Differential Privacy, Creativity & future of AI research in the LLM era | Niloofar Mireshghallah

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.***