Raymond Reiter, a professor of computer science at the University of Toronto, a fellow of the Royal Society of Canada, and winner of the International Joint Conference on Artificial Intelligence 1993 Outstanding Research Scientist Award, died September 16, 2002, after a year-long struggle with cancer. Reiter, known throughout the world as "Ray," made foundational contributions to artifi- cial intelligence, knowledge representation and databases, and theorem proving.
Visual analogical mapping and transfer can be used to derive a structural model of a drawing by analogy, and, moreover, the problem of analogical mapping can be guided by using functional knowledge. We view the interpretation drawings of designs as deriving a structural model of the components and connections of the depicted device. This problem is not deductive in nature but, rather, it is abductive, as there is no a priori reason a shape must represent one object and not another; only with significant help from the design context can a model be derived, and in particular we propose to do it by deriving the model by analogy to a similar drawing with a known structural and teleological model. This requires (1) an analogical mapping from the source (known) drawing to the target drawing that is derived on the basis of shapes and spatial relations, and (2) a transfer and adaptation process by which the old model is transferred to the new drawing and adapted to it. In this paper we are focusing, in particular, on the first task, that of analogical mapping.
Artificial intelligence (AI) systems are increasingly capable of analyzing health data such as medical images (e.g., skin lesions) and test results (e.g., ECGs). However, because it can be difficult to determine when an AI-generated diagnosis should be trusted and acted upon—especially when it conflicts with a human-generated one—many AI systems are not utilized effectively, if at all. Similarly, advances in information technology have made it possible to quickly solicit multiple diagnoses from diverse groups of people throughout the world, but these technologies are underutilized because it is difficult to determine which of multiple diagnoses should be trusted and acted upon. Here, I propose a method of soliciting and combining multiple diagnoses that will harness the collective intelligence of both human and artificial intelligence for analyzing health data.
SemNews is a semantic news service that monitors different RSS news feeds and provides structured representations of the meaning of news. As new content appears, SemNews extracts the summary from the RSS description and processes it using OntoSem, which is a sophisticated text understanding system. The OntoSem environment is a rich and extensive tool for extracting and representing meaning in a language independent way. OntoSem performs a syntactic, semantic, and pragmatic analysis of the text, resulting in its text meaning representation or TMR. The TMRs are represented using a constructed world model or an ontology that consists of about 8000 Concepts.