Hello, we’re Computer Science Professors Dr. Leo Porter and Dr. Daniel Zingaro. We’ve dedicated our careers to helping students succeed in programming and computer science courses. There are approaches we know are effective in teaching novices, such as learning from worked-out examples and using real-world problems that resonate with students. When we’re reading a book, we’re always thinking: will this book help people learn? Does it use what we know about learning to serve as an effective teaching aid? Can we use this with our students? Can we for once stop analyzing the book and just read for fun? (The answer to that last question is, unfortunately, ‘no,’ 😀 We can’t help it!)
With massive changes happening due to generative AI tools like ChatGPT and GitHub Copilot, you won’t be surprised that there’s a swarm of new books that use generative AI to teach programming to beginners or to enhance what programmers can do.
In this article, we wanted to cover our top four generative AI books that are being published by Manning Publications.
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We know, we know: you just want to use generative AI to supercharge your programming productivity. We want that, too! But we’re at the dawn of a programming revolution here, and we strongly encourage you to take the time to understand the ethical and legal concerns wrought by generative AI.
What happens when generative AI models ingest objectionable speech or personal data? Why are these models apt to produce hallucinations, and why should we care? Why is it so difficult to address bias in machine learning? What is the critical role that human feedback plays in LLM training, and what are the associated costs to humans? Does generative AI’s use of copyrighted work fall under fair use?
As informed end-users of generative AI, it’s up to us to answer these questions–to understand what data we might be using, how that data was produced, and the societal and ethical impacts of these tools. This book helps us toward those answers.
We appreciate that many claims in the book are supported with references that the reader can check for additional details. We also benefited from numerous powerful examples throughout the book, such as racial bias in movie sentiment scores, a ChatGPT data breach, and a famous virtual influencer.
We’d also like to emphasize that while the focus of the book is on the responsible use of generative AI, there is also a non-mathematical overview coverage of how generative AI tools work, which we suspect will be of interest to many readers. For example, you’ll learn more about many concepts you’ve probably heard about in passing, such as foundation models, fine-tuning, emergent properties of LLMs, zero-shot and few-shot learning, and chain-of-thought prompting.
Finally, we applaud the balanced discussion of the pros and cons of synthetic media, the ways that LLMs are and will be misused, the ways that professionals are using LLMs and–of course!–the coverage of the impacts on education.
Dhamani and Engler’s Introduction to Generative AI is a must-read foundational guide not only to understand how generative AI works but also to understand its broader societal implications.
The two of us (Daniel Zingaro and Leo Porter) wrote this book because we believe that the way new programmers learn to program has changed dramatically now that generative AI is here. We’ve both taught thousands of students to program over the years and a lot of our time needed to revolve around teaching syntax, which is the ways that words and symbols are put together to create programs that run. But generative AI handles syntax extremely well (which is a good thing, because many learners find syntax boring and frustrating). So, in writing this book, our guiding question was: what are the main skills that new programmers need to learn now?
In this book, written for absolute beginners, you’ll be writing programs that work from day one, in contrast to the before times when you would have had to learn lots of syntax first. You’ll learn how to test code that comes from the generative AI to check whether it is correct, break down large problems into smaller bits that the AI can better solve, and use a debugger to trace your code very carefully to see what it’s doing. Oh, and you’ll be learning Python along the way, too, in case you need that for your resume 🙂
David Clinton
Why would you buy and read a book with ‘obsolete’ in the title? What the author is getting at with this irreverent title is that generative AI is moving so quickly that everything written about it will be obsolete quickly. We may as well understand the foundations of effectively interacting with these tools, which is what this book focuses on.
The book starts by explaining the background concepts you need to know when working with generative AI tools. What’s a token? What are the differences between all of those GPT models? What the heck is temperature and Top P?
You need Python experience to read this one. This isn’t a programming book, though. It’s a “let’s see what we can do with generative AI!” book. You’ll generate fiction (not very good fiction… yet?), generate book cover images, convert slides to videos, and quickly obtain summaries of boring meetings and long PDF documents. The book tours many powerful generative AI tools that you may not have been aware of–it goes way beyond what the general public is doing with ChatGPT.
The key takeaway of the book is that the best results come from pairing your domain knowledge with the explosion of content you can create with generative AI.
OK — so you’re already a Python developer and you want to start using LLMs to rocket your productivity. How? By reading Crocker’s new book 😀
This book shows you how to use GitHub Copilot, ChatGPT, and Amazon CodeWhisperer (and when to use each). It assumes that you already know Python, and we further suggest that familiarity with building APIs in Python would be a plus.
Through its chapters, you’ll build an Information Technology Asset Management (ITAM) system, using generative AI for each step… from designing the system to writing the code, generating data, testing and managing the deployment, and helping with security. (Yes: generative AI is useful way beyond writing code for you!)
The pro of writing the book as one comprehensive example is that you see how a complete application is built and deployed with generative AI help. The cons are that it makes it difficult to jump around the book and that if you are not motivated by the chosen example then the book itself may not be as motivating as a collection of smaller examples. We need books of both types!
For us, the material on system design is of particular interest, because in our time working with generative AI, we have done the high-level design and left the low-level code to the AI. Crocker’s book shows that experienced programmers can indeed push generative AI into the design realm as well, including proposing designs, creating class diagrams for designs, and comparing and contrasting potential designs.
Whether you want to understand generative AI at a societal level, to learn programming from scratch “the new way,” to add generative AI to your programming toolbox, or to be inspired to use generative AI to … generate (sorry!) content, we’re confident that you’ll find value in one or more of these books.
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