The Association for the Advancement of Artificial Intelligence was pleased to present the 2010 Fall Symposium Series, held Thursday through Saturday, November 11-13, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the eight symposia are as follows: (1) Cognitive and Metacognitive Educational Systems; (2) Commonsense Knowledge; (3) Complex Adaptive Systems: Resilience, Robustness, and Evolvability; (4) Computational Models of Narrative; (5) Dialog with Robots; (6) Manifold Learning and Its Applications; (7) Proactive Assistant Agents; and (8) Quantum Informatics for Cognitive, Social, and Semantic Processes. The highlights of each symposium are presented in this report.

The Association for the Advancement of Artificial Intelligence was pleased to present the 2010 Fall Symposium Series, held Thursday through Saturday, November 11-13, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the eight symposia are as follows: (1) Cognitive and Metacognitive Educational Systems; (2) Commonsense Knowledge; (3) Complex Adaptive Systems: Resilience, Robustness, and Evolvability; (4) Computational Models of Narrative; (5) Dialog with Robots; (6) Manifold Learning and Its Applications; (7) Proactive Assistant Agents ; and (8) Quantum Informatics for Cognitive, Social, and Semantic Processes. The highlights of each symposium are presented in this report.

When we are confronted with unexpected situations, we deal of background knowledge and special-purpose reasoners to with them by falling back on our general knowledge or making support general inference. Recent advances in text mining, analogies to other things we know. When software applications crowdsourcing, and professional knowledge engineering efforts fail, on the other hand, they often do so in brittle have finally led to commonsense knowledge bases of and unfriendly ways. At the same time, new application colleagues grappling with representation and reasoning, to domains are giving fresh insights into desiderata for common Doug Lenat, Push Singh, and Lenhart Schubert conducting sense reasoners and guidance for knowledge collection large scale engineering projects to construct collections efforts.

By extending Cyc's ontology and KB approximately 2%, Cycorp and Cleveland Clinic Foundation (CCF) have built a system to answer clinical researchers' ad hoc queries. But, surprisingly often, after applying various constraints (medical domain knowledge, common sense, discourse pragmatics, syntax), there is only one single way to fit those fragments together, one semantically meaningful formal query P. The system, SRA (for Semantic Research Assistant), dispatches a series of database calls and then combines, logically and arithmetically, their results into answers to P. Seeing the first few answers stream back, the user may realize that they need to abort, modify, and re-ask their query. Besides real-time ad hoc query-answering, queries can be bundled and persist over time. Until full articulation/answering of precise, analytical queries becomes as straight-forward and ubiquitous as text search, even partial understanding of a query empowers semantic search over semi-structured data (ontology-tagged text), avoiding many of the false positives and false negatives that standard text searching suffers from.

By extending Cyc’s ontology and KB approximately 2%, Cycorp and Cleveland Clinic Foundation (CCF) have built a system to answer clinical researchers’ ad hoc queries. The query may be long and complex, hence only partially understood at first, parsed into a set of CycL (higher-order logic) fragments with open variables. But, surprisingly often, after applying various constraints (medical domain knowledge, common sense, discourse pragmatics, syntax), there is only one single way to fit those fragments together, one semantically meaningful formal query P. The system, SRA (for Semantic Research Assistant), dispatches a series of database calls and then combines, logically and arithmetically, their results into answers to P. Seeing the first few answers stream back, the user may realize that they need to abort, modify, and re-ask their query. Even before they push ASK, just knowing approximately how many answers would be returned can spark such editing. Besides real-time ad hoc query-answering, queries can be bundled and persist over time. One bundle of 275 queries is rerun quarterly by CCF to produce the procedures and outcomes data it needs to report to STS (Society of Thoracic Surgeons, an external hospital accreditation and ranking body); another bundle covers ACC (American College of Cardiology) reporting. Until full articulation/answering of precise, analytical queries becomes as straight-forward and ubiquitous as text search, even partial understanding of a query empowers semantic search over semi-structured data (ontology-tagged text), avoiding many of the false positives and false negatives that standard text searching suffers from.

On March 27, 2006, I gave a light-hearted and occasionally bittersweet presentation on “Whatever Happened to AI?” at the Stanford Spring Symposium presentation – to a lively audience of active AI researchers and formerly-active ones (whose current inaction could be variously ascribed to their having aged, reformed, given up, redefined the problem, etc.)  This article is a brief chronicling of that talk, and I entreat the reader to take it in that spirit: a textual snapshot of a discussion with friends and colleagues, rather than a scholarly article. I begin by whining about the Turing Test, but only for a thankfully brief bit, and then get down to my top-10 list of factors that have retarded progress in our field, that have delayed the emergence of a true strong AI.