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Best in 7 days
Best in 7 days
Best in 7 days
Best in 7 days
4.5 ★★★★★
Based on 2104 reviews
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Product Reviews
★★★★★ 5
Witty introduction. Not detailed, but technically accurate
Format: Kindle
I loved this book! I am a programmer and I have read several books about AI. This is an introduction for the general public. It does not have many technical details, but it is technically accurate. The writing is clear and understandable. It is also hilarious, with cute illustrations.
I think it has more technical details and more interesting, on-point examples than some reviewers appreciate. They see the jokes and illustrations, and they don't notice how much technical information comes along with them. For example, she describes some fascinating parallels with biological evolution and AI systems discovering method of locomotion on their own, such as making yourself into a tower and then toppling over in the direction you want to go. The author points out this is how some species of grass propagate their seeds, one stem-length, each growing season.
This is a short technical book. I like that! It reminds me of the classic "How to Lie With Statistics," by Darrell Huff, 1954, 142 pages. Everyone who wants to understand statistics should read this. It is also hilarious, it also has cute illustrations, and it is also one of the best introductions to the subject ever written.
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Reviewed in the United States on November 20, 2019
★★★★★ 4
Funny and scary, but with a bit of reassurance
Format: Kindle
Reassuring and disturbing at the same time, this book exposes the flaws and risks of AI, while pointing out the weaknesses that make it incapable of taking over the world.
The examples of AI foolishness entertain and startle. AI's missteps and errors and the real harm they cause provide a warning that is easier to absorb when leavened with the humor.
So what happens if I end this review with a bit of AI-generated predictive texting? Here goes:
This is a story out of India and I am not sure if I can make it to the wrong email address. Please let me know if you have any questions. I look forward to hearing from you.
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Reviewed in the United States on December 1, 2019
★★★★★ 5
Hilarious and Informative
Format: Kindle
This book was a major hit with me - it had me in hysterics at several points while teaching me a lot about machine learning and neural networks on a deep level.
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Reviewed in the United States on April 5, 2026
★★★★★ 5
An adorable book
Format: Paperback
This is an adorable book. I bought it because I needed to know more about AI, and this exemplar seemed perfect to me. And it was.
I had already read several books on AI, dozens in fact, and let's say this is one of the best. First: the tone (the author is very nice); secondly, the drawings (always adding clarity), and thirdly, the scope (it covers all the main topics, including ethical and technological issues).
The problem with books on AI is that some authors begin to talk on AI in a very effective manner, but then, before you realize, they start pontificating on all the evils that it brings with it, and the perverse people behind the scene trying to kidnap your soul (or your money). Believe me, I tremble every time I read on AI because I know what possibly is going to happen after the first fifty pages. This is not the case. Janelle Shane goes to the point, shows you the magic, and the limits of this pervasive science, without painting the horror movie some others make you watch.
Highly recommended for all those interested in passing a couple of days of good reading on AI topics, learning on them, and enjoying a very entertaining author.
Five brilliant stars.
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Reviewed in the United States on October 13, 2023
★★★★★ 5
A fun, accessible introduction to how AI works...and how it sometimes doesn't!
Format: Hardcover
Still relevant despite recent advances in AI-generated imagery and text, because the new systems still work on the same principles as the ones that were around three years ago. They just have a lot more data and processing power. This also means they have the same limitations and blind spots. What was it trained on? *How* was it trained? (This is the most obvious way human bias can leak into an AI model.) How well is the goal specified? And of course, did the AI actually latch onto relevant details, or did it notice that all the training pictures labeled sheep had green fields and blue skies, and completely ignore the actual sheep?
These are things to keep in mind as we enter the landscape of generative AI tools like ChatGPT: You can train an LLM to write a book review, and it'll give you a great piece of text that *reads* like a book review -- but it's not going to have actually evaluated the book. For that, you'd have to train *another* AI to categorize books as good, bad, interesting, dull, and so on. But even that can only be as good as its training data. (I don't remember whether the classic phrase "garbage in, garbage out" is used anywhere in the book, but it still applies today!)
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Reviewed in the United States on April 1, 2023
