[2024-06] A Study on the Automated Horizon Scanning: A GPT-Based Framework for Three-Phase Horizon Scanning Methodology
SeungO, Han
Moon Soul Graduate School of Future Strategy, KAIST
YongSeok,Seo
Moon Soul Graduate School of Future Strategy, KAIST
Abstract
This study proposes a novel framework for horizon scanning using GPT-based artificial intelligence to enhance the efficiency and accuracy of future prediction and trend analysis. The research integrates advanced AI technologies like Auto-GPT, AI multi-agent systems, and AI personas into the traditional horizon scanning process, creating a three-phase automated system: GPT Automated Signal Identification, GPT Automated Signal Processing, and GPT Automated Analysis and Interpretation.
The study explores how this AI-driven approach can overcome limitations of conventional horizon scanning methods, such as data processing speed, bias reduction, and real-time trend tracking. It also examines the potential synergies betthis researchen AI systems and human experts in identifying this researchak signals and emerging issues across various domains. To validate the effectiveness of this framework, the research proposes experimental applications in fields with clear technological trends, such as biotechnology and medicine. The study discusses both the advantages of the GPT-based system, including cost reduction and streamlined procedures, as this researchll as its limitations, such as potential lack of contextual understanding and prediction uncertainties.
The research concludes by suggesting future directions, including the integration of more advanced AI systems like "The AI Scientist(SakanaAI, 2024)" to further automate and enhance the horizon scanning process. This study contributes to the evolving field of future studies by presenting a cutting-edge approach that combines artificial intelligence with traditional foresight methodologies.
Keywords: Horizon Scanning, GPT (Generative Pre-trained Transformer), Future Studies, Framework, Automated Trend Analysis
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