Book Review: Why Machines Learn: The Elegant Math Behind Modern AI

If you wondered about the history of machine learning and what all is behind it, you would not go wrong by selecting the book by Anil Ananthaswamy. His book is now a bestseller on Amazon, and I do understand why. The book is "Why Machines Learn: The Elegant Math Behind Modern AI."It offers a rich narrative explanation of the mathematics that has brought us machine learning.

The text is heavy on mathematics, so if that is not your cup of tea, you should browse the book before buying it. I bought it as I wanted to dig into some of the logic behind machine learning when viewed from a mathematical perspective. The Amazon description gives a good background on why this book is important, and it states the following:

We are living through a revolution in machine learning-powered AI that shows no signs of slowing down. This technology is based on relatively simple mathematical ideas, some of which go back centuries, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. It took the birth and advancement of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see today.

The book is essential because it provides a rich, narrative explanation of the fundamental mathematics that underpin the current AI revolution. It demystifies complex concepts like linear algebra, calculus, probability, and statistics, which are the mathematical foundations of machine learning, making them accessible to readers with a basic math background. The book also situates these mathematical ideas within their historical and social contexts, showing how AI technologies evolved through collaborative scientific progress. This approach helps readers understand how AI works and its profound capabilities, limitations, and real-world impacts on fields such as healthcare, finance, and criminal justice.

If you ask yourself, what would be the top five reasons why you should be interested in this book, they are as follows:

• Clear Explanation of Complex Math: The book breaks down intricate mathematical concepts behind machine learning algorithms like support vector machines, neural networks, and principal component analysis in an accessible way without oversimplifying.
• Historical and Social Context: It weaves the history and stories of key AI figures, making the technical material more engaging and showing the collaborative nature of AI development.
• Insight into AI Capabilities and Limitations: The author discusses ethical dilemmas, biases in AI, and the risks of overreliance on machine decisions, encouraging a balanced understanding of AI's role in society.
• Comprehensive Coverage: From the earliest perceptrons to modern deep learning and large language models, the book offers a broad overview of machine learning’s evolution and current state, suitable for both newcomers and those wanting to deepen their knowledge.
• Engaging and Well-Researched: Ananthaswamy’s writing is praised for clarity, illustrative examples, and integration of interdisciplinary insights from neuroscience, physics, and biology, making the book both informative and enjoyable.

Readers and reviewers generally regard "Why Machines Learn" as a highly informative and well-written introduction to the math behind AI. It is praised for making challenging material understandable and connecting mathematical theory with practical applications and ethical considerations. Some note that the book contains more math than typical popular science books, which may intimidate readers without some familiarity with linear algebra or calculus. Still, this depth is appreciated by those seeking a serious understanding.

Critics mention that while the book thoroughly covers traditional machine learning, it provides only a more superficial treatment of the latest deep learning innovations and generative AI techniques like word2vec or the role of randomization in training deep networks. The book leans towards theoretical justifications and thus may underrepresent some experimental aspects of modern AI.

The book is a solid, inspiring resource for anyone interested in the elegant mathematics driving AI. It offers both technical insight and a thoughtful perspective on the technology’s impact and future. This balanced and detailed approach makes "Why Machines Learn" a valuable read for students, educators, AI enthusiasts, and professionals who want to understand modern artificial intelligence's mathematical elegance and societal implications.

It is fascinating how fast the AI space is moving, and my passion for continuous learning is deep and has always been deep. There is so much fascinating material for us to consume. The question is about focus and trying to filter the information to the most relevant, as no human being can learn and remember everything. That is why AI and these large language models have completely changed our view of learning, as we always have the information at our fingertips.

From a behavioral perspective, when did you go to a regular search engine to look for information when researching something? Right, I am sure it is a while for you, it is at least for me. I no longer have to think and see those sponsored links; I get my information from LLMs with resource links. That is why Perplexityhas become my AI tool of choice when doing write-ups and research.

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Yours,

Dr. Petri I. Salonen