Chris Olah

E1038734

AI interpretability researcher AI safety researcher artificial intelligence researcher researcher

Chris Olah is a researcher known for his pioneering work in AI interpretability and safety, including leadership roles at organizations like OpenAI and Anthropic.

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Label	Occurrences
Chris Olah canonical	1

Statements (46)

Predicate	Object
instanceOf	AI interpretability researcher ⓘ AI safety researcher ⓘ artificial intelligence researcher ⓘ researcher ⓘ
basedIn	United States of America ⓘ surface form: United States
citizenship	Canada ⓘ
contributedTo	Distill.pub NERFINISHED ⓘ
educatedAt	University of Toronto ⓘ
employer	Anthropic NERFINISHED ⓘ OpenAI NERFINISHED ⓘ
fieldOfWork	AI interpretability ⓘ AI safety ⓘ artificial intelligence ⓘ circuits in neural networks ⓘ deep learning ⓘ feature visualization ⓘ machine learning ⓘ mechanistic interpretability ⓘ neural network interpretability ⓘ
hasBlog	https://colah.github.io ⓘ
knownFor	Circuits research program on understanding neural networks ⓘ distillation of complex ML ideas into accessible explanations ⓘ feature visualization techniques for convolutional neural networks ⓘ promoting clarity and transparency in ML research communication ⓘ work on interpretability of large language models ⓘ
languageSpoken	English ⓘ
notableFor	contributions to AI safety ⓘ leadership in AI research organizations ⓘ pioneering work in AI interpretability ⓘ research on mechanistic interpretability of neural networks ⓘ
notableWork	Distill.pub articles on machine learning ⓘ “Feature Visualization” NERFINISHED ⓘ “The Building Blocks of Interpretability” NERFINISHED ⓘ “Zoom In: An Introduction to Circuits” NERFINISHED ⓘ
positionHeld	co-founder of Anthropic ⓘ head of interpretability at Anthropic ⓘ research scientist at Google Brain ⓘ research scientist at OpenAI ⓘ
researchInterest	alignment of advanced AI systems ⓘ scalable oversight via interpretability ⓘ understanding internal representations in neural networks ⓘ
writesAbout	AI interpretability ⓘ AI safety ⓘ deep learning ⓘ machine learning ⓘ neural networks ⓘ

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Full triples — surface form annotated when it differs from this entity's canonical label.

Anthropic → foundedBy → Chris Olah ⓘ