Inside the Black Box: Neuron-Level Control and Safer LLMs
AI Engineering Podcast
00:00
Using interpretability as feedback and training signal
Tobias asks how to feed metrics back; Vinay discusses post-hoc utility, experiments on interpretability-based losses, and RL ideas.
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Summary
In this episode of the AI Engineering Podcast Vinay Kumar, founder and CEO of Arya.ai and head of Lexsi Labs, talks about practical strategies for understanding and steering AI systems. He discusses the differences between interpretability and explainability, and why post-hoc methods can be misleading. Vinay shares his approach to tracing relevance through deep networks and LLMs using DL Backtrace, and how interpretability is evolving from an audit tool into a lever for alignment, enabling targeted pruning, fine-tuning, unlearning, and model compression. The conversation covers setting concrete alignment metrics, the gaps in current enterprise practices for complex models, and tailoring explainability artifacts for different stakeholders. Vinay also previews his team's "AlignTune" effort for neuron-level model editing and discusses emerging trends in AI risk, multi-modal complexity, and automated safety agents. He explores when and why teams should invest in interpretability and alignment, how to operationalize findings without overcomplicating evaluation, and the best practices for private, safer LLM endpoints in enterprises, aiming to make advanced AI not just accurate but also acceptable, auditable, and scalable.
Announcements
Interview
Contact Info
Parting Question
Closing Announcements
Links
The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
In this episode of the AI Engineering Podcast Vinay Kumar, founder and CEO of Arya.ai and head of Lexsi Labs, talks about practical strategies for understanding and steering AI systems. He discusses the differences between interpretability and explainability, and why post-hoc methods can be misleading. Vinay shares his approach to tracing relevance through deep networks and LLMs using DL Backtrace, and how interpretability is evolving from an audit tool into a lever for alignment, enabling targeted pruning, fine-tuning, unlearning, and model compression. The conversation covers setting concrete alignment metrics, the gaps in current enterprise practices for complex models, and tailoring explainability artifacts for different stakeholders. Vinay also previews his team's "AlignTune" effort for neuron-level model editing and discusses emerging trends in AI risk, multi-modal complexity, and automated safety agents. He explores when and why teams should invest in interpretability and alignment, how to operationalize findings without overcomplicating evaluation, and the best practices for private, safer LLM endpoints in enterprises, aiming to make advanced AI not just accurate but also acceptable, auditable, and scalable.
Announcements
- Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
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- Your host is Tobias Macey and today I'm interviewing Vinay Kumar about strategies and tactics for gaining insights into the decisions of your AI systems
Interview
- Introduction
- How did you get involved in machine learning?
- Can you start by giving a quick overview of what explainability means in the context of ML/AI?
- What are the predominant methods used to gain insight into the internal workings of ML/AI models?
- How does the size and modality of a model influence the technique and evaluation of methods used?
- What are the contexts in which a team would incorporate explainability into their workflow?
- How might explainability be used in a live system to provide guardrails or efficiency/accuracy improvements?
- What are the aspects of model alignment and explainability that are most challenging to implement?
- What are the supporting systems that are necessary to be able to effectively operationalize the collection and analysis of model reliability and alignment?
- "Trust", "Reliability", and "Alignment" are all words that seem obvious until you try to define them concretely. What are the ways that teams work through the creation of metrics and evaluation suites to gauge compliance with those goals?
- What are the most interesting, innovative, or unexpected ways that you have seen explainability methods used in AI systems?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on explainability/reliability at AryaXAI?
- When is evaluation of explainability overkill?
- What do you have planned for the future of AryaXAI and explainable AI?
Contact Info
Parting Question
- From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?
Closing Announcements
- Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
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Links
- Lexsi Labs
- Ayra.ai
- Deep Learning
- AlexNet
- DL Backtrace
- Gradient Boost
- SAE == Sparse AutoEncoder
- Shapley Values
- LRP == Layerwise Relevance Propagation
- IG == Integrated Gradients
- Circuit Discovery
- F1 Score
- LLM As A Judge
The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
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