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Author: Leslie Valiant
ISBN : B00BE650IQ
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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.
Download latest books on mediafire and other links compilation Free Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World
- File Size: 639 KB
- Print Length: 210 pages
- Page Numbers Source ISBN: 0465032710
- Publisher: Basic Books (June 4, 2013)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B00BE650IQ
- Text-to-Speech: Enabled
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- Lending: Not Enabled
- Amazon Best Sellers Rank: #72,688 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Free Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World
First, the Good: The author introduces a few ideas that are tasty, like the idea of evolution as computation. This notion suggests evolution as a phenomenon in which Nature (to anthropomorphize) explores possibility space. Or, the introduction of a few challenging (to be charitable) notions--evolution as actually goal-directed (in a way), evolution NOT acting on populations, etc. There is also a nice description of P/NP problems (indeed, the first part of the book is strongest). Finally, there is confrontation with that great bugaboo of science philosophy, the Problem of Induction--even more important in the age of Big Data (a phenom now in its "Screw causation, all is correlation! Yippee!" adolescent part of the Hype Cycle. Sigh.)
The Bad: None of these ideas are really developed, much less justified. From an evolutionary science POV, what he is saying is rather provocative (one thinks..see below) but never defended. Contrast with Dawkins' fantastically lucid descriptions of evolutionary mechanisms--this author's do not compare.
The Ugly: after a while, the prose is simply unreadable. The effect is a little hard to describe, but it seems that the author can't find his theme (or cannot show it to us), and cannot BUILD his ideas. In other words, he doesn't take a central idea, build it up, repeat the essentials (to keep us oriented) and push those elements out into concretes for illustration. Even worse, in trying to straddle some path between using math to show and not using math so as to avoid spooking, there is both too much and too little math.
Worst of all: There is no clear, explicit definition of what a PAC algorithm is--there is a very light introduction to venerable machine learning algorithms (e.g.
In short: whether you're a computer scientist familiar with machine learning algorithms, or whether you don't know much about artificial intelligence, this book has profound and novel insights to offer. I've been a practitioner of machine learning for a long time, and yet the book's framework relating machine learning to evolution gave me a whole bunch of "aha" moments. So pick it up and give it a read.
The book's thesis in a few words: cognitive concepts are computational, and they are acquired by a learning process, before and after birth. Nature, the grand designer, uses ecorithms to guide this process - systems whose functioning and whose parameters are learned and evolved, as opposed to written down once (like algorithms). The processes of learning, evolution and reasoning are the building blocks of ecorithms.
This, in and of itself, is not a new framework. Open any artificial intelligence textbook, and the table of contents will be organized into algorithms for "learning" and "reasoning". So nothing new there. But then, the book launches into an excellent, simple and mind-blowing thought experiment: what if nature were simply relying on the same simple learning algorithms that we as humans have been researching, with the same constraints - and evolution is just that formal learning process in action? And then: given all we know about the parameters of these learning algorithms, would evolution have been mathematically possible?
To answer that, the author goes into some detail on computational complexity theory. Computer science has shown that there are many seemingly simple processes that aren't solvable in polynomial time - meaning, if you make them big enough, solving them will take longer than the universe existed.