The U.S. Navy is testing a radical new style of long-distance drone that can take off and land vertically, making it deployable from almost anywhere. The V-Bat drone, which recently completed a fly-test over the Atlantic Ocean can be equipped with an 8 lbs. In prior test, the V-Bat pushed the boundaries of its ceiling altitude, soaring to a height of 15,000 feet and returning safely. V-Bat's defining feature, vertical landing and take-off, make it unique and in some ways, more capable, than other drones used in military operations, according to MartinUAV'With these milestones, V-BAT has demonstrated all of the key performance parameters we set for it two years ago,' said Phillip Jones, Martin UAV's Chief Operating Officer in a statement. 'The focus for the engineering team will now shift to enhancing and refining these capabilities to even better meet & exceed warfighter requirements.'

Amazon staff review thousands of audio recordings made by Alexa each day -- including snippets of couples arguing and having sex -- an investigation claims. The clips were accidentally captured by the popular digital assistant -- confusing the noises for the commands it should be listening to -- and sent off for analysis. Staff at the tech firm review one in every five-hundred recordings made by Alexa, whether of deliberate commands to the assistant or accidental recordings. According to a privacy expert, the revelation is a reminder of the extent of the personal information that the tech firm has on its users. Amazon has an English-speaking team monitoring thousands of Alexa recordings daily based in Bucharest, Romania, the Sun claims, along with similar setups in Boston, Costa Rica and India.

A team of U.S. researchers has developed a program that weeds-out fake news. The Giant Language Model Test Room is devised by IT experts at Harvard University and the Massachusetts Institute of Technology (MIT) in a bid to counter inauthentic journalism. Based around predictive language models, which allow computers and bots to write copy, the system aims to machine algorithms. According to results of their own research, GLTR helped to improve the detection-rate of forged text from 54 percent to 72 percent - meaning the days of misinformation could potentially be numbered. Due to their modeling power,automated language models have the potential to generate textual output that is indistinguishable from the real thing - AKA it's often fake news The Giant Language Model Test Room enables forensic analysis of how likely an automatic system generated a text.

Hundreds of police departments across the U.S. have partnered with Amazon to use its brand of home security devices as local surveillance networks. According to a report from Vice, documents obtained through a Freedom of Information Act (FOIA) request detail at least 200 partnerships between Amazon's security company Ring and local law enforcement agencies. The number -- taken from notes made in an email that were transcribed from a Ring representative -- is the first hard piece of data on how many departments are working with the company. Partnerships between Amazon and police departments are far more prolific than previously though according to a new report that details 200 collaborations across the U.S. Previous attempts to quantify the extent of the collaboration have estimated around three dozen direct partnerships -- a number that is clearly well below actual figures. Partnerships between the agencies and Amazon usually involve the company donating its compact security cameras -- which are often fixed to a person's door -- and then equipping them with a'neighborhood portal.'

Nearly all established sports are going through some degree of hand-wringing over attracting younger fans as their older core ages out. The death of monoculture and explosion of entertainment options, many accessible without leaving one's bedroom, have seen attendance drops across the board. MLB and NFL teams have fallen over themselves installing on-site daily fantasy lounges to lure second-screeners. Even the hidebound International Olympic Committee has made transparent plays for youth, most recently with the addition of skateboarding, surfing and three-on-three basketball to next year's Summer Olympics in Tokyo. The demographic they're so thirsty for could be found in droves over the weekend at New York's Billie Jean King National Tennis Center, where three days of sold-out crowds turned out for the biggest video game competition of all time – the Fortnite World Cup – where a 16-year-old from Pennsylvania named Kyle Giersdorf (aka Bugha) brought home the winner's share of $3m with a dominant performance in Sunday's solos competition.

Could this be the start of a new way to fight ageing? A supplement designed to slow the ageing process aims to increase the number of healthy years we enjoy towards the end of our lives. Launched for online sale in the US in July, the pill hasn't been through clinical trials. Instead, it is being marketed direct to the public as a dietary supplement. Its makers claim it is the only scientifically validated anti-ageing supplement on the market.

Learning When to Drive in Intersections by Combining Reinforcement Learning and Model Predictive Control Tommy Tram 1, 2, 3, Ivo Batkovic 1, 2, 3, Mohammad Ali 1, and Jonas Sj oberg 2 Abstract -- In this paper, we propose a decision making algorithm intended for automated vehicles that negotiate with other possibly non-automated vehicles in intersections. The decision algorithm is separated into two parts: a high-level decision module based on reinforcement learning, and a low-level planning module based on model predictive control. Traffic is simulated with numerous predefined driver behaviors and intentions, and the performance of the proposed decision algorithm was evaluated against another controller . The results show that the proposed decision algorithm yields shorter training episodes and an increased performance in success rate compared to the other controller . Interactions between road users in intersections is a complex problem to solve, making it difficult to address using conventional rule based systems. Many advancements aim to solve this problem by trying to imitate human drivers [1] or predicting what other drivers in traffic are planning to do [2]. In [3], the authors show that by modeling the decision process as a partially observable Markov decision process, the model can account for uncertainty in sensing the environment and [4] showed some probabilistic guarantees when solving the problem using reinforcement learning (RL).

Conditional independence testing is a key problem required by many machine learning and statistics tools. In particular, it is one way of evaluating the usefulness of some features on a supervised prediction problem. We propose a novel conditional independence test in a predictive setting, and show that it achieves better power than competing approaches in several settings. Our approach consists in deriving a p-value using a permutation test where the predictive power using the unpermuted dataset is compared with the predictive power of using dataset where the feature(s) of interest are permuted. We conclude that the method achives sensible results on simulated and real datasets.

Algorithms are powerful and necessary tools behind a large part of the information we use every day. However, they may introduce new sources of bias, discrimination and other unfair practices that affect people who are unaware of it. Greater algorithm transparency is indispensable to provide more credible and reliable services. Moreover, requiring developers to design transparent algorithm-driven applications allows them to keep the model accessible and human understandable, increasing the trust of end users. In this paper we present EBAnO, a new engine able to produce prediction-local explanations for a black-box model exploiting interpretable feature perturbations. EBAnO exploits the hypercolumns representation together with the cluster analysis to identify a set of interpretable features of images. Furthermore two indices have been proposed to measure the influence of input features on the final prediction made by a CNN model. EBAnO has been preliminarily tested on a set of heterogeneous images. The results highlight the effectiveness of EBAnO in explaining the CNN classification through the evaluation of interpretable features influence.

Deep learning (DL) has shown great potential in medical image enhancement problems, such as super-resolution or image synthesis. However, to date, little consideration has been given to uncertainty quantification over the output image. Here we introduce methods to characterise different components of uncertainty in such problems and demonstrate the ideas using diffusion MRI super-resolution. Specifically, we propose to account for $intrinsic$ uncertainty through a heteroscedastic noise model and for $parameter$ uncertainty through approximate Bayesian inference, and integrate the two to quantify $predictive$ uncertainty over the output image. Moreover, we introduce a method to propagate the predictive uncertainty on a multi-channelled image to derived scalar parameters, and separately quantify the effects of intrinsic and parameter uncertainty therein. The methods are evaluated for super-resolution of two different signal representations of diffusion MR images---DTIs and Mean Apparent Propagator MRI---and their derived quantities such as MD and FA, on multiple datasets of both healthy and pathological human brains. Results highlight three key benefits of uncertainty modelling for improving the safety of DL-based image enhancement systems. Firstly, incorporating uncertainty improves the predictive performance even when test data departs from training data. Secondly, the predictive uncertainty highly correlates with errors, and is therefore capable of detecting predictive "failures". Results demonstrate that such an uncertainty measure enables subject-specific and voxel-wise risk assessment of the output images. Thirdly, we show that the method for decomposing predictive uncertainty into its independent sources provides high-level "explanations" for the performance by quantifying how much uncertainty arises from the inherent difficulty of the task or the limited training examples.