Book review: Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World, by Leslie Valiant.
This book provides some nonstandard perspectives on machine learning and evolution, but doesn’t convince me there’s much advantage to using those perspectives. I’m unsure how much of that is due to his mediocre writing style. He often seems close to saying something important, but never gets there.
He provides a rigorous meaning for the concept of learnability. I suppose that’s important for something, but I can’t recall what.
He does an ok job of explaining how evolution is a form of learning, but Eric Baum’s book What is Thought? explains that idea much better.
The last few chapters, where he drifts farther from his areas of expertise, are worse. Much of what he says there only seems half-right at best.
One example is his suggestion that AI researchers ought to put a lot of thought into how teaching materials are presented (similar to how schools are careful to order a curriculum, from simple to complex concepts). I doubt that that reflects a reasonable model of human learning: children develop an important fraction of their intelligence before school age, with little guidance for the order in which they should learn concepts (cf. Piaget’s theory of cognitive development); and unschooled children seem to choose their own curriculum.
My impression of recent AI progress suggests that a better organized “curriculum” is even farther from being cost-effective there – progress seems to be coming more from better ways of incorporating unsupervised learning.
I’m left wondering why anyone thinks the book is worth reading.