Mapping AI Models 🗺
- New Research From Anthropic (the maker of AI chatbot Claude) offers a detailed look inside a modern large language model
- A subfield of AI research: Mechanistic Interpretability Aims to understand how these models work by examining their internal mechanisms
- For the first time, Anthropic made significant strides in interpreting AI models, specifically Claude 3 Sonnet, using a technique called "dictionary learning."
- Finding Patterns: They identified approximately 10 million patterns, or "features," which represent different concepts within the model.
- Examples of Features:
- San Francisco Feature: Activated when discussing San Francisco.
- Scientific Terms: Activated for topics like immunology or elements like lithium.
- When these features are triggered the Model Output changes as seen above
The work has really just begun. The features we found represent a small subset of all the concepts learned by the model during training
This is the first step! in understanding models, tracing LLMs from training data to final output.
Digital Product Designer, Entrepreneur
1moI've been fascinated by the work of the Anthropic team, specifically their focus on introspection. I avoid using the term "Mechanistic Interpretability," as it tends to confuse people. Instead, I explain that the creators of LLMs largely don't understand how the neural networks function, but they do have some insights. They are developing tools to observe how an LLM connects information and generates a response, similar to an MRI machine for an LLM. While people often find this intriguing and ask further complex questions, I always attempt to provide simple answers. It's exciting to see Anthropic making progress in this area.