[Theory] NOW: 2/14 Talks at TTIC: Daphne Ippolito, University of Pennsylvania

[Mary Marre]

*When:*        Monday, February 14th at* 11:30 am CT*


*Where:       *Talk will be given *live, in-person* at

                   TTIC, 6045 S. Kenwood Avenue

                   5th Floor, Room 530



*Where:*       Zoom Virtual Talk (*register in advance here*
<https://uchicagogroup.zoom.us/webinar/register/WN_Ijpmbu9PTpS73C_G-4kMLQ>)


*Who: *         Daphne Ippolito, University of Pennsylvania


*Title:* The Implications of (Near) Human-Level Language Generation by
Computers

*Abstract:  *One of the oldest goals of artificial intelligence is to have
computers that can use language with human-like ability. State-of-the-art
massive neural networks produce remarkably high-quality text. Does this
mean we have achieved AI’s longstanding goal? In this talk, I address two
concerns about neural text generation systems. First, I present a
large-scale, systematic study of how humans and automatic classifiers fare
at detecting generated text. I show that the strategies used to produce
text that is harder for humans to detect result in generated text that is
more detectable by automatic systems. Second, I describe how neural
language models are capable of memorizing significant amounts of their
training data (potentially raising privacy and copyright concerns). I show
that deduplication of training data is an effective mitigation strategy.
Despite their limitations, human-level text generation systems provide very
exciting opportunities, especially in the area of human-AI
collaboration–where language generation tools are used to assist rather
than replace human writers. I present Wordcraft, an AI-assisted writing
tool developed in collaboration with colleagues at Google, that gives a
glimpse into the future of next-generation word processors.

*Bio: *Daphne Ippolito is a final-year PhD student at University of
Pennsylvania and a research scientist at Google Brain. She is co-advised by
Professor Chris Callison-Burch at UPenn and Principal Scientist Douglas Eck
at Google. Her research focuses on large-scale neural language models for
text generation, spanning multiple dimensions, including decoding
strategies, evaluation methods, and use in creative-writing applications.
During her PhD, she has published over eight papers at top venues,
including ACL, EMNLP, and NeurIPS. Prior to UPenn, she completed her
bachelors at University of Toronto.

*H**ost: Karen Livescu <klivescu at ttic.edu>*
Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Chicago, IL  60637*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Mon, Feb 14, 2022 at 10:30 AM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*        Monday, February 14th at* 11:30 am CT*
>
>
> *Where:       *Talk will be given *live, in-person* at
>
>                    TTIC, 6045 S. Kenwood Avenue
>
>                    5th Floor, Room 530
>
>
>
> *Where:*       Zoom Virtual Talk (*register in advance here*
> <https://uchicagogroup.zoom.us/webinar/register/WN_Ijpmbu9PTpS73C_G-4kMLQ>
> )
>
>
> *Who: *         Daphne Ippolito, University of Pennsylvania
>
>
> *Title:* The Implications of (Near) Human-Level Language Generation by
> Computers
>
> *Abstract:  *One of the oldest goals of artificial intelligence is to
> have computers that can use language with human-like ability.
> State-of-the-art massive neural networks produce remarkably high-quality
> text. Does this mean we have achieved AI’s longstanding goal? In this talk,
> I address two concerns about neural text generation systems. First, I
> present a large-scale, systematic study of how humans and automatic
> classifiers fare at detecting generated text. I show that the strategies
> used to produce text that is harder for humans to detect result in
> generated text that is more detectable by automatic systems. Second, I
> describe how neural language models are capable of memorizing significant
> amounts of their training data (potentially raising privacy and copyright
> concerns). I show that deduplication of training data is an effective
> mitigation strategy. Despite their limitations, human-level text generation
> systems provide very exciting opportunities, especially in the area of
> human-AI collaboration–where language generation tools are used to assist
> rather than replace human writers. I present Wordcraft, an AI-assisted
> writing tool developed in collaboration with colleagues at Google, that
> gives a glimpse into the future of next-generation word processors.
>
> *Bio: *Daphne Ippolito is a final-year PhD student at University of
> Pennsylvania and a research scientist at Google Brain. She is co-advised by
> Professor Chris Callison-Burch at UPenn and Principal Scientist Douglas Eck
> at Google. Her research focuses on large-scale neural language models for
> text generation, spanning multiple dimensions, including decoding
> strategies, evaluation methods, and use in creative-writing applications.
> During her PhD, she has published over eight papers at top venues,
> including ACL, EMNLP, and NeurIPS. Prior to UPenn, she completed her
> bachelors at University of Toronto.
>
> *H**ost: Karen Livescu <klivescu at ttic.edu>*
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Chicago, IL  60637*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
>
> On Sun, Feb 13, 2022 at 4:08 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:*        Monday, February 14th at* 11:30 am CT*
>>
>>
>> *Where:       *Talk will be given *live, in-person* at
>>
>>                    TTIC, 6045 S. Kenwood Avenue
>>
>>                    5th Floor, Room 530
>>
>>
>>
>> *Where:*       Zoom Virtual Talk (*register in advance here*
>> <https://uchicagogroup.zoom.us/webinar/register/WN_Ijpmbu9PTpS73C_G-4kMLQ>
>> )
>>
>>
>> *Who: *         Daphne Ippolito, University of Pennsylvania
>>
>>
>> *Title:* The Implications of (Near) Human-Level Language Generation by
>> Computers
>>
>> *Abstract:  *One of the oldest goals of artificial intelligence is to
>> have computers that can use language with human-like ability.
>> State-of-the-art massive neural networks produce remarkably high-quality
>> text. Does this mean we have achieved AI’s longstanding goal? In this talk,
>> I address two concerns about neural text generation systems. First, I
>> present a large-scale, systematic study of how humans and automatic
>> classifiers fare at detecting generated text. I show that the strategies
>> used to produce text that is harder for humans to detect result in
>> generated text that is more detectable by automatic systems. Second, I
>> describe how neural language models are capable of memorizing significant
>> amounts of their training data (potentially raising privacy and copyright
>> concerns). I show that deduplication of training data is an effective
>> mitigation strategy. Despite their limitations, human-level text generation
>> systems provide very exciting opportunities, especially in the area of
>> human-AI collaboration–where language generation tools are used to assist
>> rather than replace human writers. I present Wordcraft, an AI-assisted
>> writing tool developed in collaboration with colleagues at Google, that
>> gives a glimpse into the future of next-generation word processors.
>>
>> *Bio: *Daphne Ippolito is a final-year PhD student at University of
>> Pennsylvania and a research scientist at Google Brain. She is co-advised by
>> Professor Chris Callison-Burch at UPenn and Principal Scientist Douglas Eck
>> at Google. Her research focuses on large-scale neural language models for
>> text generation, spanning multiple dimensions, including decoding
>> strategies, evaluation methods, and use in creative-writing applications.
>> During her PhD, she has published over eight papers at top venues,
>> including ACL, EMNLP, and NeurIPS. Prior to UPenn, she completed her
>> bachelors at University of Toronto.
>>
>> *H**ost: Karen Livescu <klivescu at ttic.edu>*
>>
>>
>>
>>
>> Mary C. Marre
>> Faculty Administrative Support
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue*
>> *Chicago, IL  60637*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>>
>> On Mon, Feb 7, 2022 at 9:07 PM Mary Marre <mmarre at ttic.edu> wrote:
>>
>>> *When:*        Monday, February 14th at* 11:30 am CT*
>>>
>>>
>>> *Where:       *Talk will be given *live, in-person* at
>>>
>>>                    TTIC, 6045 S. Kenwood Avenue
>>>
>>>                    5th Floor, Room 530
>>>
>>>
>>>
>>> *Where:*       Zoom Virtual Talk (*register in advance here*
>>> <https://uchicagogroup.zoom.us/webinar/register/WN_Ijpmbu9PTpS73C_G-4kMLQ>
>>> )
>>>
>>>
>>> *Who: *         Daphne Ippolito, University of Pennsylvania
>>>
>>>
>>> *Title:* The Implications of (Near) Human-Level Language Generation by
>>> Computers
>>>
>>> *Abstract:  *One of the oldest goals of artificial intelligence is to
>>> have computers that can use language with human-like ability.
>>> State-of-the-art massive neural networks produce remarkably high-quality
>>> text. Does this mean we have achieved AI’s longstanding goal? In this talk,
>>> I address two concerns about neural text generation systems. First, I
>>> present a large-scale, systematic study of how humans and automatic
>>> classifiers fare at detecting generated text. I show that the strategies
>>> used to produce text that is harder for humans to detect result in
>>> generated text that is more detectable by automatic systems. Second, I
>>> describe how neural language models are capable of memorizing significant
>>> amounts of their training data (potentially raising privacy and copyright
>>> concerns). I show that deduplication of training data is an effective
>>> mitigation strategy. Despite their limitations, human-level text generation
>>> systems provide very exciting opportunities, especially in the area of
>>> human-AI collaboration–where language generation tools are used to assist
>>> rather than replace human writers. I present Wordcraft, an AI-assisted
>>> writing tool developed in collaboration with colleagues at Google, that
>>> gives a glimpse into the future of next-generation word processors.
>>>
>>> *Bio: *Daphne Ippolito is a final-year PhD student at University of
>>> Pennsylvania and a research scientist at Google Brain. She is co-advised by
>>> Professor Chris Callison-Burch at UPenn and Principal Scientist Douglas Eck
>>> at Google. Her research focuses on large-scale neural language models for
>>> text generation, spanning multiple dimensions, including decoding
>>> strategies, evaluation methods, and use in creative-writing applications.
>>> During her PhD, she has published over eight papers at top venues,
>>> including ACL, EMNLP, and NeurIPS. Prior to UPenn, she completed her
>>> bachelors at University of Toronto.
>>>
>>> *H**ost: Karen Livescu <klivescu at ttic.edu>*
>>>
>>>
>>>
>>> Mary C. Marre
>>> Faculty Administrative Support
>>> *Toyota Technological Institute*
>>> *6045 S. Kenwood Avenue*
>>> *Chicago, IL  60637*
>>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>>
>>
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