“Generative AI In Practice” is a good book.Please ignore the buzzwords

I hadn't been sent a book to review in a while so I decided to take a risk on this one. Surprisingly, it's a book I can wholeheartedly recommend. As long as you ignore the first word, “Generative AI in Practice” by Bernard Marr (Wiley, ISBN: 978-1-394-25424-8) will help you.

Let's start with the speech. I've been avoiding anything with the word “generative” in it since it became a buzzword in the current hype cycle. This has been presented as a revolution in artificial intelligence (AI) because so many people like the idea of ​​revolution. In reality, evolution happens much more frequently. ChatGPT and other large-scale language models have shown that much more complex deep learning systems, powered by the cloud, have advanced the technology. And that's it. But if you put a cool adjective in front of AI, you can ask for more funding.

What's so great about this book is that it provides a very easy-to-understand overview of the current state of AI in business. If you ignore the hype, it's perfect for that purpose.

As always with these books, feel free to skip the first few chapters. The author does not understand the history of AI and tries to use chess as an example to argue that “traditional” AI followed pre-existing rules. But if you look at the Go system, you'll see that early systems had already devised tactics that people hadn't thought of, but that worked. He then tries to suggest that deep learning, or neural networks, may have magically appeared with ChatGPT and generative add-ons. It's nothing new. As we've said over the past decade, what has changed is the power of data center servers, or the cloud. Naturally things have progressed as we can now run much more complex models much faster.

I also laughed at the assertion that with modern systems, “you don't need to be a data scientist to explore data.” Really? Early AI, as well as business intelligence (BI) systems, have been doing that for ages.

Okay, without further ado, let's talk about why this book is so enjoyable.

Chapter 4 contains some standard material, but one of the key takeaways is a point he makes that has been covered elsewhere for several years. The plan is for companies to use AI to take over the simple, rudimentary tasks that many industries (insurance as one example) have. Management's argument is that it frees up time for people to handle more complex tasks. But how do new employees learn without starting with simple tasks and tackling complex ones? It's clear that CxO suites and shareholders want to automate everything, but employees need How will the transition be handled so I can train while I can?

Chapter 5 addresses another important issue. Ma is the first business management author to directly address the major employment disruptions that will occur. My only complaint is that his argument doesn't go deep enough. Again, AI is limited to a range not far from what we currently understand. Indeed, even researchers will be replaced, as Chapter 13 suggests is already happening with molecular studies of new compounds.

All chapters in Part 2 are designed for skim reading. Each focuses on a business area and explains how AI is advancing those areas. Again, ignore how he keeps pointing out generationism. He's a self-proclaimed “futurist” and “influencer,” so he has to push the hype curve like crazy. Yet, on the other hand, he gives a great overview of what is already happening and what could happen with AI in each field.

Any manager will benefit from specific examples from their own industry as well as understanding the broader impact of AI on their business. The power of systems will change the way our economies work, from local to global. This book does not address the regulatory and major societal issues that need to be addressed as more and more tasks are automated. However, for a good overview of the cutting edge of AI in business, it's a worthwhile and quick read.