If you could scan your own brains for the words "web search engines," Google would undoubtedly top the list of hits. Currently, about three quarters of worldwide digital online searches are carried out using Google's web search service. Indeed the word "google" is so well known and ubiquitous that it has become a verb in numerous languages. Statistics about Google are so impressive that it's hard to believe the company has only existed for twenty years. When it started in 1998, it wasn't the first internet search engine, but won the contest over the competition through a combination of cutting edge technology, smart marketing, and good luck.

Artificial Intelligence (AI) is fast becoming European banks' key defense against money laundering that finances terrorism and other criminal activities around the world. Anti-Money Laundering (AML) is a key priority in the EU. The latest EU Anti-Money Laundering Directive (AMLD IV), published in 2015 and confirmed by the European Parliament, mandated higher safeguards for financial flows from high-risk countries and enhanced the powers of EU Financial Intelligence Units. Penalties are at an all-time high for banks transacting with known or suspected money launderers. The responsibility lies with banks to stop money laundering.

If there is one thing about artificial intelligence (AI) most people agree on it's the fact that AI and the way in which its many'forms' are leveraged, whether it's in a context of cobots, sentiment analysis applications, autonomous decision-making in smart buildings, intelligence at the edge of IoT (Internet of Things) or any other solution, should serve human, business and societal goals one way or the other. That is of course easier said than done, it is an area of AI research for a reason. There are fears, there are different views on what societies need, there are several technologies which can be used by all industries and people (regardless of activities), there is the question about future applications enabled by rapidly evolving'forms' of AI such as deep learning, there is the aspect of regulation as lawmakers start looking at AI, there are discussions about what type of goals are ethical and there are ample pioneers, researchers and thinkers looking at machine ethics and computational ethics or even ethics towards robots. One of them is Nell Watson, a speaker at the AI for business event. Nell is, among others, an adjunct within the Artificial Intelligence and Robotics track of the Singularity University where she mainly lectures on machine intelligence, the relationship between people and robots and the future of society.

Governments could already be plotting to make use of brand new tech, including drones adapted into missiles, fake videos and auto-hacking tools, the Malicious Use of Artificial Intelligence report warns. Within five years AI could "go rogue" and be utilized by criminals. Lifelike videos and speech impersonation could be used to target individuals, while drones could be launched to physically attack a person, the report says. Miles Brundage, research fellow at Oxford University's Future of Humanity Institute, said: "AI will alter the landscape of risk for citizens, organisations and states -- whether it's criminals training machines to hack or'phish' at human levels of performance or privacy-eliminating surveillance, profiling and repression -- the full range of impacts on security is vast. "It is often the case that AI systems don't merely reach human levels of performance but significantly surpass [them].

In this paper, we construct a reference score for online peer assessments based on HodgeRank [5]. Peer assessment is a process in which students grade their peers assignments [2, 6]. A peer assignment system is used to enhance students learning process, especially in higher education. Through such a system, students are given the opportunity to not only learn knowledge from textbooks and instructors, but also from the process of making judgements on assignments completed by their peers. This process helps them understand the weaknesses and strengths in the work of others, and then to review their own. However, there are some practical issues associated with a peer assignment system.

We present the ConditionaL Neural Network (CLNN) and the Masked ConditionaL Neural Network (MCLNN) designed for temporal signal recognition. The CLNN takes into consideration the temporal nature of the sound signal and the MCLNN extends upon the CLNN through a binary mask to preserve the spatial locality of the features and allows an automated exploration of the features combination analogous to hand-crafting the most relevant features for the recognition task. MCLNN has achieved competitive recognition accuracies on the GTZAN and the ISMIR2004 music datasets that surpass several state-of-the-art neural network based architectures and hand-crafted methods applied on both datasets.

Superior performance and ease of implementation have fostered the adoption of Convolutional Neural Networks (CNNs) for a wide array of inference and reconstruction tasks. CNNs implement three basic blocks: convolution, pooling and pointwise nonlinearity. Since the two first operations are well-defined only on regular-structured data such as audio or images, application of CNNs to contemporary datasets where the information is defined in irregular domains is challenging. This paper investigates CNNs architectures to operate on signals whose support can be modeled using a graph. Architectures that replace the regular convolution with a so-called linear shift-invariant graph filter have been recently proposed. This paper goes one step further and, under the framework of multiple-input multiple-output (MIMO) graph filters, imposes additional structure on the adopted graph filters, to obtain three new (more parsimonious) architectures. The proposed architectures result in a lower number of model parameters, reducing the computational complexity, facilitating the training, and mitigating the risk of overfitting. Simulations show that the proposed simpler architectures achieve similar performance as more complex models.

The Partial Information Decomposition (PID) [arXiv:1004.2515] provides a theoretical framework to characterize and quantify the structure of multivariate information sharing. A new method (Idep) has recently been proposed for computing a two-predictor PID over discrete spaces. [arXiv:1709.06653] A lattice of maximum entropy probability models is constructed based on marginal dependency constraints, and the unique information that a particular predictor has about the target is defined as the minimum increase in joint predictor-target mutual information when that particular predictor-target marginal dependency is constrained. Here, we apply the Idep approach to Gaussian systems, for which the marginally constrained maximum entropy models are Gaussian graphical models. Closed form solutions for the Idep PID are derived for both univariate and multivariate Gaussian systems. Numerical and graphical illustrations are provided, together with practical and theoretical comparisons of the Idep PID with the minimum mutual information PID (Immi). [arXiv:1411.2832] In particular, it is proved that the Immi method generally produces larger estimates of redundancy and synergy than does the Idep method. In discussion of the practical examples, the PIDs are complemented by the use of deviance tests for the comparison of Gaussian graphical models.

The problem of low-rank approximation with convex constraints, which appears in data analysis, system identification, model order reduction, low-order controller design and low-complexity modelling is considered. Given a matrix, the objective is to find a low-rank approximation that meets rank and convex constraints, while minimizing the distance to the matrix in the squared Frobenius norm. In many situations, this non-convex problem is convexified by nuclear norm regularization. However, we will see that the approximations obtained by this method may be far from optimal. In this paper, we propose an alternative convex relaxation that uses the convex envelope of the squared Frobenius norm and the rank constraint. With this approach, easily verifiable conditions are obtained under which the solutions to the convex relaxation and the original non-convex problem coincide. An SDP representation of the convex envelope is derived, which allows us to apply this approach to several known problems. Our example on optimal low-rank Hankel approximation/model reduction illustrates that the proposed convex relaxation performs consistently better than nuclear norm regularization and may outperform balanced truncation.

Multilayer bootstrap network builds a gradually narrowed multilayer nonlinear network from bottom up for unsupervised nonlinear dimensionality reduction. Each layer of the network is a nonparametric density estimator. It consists of a group of k-centroids clusterings. Each clustering randomly selects data points with randomly selected features as its centroids, and learns a one-hot encoder by one-nearest-neighbor optimization. Geometrically, the nonparametric density estimator at each layer projects the input data space to a uniformly-distributed discrete feature space, where the similarity of two data points in the discrete feature space is measured by the number of the nearest centroids they share in common. The multilayer network gradually reduces the nonlinear variations of data from bottom up by building a vast number of hierarchical trees implicitly on the original data space. Theoretically, the estimation error caused by the nonparametric density estimator is proportional to the correlation between the clusterings, both of which are reduced by the randomization steps.