Marcus Hutter | |
---|---|
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, Jan Leike and Tor Lattimore |
Website | www |
Marcus Hutter (born April 14, 1967 in Munich) is a computer scientist, professor and artificial intelligence researcher. As a senior researcher at DeepMind, he studies the mathematical foundations of artificial general intelligence. [1] [2]
Hutter studied physics and computer science at the Technical University of Munich. In 2000 he joined Jürgen Schmidhuber's group at the Dalle Molle Institute for Artificial Intelligence Research in Manno, Switzerland. [3] [4] He developed a mathematical formalism of artificial general intelligence named AIXI. He has served as a professor at the College of Engineering, Computing and Cybernetics of the Australian National University in Canberra, Australia. [5]
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. [6] [7] [4] His book Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability was published in 2005 by Springer. [8] Also in 2005, Hutter published with his doctoral student Shane Legg an intelligence test for artificial intelligence devices. [9] In 2009, Hutter developed and published the theory of feature reinforcement learning. [10] In 2014, Lattimore and Hutter published an asymptotically optimal extension of the AIXI agent. [11]
In 2019, Hutter joined DeepMind, recruited by Shane Legg. [2] In 2022, he co-authored a paper arguing that "deploying a sufficiently advanced reinforcement learning agent would likely be incompatible with the continued survival of humanity". [12] [13]
In 2006, Hutter announced the Hutter Prize for Lossless Compression of Human Knowledge, with a total of €50,000 in prize money. [14] [15] In 2020, Hutter raised the prize money for the Hutter Prize to €500,000. [4]
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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.
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance.
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.
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.
In intelligence and artificial intelligence, an intelligent agent (IA) is an agent that perceives its environment, takes actions autonomously in order to achieve goals, and may improve its performance with learning or acquiring knowledge. An intelligent agent may be simple or complex: A thermostat or other control system is considered an example of an intelligent agent, as is a human being, as is any system that meets the definition, such as a firm, a state, or a biome.
General game playing (GGP) is the design of artificial intelligence programs to be able to play more than one game successfully. For many games like chess, computers are programmed to play these games using a specially designed algorithm, which cannot be transferred to another context. For instance, a chess-playing computer program cannot play checkers. General game playing is considered as a necessary milestone on the way to artificial general intelligence.
The Hutter Prize is a cash prize funded by Marcus Hutter which rewards data compression improvements on a specific 1 GB English text file, with the goal of encouraging research in artificial intelligence (AI).
David A. McAllester is an American computer scientist who is Professor and former chief academic officer at the Toyota Technological Institute at Chicago. He received his B.S., M.S. and Ph.D. degrees from the Massachusetts Institute of Technology in 1978, 1979 and 1987 respectively. His PhD was supervised by Gerald Sussman. He was on the faculty of Cornell University for the academic year 1987-1988 and on the faculty of MIT from 1988 to 1995. He was a member of technical staff at AT&T Labs-Research from 1995 to 2002. He has been a fellow of the American Association of Artificial Intelligence since 1997. He has written over 100 refereed publications.
Michael Lederman Littman is a computer scientist, researcher, educator, and author. His research interests focus on reinforcement learning. He is currently a University Professor of Computer Science at Brown University, where he has taught since 2012.
Dimitri Panteli Bertsekas is an applied mathematician, electrical engineer, and computer scientist, a McAfee Professor at the Department of Electrical Engineering and Computer Science in School of Engineering at the Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, and also a Fulton Professor of Computational Decision Making at Arizona State University, Tempe.
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.
Google DeepMind Technologies Limited is a British-American artificial intelligence research laboratory which serves as a subsidiary of Google. Founded in the UK in 2010, it was acquired by Google in 2014 and merged with Google AI's Google Brain division to become Google DeepMind in April 2023. The company is based in London, with research centres in Canada, France, Germany, and the United States.
Instrumental convergence is the hypothetical tendency for most sufficiently intelligent, goal directed beings to pursue similar sub-goals, even if their ultimate goals are quite different. More precisely, agents may pursue instrumental goals—goals which are made in pursuit of some particular end, but are not the end goals themselves—without ceasing, provided that their ultimate (intrinsic) goals may never be fully satisfied.
Shane Legg is a machine learning researcher and entrepreneur. With Demis Hassabis and Mustafa Suleyman, he cofounded DeepMind Technologies, and works there as the chief AGI scientist. He is also known for his academic work on artificial general intelligence, including his thesis supervised by Marcus Hutter.
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.
David Silver is a principal research scientist at Google DeepMind and a professor at University College London. He has led research on reinforcement learning with AlphaGo, AlphaZero and co-lead on AlphaStar.
In the field of artificial intelligence (AI), AI alignment aims to steer AI systems toward a person's or group's intended goals, preferences, and ethical principles. An AI system is considered aligned if it advances the intended objectives. A misaligned AI system pursues unintended objectives.
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.
Artificial intelligence and machine learning techniques are used in video games for a wide variety of applications such as non-player character (NPC) control and procedural content generation (PCG). Machine learning is a subset of artificial intelligence that uses historical data to build predictive and analytical models. This is in sharp contrast to traditional methods of artificial intelligence such as search trees and expert systems.
Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that coexist in a shared environment. Each agent is motivated by its own rewards, and does actions to advance its own interests; in some environments these interests are opposed to the interests of other agents, resulting in complex group dynamics.