"We are either kings or pawns of men," said Napoleon Bonaparte. In his famous quote, he describes possibly the biggest challenge facing humanity as we move into a world driven by ever smarter, more manipulative technology. Are we truly in control of our destiny or is something else secretly pulling our strings? As technology advances, we are quickly becoming slaves to it, unaware of our intentional manipulation. It could be a simple post on Facebook only visible to those targeted or an automated response to your political tweet intentionally trying to anger you for the express purpose of affecting your choice to vote for a particular candidate.

Algorithms are increasingly powerful, and researchers have recently been grappling with how they can operate ethically, and effectively, in society. I wrote earlier this year about a fascinating project where researchers had developed an AI capable of explaining its own workings. The researchers developed an algorithm that is not only capable of performing its task, but also translates how it achieved it into reasonably understandable English via a documentation process that is performed at each stage of its work. Despite official attempts to build this into our rules and regulations however, researchers suggest there is still a way to go. A paper, from a team from The Alan Turing Institute, suggests that the EU's General Data Protection Regulation does little to legally mandate tech companies to explain their algorithms. What's more, there are also doubts raised as to just what kind of information may be included when explanations are provided.

Nearly two-thirds of consumers are worried about home IoT devices listening in on their conversations, according to a Gartner survey released Monday. Those jitters aren't too surprising after recent news items about TV announcers inadvertently activating viewers' Amazon Echos, or about data from digital assistants being used as evidence in criminal trials. But privacy concerns are just one hurdle smart homes still have to overcome, according to the survey. In fact, Gartner found that most consumers don't feel they need what smart homes offer. Consumer IoT is still in an early-adopter phase, Gartner concluded from the online survey, which was conducted in the second half of last year in the U.S., U.K., and Australia.

The Chancellor is expected to raise taxes in his Budget speech on Wednesday, as he tries to tackle the UK's deficit while building a fund to ensure a smooth Brexit. But there will also be some spending, including a boost to Britain's digital economy. As well as up to £270m for research and development for artificial intelligence, electric cars and new pharmaceutical research, Philip Hammond will announce new funding to support university places in science, technology and engineering subjects. He will say his series of new measures are part of his strategy to increase innovation and productivity, in order to "Brexitproof" the UK in case the country encounters severe economic turbulence during and after the Brexit process. The measures follow the Chancellor's announcement on Sunday of a £500m investment in technical education in an attempt to bring "genuine parity of esteem" between academic and vocational qualifications. All of this, however, is pretty small beer when set against the enormous challenges around debt and deficit the Chancellor inherited, not from his Labour predecessors but from his immediate Conservative one, George Osborne.

We present an algorithm for model-based reinforcement learning that combines Bayesian neural networks (BNNs) with random roll-outs and stochastic optimization for policy learning. The BNNs are trained by minimizing $\alpha$-divergences, allowing us to capture complicated statistical patterns in the transition dynamics, e.g. multi-modality and heteroskedasticity, which are usually missed by other common modeling approaches. We illustrate the performance of our method by solving a challenging benchmark where model-based approaches usually fail and by obtaining promising results in a real-world scenario for controlling a gas turbine.

We propose the spatiotemporal estimation of the demand that is a function of variable effects related to traffic, pricing and weather conditions. With respect to the methodology, a single decision tree, bootstrap-aggregated (bagged) decision trees, random forest, boosted decision trees, and artificial neural network for regression have been adapted and systematically compared using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and slope. To better assess the quality of the models, they have been tested on a real case study using the data of DiDi Chuxing, the main on-demand ride-hailing service provider in China. In the current study, 199,584 time-slots describing the spatiotemporal ride-hailing demand has been extracted with an aggregated-time interval of 10 mins. All the methods are trained and validated on the basis of two independent samples from this dataset. The results revealed that boosted decision trees provide the best prediction accuracy (RMSE 16.41), while avoiding the risk of over-fitting, followed by artificial neural network (20.09), random forest (23.50), bagged decision trees (24.29) and single decision tree (33.55).

Very often features come with their own vectorial descriptions which provide detailed information about their properties. We refer to these vectorial descriptions as feature side-information. In the standard learning scenario, input is represented as a vector of features and the feature side-information is most often ignored or used only for feature selection prior to model fitting. We believe that feature side-information which carries information about features intrinsic property will help improve model prediction if used in a proper way during learning process. In this paper, we propose a framework that allows for the incorporation of the feature side-information during the learning of very general model families to improve the prediction performance. We control the structures of the learned models so that they reflect features similarities as these are defined on the basis of the side-information. We perform experiments on a number of benchmark datasets which show significant predictive performance gains, over a number of baselines, as a result of the exploitation of the side-information.

While the costs of human violence have attracted a great deal of attention from the research community, the effects of the network-on-network (NoN) violence popularised by Generative Adversarial Networks have yet to be addressed. In this work, we quantify the financial, social, spiritual, cultural, grammatical and dermatological impact of this aggression and address the issue by proposing a more peaceful approach which we term Generative Unadversarial Networks (GUNs). Under this framework, we simultaneously train two models: a generator G that does its best to capture whichever data distribution it feels it can manage, and a motivator M that helps G to achieve its dream. Fighting is strictly verboten and both models evolve by learning to respect their differences. The framework is both theoretically and electrically grounded in game theory, and can be viewed as a winner-shares-all two-player game in which both players work as a team to achieve the best score. Experiments show that by working in harmony, the proposed model is able to claim both the moral and log-likelihood high ground. Our work builds on a rich history of carefully argued position-papers, published as anonymous YouTube comments, which prove that the optimal solution to NoN violence is more GUNs.

Coordinate descent methods employ random partial updates of decision variables in order to solve huge-scale convex optimization problems. In this work, we introduce new adaptive rules for the random selection of their updates. By adaptive, we mean that our selection rules are based on the dual residual or the primal-dual gap estimates and can change at each iteration. We theoretically characterize the performance of our selection rules and demonstrate improvements over the state-of-the-art, and extend our theory and algorithms to general convex objectives. Numerical evidence with hinge-loss support vector machines and Lasso confirm that the practice follows the theory.

Bird sounds possess distinctive spectral structure which may exhibit small shifts in spectrum depending on the bird species and environmental conditions. In this paper, we propose using convolutional recurrent neural networks on the task of automated bird audio detection in real-life environments. In the proposed method, convolutional layers extract high dimensional, local frequency shift invariant features, while recurrent layers capture longer term dependencies between the features extracted from short time frames. This method achieves 88.5% Area Under ROC Curve (AUC) score on the unseen evaluation data and obtains the second place in the Bird Audio Detection challenge.