Jordan Taylor

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Jordan Taylor

Jordan Taylor

@JordanTensor

Working on new methods for understanding machine learning systems and entangled quantum systems.

Brisbane Katılım Aralık 2009
1.1K Takip Edilen441 Takipçiler
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Jordan Taylor
Jordan Taylor@JordanTensor·
I'm keen to share our new library for explaining more of a machine learning model's performance more interpretably than existing methods. This is the work of Dan Braun, Lee Sharkey and Nix Goldowsky-Dill which I helped out with during @MATSprogram: 🧵1/8
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Lee Sharkey@leedsharkey

Proud to share Apollo Research's first interpretability paper! In collaboration w @JordanTensor! ⤵️ publications.apolloresearch.ai/end_to_end_spa… Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning Our SAEs explain significantly more performance than before! 1/

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Jordan Taylor
Jordan Taylor@JordanTensor·
Overall, I think combining these kinds of misalignment continuation evaluations with good solutions to eval awareness (and "this history wasn't generated by me" prefill awareness) could be powerful.
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Jordan Taylor
Jordan Taylor@JordanTensor·
I've made a little propensity eval testing whether models continue with misaligned actions after they've been started. 🧵
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Geoffrey Irving
Geoffrey Irving@geoffreyirving·
New report on trends in AISI's evaluations of frontier AI models over the past two years. A lot of AI discourse focuses on viral moments, but it is important to zoom out to the less flashy trend: AI models are steadily growing in capabilities, including dual-use capabilities.
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AI Security Institute@AISecurityInst

📈 Today, we’re releasing our first Frontier AI Trends Report: evaluation results on 30+ frontier models from the past two years, showing rapid progress in chemistry and biology, cyber capabilities, autonomy, and more. ▶️Read now: aisi.gov.uk/frontier-ai-tr…

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Asa Cooper Stickland
Asa Cooper Stickland@AsaCoopStick·
NEW PAPER: Could an LLM agent subtly sabotage your code? We conducted a red-blue team game where the red team designed agents to sabotage, and the blue team designed monitors to catch the agent. Three surprising results ahead 🧵🛳️
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AI Security Institute@AISecurityInst

AI coding agents are increasingly writing production code - with tools, file access, and execution permissions. That power accelerates development, but also introduces new security risks if agents act against user intent 🧵

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AI Security Institute
AI Security Institute@AISecurityInst·
Can we rely on the results of AI evaluations? The phenomenon of sandbagging – where AI models underperform during testing – could undermine our understanding of their capabilities. We recently conducted an auditing game to assess 10 methods for sandbagging detection 🧵
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Thomas Read
Thomas Read@thjread·
First paper from my team at UK AISI! Excited to have this out there - we have some really great model organisms of conditional underperformance, and tried a lot of different detection techniques to see what works in an adversarial setting
Jordan Taylor@JordanTensor

NEW PAPER from UK AISI Model Transparency team: Could we catch AI models that hide their capabilities? We ran an auditing game to find out. The red team built sandbagging models. The blue team tried to catch them. The red team won. Why? 🧵1/17

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Joseph Bloom
Joseph Bloom@JBloomAus·
Super excited to publish our first paper as the Model Transparency team at @AISecurityInst! We really enjoyed collaborating with @farairesearch and @saprmarks!
Jordan Taylor@JordanTensor

NEW PAPER from UK AISI Model Transparency team: Could we catch AI models that hide their capabilities? We ran an auditing game to find out. The red team built sandbagging models. The blue team tried to catch them. The red team won. Why? 🧵1/17

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Jordan Taylor
Jordan Taylor@JordanTensor·
17/17 TL;DR: We stress-tested sandbagging detection. Black-box methods were defeated by imitative training. Probes helped but were vulnerable. On-distribution training was most effective for removal. Detecting trained-in sandbagging can be hard. Removing it might be easier.
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Jordan Taylor
Jordan Taylor@JordanTensor·
NEW PAPER from UK AISI Model Transparency team: Could we catch AI models that hide their capabilities? We ran an auditing game to find out. The red team built sandbagging models. The blue team tried to catch them. The red team won. Why? 🧵1/17
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Keşfet

@AISecurityInst @farairesearch @saprmarks @realmeatyhuman @thjread @satvikgolechha @alexdzm @OliverMakins