Examination of Structural Features of chatGPT۳.۵ and chatGPT۴

This research delves into a comparative study of structural features of chatGPT۳۵ and chatG۴, two iterations of theconversational AI model developed by Open AI. The analysis focuses on exploring key architectural differences,training methodologies, performance metrics, and advancements introduced in chatGPT۴ as compared to itspredecessor, chatGPT۳.۵. By examining various aspects such as model complexity, training data sources,conversational quality, and response generation capabilities, this study aims to provide insights into the evolutionarychanges and improvements from chatGPT۳.۵ to chatGPT۴. The findings offer valuable implications in theenhancements in chatGPT۴’s efficiency, effectiveness, and overall conversational prowess, contributing to thebroader landscape of natural language processing research and AI innovation.One of the most remarkable features of ChatGPT is its Advanced Data Analysis function. This feature transcends theboundaries of traditional AI chatbots by allowing users to directly upload data to ChatGPT. This opens up a world ofpossibilities for data-driven decision-making. Users can seamlessly write, test, and run code within the platform,making it a valuable tool for data professionals and enthusiasts alike. The platform's compatibility with various dataformats, including

Keywords:

chatGPT , Conversational AI , Natural Language Processing , chatGPT۳.۵ , chatGPT۴ , structural features