Artificial intelligence researchers have not been successful in giving intelligent agents the common-sense knowledge they need to reason about the world. Without this knowledge, it is impossible for intelligent agents to truly interact with the world. Traditionally, there have been two unsuccessful approaches to getting computers to reason about the world--symbolic logic and deep learning. A new project, called COMET, tries to bring these two approaches together. Although it has not yet succeeded, it offers the possibility of progress.

Artificial intelligence researchers have not been successful in giving intelligent agents the common-sense knowledge they need to reason about the world. Without this knowledge, it is impossible for intelligent agents to truly interact with the world. Traditionally, there have been two unsuccessful approaches to getting computers to reason about the world--symbolic logic and deep learning. A new project, called COMET, tries to bring these two approaches together. Although it has not yet succeeded, it offers the possibility of progress.

Artificial intelligence researchers have not been successful in giving intelligent agents the common-sense knowledge they need to reason about the world. Without this knowledge, it is impossible for intelligent agents to truly interact with the world. Traditionally, there have been two unsuccessful approaches to getting computers to reason about the world--symbolic logic and deep learning. A new project, called COMET, tries to bring these two approaches together. Although it has not yet succeeded, it offers the possibility of progress.

Artificial intelligence researchers have not been successful in giving intelligent agents the common-sense knowledge they need to reason about the world. Without this knowledge, it is impossible for intelligent agents to truly interact with the world. Traditionally, there have been two unsuccessful approaches to getting computers to reason about the world--symbolic logic and deep learning. A new project, called COMET, tries to bring these two approaches together. Although it has not yet succeeded, it offers the possibility of progress.

Her contributions in this endeavor include serving as PI of the SAIC Evaluation and Knowledge Infrastructure Team for the DARPA Machine Reading Program, which focused on developing methods to extract formal knowledge from free text; and PI of TAILCM, an IARPA sponsored seedling that has investigated the feasibility of translating regulatory text written in natural language to rules in formal logic programming languages. Before joining SAIC, Dr. Morgensten was Research Staff Member at the IBM T. J. Watson Research Center in Hawthorne, NY where she developed leading technologies in the areas of decision support, knowledge management, customer relationship management, business rules, and the semantic web. Dr. Morgenstern has served as technical lead for developing and deploying applications for Fortune-500 companies in a diversity of industries, including medical insurance, banking, insurance, telephony, software sales, and business continuity. Her patents have won several IBM awards due to their value to industry. Dr. Morgenstern has been on the editorial boards of Journal of Artificial Intelligence Research, Annals of Mathematics and Artificial Intelligence, and Electronic Transactions of Artificial Intelligence.