Detecting Emerging Technologies and their Evolution using Deep Learning and Weak Signal Analysis
This work addresses the problem of timely and reliable detection of emerging technologies for strategic planners and domain experts, though it appears incremental as it builds on existing methods like deep learning and signal analysis.
The paper tackles the challenge of early identification of emerging technologies by developing a multi-layer quantitative approach using deep learning and weak signal analysis on scientific publications, such as in hypersonics, to help strategic planners and experts monitor trends more effectively.
Emerging technologies can have major economic impacts and affect strategic stability. Yet, early identification of emerging technologies remains challenging. In order to identify emerging technologies in a timely and reliable manner, a comprehensive examination of relevant scientific and technological (S&T) trends and their related references is required. This examination is generally done by domain experts and requires significant amounts of time and effort to gain insights. The use of domain experts to identify emerging technologies from S&T trends may limit the capacity to analyse large volumes of information and introduce subjectivity in the assessments. Decision support systems are required to provide accurate and reliable evidence-based indicators through constant and continuous monitoring of the environment and help identify signals of emerging technologies that could alter security and economic prosperity. For example, the research field of hypersonics has recently witnessed several advancements having profound technological, commercial, and national security implications. In this work, we present a multi-layer quantitative approach able to identify future signs from scientific publications on hypersonics by leveraging deep learning and weak signal analysis. The proposed framework can help strategic planners and domain experts better identify and monitor emerging technology trends.