Formal concepts and closed itemsets proved to be of big importance for knowledge discovery, both as a tool for concise representation of association rules and a tool for clustering and constructing domain taxonomies and ontologies. Exponential explosion makes it difficult to consider the whole concept lattice arising from data, one needs to select most useful and interesting concepts. In this paper interestingness measures of concepts are considered and compared with respect to various aspects, such as efficiency of computation and applicability to noisy data and performing ranking correlation.

Kronecker product kernel provides the standard approach in the kernel methods literature for learning from graph data, where edges are labeled and both start and end vertices have their own feature representations. The methods allow generalization to such new edges, whose start and end vertices do not appear in the training data, a setting known as zero-shot or zero-data learning. Such a setting occurs in numerous applications, including drug-target interaction prediction, collaborative filtering and information retrieval. Efficient training algorithms based on the so-called vec trick, that makes use of the special structure of the Kronecker product, are known for the case where the training data is a complete bipartite graph. In this work we generalize these results to non-complete training graphs. This allows us to derive a general framework for training Kronecker product kernel methods, as specific examples we implement Kronecker ridge regression and support vector machine algorithms. Experimental results demonstrate that the proposed approach leads to accurate models, while allowing order of magnitude improvements in training and prediction time.

In this paper we introduce a model of lifelong learning, based on a Network of Experts. New tasks / experts are learned and added to the model sequentially, building on what was learned before. To ensure scalability of this process,data from previous tasks cannot be stored and hence is not available when learning a new task. A critical issue in such context, not addressed in the literature so far, relates to the decision which expert to deploy at test time. We introduce a set of gating autoencoders that learn a representation for the task at hand, and, at test time, automatically forward the test sample to the relevant expert. This also brings memory efficiency as only one expert network has to be loaded into memory at any given time. Further, the autoencoders inherently capture the relatedness of one task to another, based on which the most relevant prior model to be used for training a new expert, with finetuning or learning without-forgetting, can be selected. We evaluate our method on image classification and video prediction problems.

This paper presents an end-to-end approach for tracking static and dynamic objects for an autonomous vehicle driving through crowded urban environments. Unlike traditional approaches to tracking, this method is learned end-to-end, and is able to directly predict a full unoccluded occupancy grid map from raw laser input data. Inspired by the recently presented DeepTracking approach [Ondruska, 2016], we employ a recurrent neural network (RNN) to capture the temporal evolution of the state of the environment, and propose to use Spatial Transformer modules to exploit estimates of the egomotion of the vehicle. Our results demonstrate the ability to track a range of objects, including cars, buses, pedestrians, and cyclists through occlusion, from both moving and stationary platforms, using a single learned model. Experimental results demonstrate that the model can also predict the future states of objects from current inputs, with greater accuracy than previous work.

A growing number of threats to Android phones creates challenges for malware detection. Manually labeling the samples into benign or different malicious families requires tremendous human efforts, while it is comparably easy and cheap to obtain a large amount of unlabeled APKs from various sources. Moreover, the fast-paced evolution of Android malware continuously generates derivative malware families. These families often contain new signatures, which can escape detection when using static analysis. These practical challenges can also cause traditional supervised machine learning algorithms to degrade in performance. In this paper, we propose a framework that uses model-based semi-supervised (MBSS) classification scheme on the dynamic Android API call logs. The semi-supervised approach efficiently uses the labeled and unlabeled APKs to estimate a finite mixture model of Gaussian distributions via conditional expectation-maximization and efficiently detects malwares during out-of-sample testing. We compare MBSS with the popular malware detection classifiers such as support vector machine (SVM), $k$-nearest neighbor (kNN) and linear discriminant analysis (LDA). Under the ideal classification setting, MBSS has competitive performance with 98\% accuracy and very low false positive rate for in-sample classification. For out-of-sample testing, the out-of-sample test data exhibit similar behavior of retrieving phone information and sending to the network, compared with in-sample training set. When this similarity is strong, MBSS and SVM with linear kernel maintain 90\% detection rate while $k$NN and LDA suffer great performance degradation. When this similarity is slightly weaker, all classifiers degrade in performance, but MBSS still performs significantly better than other classifiers.

Machine Learning is fast impacting all business sectors. In fact, the right application of Machine Learning can significantly transform any business data into actionable insights, which helps enterprises grow their businesses as decision makers can more consistently make the right decisions at the right time. The continuous evolution of predictive applications allow companies to foresee what's about to happen next. Yet this represents half the challenge as businesses need to also decide what to do with the distilled insights. In some cases, highly skilled humans are replaced by machines that can perform certain complicated tasks better and more efficiently than humans.

Intel launched its new global business-to-business (B2B) marketing campaign starring "The Future" and recurring actor Jim Parsons, and focusing on sectors where Intel technology is building the future, such as artificial intelligence and autonomous driving. The campaign includes two commercials featuring "The Future" as a character representing how Intel technology empowers businesses to face the future with confidence. "The new campaign brings to life the idea that at Intel, we know the future, because we're building it," said Steve Fund, senior vice president and chief marketing officer at Intel Corporation. "We'll showcase the advances we're making in emerging technologies today to make the future of business even more amazing." Intel recently conducted research among senior business leaders, which revealed a universal anxiety about what the future will bring and a fear of being outpaced by emerging technologies.

The EU is launching an unprecedented public consultation today to find out what Europeans fear most about the future of the internet. A succession of surveys over the coming weeks will ask people for their views on everything from privacy and security to artificial intelligence, net neutrality, big data and the impact of the digital world on jobs, health, government and democracy. A dozen leading European publications, including the Guardian, are to publicise the surveys over the coming three weeks. Results will be compiled in early June. Readers can complete the first questionnaire here.

UberEats, like Deliveroo and Amazon Prime Now, can be useful if you don't have the time, supplies or energy to rustle up some grub. When you're really in a rush, however -- the morning'I must not be late for work again' dash, for instance -- it can be a nuisance to open the app, complete an order and then wait for the courier to arrive at your front door. Now, in London, Birmingham and Manchester, it's possible to schedule an UberEats delivery. It's a small addition, but one that could prove useful if you're time poor or like to have your meals organised in advance. In the UK, Uber has some tough competition.

As banking organisations, financial services providers and brands predict and plan for the way consumers will manage their money in the future, artificial intelligence (AI) is high on the business development strategy for 2017 and beyond. AI is already around us and used everyday within payments, money management and for robo-advice, particularly in the area of intelligent digital assistants that handle regular customer service enquiries and tasks. It can process'big data' far more efficiently than humans and can recognise speech, images, text, patterns of online behaviour, for example to detect fraud as well as appropriate advertisements for upselling. Smart machines and technology can turn data into customer insights and enhance service provisions, bringing the digital experience closer to the human interaction for consumers. Santander announced it is to provide secure transactions using voice recognition via its banking app, while Royal Bank of Scotland has trialled'Luvo' AI customer service assistance to interact with staff and potentially serve customers in the future.