Why Machines Learn: The Elegant Math Behind Modern AI
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✔️ File: PDF 77,71 MB • Pages: 517
A rich, narrative explanation of the mathematics that has brought us machine learning and the ongoing explosion of artificial intelligence
Machine learning systems are making life-altering decisions for us: approving mortgage loans, determining whether a tumour is cancerous, or deciding whether someone gets bail. They now influence developments and discoveries in chemistry, biology, and physics—the study of genomes, extra-solar planets, even the intricacies of quantum systems. And all this before large language models such as ChatGPT came on the scene.
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. In this enlightening book, Anil Ananthaswamy explains the fundamental math behind machine learning, while suggesting intriguing links between artifical and natural intelligence. Might the same math underpin them both?
As Ananthaswamy resonantly concludes, to make safe and effective use of artificial intelligence, we need to understand its profound capabilities and limitations, the clues to which lie in the math that makes machine learning possible.
3 reviews for Why Machines Learn: The Elegant Math Behind Modern AI
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Craig Hicks –
Anil’s storytelling added human faces to many names I was already familiar with, but only in an abstract way. That’s the history part, written in a very personal and engaging way that only a good writer can do. At the same time the history of the development of ML theory is complete and expounded upon in enough detail that anyone with college level math abilities could follow along if so desired. (I expect many will skip some of those parts either because they know it or they don’t need to know it. Perhaps those sections could be better sectioned to enable skipping.) Finally he asks very good questions about the nature of intelligence and how AI does or does not overlap with human intelligence, and well as the dangers it poses and benefits it may offer.
The way the author maintains the big picture while leading the reader through a “live” minute-by-minute narration of compelling details reminds me of the style of VS Naipal, despite being a completely different genre.
Benjamin P Enfield –
I had been working through other explanations of AI, but kept finding that I had forgotten too much of my college math to be able to fully understand the explanations. This book does an excellent job at providing explanation at the right times so I can understand each building block of the math behind AI.
It is written both as a history, and as a set of mathematical building blocks so the reader gets a person focused understanding of the history of AI, and the reader gets to re-develop their math skills from the ground up to understand (abtracted) technical details of AI.
I’d recommend this as the go-to book on AI for anyone who took multi-variable calculus in college (but forgot it in the intervening years).
A M –
It is not clear who is supposed to be the reader of this book. It explains the mathematics, starting with essential calculus, and goes on to the formulas of deep learning. It is too tricky for readers unfamiliar with calculus and redundant to those familiar with it. I mainly enjoyed reading the last chapter about the current challenges of deep learning.