The threat of terrorism has greatly accelerated the exchange of data between European states. Social media has become indispensable, both for investigative purposes and to fight propaganda. The "Fraternity Taskforce", a group of some 20 investigators, has probing into the Paris attacks of 13 November 2015 since late last year. But this team, based at Europol headquarters in The Hague, has no high-tech surveillance equipment or bullet-proof vests. Its main weapon and its biggest resource is data, vast quantities of data. The European police organisation's focus on terrorism has quickly taken off with this investigation.

Investors backed more AI companies in the first quarter of 2016 (Q1'16) than in any other quarter, according to research from venture capital analysis firm CB Insights, which supports the idea that AI is the next major revolution in computing. In Q1'16, there were over 140 deals to startups focused on AI, CB Insights data wrote on its blog on Tuesday. Data startup Trifacta, DNA testing startup Pathway Genomics, and cognitive computing business Digital Reasoning Systems were among the AI-powered companies that raised equity funding rounds in Q1 from investors including Goldman Sachs, Accel Partners, Greylock Partners, and the IBM Watson Group. So far in 2016, more than 200 AI-focused companies have collectively raised nearly 1.5 billion ( 1 billion). The pick up in AI funding activity comes as businesses look to make their platforms and systems more human-like.

Google is diving deeper into artificial intelligence, with the company opening a dedicated machine learning research center in its Zurich office, the search company announced on Thursday, June 16. The Google Research Europe center will focus on three areas: Machine intelligence, natural language processing and understanding, and machine perception. The research center aims to deliver machine learning that can be put into practical use, to improve the machine learning infrastructure, and to assist the research community overall. "Google's ongoing research in machine intelligence is what powers many of the products being used by hundreds of millions of people a day -- from Translate to Photo Search to Smart Reply for Inbox," Emmanuel Mogenet, head of Google Research Europe, wrote in the blog post announcing the center. Mogenet noted machine learning software engineers and researchers will be able to develop products and conduct research at the Zurich center, which also holds the largest Google engineering office outside of the US.

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SYDNEY: Marketers are looking toward artificial intelligence (AI) to boost capability in measurement and targeting, according to an expert in the field. Karen Nelson-Field, Associate Professor at the University of South Australia and the author of Viral Marketing: The Science of Sharing, addressed this topic at the AdNews Media Summit in Sydney. And she outlined potentially significant opportunities for advertisers in the areas of viewability, ad avoidance, audience measurement and contextual programmatic targeting in real-time. While biometrics and similar technology have been used before to track people's responses to ads in a laboratory setting, Nelson-Field argued this is too removed from how people interact with advertising in real life. She suggested that the next step for marketers is in biometrics with vision AI behind it, a phase that will harness subconscious recollection and provide a more accurate picture of how consumers interact with advertising in real life.

It is not every day you see a runaway robot causing traffic chaos in a city centre - but in one Russian suburb is has happened twice in the last week. The robot, named Promobot, was being put through its paces at a research lab in the city of Perm in central Russia's Perm Krai region. In its first escape, the robot, designed to avoid obstacles and to turn around when it reached a boundary, had been left walking around an outside yard. This is the hilarious moment a runaway robot causes traffic chaos in a city centre. The robot - called Promobot - was being put through its paces at a research lab in the city of Perm in central Russia's Perm Krai region Promobot - short for Promotional Robot - is a unique robot created by Russian scientists and is designed to work in customer relations.

Charles Radclyffe, a serial entrepreneur who has focused his career on solving tough technology challenges for some of the world's largest organisations, spoke about data philosophy and mentioned how technology is slowly changing the world and could cause a wealth distribution imbalance. The Big Data Summit (Malta) is the first event of its kind to be held in Malta aimed at bringing together an international group of business leaders, policy makers and technology leaders to discuss the future of the global economy and how big data and advanced analytics is already transforming the business world as we know it. This major event brings together thought leaders from some of the key players in Big Data today including Tableau, Qlik, Microsoft, Zendesk and Salesforce as well as accomplished independent international speakers from a variety of industries, including professional services, IT, Telco, iGaming, Academia as well as areas where some of the major breakthroughs are being made like Machine Learning and Artificial Intelligence. Mr Radclyffe spoke about data philosophy and data ethics. He said that the consequence of what we are doing through technology today will have the furthest reaching impact to date.

Process mining techniques aim to extract insights in processes from event logs. One of the challenges in process mining is identifying interesting and meaningful event labels that contribute to a better understanding of the process. Our application area is mining data from smart homes for elderly, where the ultimate goal is to signal deviations from usual behavior and provide timely recommendations in order to extend the period of independent living. Extracting individual process models showing user behavior is an important instrument in achieving this goal. However, the interpretation of sensor data at an appropriate abstraction level is not straightforward. For example, a motion sensor in a bedroom can be triggered by tossing and turning in bed or by getting up. We try to derive the actual activity depending on the context (time, previous events, etc.). In this paper we introduce the notion of label refinements, which links more abstract event descriptions with their more refined counterparts. We present a statistical evaluation method to determine the usefulness of a label refinement for a given event log from a process perspective. Based on data from smart homes, we show how our statistical evaluation method for label refinements can be used in practice. Our method was able to select two label refinements out of a set of candidate label refinements that both had a positive effect on model precision.

Tactile information is important for gripping, stable grasp, and in-hand manipulation, yet the complexity of tactile data prevents widespread use of such sensors. We make use of an unsupervised learning algorithm that transforms the complex tactile data into a compact, latent representation without the need to record ground truth reference data. These compact representations can either be used directly in a reinforcement learning based controller or can be used to calibrate the tactile sensor to physical quantities with only a few datapoints. We show the quality of our latent representation by predicting important features and with a simple control task.

Layer-wise relevance propagation (LRP) is a recently proposed technique for explaining predictions of complex non-linear classifiers in terms of input variables. In this paper, we apply LRP for the first time to natural language processing (NLP). More precisely, we use it to explain the predictions of a convolutional neural network (CNN) trained on a topic categorization task. Our analysis highlights which words are relevant for a specific prediction of the CNN. We compare our technique to standard sensitivity analysis, both qualitatively and quantitatively, using a "word deleting" perturbation experiment, a PCA analysis, and various visualizations. All experiments validate the suitability of LRP for explaining the CNN predictions, which is also in line with results reported in recent image classification studies.