Jungwirth, D., Haluza, D. (2023). AI-Based Scenario Generation for Future Planning: An Exploratory Study Using GPT-3.
J Curr Trends Comp Sci Res, 2(2), 57-67.
Jungwirth, D., Haluza, D. (2023). AI-Based Scenario Generation for Future Planning: An Exploratory Study Using GPT-3.
J Curr Trends Comp Sci Res, 2(2), 57-67.
Jungwirth, D., Haluza, D. (2023). AI-Based Scenario Generation for Future Planning: An Exploratory Study Using GPT-3.
J Curr Trends Comp Sci Res, 2(2), 57-67.
Jungwirth, D., Haluza, D. (2023). AI-Based Scenario Generation for Future Planning: An Exploratory Study Using GPT-3.
J Curr Trends Comp Sci Res, 2(2), 57-67.
Abstract
Artificial Intelligence (AI) has the power to generate scenarios and make predictions through the use of advanced algorithms and machine learning techniques. OpenAI’s GPT-3 AI is a state-of-the-art language model that has been trained on a large dataset of text, which allows it to generate hu-man-like text, and it can generate scenarios for different fields. However, GPT-3 was trained on data available up until June 2021, had no access to more recent data nor was connected to the internet. In this study, we investigated the capability of OpenAI’s GPT-3 AI to predict the Ukrainian war es-calation, which started in 2022 and had massive geopolitical effects. We used GPT-3´s capability to generate future scenarios, to check those scenarios for internal consistency, and to create a proba-bility estimate. The results showed that although GPT-3 described an open war as one of the low probability scenarios, its capability on predicting the future was limited. Furthermore, it became evident that checking internal consistency of the generated scenarios could be improved. We ap-preciated GPT-3 as very useful and powerful for generating future scenarios, but also concluded that its prediction capabilities of real-world events are limited, should be used with caution, and require further development.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
