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

BOSTON, Aug. 3, 2016 – AnDevCon – Hewlett Packard Enterprise (NYSE: HPE) today announced the availability of HPE Mobile Center 2.0, an integrated software development testing suite designed to help organizations rapidly build, test, and deliver high-quality mobile applications. HPE Mobile Center leverages HPE Haven OnDemand, HPE Fortify on Demand, and open source testing tools including Selenium and Appium, allowing developers to test any app operation and cover any user scenario. To significantly scale the number of devices that are tested, HPE Mobile Center is now integrated with Amazon Web Services Device Farm (AWS Device Farm) – a service that helps mobile app developers quickly and securely test their apps on smartphones, tablets, and other devices to improve the quality of their Android and iOS apps. HPE is expanding Mobile Center capabilities to provide users with a predictable and repeatable mechanism for testing, monitoring, and improving any mobile app. The integration with HPE Haven OnDemand and HPE Fortify on Demand provides development teams with powerful capabilities that leverage machine learning from testing data and user feedback to help guide the application development direction, with security built in from the beginning.

What comes first: the chicken or the egg? People have asked that question for ages, but nobody seems to have the answer. Which entity created the second one? How does one help the other to evolve? How do they work separately, or together? And what happens if they finally combine into one platform?

From July 20th to July 28th 2016, I had the opportunity of attending the 6th Lisbon Machine Learning School. The Lisbon Machine Learning School (LxMLS) is an annual event that brings together researchers and graduate students in the fields of NLP and Computational Linguistics, computer scientists with an interest in statistics and ML, and industry practitioners with a desire for a more in-depth understanding. Participants had a chance to join workshops and labs, where they got hands-on experience with building and exploring state-of-the-art deep learning models, as well as to attend talks and speeches by prominent deep learning and NLP researchers from a variety of academic and industrial organisations. You can find the entire programme here. In this blog post, I am going to share some of the highlights, key insights and takeaways of the summer school.

I want to create a deep neural network who's first layer maps words to vector embeddings. Essentially: Y Wx b where Y is the corresponding word embedding and x is the one-hot encoded vector for a word. However, I found feeding such a large x as a placeholder for each word full of zero makes the model extremely slow, so I was wondering if there are any alternatives aside from running word2vec in C and then just feeding the already embedded word vectors in.

Apple's Siri, Microsoft Cortana, Amazon's Echo, Google's speech recognition tech is high on trends these days. Ever wondered how these software get to know what we are saying? Well Thanks to technology of Natural Language Processing due to which language miscommunication gap has been decayed. Humans are being at the topmost position on the food-chain because of their intelligence. Everything humanity has developed in the past 10,000 years is the result of a discovery.

Check out AI Trends new report on Intelligent Bots and AI World's Intelligent Assistant Workshop, Nov 7, San Fran Starting in the 1980s, technology companies like Apple, Microsoft, and many others presented computer users with the graphical user interface as a means to make technology more user-friendly. The average consumer wasn't going to learn binary code to use a computer, so the great minds at these leading technology companies slapped a screen on technology and offered an interface that provided icons, buttons, toolbars, and other graphical elements so that the computer could be easily consumed by a mass market. Today it's hard to even imagine technological devices without a screen and a graphical presentation -- until now.

Satellites are best known for helping smartphones map driving routes or televisions deliver programs. But now, data from some of the thousands of satellites orbiting Earth are helping track things like crop conditions on rural farms, illegal deforestation, and increasingly, poverty in the hard-to-reach places around the globe. As much as that data has the potential to provide invaluable information to humanitarian organizations, watchdog groups, and policymakers, there is too much of it to sift through in order to draw insights that could influence important decisions. A team of researchers from Stanford University, however, says it has developed an efficient way. By creating a deep-learning algorithm that can recognize signs of poverty in satellite images – such as condition of roads – the team sorted through a million images to accurately identify economic conditions in five African countries, reported the scientists in the journal Science on Thursday.

Fun projects arise when you combine a problem that needs to be solved with a desire to learn. My problem was the neighbors' cats: I wanted to encourage them to hang out somewhere other than my front yard. At the same time, I wanted to learn more about neural network software and deep learning and have a bit of fun doing it. So I set out to build a system that would automatically chase cats out of my yard using artificial intelligence. This boils down to a neural network trained to recognize cats in images from a security camera and turn on the sprinkler system to scare them away. The neural network inference runs on Jetson TX1. This post is an elaboration on and continuation of my notes on the project here.

A global hub of the machinery industry, Taiwan is stepping up efforts to develop innovative smart manufacturing technologies. From May 20-24 this year, about 30,000 visitors, including buyers from Asia, Europe and the U.S., flocked to the Commercial Exhibition Center in Taichung City, central Taiwan for the Automatic Machinery and Intelligent Manufacturing Exhibition. The trade show has been staged annually in Taichung for more than three decades, though this marked the first time that intelligent manufacturing was used in the title of the event. Organizer Commercial Times, one of the country's two major financial newspapers, opted to alter the name of the show to highlight the growing focus on this field in the nation's globally competitive machinery sector. "This is the 32nd edition of our machinery show in Taichung. But unlike previous events, this year's exhibition features intelligent machines to reflect the current trend in manufacturing systems development," Chen Kuo-wei (???), president of Commercial Times, said at the opening ceremony of the five-day event, which generated business deals totaling about NT 300 million (US 9.2 million).