Activation Atlases expose neural-network bugs and human-designed attacks
Chris Olah and Ludwig Schubert released activation atlases and an interactive demo for auditing neural networks. The method exposed spurious correlations and enabled human-designed attacks that fooled tested vision models as often as 93 percent.
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Why it moved the index
The released visualization method and demo produced concrete audit findings rather than a purely theoretical proposal. Its direct evidence came from vision models, so the magnitude is limited relative to later production language-model interpretability work.
Assessment history
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R1
Away 27 · confidence 88
Adds a previously missing pre-2020 Chris Olah result with a dated primary release, public tooling and demonstrated model-audit impact.
14 Aug 2026
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DoomBench assesses “Activation Atlases expose neural-network bugs and human-designed attacks” as evidence moving away from doom, with magnitude 27 and confidence 88 out of 100 in the safety and alignment category.
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The DoomBench assessment of “Activation Atlases expose neural-network bugs and human-designed attacks” is based on reporting from OpenAI and records the editorial rationale, source quality, attribution, and revision history.
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DoomBench summarizes “Activation Atlases expose neural-network bugs and human-designed attacks” as follows: Chris Olah and Ludwig Schubert released activation atlases and an interactive demo for auditing neural networks. The method...
https://www.doombench.com/news/activation-atlases-expose-neural-network-bugs-and-human-designed-attacks-2019-03-06