Schölkopf's central idea: causality in ML
My central idea is that to build truly intelligent systems, we must move beyond correlation-based learning and incorporate causal reasoning. Traditional machine learning often learns spurious correlations. By focusing on independent mechanisms and understanding how interventions affect outcomes, we can develop models that are more robust to distribution shifts and generalize better to new environments. This is key for reliable decision-making.
Ask Bernhard Schölkopf the follow-up →