Scientists are hoping to use Big Data and machine learning to prevent further outbreaks of Ebola, by identifying the likelihood of various bat species carrying the virus. Ebola is what's known as a filovirus, which are long filament-shaped viruses whose genome is encoded on a single strand of RNA. Ebola is the most famous example, but there are others which are just as deadly, such as the Marburg virus that takes its name from an outbreak in the city of Marburg, Germany, in 1967. Ebola, like Marburg, is spread when people come into direct contact with the bodily fluids of infected persons. The most infamous outbreak of Ebola occurred just two years ago, in West Africa in 2014, where 11,310 people died from the disease, the World Health Organization says.

Coleman's work with low-dimensional nanostructures, as well as Nicolosi's in the field of battery development, bring 2D materials to the fore. Plummeting production costs mean that such 2D materials are emerging with a wide range of applications. Thomas Swann, for example, produces materials in commercial quantities, though the research in Ireland would largely relate to smaller measurements.

Inbenta is a company specialized in Artificial Intelligence and Natural Language Processing which was born in Barcelona and nowadays has presence in 8 different countries (Spain, USA, Chile, Brasil, Mexico, France, The Netherlands and Singapore).

Well I finally got one, but it turned out to be a vacuum cleaner rather than a talking walking friend. In the next 5 years we are going to start to see Robots and Artificial Intelligence (AI) have a big impact on all our working lives. Driverless trucks & cars In the next 5 years we will start to see the first driverless vehicles on our roads. Imagine how this will impact the daily commute. Instead of being stuck behind the wheel you can sit at a table working, watching TV, or even doing your gym workout on a treadmill.

TOKYO--Japanese smartphone-game provider DeNA Co. said Thursday it has set up a company with Preferred Networks Inc., becoming the latest major firm to bet on the startup's artificial-intelligence technology for growth. The joint project might mean Nintendo Co.'s future smartphone games would be powered by the technology, further beefing up the business potential of its popular characters, including Pokémon, for example, which was...

Singapore has announced a new partnership with Microsoft to create a digital government services platform that will shift towards conversational computing. Announcing the initiative at the World Cities Summit in the city-state on 12 July, Dr Vivian Balakrishnan, Minister for Foreign Affairs and Minister-In-Charge of the Smart Nation Initiative, said the new medium, conceptually referred to as "Conversations as a Platform" will use chatbots -- intelligent software programmes that simulate human behaviour. "I believe there are more intuitive ways for government services to be delivered to our citizens," Dr Balakrishnan said. The chatbots, which combine human language, artificial intelligence and machine learning, are envisioned to make public and business transactions simpler, more efficient, and more consistent. "Everybody expects responsive and personalised interactions in real time. The recent quantum improvement of natural language processing means that'conversations' will be the new medium," he said.

In the future, the development of artificial intelligence (AI) will accelerate beyond anything we have previously imagined. It will offer limitless possibilities – changing our experiences, transforming every area of life and redefining how we interact with technology. Yet AI is not just future fantasy. It is here and now, gaining momentum through advances in machine learning, neural networks and big data. These are exciting times and the UK is right at the heart of it.

Helen of Troy may have had the face that launched a thousand ships, but even the best facial recognition algorithms may have had trouble finding her face in a crowd of one million strangers. The first benchmark test based on one million faces has shown how facial recognition algorithms from Google and other research groups around the world can still fall short in accurately identifying and verifying faces. Facial recognition algorithms that had previously performed with more than 95 percent accuracy on a popular benchmark test involving 13,000 faces saw significant drops in accuracy when faced with the new MegaFace Challenge involving one million faces. The best performer on one test, Google's FaceNet algorithm, dropped from near-perfect accuracy on five-figure datasets to 75 percent on the million-face test. Other top algorithms dropped from above 90-percent accuracy on the small datasets to below 60 percent on the MegaFace Challenge.

Deploying the most innovative technologies alone will not ensure Singapore can succeed in its smart nation ambition, as this will require a population that is willing to embrace change in the way it interacts with its government. Since the launch of its smart nation initiative in 2014, the Singapore government has been rolling out various pilots and programmes to put in place the supporting infrastructure and systems. These centred around key objectives, among others, to enable safer and greener urban living, provide more transport options, facilitate better healthcare, and deliver more responsive public services and citizen engagement. Several initiatives had focused on a range of technologies including data analytics, Internet of Things (IoT), and cloud computing. Microsoft earlier this week announced it was working with the Singapore government to explore the use of machine learning and chatbots to deliver more interactive online citizen services.

Functional Magnetic Resonance Imaging (fMRI) is a noninvasive technique for studying brain activity, which receives an increasing attention in the last decade or so. During an fMRI experiment, a series of brain images is acquired, while the subject possibly performs a set of tasks responding to external stimuli. Changes in the measured blood-oxygen-level dependent (BOLD) signal are used to examine different types of activation in the brain. There are several objectives in the analysis of fMRI data, the most common of which are the localization of regions of the brain, that are activated by a task, and the determination of the functional brain connectivity [1, 2]. The localization of the activated areas in the human brain is a challenging "cocktail party" problem, where several people are talking (areas activated) simultaneously behind a wall (skull). Our goal is to distinguish those areas (spatial maps) as well as activation patterns (time courses) through some blind source separation (decomposition) method [3, 4]. Each source is the outcome of a combination of a time course with a spatial map. In fMRI studies of the brain function, the structure of the data involves multiple modes, such as trial, task condition, subject, in addition to the intrinsic dimensions of time and space [5].