A promising version of artificial intelligence: A modern approach explores the breadth and depth of the field of artificial intelligence (AI). The fourth edition introduces readers to the latest technologies, presents concepts in a more integrated way, and introduces new and expanded topics in machine learning, deep learning, transfer learning, multi-agent systems, robotics, natural language processing, causality, and probabilistic programming. It provides scope. , Privacy, Fairness and Security AI.
A Modern Approach (Pearson Series in Artifical Intelligence)
This book provides the most complete and up-to-date introduction to the theory and practice of artificial intelligence. Non-technical learning materials introduce basic concepts using intuitive explanations before moving on to mathematical or algorithmic details. Using non-technical language makes this book accessible to more readers. A Consolidated Approach to AI shows students how the various AI subfields can be combined with each other to create practical and useful programs.
This book covers
Offer the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence
- The basic definition of AI systems is generalized to eliminate the standard assumption that the objective is fixed and known by the intelligent agent; instead, the agent may be uncertain about the true objectives of the human(s) on whose behalf it operates.
- The Author-Maintained Website at http://aima.cs.berkeley.edu/ includes text-related comments and discussions, exercises, an online code repository, Instructor Resources, and more!
- Interactive student exercises are now featured on the website to allow for continuous updating and additions.
- New instructional video tutorials deepen students’ engagement and bring key concepts to life.Stay current with the latest technologies and present concepts in a more unified manner
- New chapters feature expanded coverage of probabilistic programming (Ch. 15); multiagent decision making (Ch. 18 with Michael Wooldridge); deep learning (Ch. 21 with Ian Goodfellow); and deep learning for natural language processing (Ch. 24 with Jacob Devlin and Mei-Wing Chang).
- Increased coverage of machine learning.
- Significantly updated material on robotics includes robots that interact with humans and the application of reinforcement learning to robotics.
- New section on causality by Judea Pearl.
- New sections on Monte Carlo search for games and robotics.
- New sections on transfer learning for deep learning in general and for natural language.
- New sections on privacy, fairness, the future of work, and safe AI.
- Extensive coverage of recent advances in AI applications.
- Revised coverage of computer vision, natural language understanding, and speech recognition reflect the impact of deep learning methods on these fields.