METHODOLOGY OF CHATGPT
ChatGPT is a new technology for natural language processing (NLP) that could change the way
conversational AI works.At its core, ChatGPT uses a recurrent neural network (RNN) architecture, which
lets it learn from previous conversations and contextual information about each conversation topic or
context provided by the user's input sentence(s).Also, unlike these rule-based
approaches, which are limited by the size of their preprogrammed knowledge bases, RNN architectures
can be trained on large datasets, such as those available through open source projects like OpenAI Gpt2
or Google's BERT model and can be scaled up almost indefinitely.The most important part of training is to
use reinforcement learning techniques along with supervised machine translation models like the Neural
Machine Translation (NMT) model in the Google Translate API.This makes it possible to make more correct predictions
than with the rule-based methods used by chatbots like ELIZA or AIML.It combines deep learning, reinforcement learning, and transfer learning to make
chatbots that can act like humans.