Dive into how and why large language models like ChatGPT mirror the human Mandela Effect, reproducing our collective false memories and misquotations. Arshavir Blackwell examines the science behind errors in models and minds, and explores how new techniques can counteract these uncanny AI confabulations.

How do millions of computations inside large language models add up to something like understanding? This episode explores the latest breakthroughs in mechanistic interpretability, showing how tools like representational geometry, circuit decomposition, and compression theory illuminate the missing middle between circuits and meaning. Join Arshavir Blackwell as he opens the black box and challenges what we really mean by 'understanding' in machines.

Embark on a step-by-step journey through the inner workings of transformer models like those powering ChatGPT. Arshavir Blackwell breaks down how context, attention, and high-dimensional geometry turn isolated tokens into fluent, meaningful language—revealing the mathematics of understanding inside the black box.

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.

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.

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.

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.

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.