Judea Pearl
In plain wordsAn Israeli-American computer scientist who gave AI the mathematics for reasoning under uncertainty and, later, for telling cause and effect apart from mere correlation.
Israeli-American computer scientist, also known for philosophical work on causation, who has been at the University of California, Los Angeles (UCLA) since 1970. In the 1980s he developed Bayesian networksBayesian network, n.: A diagram of causes and effects with probabilities attached — say, how rain and a sprinkler can each make grass wet — that lets a computer update its beliefs about hidden causes when it sees evidence., a term he introduced in 1985, and belief propagation, a message-passing method for computing probabilities in them; his book 'Probabilistic Reasoning in Intelligent Systems' (1988) helped move AI from rule-based to probabilistic reasoning under uncertainty. His second program turned to cause and effect: in 'Causality' (2000) and related work, structural causal models, causal diagrams and the do-calculusdo-calculus, n.: A set of mathematical rules, devised by Judea Pearl, for working out the effect of deliberately changing something using only observational data plus a diagram of what causes what. give causal inferencecausal inference, n.: Working out whether one thing actually causes another, rather than merely occurring alongside it — for example, whether a new feature made users stay longer or just happened at the same time. a mathematics that separates seeing from doing and supports reasoning about interventions and counterfactualscounterfactual, n.: A statement about what would have happened if something had been different — 'had I left earlier, I would have caught the train'. AI uses counterfactuals to explain decisions and to measure causes.. With Dana Mackenzie he popularized these ideas in 'The Book of Why' (2018), arguing that machine learning that only fits data remains on the lowest rung of a 'ladder of causation'. Turing AwardTuring Award, prop. n.: Nicknamed the 'Nobel Prize of computing', the top honor in computer science, awarded each year since 1966 by the Association for Computing Machinery. Several AI pioneers have received it. 2011.
Related
- Bayesian networkBayesian network, n.: A diagram of causes and effects with probabilities attached — say, how rain and a sprinkler can each make grass wet — that lets a computer update its beliefs about hidden causes when it sees evidence.
- probabilistic graphical modelprobabilistic graphical model, n.: A way of describing how many uncertain quantities depend on one another by drawing them as a network, which makes it practical for a computer to reason about all of them together.
- causal inferencecausal inference, n.: Working out whether one thing actually causes another, rather than merely occurring alongside it — for example, whether a new feature made users stay longer or just happened at the same time.
- do-calculusdo-calculus, n.: A set of mathematical rules, devised by Judea Pearl, for working out the effect of deliberately changing something using only observational data plus a diagram of what causes what.
- counterfactualcounterfactual, n.: A statement about what would have happened if something had been different — 'had I left earlier, I would have caught the train'. AI uses counterfactuals to explain decisions and to measure causes.
- Bayesian inferenceBayesian inference, n.: Reasoning that treats unknowns as uncertain beliefs and updates them with evidence using Bayes' theorem. The result is not one answer but a range of answers, each with a probability.
Sources
- Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann.
- Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.
Mentioned in
Bayesian networkBayesian network, n.: A diagram of causes and effects with probabilities attached — say, how rain and a sprinkler can each make grass wet — that lets a computer update its beliefs about hidden causes when it sees evidence., Turing AwardTuring Award, prop. n.: Nicknamed the 'Nobel Prize of computing', the top honor in computer science, awarded each year since 1966 by the Association for Computing Machinery. Several AI pioneers have received it., belief propagationbelief propagation, n.: A way for a network of uncertain quantities to reach conclusions by having each node pass short messages to its neighbors about what it currently believes, until the beliefs settle down., counterfactual fairnesscounterfactual fairness, n.: A fairness test that asks: had this person been of another race or sex, with everything that would have followed from that, would the system have decided the same way?, Daphne KollerDaphne Koller, prop. n.: An Israeli-American computer scientist who wrote the standard text on probabilistic reasoning in AI, co-founded the online-course company Coursera, and founded a machine-learning drug-discovery company., deep belief networkdeep belief network, n.: A many-layered neural network trained one layer at a time, introduced in 2006. It showed that deep networks could be trained after all and helped launch the deep learning boom, before simpler methods replaced it.