An engrossing guide to seeing—and communicating—more clearly from the groundbreaking course that helps FBI agents, cops, CEOs, ER docs, and others save money, reputations, and lives. How could looking at Monet’s water lily paintings help save your company millions? How can checking out people’s footwear foil a terrorist attack? How can your choice of adjective win an argument, calm your kid, or catch a thief? In her celebrated seminar, the Art of Perception, art historian Amy Herman has trained experts from many fields how to perceive and communicate better. By showing people how to look closely at images, she helps them hone their “visual intelligence,” a set of skills we all possess but few of us know how to use properly. She has spent more than a decade teaching doctors to observe patients instead of their charts, helping police officers separate facts from opinions when investigating a crime, and training professionals from the FBI, the State Department, Fortune 500 companies, and the military to recognize the most pertinent and useful information. Her lessons highlight far more than the physical objects you may be missing; they teach you how to recognize the talents, opportunities, and dangers that surround you every day. Whether you want to be more effective on the job, more empathetic toward your loved ones, or more alert to the trove of possibilities and threats all around us, this book will show you how to see what matters most to you more clearly than ever before. Please note: this ebook contains full-color art reproductions and photographs, and color is at times essential to the observation and analysis skills discussed in the text. For the best reading experience, this ebook should be viewed on a color device.

This book presents some of the emerging techniques and technologies used to handle Web data management. Authors present novel software architectures and emerging technologies and then validate using experimental data and real world applications. The contents of this book are focused on four popular thematic categories of intelligent Web data management: cloud computing, social networking, monitoring and literature management. The Volume will be a valuable reference to researchers, students and practitioners in the field of Web data management, cloud computing, social networks using advanced intelligence tools.

Most of the well-known mathematical software systems are batch oriented, though in the past few years there have been attempts to incorporate ``knowledge'' or ``expertise'' into these systems. A number of developments have helped in making the systems more powerful and user-friendly: algorithm/parameter selection for the solution of well-defined mathematical engineering problems; parallel computing; computer graphics technology; interface development tools; and of course the years of experience with these systems and the increase in available computing power have made it practical to fulfill the potential seen in the early years of their development. This book covers four main areas of the subject: Application Oriented Expert Systems, Advisory Systems, Knowledge Manipulation Issues, and User Interfaces.

This book constitutes the proceedings of the 12th International Workshop on Knowledge Management and Acquisition for Intelligent Systems, PKAW 2012, held in Kuching, Malaysia, in September 2012. The 21 full papers and 11 short papers included in this volume were carefully reviewed and selected from 141 papers. They deal with knoweldge acquisition issues and evaluation; language, text and image processing; incremental knowledge acquisition; agent based knowledge acquisition and management; ontology-based approaches; WEB 2.0 methods and applications; and other applications.

Computational Intelligence (CI) is a term corresponding to a new generation of algorithmic methodologies in artificial intelligence, which combines elements of learning, adaptation, evolution and approximate (fuzzy) reasoning to create programs that can be considered intelligent. Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods presents computational intelligence methods for addressing supply chain issues. Emphasis is given to techniques that provide effective solutions to complex supply chain problems and exhibit superior performance to other methods of operations research.

“Artificial intelligence has always inspired outlandish visions—that AI is going to destroy us, save us, or at the very least radically transform us. Erik Larson exposes the vast gap between the actual science underlying AI and the dramatic claims being made for it. This is a timely, important, and even essential book.” —John Horgan, author of The End of Science Many futurists insist that AI will soon achieve human levels of intelligence. From there, it will quickly eclipse the most gifted human mind. The Myth of Artificial Intelligence argues that such claims are just that: myths. We are not on the path to developing truly intelligent machines. We don’t even know where that path might be. Erik Larson charts a journey through the landscape of AI, from Alan Turing’s early work to today’s dominant models of machine learning. Since the beginning, AI researchers and enthusiasts have equated the reasoning approaches of AI with those of human intelligence. But this is a profound mistake. Even cutting-edge AI looks nothing like human intelligence. Modern AI is based on inductive reasoning: computers make statistical correlations to determine which answer is likely to be right, allowing software to, say, detect a particular face in an image. But human reasoning is entirely different. Humans do not correlate data sets; we make conjectures sensitive to context—the best guess, given our observations and what we already know about the world. We haven’t a clue how to program this kind of reasoning, known as abduction. Yet it is the heart of common sense. Larson argues that all this AI hype is bad science and bad for science. A culture of invention thrives on exploring unknowns, not overselling existing methods. Inductive AI will continue to improve at narrow tasks, but if we are to make real progress, we must abandon futuristic talk and learn to better appreciate the only true intelligence we know—our own.