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Introduction to Interpretable AI: Superposition and Sparse Autoencoders

Event artwork for Introduction to Interpretable AI: Superposition and Sparse Autoencoders

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About this event

What to expect

Understanding how AI systems really work is becoming crucial as these models take on increasingly complex tasks. This in-person meetup in Montreal offers a focused introduction to interpretable AI, specifically exploring the concepts of superposition and sparse autoencoders — cutting-edge techniques for peering inside neural networks to understand how they store and process information.

What You'll Learn

Presented by Khalil Sabri, this session demystifies one of the most intriguing aspects of modern AI: how neural networks represent concepts internally. The talk explores:

  • Superposition: How neural networks store multiple concepts in overlapping patterns within the same neurons, creating a compressed but often opaque representation of knowledge
  • Sparse Autoencoders: A powerful technique for extracting and isolating these hidden features, making it possible to understand what specific neurons and layers are actually learning
  • Practical implications for AI safety, debugging, and model improvement

This introduction is designed to make complex concepts accessible, walking through the fundamental principles of interpretable AI and why this research matters for anyone building or deploying AI systems.

Who Should Attend

This meetup is perfect for AI practitioners, machine learning engineers, data scientists, and anyone curious about how neural networks actually work under the hood. Whether you're building AI agents, fine-tuning models, or simply want to understand the black box problem in modern AI, you'll gain valuable insights into the emerging field of mechanistic interpretability.

The informal format encourages discussion and questions, making it ideal for both those with technical backgrounds and curious learners who want to deepen their understanding of AI systems.

Event Format

This is a casual, community-driven gathering focused on learning and discussion. After Khalil's presentation, attendees will have the opportunity to engage in Q&A and discuss the implications of interpretable AI with fellow attendees. The relaxed atmosphere at the Grande Bibliothèque (room 2.130.1) provides an ideal setting for thoughtful conversation about one of AI's most important frontiers.

As AI systems become more powerful and autonomous, understanding their internal representations becomes essential — not just for improving performance, but for ensuring thes

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