By 2020, medical data is expected to double every 73 days. With the rapidly growing amount of health data being produced, it is no surprise that medical knowledge is being drowned in the sea of scientific data. To make use of the incredible amounts of medical data, health professionals are increasingly turning to artificial intelligence (AI) to help find patterns in the deluge of data to provide better diagnostics and treatment. And now, the Chan Zuckerberg Initiative may speed progress in that area. The philanthropic arm of Facebook CEO Mark Zuckerberg and his wife Priscilla Chan has just purchased a Canadian healthcare startup, Meta, which uses AI to find, review and compile scientific papers. Meta is the Chan Zuckerberg Initiative's first outright acquisition and will be made free to use for all researchers.

Poland I have a PhD in educational science. I published 7 books and over 50 papers. I have been working in more than 20 different research projects. I am a member of the editorial boards and a reviewer of several scientific journals. My current research belongs primarily to the area of philosophy of education, cyberpsychology and cognitive neuroscience.

Russia is carrying out a sustained campaign of cyber attacks targeting democracy and critical infrastructure in the West, UK Defence Secretary Sir Michael Fallon has warned. Moscow was "weaponising misinformation" in a bid to expand its influence and destabilise Western governments and weaken Nato, he said. Vladimir Putin had chosen to become a "strategic competitor" of the West. Sir Michael said it was vital alliance members strengthened cyber defences. His speech, at the University of St Andrews, comes as Theresa May is to use an informal summit in Malta to press EU Nato members to boost defence spending.

French academics have developed an autonomous vehicle system able to mimic a human driver and a Wi-Fi network to share data between vehicles. See Also: Will Nevada be the next state to legalize self-driving cars? The system is designed with 2030 in mind, when researchers expect Wi-Fi sensors to be built into highways and 360-degree cameras and Radar to track cars far away easily. Autonomous vehicles using the EPFL system should be able to give way to other cars and know when a car is going to switch lanes or leave the highway before it happens. This self-aware system may reduce the amount of accidents on the road, according to EPFL.

Learning to learn by gradient descent by gradient descent, Andrychowicz et al., NIPS 2016 One of the things that strikes me when I read these NIPS papers is just how short some of them are – between the introduction and the evaluation sections you might find only one or two pages! A general form is to start out with a basic mathematical model of the problem domain, expressed in terms of functions. Selected functions are then learned, by reaching into the machine learning toolbox and combining existing building blocks in potentially novel ways. When looked at this way, we could really call machine learning'function learning'. Thinking in terms of functions like this is a bridge back to the familiar (for me at least).

Marking a major step forward for artificial intelligence (AI), Libratus, an AI developed by Carnegie Mellon University (CMU), has resoundingly beaten four of the best heads-up no-limit Texas hold'em poker players in the world in a marathon, 20-day competition. After 20 days and a collective 120,000 hands played, Libratus closed out the competition Monday leading the pros by a collective $1,766,250 in chips. "I'm just impressed with the quality of poker Libratus plays," pro player Jason Les, a specialist in heads-up no-limit Texas hold'em like the other three players, said at a press conference yesterday morning. "They made algorithms that play this game better than us. We make a living trying to find vulnerabilities in strategies. That's what we do every day when we play heads-up no-limit. We tried everything we could and it was just too strong."

The radiation level in the containment vessel of reactor 2 at the crippledFukushima No. 1 power plant has reached a maximum of 530 sieverts per hour, the highest since the triple core meltdown in March 2011, oTokyo Electric Power Co. Holdings Inc. said Thursday. The reading means a person could die from even brief exposure, highlighting the difficulties ahead as the government and Tepco grope their way toward dismantling all three reactors that suffered core meltdowns in the March 2011 disaster. Tepco also announced that, based on image analysis, it has discovered a 2-meter hole in the metal grating beneath the pressure vessel inside reactor 2's containment vessel, and discovered a portion of it is warped. The hole could have been caused by melted fuel penetrating the vessel after the March 11, 2011 mega-quake and massive tsunami triggered a station blackout that crippled the plant's ability to keep the reactors cool. The new radiation level, described by some experts as "unimaginable," far exceeds 73 sieverts per hour, the previously highest radiation reading monitored in the interior of the reactor.

There is a widely-accepted need to revise current forms of healthcare provision, with particular interest in sensing systems in the home. Given a multiple-modality sensor platform with heterogeneous network connectivity, as is under development in the Sensor Platform for HEalthcare in Residential Environment (SPHERE) Interdisciplinary Research Collaboration (IRC), we face specific challenges relating to the fusion of the heterogeneous sensor modalities. We introduce Bayesian models for sensor fusion, which aims to address the challenges of fusion of heterogeneous sensor modalities. Using this approach we are able to identify the modalities that have most utility for each particular activity, and simultaneously identify which features within that activity are most relevant for a given activity. We further show how the two separate tasks of location prediction and activity recognition can be fused into a single model, which allows for simultaneous learning an prediction for both tasks. We analyse the performance of this model on data collected in the SPHERE house, and show its utility. We also compare against some benchmark models which do not have the full structure, and show how the proposed model compares favourably to these methods.

Smart devices have shown that they are vulnerable to security flaws in the past, so how do we secure ourselves as we move towards smart homes? Across the world Smart homes are on the rise, with the IoT creating new technological solutions to our everyday problems. As companies like Google and Amazon roll out their smart speakers, collating our devices and apps into easily manageable hubs, smart homes will surely only increase in popularity. According to ABI Research, by 2020 there will be 40.9 billion active wireless connected devices and this will largely be non-hub devices like sensors. IDC forecasts that by 2020 the worldwide market for IoT will surpass $7 trillion. The growth of these systems in the recent and coming years is astronomical but with all of our personal data uploaded into nebulous'cloud' systems how safe are we and how safe are our homes?

Researchers have developed a new method for making effective calculations in "high-dimensional space" – and proved its worth by using it to solve a 93-dimensional problem. Researchers have developed a new technique for making calculations in "high-dimensional space" – mathematical problems so wide-ranging in their scope, that they seem at first to be beyond the limits of human calculation. In what sounds like the title of a rejected script for an Indiana Jones movie, the method improves on existing approaches to beat a well-known problem known as "The Curse Of Dimensionality". It was devised by a team of researchers at the University of Cambridge. In rough terms, the "Curse", refers to the apparent impossibility of making calculations in situations where the number of variables, attributes, and possible outcomes is so large that it seems futile even to try to comprehend the problem in the first place.