AI is changing fast. Building safe, trustworthy AI systems and establishing confidence in their behavior and robustness is crucial for their success and adoption in society.
In this conversation with Dr. Anthony Corso, he discusses techniques for building safe and reliable autonomous systems using state of the art machine learning techniques for high-stakes applications such as healthcare, transportation, and critical infrastructure.
View Anthony’s course: https://online.stanford.edu/courses/xaa101-designing-reliable-and-robust-ai-systems
About the speaker:
Anthony is the executive director of the Stanford Center for AI Safety and the associate director of research for the SAIL-Toyota Center. His current research is split between developing verifiably robust autonomy and the using AI algorithms to tackle climate change. Learn more about Anthony: https://anthonylcorso.com/
Chapters
0:00 Introduction
01:38 Dr. Corso intro to reliable AI
03:24 Risks with Autonomous Systems
04:43 How AI Systems Fail
06:13 Can AI be more safe than humans?
07:44 Challenges & Scalability
08:58 Generalizability
11:19 Existential Risks of AI
13:17 AI Ethics
14:39 Applications of AI Systems
15:29 How to build safe AI Systems
21:00 Testing for Rare Events
22:05 Testing & Formal Verification
26:40 What is Robustness?
29:20 Uncertainty Quantification & Fallback Strategies
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