Welcome to my journey of tackling the "200 Concrete Open Problems in Mechanistic Interpretability" by Neel Nanda for fun in my spare time! This repository documents my progress, findings, and insights as I dive into these intriguing and challenging problems. My goal is to contribute to the field of mechanistic interpretability by exploring and solving these problems, sharing my methods and results with the community.
Mechanistic interpretability is a fascinating area of AI research focused on understanding the inner workings of neural networks. By addressing these open problems, I hope to:
- Improve the transparency and explainability of AI systems.
- Develop novel methods and tools for interpreting complex models.
- Contribute to the broader AI alignment and safety efforts.
This repository is organized into several sections:
- Problem List: A detailed list of the 200 open problems, categorized and linked to relevant resources.
- Solutions: My solutions to each problem, including code, experiments, and detailed explanations.
- Insights and Learnings: Key insights and learnings from my work on each problem.
- Discussion and Collaboration: A space for discussion, feedback, and collaboration with the community.
The problems are categorized into the following sections:
- Foundational Questions: Fundamental questions about the nature of interpretability.
- Method Development: Creating new methods for understanding neural networks.
- Application and Validation: Applying interpretability methods to real-world models and validating their effectiveness.
- Theoretical Insights: Theoretical analysis and insights into mechanistic interpretability.
Each problem will have its own dedicated page with:
- A detailed description of the problem.
- My approach and solution.
- Results and findings.
I welcome contributions from anyone interested in mechanistic interpretability! Here's how you can get involved:
- Discussion: Join the discussion on each problem by leaving comments and suggestions.
- Collaboration: Collaborate with me on specific problems by contributing code, ideas, or resources.
- Feedback: Provide feedback on my solutions and suggest improvements.
To get started, check out the Problem List and pick a problem that interests you. You can also browse through my Solutions to see my progress and findings so far.