- Series: Jean Nicod Lectures
- Hardcover: 118 pages
- Publisher: A Bradford Book; 1 edition (March 30, 2007)
- Language: English
- ISBN-10: 0262083604
- ISBN-13: 978-0262083607
- Product Dimensions: 5.4 x 0.2 x 8 inches
- Shipping Weight: 8.8 ounces
- Average Customer Review: 2 customer reviews
- Amazon Best Sellers Rank: #3,193,966 in Books (See Top 100 in Books)
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Reliable Reasoning: Induction and Statistical Learning Theory (Jean Nicod Lectures) 1st Edition
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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)
About the Author
Gilbert Harman is Stuart Professor of Philosophy at Princeton University and the author of Explaining Value and Other Essays in Moral Philosophy and Reasoning, Meaning, and Mind.
Sanjeev Kulkarni is Professor of Electrical Engineering and an associated faculty member of the Department of Philosophy at Princeton University with many publications in statistical learning theory.
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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.