Ansible GPT — callback plugin with OpenAI for Ansible tasks and playbooks
Tutorial how to write your own Ansible Callback plugin and leverage OpenAI for Ansible code analysis.
Join us
GPT (Generative Pre-trained Transformer) is a deep learning model developed by OpenAI that has been pre-trained on massive amounts of text data using unsupervised learning techniques. GPT is designed to generate human-like text in response to prompts, and it is capable of performing a variety of natural language processing tasks, including language translation, summarization, and question-answering. The model is based on the transformer architecture, which allows it to handle long-range dependencies and generate coherent, fluent text. GPT has been used in a wide range of applications, including chatbots, language translation, and content generation.
GPT is a family of language models that have been trained on large amounts of text data using a technique called unsupervised learning. The model is pre-trained on a diverse range of text sources, including books, articles, and web pages, which allows it to capture a broad range of language patterns and styles. Once trained, GPT can be fine-tuned on specific tasks, such as language translation or question-answering, by providing it with task-specific data.
One of the key features of GPT is its ability to generate coherent and fluent text that is indistinguishable from human-generated text. This is achieved by training the model to predict the next word in a sentence given the previous words. GPT also uses a technique called attention, which allows it to focus on relevant parts of the input text when generating a response.
GPT has become increasingly popular in recent years, particularly in the field of natural language processing. The model has been used in a wide range of applications, including chatbots, content generation, and language translation. GPT has also been used to create AI-generated stories, poetry, and even music.
Will AI lead to humanity's downfall, as warned by Musk and Hawking? What is the Dead Internet Theory and its relation to Generative AI? How do figures like Hinton and Chomsky perceive the risks of Generative AI, and are they valid? How does Generative AI redefine intelligence and information access? What are the most effective Prompt Engineering techniques? How do connectionism and symbolism differ in AI, and their impact on AI system development? How have models like BERT, MUM, and GPT revolutionized Generative AI and its applications? Will Generative AI drive entrepreneurship or replace human roles? What are the projected impacts of Generative AI on global GDP and personal income? What challenges and considerations are involved in regulating AI technologies?
You'll find answers to these questions and more within the pages of our book.
This tutorial is part of my book "OpenAI GPT For Python Developers".
The goal of this book is to provide a step-by-step guide to using GPT-3 in your projects through this API but not only - many other tools and models built by OpenAI such as Whisper (an automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data), CLIP (Contrastive Language-Image Pre-Training), a neural network trained on a variety of (image, text) pairs and DALL·E 2, a new AI system that can create realistic images and art from a description in natural language.