Introducing a few books about ChatPT and AI

What ChatGPT is doing...and why it's good

Introduction

The author is Stephen Wolfram, a god-like man who invented the Mathematics software, who made the WolframAlpha website, who created a computational language called Wolfram Language, and who has always had a unique leader-like view of new technologies.

This book has been endorsed by OpenAI CEO Sam Altman as the best explanation he has ever seen. Wolfram not only makes clear the underpinnings and lifeblood of GPT, but offers an insight that could be considered astounding.

The ChatGPT Gold Rush: Profiting from the Artificial Intelligence Revolution

Introduction

This book explains how ChatGPT is impacting industries and the new gold rush opportunities that will arise from it. It details tip engineering and lists 200 ChatGPT applications for reference and learning.

The Artificial Intelligence Revolution in Medicine: GPT-4 and Beyond

Introduction

The AI Revolution in Medicine: GPT-4 and Beyond, with a foreword by OpenAI boss Sam Altman, is a 282-page pdf. GPT-4 and its competitors and followers are about to change medicine. But in life-threatening situations, you need to understand these technologies. What can they do? What can't they do yet? What can they not do? To make a decision, experience the cutting-edge technologies for yourself.

A Security Perspective on Artificial Intelligence, Machine Learning and Deep Learning

Introduction

Because AI/ML/DL security is an emerging field, many researchers and industry professionals do not yet have a detailed, comprehensive understanding of the field. This book aims to provide a complete picture of the challenges and solutions to relevant security issues in various applications. It explains how different attacks occur in advanced artificial intelligence tools and the challenges of overcoming them. Then, the book describes many promising solutions for achieving AI security and privacy. The full book is 518 pages pdf.

Understanding Deep Learning

Introduction

A 518-page pdf by Simon J.D. Prince, Professor at the University of Bath.

The title of this book is "Understanding Deep Learning" to distinguish it from books covering coding and other practical aspects. This text focuses on the ideas behind deep learning. The first part of the book introduces deep learning models, and discusses how to train them, measure their performance, and improve that performance. The next section considers architectures dedicated to image, text, and graphics data. These chapters require only an introduction to linear algebra, calculus, and probability theory, and should be appropriate for second-year undergraduates in any of the quantitative disciplines. The subsequent sections of the book deal with generative models and reinforcement learning. These chapters require more knowledge of probability and calculus and are aimed at more advanced students.