This issue of AI Magazine brings together a collection of articles on challenges, mechanisms, and research progress in turn-taking and coordination between humans and machines.

This issue of AI Magazine brings together a collection of articles on challenges, mechanisms, and research progress in turn-taking and coordination between humans and machines. The contributing authors work in interrelated fields of spoken dialog systems, intelligent virtual agents, human-computer interaction, human-robot interaction, and semiautonomous collaborative systems and explore core concepts in coordinating speech and actions with virtual agents, robots, and other autonomous systems. Several of the contributors participated in the AAAI Spring Symposium on Turn-Taking and Coordination in Human-Machine Interaction, held in March 2015, and several articles in this issue are extensions of work presented at that symposium. The articles in the collection address key modeling, methodological, and computational challenges in achieving effective coordination with machines, propose solutions that overcome these challenges under sensory, cognitive, and resource restrictions, and illustrate how such solutions can facilitate coordination across diverse and challenging domains. The contributions highlight turn-taking and coordination in human-machine interaction as an emerging and evolving research area with important implications for future applications of AI.

During the last 36 years of its illustrious history, ince its inception in 1980, AI Magazine has played an the magazine has gone through several transformations. Now the magazine is going through another transition: David Leake, the longtime editor-in-chief is moving on after 17 years of distinguished service, though fortunately he will continue to advise us as editor emeritus. I am honored and delighted to follow David. I have been a member of the Editorial Board of AI Magazine for several years, associate editor since August 2015, and editor elect since February 2016; my tenure as editor-in-chief starts with this winter 2016 issue. I thank David, Managing Editor Mike Hamilton, former AAAI President Tom Dietterich, and AAAI for recruiting me for this challenge....

Machine learning is to learn from data repetitively and to find the pattern hidden there. By applying the results of learning to new data, in other word Machine learning allows computers to analyze past data and predict future data. Machine learning is widely used in familiar places such as product recommendation system and face detection of photos. Also, as cloud machine learning services such as Microsoft's "Azure Machine Learning", Amazon's "Amazon Machine Learning", and Google's "Cloud Machine Learning" are released. This article is written to help novices and experts alike find the best Machine learning books to start with or continue their education. So here is a list of the best Machine learning Books: Book Name: Machine Learning This textbook provides a single source introduction to the primary approaches to machine learning Good content explained in very simple language. The book covers the concepts and techniques from the various fields in a unified fashion and very recent subjects such as genetic algorithms, re-enforcement learning and inductive logic programming. Writing style is clear, explanatory and precise.

In November, WIRED published a special issue guest-edited by President Obama. The magazine's features editor Maria Streshinsky says that working with the president was an exciting opportunity for everyone at WIRED, especially editor in chief Scott Dadich. "He could really recognize a lot of the language that the president would use as far as what the future could hold, and that it's well within our grasp to have an optimistic future," Streshinsky says in Episode 236 of the Geek's Guide to the Galaxy podcast. "Those are the ideas that the president was very interested in, and that just sit squarely in what Scott believes and what WIRED tries to focus on." WIRED associate editor Jason Kehe, a big science fiction fan, was particularly excited to learn more about the president's taste in science fiction.

Just picked up a super cheap copy of Artificial Intelligence: A Modern Approach from the local charity shop. The only problem is that it's 1st edition. As far as I'm aware the 1st edition is quite old now but since I'm just beginning to learn about AI, is it still ok to start with? I know some of the content will be obsolete but I would assume it covers the basics quite well?

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Who will remain a loyal customer and who won't? Which messages are most effective with which segments? How can customer value be maximized? This book supplies powerful tools for extracting the answers to these and other crucial business questions from the corporate databases where they lie buried. In the years since the first edition of this book, data mining has grown to become an indispensable tool of modern business.

The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature.

This editorial introduces answer set programming, a vibrant research area in computational knowledge representation and declarative programming. We give a brief overview of the articles that form this special issue on answer set programming and of the main topics they discuss.