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Marcus Hutter | |
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Nationality | German |
Alma mater | Technical University Munich and Ludwig Maximilian University of Munich |
Known for | Universal artificial intelligence Artificial General Intelligence |
Awards | IJCAI 2023 Alignment 2018 AGI 2016 UAI 2016 IJCAI-JAIR 2014 Kurzweil AGI 2009 Lindley 2006 Best Paper Prizes |
Scientific career | |
Fields | |
Institutions | DeepMind, Google, IDSIA, ANU, BrainLAB |
Thesis | Instantons in QCD (1996) |
Doctoral advisor | Harald Fritzsch |
Other academic advisors | Wilfried Brauer |
Doctoral students | Shane Legg and Jan Leike and Tor Lattimore |
Website | www |
Marcus Hutter (born April 14, 1967 in Munich) is a professor and artificial intelligence researcher. As a Senior Scientist at DeepMind, he is researching the mathematical foundations of artificial general intelligence. [1] He is on leave from his professorship at the ANU College of Engineering and Computer Science of the Australian National University in Canberra, Australia. [2] Hutter studied physics and computer science at the Technical University of Munich. In 2000 he joined Jürgen Schmidhuber's group at the Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (Dalle Molle Institute for Artificial Intelligence Research) in Manno, Switzerland.[ citation needed ] He developed a mathematical theory of artificial general intelligence. His book Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability was published by Springer in 2005. [3]
Starting in 2000, Hutter developed and published a mathematical theory of artificial general intelligence, AIXI, based on idealised intelligent agents and reward-motivated reinforcement learning. [4] [5] : 399 [6]
In 2005, Hutter and Legg published an intelligence test for artificial intelligence devices. [7]
In 2009, Hutter developed and published the theory of feature reinforcement learning. [8]
In 2014, Lattimore and Hutter published an asymptotically optimal extension of the AIXI agent. [9]
In 2006, Hutter announced the Hutter Prize for Lossless Compression of Human Knowledge, with a total of €50,000 in prize money. [10] In 2020, Hutter raised the prize money for the Hutter Prize to €500,000. [11] [6]
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: CS1 maint: multiple names: authors list (link)Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions in a dynamic environment in order to maximize the cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.
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Minimum Description Length (MDL) is a model selection principle where the shortest description of the data is the best model. MDL methods learn through a data compression perspective and are sometimes described as mathematical applications of Occam's razor. The MDL principle can be extended to other forms of inductive inference and learning, for example to estimation and sequential prediction, without explicitly identifying a single model of the data.
Ray Solomonoff was an American mathematician who invented algorithmic probability, his General Theory of Inductive Inference, and was a founder of algorithmic information theory. He was an originator of the branch of artificial intelligence based on machine learning, prediction and probability. He circulated the first report on non-semantic machine learning in 1956.
In algorithmic information theory, algorithmic probability, also known as Solomonoff probability, is a mathematical method of assigning a prior probability to a given observation. It was invented by Ray Solomonoff in the 1960s. It is used in inductive inference theory and analyses of algorithms. In his general theory of inductive inference, Solomonoff uses the method together with Bayes' rule to obtain probabilities of prediction for an algorithm's future outputs.
Solomonoff's theory of inductive inference is a mathematical theory of induction introduced by Ray Solomonoff, based on probability theory and theoretical computer science. In essence, Solomonoff's induction derives the posterior probability of any computable theory, given a sequence of observed data. This posterior probability is derived from Bayes' rule and some universal prior, that is, a prior that assigns a positive probability to any computable theory.
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AIXI is a theoretical mathematical formalism for artificial general intelligence. It combines Solomonoff induction with sequential decision theory. AIXI was first proposed by Marcus Hutter in 2000 and several results regarding AIXI are proved in Hutter's 2005 book Universal Artificial Intelligence.
Bayesian optimization is a sequential design strategy for global optimization of black-box functions that does not assume any functional forms. It is usually employed to optimize expensive-to-evaluate functions.
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This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.
Thomas G. Dietterich is emeritus professor of computer science at Oregon State University. He is one of the pioneers of the field of machine learning. He served as executive editor of Machine Learning (journal) (1992–98) and helped co-found the Journal of Machine Learning Research. In response to the media's attention on the dangers of artificial intelligence, Dietterich has been quoted for an academic perspective to a broad range of media outlets including National Public Radio, Business Insider, Microsoft Research, CNET, and The Wall Street Journal.
Oded Regev is an Israeli-American theoretical computer scientist and mathematician. He is a professor of computer science at the Courant institute at New York University. He is best known for his work in lattice-based cryptography, and in particular for introducing the learning with errors problem.
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