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Title: Towards natural Human-AI interactions in Vision and Language
Arjun Chandrasekaran
Computer Science PhD Student
College of Computing
Georgia Institute of Technology
http://www.prism.gatech.edu/~arjun9/
Date: Monday, October 21st, 2019
Time: 12pm to 2pm (EDT)
Location: Coda 1215 “Midtown” (756 West Peachtree St NW)
Committee:
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Dr. Devi Parikh (Advisor, College of Computing, Georgia Institute of Technology)
Dr. Dhruv Batra (College of Computing, Georgia Institute of Technology)
Dr. Sonia Chernova (College of Computing, Georgia Institute of Technology)
Dr. Mark Riedl (College of Computing, Georgia Institute of Technology)
Dr. Mohit Bansal (Department of Computer Science, University of North Carolina at Chapel Hill)
Abstract:
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Inter-human interaction is a rich form of communication. Human interactions typically leverage a good theory of mind, involve pragmatics, story-telling, humor, sarcasm, empathy, sympathy, etc. Recently, we have seen a tremendous increase in the frequency and the modalities through which humans interact with AI. Despite this, current human-AI interactions lack many of these features that characterize inter-human interactions. Towards the goal of developing AI that can interact with humans naturally (similar to other humans), I take a two-pronged approach that involves investigating the ways in which both the AI and the human can adapt to each other’s characteristics and capabilities. In my research, I study aspects
of human interactions, such as humor, story-telling, and the humans’ abilities to understand and collaborate with an AI. Specifically, in the vision and language modalities,
Through this work, I demonstrate that aspects of human interactions (such as certain forms of humor and story-telling) can be modeled with reasonable success using computational models that utilize neural networks. On the other hand, I also show that a lay person can successfully predict the outputs and failures of a deep neural network. Finally, I present evidence that suggests that a lay person who has access to interpretable explanations from the model, can collaborate more effectively with a neural network on a goal-driven task.
