Professor Marcus Hutter
The KurzweilAI.net article Hutter Prize for Lossless Compression of Human Knowledge saidMarcus Hutter has announced the 50,000 Euro Hutter Prize for Lossless Compression of Human Knowledge by compressing the 100MB file Wikipedia ‘enwik8’ file to less than the current record of 18MB.
The intent of this prize is to encourage development of intelligent compressors/programs.
“Being able to compress well is closely related to intelligence,” says the Prize for Compressing Human Knowledge” website.
“While intelligence is a slippery concept, file sizes are hard numbers. Wikipedia is an extensive snapshot of Human Knowledge. If you can compress the first 100MB of Wikipedia better than your predecessors, you(r compressor) likely has to be smart(er).”
Marcus Hutter,
Ph.D. (physics), Habil (informatics),
runs the
50,000 € Hutter Prize for Compressing Human Knowledge. He
is
an Associate Professor in the
Research School of Information Sciences and Engineering (RSISE) at
the
Australian National
University (ANU) in Canberra, Australia, and
senior research in the
National Information and Communication Technology of Australia
(NICTA).
He is also honorary
lecturer at
Technical University Munich. His current interests are centered
around reinforcement learning, algorithmic information theory and
statistics, universal induction schemes, adaptive control theory, and
related areas.
Marcus Hutter authored the book
Universal Artificial
Intelligence
in which he develops a
parameter-free theory of an optimal reinforcement learning agent
embedded in an arbitrary unknown environment, based on a
formal mathematical definition of general intelligence.
He also authored
Fitness Uniform Selection to Preserve Genetic Diversity,
Instantons in QCD: Theory and Application of
the Instanton Liquid Model,
Robust Estimators under the Imprecise Dirichlet Model, and
The Fastest and Shortest Algorithm for All Well-Defined
Problems.
He coauthored
Hybrid Rounding Techniques for Knapsack Problems,
Adaptive Online Prediction by Following the Perturbed
Leader,
Asymptotics of Discrete MDL for Online Prediction,
Distribution of Mutual Information from Complete and Incomplete
Data,
Optimality of Universal Bayesian Sequence
Prediction for General Loss and Alphabet, and
Family Structure from Periodic Solutions of an Improved Gap Equation.
Read his
full list of publications!
Learn about his lectures.
He is reviewer for the journals
IEEE-TPAMI,
IEEE-TIT,
IEEE-SMC,
IEEE-TEC,
JCSS,
MLJ,
JMLR,
Marcus earned a Bachelors degree in computer science in 1989, a
Bachelors degree in Physics in 1990, and a Masters degree in computer
science in 1992 at the Technical University in Munich, Germany. He
earned a PhD in theoretical particle physics there in 1995. In 2003 he
completed his Habilitation (2nd PhD) at the Technical University Munich
in Optimal Sequential Decisions based on Algorithmic Probability, and
has since then been an honorary official lecturer there.
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