Commonsense Intelligence: Cracking the Longstanding Challenge in AI

Speaker: Yejin Choi (University of Washington)

Date and Time: 10am-11am, October 4 (Friday)

Place: Room 4405, Sibel Center

Abstract:

Despite considerable advances in deep learning, AI remains to be narrow and brittle. One fundamental limitation comes from its lack of commonsense intelligence: reasoning about everyday situations and events, which in turn, requires knowledge about how the physical and social world works. In this talk, I will share some of our recent efforts that attempt to crack commonsense intelligence.

First, I will introduce ATOMIC, the atlas of everyday commonsense knowledge and reasoning, organized as a graph of 877k if-then rules (e.g., "if X pays Y a compliment, then Y will likely return the compliment”). Next, I will introduce COMET, our deep neural networks that can learn from and generalize beyond the ATOMIC commonsense graph. Finally, I will present RAINBOW, a collection of seven benchmarks that aims to cover a wide spectrum of commonsense intelligence from natural language inference to adductive reasoning to visual commonsense reasoning. I will conclude the talk by discussing major open research questions, including the importance of algorithmic solutions to reduce incidental biases in data that can lead to overestimation of true AI capabilities.

Bio:

Yejin Choi is an associate professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington and also a senior research manager at AI2 overseeing the project Mosaic. Her research interests include language grounding with vision, physical and social commonsense knowledge, language generation with long-term coherence, conversational AI, and AI for social good. She was a recepient of Borg Early Career Award (BECA) in 2018, among the IEEE’s AI Top 10 to Watch in 2015, a co-recipient of the Marr Prize at ICCV 2013, and a faculty advisor for the Sounding Board team that won the inaugural Alexa Prize Challenge in 2017. Her work on detecting deceptive reviews, predicting the literary success, and interpreting bias and connotation has been featured by numerous media outlets including NBC News for New York, NPR Radio, New York Times, and Bloomberg Business Week. She received her Ph.D. in Computer Science from Cornell University.