The Workshop Program of the Association for the Advancement of Artificial Intelligence’s Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) was held at the beginning of the conference, February 12-13, 2016. Workshop participants met and discussed issues with a selected focus — providing an informal setting for active exchange among researchers, developers and users on topics of current interest. To foster interaction and exchange of ideas, the workshops were kept small, with 25-65 participants. Attendance was sometimes limited to active participants only, but most workshops also allowed general registration by other interested individuals. The AAAI-16 Workshops were an excellent forum for exploring emerging approaches and task areas, for bridging the gaps between AI and other fields or between subfields of AI, for elucidating the results of exploratory research, or for critiquing existing approaches. The fifteen workshops held at AAAI-16 were Artificial Intelligence Applied to Assistive Technologies and Smart Environments (WS-16-01), AI, Ethics, and Society (WS-16-02), Artificial Intelligence for Cyber Security (WS-16-03), Artificial Intelligence for Smart Grids and Smart Buildings (WS-16-04), Beyond NP (WS-16-05), Computer Poker and Imperfect Information Games (WS-16-06), Declarative Learning Based Programming (WS-16-07), Expanding the Boundaries of Health Informatics Using AI (WS-16-08), Incentives and Trust in Electronic Communities (WS-16-09), Knowledge Extraction from Text (WS-16-10), Multiagent Interaction without Prior Coordination (WS-16-11), Planning for Hybrid Systems (WS-16-12), Scholarly Big Data: AI Perspectives, Challenges, and Ideas (WS-16-13), Symbiotic Cognitive Systems (WS-16-14), and World Wide Web and Population Health Intelligence (WS-16-15).

It was a which I have done since 1999. It was a special pleasure to work with an outstanding team of volunteers---the editorial board, column editors, and others---and with the authors and reviewers, as well as with Mike Hamilton, managing editor, and the AAAI staff. As my administrative duties have expanded at Indiana University, where I am now executive associate dean of the School of Informatics and Computing, the time has come for me to pass the torch. The editorship provided me with a birdseye view of the field of AI that brought its stunning progress into focus. Research advances and the integration of AI into everyday life today give artificial intelligence unprecedented practical impact.

The conference The goal of the AAAI-17 Student evening poster programs, and location is a great starting Abstract and Poster program is to provide will have a short paper included in the point to explore the City's tremendous a forum in which students can proceedings. Submissions from everyone, ethnic and cultural diversity and its present and discuss their work during including authors of paper submissions wide variety of offerings. San Francisco its early stages, meet some of their to AAAI, IAAI, and AAAI-17 is also perfectly positioned to explore peers who have related interests, and workshops, are encouraged. Work submitted the entire Bay Area, whether for recreation introduce themselves to more senior to other tracks (such as the or business.

Marvin Minsky, one of the pioneers of artificial intelligence and a renowned mathematicial and computer scientist, died on Sunday, 24 January 2016 of a cerebral hemmorhage. He was 88. In this article, AI scientists Kenneth D. Forbus (Northwestern University), Benjamin Kuipers (University of Michigan), and Henry Lieberman (Massachusetts Institute of Technology) recall their interactions with Minksy and briefly recount the impact he had on their lives and their research. A remembrance of Marvin Minsky was held at the AAAI Spring Symposium at Stanford University on March 22. Video remembrances of Minsky by Danny Bobrow, Benjamin Kuipers, Ray Kurzweil, Richard Waldinger, and others can be on the sentient webpage1 or on youtube.com.

It knew enough about medical diagnoses and literature to beat "Jeopardy!" Now, an IBMcomputer platform called Watson is taking on something really tough: teaching third-grade math. For the past two years, the IBM Foundation has worked with teachers and their union, the American Federation of Teachers, to build Teacher Advisor, a program that uses artificial-intelligence technology to answer questions from educators and help them build personalized lesson plans. By the end of the year, it will be available free to third-grade math teachers across the country and will add subject areas and grade levels over time. "The idea was to build a personal adviser, so a teacher would be able to find the best lesson and then customize the lesson based upon their classroom needs," said Stanley S. Litow, president of the IBM Foundation. "By loading a massive amount of content, of teaching strategies, lesson plans, you'd actually make Watson the teacher coach," Mr. Litow said.

SAN FRANCISCO -- Google is opening tech labs in Oakland, Calif., and Harlem to build bridges to underserved communities as it seeks the next generation of African-American and Latino computer scientists. Code Next, a new initiative which officially launched Thursday, puts on free programs for middle school and high school students, working with local organizations such as Black Girls Code and local schools to nurture their interest in computer science. Google says its research shows that 51% of African-American students and 47% of Hispanic students don't have access to computer science classes in school. Code Next aims to fills that gap with hands-on curriculum that encourages creativity and experimentation, showing young people overlooked by the tech industry the possibilities that industry offers. Eventually Google, which is developing the two labs in collaboration with MIT Media Lab, plans to make the curriculum available to educators.

Duolingo has been offering language learning tools for a while now, but today the company debuted a new tool inside its iPhone app that could make the task a bit easier. Thanks to AI-powered chatbots, the language-learning app offers a way to have conversations while you're trying to learn French, German and Spanish. That's a short list of languages for now, but Duolingo says more options are on the way. Right now, you can only interact with the chatbots via text, but the company does have plans to add spoken conversations in the future. Duolingo gave these bots a bit of personality to make them more like real people and created them to be flexible with the answers they'll accept when there's multiple ways for you to respond.

On stage at TechCrunch Disrupt last month, Udacity founder Sebastian Thrun announced that the online education company would be building its own autonomous car as part of its self-driving car nanodegree program. To get there, Udacity has created a series of challenges to leverage the power of community to build the safest car possible -- meaning anyone and everyone is welcome to become a part of the open-sourced project. Challenge one was all about building a 3D model for a camera mount, but challenge two has brought deep learning into the mix. In the latest challenge, participants have been tasked with using driving data to predict steering angles. Initially, Udacity released 40GB of data to help at-home tinkerers build competitive models without access to the type of driving data that Tesla of Google would have.

The education field is also already feeling the impact of AI. According to the Accenture study, math students that used virtual tutors saw pass rates increase by 11 percent. At the same time, dropout rates tumbled by 56 percent. Even further, since these tutors allow students to advance at their own pace, more than half of students completed classes a month earlier than before.

Machine learning, which involves programs that get more accurate with experience, is fundamentally different from any kind of computing that's come before. "There's always been a simple division of labor: machines do number crunching, and humans make decisions," says Pradeep Dubey, an Intel Fellow at the company's Intel Labs division. Machine-learning programs--and in particular the high-profile deep-learning subset that can teach themselves--are different. These programs have the potential to discover new drug compounds or identify consumer trends without human intervention. For Dubey and others at Intel, it was clear that they needed to find a way to make machine-learning programs work well on Intel's architecture.