Every month for more than a year, company chairman Michael Edwards went to a hospital miles from his home to have an injection into his eyeball. The treatment was painful and often inconvenient -- but he endured it for 15 months because it was saving his sight. 'However, while my sight didn't worsen, it didn't make any improvement,' says Michael. 'I still couldn't read.' Yet now, thanks to a robot and the type of treatment usually used for cancer, he can see more clearly -- and the same technique could one day improve the lives of countless others. Sixteen months ago, Michael, from Fawsley in Northamptonshire, was diagnosed with age-related macular degeneration (AMD), a leading cause of blindness in the over-60s.

She consults an app on her phone, which asks an increasingly sophisticated series of diagnostic questions. The app also takes in data from Janet's fitness trackers that monitor heart rate, blood pressure and blood sugar. The app decides that Janet's symptoms look serious, and it arranges a video chat with a human doctor to discuss options so that potentially bad news can be presented in a more'human' way. The doctor has access to Janet's data remotely, along with access to a more sophisticated diagnostic, Artificial Intelligence. During that consultation Janet is booked into a clinic for medical imaging scans to aid in further diagnosis.

Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large data streams that will make land cover/use classification difficult. Machine learning classifiers can help at this, and many methods are currently available. A popular kernel classifier is the Gaussian process classifier (GPC), since it approaches the classification problem with a solid probabilistic treatment, thus yielding confidence intervals for the predictions as well as very competitive results to state-of-the-art neural networks and support vector machines. However, its computational cost is prohibitive for large scale applications, and constitutes the main obstacle precluding wide adoption. This paper tackles this problem by introducing two novel efficient methodologies for Gaussian Process (GP) classification. We first include the standard random Fourier features approximation into GPC, which largely decreases its computational cost and permits large scale remote sensing image classification. In addition, we propose a model which avoids randomly sampling a number of Fourier frequencies, and alternatively learns the optimal ones within a variational Bayes approach. The performance of the proposed methods is illustrated in complex problems of cloud detection from multispectral imagery and infrared sounding data. Excellent empirical results support the proposal in both computational cost and accuracy.

In the emerging advancement in the branch of autonomous robotics, the ability of a robot to efficiently localize and construct maps of its surrounding is crucial. This paper deals with utilizing thermal-infrared cameras, as opposed to conventional cameras as the primary sensor to capture images of the robot's surroundings. For localization, the images need to be further processed before feeding them to a navigational system. The main motivation of this paper was to develop an edge detection methodology capable of utilizing the low-SNR poor output from such a thermal camera and effectively detect smooth edges of the surrounding environment. The enhanced edge detector proposed in this paper takes the raw image from the thermal sensor, denoises the images, applies Canny edge detection followed by CSS method. The edges are ranked to remove any noise and only edges of the highest rank are kept. Then, the broken edges are linked by computing edge metrics and a smooth edge of the surrounding is displayed in a binary image. Several comparisons are also made in the paper between the proposed technique and the existing techniques.

Deep learning models are impactful in many real-world applications, and the transfer of this technology to society has created new emerging issues, like the need of model interpretability [14]. The General Data Protection Regulation approved in 2016 by the European parliament, which will be effective in 2018, is a concrete example of the need to provide human understandable justifications for decisions taken by automated data-processing systems [15]. Research could probably be inspired by old literature in neural networks to find better explanations about the dynamics of deep learning and provide more human interpretable solutions. An example of such process is found in standard logistic activation functions, that have been studied extensively in the past, but tend to be substituted by other activation functions in modern neural networks. To understand why this may be the case, it is important to recall the unique properties of the logistic function and therefore analyze the reasons why it has been introduced in neural networks. Firstly, the standard logistic function is biologically plausible.

We introduce a novel kernel that models input-dependent couplings across multiple latent processes. The pairwise joint kernel measures covariance along inputs and across different latent signals in a mutually-dependent fashion. A latent correlation Gaussian process (LCGP) model combines these non-stationary latent components into multiple outputs by an input-dependent mixing matrix. Probit classification and support for multiple observation sets are derived by Variational Bayesian inference. Results on several datasets indicate that the LCGP model can recover the correlations between latent signals while simultaneously achieving state-of-the-art performance. We highlight the latent covariances with an EEG classification dataset where latent brain processes and their couplings simultaneously emerge from the model.

As future wars will be increasingly fought by intelligent robots that effectively replace human soldiers, Norwegian researcher Morten Hansbø fears that his country might be caught unawares by the latest trends in unmanned warfare. Only years from now, war robots fully capable of understanding their surroundings, adapting to weather conditions and navigating without GPS may become a reality, wrote researcher Morten Hansbø of the Norwegian Defense Research Institute (FFI) in his article "Robotics, Combat Power and Sustainability in the Future Defense." Countries that don't use robotics may become easy prey in future warfare, Hansbø claimed in an interview with the Norwegian daily Aftenposten. "A defense that does not rely on robotics will have little credibility 15 years from now," Morten Hansbø said. Hansbø argued that Norway's wait-and-see attitude may soon backfire.

Nvidia is on a quest to find the most disruptive artificial intelligence startups. This quest is part of a larger contest, dubbed Nvidia Inception, which is screening more than 600 entrants to cull the best AI startups in three big categories. We wrote about the first four candidates for the hottest emerging startup on Friday. And now we're focusing on the next five candidates in the category dubbed the "most disruptive" startups. Jen-Hsun Huang, CEO of Nvidia, hosted a Shark Tank-style event this week as part of the search to find the best AI startups. Huang and a panel of judges listened to pitches from 14 AI startups across three categories.

Estonia is known for its'firsts'. We were the first country to declare internet access as a human right, the first country to hold a nationwide election online, the first country in Europe to both legalise ride sharing and delivery bots, and -- of course -- the first country to offer e-Residency. Countries around the world now face the challenge of understanding the rise of Artifical Intelligence, which is increasingly affecting the daily lives of their populations, so which country willl be the first in developing a comprehensive legal framework that ensures the technology can be developed in an ethical and sustainable way? We think the answer once again should be Estonia. This work to understand AI in Estonia started with our self-driving vehicles task force.

A few months ago, Bumble announced that it would be adding business networking features to its dating app. Now it's here, and it's called Bizz. It launches in the US, UK, Germany, France and Canada today. Bumble's claim to fame in the crowded dating app market is that it allows women to choose who they want to talk to; men must wait to be contacted. It's a measure that's also integrated into Bizz.