| Marcus Hutter | |
| Nationality: | German |
| Workplaces: | DeepMind, Google, IDSIA, ANU, BrainLAB |
| Alma Mater: | Technical University Munich and Ludwig Maximilian University of Munich |
| Thesis Title: | Instantons in QCD |
| Thesis Url: | https://arxiv.org/abs/hep-ph/0107098 |
| Thesis Year: | 1996 |
| Doctoral Advisor: | Harald Fritzsch |
| Academic Advisors: | Wilfried Brauer |
| Doctoral Students: | Shane Legg, Jan Leike and Tor Lattimore |
| 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 |
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] 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.[4]
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.[5] [6] His book Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability was published in 2005 by Springer.[7] Also in 2005, Hutter published with his doctoral student Shane Legg an intelligence test for artificial intelligence devices.[8] In 2009, Hutter developed and published the theory of feature reinforcement learning.[9] In 2014, Lattimore and Hutter published an asymptotically optimal extension of the AIXI agent.[10]
In 2019, Hutter joined DeepMind, recruited by Shane Legg. 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".[11] [12]
See main article: Hutter Prize. In 2006, Hutter announced the Hutter Prize for Lossless Compression of Human Knowledge, with a total of €50,000 in prize money.[13] [14] In 2020, Hutter raised the prize money for the Hutter Prize to €500,000.[15]