You might think that between your family thread on Facebook Messenger, your various friend groups on WhatsApp, and the trusted text a few acquaintances still insist on using, you've got all possible lines of communication covered. But spare a thought for Allo, Google's latest attempt to be more than just a fringe player in messaging. Allo made its debut on far-flung shores earlier this week, but as of today it's now available in the UK for Android and iOS. Allo lets you to exchange words, images, stickers and the like in individual or group chats. Beyond the standard fare, you can increase or decrease message font size to "shout or whisper," and there's a smart reply option that drafts quick-responses on your behalf (with ambiguous success).

LONDON, UK / ACCESSWIRE / September 22, 2016 / Almax Analytics, a software platform delivering actionable insights, announced today they have emerged from stealth (development) with a customer ready product for the Financial Markets. Almax debuted in January 2016 as an Innovative Company to Watch by KPMG Luxembourg and quickly built demand and investor interest, thereby closing the seed round April 2016 to develop this first to market technology. "We are excited to present our product to the market and have worked hard to deliver this technology," says Balazs Klemm, Chief Executive and Founder. "On behalf of the company I need to thank our loyal supporters and the Almax team for their commitment and patience. We look forward to growing the business and already engaged in Series A financing discussions."

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

While robots and computers will probably never completely replace doctors and nurses, machine learning/deep learning and AI are transforming the healthcare industry, improving outcomes, and changing the way doctors think about providing care. Machine learning is improving diagnostics, predicting outcomes, and just beginning to scratch the surface of personalized care. Imagine walking in to see your doctor with an ache or pain. After listening to your symptoms, she inputs them into her computer, which pulls up the latest research she might need to know about how to diagnose and treat your problem. You have an MRI or an xray and a computer helps the radiologist detect any problems that could be too small for a human to see.

Google's artificial intelligence unit DeepMind is getting serious about healthcare - with ambitious plans to digitise the NHS - but first it needs to convince patients to hand over their medical records. Back in February, it began work with the Royal Free to create an app to help doctors spot patients who might be at risk of developing kidney disease. The first most knew of the partnership was when it emerged some months later that it would be accessing 1.6 million patient records as part of the deal. That led to some pretty negative headlines and questions from some of the patients involved as to why they had not been informed their data was being used in this way. The app - dubbed Streams - is now under investigation by the Information Commissioner's Office (ICO) while the National Data Guardian, which is tasked with safeguarding health data, is also looking at it. Newly determined to forge a better relationship with the public, Google hosted its first ever patient engagement forum this week at its new headquarters in King's Cross, pledging that it wanted, in future, to work in closer partnership with the public.

A big challenge in environmental monitoring is the spatiotemporal variation of the phenomena to be observed. To enable persistent sensing and estimation in such a setting, it is beneficial to have a time-varying underlying environmental model. Here we present a planning and learning method that enables an autonomous marine vehicle to perform persistent ocean monitoring tasks by learning and refining an environmental model. To alleviate the computational bottleneck caused by large-scale data accumulated, we propose a framework that iterates between a planning component aimed at collecting the most information-rich data, and a sparse Gaussian Process learning component where the environmental model and hyperparameters are learned online by taking advantage of only a subset of data that provides the greatest contribution. Our simulations with ground-truth ocean data shows that the proposed method is both accurate and efficient.

The multivariate normal density is a monotonic function of the distance to the mean, and its ellipsoidal shape is due to the underlying Euclidean metric. We suggest to replace this metric with a locally adaptive, smoothly changing (Riemannian) metric that favors regions of high local density. The resulting locally adaptive normal distribution (LAND) is a generalization of the normal distribution to the "manifold" setting, where data is assumed to lie near a potentially low-dimensional manifold embedded in $\mathbb{R}^D$. The LAND is parametric, depending only on a mean and a covariance, and is the maximum entropy distribution under the given metric. The underlying metric is, however, non-parametric. We develop a maximum likelihood algorithm to infer the distribution parameters that relies on a combination of gradient descent and Monte Carlo integration. We further extend the LAND to mixture models, and provide the corresponding EM algorithm. We demonstrate the efficiency of the LAND to fit non-trivial probability distributions over both synthetic data, and EEG measurements of human sleep.

We consider the task of predicting lexical entailment using distributional vectors. We perform a novel qualitative analysis of one existing model which was previously shown to only measure the prototypicality of word pairs. We find that the model strongly learns to identify hypernyms using Hearst patterns, which are well known to be predictive of lexical relations. We present a novel model which exploits this behavior as a method of feature extraction in an iterative procedure similar to Principal Component Analysis. Our model combines the extracted features with the strengths of other proposed models in the literature, and matches or outperforms prior work on multiple data sets.

There were two "tracks" for agents to compete on, offering very different challenges. Track 1 featured a map known to the teams, and rocket launchers were the only weapons. The agents started with a weapon but were able to collect ammo and health kits. Track 2 was a far harder challenge. It featured three maps, unknown to teams, and a full array of weapons and items.

Approximately 8 of the 319 million people in the United States read the Wall Street Journal, good for 2 percent of the population. If you look at the language -- standardized English -- being fed into many natural language processing units, it's based on the language of that 2 percent. And many machines literally use the venerable, business-focused newspaper to better understand English language. It might seem like an obvious choice. Standardized English is taught in schools, it's used in legal documents, and it sets the basis for formal society.