Improved Convolutional Neural Network based Approach for Analyzing the ChatGPT and Different Fields Impact

Abstract

The recently developed ChatGPT NLP model enhances OpenAI’s GPT-2 transformer-based language model by training on the GPT-3 set of enormous language patterns (an approach to transfer learning). With this concept in place, humans and AIs can carry on discussion using only text. It could be used in chatbots and other voice-and text-based customer support applications. The subject detection, emotion recognition, and sentiment analysis functions built into ChatGPT help users gain a deeper comprehension of the other individuals with whom they are interacting. The ability to produce many conversation threads allows for the creation of more natural interactions between user and bot. And will also discuss some of the obstacles on the path of AI advancement and discuss potential solutions. Recent advancements in artificial intelligence (AI) are the topic of this system. The field of artificial intelligence (AI) has made greater strides in recent years, with numerous breakthroughs in both application and technology and to talk about how some of these innovations are already bettering people’s lives. The proposed method first employs preprocessing to cleanse the data before turning to event extraction. The new method is compared to two well-established alternatives, namely CNN and SVM. When compared to two more conventional approaches, the proposed strategy performs efficiently.

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