In order to mitigate the displacement effect on this large portion of the workforce, as well as to help members of the UK's labour pool who have already found themselves out of work, PwC issued a number of recommendations to accompany its report. Most importantly, the firm suggests that the UK Government should invest more in'STEAM' skills that will be most useful to people in this increasingly automated world. While this does mean pushing for schools to focus more on STEM subjects (science, technology, engineering and mathematics), it also means Britain should explore how art and design – the'A' in'STEAM' – can feature at the heart of innovation. It is not solely about educating new labour, though, and PwC also states that governments have a responsibility to encourage workers to continually update and adapt their skills so as to complement what new machines and AI can do. Meanwhile, the UK Government should strengthen the state's safety net for those who find it hard to adjust to technological changes.

You will soon be working with, or competing against, a new type of robot with the potential to change the game of international politics forever. Diplomacy is similar to a strategic board game. A country makes a move, the other(s) respond. Artificial intelligence is good at board games. To get the game started, the system analyses previous play, learns lessons from defeats or even repeatedly plays against itself to devise a strategy that can be never thought of before by humans.

Robots could replace as many as 10,000 human jobs at the banking giant Citi within five years, its president told the Financial Times. "We've got 20,000 operational roles. Over the next five years could you make it 10,000?" Jamie Forese, the president of Citi and chief executive of the bank's institutional clients group, told the Financial Times in an interview . Forese said the most likely areas for automation were in technology and operations, which accounts for almost 40% of the headcount at Citi's investment-banking arm.

The underlying factors affecting users' choices of what to watch on TV have for several years been of interest to commercial and academic research. In the midst of a rapidly changing device and multimedia landscape, TVs continue to be at the core of multimedia consumption in the home with scenarios covering, among others, social gatherings and solitary immersive moments. The inherent complexity of viewing situations challenges the creation of experiences that match personal preferences as well as temporal and social contexts. Due to the increased availability of multimedia, research has been focused on improving the users' decision process by reducing large catalogs of content to a few personalized suggestions [1]. Commercial recommender solutions are now considered core to the business of engaging users and thereby preventing abandonment [2]. To do so, recommender systems have explored various features for personalization, such as history of watching, ratings, user/item similarity, and time of the day, the last of which is an example of features characteristic to context-aware recommender systems (CARS) [3]. The main objective of a recommender system is to personalize the experience to the individual, often by studying the user-item matrix. This could be an issue, since an account on a TV is often shared by multiple members of a household that end up diluting the user profile.

Random forests are a powerful method for non-parametric regression, but are limited in their ability to fit smooth signals, and can show poor predictive performance in the presence of strong, smooth effects. Taking the perspective of random forests as an adaptive kernel method, we pair the forest kernel with a local linear regression adjustment to better capture smoothness. The resulting procedure, local linear forests, enables us to improve on asymptotic rates of convergence for random forests with smooth signals, and provides substantial gains in accuracy on both real and simulated data.

Malware authors have always been at an advantage of being able to adversarially test and augment their malicious code, before deploying the payload, using anti-malware products at their disposal. The anti-malware developers and threat experts, on the other hand, do not have such a privilege of tuning anti-malware products against zero-day attacks pro-actively. This allows the malware authors to being a step ahead of the anti-malware products, fundamentally biasing the cat and mouse game played by the two parties. In this paper, we propose a way that would enable machine learning based threat prevention models to bridge that gap by being able to tune against a deep generative adversarial network (GAN), which takes up the role of a malware author and generates new types of malware. The GAN is trained over a reversible distributed RGB image representation of known malware behaviors, encoding the sequence of API call ngrams and the corresponding term frequencies. The generated images represent synthetic malware that can be decoded back to the underlying API call sequence information. The image representation is not only demonstrated as a general technique of incorporating necessary priors for exploiting convolutional neural network architectures for generative or discriminative modeling, but also as a visualization method for easy manual software or malware categorization, by having individual API ngram information distributed across the image space. In addition, we also propose using smart-definitions for detecting malwares based on perceptual hashing of these images. Such hashes are potentially more effective than cryptographic hashes that do not carry any meaningful similarity metric, and hence, do not generalize well.

Nowadays, a lot of scientific efforts are concentrated on the diagnosis of Alzheimer's Disease (AD) applying deep learning methods to neuroimaging data. Even for 2017, there were published more than a hundred papers dedicated to AD diagnosis, whereas only a few works considered a problem of mild cognitive impairments (MCI) conversion to the AD. However, the conversion prediction is an important problem since approximately 15% of patients with MCI converges to the AD every year. In the current work, we are focusing on the conversion prediction using brain Magnetic Resonance Imaging and clinical data, such as demographics, cognitive assessments, genetic, and biochemical markers. First of all, we applied state-of-the-art deep learning algorithms on the neuroimaging data and compared these results with two machine learning algorithms that we fit using the clinical data. As a result, the models trained on the clinical data outperform the deep learning algorithms applied to the MR images. To explore the impact of neuroimaging further, we trained a deep feed-forward embedding using similarity learning with Histogram loss on all available MRIs and obtained 64-dimensional vector representation of neuroimaging data. The use of learned representation from the deep embedding allowed to increase the quality of prediction based on the neuroimaging. Finally, the current results on this dataset show that the neuroimaging does affect conversion prediction, however, cannot noticeably increase the quality of the prediction. The best results of predicting MCI-to-AD conversion are provided by XGBoost algorithm trained on the clinical and embedding data. The resulting accuracy is 0.76 +- 0.01 and the area under the ROC curve - 0.86 +- 0.01.

Estimation is the computational task of recovering a hidden parameter $x$ associated with a distribution $D_x$, given a measurement $y$ sampled from the distribution. High dimensional estimation problems arise naturally in statistics, machine learning, and complexity theory. Many high dimensional estimation problems can be formulated as systems of polynomial equations and inequalities, and thus give rise to natural probability distributions over polynomial systems. Sum-of-squares proofs provide a powerful framework to reason about polynomial systems, and further there exist efficient algorithms to search for low-degree sum-of-squares proofs. Understanding and characterizing the power of sum-of-squares proofs for estimation problems has been a subject of intense study in recent years. On one hand, there is a growing body of work utilizing sum-of-squares proofs for recovering solutions to polynomial systems when the system is feasible. On the other hand, a general technique referred to as pseudocalibration has been developed towards showing lower bounds on the degree of sum-of-squares proofs. Finally, the existence of sum-of-squares refutations of a polynomial system has been shown to be intimately connected to the existence of spectral algorithms. In this article we survey these developments.

Modern measurement technologies provide the means to measure high density spatial and geometric data in three-dimensional (3D) coordinate systems, referred to as point clouds. Point cloud data analysis has broad applications in advanced manufacturing and metrology for measuring dimensional accuracy and shape analysis, in geographic information systems (GIS) for digital elevation modeling and analysis of terrains, in computer graphics for shape reconstruction, and in medical imaging for volumetric measurement to name a few. The role of point cloud data in manufacturing is now more important than ever, particularly in the field of smart and additive manufacturing processes, where products with complex shape and geometry are manufactured with the help of advanced technologies (Gibson et al., 2010). In these processes, the dimensional and geometric accuracy of manufactured parts are measured in the form of point clouds using modern sensing devices, including touch-probe coordinate measuring machines (CMM) and optical systems, such as laser scanners. Modeling the relationship of the dimensional accuracy, encapsulated in point clouds, with process parameters and machine settings is vital for variation reduction and process optimization.

This paper studies clustering of data sequences using the k-medoids algorithm. All the data sequences are assumed to be generated from \emph{unknown} continuous distributions, which form clusters with each cluster containing a composite set of closely located distributions (based on a certain distance metric between distributions). The maximum intra-cluster distance is assumed to be smaller than the minimum inter-cluster distance, and both values are assumed to be known. The goal is to group the data sequences together if their underlying generative distributions (which are unknown) belong to one cluster. Distribution distance metrics based k-medoids algorithms are proposed for known and unknown number of distribution clusters. Upper bounds on the error probability and convergence results in the large sample regime are also provided. It is shown that the error probability decays exponentially fast as the number of samples in each data sequence goes to infinity. The error exponent has a simple form regardless of the distance metric applied when certain conditions are satisfied. In particular, the error exponent is characterized when either the Kolmogrov-Smirnov distance or the maximum mean discrepancy are used as the distance metric. Simulation results are provided to validate the analysis.