Howdy:
I thought I would pull together four books you might like to consider putting into your reading queue about AI and us all. These are all generally well-respected books (by respected authors) and I think you’ll find them illuminating.
“AI Engineering: Building Applications with Foundation Models” is about what most of us actually want to learn how to do. Sure, making a model is a fascinating exploration of linear algebra and GPUs, but you know, most of us just want to use the big models to do useful stuff. In this book, widely praised by many, author Chip Huyen discusses AI engineering and the process of building applications with readily available foundation models like ChatGPT and Claude.
“AI-Assisted Programming: Better Planning, Coding, Testing, and Deployment” by Tom Taulli is a comprehensive guide that explores how developers can leverage AI to enhance various stages of the software development lifecycle. As you all know, software development is not just about generating code. The book provides a practical and pragmatic approach, offering best practices for using AI tools in programming tasks like planning, coding, debugging, testing, and deployment.
“AI and Machine Learning for Coders: A Programmer’s Guide to Artificial Intelligence” by Laurence Moroney is a highly regarded introductory book for programmers interested in learning about AI and machine learning. People like it for its hands-on, code-first approach, making it accessible to beginners. And while the advanced topics can be hard to get your mind around, the book emphasizes practical application using TensorFlow and the Keras Sequential API, covering topics like computer vision, NLP, and sequence modeling.
Finally, a couple of friends of Zip Code, Wolf Ruzicka and Victor Shilo, have written “AI Driven: Staying Alive in the Age of Digital Darwinism,” which is very interesting as it talks about actual customers who have done more than just use ChatGPT to write emails.
As Amazon says, “Through five real-world case studies, Ruzicka and Shilo demystify AI in clear prose tailored to executives overseeing complex organizations. The examples range from adding predictive analytics to real-time structured data streams to probabilistic engineering on top of unstructured data to the correct use of generative AI on an enterprise scale. While underscoring AI’s competitive imperative, the authors detail proven methodologies to assess organizational readiness, drive cross-functional execution, and balance innovation velocity with economic viability.”
So, real wisdom, real trench stories. I like this one a lot.
They’re all on Amazon, and probably available for free from O’Reilly if you still have that Bookshelf subscription you keep lying around.
Cheers,
–Kris
