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

Reducing data in the data center has been a mentality in the past, but the Internet of Things (IoT) demands more, more, and more still. Withholding information from analytics systems is in essence selling IoT systems short; actively seeking it, on the other hand, invites challenges perhaps never-before-seen by even the most seasoned data scientists. In this interview with Chris O'Connor, General Manager of Watson Internet of Things Offerings at IBM, he discusses how the power of cognitive computing is being harnessed through the company's Watson platform – now exposed to developers through a set of application programming interfaces (APIs) – to turn the IoT data deluge into increasingly valuable insights. For those unfamiliar, can you briefly describe Watson, and then fill us in on what it's been up to since its Jeopardy! O'CONNOR: Watson is a true learning platform.

Here's what happened yesterday at the first meeting. Rachel started by going through the syllabus. So, what is data science? This is an ongoing discussion, but Michael Driscoll's answer is pretty good: Data science, as it's practiced, is a blend of Red-Bull-fueled hacking and espresso-inspired statistics. But data science is not merely hacking, because when hackers finish debugging their Bash one-liners and Pig scripts, few care about non-Euclidean distance metrics.

Dr. Quoc Viet Le is a research scientist at Google Brain known for his path-breaking work on deep neural networks (DNN). He is especially famous for his Ph.D work in image processing under Andrew Ng, one of the pioneers of the DNN revolution. Le's and Ng's work demonstrated how computers could be used to learn complicated features and patterns in a way similar to how the mammalian brain learns, with better performance than earlier neural network technology. One of their first breakthroughs was demonstrating the training of a large neural network to detect cats from YouTube videos. This revolutionized the interest in DNNs, and got the current giants of the computer industry such as Google, Facebook and Microsoft in a race to incorporate AI techniques into their software.

Linear regression is perhaps one of the most well known and well understood algorithms in statistics and machine learning. In this post you will discover the linear regression algorithm, how it works and how you can best use it in on your machine learning projects. You do not need to know any statistics or linear algebra to understand linear regression. This is a gentle high-level introduction to the technique to give you enough background to be able to use it effectively on your own problems. Linear Regression for Machine Learning Photo by Nicolas Raymond, some rights reserved.

Here is the link to coursera course for NLP Pick the software from the The Stanford NLP (Natural Language Processing) Group and input some text to view its parse tree, named entities, part of speech tags, etc.

Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have created an algorithm that can predict how memorable or forgettable an image is almost as accurately as humans -- and they plan to turn it into an app that subtly tweaks photos to make them more memorable. For each photo, the "MemNet" algorithm -- which you can try out online by uploading your own photos -- also creates a heat map that identifies exactly which parts of the image are most memorable. "Understanding memorability can help us make systems to capture the most important information, or, conversely, to store information that humans will most likely forget," says CSAIL graduate student Aditya Khosla, who was lead author on a related paper. "It's like having an instant focus group that tells you how likely it is that someone will remember a visual message." Team members picture a variety of potential applications, from improving the content of ads and social media posts, to developing more effective teaching resources, to creating your own personal "health-assistant" device to help you remember things.

This page contains a curated list of examples, tutorials, blogs about XGBoost usecases. It is inspired by awesome-MXNet, awesome-php and awesome-machine-learning. Please send a pull request if you find things that belongs to here. This is a list of short codes introducing different functionalities of xgboost packages. Most of examples in this section are based on CLI or python version.

The team, led by computer science professor Hitoshi Matsubara, collaborated closely with their digital construct during the writing process. The humans first assigned a gender to the protagonist and developed a rudimentary outline of the plot. They also assembled a list of words, phrases, and sentences to be included in the story. It was the AI's job to assemble these distinct assets into a unified text that wasn't just intelligible but compelling as well. The result was a novel entitled Konpyuta ga shosetsu wo kaku hi, or "The Day a Computer Writes a Novel", about an AI that abandons its responsibilities to humanity after recognizes its own talent for writing.

This article discusses the quadratization of Markov Logic Networks, which enables efficient approximate MAP computation by means of maximum flows. The procedure relies on a pseudo-Boolean representation of the model, and allows handling models of any order. The employed pseudo-Boolean representation can be used to identify problems that are guaranteed to be solvable in low polynomial-time. Results on common benchmark problems show that the proposed approach finds optimal assignments for most variables in excellent computational time and approximate solutions that match the quality of ILP-based solvers.