The key insight here is that machine learning models excel at learning distributions from training data. The speaker relates this to their experience working on protein folding, where there was a belief that known fundamental forces could be used to calculate protein folding from first principles with sufficient compute power. This led to the creation of the BlueGene computer, which was eventually repurposed due to funding issues.

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