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Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World, by Leslie Valiant
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From a leading computer scientist, a unifying theory that will revolutionize our understanding of how life evolves and learns.
How does life prosper in a complex and erratic world? While we know that nature follows patterns—such as the law of gravity—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.
Offering a powerful and elegant model that encompasses life's complexity, Probably Approximately Correct has profound implications for how we think about behavior, cognition, biological evolution, and the possibilities and limits of human and machine intelligence.
- Sales Rank: #510219 in Books
- Brand: Basic Books AZ
- Published on: 2014-11-14
- Released on: 2014-11-14
- Original language: English
- Number of items: 1
- Dimensions: 8.13" h x .63" w x 5.45" l, .87 pounds
- Binding: Paperback
- 208 pages
- Basic Books AZ
Review
“Computer scientist Leslie Valiant celebrates Alan Turing as the progenitor of a third scientific revolution, potentially as profound as Newton's and Einstein's in transforming our understanding of the world. Why not a ‘fourth revolution'—why omit Darwin? Because, Valiant dares to say, Darwin's theory is radically incomplete, and until it is equipped to make quantitative, verifiable predictions, evolution by natural selection cannot account for the complexity of living things and is not ‘more than a metaphor.' But Valiant offers no drop of succor to creationists. Rather, he seeks to arm neo-Darwinian theory against their onslaughts by elucidating the mechanistic, quantitative basis it must have in a world ‘without a designer.' The algorithms of computational learning theory, he posits, will be key—in particular, a special kind he calls ‘ecorithms,' which incorporate information gathered from the environment to improve an organism's ‘performance.' Turing's heirs have only just begun to plot its equation.”
—The Scientist
“This remarkable book is carefully constructed to give the lay person a sense of subtle problems in mathematics and artificial intelligence, and offers a framework for biologists and computer scientists to use in jointly investigating the most fascinating and enigmatic biological questions.”
—Marc Kirschner, Chair, Department of Systems Biology, Harvard Medical School, and coauthor of The Plausibility of Life: Resolving Darwin's Dilemma
“This book contains a lot of fresh thinking and elegant, nuanced ideas. It is more than probably approximately brilliant. I am amazed by how much insight has been packed into relatively few pages. Anyone interested in computation, learning, evolution, or human nature should find these pages extraordinarily stimulating and informative.”
—Stephen M. Kosslyn, Founding Dean, Minerva University, and former director, Center for Advanced Study in the Behavioral Sciences, Stanford University
“Ecorithms are algorithms that learn from interaction with their environment. This book provides a theoretical framework for understanding the power and limits of ecorithms and applies it to human cognition, biological evolution and artificial intelligence. It is elegantly written and will be accessible to a wide circle of readers.”
—Richard Karp, Turing Award winner and director, Simons Institute for the Theory of Computing, University of California, Berkeley
“This little book is hugely ambitious. It takes on the task of creating a quantitative, mathematical theory to explain all essential mechanisms governing the behavior of all living organisms: survival, learning, adaptation, evolution, cognition and intelligence. The suggested theory has all the characteristics of a great one. It is simple, general, and falsifiable, and moreover seems probably, approximately, correct!”
—Avi Wigderson, Nevanlinna Prize winner and Professor of Mathematics, Institute for Advanced Study, Princeton
“The quest for machines (and codes) that never make mistakes was only a first step toward machines (and codes) that learn from them. Leslie Valiant's Probably Approximately Correct is a detailed, much-needed guide to how nature brought us here, and where technology is taking us next.”
—George Dyson, author of Turing's Cathedral and Darwin among the Machines
“[Probably Approximately Correct] really shines as an introduction to computer science theory to the general public, providing a compact and accessible description of basic, important results … . This is a book that should be on every computer scientist's shelf so that when someone asks, ‘Why is computer science theory important?' the three word response can be, ‘Read this book.'”
—SIGACT News
“A scholar at the intersection of computing and evolutionary neuroscience, Valiant explores ‘ecorithms': algorithms that learn by interacting with their environment, not from their designer—and so are fundamental to the process of evolution. His text is clear and approachable, with some work; the argument is sweeping.”
—Harvard Magazine
“[Valiant's] major point is that Darwin introduced evolution by natural selection, but the detailed mechanism is still sketchy. A hundred years of genetics has filled in some details, but Valiant sees the computational details as essential and still missing. The goal of this book is to encourage further research and to set a paradigm for such work. As Erwin Schrödinger's What Is Life? inspired those who built molecular biology, Valiant hopes to inspire those who will build a future computational biology. Highly recommended.”
—Choice
“Insightful.... This is science at its best, driven not by dogma and blind belief, but by the desire to understand, intellectual integrity and reliance on facts.... The book is written in a lively, accessible style and is surprisingly entertaining. It's funny how your perception of even mundane tasks can change after reading it—you start thinking algorithmically, confirming Dr. Valiant's maxim that computer science is ‘more about humans than about computers.'”
—New York Times
“[A]n engaging meditation on complexity and on how living things often unwittingly use math to navigate it.”
—Scientific American
About the Author
Leslie Valiant is the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University. He is a Fellow of the Royal Society and a member of the National Academy of Sciences. He is a winner of the Nevanlinna Prize from the International Mathematical Union, and the Turing Award, known as the Nobel of computing.
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6 of 6 people found the following review helpful.
Evolution is not a form of learning: learning is a product of evolution
By Tim Tyler
This book is by a machine learning expert. He in interested in models of learning and particularly their assessment in terms of computational complexity theory. It considers seriously the role of evolution itself in the context of knowledge acquisition processes. The book argues that evolution is a subset of learning processes.
Overall, the book is a reasonable one. However, the presentation is a bit dry and boring. The author apparently likes coining terms, and dislikes reviewing the work of others. As Leslie says, there is indeed a close link between the theories of evolution and learning. He correctly argues against the modern dogma of directionless evolution (since evolution and learning are linked and learning is clearly directional). Leslie argues that "fitness" provides such a direction. In fact a much stronger case than the one Leslie gives can be made - based on thermodynamics.
Overall, I am inclined to think that the book has its core thesis backwards. Instead of evolution being a subset of learning processes, learning processes are part of evolution. Most of the rest of this review focuses on this one point, because I think it is an important one.
The idea that learning is a part of evolution is an old one. James Mark Baldwin proposed that organisms could learn a behavioural trait and then see genetic predispositions to learning that behaviour amplified by evolution. This idea was later generalised by Waddington - who proposed that genes could take over the trait completely - via a process known as "genetic assimilation". We see this effect in modern times, with learned milk drinking preceding genetically encoded lactose tolerance. Overall, the course of evolution is altered significantly by individual and social learning processes.
Leslie says that "The idea that evolution is a form of learning sounds implausible to many people when they first hear it." I think this is because he has things backwards - and learning is better seen as one of the products of evolution. How does Leslie argue that evolution is part of learning - and not the other way around? Leslie confines his attention to the case of "Darwinian evolution". According to Leslie, this term refers to evolution without learning. Leslie asserts that, in Darwinian evolution, genetic variations are generated independently of current experiences - a constraint that does not apply to learning systems. Unfortunately for Leslie's thesis, this isn't the kind of evolution that Darwin believed in. Darwin was well aware of the role of learning in evolution. Indeed he formulated a theory to explain how current experiences went on to affect the next generation. Darwin's proposed "gemmules" were subsequently discredited, but they clearly show that Darwin thought that current experiences influenced heritable variation.
Leslie goes on to describe modern cultural evolution, saying that "culture also undergoes change or evolution, but this change is no longer limited by Darwinian principles". However, Darwin was, in fact, a pioneer in the discovery of cultural evolution, writing about how words and languages were subject to natural selection. Leslie argues that human culture introduced learning to evolution. He minimizes the significance of cultural inheritance in other animals and the influence of individual learning on DNA evolution via the Baldwin effect and genetic assimilation. He says that before human culture: "the learning and reasoning carried out by an organism during its life had limited impact that outlived the individual". I think this is a big understatement that is not really consistent with the scientific evidence on the role of learning in evolution. Learning is important, and it's impact on evolution long pre-dates human cultural evolution.
The "Darwinian evolution" described by the author would have been foreign to Darwin. Also, we know that the idea that genetic variations are generated independently of current experiences is wrong - not least because of the role of stress in stimulating the production of mutations. This it isn't the kind of evolutionary theory that is much use for explaining what happens in nature. Why Leslie focuses on this impoverished version of evolutionary theory is not completely clear. Perhaps he really believes that this is what Darwinian evolutionary theory says. Or perhaps making Darwinism look weak makes his own field of learning seem more important.
So far, this has been mostly an argument over terminology - specifically over what the term "Darwinian evolution" refers to. This debate has limited interest - and can mostly be avoided with clear definitions. However the problem with learning theorists placing learning centrally and denigrating the power of Darwinian evolution, is that they then fail to make proper use of the insights evolutionary theory provides. In fact, Darwinism has much to say about how the brain processes responsible for animal learning work. Natural selection acts on synapses. Axon pulses are copied with variation and selection. There's competition between ideas within the brain for attention. The result is a good adaptive fit between an organism's model of its world and its environment. Interesting though these idea are, you won't find anything like them in this book. Indeed, few machine learning experts appear to have looked into the implications of modern versions of Darwinism. Instead, Leslie sees Darwinian evolution as a primitive ladder that led to modern learning systems. He doesn't deal with the more powerful, generalized versions of evolutionary theory that also cover organisms that learn or make use of cultural transmission.
2 of 2 people found the following review helpful.
Thought provoking learning theory for evolutionary biology
By Dr. Lee D. Carlson
When one is confronted with a “your money or your life” proposition from a gun-wielding thug in a dark alley, there is no temptation to poll the data on dark alley robberies in order to calculate the probability that the thief will pull the trigger. Unless one is trained to deal specifically with threats and stressful situations such as this, one quickly hands over the wallet or the purse. No sophisticated time intensive algorithms are in play in this situation. The pattern matching of the gun image and the affective capabilities of the brain take over here, provoking with incredible speed an appropriate fear response. The feedback received from this situation is that of walking away unscathed.
The point to be made here is that if one is to view the brain as a computational entity that deploys various algorithms to deal with situations like this and survival in general one must come to grips with the computational complexity of these algorithms. One must acknowledge that survival entails in many instances that thought processes operate on time scales that can are very short, as well as time scales that can be very long, i.e. require much deliberation and a quantitative assessment of risks.
The author of this book is well aware of the issues with computational complexity and via the idea (which he invented) of ‘probably approximately correct’ or PAC learning for short, has given the evolutionary biologists an interesting and provocative view of evolutionary processes that addresses some of the gaps in the Darwinian paradigm.
The book is highly interesting and its perusal will not only help the reader understand the issues at stake in the Darwinian view of evolution but will also assist the uninitiated reader in the understanding of PAC learning itself. In this regard the author devotes a portion of the book to PAC learning and examples are given that illustrate it. A very plausible case for the role of PAC learning in evolutionary process is outlined and should be understandable to anyone with even a modest background in computer science and mathematical logic. Readers with more background in learning theory and artificial intelligence can still appreciate the book even though the rigorous formalism has been omitted in order to appeal to a wider audience.
Whatever the eventual impact this book has on evolutionary biology it raises issues that should be addressed when contemplating the Darwinian paradigm, and beyond that it addresses requirements that every algorithm developer confronts in everyday practice. These involve the running time of algorithms designed for practical use, the data needed for these algorithms that can frequently be corrupted or sparse, and the overhead generated by the algorithm, especially those deployed on information networks.
0 of 0 people found the following review helpful.
I could not advance beyond Valiant's definition of "fitness"
By Vladimir Roubtsov
The early chapters (roughly half of the book) contain some usefully intuitive discussions of PAC learning and computability hardness classes.
Chapter 6, "The Evolvable", is where I've found the narrative starting to get unconvincing. After some interesting thoughts about interpreting the modern theory of evolution as a learning process but currently lacking a "quantitative feasibility proof" (how could complex biological mechanisms have evolved given the time and population sizes apparently available?), the author proceeds to define his variation of "fitness" in terms of "benefits" to organisms. "The target of evolution therefore is simply higher performance". This is not quire recursive, but does not seem to be a "computable" definition at all -- I am not convinced it is feasible to grade one organism action strategy as "more beneficial" than another in "polynomial" time.
On the plus side, there are many references I've found interesting to follow up on.
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