Building Knowledgeable Machines
Submitted by Hari (ഹരി) (@haricm) on Jun 6, 2018
Knowledge harvesting from Web-scale text datasets has emerged as an important and active research area over the last decade or so, resulting in the automatic construction of large knowledge graphs (KGs) consisting of millions of entities and relationships among them. This has the potential to revolutionize Artificial Intelligence and intelligent decision making by removing the knowledge bottleneck which has plagued systems in these areas all along.
In this talk, I shall present an overview of my group’s research in this exciting and emerging area.
Partha Talukdar is an Assistant Professor in the Department of Computational and Data Sciences (CDS) at the Indian Institute of Science (IISc), Bangalore. He is also the founder of Kenome, an AI startup helping enterprises make sense of dark data using cutting edge machine learning, NLP and deep learning. Previously, he was a Postdoctoral Fellow in the Machine Learning Department at Carnegie Mellon University, working with Tom Mitchell on the NELL project. Partha received his PhD (2010) in CIS from the University of Pennsylvania, working under the supervision of Fernando Pereira, Zack Ives, and Mark Liberman. Partha is broadly interested in Machine Learning, Natural Language Processing, and Cognitive Neuroscience, with particular interest in large-scale learning and inference. Partha is a recipient of IBM Faculty Award, Google’s Focused Research Award, and Accenture Open Innovation Award. He is a co-author of a book on Graph-based Semi-Supervised Learning published by Morgan Claypool Publishers. Homepage: http://talukdar.net