Lifelong Machine Learning and Reasoning
June 11, 2014 (11:30 am)
Speaker: Danny Silver (Acadia University) Title: Lifelong Machine Learning and Reasoning Date: June 11, 2014 Time: 11:30am Location: Slonim Conference room (#430) Goldberg Computer Science Building Dalhousie University 6050 University Avenue, Halifax
Lifelong Machine Learning (LML) considers intelligent systems that learn many tasks over a lifetime, accurately and efficiently retaining the knowledge they have learned and using that knowledge to more quickly and accurately learn new tasks. In this tutorial we will review a framework for LML, define its requirements, and present solutions for the key problems that involve knowledge consolidation and transfer learning using multiple task learning methods. Links to artificial general intelligence and neural-symbolic integration are made. The final part of the talk will discuss recent work on extending LML to the learning of common background knowledge for the purposes of reasoning (this extension we call Lifelong Machine Learning and Reasoning, or LMLR). Opportunities for advances in artificial intelligence lie at the locus of machine learning and knowledge representation; specifically, knowledge consolidation can provide insights into common knowledge representation for use in learning and reasoning. This talk is for those who feel it is time for the machine learning community to move beyond learning algorithms to systems that are capable of learning, retaining and using knowledge over a lifetime.
Dr. Danny Silver is the Director of the Acadia Institute for Data Analytics. He is also a professor in and the former Director of the Jodrey School of Computer Science at Acadia University. His areas of research and development are machine learning, data mining, and adaptive systems. He has published over 60 scientific papers, edited special journal editions, and has co-chaired or been part of the program committee for a number of national and international conferences, seminars and workshops. He is on the editorial board for the Journals of Artifical General Intelligence and Brain Informatics and was the President of the Canadian Artificial Intelligence Association (CAIAC) from 2007-2009. Danny has held a NSERC Discovery Grant since 2000, and most recently was awarded a Harrison McCain Foundation Award for research into advance machine learning methods. Since 1993, he has worked on machine learning and data mining projects in the private and public sector providing situation analysis and problem definition, project management and guidance, and predictive analytic services. In 2011, he received the Science Champion Award from the Nova Scotia Discovery Center for his work on youth robotics and the advancement of STEM education.