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What Actually Matters in AI? - with Zhuang Liu (Princeton)
In this episode, we hosted Zhuang Liu, Assistant Professor at Princeton and former researcher at Meta, for a conversation about what actually matters in modern AI and what turns out to be a historical accident.
Zhuang is behind some of the most important papers in recent years (with more than 100k citations): ConvNeXt (showing ConvNets can match Transformers if you get the details right), Transformers Without Normalization (replacing LayerNorm with dynamic tanh), ImageBind, Eyes Wide Shut on CLIP's blind spots, the dataset bias work showing that even our biggest "diverse" datasets are still…
The skinny
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