Detecting Emerging Technologies in Artificial Intelligence Scientific Ecosystem Using an Indicator-based Model

TL;DR

This paper presents a new indicator-based model for detecting emerging AI technologies by integrating collaboration and impact attributes, successfully identifying and ranking emerging topics with detailed attribute scores.

Contribution

Introduces a novel method incorporating collaboration and impact attributes for early detection of emerging AI topics, enabling comprehensive ranking and analysis.

Findings

The new method effectively identifies emerging AI topics.

It provides attribute-specific scores for detailed analysis.

The approach successfully ranks topics by emergence level.

Abstract

Early identification of emergent topics is of eminent importance due to their potential impacts on society. There are many methods for detecting emerging terms and topics, all with advantages and drawbacks. However, there is no consensus about the attributes and indicators of emergence. In this study, we evaluate emerging topic detection in the field of artificial intelligence using a new method to evaluate emergence. We also introduce two new attributes of collaboration and technological impact which can help us use both paper and patent information simultaneously. Our results confirm that the proposed new method can successfully identify the emerging topics in the period of the study. Moreover, this new method can provide us with the score of each attribute and a final emergence score, which enable us to rank the emerging topics with their emergence scores and each attribute score.