Emmanuel Ameisen

Emmanuel Ameisen, a research engineer at Anthropic specializing in interpretability research, shares insights from his recent studies on large language models. He discusses how mechanistic interpretability methods shed light on internal processes, showing how models plan creative tasks like poetry and calculate math using unique algorithms. The conversation dives into neural pathways, revealing how hallucinations stem from separate recognition circuits. Emmanuel highlights the challenges of accurately interpreting AI behavior and the importance of understanding these systems for safety and reliability.

Emmanuel Ameisen, an interpretability researcher at Anthropic and author, dives into the workings of large language models. He explains how these models can resemble biological systems and reveals surprising problem-solving patterns, like predicting multiple tokens at once. Emmanuel also addresses the misleading nature of reasoning outputs and the neural mechanics behind hallucinations. He emphasizes the importance of model calibration, debugging tools, and even shares practical advice for developers. It's a fascinating look at the complexity of AI behavior!

Emmanuel Ameisen, an interpretability researcher who previously worked at Anthropic, shares fascinating insights into large language models. He dives into how these models resemble biological systems, revealing surprising patterns like multi-token planning and shared neurons across languages. Emmanuel discusses the mechanisms behind hallucinations and the importance of model calibration. He also explores practical applications in medicine and offers invaluable advice for developers on understanding and evaluating model behavior.