Controlling AI Models from the Inside

Controlling AI Models from the Inside

Author: Practical AI LLC January 20, 2026 Duration: 43:55

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. 

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There's a lot of noise out there about artificial intelligence, but cutting through the hype to find what's genuinely useful can be a challenge. That's the space where Practical AI operates. Hosted by the team at Practical AI LLC, this technology podcast moves beyond abstract theory to explore how AI, machine learning, and large language models are actually being applied right now. Each episode features unscripted conversations with a diverse mix of experts, developers, business leaders, and curious minds. You'll hear tangible discussions about implementing machine learning systems, the realities of MLOps, the evolution of neural networks, and the practical implications of breakthroughs in deep learning and GANs. The dialogue is grounded in real-world scenarios, focusing on how these technologies solve problems, drive productivity, and create value in accessible ways. Whether you're a professional building models, a business person integrating AI tools, or an enthusiast eager to understand the landscape, this podcast offers a clear, conversational entry point. It’s about making sense of a complex field through the lens of practical application, demystifying the concepts that are shaping our world without losing sight of how they work on the ground.
Author: Language: en-us Episodes: 100

Practical AI
Podcast Episodes
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