Most Relevant Explanation (MRE) is an inference task in Bayesian networks that finds the most relevant partial instantiation of target variables as an explanation for given evidence by maximizing the Generalized Bayes Factor (GBF). No exact MRE algorithm has been developed previously except exhaustive search. This paper fills the void by introducing two Breadth-First Branch-and-Bound (BFBnB) algorithms for solving MRE based on novel upper bounds of GBF. One upper bound is created by decomposing the computation of GBF using a target blanket decomposition of evidence variables. The other upper bound improves the first bound in two ways. One is to split the target blankets that are too large by converting auxiliary nodes into pseudo-targets so as to scale to large problems. The other is to perform summations instead of maximizations on some of the target variables in each target blanket. Our empirical evaluations show that the proposed BFBnB algorithms make exact MRE inference tractable in Bayesian networks that could not be solved previously.

Network data appear in a number of applications, such as online social networks and biological networks, and there is growing interest in both developing models for networks as well as studying the properties of such data. Since individual network datasets continue to grow in size, it is necessary to develop models that accurately represent the real-life scaling properties of networks. One behavior of interest is having a power law in the degree distribution. However, other types of power laws that have been observed empirically and considered for applications such as clustering and feature allocation models have not been studied as frequently in models for graph data. In this paper, we enumerate desirable asymptotic behavior that may be of interest for modeling graph data, including sparsity and several types of power laws. We outline a general framework for graph generative models using completely random measures; by contrast to the pioneering work of Caron and Fox (2015), we consider instantiating more of the existing atoms of the random measure as the dataset size increases rather than adding new atoms to the measure. We see that these two models can be complementary; they respectively yield interpretations as (1) time passing among existing members of a network and (2) new individuals joining a network. We detail a particular instance of this framework and show simulated results that suggest this model exhibits some desirable asymptotic power-law behavior.

Parallel texts are a relatively rare language resource, however, they constitute a very useful research material with a wide range of applications. This study presents and analyses new methodologies we developed for obtaining such data from previously built comparable corpora. The methodologies are automatic and unsupervised which makes them good for large scale research. The task is highly practical as non-parallel multilingual data occur much more frequently than parallel corpora and accessing them is easy, although parallel sentences are a considerably more useful resource. In this study, we propose a method of automatic web crawling in order to build topic-aligned comparable corpora, e.g. based on the Wikipedia or Euronews.com. We also developed new methods of obtaining parallel sentences from comparable data and proposed methods of filtration of corpora capable of selecting inconsistent or only partially equivalent translations. Our methods are easily scalable to other languages. Evaluation of the quality of the created corpora was performed by analysing the impact of their use on statistical machine translation systems. Experiments were presented on the basis of the Polish-English language pair for texts from different domains, i.e. lectures, phrasebooks, film dialogues, European Parliament proceedings and texts contained medicines leaflets. We also tested a second method of creating parallel corpora based on data from comparable corpora which allows for automatically expanding the existing corpus of sentences about a given domain on the basis of analogies found between them. It does not require, therefore, having past parallel resources in order to train a classifier.

First of all, though, there might still be things left to achieve with Go. Ke Jie, an 18-year-old Go virtuoso from China ranked #1 in the world, seemed cautiously optimistic about his own chances following Lee's first defeat last week, saying "it's 60 percent in favor of me." And many Go players have said they want to learn as much about AlphaGo as possible -- after all, it's only ever played a handful of games in public, demonstrating unorthodox, crushing tactics. It seems likely that AlphaGo will eventually be released to the public, and don't be surprised to see a match against Ke at some point; Lee Se-dol was chosen for his iconic stature and long career, but Ke is considered the stronger player today. DeepMind founder Demis Hassabis (above) has also said the company plans to test a version without any human training at all -- just the program teaching itself.

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Apple has revealed a 29 armed robot that can rip apart an iPhone in 11 seconds for recycling. It is hoped the machine will help recycle silver, tungsten and other metals from the handsets. The system started to operate at full capacity last month and can take apart one iPhone 6 every 11 seconds to recover aluminum, copper, tin, tungsten, cobalt, gold and silver parts, according to Apple. The system started to operate at full capacity last month and can take apart one iPhone 6 every 11 seconds to recover aluminum, copper, tin, tungsten, cobalt, gold and silver parts, according to Apple. It has already been installed near Apple's HQ in Cupertino, and it plans to build a second in Europe.

Engineers are testing a new robot rescue system in the Greek islands, hoping it will be able to save some refugees while trying to cross from Turkey to Greece.

"I wish I could touch you," Theodore says, laying in bed. Until she speaks up, tentatively. "How would you touch me?" It's a famously poignant scene from the movie Her, as the character Theodore is about to make vocal love to an artificial intelligence living in his ear. But according to half a dozen experts I interviewed, ranging from industrial designer Gadi Amit to the usability guru Don Norman, in-ear assistants aren't science fiction. In fact, a notable pile of discreet, wireless earbuds enabling just this idea are coming to market now. Sony recently released its first in-ear assistant, the Xperia Ear. Intel showed off a similar proof-of-concept last year. The talking, bio-monitoring Bragi Dash will be reaching early Kickstarters soon, while fellow startup Here has raised 17 million to compete in the smart earbud space.

When a person's intelligence is tested, there are exams. When artificial intelligence is tested, there are games. But what happens when computer programs beat humans at all of those games? This is the question AI experts must ask after a Google-developed program called AlphaGo defeated a world champion Go player in four out of five matches in a series that concluded Tuesday. Long a yardstick for advances in AI, the era of board-game testing has come to an end, said Murray Campbell, an IBM research scientist who was part of the team that developed Deep Blue, the first computer program to beat a world chess champion.