How can AI learn causality for better science?
The quest for AI to understand causality is paramount for accelerating scientific discovery. Current deep learning models often learn correlations, but true understanding requires grasping cause-and-effect. My work on causal representation learning aims to equip AI with the ability to infer causal structures from data, enabling it to propose hypotheses, design experiments, and interpret scientific findings in a more profound and reliable manner.
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