Jerry H. Gill

"In this survey of Native American worldviews, philosopher of religion Jerry H. Gill emphasizes the value and broad contours of Native American belief systems. He presents an integrated view to serve as an introduction to ways of life and perspectives on the world far different from those of the dominant Euro-American culture." "Drawing on the scholarship of anthropologists and specialists in American Indian Studies, Gill brings together much original research in broad, accessible chapters. He explores Native American origin stories, the special connotations given to spatial concepts such as the cardinal directions and the circle, the influence of the seasons and the cycles of life on different cultures, and clan and kinship systems. Separate chapters are devoted the key ceremonies and customs as well as to concepts of health, harmony, virtues, wisdom, and beauty."--BOOK JACKET.

"The Tacit Mode exposes and explores the central insights in Michael Polanyi's major works. It focuses on his epistemological insights concerning tacit knowing, and explores their ramifications for philosophy, science, art, language, political theory, and religion. The notion of tacit knowledge reconstructs the modern concept of objectivity while avoiding the self-stultifying effects of "deconstructivist" postmodernism and puts Polanyi on the cutting edge of contemporary philosophy."--BOOK JACKET.

"In this book, a philosopher combines famous cases with a lifetime of study to examine the threshold of language--that point "between speech and not quite speech." He provides fascinating accounts of the deaf and blind Helen Keller, of chimpanzees like Washoe, and of feral children such as Victor, the "wild child of Aveyron," putting a new spin on their stories. When does it start, he asks, that miracle most of us take for granted? Where does it come from, that uniquely human power to transform perception and action into thought and the singular activity we call speech?"--Back cover.

Over the past three decades or so, research on machine learning and data mining has led to a wide variety of algorithms that learn general functions from experience. As machine learning is maturing, it has begun to make the successful transition from academic research to various practical applications. Generic techniques such as decision trees and artificial neural networks, for example, are now being used in various commercial and industrial applications. Learning to Learn is an exciting new research direction within machine learning. Similar to traditional machine-learning algorithms, the methods described in Learning to Learn induce general functions from experience. However, the book investigates algorithms that can change the way they generalize, i.e., practice the task of learning itself, and improve on it. To illustrate the utility of learning to learn, it is worthwhile comparing machine learning with human learning. Humans encounter a continual stream of learning tasks. They do not just learn concepts or motor skills, they also learn bias, i.e., they learn how to generalize. As a result, humans are often able to generalize correctly from extremely few examples - often just a single example suffices to teach us a new thing. A deeper understanding of computer programs that improve their ability to learn can have a large practical impact on the field of machine learning and beyond. In recent years, the field has made significant progress towards a theory of learning to learn along with practical new algorithms, some of which led to impressive results in real-world applications. Learning to Learn provides a survey of some of the most exciting new research approaches, written by leading researchers in the field. Its objective is to investigate the utility and feasibility of computer programs that can learn how to learn, both from a practical and a theoretical point of view.