Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/300545 
Year of Publication: 
2024
Citation: 
[Conference:] R&D Management Conference 2024, 17-19 June, 2024, Stockholm, KTH Royal Institute of Technology [Year:] 2024
Publisher: 
Fraunhofer-Gesellschaft, München
Abstract: 
In the fast-paced realm of technological evolution, accurately forecasting emerging trends is critical for both academic inquiry and industry application. Traditional trend analysis methodologies, while valuable, struggle to efficiently process and interpret the vast datasets of today's information age. This paper introduces a novel approach that synergizes Generative AI and Bidirectional Encoder Representations from Transformers (BERT) for semantic insights and trend forecasting, leveraging the power of Retrieval-Augmented Generation (RAG) and the analytical prowess of BERT topic modeling. By automating the analysis of extensive datasets from publications and patents, the presented methodology not only expedites the discovery of emergent trends but also enhances the precision of these findings by generating a short summary for found emergent trends. For validation, three technologies - reinforcement learning, quantum machine learning, and Cryptocurrencies - were analysed prior to their first appearance in the Gartner Hype Cycle. Research highlights the integration of advanced AI techniques in trend forecasting, providing a scalable and accurate tool for strategic planning and innovation management. Results demonstrated a significant correlation between model's predictions and the technologies' appearances in the Hype Cycle, underscoring the potential of this methodology in anticipating technological shifts across various sectors
Subjects: 
BERT
Topic modelling
RAG
Gartner Hype Cycle
LLM
BERTopic
Published Version’s DOI: 
Document Type: 
Conference Paper
Document Version: 
Manuscript Version (Preprint)
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