Discussions will focus on how the technological developments will shape the future of Europe and building a strong Digital Single Market with increased investment and digital skills is crucial. After last year's Digital Day in Rome that triggered successful cooperation in areas such as high-performance computing, connected mobility and the digitisation of industry, the Commission is repeating the initiative to encourage more cooperation on digital issues. Within a year, major progress has been made towards a Digital Single Market. The end of roaming charges and the portability of online content are now part of the lives of Europeans. Stronger rules on the protection of personal data and the first EU-wide rules on cybersecurity will become a reality in May 2018.

The United States and the United Kingdom are leading this initiative. Denmark, Germany, the Netherlands, Norway and Spain are also involved (see go.nature.com/2hebxnt). Artificial intelligence (AI) is poised to revolutionize this activity.

Although ambulance crews are undoubtedly essential players in the process of saving the lives of people in medical distress, dispatchers are the ones who initially assess the situation and make crucial decisions about the urgency of the situation and what kind of help to send. They consistently display excellence in their work and show calm demeanors many people can't help but admire and appreciate. But, like almost everyone else, emergency dispatchers could benefit from additional insights -- especially those that aren't immediately evident over the phone. That's where an AI software application called Corti comes into play. If a person is having a heart attack, he or she may not be in the appropriate condition to go into substantial detail about symptoms.

Extracting information about entities remains an important research area. This paper addresses the problem of corpus-level entity typing, i.e., inferring from a large corpus that an entity is a member of a class, such as "food" or "artist". The application of entity typing we are interested in is knowledge base completion, specifically, to learn which classes an entity is a member of. We propose FIGMENT to tackle this problem. FIGMENT is embedding-based and combines (i) a global model that computes scores based on global information of an entity and (ii) a context model that first evaluates the individual occurrences of an entity and then aggregates the scores. Each of the two proposed models has specific properties. For the global model, learning high-quality entity representations is crucial because it is the only source used for the predictions. Therefore, we introduce representations using the name and contexts of entities on the three levels of entity, word, and character. We show that each level provides complementary information and a multi-level representation performs best. For the context model, we need to use distant supervision since there are no context-level labels available for entities. Distantly supervised labels are noisy and this harms the performance of models. Therefore, we introduce and apply new algorithms for noise mitigation using multi-instance learning. We show the effectiveness of our models on a large entity typing dataset built from Freebase.

Belief change within the framework of fragments of propositional logic is one of the main and recent challenges in the knowledge representation research area. While previous research works focused on belief revision, belief merging, and belief contraction, the problem of belief update within fragments of classical logic has not been addressed so far. In the context of revision, it has been proposed to refine existing operators so that they operate within propositional fragments, and that the result of revision remains in the fragment under consideration. This approach is not restricted to the Horn fragment but also applicable to other propositional fragments like Krom and affine fragments. We generalize this notion of refinement to any belief change operator. We then focus on a specific belief change operation, namely belief update. We investigate the behavior of the refined update operators with respect to satisfaction of the KM postulates and highlight differences between revision and update in this context.

Ensembles are popular methods for solving practical supervised learning problems. They reduce the risk of having underperforming models in production-grade software. Although critical, methods for learning heterogeneous regression ensembles have not been proposed at large scale, whereas in classical ML literature, stacking, cascading and voting are mostly restricted to classification problems. Regression poses distinct learning challenges that may result in poor performance, even when using well established homogeneous ensemble schemas such as bagging or boosting. In this paper, we introduce MetaBags, a novel, practically useful stacking framework for regression. MetaBags is a meta-learning algorithm that learns a set of meta-decision trees designed to select one base model (i.e. expert) for each query, and focuses on inductive bias reduction. A set of meta-decision trees are learned using different types of meta-features, specially created for this purpose - to then be bagged at meta-level. This procedure is designed to learn a model with a fair bias-variance trade-off, and its improvement over base model performance is correlated with the prediction diversity of different experts on specific input space subregions. The proposed method and meta-features are designed in such a way that they enable good predictive performance even in subregions of space which are not adequately represented in the available training data. An exhaustive empirical testing of the method was performed, evaluating both generalization error and scalability of the approach on synthetic, open and real-world application datasets. The obtained results show that our method significantly outperforms existing state-of-the-art approaches.

Recent studies utilize multiple kernel learning to deal with incomplete-data problem. In this study, we introduce new methods that do not only complete multiple incomplete kernel matrices simultaneously, but also allow control of the flexibility of the model by parameterizing the model matrix. By imposing restrictions on the model covariance, overfitting of the data is avoided. A limitation of kernel matrix estimations done via optimization of an objective function is that the positive definiteness of the result is not guaranteed. In view of this limitation, our proposed methods employ the LogDet divergence, which ensures the positive definiteness of the resulting inferred kernel matrix. We empirically show that our proposed restricted covariance models, employed with LogDet divergence, yield significant improvements in the generalization performance of previous completion methods.

In one of the most common settings in statistical relational learning (SRL), we are given a single relational structure from which we need to learn a model, and this model is then used to make predictions on previously unseen structures. For example, the global relational structure could correspond to a large social network, with the training data specifying the relationships that hold among a small subset of the users, along with their attributes. Clearly, in order to provide any guarantees on the accuracy of these predictions, we need to make (simplifying) assumptions about how the training and test structures are related. In this paper, we follow the setting from [10, 9], where it is assumed that these structures, which we will call relational examples, are all obtained by sampling domain elements from a larger global structure (uniformly and without replacement).

Simultaneously utilizing several complementary solvers is a simple yet effective strategy for solving computationally hard problems. However, manually building such solver portfolios typically requires considerable domain knowledge and plenty of human effort. As an alternative, automatic construction of parallel portfolios (ACPP) aims at automatically building effective parallel portfolios based on a given problem instance set and a given rich design space. One promising way to solve the ACPP problem is to explicitly group the instances into different subsets and promote a component solver to handle each of them.This paper investigates solving ACPP from this perspective, and especially studies how to obtain a good instance grouping.The experimental results showed that the parallel portfolios constructed by the proposed method could achieve consistently superior performances to the ones constructed by the state-of-the-art ACPP methods,and could even rival sophisticated hand-designed parallel solvers.

WASHINGTON – China is slightly ahead of South Korea and the United States in the race to develop fifth generation wireless networks, or 5G, a U.S. study showed Monday. The study released by the CTIA, a U.S.-based industry association of wireless carriers, suggested that the United States is lagging in the effort to deploy the superfast wireless systems that will be needed for self-driving cars, telemedicine and other technologies. The report prepared by the research firm Analysys Mason found that all major Chinese providers have committed to specific launch dates and the government has committed to allocate spectrum for the carriers. The 10-nation study said the U.S. is in the "first tier" of countries in preparing deployment of 5G, along with China, South Korea and Japan. In the second tier are key European markets, including France, Germany and Britain, with Singapore, Russia and Canada in the third tier.