Data Citizens: A Distinguished Lecture Series presents Marjorie McShane

Join us on Thursday, May 20, as we welcome Marjorie McShane, Associate Professor, Rensselaer Polytechnic Institute Cognitive Science Department, and Co-Director of the Language-Endowed Intelligent Agents (LEIA) Lab for a talk on artificial intelligence and language processing.

Data Citizens: A Distinguished Lecture Series is an ongoing course of in-depth talks by experts in the field of data science on a wide variety of topics including data visualization, big data, artificial intelligence, and predictive analytics.

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Thursday, May 20, 2021  |  1:00-2:00 PM 

Talk Title:  Toward Broad and Deep Language Understanding for Intelligent Systems

Abstract:  The early vision of AI included the goal of endowing intelligent systems with human-like language processing capabilities. This proved harder than expected, leading the vast majority of natural language processing practitioners to pursue less ambitious, shorter-term goals. Whereas the utility of human-like language processing is unquestionable, its feasibility is quite justifiably questioned. In this talk, I will not only argue that some approximation of human-like language processing is possible, but I will also present a program of R&D that is working on making it a reality. This vision and progress to date are described in the book Linguistics for the Age of AI (MIT Press, 2021).

About Marjorie McShane:  Dr. McShane is a cognitive scientist, computational linguist, and knowledge engineer who develops cognitive models of intelligent agents that can collaborate with people in task-oriented, dialog applications. She is particularly interested in the integration of functionalities that are often treated in isolation, such as physiological stimulation, emotion modeling, and the many aspects of cognition.

One aspect of cognition to which she has devoted particular attention is natural language processing, approached from a cross-linguistic perspective and with the goal of producing machine-tractable descriptions that can support sophisticated conversational agents. McShane was a central contributor to the Boas system, a proof-of-concept system that elicited knowledge about any of the world’s languages from linguistically untrained native speakers. Boas used a mixed-initiative strategy, by which the system guided certain aspects of the knowledge compilation process and the user took the lead in others. Among the key requirements were: that the system accommodates descriptions of not only anticipated but also unanticipated phenomena, that the descriptions be sufficiently formal to directly provide support to a generic machine translation engine, and that the system be usable by informants without the support of developers.

McShane has also worked extensively on cognitive modeling in the medical domain, to support the configuration of intelligent agents playing the roles of virtual patients and tutors in training applications such as the Maryland Virtual Patient system. Guided by the functional needs of such agents, McShane has recently begun to pursue the modeling of “mindreading” (otherwise known as mental model ascription), defined as inferring features of another human or artificial agent that cannot be directly observed, such as that agent’s beliefs, plans, goals, intentions, personality traits, mental and emotional states, and knowledge about the world. This capability is an essential functionality of intelligent agents if they are to engage in sophisticated collaborations with people.

McShane has authored three books, Linguistics for the Age of AI (co-authored with Sergei Nirenburg, The MIT Press, 2021), A Theory of Ellipsis (Oxford University Press, 2005) and An Innovative, Practical Approach to Polish Inflection (Lincom Europa, 2003), and has published extensively in the areas of linguistics, natural language processing, cognitive modeling, and knowledge representation.

She earned her PhD in Slavic Languages and Literatures from Princeton University, and since then, has been working on computational linguistics, intelligent agent modeling, and natural language processing.