Pushing the Envelope: Investigating the Potential and Limitations of ChatGPT and Artificial Intelligence in Advancing Computer Science Research

Abstract

Computer science has been revolutionized by artificial intelligence (AI), which has opened up new paths for data analysis, modelling, and prediction. ChatGPT, a language model based on deep neural networks, has shown promise in natural language processing, picture identification, and data analysis. However, integrating ChatGPT and other AI models into scientific research presents obstacles and constraints. This study aims to analyze the limitations of ChatGPT and AI in computer science research and provide methods to address these shortcomings. Specifically, we concentrate on two primary goals: first, Domain expertise restrictions: There may be a lack of specialized subject expertise in ChatGPT and comparable language models, providing difficulties for researchers in particular domains. We investigate techniques to improve domain knowledge integration into AI models, allowing for more precise and contextually relevant predictions. Second, interpretability difficulties: The interpretability of AI models continues to be a significant challenge, impeding researchers' ability to comprehend how algorithms create predictions. We address this issue by suggesting strategies for enhancing interpretability, giving researchers insights into the decision-making processes of AI models. We hope to promote AI's ethical and responsible use in scientific research by addressing these objectives. We examine various strategies to improve the representation of domain knowledge within AI models and its interpretability, enabling researchers to utilize the benefits of ChatGPT and AI while limiting their limitations.

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