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AI red teaming is exploding in popularity: At DEF CON this year, more than 2,500 hackers descended to red-team AI systems. Every organization investing in AI—from Microsoft to Google to Meta to NVIDIA—has AI red teams to actively secure their AI systems
This workshop will cover how NVIDIA RAPIDS offers a seamless experience to enable GPU-acceleration for many existing data science tasks with zero code changes. You will learn how to use GPU-accelerated tools to conduct data science faster, leading to more scalable, reliable, and cost-effective results!
This workshop will cover how NVIDIA RAPIDS offers a seamless experience to enable GPU-acceleration for many existing data science tasks with zero code changes. You will learn how to use GPU-accelerated tools to conduct data science faster, leading to more scalable, reliable, and cost-effective results!
But what does it even mean to red-team AI systems? Grounded in case studies from Microsoft, Siva Kumar contextualizes how red-teaming AI systems differs from red-teaming traditional software systems, discusses how it intersects with previous lines of inquiry such as adversarial examples, and distills eight lessons from a practitioner’s perspective.
