Michael I. Jordan

American computer scientist and statistician whose probabilistic methods and many students made him a central figure in machine learningmachine learning, n.: A way of building software that learns from examples instead of following rules written by a programmer. Shown thousands of labeled photos, for instance, a program can learn to recognize cats.; after MIT he moved in 1998 to the University of California, Berkeley, and later also became a researcher at Inria Paris. Trained in cognitive science under David RumelhartDavid Rumelhart, prop. n.: An American psychologist who, with Geoffrey Hinton and Ronald Williams, showed in 1986 how to train multilayer neural networks, launching the modern revival of the field., he proposed an early recurrent neural networkrecurrent neural network, n.: A neural network that reads a sequence, such as a sentence or an audio clip, one piece at a time, carrying a running summary forward so that what came earlier can shape how later pieces are understood. (the Jordan network, 1986) and, with Robert Jacobs, Steven Nowlan and Geoffrey HintonGeoffrey Hinton, prop. n.: A British-Canadian scientist whose decades of persistent work on brain-inspired neural networks laid the foundations of modern AI; often called a 'godfather of AI', he now warns publicly about its dangers., the mixture of expertsmixture of experts, n.: A way to build very large AI models cheaply: the model contains many specialist sub-networks, and for each word only a few are switched on, picked by a small dispatcher network. (1991). He then helped move the field toward probabilistic modeling: his work on graphical modelsprobabilistic 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. and variational inferencevariational inference, n.: A way of approximating a hard-to-compute probability distribution by picking the closest match from a simpler family of distributions, turning a hard problem of adding up every possibility into an optimization problem., on latent Dirichlet allocationlatent Dirichlet allocation, n.: A popular topic-finding method that assumes every document is a blend of a few themes and every theme favors certain words, then works backward from the words to find both. (2003, with David Blei and Andrew NgAndrew Ng, prop. n.: A British-born American computer scientist whose online courses have taught machine learning to millions; he also co-founded Google's deep learning team, Google Brain, and the online-course company Coursera.) and on Bayesian nonparametric models joined machine learning with statistics. Many leading researchers trained with him, including Ng, Blei, Zoubin Ghahramani and Percy LiangPercy Liang, prop. n.: An American computer scientist at Stanford whose center coined the term 'foundation model' and builds tools for evaluating and documenting large AI models.. He has criticized loose talk of artificial intelligence, arguing that the field is better seen as a new engineering discipline. Honors include the IEEE John von Neumann Medal (2020) and the inaugural World Laureates Association Prize in computer science or mathematics (2022).