Probably, Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World

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 i...

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Bibliographic Details
Main Author: Leslie Valiant
Format: Printed Book
Published: New York Basic Books 2013
Subjects:
Human behavior > Mathematical models. Algorithms. Nature > Mathematical models. Mathematics in nature.
Table of Contents:
  • 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.

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