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AMD acquires Fei-Fei Li's World Labs for $8.2 billion

The all-stock deal closes by year-end, with Li becoming AMD chief scientist reporting directly to Lisa Su.

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SITUATION EXPLAINED: AMD is buying World Labs for $8.2 billion, and Fei-Fei Li becomes its chief scientist. • All stock, closing by the end of the year. Li reports directly to Lisa Su • AMD was already an investor in World Labs' $1 billion round in February. So was Nvidia • @LisaSu : "The more you understand end-to-end, the better system you're going to build." World Labs' research should show AMD how workloads are evolving and shape its hardware roadmap • Two products. Marble generates persistent high-fidelity 3D worlds but static ones. Atlas, unreleased, is a 4D simulator you can move through in space and time • Li's doctoral students included both Timnit Gebru and Andrej Karpathy @theojaffee: "Fei-Fei Li is like one of the most impressive people in AI history, basically. She has this…
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AMD just paid $8.2 billion for the researcher who accidentally made Nvidia the most valuable company on Earth. Back in 2007, Fei-Fei Li was a junior professor with an idea her mentors warned would end her career. She wanted to label millions of internet photos so machines could learn to see. Funding agencies passed. Senior colleagues told her the field advanced on algorithms and that data collection was grunt work beneath a Princeton PhD. She built it anyway. Roughly 49,000 workers across 167 countries spent two and a half years labeling images. Then she released the whole dataset, ImageNet, for free. In 2012, a Toronto grad student named Alex Krizhevsky trained a neural network on her free dataset using two $500 Nvidia gaming cards. AlexNet nearly cut the error rate in half, and that…
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Fei-Fei Li spent years hand-labeling 14 million images, gave the entire thing away for free, and watched it create a multi-trillion dollar industry that paid her nothing. Sixteen years later, AMD is finally paying her. $8.2 billion. Rewind to 2007. She's a junior professor at Princeton, and the field's consensus is that progress comes from better algorithms, with data as an afterthought. Colleagues warn her that building a giant image dataset will kill her career. Money gets so tight she considers reopening her family's dry cleaning business in New Jersey, the same one she ran on weekends as a Princeton undergrad, to fund the project. She builds ImageNet anyway and releases it free in 2009. For three years, almost nothing happens. Then in 2012, two of Geoffrey Hinton's students train a…
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