Why Nick Cammarata thinks neural networks may be easier to understand

What happened

Goodfire researcher Nick Cammarata argues that neural networks may be more interpretable than previously thought, drawing on extensive neuron inspection. Revisiting Inception v1, he linked a neuron’s responses to cars and cats to shared V shapes rather than unrelated concepts. Other researchers caution that interpreting individual neurons does not explain whole circuits or establish that modern LLMs can be fully understood; technical results from his work on modern LLMs are forthcoming.

The story so far

Goodfire researcher Nick Cammarata argues that neural networks may be easier to understand than previously thought, but other researchers caution that his observations about individual neurons do not establish that modern large language models can be fully understood. Drawing on extensive neuron inspection, Cammarata revisited Inception v1 and linked a neuron’s responses to cars and cats to V shapes shared by both, rather than to unrelated concepts. The disagreement concerns how far such findings reach: interpreting individual neurons does not explain the larger circuits they form, other researchers caution. Technical results from Cammarata’s work on modern LLMs are forthcoming.

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