Conversational AI for Natural Language Processing: An Review of ChatGPT

Abstract

ChatGPT is a conversational artificial intelligence model developed by OpenAI, which was introduced in 2019. It employs a transformer-based neural mesh to produce human being responses in real-time, allowing for natural language conversations with a machine. ChatGPT is instructed on huge quantities of data captured using the internet, making it knowledgeable in an extensive span of topics, from news & entertainment to politics and sports. This allows it to generate contextually relevant responses to questions and statements, making the conversation seem more lifelike. The model can be used in various applications, including customer service, personal assistants, and virtual assistants. ChatGPT has also shown promising results in generating creative content, such as jokes and poetry, showcasing its versatility and potential for future applications.This paper provides a comprehensive review of the existing literature on ChatGPT, highlighting its key advantages, such as improved accuracy and flexibility compared to traditional NLP tools, as well as its limitations and the need for further research to address potential ethical concerns. The review also highlights the potential for ChatGPT to be used in NLP applications, including question-answering and dialogue generation, and highlights the need for further research and development in these areas.

Modelling and Classification of Optical Beam Profiles using Vision Transformer

“…Due to their self-attention mechanism, the transformer's ability to process long, parallel sequences of data exhibits superior performance compared to traditional recurrent neural networks (RNNs) in handling sequential data types [1]. Attributing to this superior performance, transformer neural networks have been widely used in the field of natural language processing (NLP) [2][3][4]. Leveraging on its scaling success in NLP, Google Inc. attempted to apply transformer neural networks to image classification with minimal modifications, resulting in the vision transformer (ViT) model [5].…”