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What and Who
Title:Towards Literate Artificial Intelligence
Speaker:Mrinmaya Sachan
coming from:Carnegie Mellon University
Speakers 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.

Event Type:SWS Colloquium
Visibility:SWS, RG1, MMCI
We use this to send out email in the morning.
Level:AG Audience
Language:English
Date, Time and Location
Date:Tuesday, 5 March 2019
Time:10:30
Duration:90 Minutes
Location:Saarbrücken
Building:E1 5
Room:029
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 AI systems in the same way as we evaluate our children - by giving them standardized tests.
Standardized tests are administered to students to measure the knowledge and skills gained by them. Thus, it is natural to use these tests
to measure the intelligence of our AI systems. 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 P2P to build 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.
Contact
Name(s):Annika Meiser
Phone:93039105
EMail:--email address not disclosed on the web
Video Broadcast
Video Broadcast:YesTo Location:Kaiserslautern
To Building:G26To Room:111
Meeting ID:
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Created:
Annika Meiser/MPI-SWS, 02/26/2019 09:32 AM
Last modified:
Uwe Brahm/MPII/DE, 03/05/2019 07:01 AM
  • Annika Meiser, 02/26/2019 09:37 AM -- Created document.