In this article, the author discusses the importance of understanding the inner workings of AI models and the need for interpretability. They introduce the concept of neuron activations and the challenges of deciphering their meaning. The author explains the use of dictionary learning to isolate patterns in neuron activations and how this technique can be scaled up to larger AI language models. They provide insights into the features extracted from Claude Sonnet, a large language model, and discuss how these features correspond to various concepts and entities. Additionally, they highlight the ability to manipulate these features and how it affects the model’s behavior. The article concludes with a discussion on the potential applications of these findings for improving the safety of AI models.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Understanding the inner workings of large language models | Understanding the internal processes of AI models | AI models will be safer and more reliable | Increasing concerns about the safety and reliability of AI models. |