3 of 3 people found the following review helpful
on 3 November 2013
Recently an idea of computing nature started to take prominent place among philosophers and theoreticians of computing. This is a highly informative book by leading computer scientist framing the topic of computing nature into his central thesis of PAC (Probably Approximately True) learning algorithms. Learning (that is ability of adequate adaptation) is central idea and it is based on the awareness of the physical constraints learning system has - it is never infinite or equipped with perfect information.
Valiant coined the term "ecorithms" for the computational rules which help a system to learn through interactions with the environment. The book relates adaptation with learning (PAC), evolution and cognition in a common naturalistic computational framework. Recommended to anyone interested in how nature works and how its functioning relies on computational strategies.
on 18 October 2015
Some of the comments which complain about style are probably fair. This is what can happen if a book is written by a professional, rather than a professional author. On the other hand, I found this to be one of the most stimulating books I've read all year. I do some machine learning for a living, but there's a difference between understanding how some algorithms work, and understanding why it is even possible to learn in the first place. This book is great at generating "aha" moments. Books that do that are rare, hence my five stars. However, be prepared to go elsewhere if you want to follow up on those insights. This is not an exhaustive textbook on PAC theory.