The Forum for Artificial Intelligence meets every other week (or so) to discuss scientific, philosophical, and cultural issues in artificial intelligence. Both technical research topics and broader inter-disciplinary aspects of AI are covered, and all are welcome to attend!

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A Framework for Learning to Query Heterogeneous Data

Dr. William W. Cohen   [homepage]

Machine Learning Department
Carnegie Mellon University

[Sign-up schedule for individual meetings]

A long-term goal of research on data integration is to develop data models and query languages that make it easy to answer structured queries using heterogeneous data. In this talk, I will describe a very simple query language, based on typed similarity queries, which are answered based on a graph containing a heterogeneous mixture of textual and non-textual objects. The similarity metric proposed is based on a lazy graph walk, which can be approximated efficiently using methods related to particle filtering. Machine learning techniques can be used to improve this metric for specific tasks, often leading to performance far better than plausible task-specific baseline methods. We experimentally evaluate several classes of similarity queries from the domains of analysis of biomedical text and personal information management: for instance, in one set of experiments, a user's personal information is represented as a graph containing messages, calendar information, social network information, and a timeline, and similarity search is used to find people likely to attend a meeting.

This is joint work with Einat Minkov and Andrew Ng.

About the speaker:

William Cohen received his bachelor's degree in Computer Science from Duke University in 1984, and a PhD in Computer Science from Rutgers University in 1990. From 1990 to 2000 Dr. Cohen worked at AT&T Bell Labs and later AT&T Labs-Research, and from April 2000 to May 2002 Dr. Cohen worked at Whizbang Labs, a company specializing in extracting information from the web. Dr. Cohen is member of the board of the International Machine Learning Society, and has served as an action editor for the Journal of Machine Learning Research, the journal Machine Learning and the Journal of Artificial Intelligence Research. He co-organized the 1994 International Machine Learning Conference, is the co-Program Committee Chair for the 2006 International Machine Learning Conference, and has served on more than 20 program committees or advisory committees.

Dr. Cohen's research interests include information integration and machine learning, particularly information extraction, text categorization and learning from large datasets. He holds seven patents related to learning, discovery, information retrieval, and data integration, and is the author of more than 100 publications.

Meaning Machines

Dr. Deb Roy   [homepage]

MIT Media Laboratory
MIT

People use words to refer to the world as a means for influencing the beliefs and actions of others. Although many isolated aspects of the structure and use of language have been extensively studied, a unified model of situated language use remains unexplored. Any attempt to explain unconstrained adult language use appears futile due to the overwhelming complexity of the physical, cognitive, and cultural factors at play. A strategy for making progress towards a holistic account of language use is to study simple forms of language (e.g., conversational speech about objects and events in the here-and-now in limited social contexts) and strive for "vertically integrated" computational models. I will present experiments guided by this strategy in building conversational robots and natural language interfaces for video games. An emerging framework suggests a semiotic perspective may be useful for designing systems that process language grounded in social and physical context.

About the speaker:

Deb Roy is Associate Professor of Media Arts and Sciences at the Massachusetts Institute of Technology. He is Director of the Cognitive Machines Group at the MIT Media Laboratory which he founded in 2000. Roy also directs the 10x research program, a lab-wide effort to design new technologies for enhancing human cognitive and physical capabilities. Roy has published numerous peer-reviewed papers in the areas of knowledge representation, speech and language processing, machine perception, robotics, information retrieval, cognitive modeling, and human-machine interaction, and has served as guest editor of the journal Artificial Intelligence. He has lectured widely in academia and industry. His work has been featured in various popular press venues including the New York Times, the Globe and Mail, CNN, BBC, and PBS. In 2003 Roy was appointed AT&T Career Development Professor. He holds a B.A.Sc. in Computer Engineering from University of Waterloo, and a Ph.D. in Media Arts and Sciences from MIT.