Low-rank tensor completion problem aims to recover a tensor from limited observations, which has many real-world applications. Due to the easy optimization, the convex overlapping nuclear norm has been popularly used for tensor completion. However, it over-penalizes top singular values and lead to biased estimations. In this paper, we propose to use the nonconvex regularizer, which can less penalize large singular values, instead of the convex one for tensor completion. However, as the new regularizer is nonconvex and overlapped with each other, existing algorithms are either too slow or suffer from the huge memory cost. To address these issues, we develop an efficient and scalable algorithm, which is based on the proximal average (PA) algorithm, for real-world problems. Compared with the direct usage of PA algorithm, the proposed algorithm runs orders faster and needs orders less space. We further speed up the proposed algorithm with the acceleration technique, and show the convergence to critical points is still guaranteed. Experimental comparisons of the proposed approach are made with various other tensor completion approaches. Empirical results show that the proposed algorithm is very fast and can produce much better recovery performance.

In this article, we consider the sparse tensor singular value decomposition, which aims for dimension reduction on high-dimensional high-order data with certain sparsity structure. A method named Sparse Tensor Alternating Thresholding for Singular Value Decomposition (STAT-SVD) is proposed. The proposed procedure features a novel double projection \& thresholding scheme, which provides a sharp criterion for thresholding in each iteration. Compared with regular tensor SVD model, STAT-SVD permits more robust estimation under weaker assumptions. Both the upper and lower bounds for estimation accuracy are developed. The proposed procedure is shown to be minimax rate-optimal in a general class of situations. Simulation studies show that STAT-SVD performs well under a variety of configurations. We also illustrate the merits of the proposed procedure on a longitudinal tensor dataset on European country mortality rates.

The ability to conduct logical reasoning is a fundamental aspect of intelligent behavior, and thus an important problem along the way to human-level artificial intelligence. Traditionally, symbolic methods from the field of knowledge representation and reasoning have been used to equip agents with capabilities that resemble human logical reasoning qualities. More recently, however, there has been an increasing interest in using machine learning rather than logic-based formalisms to tackle these tasks. In this paper, we employ state-of-the-art methods for training deep neural networks to devise a novel model that is able to learn how to effectively perform basic ontology reasoning. This is an important and at the same time very natural reasoning problem, which is why the presented approach is applicable to a plethora of important real-world problems. We present the outcomes of several experiments, which show that our model learned to perform precise reasoning on diverse and challenging tasks. Furthermore, it turned out that the suggested approach suffers much less from different obstacles that prohibit symbolic reasoning, and, at the same time, is surprisingly plausible from a biological point of view.

We analyze the value-loading problem. This is the problem of encoding moral values into an AI agent interacting with a complex environment. Like many before, we argue that this is both a major concern and an extremely challenging problem. Solving it will likely require years, if not decades, of multidisciplinary work by teams of top scientists and experts. Given how uncertain the timeline of human-level AI research is, we thus argue that a pragmatic partial solution should be designed as soon as possible. To this end, we propose a preliminary research program. This roadmap identifies several key steps. We hope that this will allow scholars, engineers and decision-makers to better grasp the upcoming difficulties, and to foresee how they can best contribute to the global effort.

While modern machine translation has relied on large parallel corpora, a recent line of work has managed to train Neural Machine Translation (NMT) systems from monolingual corpora only (Artetxe et al., 2018c; Lample et al., 2018). Despite the potential of this approach for low-resource settings, existing systems are far behind their supervised counterparts, limiting their practical interest. In this paper, we propose an alternative approach based on phrase-based Statistical Machine Translation (SMT) that significantly closes the gap with supervised systems. Our method profits from the modular architecture of SMT: we first induce a phrase table from monolingual corpora through cross-lingual embedding mappings, combine it with an n-gram language model, and fine-tune hyperparameters through an unsupervised MERT variant. In addition, iterative backtranslation improves results further, yielding, for instance, 14.08 and 26.22 BLEU points in WMT 2014 English-German and English-French, respectively, an improvement of more than 7-10 BLEU points over previous unsupervised systems, and closing the gap with supervised SMT (Moses trained on Europarl) down to 2-5 BLEU points. Our implementation is available at https:// github.com/artetxem/monoses.

Structured scene descriptions of images are useful for the automatic processing and querying of large image databases. We show how the combination of a semantic and a visual statistical model can improve on the task of mapping images to their associated scene description. In this paper we consider scene descriptions which are represented as a set of triples (subject, predicate, object), where each triple consists of a pair of visual objects, which appear in the image, and the relationship between them (e.g. man-riding-elephant, man-wearing-hat). We combine a standard visual model for object detection, based on convolutional neural networks, with a latent variable model for link prediction. We apply multiple state-of-the-art link prediction methods and compare their capability for visual relationship detection. One of the main advantages of link prediction methods is that they can also generalize to triples, which have never been observed in the training data. Our experimental results on the recently published Stanford Visual Relationship dataset, a challenging real world dataset, show that the integration of a semantic model using link prediction methods can significantly improve the results for visual relationship detection. Our combined approach achieves superior performance compared to the state-of-the-art method from the Stanford computer vision group.

Scientists have used artificial intelligence to predict how cancers will progress and evolve, which could help doctors design the most effective treatment for each patient. A team led by the Institute of Cancer Research, London (ICR) and the University of Edinburgh developed a new technique known as Revolver (Repeated evolution of cancer), which picks out patterns in DNA mutation within cancers and uses the information to forecast future genetic changes. They said the ever-changing nature of tumours is one of the biggest challenges of treatment – with cancers often evolving to a drug-resistant form. Parents think children should be taught signs of cancer, poll finds'Exciting' cancer drug combination shrinks tumours and stops growth Parental cancer has lifetime impact on children's education and earnin Vaping causes DNA mutations which could lead to cancer, says study'Exciting' cancer drug combination shrinks tumours and stops growth Parental cancer has lifetime impact on children's education and earnin But if doctors can predict how a tumour will evolve, they could intervene earlier to stop cancer in its tracks before it has had a chance to evolve or develop resistance, increasing the patient's chances of survival. The team also found a link between certain sequences of repeated tumour mutations and survival outcome.

Search has always been a key enterprise technology going back to the days of the first enterprise content management systems. This is hardly surprising given how important finding the right data is for any of the applications used by enterprises in their business processes. Since the rise of big data and the use of big data sets, search has become even more important. If enterprise data is the real wealth of a business, then search is the tool that uncovers that wealth. But what do you do with the increasingly large amounts of data that enterprises now have access to?

As a teenager in Nigeria, I tried to build an artificial intelligence system. I was inspired by the same dream that motivated the pioneers in the field: That we could create an intelligence of pure logic and objectivity that would free humanity from human error and human foibles. I was working with weak computer systems and intermittent electricity, and needless to say my AI project failed. Eighteen years later--as an engineer researching artificial intelligence, privacy, and machine-learning algorithms--I'm seeing that so far, the premise that AI can free us from subjectivity or bias is also disappointing. We are creating intelligence in our own image.

Dimension reduction is a main step in learning process which plays a essential role in many applications. The most popular methods in this field like SVD, PCA, and LDA, only can apply to vector data. This means that for higher order data like matrices or more generally tensors, data should be fold to a vector. By this folding, the probability of overfitting is increased and also maybe some important spatial features are ignored. Then, to tackle these issues, methods are proposed which work directly on data with their own format like GLRAM, MPCA, and MLDA. In these methods the spatial relationship among data are preserved and furthermore, the probability of overfitiing has fallen. Also the time and space complexity are less than vector-based ones. Having said that, because of the less parameters in multilinear methods, they have a much smaller search space to find an optimal answer in comparison with vector-based approach. To overcome this drawback of multilinear methods like GLRAM, we proposed a new method which is a general form of GLRAM and by preserving the merits of it have a larger search space. We have done plenty of experiments to show that our proposed method works better than GLRAM. Also, applying this approach to other multilinear dimension reduction methods like MPCA and MLDA is straightforwar