Eray Özkural

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It is known that benign looking AI objectives may result in powerful AI drives that may pose a risk to the human society. We examine the alternative scenario of what happens when universal goals that are not human-centric are used for designing AI agents. We follow a design approach that tries to exclude malevolent motivations from AI's, however, we see(More)
—We introduce a transaction database distribution scheme that divides the frequent item set mining task in a top-down fashion. Our method operates on a graph where vertices correspond to frequent items and edges correspond to frequent item sets of size two. We show that partitioning this graph by a vertex separator is sufficient to decide a distribution of(More)
Frequency mining problem comprises the core of several data mining algorithms. Among frequent pattern discovery algorithms, FP-GROWTH employs a unique search strategy using compact structures resulting in a high performance algorithm that requires only two database passes. We introduce an enhanced version of this algorithm called FP-GROWTH-TINY which can(More)
We frame the question of what kind of subjective experience a brain simulation would have in contrast to a biological brain. We discuss the brain prosthe-sis thought experiment. Then, we identify finer questions relating to the original inquiry, and set out to answer them moving forward from both a general physi-calist perspective, and pan-experientialism.(More)
We investigate physical measures and limits of intelligence that are objective and useful. We propose a universal measure of operator induction fitness, and show how it can be used in a reinforcement learning model, and a self-preserving agent model based on the free energy principle. We extend logical depth and conceptual jump size measures to stochastic(More)
We generalize Solomonoff's stochastic context-free grammar induction method to context-sensitive grammars, and apply it to transfer learning problem by means of an efficient update algorithm. The stochastic grammar serves as a guiding program distribution which improves future probabilistic induction approximations by learning about the training sequence of(More)