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Probably Approximately Correct
Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World | Leslie Valiant
1 post | 1 read | 1 to read
We have effective theories for very few things. Gravity is one, electromagnetism another. But for most things—whether as mundane as finding a mate or as major as managing an economy—our theories are lousy or nonexistent. Fortunately, we don't need them, any more than a fish needs a theory of water to swim; we're able to muddle through. But how do we do it? In Probably Approximately Correct, computer scientist Leslie Valiant presents a theory of the theoryless. The key is “probably approximately correct” learning, Valiant's model of how anything can act without needing to understand what is going on. The study of probably approximately correct algorithms reveals the shared computational nature of evolution and cognition, indicates how computers might possess authentic intelligence, and shows why hacking a problem can be far more effective than developing a theory to explain it. After all, finding a mate is a lot more satisfying than finding a theory of mating. Offering an elegant, powerful model that encompasses all of life's complexity, Probably Approximately Correct will revolutionize the way we look at the universe's greatest mysteries.
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swynn
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Mehso-so

This is a discussion of the idea and applications of a machine learning model. Valiant argues that PAC can be generalized to study human learning, cognitive development, and biological evolution. Alas, the discussion is not on a popular level and I did not always follow his arguments, nor could I judge their validity. But to the extent I could, I found it a useful grounding for thinking about how we got here & how we keep going.

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