Principles Of Artificial Intelligence

A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used. Principles of Artificial Intelligenceevolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.

The artificial intelligence (AI) landscape has evolved significantly from 1950 when Alan Turing first posed the question of whether machines can think. Today, AI is transforming societies and economies. It promises to generate productivity gains, improve well-being and help address global challenges, such as climate change, resource scarcity and health crises.

Computing Methodologies -- Artificial Intelligence.

"Artificial intelligence (AI) technologies are one of the top investment priorities in these days. We may expect that by 2030, some 800 million jobs will have disappeared and taken over by machines, and artificial intelligence will reach human levels by around 2029. Follow that out further to 2045, we will have multiplied the intelligence, the human biological machine intelligence of our civilization a billion-fold. The time of machines requires new forms of work and new ways of business education. This book is authored by a range of international experts with a diversity of backgrounds and perspectives hopefully bringing us closer to the responses for the questions like how may AI be used /or is a threat for PRME implementation, how will AI impact the business education world or what we should teach in business school in the time of AI (what the 'right' set of future skills is)? In our book, we will try to address the following questions: 1. How will AI impact the business education world? 2. How may AI be used /or is it a threat for PRME implementation? 3. What we should teach (what the 'right' set of future skills is)? 4. How should we teach (the way in which schools should teach and assess them)? 5. Where should we teach (what implications does AI have for today's education infrastructure)?"--

Previous treatments of Artificial Intelligence (AI) divide the subject into its major areas of application, namely, natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, intelligent data retrieval systems, etc. The major difficulty with this approach is that these application areas are now so extensive, that each could, at best, be only superficially treated in a book of this length. Instead, I have attempted here to describe fundamental AI ideas that underlie many of these applications. My organization of these ideas is not, then, based on the subject matter of their application, but is, instead, based on general computational concepts involving the kinds of data structures used, the types of operations performed on these data struc tures, and the properties of con'trol strategies used by AI systems. I stress, in particular, the important roles played in AI by generalized production systems and the predicate calculus. The notes on which the book is based evolved in courses and seminars at Stanford University and at the University of Massachusetts at Amherst. Although certain topics treated in my previous book, Problem solving Methods in Artificial Intelligence, are covered here as well, this book contains many additional topics such as rule-based systems, robot problem-solving systems, and structured-object representations.

Presents results of research into computer systems that can improve their own performance. For undergraduates, graduates, and professionals intending to write or use such systems. The various perspectives of over a dozen contributors are abstracted into the unifying principle: generate + test, which makes possible a provisional taxonomy of machine learning algorithms. The sections cover a background to induction, biologically inspired systems, automated discovery, and long-term perspectives. The paper edition ($29.95) was not seen. Annotation copyrighted by Book News, Inc., Portland, OR

"This book focuses on the Explainable Artificial Intelligence (XAI) for healthcare, providing a broad overview of state-of-art approaches for accurate analysis and diagnosis, and encompassing computational vision processing techniques that handle complex data like physiological information, electronic healthcare records, medical imaging data that assist in earlier prediction"--