Executive summary: This post presents a satirical interpretation of Sparse Autoencoder (SAE) features, highlighting their potential to capture meaningful computational structure in AI models, while also poking fun at the challenges and absurdities of feature interpretation.
Key points:
The post humorously explores whether SAE features reflect properties of the model or just correlational structure in the underlying data distribution.
Several fictional SAE features are presented, such as the “Scripture Feature,” “Perseverance Feature,” and “Teamwork Feature,” each with a tongue-in-cheek interpretation.
The post jokingly suggests that deciphering feature activations using quantization can reveal hidden messages, like a plea for help in Morse code.
A fictional “Neel Nanda Feature” is introduced, which supposedly fires on text related to mechanistic interpretability and methods Neel Nanda is excited about.
The post concludes with a series of nested “Criticism of Effective Altruism” features, highlighting the potential for hierarchical feature activations and the absurdity of over-interpretation.
The authors acknowledge that this post is an April Fools’ joke, but encourage readers to explore real SAE research and feature dashboards on Neuronpedia.org.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, andcontact us if you have feedback.
As an author on this post,I think this is a surprisingly good summary. Some notes:
While all of the features are fictional, the more realistic ones are not far from reality. We’ve seen scripture features of various kinds in real models. A scripture intersect Monty Python feature just wouldn’t be that surprising.
Some of the other features were more about tying in interesting structure in reality than playing anything else (eg criticism of criticism feature).
In terms of the absurdities of feature interpretation, I think the idea was to highlight awareness of possible flaws like buying into overly complicated stories we could tell if we work too hard to explain our results. We’re not sure what we’re doing yet in this pre-paradigmatic science so having a healthy dose of self-awareness is important!
Executive summary: This post presents a satirical interpretation of Sparse Autoencoder (SAE) features, highlighting their potential to capture meaningful computational structure in AI models, while also poking fun at the challenges and absurdities of feature interpretation.
Key points:
The post humorously explores whether SAE features reflect properties of the model or just correlational structure in the underlying data distribution.
Several fictional SAE features are presented, such as the “Scripture Feature,” “Perseverance Feature,” and “Teamwork Feature,” each with a tongue-in-cheek interpretation.
The post jokingly suggests that deciphering feature activations using quantization can reveal hidden messages, like a plea for help in Morse code.
A fictional “Neel Nanda Feature” is introduced, which supposedly fires on text related to mechanistic interpretability and methods Neel Nanda is excited about.
The post concludes with a series of nested “Criticism of Effective Altruism” features, highlighting the potential for hierarchical feature activations and the absurdity of over-interpretation.
The authors acknowledge that this post is an April Fools’ joke, but encourage readers to explore real SAE research and feature dashboards on Neuronpedia.org.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
As an author on this post,I think this is a surprisingly good summary. Some notes:
While all of the features are fictional, the more realistic ones are not far from reality. We’ve seen scripture features of various kinds in real models. A scripture intersect Monty Python feature just wouldn’t be that surprising.
Some of the other features were more about tying in interesting structure in reality than playing anything else (eg criticism of criticism feature).
In terms of the absurdities of feature interpretation, I think the idea was to highlight awareness of possible flaws like buying into overly complicated stories we could tell if we work too hard to explain our results. We’re not sure what we’re doing yet in this pre-paradigmatic science so having a healthy dose of self-awareness is important!