIRG-0069)Altres ajuts: Russian Science Foundation (RSF grant number 19-18-00262)Taking robotic patents between 1977 and 2017 and building upon the topic modeling technique, we extract their latent topics, analyze how important these topics are over time, and how they are related to each other looking at how often they are recombined in the same patents. This allows us to differentiate between more and less important technological trends in robotics based on their stage of diffusion and position in the space of knowledge represented by a topic graph, where some topics appear isolated while others are highly interconnected. Furthermore, utilizing external reference texts that characterize service robots from a technical perspective, we propose and apply a novel approach to match the constructed topics to service robotics. The matching procedure is based on frequency and exclusivity of words overlapping between the patents and the reference texts. We identify around 20 topics belonging to service robotics. Our results corroborate earlier findings, but also provide novel insights on the content and stage of development of application areas in service robotics. With this study we contribute to a better understanding of the highly dynamic field of robotics as well as to new practices of utilizing the topic modeling approach, matching the resulting topics to external classifications and applying to them metrics from graph theory

Tracing the evolution of service robotics : Insights from a topic modeling approach

Altres ajuts: Acord transformatiu CRUE-CSICAltres ajuts: Helmholtz Association (HIRG-0069)Altres ajuts: Russian Science Foundation (RSF grant number 19-18-00262)Taking robotic patents between 1977 and 2017 and building upon the topic modeling technique, we extract their latent topics, analyze how important these topics are over time, and how they are related to each other looking at how often they are recombined in the same patents. This allows us to differentiate between more and less important technological trends in robotics based on their stage of diffusion and position in the space of knowledge represented by a topic graph, where some topics appear isolated while others are highly interconnected. Furthermore, utilizing external reference texts that characterize service robots from a technical perspective, we propose and apply a novel approach to match the constructed topics to service robotics. The matching procedure is based on frequency and exclusivity of words overlapping between the patents and the reference texts. We identify around 20 topics belonging to service robotics. Our results corroborate earlier findings, but also provide novel insights on the content and stage of development of application areas in service robotics. With this study we contribute to a better understanding of the highly dynamic field of robotics as well as to new practices of utilizing the topic modeling approach, matching the resulting topics to external classifications and applying to them metrics from graph theory

The emergence of care robotics - A patent and publication analysis

Care robots are a means to support elderly people affected by physical or mental handicaps to remain as autonomous as possible or regain already lost autonomy (e.g. running stairs). They also support care-takers when working with handicapped. We review the emergence of care robotics and particularly offer answers to two research questions: Which organizations and individuals in which countries have been and are active in research and development? How has research and development emerged with regard to activity focus, intensity levels and cooperation? The analysis rests on PATSTAT patent and ISI Web of Science publication data. Bibliographic and network analyses are conducted on country, organization (i.e. universities and firms) and individual levels. We find that care robotics research and development activities have constantly increased since the late 1970s. Today Japanese universities and firms are the most active players, while in early stages US and European organizations pioneered care robotics research. Starting from six disjunctive small networks, several highly interconnected care robotics research networks have evolved. However, most cooperation clusters are still found within the same country. Only few international hubs emerged. Among them are two Japanese organizations (ATR, AIST) and Carnegie Mellon University, US.This is the accepted manuscript. The final version is available from Elsevier at http://www.sciencedirect.com/science/article/pii/S004016251400275