How a Pioneer of Machine Learning Became One of Its Sharpest Critics

A future of truly intelligent machines requires causal reasoning, not simply "nontrivial curve fitting" (the probabilistic association of cause and effect), argues Judea Pearl. Development of true reasoning – why a given action has a certain outcome, not just that they're correlated – would allow machines to "ask counterfactual questions" – in effect, to predict how a change creates a likely outcome that has never been seen before – and potentially even develop agency and free will. He puts lack of progress in this area down to a missing "calculus for asymmetrical relations" (knowing that the sun causes the grass to grow and not vice versa).
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Judea Pearl helped artificial intelligence gain a strong grasp on probability, but laments that it still can’t compute cause and effect.