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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Talk Title: Towards Literate Artificial Intelligence
Abstract: Over the past decade, the field of artificial intelligence (AI) has seen striking developments. Yet, today’s AI systems sorely lack the essence of human intelligence i.e. our ability to (a) understand language and grasp its meaning, (b) assimilate common-sense background knowledge of the world, and (c) draw inferences and perform reasoning. Before we even begin to build AI systems that possess the aforementioned human abilities, we must ask an even more fundamental question: How would we even evaluate an AI system on the aforementioned abilities? In this talk, I will argue that we can evaluate our AI systems in the same way as we evaluate our children - by giving them standardized tests. Standardized tests are regularly administered to students to evaluate the knowledge and the skills gained by them as they progress through the formal education system. Thus, it is a natural proposition to use these tests to measure the intelligence of our AI systems as well. Then, I will describe Parsing to Programs (P2P), a framework that combines ideas from semantic parsing and probabilistic programming for situated question answering. We used the P2P framework to build two systems that can solve pre-university level Euclidean geometry and Newtonian physics examinations. P2P achieves a performance at least as well as the average student on questions from textbooks, geometry questions from previous SAT exams, and mechanics questions from Advanced Placement (AP) exams. I will conclude by describing the implications of this research and some ideas for future work.
Bio: Mrinmaya Sachan is a Ph.D. candidate in the Machine Learning Department in the School of Computer Science at Carnegie Mellon University. His research is in the interface of machine learning, natural language processing, knowledge discovery, and reasoning. He received an outstanding paper award at ACL 2015, multiple fellowships (IBM fellowship, Siebel scholarship and CMU CMLH fellowship) and was a finalist for the Facebook fellowship. Before graduate school, he graduated with a B.Tech. in Computer Science and Engineering from IIT Kanpur with an Academic Excellence Award.
