Everyone is talking about ChatGPT. Some say it is a robot, while others say it is an alien application. But the truth is ChatGPT is an extension of GPT (Generative Pre-trained Transformer) which was developed by OpenAI in 2018. ChatGPT was trained on a massive dataset of internet text and was able to generate human-like text in a variety of styles and formats.

Following the success of GPT, in 2019 OpenAI introduced GPT-2, a larger version of GPT with more than 1.5 billion parameters. GPT-2 was able to generate more coherent and fluent text, but it also raised concerns about its potential misuse, such as generating fake news. As a result, OpenAI decided to release a smaller version of GPT-2 with fewer parameters, called GPT-2-117M.

Brief history of ChatGPT

In 2020, OpenAI introduced GPT-3, which is currently the largest language model with 175 billion parameters. GPT-3 is able to perform a wide range of natural language tasks, including translation, summarization, and question answering. Through assistance of a Kenyan company called SAMA, OpenAI was able to build large amount of data that helped it to filter and also to build dataset.

ChatGPT is a version of GPT-3 that is fine-tuned for conversational tasks such as text completion, text generation and text classification.

It is a large language model developed by OpenAI that can generate human-like text. It is trained on a diverse set of internet text and is able to generate text in a variety of styles and formats, including conversation, news articles, stories, and more. The model is designed to be fine-tuned to specific tasks such as conversation and text completion.

How it works

The development of ChatGPT is based on a process called pre-training, which involves training a large language model on a massive dataset of text. In the case of ChatGPT, the model is trained on a diverse set of internet text, including books, articles, and websites.

Once the model is pre-trained, it can be fine-tuned for specific tasks such as conversation, text completion, text generation, and text classification. The fine-tuning process involves training the model on a smaller dataset of task-specific text, which helps the model learn to perform the task more accurately. It has an answer for everything that one types. It computes and comes up with words, paragraphs, explaining what one asks or texts. It is a mind-blowing AI technology, which everyone wants to use making work easier.

The chatGPT model is based on the transformer architecture which was introduced in 2017 by Google. The transformer architecture is a neural network architecture that can process input sequences in parallel, making it well suited for language tasks.

Overall, the development of ChatGPT involves a combination of pre-training, fine-tuning, and the use of advanced neural network architectures such as the transformer, which allows the model to generate human-like text and perform a wide range of natural language tasks.

How to use ChatGPT

Depending on what you want to accomplish, there are several ways to use ChatGPT. Here are a few examples:

  • Text completion: You can use it to complete a given text prompt, by providing the model with the first few words of a sentence, and it will generate the rest of the sentence.
  • Text generation: You can use ChatGPT to generate text on a specific topic. For example, you can provide the model with a topic such as “sports” and it will generate a text about sports.
  • Text classification: This classifying text into different categories. For example, you can train the model to classify text into categories such as “positive” or “negative” sentiment.
  • Dialogue generation: It can generate dialogue between two or more people.

To use ChatGPT, you will need to access the OpenAI API which requires an API key, and you can use several libraries such as transformers, Hugging Face to use GPT-3 in your application.

It’s important to note that ChatGPT is a powerful tool and should be used with caution, as the model is able to generate human-like text that may not always be accurate or appropriate. It’s recommended to use the model with some human supervision and to use the model for the specific task it was fine-tuned for.