University of Maryland Baltimore County
Manas Gaur is an assistant professor in the department of computer science and electrical engineering at the University of Maryland Baltimore County. He is an affiliate faculty member of Ebiquity Research Center at UMBC and directs the Knowledge-infused AI and Inference lab (https://www.csee.umbc.edu/research/research-labs/). As the AAAI New Faculty, his noted research in knowledge-infused learning explores methods and metrics to develop safe and explainable conversational systems. He has been an Eric and Wendy Schmidt DSSG Fellow with UChicago, AI for Social Good Fellow with Dataminr Inc. and Visiting Researcher at Alan Turing Institute.
Talks and Events
Targeted Knowledge Infusion To Make Conversational AI Explainable and Safe
Conversational Systems (CSys) represent practical and tangible outcomes of advances in NLP and AI. CSys see continuous improvements through unsupervised training of large language models (LLMs) on a humongous amount of generic training data. However, when these CSys are suggested for use in domains like Mental Health, they fail to match the acceptable standards of clinical care, such as the clinical process in Patient Health Questionnaire (PHQ-9). The talk will present, Knowledge-infused Learning (KiL), a paradigm within NeuroSymbolic AI that focuses on making machine/deep learning models (i) learn over knowledge-enriched data, (ii) learn to follow guidelines in process-oriented tasks for safe and reasonable generation, and (iii) learn to leverage multiple contexts and stratified knowledge to yield user-level explanations. KiL established Knowledge-Intensive Language Understanding, a set of tasks for assessing safety, explainability, and conceptual flow in CSys.
Track: Deep Learning for and with Knowledge Graphs
- Knowledge-infused Learning
- Process Knowledge
- Explainable AI
- Safe AI
- Conversational Systems
- Social Good