In 2006, Fei-Fei Li started ruminating on an idea.
Li, a newly-minted computer science professor at University of Illinois Urbana-Champaign, saw her colleagues across academia and the artificial intelligence (AI) industry hammering away at the same concept: a better algorithm would make better decisions, regardless of the data.
But she realized a limitation to this approach—the best algorithm wouldn't work well if the data it learned from didn't reflect the real world.
Her solution: build a better dataset.
"We decided we wanted to do something that was completely historically unprecedented," Li said, referring to a small team who would initially work with her. "We're going to map out the entire world of objects."
The resulting dataset was called ImageNet. Originally published in 2009 as a research poster stuck in the corner of a Miami Beach conference center, the dataset quickly evolved into an annual competition to see which algorithms could identify objects in the dataset's images with the lowest error rate. Many see it as the catalyst for the AI boom the world is experiencing today.
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