"Their implied promise is that you could somehow model our causal world from recordings, measurements, and mathematical optimization alone."
Hornik and colleagues established that feedforward neural networks possess universal function approximation capabilities under specified mathematical conditions. This ambitious theoretical result provides historical context for the broad modeling promise associated with neural networks. The manuscript describes that promise as an expectation rather than asserting that ANNs actually reconstruct causal reality.
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