# How a Pioneer of Machine Learning Became One of Its Sharpest Critics https://blog.cynexia.com/how-a-pioneer-of-machine-learning-became-one-of-its-sharpest-critics Author: [Rob Aleck](https://blog.cynexia.com/pages/about) · https://github.com/mnbf9rca · https://www.linkedin.com/in/robertaleck/ · https://family-foqos.app/ Published: 2018-05-31 Categories: LinkedIn cross-post 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). [https://www.theatlantic.com/technology/archive/2018/05/machine-learning-is-stuck-on-asking-why/560675/?single_page=true]()Judea Pearl helped artificial intelligence gain a strong grasp on probability, but laments that it still can't compute cause and effect.