Dr Hyrum Anderson is a Distinguished Machine Learning Engineer at Robust Intelligence. Prior to that, he was Principal Architect of Trustworthy Machine Learning at Microsoft where he also founded Microsoft’s AI Red Team; he also led security research at MIT Lincoln Laboratory, Sandia National Laboratories, and Mendiant, and was Chief Scientist at Endgame (later acquired by Elastic). He’s also the co-author of the book “Not a Bug, But with a Sticker” and his research interests include assessing the security and privacy of ML systems and building Robust AI models.

Timestamps of the conversation 00:50 Introduction 01:40 Background in AI and ML security 04:45 Attacks on ML systems 08:20 Fractions of ML systems prone to Attacks 10:38 Operational risks with security measures 13:40 Solution from an algorithmic or policy perspective 15:46 AI regulation and policy making 22:40 Co-development of AI and security measures 24:06 Risks of Generative AI and Mitigation 27:45 Influencing an AI model 30:08 Prompt stealing on ChatGPT 33:50 Microsoft AI Red Team 38:46 Managing risks 39:41 Government Regulations 43:04 What to expect from the Book 46:40 Black in AI & Bountiful Children’s Foundation Check out Rora: https://teamrora.com/jayshah Guide to STEM Ph.D. AI Researcher + Research Scientist pay: https://www.teamrora.com/post/ai-researchers-salary-negotiation-report-2023 Rora's negotiation philosophy: https://www.teamrora.com/post/the-biggest-misconception-about-negotiating-salaryhttps://www.teamrora.com/post/job-offer-negotiation-lies Hyrum's Linkedin: https://www.linkedin.com/in/hyrumanderson/ And Research: https://scholar.google.com/citations?user=pP6yo9EAAAAJ&hl=en Book - Not a Bug, But with a Sticker: https://www.amazon.com/Not-Bug-But-Sticker-Learning/dp/1119883989/ About the Host: Jay is a Ph.D. student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information contained 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.***

Meredith is an associate professor at New York University and research director at the NYU Alliance for Public Interest Technology. Her research interests include using data analysis for good and ethical AI. She is also the author of the book “More Than a Glitch: Confronting Race, Gender, and Ability Bias in Tech” and we will discuss more about this with her in this podcast. Time stamps of the conversation 00:42 Introduction 01:17 Background 02:17 Meaning of “it is not a glitch” in the book title 04:40 How are biases coded into AI systems? 08:45 AI is not the solution to every problem 09:55 Algorithm Auditing 11:57 Why do organizations don't use algorithmic auditing more often? 15:12 Techno-chauvinism and drawing boundaries 23:18 Bias issues with ChatGPT and Auditing the model 27:55 Using AI for Public Good - AI on context 31:52 Advice to young researchers in AI Meredith's homepage: https://meredithbroussard.com/ And her Book: https://mitpress.mit.edu/9780262047654/more-than-a-glitch/ About the Host: Jay is a Ph.D. student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information contained 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.***

Part-2 of my podcast with David Stutz. (Part-1: https://youtu.be/J7hzMYUcfto) David is a research scientist at DeepMind working on building robust and safe deep learning models. Prior to joining DeepMind, he was a PhD student at the Max Plank Institute of Informatics. He also maintains a fantastic blog on various topics related to machine learning and graduate life which is insightful to young researchers out there. 00:00:00 Working at DeepMind 00:08:20 Importance of Abstraction and Collaboration in Research 00:13:08 DeepMind internship project 00:19:39 What drives research projects at DeepMind 00:27:45 Research in Industry vs Academia 00:30:45 Interview tips for research roles, at DeepMind or other companies 00:44:38 Finding the right Advisor & Institute for PhD 01:02:12 Do you really need a Ph.D. to do AI/ML research? 01:08:28 Academia vs Industry: Making the choice 01:10:49 Pressure to publish more papers 01:21:35 Artificial General Intelligence (AGI) 01:33:24 Advice to young enthusiasts on getting started David's Homepage: https://davidstutz.de/ And his blog: https://davidstutz.de/category/blog/ Research work: https://scholar.google.com/citations?user=TxEy3cwAAAAJ&hl=en About the Host: Jay is a Ph.D. student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries. Stay tuned for upcoming webinars! ***Disclaimer: The information contained 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.***