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Leslie Valiant

Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World Hardcover – 4 Jun 2013

by Leslie Valiant (Author)

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From a leading computer scientist, a unifying theory that will revolutionize our understanding of how life evolves and learns.How does life prosper in a complex and erratic world? While we know that nature follows patterns,such as the law of gravity,our everyday lives are beyond what known science can predict. We nevertheless muddle through even in the absence of theories of how to act. But how do we do it?In Probably Approximately Correct , computer scientist Leslie Valiant presents a masterful synthesis of learning and evolution to show how both individually and collectively we not only survive, but prosper in a world as complex as our own. The key is probably approximately correct" algorithms, a concept Valiant developed to explain how effective behaviour can be learned. The model shows that pragmatically coping with a problem can provide a satisfactory solution in the absence of any theory of the problem. After all, finding a mate does not require a theory of mating. Valiant's theory reveals the shared computational nature of evolution and learning, and sheds light on perennial questions such as nature versus nurture and the limits of artificial intelligence.Offering a powerful and elegant model that encompasses life's complexity, Probably Approximately Correct has profound implications for how we think about behaviour, cognition, biological evolution, and the possibilities and limits of human and machine intelligence.

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Product details

Hardcover: 208 pages
Publisher: Basic Books (4 Jun. 2013)
Language: English
ISBN-10: 0465032710
ISBN-13: 978-0465032716
Product Dimensions: 16.5 x 2.2 x 24.1 cm
Average Customer Review: 5 customer reviews
Amazon Bestsellers Rank: 1,125,298 in Books (See Top 100 in Books)
- #743 in Books > Computers & Internet > Computer Science > Algorithms
- #1343 in Books > Computers & Internet > Computer Science > Artificial Intelligence
- #2110 in Books > Computers & Internet > Computer Science > Programming > Introduction to Programming

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"This remarkable book is carefully constructed to give the lay person a sense of subtle problems in mathematics and artificial intelligence, and offers a framework for biologists and computer scientists to use in jointly investigating the most fascinating and enigmatic biological questions."--Marc Kirschner, Chair, Department of Systems Biology, Harvard Medical School, and coauthor of "The Plausibility of Life: Resolving Darwin's Dilemma"
"This book contains a lot of fresh thinking and elegant, nuanced ideas. It is more than probably approximately brilliant. I am amazed by how much insight has been packed into relatively few pages. Anyone interested in computation, learning, evolution, or human nature should find these pages extraordinarily stimulating and informative."--Stephen M. Kosslyn, Founding Dean, Minerva University, and former director, Center for Advanced Study in the Behavioral Sciences, Stanford University
"Ecorithms are algorithms that learn from interaction with their environment. This book provides a theoretical framework for understanding the power and limits of ecorithms and applies it to human cognition, biological evolution and artificial intelligence. It is elegantly written and will be accessible to a wide circle of readers."--Richard Karp, Turing Award winner and director, Simons Institute for the Theory of Computing, University of California, Berkeley
"This little book is hugely ambitious. It takes on the task of creating a quantitative, mathematical theory to explain all essential mechanisms governing the behavior of all living organisms: survival, learning, adaptation, evolution, cognition and intelligence. The suggested theory has all the characteristics of a great one. It is simple, general, and falsifiable, and moreover seems probably, approximately, correct!" --Avi Wigderson, Nevanlinna Prize winner and Professor of Mathematics, Institute for Advanced Study, Princeton
"The quest for machines (and codes) that ne

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A leading computer scientist shows why understanding computation is the key to understanding life

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SimpleSimon

Explains how evolution computes, but read it back to front

24 February 2017

Format: PaperbackVerified Purchase

In summary, I found this book to be full of powerful and exciting ideas, so worth the 5*'s. But it's not a perfect book. I found it quite hard going. I felt that he had in mind as his audience someone rather like himself - a top mathematician who is totally versed in the subject. He rarely loosens up and engages with the laity, like me, so I had to do more work (not a bad thing, necessarily).

Leslie Valiant sets out to show that it is computationally feasible for life to evolve its amazingly complex solutions to problems of survival, given the time (3.5bn years) and resource available. He shows, using a lot of maths concepts, that at the heart of evolution and learning there must be a relatively simple computation performed in a relatively simple processor that enables life to learn from examples and ultimately create sophisticated solutions that work (or persist). He explains that unlike computer-based computations, which are logical and algorithmic, life's computations are 'theory-less' in that they cannot apply generally outside the domain they have been learned in; they work tolerably in those domains - well enough for survival; they get better (fewer errors) when needed by adding learning from more examples (they adapt) - so they are robust in that they can be improved if needed and fail gracefully; the solutions aren't necessarily logical but they are powerful because the computation can powerfully chain solutions in a form of reasoning. This model can apply not just to evolution of persistent life forms but also to learning persistent ideas (knowledge). He calls these algorithms, ecorithms.

Along the way he offers all sorts of delightful insights, such as that life works on problems as if it was 'decrypting' the key to the answer.

Like many mathematicians, Valiant wants to show us the working before telling us the answer. I'd suggest reading this book back to front. Start with the Glossary to understand the language, then look at the notes to get an idea of the context, then go to section 7.7. where he finally reveals the exciting answer on how computation is done by evolution. Now you are ready to be told the story.

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JoeBloggs

A diificult but rewarding read

15 December 2018

Format: Kindle EditionVerified Purchase

Before going any further I must declare an interest as I went to school with the author, though that was 55 years ago and we have not kept in touch.
Despite difficulties in understanding quite a bit of the book I have, in common with some other reviewers, given it 5 stars for the various fascinating parts that I could follow.
The book covers a variety of topics including the Turing machine and complexity theory but I take the two main subjects to be computer learning (or Artificial Intelligence), and a theory of evolution based around genetic learning.
Humans learn and can perform complex operations without having any knowledge of an underlying theory of how something works – we do not need defined algorithms. Computers however need defined rules before they can do anything. Professor Valiant’s method of PAC (see the book title) is a way of looking at learning without having an exact formula or algorithm to get to an answer. I broadly understood the concept of PAC but can’t say I got any idea of how it could actually be used for computer learning.
Moving on from computers Professor Valiant applies PAC to the method of evolution.
Darwin’s Natural Selection, i.e. the stronger of random mutations winning out, has been challenged by Intelligent Design where some external agent, whether alien or God has influenced the development of DNA.
Professor Valiant maintains that the probability of the creation of DNA and subsequent evolution happening by chance is not mathematically justifiable.
He proposes that the evolution of DNA is controlled neither by chance nor by external design but by a learning feedback method he calls ecorithms, and he applies PAC to this.

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Kaz

Not elegant, but stimulating.

18 October 2015

Format: PaperbackVerified Purchase

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.

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GordanaDodig

Excellent book on computing nature!

3 November 2013

Format: HardcoverVerified Purchase

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.

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