[Theory] CORRECTION: 10/7 TTIC Colloquium: Yejin Choi, University of Washington

[Mary Marre]

*When:*      Monday, October 7th at 11:00 am



*Where:*     TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526



*Who: *      Yejin Choi, University of Washington


*Title: *       Commonsense Intelligence: Cracking the Longstanding
Challenge in AI

*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.

Host: David McAllester <mcallester at ttic.edu>


Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 517*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Mon, Sep 30, 2019 at 3:29 PM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*      Monday, October 7th at 11:00 am
>
>
>
> *Where:*     TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>
>
>
> *Who: *      Yejin Choi, University of Washington
>
>
> *Title: *       Commonsense Intelligence: Cracking the Longstanding
> Challenge in AI
>
> *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.
>
> Host: David McAllester <mcallester at ttic.edu>
>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 517*
> *Chicago, IL  60637*
> *p:(773) 834-1757*
> *f: (773) 357-6970*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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