1. Ecorithms. -- 2. Prediction and adaptation. -- 3. The computable: not everything that can be defined can be computed. The Turing Paradigm -- Robust computational models -- The character of computational laws -- Polynomial time computation -- Possible ultimate limitations -- Simple algorithms with complicated behavior -- The perceptron algorithm -- 4. Mechanistic explanations of nature : what might we look for? -- 5. The learnable : how can one draw general lessons from particular experiences? -- Cognition -- The problem of induction -- Induction in an urn -- Error control -- Toward PAC learnability -- PAC learnability -- Occam: when to trust a hypothesis -- Are there limits to learnability? -- Teaching and learning -- Learnable target pursuit -- PAC learning as a basis of cognition -- 6. The evolvable : how can complex mechanisms evolve from simpler ones? -- Is there a gap? -- How can the gap be filled? -- Does evolution have a target? -- Evolvable target pursuit -- Evolution versus learning -- Evolution as a form of learning -- Definition of evolvability -- Extent and limits -- Real-based evolution -- Why is this theory so different? -- 7. The deducible : how can one reason with imprecise concepts? -- Reasoning -- The need for reasoning even with the theoryless -- The challenge of complexity -- The challenge of brittleness -- The challenge of semantics -- The challenge of grounding -- The mind's eye: a pinhole to the world -- Robust logic: reasoning in an unknowable world -- Thinking -- 8. Humans as ecorithms. -- Introduction -- Nature versus nurture -- Naiveté -- Prejudice and rush to judgment -- Personalized truth -- Personal feelings -- Delusions of reason -- Machine-aided humans -- Is there something more? -- 9. Machines as ecorithms : why is artificial intelligence difficult to achieve? -- Introduction -- Machine learning -- Artificial intelligence -- where is the difficulty? -- The artificial in artificial intelligence -- Unsupervised learning -- Artificial intelligence-where next? -- Need we fear artificial intelligence?-- Questions. -- Science -- A more strongly ecorithmic future -- How to act? -- Mysteries.

How does life prosper in a complex and erratic world? While we know that nature follows patterns ââ,¬â€œ such as the law of gravity ââ,¬â€œ 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.