Seminar · Deep Learning
Big Boxes, Not Black Boxes: What we can compute about LLMs, and what it may say about AGI
Zohar Ringel · Hebrew University of Jerusalem
Wed, Oct 7, 2026 · 18:00 UTC
Deep networks are often thought of as black boxes. Their ability to encompass vast swathes of knowledge indeed makes them hard to explain. Yet many of their behaviours — generalization under overparametrization, grokking, OOD failures, neural scaling laws — recur across architectures and scales, and each, however surprising, can be reproduced and explained in controlled settings. I will review these efforts to identify and explain the universal phenomena of deep learning, and suggest that an LLM may amount to a sum of such tractable sub-phenomena, interpolative in nature. Finally, leaving scie