AI Alignment: Mechanistic Interpretability EN

AI Alignment and Interpretability: Essential for Your Health

This investigation examines mechanistic interpretability in artificial intelligence, focusing on understanding how deep learning models, especially transformers, work internally. Several sources delve into key concepts such as binary features, privileged bases, and feature superposition, as well as transformer architectures such as GPT-2 and the role of attention heads and neurons. Training techniques such as stochastic gradient descent and loss functions are also explored.

Furthermore, AI alignment, which seeks to ensure that AI systems adhere to human values, is addressed, discussing the RICE paradigm and challenges such as the "AI alignment paradigm," where greater alignment can paradoxically make models more susceptible to malicious misalignment. Finally, the texts assess the feasibility and limits of these techniques for achieving a deep understanding of complex models.

References

AI Alignment
https://alignmentsurvey.com/

The AI Alignment Paradox
https://cacm.acm.org/opinion/the-ai-alignment-paradox/

What is AI alignment?
https://www.ibm.com/think/topics/ai-alignment

Interpretability: Understanding how AI models think
https://www.youtube.com/watch?v=fGKNUvivvnc

Arthur Conmy - Mechanistic Interpretability Research Frontiers
https://www.youtube.com/watch?v=ibOceQDRnkI

Mechanistic Interpretability for AI Alignment
https://www.youtube.com/watch?v=_pgwIsiziEc

Mechanistic Interpretability for AI Safety -- A Review
https://arxiv.org/abs/2404.14082

The Misguided Quest for Mechanistic AI Interpretability
https://ai-frontiers.org/articles/the-misguided-quest-for-mechanistic-ai-interpretability

A Comprehensive Mechanistic Interpretability Explainer & Glossary
https://www.neelnanda.io/mechanistic-interpretability/glossary