Mechanistic Interpretability

Neural networks work. They generate coherent text, recognize faces, translate languages, and write code. But nobody—including the people who built them—knows exactly how they do it.

Until recently, AI systems were black boxes: weights and activations arranged in patterns we couldn't decode. We could measure performance, but we couldn't read their minds. That's changing. A new field called mechanistic interpretability is learning to reverse-engineer neural networks, discovering the circuits and features that implement computation in silicon minds.

And what they're finding is strange and beautiful: networks that pack more concepts than they have dimensions, circuits that suddenly "get it" after prolonged training, and organizational principles that might teach us as much about biological brains as artificial ones.

Why This Matters for Coherence

Understanding how neural networks maintain internal coherence—how they represent concepts, compose information, and generalize beyond training data—illuminates fundamental questions about cognition itself. These systems achieve coherence through mechanisms we're only beginning to understand: superposition, grokking, sparse distributed representations, and emergent algorithmic structure.

Interpretability isn't just about AI safety, though that matters. It's about understanding what coherence looks like when implemented in a system we can actually take apart and examine piece by piece.

What This Series Covers

This series explores the mechanistic interpretability revolution and its implications for understanding coherence in both artificial and biological systems. We'll examine:

• How neural networks use superposition to represent more features than dimensions
• The discovery of circuits implementing specific computational patterns
• Grokking and sudden generalization after apparent memorization
• Sparse autoencoders as tools for decomposing neural representations
• Connections between interpretability and information geometry
• What studying AI internals teaches us about biological cognition
• Why interpretability matters for human-AI collaboration

By the end of this series, you'll understand why the question "How do neural networks work?" finally has answers—and why those answers matter for understanding minds in general.

Articles in This Series

Reading the Mind of AI: The Mechanistic Interpretability Revolution

Introduction to mechanistic interpretability—why understanding AI internals matters for alignment, safety, and cognition science.

IdeasthesiaRyan Collison

Superposition: How Neural Networks Pack More Concepts Than Neurons

Deep dive into superposition—how networks represent more features than dimensions using interference patterns.

IdeasthesiaRyan Collison

Circuits in Silicon Minds: How Neural Networks Compute

Circuit-level analysis of neural network computation—from induction heads to complex algorithmic structures.

IdeasthesiaRyan Collison

Grokking: When Neural Networks Suddenly Understand

The grokking phenomenon—sudden generalization after apparent memorization and what it reveals about learning dynamics.

IdeasthesiaRyan Collison

Sparse Autoencoders: Extracting the Dictionary of Neural Concepts

How sparse autoencoders decompose neural activations into interpretable features—the current frontier technique.

IdeasthesiaRyan Collison

Where Interpretability Meets Information Geometry

Connecting interpretability findings to information geometry—feature manifolds and coherence in artificial systems.

IdeasthesiaRyan Collison

What AI Interpretability Teaches Us About Biological Brains

Bidirectional insights between AI interpretability and neuroscience—silicon as model system for understanding wetware.

IdeasthesiaRyan Collison

Human-AI Coherence Teams: Why Interpretability Matters for Collaboration

How interpretability enables better human-AI teaming—understanding AI cognition to leverage complementary coherence.

IdeasthesiaRyan Collison

Synthesis: What Neural Network Internals Teach Us About Coherence

Integration showing how interpretability findings illuminate coherence geometry—artificial systems as window into natural coherence.

IdeasthesiaRyan Collison