[Colloquium] REMINDER: 11/30 TTIC Colloquium: Oriol Vinyals, Google DeepMind

[Mary Marre]

*When:*      Monday, November 30th at 11:10 am



*Where:*     Zoom Virtual Talk (*register in advance here
<https://uchicagogroup.zoom.us/webinar/register/WN_4ZolnR7FQsK1MTmfUfss8g>*)



*Who: *       Oriol Vinyals, Google DeepMind



*Title:*        Model-free vs Model-based Reinforcement Learning


*Abstract: *In this talk, we will review model-free and model-based RL, two
paradigms that have enabled global breakthroughs in AI research. This
research included the ability to defeat professionals at the games of Go,
Poker, StarCraft, or DOTA, and in other fields such as Robotics. Using the
examples of the AlphaGo and AlphaStar agents, I'll present two approaches
from these paradigms in RL and will conclude the talk by presenting some
exciting new research directions that may unlock the power of model-based
RL in a wider variety of environments, including stochastic, partial
observable, with complex observation and action spaces.



*Bio:* Oriol Vinyals is a Principal Scientist at Google DeepMind, and a
team lead of the Deep Learning group. His work focuses on Deep Learning and
Artificial Intelligence. Prior to joining DeepMind, Oriol was part of the
Google Brain team. He holds a Ph.D. in EECS from the University of
California, Berkeley and is a recipient of the 2016 MIT TR35 innovator
award. His research has been featured multiple times at the New York Times,
Financial Times, WIRED, BBC, etc., and his articles have been cited over
90000 times. Some of his contributions such as seq2seq, knowledge
distillation, or TensorFlow are used in Google Translate, Text-To-Speech,
and Speech recognition, serving billions of queries every day, and he was
the lead researcher of the AlphaStar project, creating an agent that
defeated a top professional at the game of StarCraft, achieving Grandmaster
level, also featured as the cover of Nature. At DeepMind he continues
working on his areas of interest, which include artificial intelligence,
with particular emphasis on machine learning, deep learning and
reinforcement learning.


*Host:* David McAllester <mcallester at ttic.edu>

For more information on the colloquium series or to subscribe to the
mailing list, please see http://www.ttic.edu/colloquium.php


Mary C. Marre
Faculty Administrative Support
*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 Sun, Nov 29, 2020 at 3:02 PM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*      Monday, November 30th at 11:10 am
>
>
>
> *Where:*     Zoom Virtual Talk (*register in advance here
> <https://uchicagogroup.zoom.us/webinar/register/WN_4ZolnR7FQsK1MTmfUfss8g>*
> )
>
>
>
> *Who: *       Oriol Vinyals, Google DeepMind
>
>
>
> *Title:*        Model-free vs Model-based Reinforcement Learning
>
>
> *Abstract: *In this talk, we will review model-free and model-based RL,
> two paradigms that have enabled global breakthroughs in AI research. This
> research included the ability to defeat professionals at the games of Go,
> Poker, StarCraft, or DOTA, and in other fields such as Robotics. Using the
> examples of the AlphaGo and AlphaStar agents, I'll present two approaches
> from these paradigms in RL and will conclude the talk by presenting some
> exciting new research directions that may unlock the power of model-based
> RL in a wider variety of environments, including stochastic, partial
> observable, with complex observation and action spaces.
>
>
>
> *Bio:* Oriol Vinyals is a Principal Scientist at Google DeepMind, and a
> team lead of the Deep Learning group. His work focuses on Deep Learning and
> Artificial Intelligence. Prior to joining DeepMind, Oriol was part of the
> Google Brain team. He holds a Ph.D. in EECS from the University of
> California, Berkeley and is a recipient of the 2016 MIT TR35 innovator
> award. His research has been featured multiple times at the New York Times,
> Financial Times, WIRED, BBC, etc., and his articles have been cited over
> 90000 times. Some of his contributions such as seq2seq, knowledge
> distillation, or TensorFlow are used in Google Translate, Text-To-Speech,
> and Speech recognition, serving billions of queries every day, and he was
> the lead researcher of the AlphaStar project, creating an agent that
> defeated a top professional at the game of StarCraft, achieving Grandmaster
> level, also featured as the cover of Nature. At DeepMind he continues
> working on his areas of interest, which include artificial intelligence,
> with particular emphasis on machine learning, deep learning and
> reinforcement learning.
>
>
> *Host:* David McAllester <mcallester at ttic.edu>
>
> For more information on the colloquium series or to subscribe to the
> mailing list, please see http://www.ttic.edu/colloquium.php
>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *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, Nov 23, 2020 at 4:25 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:*      Monday, November 30th at 11:10 am
>>
>>
>>
>> *Where:*     Zoom Virtual Talk (*register in advance here
>> <https://uchicagogroup.zoom.us/webinar/register/WN_4ZolnR7FQsK1MTmfUfss8g>*
>> )
>>
>>
>>
>> *Who: *       Oriol Vinyals, Google DeepMind
>>
>>
>>
>> *Title:*        Model-free vs Model-based Reinforcement Learning
>>
>>
>> *Abstract: *In this talk, we will review model-free and model-based RL,
>> two paradigms that have enabled global breakthroughs in AI research. This
>> research included the ability to defeat professionals at the games of Go,
>> Poker, StarCraft, or DOTA, and in other fields such as Robotics. Using the
>> examples of the AlphaGo and AlphaStar agents, I'll present two approaches
>> from these paradigms in RL and will conclude the talk by presenting some
>> exciting new research directions that may unlock the power of model-based
>> RL in a wider variety of environments, including stochastic, partial
>> observable, with complex observation and action spaces.
>>
>>
>>
>> *Bio:* Oriol Vinyals is a Principal Scientist at Google DeepMind, and a
>> team lead of the Deep Learning group. His work focuses on Deep Learning and
>> Artificial Intelligence. Prior to joining DeepMind, Oriol was part of the
>> Google Brain team. He holds a Ph.D. in EECS from the University of
>> California, Berkeley and is a recipient of the 2016 MIT TR35 innovator
>> award. His research has been featured multiple times at the New York Times,
>> Financial Times, WIRED, BBC, etc., and his articles have been cited over
>> 90000 times. Some of his contributions such as seq2seq, knowledge
>> distillation, or TensorFlow are used in Google Translate, Text-To-Speech,
>> and Speech recognition, serving billions of queries every day, and he was
>> the lead researcher of the AlphaStar project, creating an agent that
>> defeated a top professional at the game of StarCraft, achieving Grandmaster
>> level, also featured as the cover of Nature. At DeepMind he continues
>> working on his areas of interest, which include artificial intelligence,
>> with particular emphasis on machine learning, deep learning and
>> reinforcement learning.
>>
>>
>> *Host:* David McAllester <mcallester at ttic.edu>
>>
>> For more information on the colloquium series or to subscribe to the
>> mailing list, please see http://www.ttic.edu/colloquium.php
>>
>>
>>
>>
>> Mary C. Marre
>> Faculty Administrative Support
>> *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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