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 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.
Turing Award winning computer scientist and Harvard professor Valiant introduces readers to "ecorithms," his term for formalized trial-and-error approaches to problem solving that provide valuable insight into everything from evolution to artificial intelligence. His concept a portmanteau of "eco-" and "algorithm" is modeled on the coping mechanisms and adaptations life forms use to survive and thrive. By codifying these processes to be applicable to any environment, Valiant says, researchers can create a "probably approximately correct" (PAC) model for learning that links Darwin's theory of evolution with problems at the heart of computer science. He grounds his hypotheses in solid computational theory, drawing on Alan Turing's pioneering work on "robust" problem-solving and algorithm design, and in successive chapters he demonstrates how ecorithms can depict evolution as a search for optimized performance, as well as help computer scientists create machine intelligence. While Valiant's basic idea may seem obvious to many readers, his book offers a broad look at how ecorithms may be applied successfully to a variety of challenging problems. 17 b&w figures & glossary.