Why Machines Learn

The Elegant Math Behind Modern AI

De: Anil Ananthaswamy

Narrado por: Rene Ruiz

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.

*This audiobook contains a PDF of equations, graphs, and illustrations.

Great history, hard to digest via audiobook

A lot of cool moments in the past century have led to AI’s current state, and this book does a great job capturing those moments with a glimpse into the math behind the breakthroughs. However, if you’ve taken ML/AI courses, you have already encountered a majority of the content in a more applied manner than the high-level proofs included here. An interesting read undoubtedly, but not the groundbreaking guide you might be looking for to understand & reverse-engineer why machines learn for your own career.

Great book

Very in-depth. Following the pdf is a must if you want to understand the math, but not if you want the general ideas and the stories behind them.

Great listen, but keep the PDF handy.

There is a lot of math, so pay attention. I found I needed to reference the PDF throughout the book if I really wanted to understand what the chapter was trying to say.

The math made it a cumbersome listen

The math made it a cumbersome listen. Not particularly complex math but the math got in the way of grasping the narration. I think I have a better understanding of the logical basis of artificial intelligence.

A great listen, but a physical book is pre appropriate

I enjoyed all the technical details and history. When the times came to look at a pdf, I wish I also had the physical copy.