Marcus Hutter

Last updated

Marcus Hutter
Marcus Hutter.jpg
NationalityGerman
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.hutter1.net

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]

Contents

Research

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]

Hutter Prize

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]

See also

Published works

Related Research Articles

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.

Computer science is the study of the theoretical foundations of information and computation and their implementation and application in computer systems. One well known subject classification system for computer science is the ACM Computing Classification System devised by the Association for Computing Machinery.

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.

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.

<span class="mw-page-title-main">Algorithmic probability</span>

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.

In physics and cosmology, the mathematical universe hypothesis (MUH), also known as the ultimate ensemble theory, is a speculative "theory of everything" (TOE) proposed by cosmologist Max Tegmark. According to the hypothesis, the universe is a mathematical object in and of itself. Furthermore, Tegmark suggests that not only is the universe mathematical, but it is also computable.

<span class="mw-page-title-main">Multi-armed bandit</span> Resource problem in machine learning

In probability theory and machine learning, the multi-armed bandit problem is a problem in which a decision maker iteratively selects one of multiple fixed choices when the properties of each choice are only partially known at the time of allocation, and may become better understood as time passes. A fundamental aspect of bandit problems is that choosing an arm does not affect the properties of the arm or other arms.

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).

<span class="mw-page-title-main">Dimitri Bertsekas</span>

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.

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.

<span class="mw-page-title-main">Quantum machine learning</span> Interdisciplinary research area at the intersection of quantum physics and machine learning

Quantum machine learning is the integration of quantum algorithms within machine learning programs.

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.

<span class="mw-page-title-main">Thomas G. Dietterich</span>

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.

Sébastien Bubeck is a French-American computer scientist and mathematician. He is currently Microsoft's Vice President of GenAI and leads the Machine Learning Foundations group at Microsoft Research Redmond. Bubeck was formerly professor at Princeton University and a researcher at the University of California, Berkeley. He is known for his contributions to online learning, optimization and more recently studying deep neural networks, and in particular transformer models.

References

  1. . DeepMind. Accessed February 2019.
  2. . The Australian National University, Canberra. Accessed December 2016.
  3. Marcus Hutter (2005). Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability. Berlin; Heidelberg; New York: Springer. ISBN   9783540221395.
  4. Marcus Hutter (2002). "The Fastest and Shortest Algorithm for All Well-Defined Problems". International Journal of Foundations of Computer Science. 13 (3): 431–443. arXiv: cs/0206022 . Bibcode:2002cs........6022H. doi:10.1142/S0129054102001199. S2CID   5496821.
  5. Bill Hibbard (2008). Adversarial Sequence Prediction. In: Pei Wang (editor) (2008). Artificial General Intelligence, 2008: Proceedings of the First AGI Conference. IOS Press. ISBN   9781586038335. Pages 399–403
  6. 1 2 Marcus Hutter. "500'000€ Prize for Compressing Human Knowledge". hutter1.net. Retrieved 25 February 2020.
  7. Duncan Graham-Rowe (12 August 2005). IQ test for AI devices gets experts thinking. New Scientist.
  8. Marcus Hutter (2009). "Feature Reinforcement Learning: Part {I}: Unstructured {MDP}s" (PDF). Journal of Artificial General Intelligence. ISSN   1946-0163.
  9. Tor Lattimore and Marcus Hutter (2014). "Bayesian Reinforcement Learning with Exploration" (PDF). Algorithmic Learning Theory. Proc. 25th International Conf. on Algorithmic Learning Theory ({ALT'14}). Lecture Notes in Computer Science. Vol. 8776. pp. 170–184. doi:10.1007/978-3-319-11662-4_13. hdl: 1885/14709 . ISBN   978-3-319-11661-7.
  10. Marcus Hutter. "50'000€ Prize for Compressing Human Knowledge". hutter1.net. Retrieved 29 November 2016.
  11. Sagar, Ram (7 April 2020). "Compress Data And Win Hutter Prize Worth Half A Million Euros". Analytics India Magazine. Retrieved 7 March 2024.