He continued: "We wanted to see if we could build a system that could learn to play and beat the best Go players by just providing the games of professional players. We are thrilled to have achieved this milestone, which has been a lifelong dream of mine. Our hope is that in the future we can apply these techniques to other challenges -- from instant translation to smartphone assistants to advances in health care."

For the vast majority of queries (for example, navigation, simple fact lookup, and others), search engines do extremely well. They also highlight many of the issues that are common to sophisticated AI question-answering systems. Rapid and ready-at-hand access, depth of processing, and the way they enable people to offload some ordinary memory tasks suggest that search engines have become more of a cognitive amplifier than a simple repository or front-end to the Internet. Although search engines are superb at finding and presenting information--up to and including extracting complex relations and making simple inferences--knowing how to frame questions and evaluate their results for accuracy and credibility remains an ongoing challenge.

Active problem solving has been shown to be one of the most effective ways to acquire complex skills. Whether one is learning a programming language by implementing a computer program, or learning calculus by solving problems, context sensitive feedback and guidance are crucial to keeping problem solving efforts fruitful and efficient. This article reviews AI-based algorithms that can diagnose student difficulties during active problem solving and serve as the basis for providing context-sensitive and individualized guidance. The article also describes the crucial role sensor based estimates of cognitive resources such as working memory capacity and attention can play in enhancing the diagnostic capabilities of intelligent instructional systems.

The adoption of probabilistic and decision-theoretic representations and statistical learning methods has led to a large degree of integration and cross-fertilization among AI, machine learning, statistics, control theory, neuroscience, and other fields. The progress in AI research makes it timely to focus research not only on making AI more capable, but also on maximizing the societal benefit of AI. We recommend expanded research aimed at ensuring that increasingly capable AI systems are robust and beneficial: our AI systems must do what we want them to do. In summary, we believe that research on how to make AI systems robust and beneficial is both important and timely, and that there are concrete research directions that can be pursued today.

CiteSeerX is a digital library search engine providing access to more than five million scholarly documents with nearly a million users and millions of hits per day. These AI technologies have been developed by CiteSeerX group members over the past 5–6 years. We also present AI technologies implemented in table and algorithm search, which are special search modes in CiteSeerX. While it is challenging to rebuild a system like CiteSeerX from scratch, many of these AI technologies are transferable to other digital libraries and/or search engines.

The AAAI 2014 Fall Symposium Series was held Thursday through Saturday, November 13–15, at the Westin Arlington Gateway in Arlington, Virginia adjacent to Washington, DC. The titles of the seven symposia were Artificial Intelligence for Human-Robot Interaction, Energy Market Prediction, Expanding the Boundaries of Health Informatics Using AI, Knowledge, Skill, and Behavior Transfer in Autonomous Robots, Modeling Changing Perspectives: Reconceptualizing Sensorimotor Experiences, Natural Language Access to Big Data, and The Nature of Humans and Machines: A Multidisciplinary Discourse. The highlights of each symposium are presented in this report.

The Twenty-Ninth AAAI Conference on Artificial Intelligence, (AAAI-15) was held in January 2015 in Austin, Texas (USA) The conference program was cochaired by Sven Koenig and Blai Bonet. This report contains reflective summaries of the main conference, the robotics program, the AI and robotics workshop, the virtual agent exhibition, the what's hot track, the competition panel, the senior member track, student and outreach activities, the student abstract and poster program, the doctoral consortium, the women's mentoring event, and the demonstrations program.

This project continued to evolve and we explored the design space of a contextual software command recommender system and completed a six-week user study (Li et al. We then expanded the scope of our project by implementing CommunityCommands, a fully functional and deployable recommender system. During a one-year period, the recommender system was used by more than 1100 users. We also present our system usage data and payoff, and provide an in-depth discussion of the challenges and design issues associated with developing and deploying the software command recommender system.

Online dating is a prime application area for recommender systems, as users face an abundance of choice, must act on limited information, and are participating in a competitive matching market. The deployment was the result of thorough evaluation and an online trial of a number of methods, including profile-based, collaborative filtering and hybrid algorithms. Results taken a few months after deployment show that the recommender system delivered its projected benefits.

This article presents techniques for recognizing students activities in ELEs and visualizing these activities to students. It describes a new plan recognition algorithm that takes into account repetition and interleaving of activities. It was able to outperform the state-of-the-art plan recognition algorithms when compared to a gold-standard that was obtained by a domain-expert. We also show that visualizing students' plans improves their performance on new problems when compared to an alternative visualization that consists of a step-by-step list of actions.