Introduction to Mechanistic Interpretability - by Sarah
Mechanistic Interpretability is an emerging field that seeks to understand the internal reasoning processes of trained neural networks and gain insight into how and why they produce the outputs that they do.
Blog Introduction to Mechanistic Interpretability Sarah Aug 19, 2024 30 3 1 Share Mechanistic Interpretability is an emerging field that seeks to understand the internal reasoning processes of trained neural networks and gain insight into how and why they produce the outputs that they do. AI researchers currently have very little understanding of what is happening inside state-of-the-art models. 1 Current frontier models are extremely large – and extremely complicated. They might contain billions, or even trillions of parameters, spread across over 100 layers. Though we control the data that i
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