Abstract
The emergence of advanced NLP models, like ChatGPT and other conversational AI models, has triggered a revolutionary transformation. This chapter explores the burgeoning field of ChatGPT applications, conducting a comprehensive analysis of their impact across various domains. The chapter assesses their capabilities, challenges, and potential uses, examining the underlying architecture and training methods that enable them to generate contextually relevant and coherent responses. Ethical considerations are also addressed, encompassing concerns about bias, misinformation, and user privacy in real-world conversations. The chapter also acknowledges drawbacks, including occasional inaccuracies or sensitive content generation. In conclusion, ongoing research is vital to enhance model robustness, user experience, and ethical deployment in conversational AI. ChatGPT and similar models are poised to reshape human-machine communication, fostering dynamic, engaging, and valuable conversations.
Top1. Introduction
Over the past few decades, the field of artificial intelligence (AI) has undergone significant evolution. Initially, AI systems were designed for specific tasks, such as playing chess or go. Advances in machine learning and deep learning have driven considerable progress in AI's development, resulting in enhancements across various applications like robotics, healthcare, finance, and image recognition. (Suresh Babu C.V., 2022),
AI systems now possess decision-making abilities honed through machine learning and deep learning algorithms, enabling them to analyze vast datasets and uncover patterns, insights, and correlations that may elude human perception. This data-driven approach empowers AI systems to make precise decisions in diverse domains.
One pivotal milestone in AI's journey was the invention of neural networks in the 1950s, inspired by the human brain's learning process. These networks revolutionized AI by enabling the execution of previously impossible tasks. They empowered AI systems to accurately transcribe human speech, and recognize objects, individuals, and situations in images.
Deep learning models, particularly transformer-based designs like Bert and GPT (generative pre-trained transformers), have significantly enhanced natural language processing (NLP) capabilities. Tasks such as machine translation, chatbot development, sentiment analysis, and other language-related applications have advanced due to their ability to comprehend and generate human-like language.