Monday, April 01, 2019
Carnegie Mellon University
Towards Literate Artificial Intelligence
Over the past decade, the field of artificial intelligence (AI) has seen striking developments. Yet, todayís 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 though 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 implications of this research and some ideas for future work.
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.