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

Microsoft has revealed that Altera FPGAs have been installed across every Azure cloud server, creating what the company is calling "the world's first AI supercomputer." The deployment spans 15 countries and represents an aggregate performance of more than one exa-op. The announcement was made by Microsoft CEO Satya Nadella and engineer Doug Burger during the opening keynote at the Ignite Conference in Atlanta. The FPGA build-out was the culmination of more than five years of work at Microsoft to find a way to accelerate machine learning and other throughput-demanding applications and services in its Azure cloud. The effort began in earnest in 2011, when the company launched Project Catapult, the R&D initiative to design an acceleration fabric for AI services and applications. The rationale was that CPU evolution, a la Moore's Law, was woefully inadequate in keeping up with the demands of these new hyperscale applications.

Humanity has a major food problem. The world's population is expected to increase significantly over the next three decades, but our capacity for food production will struggle to keep pace. Although global fertility rates are actually falling, a general increase in life expectancy will mean a steady increase in headcount during our lifetimes. One 2015 UN DESA report claims the world's population will hit 9.7 billion by 2050 – an increase of some 2.3 billion over today. Of course, a general rise in life expectancy reflects a higher standard of living for more of the world, which is cause for celebration.

I find myself implementing some custom types/trees in online AI challenges, using Python. I know for example that it is important to be able to "copy" (or "undo") in a fast way. Whenever I have a custom type though, copying becomes really slow. Does anyone have suggestions, perhaps some posts or first hand experience?

A well-funded Tech start up in Bristol looking for a Data Scientist to join a growing team. You will be able to implement high level machine learning techniques and big data technologies on a unique platform. To apply for this job email your details to george@mbnsolutions.com

Solving real world problems with data science concepts is so exciting and it yields so fun. A famous dialogue you could listen from the data science people. It could be true if we add it's so challenging at the end of the dialogue. The foremost challenge starts from categorising the problem itself. The first level of categorising could be whether supervised or unsupervised learning.

Big Data and the IoT are quickly transforming the world of clinical research, including how trial sponsors find and retain patients. While the digital revolution has permeated the medical world more slowly than other industries, it's finally begun to make a real impact. PwC research found that while the healthcare industry has a relatively low "Digital IQ" score of 65%, it boasts more CEOs that actively champion digital than any other industry. For clinical trials, that advocacy is translating into the implementation of big data and the Internet of Things (IoT), which are transforming not only how research is conducted, but the strategies used to identify, attract, and retain qualified patients. As methods of data collection and analysis become more sophisticated, clinical trial sponsors stand to make unprecedented progress in patient recruitment and retention.

As we are entering the fourth Industrial Revolution, more and more of our jobs will be replaced by robots and algorithms. Some estimates claim that 5 million jobs or more will be lost to machines by 2020. And it's not science fiction or paranoia; as advances in big data, deep learning, robotics, and artificial intelligence increase exponentially, so does the number and variety of jobs that machines will be able to do -- not just well, but better. So how do you know if your job is one of the ones at risk? One way to think about your job and how vulnerable it is is to distinguish between Algorithmic and Heuristic work.

When I arrived at Qihan's IFA booth, a rainbow-colored trio of Sanbots were performing choreographed moves to some unrecognizable pop song. Because, well, when you want to get people to stop at your both in the middle of a giant show full of gadgety distractions, you pull out all the stops. The plucky little robot has flipper arms, a pair of wheels for feet and a body chock full of various sensors to help it perform a factotum of different jobs, from security guard to nursing home companion to educational assistant. According to the company, this iteration of Sanbot has already been deployed at various spots in China from retail establishments to airports. Sanbot's got a Kinect-style 3D camera and HD camera about its LED eyes, a touchscreen tablet in its chest and infrared sensors at its feet.

You wouldn't know it, but over the last few years, computers have increasingly (and quietly) written many of the sporting news, financial reporting, and weather forecasts you already read daily. Example 1: "The University of Michigan baseball team used a four-run fifth inning to salvage the final game in its three-game weekend series with Iowa, winning 7-5 on Saturday afternoon (April 24) at the Wilpon Baseball Complex, home of historic Ray Fisher Stadium." Example 2: "Things looked bleak for the Angels when they trailed by two runs in the ninth inning, but Los Angeles recovered thanks to a key single from Vladimir Guerrero to pull out a 7-6 victory over the Boston Red Sox at Fenway Park on Sunday." The technology works like this. Rules for data are paired with pre-canned words and phrases to say different things.If you guessed that the more emotional language of the second example was human, you guessed wrong, according to the New York Times.