Inside the Black Box: Cracking AI and Deep Learning

Can Smaller Language Models Be Smarter?

Today we explore whether mechanistic interpretability could hold the key to building leaner, more transparent—and perhaps even smarter—large language models. From knowledge distillation and pruning to low-rank adaptation, we examine cutting-edge strategies to make AI models both smaller and more explainable. Join Arshavir as he breaks down the surprising challenges of making models efficient without sacrificing understanding.

The Weird Geometry That Makes AI Think

Explore how large language models use high-dimensional geometry to produce intelligent behavior. We peer into the mathematical wilderness inside transformers, revealing how intuition fails, and meaning emerges.

Can We Fix It?

Arshavir Blackwell takes you on a journey inside the black box of large language models, showing how cutting-edge methods help researchers identify, understand, and even fix the inner quirks of AI. Through concrete case studies, he demonstrates how interpretability is evolving from an arcane art to a collaborative science—while revealing the daunting puzzles that remain. This episode unpacks the step-by-step workflow and surprising realities of mechanistically mapping model cognition.

Using Symbolic AI to Explain LLMs

Delve into the mysterious world of neural circuits within large language models. We’ll dismantle the jargon, connect these abstract ideas to real examples, and discuss how circuits help bridge the gap between machine learning and human cognition.

Peering Inside the Black Box

Mechanistic interpretability and artificial psycholinguistics are transforming our understanding of large language models. In this episode, Arshavir Blackwell explores how probing neural circuits, behavioral tests, and new tools are unraveling the mysteries of AI reasoning.