Tom Mitchell

American computer scientist at Carnegie Mellon University, where he founded and chaired the world's first academic department of machine learning (2006). His doctoral work at Stanford introduced version spaces (1977), a way of representing all hypotheses consistent with a set of training examples, and his textbook 'Machine Learning' (1997) gave the field's most quoted formal definition, built on three letters: a program learns if gaining experience E makes it better at some class of tasks T, as judged by a performance measure P. He led the Never-Ending Language Learner (NELL, from 2010), which read the web continuously to grow a knowledge baseknowledge base, n.: A store of facts and rules that a program can reason with: like a database, but organized so that new conclusions can be worked out from it, not just looked up., an early experiment in continual learningcontinual learning, n.: The ability of an AI to keep learning new things over time from a stream of experience, without forgetting what it already knew and without being retrained from scratch.; showed with colleagues that brain activity evoked by concrete nouns could be predicted from word statistics (2008); and with Erik BrynjolfssonErik Brynjolfsson, prop. n.: An American economist who studies how digital technology and AI change productivity and jobs, and who warns against AI built to replace people rather than to augment them. assessed which occupational tasks 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. could perform (see task exposuretask exposure, n.: A measure of how much of a job's work AI could, in principle, do or speed up. High exposure means many of the job's tasks are within AI's reach, not that the job will vanish.). He is a member of the US National Academy of Engineering.