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SUMMARY:Applications of Algorithmic Information Theory - Prof Marcus Hutte
 r\, Google DeepMind\, London
DTSTART:20260218T140000Z
DTEND:20260218T150000Z
UID:TALK241480@talks.cam.ac.uk
CONTACT:Dr Varun Jog
DESCRIPTION:Algorithmic information theory has a wide range of application
 s\, despite the fact that its core quantity\, Kolmogorov complexity\, is i
 ncomputable. Most importantly\, AIT allows to quantify Occam’s razor\, t
 he core scientific paradigm that ”among two models that describe the dat
 a equally well\, the simpler one should be preferred”. This led to unive
 rsal theories of induction and action in the field of machine learning and
  artificial intelligence\, and practical versions like the Minimum Encodin
 g Length (MDL/MML) principles. The universal similarity metric probably sp
 awned the greatest practical success of AIT. Approximated by standard comp
 ressors like Lempel-Ziv (zip) or bzip2 or PPMZ\, it leads to the normalize
 d compression distance\, which has been used to fully automatically recons
 truct language and phylogenetic trees\, and many other clustering problems
 . AIT has been applied in disciplines as remote as Cognitive Sciences\, Bi
 ology\, Physics\, and Economics\n\nMini biography: Marcus Hutter is Senior
  Researcher at DeepMind and Professor in the RSCS at the Australian Nation
 al University. He received his PhD and BSc in physics from the LMU in Muni
 ch and a Habilitation\, MSc\, and BSc in informatics from the TU Munich. S
 ince 2000\, his research at IDSIA and ANU and DeepMind has centered around
  the information-theoretic foundations of inductive reasoning and reinforc
 ement learning\, which has resulted in 200+ publications and several award
 s. His books on “Universal Artificial Intelligence” develop the first 
 sound and complete theory of super-intelligent machines (ASI). He also run
 s the Human Knowledge Compression Contest (500'000€ H-prize). See https:
 //www.hutter1.net/\n
LOCATION:MR5\, CMS Pavilion A
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