Robots may take four million British jobs in the private sector within the next decade, some business leaders believe. Those surveyed for by YouGov for the Royal Society of Arts said 15 per cent of all jobs were under threat. The most vulnerable fields are finance and accounting, transportation and distribution, manufacturing and marketing and public relations, the survey found. But the research was not all doom and gloom, noting that technological advance creates new jobs, partly because increased productivity reduces prices freeing up consumers to spend money elsewhere in the economy. The RSA added that AI and robotics will mostly automate individual tasks rather than replace whole jobs.

Amazon is planning to release a pair of Alexa-enabled smartglasses as the latest addition to its range of voice-controlled devices, according to reports. Unlike most previous smartglasses, such as the ill-fated Google Glass experiment and Snapchat's Spectacles, the Amazon glasses won't feature a camera in any form, bypassing the privacy concerns that have plagued the form-factor in the past. Instead, they will focus on providing a link to Alexa, Amazon's voice-controlled personal assistant, through a bone-conduction audio system, which transmits sounds into the wearer's head by vibrating their skull, rather than through headphones inserted in their ear. According to a report by the Financial Times, the glasses could be revealed at a product launch event expected to be held soon alongside a home security camera, designed to tie in with its Echo Show video screen. Other reports have suggested the company will shortly release a new version of the Fire TV, its streaming media set-top box, with an Echo-style speaker system built-in.

User-generated content, bring your own device and big data were some of the fastest growing EdTech topics of 2016. Here are eight ideas and expert predictions for the year ahead. As well as placing a greater emphasis on STEM education, many schools and universities are working to cultivate skills like creativity and empathy, which are thought to be harder for machines to replicate. EdTechX Global co-founder, Benjamin Vedrenne-Cloquet, says'I think we will see future skills proofing in both primary and secondary schools.' 2. More Learning Outside the Classroom 2016 saw flipped learning become mainstream in many schools and there now exist many opportunities for digitally assisted learning that can happen from any location. Technology at school is now better linked with technology at home and many predict that 2017 will see further disintegration of the classroom walls.

Drones and other autonomous robots require mobile and efficient solutions to real-life issues, from mundane package transportation to urgent search and rescue missions. Using machine learning and a vector-based navigation system inspired by insects, agents could navigate to key locations without relying on a GPS -- becoming truly autonomous. Robots could learn to navigate independently to wildfires based on environmental sensory cues, using information from cameras and other sensors. Since vectors are represented in a geocentric context, multiple agents could communicate locations with each other, which could, for example, speed up efforts to perform rescues and put out fires. Such flexibility and speed of coordination would largely improve the success and efficiency of rescue missions during natural disasters -- and save lives.

Depending on the stories you read, artificial intelligence (AI) can be absolutely terrifying or downright hilarious (or both). Sometimes it can be lifesaving. Scientists are constantly discovering new ways this exciting technology can be used to improve our health and extend lifespans, whether that's software to detect skin cancer or a program that can pick the most viable embryo option for IVF treatment. Now, researchers are using AI scans to detect Alzheimer's almost a decade earlier than doctors making a diagnosis based on symptoms alone. Alzheimer's is the most common form of dementia, affecting over 5 million Americans.

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The work of B. Zieliński was supported by the National Science Centre (Poland) under grant agreement no 2015/19/D/ST6/01215; 2016-2019. The work of P. Spurek was supported by the National Science Centre (Poland) under grant agreement no. The work of K. Misztal was supported by the National Science Centre (Poland) under grant agreement no. Competing interests: The authors have declared that no competing interests exist.

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In this paper, we propose a novel recurrent neural network architecture for speech separation. This architecture is constructed by unfolding the iterations of a sequential iterative soft-thresholding algorithm (ISTA) that solves the optimization problem for sparse nonnegative matrix factorization (NMF) of spectrograms. We name this network architecture deep recurrent NMF (DR-NMF). The proposed DR-NMF network has three distinct advantages. First, DR-NMF provides better interpretability than other deep architectures, since the weights correspond to NMF model parameters, even after training. This interpretability also provides principled initializations that enable faster training and convergence to better solutions compared to conventional random initialization. Second, like many deep networks, DR-NMF is an order of magnitude faster at test time than NMF, since computation of the network output only requires evaluating a few layers at each time step. Third, when a limited amount of training data is available, DR-NMF exhibits stronger generalization and separation performance compared to sparse NMF and state-of-the-art long-short term memory (LSTM) networks. When a large amount of training data is available, DR-NMF achieves lower yet competitive separation performance compared to LSTM networks.

We propose the kl-UCB ++ algorithm for regret minimization in stochastic bandit models with exponential families of distributions. We prove that it is simultaneously asymptotically optimal (in the sense of Lai and Robbins' lower bound) and minimax optimal. This is the first algorithm proved to enjoy these two properties at the same time. This work thus merges two different lines of research with simple and clear proofs.

In evolving complex systems such as air traffic and social organizations, collective effects emerge from their many components' dynamic interactions. While the dynamic interactions can be represented by temporal networks with nodes and links that change over time, they remain highly complex. It is therefore often necessary to use methods that extract the temporal networks' large-scale dynamic community structure. However, such methods are subject to overfitting or suffer from effects of arbitrary, a priori imposed timescales, which should instead be extracted from data. Here we simultaneously address both problems and develop a principled data-driven method that determines relevant timescales and identifies patterns of dynamics that take place on networks as well as shape the networks themselves. We base our method on an arbitrary-order Markov chain model with community structure, and develop a nonparametric Bayesian inference framework that identifies the simplest such model that can explain temporal interaction data.