Controlling AI Models from the Inside
As generative AI moves into production, traditional guardrails and input/output filters can prove too slow, too expensive, and/or too limited. In this episode, Alizishaan Khatri of Wrynx joins Daniel and Chris to explore a fundamentally different approach to AI safety and interpretability. They unpack the limits of today’s black-box defenses, the role of interpretability, and how model-native, runtime signals can enable safer AI systems.
Featuring:
Featuring:
- Alizishaan Khatri – LinkedIn
- Chris Benson – Website, LinkedIn, Bluesky, GitHub, X
- Daniel Whitenack – Website, GitHub, X
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Creators and Guests
Host
Chris Benson
Cohost @ Practical AI Podcast • Principal AI / Autonomy Research Engineer specializing in fully autonomous UxS swarming with embodied intelligence.
