johnny
@johnnylin
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@neuronpedia. prev @apple.
San Francisco, CA
Joined January 2009
RT @AnthropicAI: Researchers can use the Neuronpedia interactive interface here: And we’ve provided an annotated w….
github.com
Contribute to safety-research/circuit-tracer development by creating an account on GitHub.
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RT @neuronpedia: Announcement: we're open sourcing Neuronpedia! 🚀. This includes all our mech interp tools: the interpretability API, steer….
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RT @CurtTigges: Neuronpedia now hosts Chain-of-Thought! Steer and inspect Deepseek-R1-Distill-Llama-8B with SAEs trained by @Open_MOSS on @….
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RT @techreview: Google DeepMind has a new way to look inside an AI’s “mind”
technologyreview.com
Autoencoders are letting us peer into the black box of artificial intelligence. They could help us create AI that is better understood, and more easily controlled.
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RT @GoogleDeepMind: Gemma Scope allows us to study how features evolve throughout the model and interact to create more complex ones. Want….
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RT @NeelNanda5: Want to learn more? @neuronpedia have made a gorgeous interactive demo walking you through what Sparse Autoencoders are, an….
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RT @NeelNanda5: Sparse Autoencoders act like a microscope for AI internals. They're a powerful tool for interpretability, but training cost….
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exciting new research from @apolloaisafety and @jordantensor: E2E SAEs (w/ ~700k features) are now live on @neuronpedia - the first to use dual UMAPs for visual comparison and exploration between SAE training methods. check it out at
Proud to share Apollo Research's first interpretability paper! In collaboration w @JordanTensor!.⤵️. Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning. Our SAEs explain significantly more performance than before! .1/.
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Terrific work by @saprmarks and team! 🥳.We really enjoyed working with them to get their Sparse Autoencoders onto @neuronpedia. You can explore, search, and test their 622,594 features here:
neuronpedia.org
Under Peer Review
Can we understand & edit unanticipated mechanisms in LMs?. We introduce sparse feature circuits, & use them to explain LM behaviors, discover & fix LM bugs, & build an automated interpretability pipeline! Preprint w/ @can_rager, @ericjmichaud_, @boknilev, @davidbau, @amuuueller
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6/ Oh and of course, @neuronpedia is publicly available for anyone to experiment and play with at Let us know what you think!.
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5/ Thanks to @JBloomAus for support, @NeelNanda5 for TransformerLens, @ch402 @nickcammarata for inspiration from OpenAI Microscope, and William Saunders for Neuron Viewer. It's time to accelerate (interpretability research). 🚀🔬.
lesswrong.com
This posts assumes basic familiarity with Sparse Autoencoders. For those unfamiliar with this technique, we highly recommend the introductory section…
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4/ Our goal is to build fantastic infrastructure, UI, and tools so you can focus on the research, experiments, and collaboration. If you're working on SAEs, fill out this short form to get hosted on Neuronpedia, including generating feature dashboards:
docs.google.com
Time Estimate: < 5 Minutes Complete this application. We respond to you within 72 hours by email.
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3/ Neuronpedia lets you wrangle hundreds of thousands of features with a few clicks. 🤠. Here, we search a custom sentence (via life inference), then sort results by the sum of the activations of two specific tokens, and finally, we filter the results to layer 10 only. Not bad!
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2/ Neuronpedia makes interp research both visual and interactive. ✨. Here, we filter for "twitter" features in GPT2, layer 9's residuals. Several matches lights up, and we zoom into a specific cluster. Finally, we save three features to a new list that can be shared publicly.
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1/ Introducing Neuronpedia: an open platform for interpretability research with hosting, visualizations, and tooling for Sparse Autoencoders (SAEs). Let's try it out! ➡️. Neuronpedia lets us instantly test activations of SAE features with custom text. Here's a Star Wars feature:
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RT @JBloomAus: Super impressed by @johnnylin's Interactive Interface for exploring my GPT2 Small SAE Features. Fi….
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best IoT feature: devices that automatically update for daylight savings time.
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