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

Cian Westmoreland was 18 years old when he enlisted in the US Air Force. Now 28, the former serviceman served with the 606 Air Control Squadron in Germany and the 73rd Expeditionary Air Control Squadron in Kandahar, Afghanistan, as an Air Force Technician. He built the communications infrastructure for the US military's drone programme in Afghanistan, which, according to a 2015 report by The Intercept led to the deaths of hundreds of civilians. In 2010, after four years in the military, he left the Air Force and joined other whistle-blowers speaking out about US drone policy. The group of technicians and operators wrote an open letter to US President Barack Obama.

During my last interview I had a great talk with Daniel McDuff. Daniel's research is at the intersection of psychology and computer science. He is interested in designing hardware and algorithms for sensing human behavior at scale, and in building technologies that make life better. Applications of behavior sensing that he is most excited about are in: understanding mental health, improving online learning and designing new connected devices (IoT). Listen to more about why it is important to collect data from much larger scales and help computers read our emotional state. Key Learning Points: 1. Understanding the impact, intersection, and meaning of Psychology and Computer Science 2. Facial Expression Recognition 3. How to define Artificial Intelligence, Deep Learning, and Machine Learning 4. Applications of behavior sensing with Online Learning, Health, and Connected Devices 5. Visual Wearable sensors and heart health 6. The impact of education and learning 7. How to build computers to measure phycology, our reactions, emotions, etc 8. Daniel is building and utilizing scalable computer vision and machine learning tools to enable the automated recognition and analysis of emotions and physiology. He is currently Director of Research at Affectiva, a post-doctoral research affiliate at the MIT Media Lab and a visiting scientist at Brigham and Womens Hospital. At Affectiva Daniel is building state-of-the-art facial expression recognition software and leading analysis of the world's largest database of human emotion responses. Daniel completed his PhD in the Affective Computing Group at the MIT Media Lab in 2014 and has a B.A. and Masters from Cambridge University. His work has received nominations and awards from Popular Science magazine as one of the top inventions in 2011, South-by-South-West Interactive (SXSWi), The Webby Awards, ESOMAR, the Center for Integrated Medicine and Innovative Technology (CIMIT) and several IEEE conferences. His work has been reported in many publications including The Times, the New York Times, The Wall Street Journal, BBC News, New Scientist and Forbes magazine. Daniel has been named a 2015 WIRED Innovation Fellow.

What if the computer algorithms could tell more compelling stories than journalists, writers or business analysts? Well, this is increasingly becoming a reality. A new generation of Big Data tools are being put to automate story telling. The ideas behind this application of analytics were first put to use generating automated news reports, covering sports and financial stories. Take the recent Wimbledon tennis championships as an example.

Alan Turing, Albert Einstein, Stephen Hawking, John Nash--these "beautiful" minds never fail to enchant the public, but they also remain somewhat elusive. How do some people progress from being able to perform basic arithmetic to grasping advanced mathematical concepts and thinking at levels of abstraction that baffle the rest of the population? Neuroscience has now begun to pin down whether the brain of a math wiz somehow takes conceptual thinking to another level. Specifically, scientists have long debated whether the basis of high-level mathematical thought is tied to the brain's language-processing centers--that thinking at such a level of abstraction requires linguistic representation and an understanding of syntax--or to independent regions associated with number and spatial reasoning. In a study published this week in Proceedings of the National Academy of Sciences, a pair of researchers at the INSERM–CEA Cognitive Neuroimaging Unit in France reported that the brain areas involved in math are different from those engaged in equally complex nonmathematical thinking.

At the GPU Technology Conference, Nvidia unveiled the Tesla P100, the latest addition to Nvidia's Tesla Accelerated Computing Platform (TACP). The accelerator unit is being marketed as the most advanced hyperscale datacenter accelerator ever built – with a claimed 12x improvement over the previous Maxwell architecture, thanks to the new Pascal architecture. Designed to provide the equivalent performance of hundreds of general purpose CPUs in a much smaller package, and with significantly lower opex costs, Nvidia is targeting the next-gen datacenter use cases, which consist largely of artificial intelligence applications – which require very different compute resources than most current datacenters can provide. Cloud computing and the supercomputing that powers dense data analytics are very important for the progression of the Internet of Things (IoT). With the image-recognition that will power computer visions, smart grid management, smart city operations, and the massive amounts of sensor data that need to be crunched to realize more efficient business practices, systems like Nvidia's provide a very capable alternative to gigantic arrays of general purpose compute resources in datacenters.

Above the Trend Line: machine learning industry rumor central, is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz. Our intent is to provide our readers a one-stop source of late-breaking news to help keep you abreast of this fast-paced ecosystem. We're working hard on your behalf with our extensive vendor network to give you all the latest happenings. Be sure to Tweet Above the Trend Line articles using the hashtag: #abovethetrendline.

We focus on the problem of estimating and quantifying uncertainties on the excursion set of a function under a limited evaluation budget. We adopt a Bayesian approach where the objective function is assumed to be a realization of a Gaussian random field. In this setting, the posterior distribution on the objective function gives rise to a posterior distribution on excursion sets. Several approaches exist to summarize the distribution of such sets based on random closed set theory. While the recently proposed Vorob'ev approach exploits analytical formulae, further notions of variability require Monte Carlo estimators relying on Gaussian random field conditional simulations. In the present work we propose a method to choose Monte Carlo simulation points and obtain quasi-realizations of the conditional field at fine designs through affine predictors. The points are chosen optimally in the sense that they minimize the posterior expected distance in measure between the excursion set and its reconstruction. The proposed method reduces the computational costs due to Monte Carlo simulations and enables the computation of quasi-realizations on fine designs in large dimensions. We apply this reconstruction approach to obtain realizations of an excursion set on a fine grid which allow us to give a new measure of uncertainty based on the distance transform of the excursion set. Finally we present a safety engineering test case where the simulation method is employed to compute a Monte Carlo estimate of a contour line.

The 2016 winners were as follows: Tom Dietterich, AAAI President, for AAAI 2017 Awards, please Manuela Veloso, AAAI Past President contact Carol Hamilton at hamilton@aaai.org.

WWTS (What Would Turing Say?) Turing's Imitation Game was a brilliant Turing was heavily influenced by the World War II "game" If Turing were alive today, what sort of test might he propose? If a machine could fool interrogators as often as a typical man, then one would have to conclude that that machine, as programmed, was as intelligent as a person (well, as intelligent as men.) As Judy Genova (1994) puts it, Turing's originally proposed game involves not a question of species, but one of gender. The current version, where the interrogator is told he or she needs to distinguish a person from a machine, is (1) much more difficult to get a program to pass, and (2) almost all the added difficulties are largely irrelevant to intelligence! And it's possible to muddy the waters even more by some programs appearing to do well at it due to various tricks, such as having the interviewee program claim to be a 13-year-old Ukrainian who doesn't speak English well (University of Reading 2014), and hence having all its wrong or bizarre responses excused due to cultural, age, or language issues.