Why Machines Learn: The Elegant Maths Behind Modern AI

by Anil Ananthaswamy

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 discoveries in chemistry, biology and physics - the study of genomes, extra-solar planets, even the intricacies of quantum systems.

We are living through a revolution in artificial intelligence that is not slowing down. This major shift is based on simple mathematics, some of which goes back centuries: linear algebra and calculus, the stuff of eighteenth-century mathematics. Indeed by the mid-1850s, a lot of the groundwork was all done. It took the development of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see all around us today. In this enlightening book, Anil Ananthaswamy explains the fundamental maths behind AI, which suggests that the basics of natural and artificial intelligence might follow the same mathematical rules.

As Ananthaswamy resonantly concludes, to make the most of our most wondrous technologies we need to understand their profound limitations - the clues lie in the maths that makes AI possible.

• ISBN10: 1802060871
• ISBN13: 9781802060874
• Publish Date: 16 July 2024
• Publish Status: Forthcoming
• Publish Country: GB
• Publisher: Penguin Random House India
• Imprint: Penguin
• Format: eBook (EPUB)
• Language: English

Reviews

[annieb123]

Originally posted on my blog Nonstop Reader.

Why Machines Learn is a fascinating, thorough, and semi-accessible monograph on AI and how machine learning algorithms are changing -everything- in modern society, meticulously delineated by Anil Ananthaswamy. Due out 16th July 2024 from Penguin Random House on their Dutton imprint, it's 480 pages and available in hardcover, audio, and ebook formats. It's worth noting that the ebook format has a handy interactive table of contents as well as interactive links throughout. The ebook format's search function is also helpful for finding info and references (recommended). 

The author does a very good job covering the background and development of machine learning and relates a lot of the human history and key players from the 50s to the current day. It's a timeline with an ever increasing pace and he draws a clear line from the creeping forward progress to the hurtling (scary) pace of the current day. 

Oddly enough, the author doesn't speculate about the near (or far) future of AI and machine learning, and his insights would've been a valuable addition to the book. The specific mathematics included in the book were at an odd level as well; too simple for people conversant with the material, and probably too complex for non-math-inclined laypeople. 

Although written in mostly accessible, non-rigorous language, it's meticulously annotated throughout, and the chapter notes will provide readers with many hours of further reading.

Four stars. Definitely a niche book, but well written and interesting. It would be an excellent choice for public library or post-secondary library acquisition, for home use, or possibly as adjunct/support text for more formal math/science history classes at the post-secondary level. 

Disclosure: I received an ARC at no cost from the author/publisher for review purposes