Jul 17, 2019
Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World
Posted by Leslie Valiant

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 theoriesFrom 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 behavior 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 behavior, cognition, biological evolution, and the possibilities and limits of human and machine intelligence.

  • Title: Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World
  • Author: Leslie Valiant
  • ISBN: 9780465032716
  • Page: 428
  • Format: Hardcover
  • Probably Approximately Correct Nature s Algorithms for Learning and Prospering in a Complex World 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 na


    This book introduces a very interesting idea – what the author calls ecorithms. An algorithm is a step-by-step instruction set to achieve the exact desired result in a controlled environment. An ecorithm, by contrast, is run in an environment unknown to the designer and it can interact with the environment and learn from it. Valiant postulates that a lot of natural phenomena, such as evolution and human cognition and behavior, are based on ecorithms. The book didn't really deliver for me. It's [...]


    There is no greater satisfaction for me to read a book that so nicely bridges corner stone of computer science which is computational complexity, learning systems, human cognition and evolution. Putting forward ideas that are indeed fascinating and enlightening and relieving us from great burden caused by being lost in this world with so many mysteries. For me, I hope, most of these ideas present itself ultimately as a mode of life in which one is more informed and humble but at same time focuse [...]


    I was surprised to see a non-technical book on such a rather arcane and technical subject (though with rich implications in many areas).The book gives a decent low-tech introduction to PAC learning, but if I have to make one complaint Leslie Valiant is not really an engaging writer (or a writer not interested in being engaging) - his examples and approach is extremely dry (you know drawing balls from urns etc.)He even manages to introduce the perceptron in the most boring manner. I'd say it is a [...]

    Aaron Terrazas

    Fascinating concept and several interesting parts, but a lot in the weeds.My favorite quote comes early on:"Much of everyday human decision making appears to be based on a competent ability to predict from past observation without any good articulation of how the prediction is made or any claim of fundamental understanding of the phenomenon in question. The predictions need not be perfect or the best possible. They need merely to be useful enough." (p 8)

    Zhaodan Kong

    Disclaimer: I finish the book in a period of a few months. My memory may not be perfectly accurate on such an occasion. So please check other peoples' comments for more serious reviews.I would say the key to this book is "Ecorithm", a term the author coins to define the algorithms that animals and humans may use to adapt to the environments that they reside in. The adaptation can happens in a larger time scale (evolution) or a smaller time scale (learning). Thus, from such a perspective, evoluti [...]


    Computer Science is no more about computers than astronomy is about telescopes.Nell’accezione comune l’informatica è vista solo come quella tecnologia che permette di scrivere documenti, preparare presentazioni, ritoccare foto e spedirle in tempo reale in giro per il mondo. In realtà, l’informatica è una scienza (non a caso, in inglese è denominata computer science) e probabilmente negli ultimi 50 anni è stata la più prolifica delle scienze e quella che ha fornito i maggiori contribu [...]


    Can't give a star rating because I was in so far over my head. Will put a few definitions down in case I come across them again in reading about machine learning or something related to Alan Turing. Ecorithm - algorithm that takes information from its environment so as to perform better in that environment. Algos for machine learning, evolution, and learning for the purpose of reasoning are all examples. Theoryless - denotes decisions for which there is not a good explanatory and predictive theo [...]

    Max Shen

    A challenging read that above all stays faithful to the discipline and integrity of academia to the sacrifice of wider accessibility. Nevertheless, the ideas are truly thought-provoking, the perspectives and paradigm of thinking quite novel and enlightening, and due to Valiant's ever-present rigor, meaningful and concrete.If you are the type to appreciate an understated yet subtly powerful and rigorously built idea over exaggerated could-be's and fanciful speculations dressed up in scientific wo [...]

    David Wiley

    If you're interested in how people learn, you will definitely enjoy this book. It presents an interesting view on learning and how it emerges from interactions with the environment. There's a lot in this book to appreciate in terms of developing a better understanding of learning. I found myself agreeing frequently - but not always - with the author.

    Alexander Swenson

    Wonderfully dry prose that sandwiches some mindblowing ideas to the uninitiated. Anyone who shares Chomsky's crabbiness about the rise of probabilistic models should read this as a detente for theoryful/theoryless science and our impending theoryful/theoryless world.


    A decent introduction to PAC learning. Light on technical details and the less sciency chapters near the end aren't that compelling.

    Shubhendu Trivedi

    -- "Biological evolution is a form of Computational Learning" Popular Science version -- The punchline of this book is perhaps: "Changing or increasing functionality of circuits in biological evolution is a form of computational learning"; although it also speaks of topics other than evolution, the underlying framework is of the Probably Approximately Correct model from the theory of Machine Learning, from which the book gets its name.I had first heard of this explicit connection between Machine [...]

    Debasish Ghosh

    Very interesting theory, but slightly prosaic read.


    An argument that constraints on algorithms are critical in understanding evolution and learning.The book takes us from a discussion of evolution's lack of detail as an algorithm, to discussions on computability ("his [Turing's] importance demands comparison with that of Issac Newton", p. 28), polynomial time, P ≠ NP, and the balance between algorithm power and what can be computed (or evolved) in practice and in principle. A useful introduction to the importants of constraints on algorithms, a [...]


    I found this piece very intriguing. My favourite chapter had to be the one on trying to quantifying human behaviour, particularly the theoretical “mind’s eye” that allows for humans to create an opinion. The “mind’s eye” acts as a filter between the observed world and what we commit to memory. Going into this book I had basically no previous knowledge on the development of artificial intelligence. Movies that depict robots that mimic humans perfectly, seem even more in the realm of [...]

    Stephen Lee

    Skip if you are familiar with Computer Science and/or machine learning. I can't judge how good it is as an introduction to either.

    Bill Pritchard

    The score I gave to "Probably Approximately Correct" is more a reflection of my lack of knowledge than the qualities of the book. There are times when you may be suggested to read a book and find that the material is way "above your paygrade". Leslie Valiant is a professor of Computer Science and Applied Mathematics at Harvard. He is the Nevalinna Prize winner from the International Mathematical Union. He is obviously extremely qualified to speak of the Probably Approximately Correct Algorithms [...]


    A fascinating call to action about trying to explain the gaps in the theory of evolution with computer algorithms. It is illuminating as to the gaps in evolution, which are ignored by some quarters, and exploited by others for an explanation by magical forces. It is not a clarion call to the mystical, but it is admirably humble about the subject of the "theoryless" aspects of evolution and intelligence. It contains an interesting twist through some computer science concepts which will be unfamil [...]


    Inspiring look at Valiant's view of evolution as a particular type of algorithm. He explains how he'd like to prove evolution quantitatively (as opposed to qualitatively). His outlook is very abstract, which for me--a reader not too familiar with theoretical computer science--offered many new perspectives on how to take complex systems and categorize them based on complexity classes from computer science. The book, however, does more to explain and promote new thinking than offer any new solutio [...]


    The key learning for me is that we do not need to be afraid that computers might take over from humans in future. Though the so called intelligence and the processing speed of the computers is unbelievably high and growing like never before, they are likely to be subservient to us. There is a wealth of knowledge and algorithm based theory that is pioneered in this book and though the reading is laboured due to the dry style of writing, one can start understanding the implications if one persists [...]


    "It is a book that is changing how I think about everyday things, education, and especially legal theory. It connects machine learning, artificial intelligence, and evolutionary theory. Among other things, it’s a terrific way to see why the new generation finds computer science the field to study." - Saul Levmore


    I liked the questions and ideas from the book, and also the presentation of the PAC learning from its author. But I doubt this book can be enjoyed by a reader who has never heard of machine learning. (S)he would probably close it very early not even getting to more interesting chapters. Anyways, the main proposal of the book is about marrying supervised learning with evolutionary biology, and it sounds like a whole new exciting field can emerge as a result.


    This book is interesting if you have at least some background in computer science and discrete math/logic, and a basic understanding of the theory of evolution. Be warned that it mostly reads like a doctoral thesis - don't expect a ton of watered-down explanations or definitions for the general reader.

    Alexi Parizeau

    Excellently written with a passion for the subject that's contagious (at least to me!). I'd say for a general audience it would also be considered easy to understand since it had little in terms of technical distractions. There was also enough in the Notes section to get me started on the key breakthroughs in Learning Theory.[First Reading: April 5-6, 2015]

    Faust Mephisto

    The writing is a bit dry but overall an informative book. It touches on some very interesting issues, like the Bayesian statistical component of evolution and the associated questions of evolutionary learning and memory.


    This book is dense on mathematics, complex conceptually but in all places lucid. It challenges the reader but offers rewards as well. The notion of PAC learning applied to evolution in particular is quite interesting. Approach the book with caution however, it's quite demanding.


    Recommended by C, but not in lib


    May be researchers should just refrain from writing pop-sci books.

    Timothy Corbett-Clark

    Disappointing following a really interesting start.


    Had high hopes. Book stayed in the details, never evolving them to a larger theory that could be used or applied. Got bored. Stopped reading.

    • Free Read [Classics Book] ↠ Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World - by Leslie Valiant ↠
      428 Leslie Valiant
    • thumbnail Title: Free Read [Classics Book] ↠ Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World - by Leslie Valiant ↠
      Posted by:Leslie Valiant
      Published :2019-03-06T14:05:52+00:00