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

Nowadays, recommender systems are used to personalize your experience on the web, telling you what to buy, where to eat or even who you should be friends with. People's tastes vary, but generally follow patterns. People tend to like things that are similar to other things they like, and they tend to have similar taste as other people they are close with. Recommender systems try to capture these patterns to help predict what else you might like. E-commerce, social media, video and online news platforms have been actively deploying their own recommender systems to help their customers to choose products more efficiently, which serves win-win strategy.

It's also an example of a deceptively straightforward question that reveals the challenges and potential of artificial intelligence in travel. You make thousands of decisions in the course of planning a trip, not always consciously. For instance, you might have a preference for aisle seats, but an even stronger preference to avoid aisle seats close to the lavatory. So when selecting your seat on a flight, you intuitively look for ones that are on the aisle and far from the back of the cabin. It's a relatively simple thought process, one that you've probably gone through hundreds of times before.

In this case study, we describe a proof-of-concept implementation of a highly optimized machine learning application for Intel Architecture. Our results demonstrate the capabilities of Intel Architecture, particularly the 2nd generation Intel Xeon Phi processors (formerly codenamed Knights Landing), in the machine learning domain. Download as PDF: Colfax-NeuralTalk2-Summary.pdf (814 kB) -- this file is available only to registered users. Register or Log In. or read online below. It is common in the machine learning (ML) domain to see applications implemented with the use of frameworks and libraries such as Torch, Caffe, TensorFlow, and similar.

CIOs ignore the AI wave at their peril. That's because, going forward--according to Toby Redshaw, consultant and former American Express CIO--the company that enters a market without benefit of AI-powered technology will be "the guy at the gunfight with a knife." Subscribe to the weekly Cloud Leader newsletter by visiting the Oracle account page and updating your subscription preferences to include SaaS, PaaS, IaaS, or DaaS. If you do not already have an Oracle account, you can create one here.

Google's artificial intelligence researchers are starting to have to code around their own code, writing patches that limit a robot's abilities so that it continues to develop down the path desired by the researchers -- not by the robot itself. It's the beginning of a long-term trend in robotics and AI in general: once we've put in all this work to increase the insight of an artificial intelligence, how can we make sure that insight will only be applied in the ways we would like? That's why researchers from Google's DeepMind and the Future of Humanity Institute have published a paper outlining a software "killswitch" they claim can stop those instances of learning that could make an AI less useful -- or, in the future, less safe. It's really less a killswitch than a blind spot, removing from the AI the ability to learn the wrong lessons. Specifically, they code the AI to ignore human input and its consequences for success or failure. If going inside is a "failure" and it learns that every time a human picks it up, the human then carries it inside, the robot might decide to start running away from any human who approaches.

Not satisfied with launching reusable rockets and designing electric supercars, Elon Musk is looking to create domestic robots to help people around the house. The billionaire entrepreneur won't be working through SpaceX or Tesla, but through another branch of his growing tech empire, collaborative artificial intelligence company Open AI. The firm, chaired by Musk and president of start-up incubator Y Combinator, Sam Altman, plans to use'off the shelf' robots rather than building them from scratch. Open AI, a non-profit founded by Elon Musk and president of start-up incubator Y Combinator, Sam Altman, plans to use'off the shelf' robots rather than building them from scratch, tweaking the robots to become mechanical maids (stock image) Open AI is a collaborative non-profit artificial intelligence company set up at the send of last year by Elon Musk and president of start-up incubator Y Combinator, Sam Altman. It received more than 1 billion in funding when it launched and sees robotics, chatbots and games as practical fields where it develop and can flex its AI muscle.

Twitter has demonstrated its commitment to artificial intelligence and machine learning by splashing a reported 150m on London-based Magic Pony Technology. The aim is to use the company's advanced technology to improve Twitter's live and video experiences. Jack Dorsey describes Magic Pony Technology as a'company that has developed novel machine learning techniques for visual processing'. Thus far this has been used to sharpen blurry images, create computer-generated images from scratch, and to learn to recognize objects. While Magic Pony Technology's AI is not exactly unique, where it stands apart from the competition is in terms of raw speed.

The pace at which robots and intelligent machines are able to take over the jobs traditionally performed by humans will result in more than half the population being unemployed within 30 years, an expert in computing has predicted. While some may look forward to a life of leisure, many others face the dismal prospect of long-term unemployment as a result of the rise of smart machines, from self-driving cars and intelligent drones to smart financial-trading machines, said Moshe Vardi, professor of computational engineering at Rice University in Houston, Texas. Speaking at the American Association for the Advancement of Science (AAAS) annual meeting in Washington, Professor Vardi predicted that developments in robotics and artificial intelligence will create a workplace revolution unlike any other seen since the start of the industrial age more than two centuries ago. "We are approaching a time when machines will be able to outperform humans at almost any task. I believe that society needs to confront this question before it is upon us," Professor Vardi said. "I do believe that, by 2045, machines will be able to do a very significant fraction of the work a man can do.

Pathologists have been largely diagnosing disease the same way for the past 100 years, by manually reviewing images under a microscope. But new work suggests that computers can help doctors improve accuracy and significantly change the way cancer and other diseases are diagnosed. A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) recently developed artificial intelligence (AI) methods aimed at training computers to interpret pathology images, with the long-term goal of building AI-powered systems to make pathologic diagnoses more accurate. "Our AI method is based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition," explained pathologist Andrew Beck, MD, PhD, Director of Bioinformatics at the Cancer Research Institute at Beth Israel Deaconess Medical Center (BIDMC) and an Associate Professor at Harvard Medical School. "This approach teaches machines to interpret the complex patterns and structure observed in real-life data by building multi-layer artificial neural networks, in a process which is thought to show similarities with the learning process that occurs in layers of neurons in the brain's neocortex, the region where thinking occurs."