Neuronpedia, a platform for mechanistic interpretability, partnered with DeepMind in July to build a demo of Gemma Scope that you can play around with right now. In the demo, you can test out different prompts and see how the model breaks up your prompt and what activations your prompt lights up. You can also mess around with the model. For example, if you turn the feature about dogs way up and then ask the model a question about US presidents, Gemma will find some way to weave in random babble about dogs, or the model may just start barking at you.
One interesting thing about sparse autoencoders is that they are unsupervised, meaning they find features on their own. That leads to surprising discoveries about how the models break down human concepts. “My personal favorite feature is the cringe feature,” says Joseph Bloom, science lead at Neuronpedia. “It seems to appear in negative criticism of text and movies. It’s just a great example of tracking things that are so human on some level.”
You can search for concepts on Neuronpedia and it will highlight what features are being activated on specific tokens, or words, and how strongly each one is activated. “If you read the text and you see what’s highlighted in green, that’s when the model thinks the cringe concept is most relevant. The most active example for cringe is somebody preaching at someone else,” says Bloom.
Some features are proving easier to track than others. “One of the most important features that you would want to find for a model is deception,” says Johnny Lin, founder of Neuronpedia. “It’s not super easy to find: ‘Oh, there’s the feature that fires when it’s lying to us.’ From what I’ve seen, it hasn’t been the case that we can find deception and ban it.”
DeepMind’s research is similar to what another AI company, Anthropic, did back in May with Golden Gate Claude. It used sparse autoencoders to find the parts of Claude, their model, that lit up when discussing the Golden Gate Bridge in San Francisco. It then amplified the activations related to the bridge to the point where Claude literally identified not as Claude, an AI model, but as the physical Golden Gate Bridge and would respond to prompts as the bridge.
Although it may just seem quirky, mechanistic interpretability research may prove incredibly useful. “As a tool for understanding how the model generalizes and what level of abstraction it’s working at, these features are really helpful,” says Batson.
For example, a team lead by Samuel Marks, now at Anthropic, used sparse autoencoders to find features that showed a particular model was associating certain professions with a specific gender. They then turned off these gender features to reduce bias in the model. This experiment was done on a very small model, so it’s unclear if the work will apply to a much larger model.
Mechanistic interpretability research can also give us insights into why AI makes errors. In the case of the assertion that 9.11 is larger than 9.8, researchers from Transluce saw that the question was triggering the parts of an AI model related to Bible verses and September 11. The researchers concluded the AI could be interpreting the numbers as dates, asserting the later date, 9/11, as greater than 9/8. And in a lot of books like religious texts, section 9.11 comes after section 9.8, which may be why the AI thinks of it as greater. Once they knew why the AI made this error, the researchers tuned down the AI’s activations on Bible verses and September 11, which led to the model giving the correct answer when prompted again on whether 9.11 is larger than 9.8.
