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Practical reasoning is not just a matter of determining how to get what you want, but of working out what to want in the first place. In Practical Induction Elijah Millgram argues that experience plays a central role in this process of deciding what is or is not important or worth pursuing. He takes aim at instrumentalism, a view predominant among philosophers today, which holds that the goals of practical reasoning are basic in the sense that they are given by desires that are not themselves the product of practical reasoning. The view Millgram defends is "practical induction," a method of reasoning from experience similar to theoretical induction. What are the practical observations that teach us what to want? Millgram suggests they are pleasant and unpleasant experiences on the basis of which we form practical judgments about particular cases. By generalizing from these judgments--that is, by practical induction--we rationally arrive at our views about what matters. Learning new priorities from experience is necessary if we are to function in a world of ever-changing circumstances. And we need to be able to learn both from our own and from others' experience. It is this, Millgram contends, that explains the cognitive importance of both our capacity for pain and pleasure and our capacity for love. Pleasure's role in cognition is not that of a goal but that of a guide. Love's role in cognition derives from its relation to our trusting the testimony of others about what does and does not matter and about what merits our desire. Itself a pleasure to read, this book is full of inventive arguments and conveys Millgram's bold thesis with elegance and force. It will alter the direction of current debates on practical reasoning.
Practical Induction Heat Treating, Second Edition is a quick reference source for induction heaters. This book ties-in the metallurgy, theory, and practice of induction heat treating from a hands-on explanation of what floor people need to know. This book includes practical tables and process analysis of induction heating.
One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. which involves the study and development of computational models of learning processes. A major goal of research in this field is to build computers capable of improving their performance with practice and of acquiring knowledge on their own. The intent of this book is to provide a snapshot of this field through a broad. representative set of easily assimilated short papers. As such. this book is intended to complement the two volumes of Machine Learning: An Artificial Intelligence Approach (Morgan-Kaufman Publishers). which provide a smaller number of in-depth research papers. Each of the 77 papers in the present book summarizes a current research effort. and provides references to longer expositions appearing elsewhere. These papers cover a broad range of topics. including research on analogy. conceptual clustering. explanation-based generalization. incremental learning. inductive inference. learning apprentice systems. machine discovery. theoretical models of learning. and applications of machine learning methods. A subject index IS provided to assist in locating research related to specific topics. The majority of these papers were collected from the participants at the Third International Machine Learning Workshop. held June 24-26. 1985 at Skytop Lodge. Skytop. Pennsylvania. While the list of research projects covered is not exhaustive. we believe that it provides a representative sampling of the best ongoing work in the field. and a unique perspective on where the field is and where it is headed.
The implications for philosophy and cognitive science of developments in statistical learning theory. 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.
Artificial Intelligence and Scientific Method examines the remarkable advances made in the field of AI over the past twenty years, discussing their profound implications for philosophy. Taking a clear, non-technical approach, Donald Gillies shows how current views on scientific method are challenged by this recent research, and suggests a new framework for the study of logic. Finally, he draws on work by such seminal thinkers as Bacon, Gödel, Popper, Penrose, and Lucas, to address the hotly contested question of whether computers might become intellectually superior to human beings.
Mapping geographies of power and knowledge in qualitative research "In this foundational tome, Professor Celine-Marie Pascale critiques methodology in relationship to specific qualitative methods and argues cogently that despite good intentions, most of this research is still tethered to the Cartesian paradigm thus limiting its emancipatory potential. This is an impressive book that will likely become a classic!" — Eduardo Bonilla-Silva, Duke University, co-author with Tukufu Zuberi, White Logic, White Methods: Racism and Methodology 2012 Winner of the International Congress of Qualitative Inquiry Distinguished Book Award! Using clear language and concrete examples, this text examines theoretical and historical foundations that shape the premise and logic of qualitative social research. It analyzes qualitative methodology and methods in relationship to issues of agency, subjectivity, and experience. Rooted to feminist, critical race, and post-structural literature, it is concerned with social justice as it critiques current research paradigms and advances broad alternatives. This is an ideal text for students in graduate-level courses in Methodology, Epistemology, Qualitative Research Methods, Data Analysis, Ethnomethodology, Symbolic Interaction, Phenomenology, Grounded Theory, and related courses the social, behavioral, and health sciences.
As the gateway to scientific thinking, an understanding of the scientific method is essential for success and productivity in science. This book is the first synthesis of the practice and the philosophy of the scientific method. It will enable scientists to be better scientists by offering them a deeper understanding of the underpinnings of the scientific method, thereby leading to more productive research and experimentation. It will also give scientists a more accurate perspective on the rationality of the scientific approach and its role in society. Beginning with a discussion of today's 'science wars' and science's presuppositions, the book then explores deductive and inductive logic, probability, statistics, and parsimony, and concludes with an examination of science's powers and limits, and a look at science education. Topics relevant to a variety of disciplines are treated, and clarifying figures, case studies, and chapter summaries enhance the pedagogy. This adeptly executed, comprehensive, yet pragmatic work yields a new synergy suitable for scientists and instructors, and graduate students and advanced undergraduates.