Generative AI For Dummies (For Dummies (Business & Personal Finance))

Very informative and easy to read. Especially for people that are not that tech savvy and older.

For over twenty years Wiley's "For Dummies" series has been my go to source for learning about new fields ranging from philosophy to neuroscience, so I am happy to report that Pam Baker's "Generative AI for Dummies" is a wonderful introduction for anyone who wants to understand the hype behind the rush to use tools like ChatGPT.Not only does the book contain numerous practical examples of how to use generative AI (also called GenAI) in the real world, but it addresses both its promise and its shortcomings. Most important it includes several key ideas that are sometimes neglected:1. GenAI does not actually understand anything we tell it. *2. GenAI generates prediction, it does NOT create.3. GenAI can make mistakes and this goes beyond intentional deep fakes. It doesn't know if it's telling you something that's not true. It can even generate faults false references so you must fact check everything. (Note: it can even generate false references so you have to look them up!)4. GenAI will create more jobs than it eliminates, but if you don't learn how to use it you could lose your job to someone does.Throughout "Generative AI for Dummies" Baker reminds us that GenAI PREDICTs what we want to know and it does this by processing huge amounts of data. Its ability to process data obviously outstrips human abilities, but human creativity remains essential to making meaningful use out of the results.Overall "Generative AI for Dummies" is a wonderful introduction to this emerging field.*I realize that some experts argue that AI does understand, but this gets down to debates about the difference between intelligence and consciousness, which is beyond the scope of this review.

I bought this to see how an author might approach creating a starter guide to a very complex subject. I expected everything to be dumbed down. Not at all the case. Yes, it provides what "dummies" need. But it's also a treasure trove of insights and advice that I've not seen anywhere else. The author easily demonstrates her command of the subject. Highly recommended.

I needed a desktop reference book while learning Coursera AI Prompting. The course was great and the Gen AI book helped fill in the gaps. A very good primer.

Good book and it came to me ontime.

Pam Baker has written a very useful resource for AI beginners and experts alike. Don’t let the “Dummies” title fool you into thinking otherwise. This is also a book that is hard to get your hands around – in that respect it mirrors what GenAI itself is like. Think of it as a practical tutorial into how to incorporate GenAI into your working life to make you a more productive and potent human. It is also not a book that you can read in some linear front-to-back sense: there are far too many tips, tricks, strategies and things to think about as you move through your AI journey. But it is a book that is absolutely essential, especially if you have been frustrated at learning how to better use AI.Underlying it all is Baker’s understanding what the winning formula for using GenAI is – to understand that the output from the computer sounds like a human. But to be really effective, the human must think like a machine and tell GenAI what you what with better prompt engineering. (She spends an entire chapter on that subject with lots of practical suggestions that combine the right mix of clarity, context and creativity. And so you will find out there is a lot more depth to this than you think.) “You must provide the vision, the passion, and the impetus in your prompts,” Baker writes. Part of that exploration is understanding how to best collaborate with GenAI. To that end, she recommends starting with a human team to work together as moderators in crafting prompts and refining the results from the GenAI tool.” The more information the AI has, the more tailored and sophisticated the outputs will be,” she writes.To that end, Baker used this strategy to create this very book and was the first such effort for its publisher Wiley. She says it took about half the time to write this, when compared to other books that she has written on technology. This gives the book a certain verisimilitude and street cred. This doesn’t mean ripping the output and setting it in type: that would have been a disaster. Instead, she used AI to hone her research and find sources, then go to those citations and find out if they really exist, adding to her own knowledge along the way. “It really sped up the research I needed to do in the early drafts,” she told me. “I still used it to polish the text in my voice. And you still need to draft in chunks and be strategic about what you share with the models that have a public internet connection, because I didn’t want my book to be incorporated into some model.” All of this is to say that you should use AI to figure out what you do best and that will narrow down the most appropriate tools to use to eliminate the more tedious parts.Baker makes the point that instead of wasting effort on trying to use GenAI to automate jobs and cut costs, we should use it to create rather than eliminate. It is good advice, and her book is chock full of other suggestions on how you can find the sweet spot for your own creative expressions. She has an intriguing section on how to lie to the model to help improve your own productivity, what she calls a “programming correction.” The flip side of this is also important, and she has several suggestions on how to prevent models from generating false information.She catalogs the various GenAI vendors and their GenAI tools into how they craft different text, audio and visual outputs, and then summarizes several popular uses, such as in generating photorealistic artworks from text descriptions, some of which she has included in this book. She also explodes several AI myths, such as AI will take over the world or lead towards massive unemployment. She has several recommendations on how to stay on top of AI developments, including continuously upskill your knowledge and tools, become more agile to embrace changes in the field, have the right infrastructure in place to run the models, and keep on top of ethical considerations for its use.