Manfred Hutter (ed.): Religionsinterne Kritik und religiöser Pluralismus im gegenwärtigen Südostasien: Frankfurt/Main: Peter Lang, 2008, [= Religionswissenschaft 15], ISBN 978-3-631-57500-0, 253 pages [Rezension]

Review of the edited volume: Manfred Hutter (ed.): Religionsinterne Kritik und religiöser Pluralismus im gegenwärtigen Südostasien Frankfurt/Main: Peter Lang, 2008, [= Religionswissenschaft 15], ISBN 978-3-631-57500-0, 253 pagesBuchbesprechung: Manfred Hutter (ed.): Religionsinterne Kritik und religiöser Pluralismus im gegenwärtigen Südostasien. Frankfurt/Main: Peter Lang, 2008, [= Religionswissenschaft 15], ISBN 978-3-631-57500-0, 253 page

Bad Universal Priors and Notions of Optimality

A big open question of algorithmic information theory is the choice of the universal Turing machine (UTM). For Kolmogorov complexity and Solomonoff induction we have invariance theorems: the choice of the UTM changes bounds only by a constant. For the universally intelligent agent AIXI (Hutter, 2005) no invariance theorem is known. Our results are entirely negative: we discuss cases in which unlucky or adversarial choices of the UTM cause AIXI to misbehave drastically. We show that Legg-Hutter intelligence and thus balanced Pareto optimality is entirely subjective, and that every policy is Pareto optimal in the class of all computable environments. This undermines all existing optimality properties for AIXI. While it may still serve as a gold standard for AI, our results imply that AIXI is a relative theory, dependent on the choice of the UTM.Comment: COLT 201

Do something good: teach

Do something good. That was the advice Jack Hutter received from a high school teacher. Hutter thought he would do good as a lawyer. But eventually, he was drawn to the classroom

Intelligence via ultrafilters: structural properties of some intelligence comparators of deterministic Legg-Hutter agents

Legg and Hutter, as well as subsequent authors, considered intelligent agents through the lens of interaction with reward-giving environments, attempting to assign numeric intelligence measures to such agents, with the guiding principle that a more intelligent agent should gain higher rewards from environments in some aggregate sense. In this paper, we consider a related question: rather than measure numeric intelligence of one Legg- Hutter agent, how can we compare the relative intelligence of two Legg-Hutter agents? We propose an elegant answer based on the following insight: we can view Legg-Hutter agents as candidates in an election, whose voters are environments, letting each environment vote (via its rewards) which agent (if either) is more intelligent. This leads to an abstract family of comparators simple enough that we can prove some structural theorems about them. It is an open question whether these structural theorems apply to more practical intelligence measures

Model Selection by Loss Rank for Classification and Unsupervised Learning

Hutter (2007) recently introduced the loss rank principle (LoRP) as a general purpose principle for model selection. The LoRP enjoys many attractive properties and deserves further investigations. The LoRP has been well-studied for regression framework in Hutter and Tran (2010). In this paper, we study the LoRP for classification framework, and develop it further for model selection problems in unsupervised learning where the main interest is to describe the associations between input measurements, like cluster analysis or graphical modelling. Theoretical properties and simulation studies are presented