Latent factor models for recommender systems represent users and items as low dimensional vectors. Privacy risks have been previously studied mostly in the context of recovery of personal information in the form of usage records from the training data. However, the user representations themselves may be used together with external data to recover private user information such as gender and age. In this paper we show that user vectors calculated by a common recommender system can be exploited in this way. We propose the privacy-adversarial framework to eliminate such leakage, and study the trade-off between recommender performance and leakage both theoretically and empirically using a benchmark dataset. We briefly discuss further applications of this method towards the generation of deeper and more insightful recommendations.

Despite the improved accuracy of deep neural networks, the discovery of adversarial examples has raised serious safety concerns. In this paper, we study two variants of pointwise robustness, the maximum safe radius problem, which for a given input sample computes the minimum distance to an adversarial example, and the feature robustness problem, which aims to quantify the robustness of individual features to adversarial perturbations. We demonstrate that, under the assumption of Lipschitz continuity, both problems can be approximated using finite optimisation by discretising the input space, and the approximation has provable guarantees, i.e., the error is bounded. We then show that the resulting optimisation problems can be reduced to the solution of two-player turn-based games, where the first player selects features and the second perturbs the image within the feature. While the second player aims to minimise the distance to an adversarial example, depending on the optimisation objective the first player can be cooperative or competitive. We employ an anytime approach to solve the games, in the sense of approximating the value of a game by monotonically improving its upper and lower bounds. The Monte Carlo tree search algorithm is applied to compute upper bounds for both games, and the Admissible A* and the Alpha-Beta Pruning algorithms are, respectively, used to compute lower bounds for the maximum safety radius and feature robustness games. When working on the upper bound of the maximum safe radius problem, our tool demonstrates competitive performance against existing adversarial example crafting algorithms. Furthermore, we show how our framework can be deployed to evaluate pointwise robustness of neural networks in safety-critical applications such as traffic sign recognition in self-driving cars.

While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments.

This paper proposes an approach to detect emotion from human speech employing majority voting technique over several machine learning techniques. The contribution of this work is in two folds: firstly it selects those features of speech which is most promising for classification and secondly it uses the majority voting technique that selects the exact class of emotion. Here, majority voting technique has been applied over Neural Network (NN), Decision Tree (DT), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Input vector of NN, DT, SVM and KNN consists of various acoustic and prosodic features like Pitch, Mel-Frequency Cepstral coefficients etc. From speech signal many feature have been extracted and only promising features have been selected. To consider a feature as promising, Fast Correlation based feature selection (FCBF) and Fisher score algorithms have been used and only those features are selected which are highly ranked by both of them. The proposed approach has been tested on Berlin dataset of emotional speech [3] and Electromagnetic Articulography (EMA) dataset [4]. The experimental result shows that majority voting technique attains better accuracy over individual machine learning techniques. The employment of the proposed approach can effectively recognize the emotion of human beings in case of social robot, intelligent chat client, call-center of a company etc.

Geospatial analytics, an industry where satellites are used to track everything from retail footfall to food production. Companies working on the technology have attracted big money. Orbital Insight raised $50 million in funding last year, while Descartes Labs attracted $30 million and SpaceKnow raised $4 million. One of the industry's pioneers is James Crawford, who worked for NASA and Google before founding Orbital Insight in 2013. "We were seeing an explosion in commercial satellites," said Crawford.

Football (popularly know as soccer in USA) as a sport has always been the center of attraction and excitement among the sports lovers as well as among common mass all over the world. Although there are few other sports that has gained popularity in different subcontinents here and there in last few decades but none of them have ever dared to challenge the popularity of football anytime in the past or at present. In fact the popularity and attraction for both football and footballers has increased exponentially over the past few decades with the introduction of humongous platforms like "World Cup Football" organized by prestigious association like FIFA and support from various other independent affluent football clubs. Today, it has become the sign of dignity and status symbol for a country to host a mega-event like World Cup Football and take advantage of the tourism and business opportunities associated with it. Behind the scene a country can showcase the strength of it's infrastructure and attract foreign tourists & investors and can create huge business opportunities by hosting such an event.

Facebook has come under fire for its controversial facial recognition technology. The social media giant primarily uses it to assist in tagging users in photos, but consumer groups and advocates say it may violate users' privacy, according to the New York Times. The scrutiny comes as Facebook continues to grapple with the fallout from the Cambridge Analytica scandal. Facebook has come under fire for its controversial facial recognition technology. Privacy advocates have specifically taken issue with how Facebook markets its facial recognition technology, telling users that it can'help protect you from a stranger using your photo to impersonate you.' Proponents of the technology say it can even be an effective tool for spotting criminals.

Timehop is at the centre of a huge hack, which could have leaked the personal data of millions of its users. People who have used the app – which gets access to social media accounts and then flags up interesting events that happened in the past – may have had some of their most sensitive information stolen during the breach, Timehop said. The app says 21 million of its users were caught up in the attack, and that it was still checking whether earlier breaches had occurred. Since many people use the app only in passing, many of those users may no longer be actively using the app. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

Artificial intelligence (AI) has been around longer than most people realize. The intent behind much of AI is to free us from mundane repetitive tasks, giving us more time to grow our intellects and businesses, with more interesting, evolving actions. We want what we want when we want it. AI offers us that access with speed and accuracy when we need it. In London, self-driving robots deliver food.

It is a stunning new view of the moon - taken from a unique viewpoint. Posted to Twitter by @Astro_Alex, European Space Agency astronaut Alexander Gerst, this image shows our planet's Moon as seen from the International Space Station. He posted the stunning image to illustrate just how far astronauts aboard the ISS travel as they orbit the Earth. European Space Agency astronaut Alexander Gerst posted the amazing image of the Moon as seen from the International Space Station. 'By orbiting the Earth almost 16 times per day, the #ISS crew travel the distance to the Moon and back – every day,' he said.