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Goodfire
415 posts
Goodfire
@GoodfireAI
Using interpretability to understand, learn from, and design AI.
San Francisco Bergabung Ağustos 2024
28 Mengikuti13.1K Pengikut
Goodfire me-retweet
Come discuss autonomous science next Tuesday at the Academy of Sciences!
Excited to talk about what we're building @GoodfireAI and looking forward to hearing talks from @Ginkgo, Monomer Bio, and Tetsuwan!
Cristian Ponce@c_m_ponce
We rented out the Academy of Sciences next Tuesday for a series of talks on autonomous science with @Ginkgo, @GoodfireAI, and Monomer Bio! Come by! luma.com/rmuagyc0
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New paper from Atticus Geiger and collaborators, led by @arunasank!
Aruna S@arunasank
Interpretability methods usually study single-token behavior. But real model behaviors, like sycophancy or writing style, are diffuse across many tokens. Can these diffuse behaviors be localized and controlled from long-form responses? YES!
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Goodfire me-retweet
In <2 years, @goodfireai has built an all-star team, landed their first blue-chip customers, published a ton of research, and now...
Raised $150M at a $1.25B valuation to become the first interpretability unicorn. 🦄
Here @DanJBalsam & @banburismus_ explain the motivation & vision for their new *Intentional Design" research agenda, which aims to understand and control what models learn in training.
Full episode ↓
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Authors: @sidbop1, @annabelma314, @maxsloef, @RaphaelSarfati, @EricBigelow, Atticus Geiger, Owen Lewis, & @jack_merullo_
Paper: arxiv.org/abs/2603.05488
Read more in the blog post: goodfire.ai/research/reaso…
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Interpretability methods can augment monitoring by identifying relevant information internally that isn’t reliably expressed.
Plus, well-calibrated probes give us a knob to control the tradeoff between accuracy and token usage, enabling adaptive inference-time compute! (6/7)
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LLMs often reason “performatively” well after deciding on a final answer - something that CoT monitors are slow to catch.
Our new paper finds that:
- probes can help monitor for this
- it seems to track with task difficulty
- probes enable early CoT exit, saving tokens! (1/7)
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Welcome @thomas_fel_! We're excited to have you at Goodfire.
Kempner Institute at Harvard University@KempnerInst
After 2 years probing #visionmodels at the #KempnerInstitute, @thomas_fel_ reflects on what he’s learned—and what pieces of the #interpretability puzzle remain hidden—as he heads to @GoodfireAI. Read the interview: bit.ly/4aEBzmp 🎙️🧩
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Stanford students - check out this spring quarter course on interp that we're helping support:
Amir Zur@AmirZur2000
Excited to launch a course on mechanistic interpretability at Stanford next quarter! So grateful to be part of an amazing teaching team: Atticus Geiger, Jing Huang, Junyi Tao, and Thomas Icard (who all share the admirable trait of not having a twitter account)
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Read more on how we interpreted Evo 2: goodfire.ai/research/inter…
Plus some follow-up work since then:
goodfire.ai/research/phylo…
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Congrats to @pdhsu @BrianHie @davey_burke @garykbrixi and all our other coauthors at @arcinstitute @nvidia @Stanford @UCSF @UCBerkeley @UW!
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Not every day nine of your teammates get published in Nature!
We've been working with Evo 2 since its release, and have found a number of exciting results with our interpretability tools - including discovering numerous biologically relevant features in the model.
Arc Institute@arcinstitute
Evo 2, the largest fully open biological AI model to date, is now published in @Nature.
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Read the post: goodfire.ai/blog/interpret…
Join us: goodfire.ai/careers
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Those optimizations let us harvest 3 billion activations from Kimi in an overnight run, at 14,000 tokens-per-second on an 8 GPU node.
That (+ other work) let us steer Kimi's chain of thought in real time! (5/6)
Ryan Panwar@RyanPanwar
Kimi is just like me frfr Steering @Kimi_Moonshot's K2 Thinking's reasoning in the Kimi CLI
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