Eugene Charniak's Books

AI & I

An Intellectual History of Artificial Intelligence

By: Eugene Charniak

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AI & I

An Intellectual History of Artificial Intelligence

By: Eugene Charniak

A concise and illuminating history of the field of artificial intelligence from one of its earliest and most respected pioneers. AI & I is an intellectual history of the field of artificial intelligence from the perspective of one of its first practitioners, Eugene Charniak. Charniak entered the field in 1967, roughly 12 years after AI’s founding, and was involved in many of AI’s formative milestones. In this book, he traces the trajectory of breakthroughs and disappointments of the discipline up to the current day, clearly and engagingly demystifying this oft revered and misunderstood technology. His argument is controversial but well supported: that classical AI has been almost uniformly unsuccessful and that the modern deep learning approach should be viewed as the foundation for all the exciting developments that are to come. Written for the scientifically educated layperson, this book chronicles the history of the field of AI, starting with its origin in 1956, as a topic for a small academic workshop held at Dartmouth University. From there, the author covers reasoning and knowledge representation, reasoning under uncertainty, chess, computer vision, speech recognition, language acquisition, deep learning, and learning writ large. Ultimately, Charniak takes issue with the controversy of AI—the fear that its invention means the end of jobs, creativity, and potentially even humans as a species—and explains why such concerns are unfounded. Instead, he believes that we should embrace the technology and all its potential to benefit society.

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Page Count

197

Publisher

MIT Press

Published Date

ISBN 10

0262548739

ISBN 13

9780262548731

Introduction to Deep Learning

By: Eugene Charniak

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Introduction to Deep Learning

By: Eugene Charniak

A project-based guide to the basics of deep learning. This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach. Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.

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Page Count

187

Publisher

MIT Press

Published Date

ISBN 10

0262039516

ISBN 13

9780262039512

AI & I

An Intellectual History of Artificial Intelligence

By: Eugene Charniak

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AI & I

An Intellectual History of Artificial Intelligence

By: Eugene Charniak

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197

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MIT Press

Published Date

ISBN 10

0262379244

ISBN 13

9780262379243

Introduction to Deep Learning

By: Eugene Charniak

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Introduction to Deep Learning

By: Eugene Charniak

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187

Publisher

MIT Press

Published Date

ISBN 10

0262351641

ISBN 13

9780262351645

Artificial Intelligence Programming

By: Eugene Charniak,Christopher K. Riesbeck,Drew V. McDermott,James R. Meehan

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Artificial Intelligence Programming

By: Eugene Charniak,Christopher K. Riesbeck,Drew V. McDermott,James R. Meehan

Artificial intelligence research has thrived in the years since this best-selling AI classic was first published. The revision encompasses these advances by adapting its coding to Common Lisp, the well-documented language standard, and by bringing together even more useful programming tools. Today's programmers in AI will find this volume's superior coverage of programming techniques and easily applicable style anything but common.

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Page Count

554

Publisher

Psychology Press

Published Date

ISBN 10

1317767993

ISBN 13

9781317767992

Statistical Language Learning

By: Eugene Charniak

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Statistical Language Learning

By: Eugene Charniak

This text introduces statistical language processing techniques--word tagging, parsing with probabilistic context free grammars, grammar induction, syntactic disambiguation, semantic word classes, word-sense disambiguation--along with the underlying mathematics and chapter exercises.

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196

Publisher

MIT Press

Published Date

ISBN 10

0262531410

ISBN 13

9780262531412

Professional Automated Trading

Theory and Practice

By: Eugene A. Durenard

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Professional Automated Trading

Theory and Practice

By: Eugene A. Durenard

An insider's view of how to develop and operate an automated proprietary trading network Reflecting author Eugene Durenard's extensive experience in this field, Professional Automated Trading offers valuable insights you won't find anywhere else. It reveals how a series of concepts and techniques coming from current research in artificial life and modern control theory can be applied to the design of effective trading systems that outperform the majority of published trading systems. It also skillfully provides you with essential information on the practical coding and implementation of a scalable systematic trading architecture. Based on years of practical experience in building successful research and infrastructure processes for purpose of trading at several frequencies, this book is designed to be a comprehensive guide for understanding the theory of design and the practice of implementation of an automated systematic trading process at an institutional scale. Discusses several classical strategies and covers the design of efficient simulation engines for back and forward testing Provides insights on effectively implementing a series of distributed processes that should form the core of a robust and fault-tolerant automated systematic trading architecture Addresses trade execution optimization by studying market-pressure models and minimization of costs via applications of execution algorithms Introduces a series of novel concepts from artificial life and modern control theory that enhance robustness of the systematic decision making—focusing on various aspects of adaptation and dynamic optimal model choice Engaging and informative, Proprietary Automated Trading covers the most important aspects of this endeavor and will put you in a better position to excel at it.

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Page Count

388

Publisher

John Wiley & Sons

Published Date

ISBN 10

1118419294

ISBN 13

9781118419298

Artificial Intelligence Programming

By: Eugene Charniak,Christopher K. Riesbeck,Drew V. McDermott,James R. Meehan

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Artificial Intelligence Programming

By: Eugene Charniak,Christopher K. Riesbeck,Drew V. McDermott,James R. Meehan

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Page Count

568

Publisher

Psychology Press

Published Date

ISBN 10

1317767985

ISBN 13

9781317767985

Book's by Eugene Charniak

AI & I

AI & I

Introduction to Deep Learning

Introduction to Deep Learning

AI & I

AI & I

Introduction to Deep Learning

Introduction to Deep Learning

Artificial Intelligence Programming

Artificial Intelligence Programming

Statistical Language Learning

Statistical Language Learning

Professional Automated Trading

Professional Automated Trading

Artificial Intelligence Programming

Artificial Intelligence Programming

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