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 patternssuch as the law of gravityour 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.
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About the Author
Leslie Valiant is the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University. He is a Fellow of the Royal Society and a member of the National Academy of Sciences. He is a winner of the Nevanlinna Prize from the International Mathematical Union, and the Turing Award, known as the Nobel of computing.
Table of Contents
2. Prediction and Adaptation
3. The Computable
4. Mechanistic Explanations of Nature
5. The Learnable
6. The Evolvable
7. The Deducible
8. Humans as Ecorithms
9. Machines as Ecorithms
Most Helpful Customer Reviews
This book changed the way I think about adaptation. Profoundly useful.
The book is a difficult read with some fairly difficult mathematical definitions and only limited examples. I was hoping for a closer tie with actual subsets of genes and proteins that could illustrate his PAC models. Cited though not really explored was Bayesian learning. (apriori knowledge used to help aposteriori knowledge) e.g. as it applies to the brain as it economizes and optimizes muscle coordination. However, the book's well-researched presentation seems as complete as the author chose to take it.