DigiNews

Summary

Billion-Parameter Theories argues that complex systems are not merely complicated and that large neural networks function as a new kind of theory by embedding understandings in architecture and training data rather than compact equations. It traces the shift from theory-first to practice-first approaches and suggests that the missing medium for modeling complexity is the ability to hold and manipulate extremely large models, with mechanistic interpretability as a path to transferable insights.