Softbank's robot Pepper is set to be the first non-human to testify in front of the UK Parliament to give evidence about the fourth industrial revolution. Pepper will be attempting to explain topics such as AI and robotics to The Commons Education Select Committee. "If we've got the march of the robots, we perhaps need the march of the robots to our select committee to give evidence," Committee chair Robert Halfon toldTes. "The fourth industrial revolution is possibly the most important challenge facing our nation over the next 10, 20, to 30 years." AI and robotics will drastically change our societies, and not always for the better.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. With all the hype about SpotMini recently, it's a good time to take a look back at another quadruped that Boston Dynamics helped develop. This system is the first of its kind that can automatically keep a cluttered room neat and tidy at a practical level, something that has been difficult to achieve using conventional robot system.

Attribute-aware CF models aims at rating prediction given not only the historical rating from users to items, but also the information associated with users (e.g. age), items (e.g. price), or even ratings (e.g. rating time). This paper surveys works in the past decade developing attribute-aware CF systems, and discovered that mathematically they can be classified into four different categories. We provide the readers not only the high level mathematical interpretation of the existing works in this area but also the mathematical insight for each category of models. Finally we provide in-depth experiment results comparing the effectiveness of the major works in each category.

Sleep disorders are implicated in a growing number of health problems. In this paper, we present a signal-processing/machine learning approach to detecting arousals in the multi-channel polysomnographic recordings of the Physionet/CinC Challenge2018 dataset. Methods: Our network architecture consists of two components. Inputs were presented to a Scattering Transform (ST) representation layer which fed a recurrent neural network for sequence learning using three layers of Long Short-Term Memory (LSTM). The STs were calculated for each signal with downsampling parameters chosen to give approximately 1 s time resolution, resulting in an eighteen-fold data reduction. The LSTM layers then operated at this downsampled rate. Results: The proposed approach detected arousal regions on the 10% random sample of the hidden test set with an AUROC of 88.0% and an AUPRC of 42.1%.

In this paper we present a hybrid active sampling strategy for pairwise preference aggregation, which aims at recovering the underlying rating of the test candidates from sparse and noisy pairwise labelling. Our method employs Bayesian optimization framework and Bradley-Terry model to construct the utility function, then to obtain the Expected Information Gain (EIG) of each pair. For computational efficiency, Gaussian-Hermite quadrature is used for estimation of EIG. In this work, a hybrid active sampling strategy is proposed, either using Global Maximum (GM) EIG sampling or Minimum Spanning Tree (MST) sampling in each trial, which is determined by the test budget. The proposed method has been validated on both simulated and real-world datasets, where it shows higher preference aggregation ability than the state-of-the-art methods.

Previous approaches using deep learning algorithms to classify glaucomatous damage on fundus photographs have been limited by the requirement for human labeling of a reference training set. We propose a new approach using spectral-domain optical coherence tomography (SDOCT) data to train a deep learning algorithm to quantify glaucomatous structural damage on optic disc photographs. The dataset included 32,820 pairs of optic disc photos and SDOCT retinal nerve fiber layer (RNFL) scans from 2,312 eyes of 1,198 subjects. A deep learning convolutional neural network was trained to assess optic disc photographs and predict SDOCT average RNFL thickness. The performance of the algorithm was evaluated in an independent test sample. The mean prediction of average RNFL thickness from all 6,292 optic disc photos in the test set was 83.3$\pm$14.5 $\mu$m, whereas the mean average RNFL thickness from all corresponding SDOCT scans was 82.5$\pm$16.8 $\mu$m (P = 0.164). There was a very strong correlation between predicted and observed RNFL thickness values (r = 0.832; P<0.001), with mean absolute error of the predictions of 7.39 $\mu$m. The areas under the receiver operating characteristic curves for discriminating glaucoma from healthy eyes with the deep learning predictions and actual SDOCT measurements were 0.944 (95$\%$ CI: 0.912- 0.966) and 0.940 (95$\%$ CI: 0.902 - 0.966), respectively (P = 0.724). In conclusion, we introduced a novel deep learning approach to assess optic disc photographs and provide quantitative information about the amount of neural damage. This approach could potentially be used to diagnose and stage glaucomatous damage from optic disc photographs.

Digital natives represent approximately a quarter of France's population, i.e. 16 million people. Despite being hyper-connected and able to adopt the latest technologies in the blink of an eye, millennials seek relationships with the perfect combination of human and digital aspects: 82 percent of them agree that we'll never completely outgrow the need for flesh-and-blood consultants!(1) At the same time, they're more demanding than the previous generation when it comes to quality of service and the customer relationship.(2) So, how do we speak the language of the millennials? What if this challenge represented an opportunity for evolution in your customer relations?

"I'm used to bad news," Merkel said, according to a participant's recollection. The German chancellor had just returned from China, where she spent a day in the Shenzhen tech hub visiting companies like ICarbonX, an artificial intelligence (AI) startup focused on disease detection. A trained physicist, Merkel had been impressed by what she saw. The money and manpower China poured into AI left the 64-year-old with little doubt that the country viewed the technology as its key to becoming a global superpower. "We really do have to walk the extra mile to make sure we're not left behind" -- Jörg Bienert, president of a new association representing more than 50 AI startups Germany, by contrast, had no plan for AI. So on her return to Berlin, Merkel met the country's top 32 AI experts at the chancellery to hear how the country was doing. Their assessment was sobering: Germany, they said, has a good track record in AI research, but it suffers from problems ranging from brain drain to a weak record in transforming basic research into real-world applications that are hampering its ability to compete in a new technology race. After three hours, Merkel left concerned -- and made her worries public a month later. "For centuries, or let's say since the age of Enlightenment, we in Europe were used to being the first ones to come up with technological innovations," she told a tech conference.

If you're looking for it, there is plenty of bad news in the tech world. From concerns about hacking and identity theft to a 2017 survey out of England that ranked Instagram as "worst for young people's mental health" compared to four other social platforms, it can be enough to make you want to become a Luddite. But the other side of the issue might be able to put a smile on your face: Tech companies and researchers are turning to AI and other software to try to solve just about any problem you can think of, from identifying fake news, to noticing if someone falls, to looking for ways to speed up the amount of time an MRI scan takes. Some companies are building software to help you change your thoughts for the better or even analyze a voice for signs of depression. For example, Woebot is a cute chatbot app designed to be an on-call emotional helper.

The random-forest technique has emerged in recent years as a powerful way to analyze large data sets while avoiding some of the pitfalls of other data-mining methods. It is based on the idea that some future event can be determined by a decision tree in which an outcome is calculated at each branch by reference to a set of training data. However, decision trees suffer from a well-known problem. In the latter stages of the branching process, decisions can become severely distorted by training data that is sparse and prone to huge variation at this kind of resolution, a problem known as overfitting. The random-forest approach is different.