Books, Generative AI
Book Review: The Complete Obsolete Guide to Generative AI
After reading the book by David Clinton, I summarized what I had learned.
David Clinton won me over for his writing style, which was never overly ironic or humorous, and for the book's content. The Complete Obsolete Guide to Generative AI is a comprehensive resource guiding the reading throughout the journey of Generative AI.
The table of contents contains the following chapters:
- Understanding Generative AI Basics
- Managing Generative AI
- Creating Text and Code
- Creating with Media Resources
- Feeding Data to Your Generative AI Models
- Prompt Engineering: Optimizing Your Experience
- Outperforming Legacy Research and Learning Tools
- Understanding Stu Better
- Building and Running Your Own Large Language Model
- HowILearnedtoStopWorryingandLovetheChaos
- Experts weigh in on putting AI to work
- Appendix A. Important Definitions and a Brief History
- Appendix B. Generative AI Resources
- Appendix C. Installing Python
The Structure of the Book
In my opinion, the book can be divided into two main parts:
- In the first part, the concept of Generative AI is introduced, with some practical examples, in Python or bash, on how to make a Generative AI model work
- In the second part, the broad context of Generative AI is described, with various references here and there, using multiple techniques without ever going into detailed examples.
The examples are implemented using GPT but can be easily adapted to other Generative AI models.
At the end of the book, there is also a handy glossary with the definitions of the most important terms related to Generative AI.
I have already written about the book's first chapter in my previous article. In case you missed it, here is the reference:
Author’s Thought
During the reading, you can also see the author’s optimistic thoughts toward Generative AI and many reflections on essential topics, such as the future use of Generative AI in various contexts and any problems associated with the use of Generative AI.
For example, in the book, we find the concept of GAN (Generative Adversarial Network) described simply:
GANs are a class of generative AI models that consist of two neural networks: the generator and the discriminator. They work in tandem, with the generator trying to create realistic data, and the discriminator trying to distinguish between real and generated data. (David Clinton)
And the concept of AGI (Artificial General Intelligence):
It refers to highly autonomous systems or machines that possess the ability to understand, learn, and perform intellectual tasks at a level equal to or surpassing human capabilities across a wide range of domains. (David Clinton)
For those looking for an introductory book on the topic of Generative AI, this book is undoubtedly suitable because, without going into too much technical detail, it puts anyone in a position to navigate this topic.
My Overall Score
I found the book very interesting because it also opened me up to new scenarios, such as the hardware problems linked to the use of Generative AI or the potential that the combination of Generative AI and Quantum Computing could have.
What struck me most about this book is the ease with which the author describes such a complex topic. This shows his extraordinary competence in writing. It seems the author enjoyed writing the book, as I did reading it.
Well done, David Clinton!
