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Listen "Differential Privacy, Creativity & future of AI research in the LLM era | Niloofar Mireshghallah"
Episode Synopsis
Niloofar is a Postdoctoral researcher at University of Washington with research interests in building privacy preserving AI systems and studying the societal implications of machine learning models. She received her PhD in Computer Science from UC San Diego in 2023 and has received multiple awards and honors for research contributions.
Time stamps of the conversation
00:00:00 Highlights
00:01:35 Introduction
00:02:56 Entry point in AI
00:06:50 Differential privacy in AI systems
00:11:08 Privacy leaks in large language models
00:15:30 Dangers of training AI on public data on internet
00:23:28 How auto-regressive training makes things worse
00:30:46 Impact of Synthetic data for fine-tuning
00:37:38 Most critical stage in AI pipeline to combat data leaks
00:44:20 Contextual Integrity
00:47:10 Are LLMs creative?
00:55:24 Under vs. Overpromises of LLMs
01:01:40 Publish vs. perish culture in AI research recently
01:07:50 Role of academia in LLM research
01:11:35 Choosing academia vs. industry
01:17:34 Mental Health and overarching
More about Niloofar: https://homes.cs.washington.edu/~niloofar/
And references to some of the papers discussed:
https://arxiv.org/pdf/2310.17884
https://arxiv.org/pdf/2410.17566
https://arxiv.org/abs/2202.05520
About the Host:
Jay is a PhD student at Arizona State University working on improving AI for medical diagnosis and prognosis.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: http://jayshah.me/ for any queries.
Stay tuned for upcoming webinars!
***Disclaimer: The information in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***
Time stamps of the conversation
00:00:00 Highlights
00:01:35 Introduction
00:02:56 Entry point in AI
00:06:50 Differential privacy in AI systems
00:11:08 Privacy leaks in large language models
00:15:30 Dangers of training AI on public data on internet
00:23:28 How auto-regressive training makes things worse
00:30:46 Impact of Synthetic data for fine-tuning
00:37:38 Most critical stage in AI pipeline to combat data leaks
00:44:20 Contextual Integrity
00:47:10 Are LLMs creative?
00:55:24 Under vs. Overpromises of LLMs
01:01:40 Publish vs. perish culture in AI research recently
01:07:50 Role of academia in LLM research
01:11:35 Choosing academia vs. industry
01:17:34 Mental Health and overarching
More about Niloofar: https://homes.cs.washington.edu/~niloofar/
And references to some of the papers discussed:
https://arxiv.org/pdf/2310.17884
https://arxiv.org/pdf/2410.17566
https://arxiv.org/abs/2202.05520
About the Host:
Jay is a PhD student at Arizona State University working on improving AI for medical diagnosis and prognosis.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: http://jayshah.me/ for any queries.
Stay tuned for upcoming webinars!
***Disclaimer: The information in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***
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