What Is GPT-3 and How Does It Work?

Understanding the technology powering the top AI-assisted tools

Image sourced from Playground AI

GPT-3 (short for “Generative Pre-trained Transformer 3”) is a language generation model developed by OpenAI. OpenAI is the research organization promoting and developing friendly artificial intelligence in ways most likely to benefit humanity as a whole.

Or so it told me…

A language generation model, like GPT-3, is a type of artificial intelligence that is able to generate natural language text. The goal of a language generation model is to produce text that is coherent, grammatically correct, and reads like it was written by a human. Language generation models can be used for a wide range of applications, including generating news articles, stories, and poems, as well as performing language tasks such as translation, summarization, and question answering.

Language generation models, like GPT-3, are trained on large datasets of text and use machine learning techniques to predict the next word or sequence of words in a given piece of text.

Machine learning is a method of teaching computers to learn and make decisions on their own, without being explicitly programmed.

There are various techniques that can be used in machine learning, including Supervised learning, Unsupervised learning, Reinforcement learning, and Deep learning. Though different, each machine learning technique teaches computers to learn and make decisions on their own, without being explicitly programmed.

Specific to GPT-3, it is trained using a combination of supervised and unsupervised learning and a type of machine learning called “pre-training” followed by fine-tuning.

In the pre-training stage, GPT-3 is trained on a large dataset of text, such as books, articles, and websites. The goal of pre-training is to learn the general patterns and structures of language so that the model can then be fine-tuned for specific tasks.

During fine-tuning, GPT-3 is further trained on a smaller dataset that is specific to the task at hand. For example, if the goal is to use GPT-3 for language translation, the model would be fine-tuned on a dataset of translated sentences.