The UK's Pensions Regulator (TPR) has built a machine-learning tool to help it focus on pension schemes most at risk of breaching its guidelines. Peter Jackson, head of data at TPR, outlined on a government blog how the watchdog had worked with data scientists to make better use of the "scheme returns", TPR's main source of pension scheme data.

This article was first published on the IEC e-tech website. Advances in sensors have increased the ability of assistive robots to perform domestic handling and mobility assistance tasks as well as to analyze the environment and individuals around them so that they can carry out monitoring functions. About 4 700 elderly assistance robots were sold globally in 2015, according to the International Federation of Robotics in Frankfurt, which forecasts that sales will increase to 37 500 units between 2016 and 2019. Robots assisting with healthcare delivery in the home not only increase their users' autonomy but also have the potential to relieve the burgeoning demands that elderly populations place on health and care services and informal caregivers. Meanwhile, increasingly sophisticated "companion robots" are being developed to interact with users, make social connections with them and even provide companionship and emotional support, subject to personal choice.

The European Robotics Forum in Edinburgh late March brought together over 800 people from industry, academia and government. The 3-day event packed it's lot of talks, workshops, and panel discussions. So much to see, meet and listen at #ERF2017 @eu_Robotics pic.twitter.com/b640q25IIK The event kicked off with a dive into deep learning and its applications in robotics with keynotes by Senior Research Scientist at DeepMind Raia Hadsell, CEO of FiveAI Stan Boland, and Member of the Scottish Parliament Keith Brown. The theme of the forum this year was "living and working with robots".

Mnuchin's statements contrast with warnings issued by the last administration, which produced reports looking at the economic impact of automation. It said, for instance, that 1.3 million to 1.7 million truck drivers could lose their jobs as a result of self-driving technology. "We are going to have to have a societal conversation about how we manage [robots and automation]," Obama told Wired. You can put the divide between Obama and Mnuchin down to politics (surprise!). The Trump team has blamed trade policies, overregulation, and immigrants for job losses in the heartland, not automation. But the contrast is also reflected in the views of economists.

We determine the quality of randomized social choice algorithms in a setting in which the agents have metric preferences: every agent has a cost for each alternative, and these costs form a metric. We assume that these costs are unknown to the algorithms (and possibly even to the agents themselves), which means we cannot simply select the optimal alternative, i.e. the alternative that minimizes the total agent cost (or median agent cost). However, we do assume that the agents know their ordinal preferences that are induced by the metric space. We examine randomized social choice functions that require only this ordinal information and select an alternative that is good in expectation with respect to the costs from the metric. To quantify how good a randomized social choice function is, we bound the distortion, which is the worst-case ratio between the expected cost of the alternative selected and the cost of the optimal alternative. We provide new distortion bounds for a variety of randomized algorithms, for both general metrics and for important special cases. Our results show a sizable improvement in distortion over deterministic algorithms.

As a highlighting research topic in the multimedia area, cross-media retrieval aims to capture the complex correlations among multiple media types. Learning better shared representation and distance metric for multimedia data is important to boost the cross-media retrieval. Motivated by the strong ability of deep neural network in feature representation and comparison functions learning, we propose the Unified Network for Cross-media Similarity Metric (UNCSM) to associate cross-media shared representation learning with distance metric in a unified framework. First, we design a two-pathway deep network pretrained with contrastive loss, and employ double triplet similarity loss for fine-tuning to learn the shared representation for each media type by modeling the relative semantic similarity. Second, the metric network is designed for effectively calculating the cross-media similarity of the shared representation, by modeling the pairwise similar and dissimilar constraints. Compared to the existing methods which mostly ignore the dissimilar constraints and only use sample distance metric as Euclidean distance separately, our UNCSM approach unifies the representation learning and distance metric to preserve the relative similarity as well as embrace more complex similarity functions for further improving the cross-media retrieval accuracy. The experimental results show that our UNCSM approach outperforms 8 state-of-the-art methods on 4 widely-used cross-media datasets.

Boosting combines weak (biased) learners to obtain effective learning algorithms for classification and prediction. In this paper, we show a connection between boosting and kernel-based methods, highlighting both theoretical and practical applications. In the context of $\ell_2$ boosting, we start with a weak linear learner defined by a kernel $K$. We show that boosting with this learner is equivalent to estimation with a special {\it boosting kernel} that depends on $K$, as well as on the regression matrix, noise variance, and hyperparameters. The number of boosting iterations is modeled as a continuous hyperparameter, and fit along with other parameters using standard techniques. We then generalize the boosting kernel to a broad new class of boosting approaches for more general weak learners, including those based on the $\ell_1$, hinge and Vapnik losses. The approach allows fast hyperparameter tuning for this general class, and has a wide range of applications, including robust regression and classification. We illustrate some of these applications with numerical examples on synthetic and real data.

An increasingly popular concern is that robots will eat up labour's share of income at an accelerating rate, leaving ordinary workers impoverished and unemployed. A common dinner conversation topic in Silicon Valley is universal basic income, and the typical argument advanced for UBI is that we are destined to indefinitely continue losing jobs faster than we replace them. Variants on this theme have circulated since the dawn of the Industrial Revolution. Improvements in farming technology have been greeted with skepticism since ancient times for these reasons. Mechanical contraptions for sewing and other tasks were decried as potentially ruinous to workers in Elizabethan England.

You can now put on a virtual reality headset with sensors which will predict your facial expressions before you even make them and have them instantly mirrored on an avatar. MindMaze unveiled MASK on Wednesday, a lightweight foam with sensors that can be applied to VR headsets, which brings human emotion to VR gaming and social networking. MindMaze, a Switzerland based company, has developed products to help stroke victims and amputees using virtual reality, computer graphics, brain imaging & neuroscience -- and now it's bringing that technology to gaming. "There's a lot of technology but none of it is human," CEO and founder of MindMaze, Tej Tadi, told International Business Times about the VR world. MindMaze unveils MASK, which predicts facial expressions and mirrors them on VR avatars.

Matthew is PCMag's UK-based editor and news reporter. Prior to joining the team, he spent 14 years writing and editing content on our sister site Geek.com and has covered most areas of technology, but is especially passionate about games tech. Matthew holds a BSc degree in Computer Science from Birmingham University and a Masters in Computer Games Development from Abertay University.