Semantic web technologies (Hitzler, Krötzsch, and Rudolph 2010) are meant to deal with these issues, and indeed since the advent of linked data (Bizer, Heath, and Berners-Lee 2009) a few years ago, they have become central to mainstream semantic web research and development. We can easily understand linked data as being a part of the greater big data landscape, as many of the challenges are the same (Hitzler and Janowicz 2013). The linking component of linked data, however, puts an additional focus on the integration and conflation of data across multiple sources. This issue of AI Magazine is a followup from that meeting and contains significantly extended, enhanced, and updated contributions. We summarize the articles in the following paragraphs.

This issue features expanded versions of articles selected from the 2015 AAAI Conference on Innovative Applications of Artificial Intelligence held in Austin, Texas. We present a selection of three articles describing deployed applications plus two more articles that discuss work on emerging applications. Since then, we have seen examples of AI applied to domains as varied as medicine, education, manufacturing, transportation, user modeling, military operations, and citizen science. The 2015 conference continued the tradition with a selection of 6 deployed applications describing systems in use by their intended end users, 13 emerging applications describing works in progress, and three papers in a new category for challenge problems. In the first article, Activity Planning for a Lunar Orbital Mission, John Bresina describes a deployed application of current planning technology in the context of a NASA mission called LADEE (Lunar Atmospheric and Dust Environment Explorer).

We present a selection of four articles describing deployed applications plus two more articles that discuss work on emerging applications. Since then, we have seen examples of AI applied to domains as varied as medicine, education, manufacturing, transportation, user modeling, and citizen science. The 2014 conference continued the tradition with a selection of 7 deployed applications describing systems in use by their intended end users, and 14 emerging applications describing works in progress. This year's special issue on innovative applications features articles describing four deployed and two emerging applications. The articles include three different types of recommender systems, which may be as much of a critique of the role of technology in society as it is an indication of recent research trends.

This special issue of AI Magazine brings seven articles presenting extended versions of papers from IAAI 2013. These articles were selected for their description of AI technologies that are either in practical use or close to it. Five of the articles describe deployed application case studies. These articles present fielded AI applications that distinguish themselves for their innovative use of AI technology. One article describes an emerging application.

"An Innovative Application from the DARPA Knowledge Bases Programs: Rapid Development of a Course-of-Action Critiquer," by Gheorghe Tecuci, Mihai Boicu, Mike Bowman, and Dorin Marcu, describes a critiquing agent for military courses of action, a challenge problem set by the Defense Advanced Research Projects Agency's (DARPA) High-Performance Knowledge Bases Program. Murray Burke, the DARPA manager for this program, introduces the article by setting the context for the application. Ontologies also play a key role in the creation and management of a web portal developed by Steffen Staab and his colleagues at the University of Karlsruhe, discussed in their article, "Knowledge Portals: Ontologies at Work." "L As in past years, papers were solicited in two categories: (1) deployed applications and (2) emerging applications and technologies. Deployed applications are systems that have been in use for at least several months by individuals or organizations other than their developers, have measurable benefits, and incorporate AI technologies. Emerging applications are systems that are close to deployment and clearly show an innovative implementation of AI technologies. Papers submitted in this track can also describe efforts that examine the utility of different AI techniques for specific applications. All these case studies are of value not only to other application developers looking for guidance in applying various techniques to their own applications but also to researchers who need to understand the technical challenges provided by real-world problems. Six deployed applications and 12 emerging application papers were presented plus 2 invited talks. Although no single theme emerges from this panoply of excellent applications, they served to demonstrate that the field continues to be fertile ground for innovation.

Ramasamy Uthurusamy was the program chair, and Barbara Hayes-Roth was the program cochair. IAAI-99 was a special occasion that provided an opportunity to reflect on a decade of IAAI conferences and contemplate the potential contributions in the coming decade. In addition to the three invited talks that specifically addressed these areas, IAAI-99 showcased some exciting and innovative applications. Although all the IAAI-99 papers and talks were certainly interesting and important, we present in this special issue of AI Magazine only a select subset because of page and other limitations. We include two invited talks and four applications as a snapshot of IAAI-99.

This editorial introduction provides an overview of artificial intelligence for computational sustainability, and introduces the special issue articles that appear in this issue and the previous issue of AI Magazine. The emerging interdisciplinary field of computational sustainability (Gomes 2009) draws techniques from computer science, information science, mathematics, statistics, operations research, and related disciplines to help balance environmental and socioeconomic needs for sustainable development. Artificial intelligence (AI) techniques play a key role in computational sustainability research, enabling the solution of sustainability problems that involve modeling or decision making in dynamic and uncertain environments. Since 2011, the main AAAI conference has included a special track on computational sustainability, encouraging AI research in this area and broader participation of sustainability researchers in the AAAI community. Sustainable solutions must balance between environmental, societal, and economic demands (United Nations General Assembly 2005).

The articles in this special issue of AI Magazine include those that propose specific tests and those that look at the challenges inherent in building robust, valid, and reliable tests for advancing the state of the art in AI. To people outside the field, the test -- which hinges on the ability of machines to fool people into thinking that they (the machines) are people -- is practically synonymous with the quest to create machine intelligence. Within the field, the test is widely recognized as a pioneering landmark, but also is now seen as a distraction, designed over half a century ago, and too crude to really measure intelligence. Intelligence is, after all, a multidimensional variable, and no one test could possibly ever be definitive truly to measure it. Moreover, the original test, at least in its standard implementations, has turned out to be highly gameable, arguably an exercise in deception rather than a true measure of anything especially correlated with intelligence.

This editorial introduction provides an overview of artificial intelligence for computational sustainability, and introduces the next two special issue articles that will appear in AI Magazine. The emerging interdisciplinary field of computational sustainability (Gomes 2009) draws techniques from computer science, information science, mathematics, statistics, operations research, and related disciplines to help balance environmental and socioeconomic needs for sustainable development. Artificial intelligence (AI) techniques play a key role in computational sustainability research, enabling the solution of sustainability problems that involve modeling or decision making in dynamic and uncertain environments. Since 2011, the main AAAI conference has included a special track on computational sustainability, encouraging AI research in this area and broader participation of sustainability researchers in the AAAI community. Sustainable solutions must balance between environmental, societal, and economic demands (United Nations General Assembly 2005).

He noted the shared interest of the members of this community in studying high-level cognition, structured representations, comprehensive system development, heuristics, and openness to insights into human cognition. The developments of the last five years warrant a new look at the issues. The five thematic articles in this issue offers such a look. The contributions are diverse and cover a representative -- though by no means a complete -- set of issues and opinions. Sergei Nirenburg's introductory essay offers a bird's eye view of the current directions of research in the field and suggests some aspirational issues that need attention for the cognitive systems community to make a lasting impact.