Book Image

Modern Generative AI with ChatGPT and OpenAI Models

By : Valentina Alto
4.9 (8)
Book Image

Modern Generative AI with ChatGPT and OpenAI Models

4.9 (8)
By: Valentina Alto

Overview of this book

Generative AI models and AI language models are becoming increasingly popular due to their unparalleled capabilities. This book will provide you with insights into the inner workings of the LLMs and guide you through creating your own language models. You’ll start with an introduction to the field of generative AI, helping you understand how these models are trained to generate new data. Next, you’ll explore use cases where ChatGPT can boost productivity and enhance creativity. You’ll learn how to get the best from your ChatGPT interactions by improving your prompt design and leveraging zero, one, and few-shots learning capabilities. The use cases are divided into clusters of marketers, researchers, and developers, which will help you apply what you learn in this book to your own challenges faster. You’ll also discover enterprise-level scenarios that leverage OpenAI models’ APIs available on Azure infrastructure; both generative models like GPT-3 and embedding models like Ada. For each scenario, you’ll find an end-to-end implementation with Python, using Streamlit as the frontend and the LangChain SDK to facilitate models' integration into your applications. By the end of this book, you’ll be well equipped to use the generative AI field and start using ChatGPT and OpenAI models’ APIs in your own projects.
Table of Contents (17 chapters)
1
Part 1: Fundamentals of Generative AI and GPT Models
4
Part 2: ChatGPT in Action
11
Part 3: OpenAI for Enterprises

Ethical implications of Generative AI and why we need Responsible AI

The previous section highlighted how, alongside the widespread knowledge and adoption of Generative AI technologies, a general concern is rising.

The rapid advancement of AI technologies brings forth a plethora of ethical considerations and challenges that must be carefully addressed to ensure their responsible and equitable deployment. Some of them are listed here:

  • Data privacy and security: As AI systems rely heavily on data for their learning and decision-making processes, ensuring data privacy and security becomes paramount. In Chapter 9, we already saw how Microsoft addressed the topic of data privacy with Azure OpenAI Service, in order to guarantee the Service-Level Agreements (SLAs) and security practices expected of the Azure cloud. However, this data privacy topic also affects the data that is used to train the model in the first instance: even though the knowledge base used by ChatGPT to generate...