The last 10 years have seen the development of novel architectures and technologies for domainfocused, task-specific systems that know many things, such as who (identities, profile, history) they are with (social context) and in what role (responsibility, security, privacy); when and where (event, time, place); why (goals, shared or personal); how are they doing it (methods, applications); and using what resources (device, services, access, and ownership). Smart spaces and devices will increasingly use such contextual knowledge to help users move seamlessly between devices and applications, without having to explicitly carry, transfer, and exchange activity context. Such systems will qualitatively shift our lives both at work and play and significantly change our interactions both with our physical and virtual worlds. This dream of seamlessly interacting with our virtual environment has a long history as can be seen in Apple Inc.'s Knowledge Navigator 1987 concept video. However, the combination of dramatic progress in low-power mobile computing devices and sensors, with advances in artificial intelligence and human-computer interaction (HCI) in the last decade, have provided the kind of platforms and algorithms that are enabling context-aware virtual personal assistants that plan activities and recognize intent. This has lead to an increase in work designed to bring these ideas into real world application and address the final technical hurdles that will make such systems a reality.

We propose a reference architecture for intelligent context-aware natural speech-enabled systems delivering complex functionality, with direct access to information, simplifying business processes and activities while providing domain-specific and task-specific depth in interactive banking, insurance, wealth management, finance, clinical, legal, telecom customer service, operations, supply chain, connected living room, and personal assistants. This system understands not just words, but intentions, and context of the interaction. We accomplish this through a marriage of speech recognition with advanced natural language processing techniques, scalable inference and semantic technologies. This architecture is expected to dramatically improve the quality of proactive decision support provided by virtual agents by enabling them to seek explanations, make predictions, generate and test hypothesis and perform what-if-analyses using scalable inference engines. The system can provide extreme personalization (N=1) by inferring user intent, making relevant suggestions, maintaining context, carrying out cost-benefit analysis from multiple perspectives, finding similar cases before they are searched for, finds relevant documents and answers, issues resolved by experts in similar situations. The architecture enables meaningfully correlating, finding and connecting people and information sources through discovery of causal, temporal and spatial relations. We present two examples of demonstrations of concept that we are in the process of building out.

The AAAI-12 Workshop program was held Sunday and Monday, July 22–23, 2012 at the Sheraton Centre Toronto Hotel in Toronto, Ontario, Canada. The AAAI-12 workshop program included 9 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages, AI for Data Center Management and Cloud Computing, Cognitive Robotics, Grounding Language for Physical Systems, Human Computation, Intelligent Techniques for Web Personalization and Recommendation, Multiagent Pathfinding, Neural-Symbolic Learning and Reasoning, Problem Solving Using Classical Planners, Semantic Cities. This article presents short summaries of those events.