Generative Artificial Intelligence Technology for Systems Engineering Research: Contribution and Challenges
Yehia Ibrahim Alzoubi, Alok Mishra, Ahmet E. Topcu, Ali Osman Çıbıkdiken
Abstract
IntroductionAI has had a significant impact on all companies, communities, and individuals.As it anticipates in its ecosystems, AI provides systematic capacity for reasoning based on information, and it learns via the many types of anticipated results [1].When AI first emerged, the emphasis of systems was mostly on unsupervised and supervised learning, where it took cues from biological creatures and physical laws of nature and constructed these laws digitally to address issues requiring large amounts of data [2].However, structured data was necessary for building models and data processing in classical AI systems.Due to these restrictions, the capacities of these AI algorithms, like decision trees, random forests, and neural networks, were relatively constrained [3].Many practical applications of natural language processing, including voice and phrase identification, distillation, translators, and textual production, have seen substantial achievements in recent years [4].However, the established generative AI models, like chatbots, have limits and are unable to deal with natural language messages with highly dependent relation-The advancement of artificial intelligence technology in recent years has had a significant impact on various industries, including the field of systems engineering.Generative Artificial Intelligence (AI), like OpenAI's ChatGPT, is one such tool that has garnered attention.While this technology offers researchers in systems engineering intriguing possibilities, it also introduces certain risks to the traditional research framework.The aim of this paper is to investigate the advantages and drawbacks associated with embracing generative AI.We conducted a comprehensive literature review utilizing resources like Google Scholar, Web of Science, and the Scopus database, along with professional websites and white papers.The analysis highlights the potential benefits of generative AI in systems engineering research, including data processing, analysis, hypothesis formulation, prediction and forecasting, and collaboration enhancement.However, it also underscores various risks, such as potential data bias, the generation of human-like text, potential loss of analytical capabilities, and difficulties in analyzing output from these AI tools.As emphasized in this paper, numerous concerns still need to be addressed regarding the use of generative AI tools due to their relatively new nature and evolving capabilities.