Conversational systems: Past, Present and Future
November 17th, 2021 11:00 PM - 11:45 AM EST
James F. Allen
John H. Dessauer Professor of Computer Science
University of Rochester and Florida Institute for Human and Machine Cognition
Website: University of Rochester and IHMC
Abstract: A system that can carry on an extended conversation with a person involving substantive content is the holy grail of Artificial Intelligence research. It has been an active area of research for over fifty years. This talk will examine the history of ideas and advances, somewhat biased by the work from my research group over the years. I will also cover recent work in the area involving deep learning and consider whether such techniques bring the dream within reach. I’ll conclude by examining the tension between the desire for explanatory and inspectable models vs apparently high performing but completely opaque systems, and speculate on how the future of work in the area will unfold.
Bio: James Allen is the John H. Dessauer Professor of Computer Science at the University of Rochester and also an Associate Director and a Senior Researcher at the Institute for Human and Machine Cognition in Pensacola, FL. He received his PhD in Computer Science from the University of Toronto and was a recipient of the Presidential Young Investigator award from NSF in 1984. Dr. Allen is an international leader in the areas of natural language understanding and collaborative human-machine interaction. A Founding Fellow of the American Association for Artificial Intelligence (AAAI), he was editor-in-chief of the journal Computational Linguistics from 1983-1993. He was general chair of the Second International conference on Principles of Knowledge Representation held in Boston in 1991, and the Fourth International Conference on AI Planning Systems in Pittsburgh in 1999. Dr. Allen’s research concerns defining computational models of intelligent collaborative and conversational agents that can interact effectively with humans in a wide range of problem solving and analysis tasks. This body of research is unique in its focus on combining what are often treated as separate fields in Artificial Intelligence, including knowledge representation and reasoning, language understanding, planning, intention recognition and learning. He is particularly interested in the overlap between natural language understanding and reasoning. While most of the NLP field has moved to statistical learning methods as the paradigm for language processing, he believes that deep language understanding can only currently be achieved by significant hand-engineering of semantically-rich formalisms coupled with statistical preferences. The TRIPS project is a long-term effort to build generic technology for dialogue systems (both spoken and 'chat' systems), which has been pursued for several decades. This includes broad-coverage domain-general natural language processing, dialogue agents built using models of collaborative problem solving, dynamic context-sensitive language modeling, and a rich engineering framework for building dialogue systems in new domains.