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.

The Python Data Science Handbook provides a reference to the breadth of computational and statistical methods that are central to data-intensive science, research, and discovery. People with a programming background who want to use Python effectively for data science tasks will learn how to face a variety of problems: e.g., how can I read this data format into my script? How can I manipulate, transform, and clean this data? How can I visualize this type of data? How can I use this data to gain insight, answer questions, or to build statistical or machine learning models?

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?

The Python Data Science Handbook provides a reference to the breadth of computational and statistical methods that are central to data-intensive science, research, and discovery. People with a programming background who want to use Python effectively for data science tasks will learn how to face a variety of problems: e.g., how can I read this data format into my script? How can I manipulate, transform, and clean this data? How can I visualize this type of data? How can I use this data to gain insight, answer questions, or to build statistical or machine learning models?

Want to watch this again later? Report Need to report the video? Report Need to report the video? Need to report the video? This feature is not available right now.

The BBC recently released a handy online calculator that allows you to assess the risk of automation by job title. As a journalist, I apparently have an 8.4 per cent risk of being replaced by a robot, although I note with some concern that the LA Times already uses a robot for its live earthquake coverage. Unsurprisingly, jobs with a mechanical element, such as train drivers (67.8 per cent at risk) or taxi drivers (57 per cent), are quite exposed, while those requiring emotional intelligence such as nurses (0.9 per cent) and psychologists (0.7 per cent) are the most secure. Creativity also looks like a safe bet, so artists (3.8 per cent) and musicians (4.5 per cent) can relax – for now.

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.

Phenomena such as placebo analgesia or pain relief through distraction highlight the powerful influence cognitive processes and learning mechanisms have on the way we perceive pain. Although contemporary models of pain acknowledge that pain is not a direct readout of nociceptive input, the neuronal processes underlying cognitive modulation are not yet fully understood. Modern concepts of perception--which include computational modeling to quantify the influence of cognitive processes--suggest that perception is critically determined by expectations and their modification through learning. Research on pain has just begun to embrace this view. Insights into these processes promise to open up new avenues to pain prevention and treatment by harnessing the power of the mind.

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.