- Hardcover: 114 pages
- Publisher: MIT Press; 1 edition (8 May 2007)
- Language English
- ISBN-10: 0262083604
- ISBN-13: 978-0262083607
- Product Dimensions: 21.1 x 14.7 x 1.4 cm
- Amazon Bestsellers Rank: 1,635,326 in Books (See Top 100 in Books)
Reliable Reasoning
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Review
"In their interesting and stimulating book Reliable Reasoning, Harman, a philosopher, and Kulkarni, an information scientist, illuminate the philosophical issues related to inductive reasoning by studying it in terms of the mathematics of probabilistic learning. One of the great virtues of this approach is that the inductive inference made through learning can survive changes in the probabilistic modeling assumptions. I find that the authors have made a convincing and persuasive case for rigorously studying the philosophical issues related to inductive inference using recent ideas from the science of artificial intelligence." Sanjoy K. Mitter , Professor of Electrical Engineering, MIT "This thoroughly enjoyable little book on learning theory reminds me of many classics in the field, such as Nilsson's *Learning Machines* or Minksy and Papert's *Perceptrons*: It is both a concise and timely tutorial 'projecting' the last decade of complex learning issues into simple and comprehensible forms and a vehicle for exciting new links among cognitive science, philosophy, and computational complexity." Stephen J. Hanson , Department of Psychology, Rutgers University "This thoroughly enjoyable little book on learning theory reminds me of many of classics in the field, such as Nilsson's *Learning Machines* or Minksy and Papert's *Perceptrons*: It is both a concise and timely tutorial 'projecting' the last decade of complex learning issues into simple and comprehensible forms and a vehicle for exciting new links between cognitive science, philosophy, and computational complexity."--Stephen J. Hanson, Department of Psychology, Rutgers University "In their interesting and stimulating book *Reliable Reasoning*, Harman, a philosopher, and Kulkarni, an information scientist, illuminate the philosophical issues related to inductive reasoning by studying it in terms of the mathematics of probabilistic learning. One of the great virtues of this approach is that the inductive inference made through learning can survive changes in the probabilistic modeling assumptions. I find that the authors have made a convincing and persuasive case for rigorously studying the philosophical issues related to inductive inference using recent ideas from the science of artificial intelligence."--Sanjoy K. Mitter, Professor of Electrical Engineering, MIT
Product Description
In Reliable Reasoning, Gilbert Harman and Sanjeev Kulkarni--a philosopher and an engineer--argue that philosophy and cognitive science can benefit from statistical learning theory (SLT), the theory that lies behind recent advances in machine learning. The philosophical problem of induction, for example, is in part about the reliability of inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors--a central topic in SLT. After discussing philosophical attempts to evade the problem of induction, Harman and Kulkarni provide an admirably clear account of the basic framework of SLT and its implications for inductive reasoning. They explain the Vapnik-Chervonenkis (VC) dimension of a set of hypotheses and distinguish two kinds of inductive reasoning. The authors discuss various topics in machine learning, including nearest-neighbor methods, neural networks, and support vector machines. Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT.
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Front Cover | Copyright | Table of Contents | Excerpt | Index
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5 of 5 people found the following review helpful
A great little book
6 Jun 2007
Format:Hardcover
I had the great priviledge of taking the class upon which this book was based last semester at Princeton University under professors Harman and Kulkarni. It is a fascinating little book, which manages to distill decades of debate and research into concise, readable chapters that carry the presentation forward. The authors' approach is original but commonsensical and they clearly demonstrate the value of interdisciplinary work in their twin fields of philosophy and electrical engineering!
The book is not without its flaws, however. The first chapter seems to take off 'in medias res' expecting the reader to be fully caught up with the latest discussion on the problem of induction, and it is not always clear exactly what a 'process of reasoning' might be compared to deductive arguments. The discussion could have benefited from incorporating material from the other draft textbook we used in class, on "The Nature and Limits of Learning", and even from the lecture handouts. The discussion of simplicity, as well, could have been clarified, especially with regard to Goodman's new riddle of induction and Karl Popper's philosophy of science.
Also rather disappointing in class was the discovery that Harman and Kulkarni's method do not warrant going beyond instrumentalism in scientific theorizing. I was hoping for something a little more robust. In any case, this book should be read by anyone interested in the issues they raise. It sure got me thinking and I will definitely refer to it later on as my research in philosophy brings me in contact again with the issues they discuss.
The book is not without its flaws, however. The first chapter seems to take off 'in medias res' expecting the reader to be fully caught up with the latest discussion on the problem of induction, and it is not always clear exactly what a 'process of reasoning' might be compared to deductive arguments. The discussion could have benefited from incorporating material from the other draft textbook we used in class, on "The Nature and Limits of Learning", and even from the lecture handouts. The discussion of simplicity, as well, could have been clarified, especially with regard to Goodman's new riddle of induction and Karl Popper's philosophy of science.
Also rather disappointing in class was the discovery that Harman and Kulkarni's method do not warrant going beyond instrumentalism in scientific theorizing. I was hoping for something a little more robust. In any case, this book should be read by anyone interested in the issues they raise. It sure got me thinking and I will definitely refer to it later on as my research in philosophy brings me in contact again with the issues they discuss.
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