The early detection of emerging technologies is crucial for organizations to stay ahead of the technological curve and adapt to the changing market landscape. For this matter, several approaches were proposed to detect emerging technologies. However, these methods often suffer from limitations such as subjectivity because of manual curation and lack of scalability and predictability. In this extended abstract, we present a hybrid approach to detect emerging technology terms using the burst detection algorithm and predict their future trends by combining machine learning techniques with burst detection. We test the proposed approach in artificial intelligence (AI)-related patents. The results show that the proposed approach can predict the future prevalence of AI technology terms with an accuracy of 90.5%. It is also demonstrated that deploying a deep neural network classifier can increase the accuracy by 10-15% compared to a conventional tree-based classifier. We hope the proposed framework supports decision-makers in better identifying emerging terms, especially in evolving multidisciplinary fields such as AI.
Article ID: 2023GL4
Publisher: Canadian Artificial Intelligence Association