Locator: 50551AI.
Notes on this book are kept here.
If you can read at college level, which generally means the ability to read at some level of the average high school junior, you should be able to slog your way through Anil Aanthasway's book even if you know no mathematics beyond your middle school years.
The narrative was excellent. Very, very easy to read, though as one gets deeper and deeper into the book, the jargon becomes as difficult as the math.
Even so, one can learn much about AI, certainly more than where you started. It's very similar to putting up a Christmas tree, and gradually adding ornaments. Or, similarly, putting up scaffolding to build a complex structure, like, say, the Egyptian pyramids.
Keeping with the Christmas tree ornaments analogy, which is a much better analogy than the pyramid scaffolding, you can keep adding ornaments as you read additional newspaper articles, magazine essays, and books on the subject. Without question, the best ornaments will be added after you spend evening dinners and/or cocktail hours with AI engineers at any level. The jargon alone is worth the price of admission.
And Ananthaswamy's book is a great introduction to AI jargon.
The math was way beyond anything I could follow. But one can scan through those pages. I don't think you want to literally skip any page with math on it because in between the formulas there is likely to be some jargon, some explanation, some context.
Names of pioneers in this field and the universities and countries from which they come were some of the best Christmas tree ornaments. You could, for example, put Geoffrey Hinton at the top of the tree. A lot of those pioneers at age 17 a few years ago are now CEOs or chief engineers at famous AI corporations and making more money than I ever made and will have more impact on humanity than I ever will.
What we now know about what we don't know about AI is absolutely fascinating. Some say scary. Luddites will ban AI from their homes.
The anecdotes about what AI engineers are learning is absolutely fascinating. The best analogy is our discovery and/or [lack of] understanding of quantum theory with the "breakthrough" in 1925 - 1926. One needs to read Richard Feynman's supposed quote on one's understanding of quantum mechanics. But despite that, researchers pressed on. It was a dual track: theorists thinking while smoking pipes and laboratory physicists screwing clamps to their laboratory desks. We are the same spot with regard to AI.
There are two schools of thought: some feel the theory must be worked out before we press on with AI (that won't happen). Others feel that regardless of the theories, we must keep pressing on. Obviously, we will do both.
At the end of the book, I can say this is best I've read on the subject so far. It is a great jumping off point for me. It becomes a reference book to re-read.
