How is ChatGPT different from Google search? Where does ChatGPT get its data?

 How is ChatGPT different from Google search?

Google search and ChatGPT are two distinct technologies with distinct applications.

ChatGPT is a language model that responds to user input with human-like language generated by deep learning algorithms. It is made to look like a human conversation and can respond to a wide range of inputs. Most of the time, ChatGPT is used to answer questions, give advice, or have a casual conversation.

On the other hand, Google Search is a search engine that shows users a list of web pages and other online resources that are related to their search query. Algorithms are used by Google Search to rank web pages and other online resources based on how relevant they are to the search query. Most of the time, Google search is used to find information, do research on a subject, or buy things.

In conclusion, Google search focuses on finding relevant online resources, whereas ChatGPT is focused on producing responses in natural language. Although some of their capabilities may be comparable, they are fundamentally distinct technologies with distinct applications.

Where does ChatGPT get its data?

ChatGPT gets its information from a wide assortment of sources, including books, articles, and sites. The large and diverse corpus of text that serves as the training data for ChatGPT is typically curated by the model's creators.

The underlying rendition of ChatGPT, for instance, was prepared on a dataset of over 40GB of text information, which incorporated various sources, for example, books, Wikipedia articles, and site pages. The training data were carefully chosen to include a wide range of topics and writing styles, assisting the model in learning to produce responses in natural language that are appropriate for various contexts.

ChatGPT can be tailored to specific applications or domains using additional datasets in addition to the initial training data. A medical chatbot that can answer questions about health and wellness could be created, for instance, by fine-tuning a ChatGPT model on a dataset of medical text.

It is important to note that the performance of ChatGPT can be significantly affected by the variety and quality of the training data. To ensure that the training data is of high quality and contains a diverse range of text that is representative of human language, developers typically devote a significant amount of time to curating and preprocessing it.