In this article, we look at the impressive power of OpenAI's GPT-3 engines by looking at an example of summarizing complex text, which in our case is an excerpt of Montana corporate law. This article is an excerpt from the book Transformers for Natural Language Processing, Second Edition. This edition includes working with GPT-3 engines, more use cases, such as casual language analysis and computer vision tasks, and an introduction to OpenAI's Codex. Then go to the examples page. You'll see many examples, from converting movie titles to emojis to generating ads to creating micro horror stories.

With Open AI releasing its avant-garde pre-trained language model -- GPT-3 has suddenly become an obsession for the machine learning community, where it can not only generate codes but also human-like stories. Along with its wide range of utilities, it has also surprised the developers and programmers with its generalised intelligence, which is relatively more advanced than the previous pre-trained language models. Previously, the NLP systems continued to struggle in learning from a few examples; however, with GPT-3, language models can significantly improve with even reaching competitiveness with prior advanced fine-tuning approaches. That being said, to use GPT-3 with 175 billion trainable parameters, developers and programmers must understand what's going on under the hood of the neural-network-powered language model. Not only can it be new and complex to understand for first-timers but can also be overwhelming with its big size.