Is Mars ready for its close-up? ExoMars rover will be sent on its mission in 2020; should be on surface by 2021 Camera on rover to contain mirror so it can beam images of itself back to Earth Selfie mirror is designed by team of scientists at Aberystwyth University Dr Matthew Gunn: 'For the first time, the selfie mirror will allow the rover to see itself on the Martian surface' Dr Matthew Gunn: 'For the first time, the selfie mirror will allow the rover to see itself on the Martian surface' British scientists have come up with a new strategy in their quest to find life on Mars - a robot armed with a selfie stick with a mirror on it. Is this proof of life on Mars? NASA rover discovers a large... David Bowie had it right! The igloos of Mars: Radical designs reveal the ice homes of... Earth's escaping atmosphere could WIPE OUT life and... Is this proof of life on Mars?

Over the last one year, Intuitive Surgical has generated returns in excess of 50%. An economic moat, a lack of competition and financial out-performance are some of the reasons for this excellent run. In this analysis, we look at the factors that can impact Intuitive's growth going ahead: Competition from other players: Intuitive Surgical faces little competition in the robotic surgery market, as there are significant barriers to entry and the company holds more than 1,000 patents worldwide. So far Intuitive has enjoyed a free run. TransEnterix was expected to be the challenger with its SurgiBot Systems.

In 2017, tech companies will have to adapt to a new reality. President Trump and his cabinet of billionaires will shape government regulation for companies across the tech industry. And while pre-election, many thought markets would collapse with a Trump win, and post-election, many questioned their naïveté that business wouldn't respond well to a Trump presidency, it's actually a mix of both for the tech world. "If you're a company whose product is enmeshed in a core Democratic philosophy like cannabis or ed tech, this could be more challenging for you," Bradley Tusk, a consultant who works with Uber and other tech companies on their regulatory strategies, told Mashable. "If you're a company who just wants to go about your business without being over-regulated, you're probably going to do pretty well."

Manoj Saxena is the executive chairman of CognitiveScale and a founding managing director of The Entrepreneurs' Fund IV (TEF), a $100m seed fund focused exclusively on the cognitive computing space. Saxena is also the chairman of Federal Reserve Bank of Dallas, San Antonio branch and Chairman, SparkCognition an Austin based cognitive security and safety analytics company. Prior to joining TEF, Saxena was general manager, IBM Watson, where his team built the world's first cognitive systems in healthcare, financial services, and retail. Earlier he founded, built and sold two Austin based software startups. Saxena will be speaking at Gigaom AI Now in San Francisco, February 15-16th.

Politicians are the lowest form of life on earth. Liberal Democrats are the lowest form of politicians.

When Donald Trump met with technology leaders in December to tell them he wanted them to create jobs in the U.S., their heads probably tilted to the side, as if you tried to explain physics to your dog and she just watched your lips moving and wondered when, among all those unfamiliar sounds, she was going to hear the word treat. They're in business to help us do more with less. They like innovation and disruption and software eating the world. In his own Chance-the-gardener way, the president-elect might be onto something. His victory was a middle-finger salute from those who feel left out by technology and globalization.

It's hard to bet against the continued rise of automation, robots, and artificial intelligence (AI), all of which are already having major impacts on how we work, learn, shop, and play. But being able to predict that robotics and AI will change our lives is a lot easier than predicting they will change our lives. In a recent forecast for 2017 and beyond, for instance, analyst firm IDC said we can expect to continue seeing robotic and AI technologies keep growing more affordable, more capable, and easier to use. The Obama White House said it expects the same, but also warns in a new report that "growth will not be costless" and could harm workers lacking the skills to compete in an AI-driven economy. How the incoming administration plans to address such issues is also uncertain. While President-elect Donald Trump's campaign promised to revive U.S. manufacturing and spend $1 trillion on the nation's infrastructure, he has also tapped Hardee's/Carl's Jr. chief Andrew Puzder -- who supports the use of automation to save on employment costs -- as secretary of the U.S. Department of Labor.

While many models are purposed for detecting the occurrence of significant events in financial systems, the task of providing qualitative detail on the developments is not usually as well automated. We present a deep learning approach for detecting relevant discussion in text and extracting natural language descriptions of events. Supervised by only a small set of event information, comprising entity names and dates, the model is leveraged by unsupervised learning of semantic vector representations on extensive text data. We demonstrate applicability to the study of financial risk based on news (6.6M articles), particularly bank distress and government interventions (243 events), where indices can signal the level of bank-stress-related reporting at the entity level, or aggregated at national or European level, while being coupled with explanations. Thus, we exemplify how text, as timely, widely available and descriptive data, can serve as a useful complementary source of information for financial and systemic risk analytics.

Modeling continuous-time physiological processes that manifest a patient's evolving clinical states is a key step in approaching many problems in healthcare. In this paper, we develop the Hidden Absorbing Semi-Markov Model (HASMM): a versatile probabilistic model that is capable of capturing the modern electronic health record (EHR) data. Unlike exist- ing models, an HASMM accommodates irregularly sampled, temporally correlated, and informatively censored physiological data, and can describe non-stationary clinical state transitions. Learning an HASMM from the EHR data is achieved via a novel forward- filtering backward-sampling Monte-Carlo EM algorithm that exploits the knowledge of the end-point clinical outcomes (informative censoring) in the EHR data, and implements the E-step by sequentially sampling the patients' clinical states in the reverse-time direction while conditioning on the future states. Real-time inferences are drawn via a forward- filtering algorithm that operates on a virtually constructed discrete-time embedded Markov chain that mirrors the patient's continuous-time state trajectory. We demonstrate the di- agnostic and prognostic utility of the HASMM in a critical care prognosis setting using a real-world dataset for patients admitted to the Ronald Reagan UCLA Medical Center.

AI has been hot in 2016, and it's not cooling off anytime soon. The investments, acquisitions, trials, reorganizations and breakthroughs of the past year have set the AI industry up to have tremendous impact over the next twelve months. We'll stop talking about far-fetched, man-versus-machine Skynet predictions and instead figure out how to harness AI to turn the slag pile of big data into the orderly summit of our dreams. The hype curve will calm down as people realize what AI can do and is doing, and thus form more realistic pictures of what it will do. We'll embrace AI as critical for our economic productivity.