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Fei-Fei Li

1976–present · Chinese-American

Created the labeled dataset that showed deep neural networks working at scale in 2012.

Contribution: Led ImageNet — a massive labeled image dataset that let researchers train and compare vision models at scale.
Enabled the 2012 deep-learning surge when networks trained on ImageNet shocked the field with accuracy jumps.
Advocates for human-centered AI: capability paired with care for people and society.
Key observations: Data quality and labeling can unlock methods that looked weak on tiny sets.
Benchmarks focus a community — shared tests make progress visible and competitive.
Who appears in the photos, and who labels them, shapes what the model "sees" as normal.
Conclusions: Scale of carefully labeled examples was as important as clever algorithms.
When you evaluate AI, ask what dataset it was graded on — and what that dataset left out.
Building AI responsibly includes the people in the data, not only the accuracy score.

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