Chapter 1: Introducing GPT-3 and the OpenAI API

The buzz about Generative Pre-trained Transformer Version 3 (GPT-3) started with a blog post from a leading Artificial Intelligence (AI) research lab, OpenAI, on June 11, 2020. The post began as follows:

We're releasing an API for accessing new AI models developed by OpenAI. Unlike most AI systems which are designed for one use-case, the API today provides a general-purpose "text in, text out" interface, allowing users to try it on virtually any English language task.

Online demos from early beta testers soon followed—some seemed too good to be true. GPT-3 was writing articles, penning poetry, answering questions, chatting with lifelike responses, translating text from one language to another, summarizing complex documents, and even writing code. The demos were incredibly impressive—things we hadn't seen a general-purpose AI system do before—but equally impressive was that many of the demos were created by people with a limited or no formal background in AI and Machine Learning (ML). GPT-3 had raised the bar, not just in terms of the technology, but also in terms of AI accessibility.

GPT-3 is a general-purpose language processing AI model that practically anybody can understand and start using in a matter of minutes. You don't need a Doctor of Philosophy (PhD) in computer science—you don't even need to know how to write code. In fact, everything you'll need to get started is right here in this book. We'll begin in this chapter with the following topics:

• Introduction to GPT-3
• Democratizing NLP
• Understanding prompts, completions, and tokens
• Introducing Davinci, Babbage, Curie, and Ada
• Understanding GPT-3 risks