Fresh supplies are due to ship out late Tuesday for the International Space Station, where the shelves finally are getting full after a string of failed deliveries. An unmanned Atlas V rocket is scheduled to blast off at 11:05 p.m. by the light of a nearly full moon. Orbital ATK's Cygnus capsule holds nearly 8,000 pounds of food, equipment and scientific research for NASA, including a commercial-quality 3-D printer anyone can rent and experimental robotic grippers modeled after the thousands of sticky hairs on geckos' feet. There's also a fire experiment that will remain on the Cygnus. Researchers will ignite a large-scale blaze, in a contained box, to see how it spreads in weightlessness. The fire will not be set until the Cygnus departs the space station in May, full of trash for a destructive re-entry.

So called predictive policing has long been thought of as a possibility, whether it was the Orwellian thought police or the technical wizardry highlighted in Hollywood films like Minority Report. Such capabilities are increasingly possible however. Last year I wrote about the ATHENA project that is being run by West Yorkshire police. The project is aiming to bring the public into policing a lot more than is currently the case by using social and mobile tools as a force for good. The project team concede that the public are usually first on the scene of an incident, and are often therefore well placed to relay crucial information to officials.

Natural language processing (NLP) involves machine learning, artificial intelligence, algorithms and linguistics related to interactions between computers and human languages. One important goal of NLP is to design and build software that will understand and analyze human languages to simplify and optimize human - computer communication. NLP algorithms are usually based on probability theory and machine learning grounded in statistical inference -- to automatically learn rules through analysis of real-world usage. It includes word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, question answering and requires both syntactic and semantic analysis at various levels. NLP applications today involve spelling and grammar correction in word processors, machine translation, sentiment analysis and email spam detection.

Stanford CoreNLP is an extensible, open source, JVM-based NLP toolkit with good quality core natural language analysis components, quite widely used in academia, companies, and government. This talk will give an overview, look at use from the command-line, code, and the web API, including the new server and new annotators, and provide a deeper dive going through a pipeline we recently built for a machine reading task. We'd also welcome questions (and requests!) from people who have used CoreNLP. We'll open at 7 and the talk will begin at 7:30, co-presented by Christopher Manning, Professor at Stanford University and Jason Bolton, Research Engineer at Stanford University.

I'm planning to do my graduate thesis on Deep Learning. I know the basics of Machine Learning (from Andrew Ng's Coursera Class) and Deep Learning (Nando de Freitas's Lectures). I've developed "deep" interest in this amazing field. As a total beginner in this field, what do you guys suggest should I do my master's thesis on? What specific deep neural network architecture should I choose?

In recent years, deep learning approaches have come to dominate discriminative problems in many sub-areas of machine learning. Alongside this, they have also powered exciting improvements in generative and conditional modeling of richly structured data such as text, images, and audio. This talk, led by indico's Head of Research, Alec Radford, will serve as an introduction to several emerging application areas of generative modeling and provide a survey of recent techniques in the field.

Most data scientists and statisticians agree that predictive modeling is both art and science yet, relatively little to no air time is given to describing the art. This post describes one piece of the art of modeling called feature engineering which expands the number of variables you have to build a model. I offer six ways to implement feature engineering and provide examples of each. Using methods like these is important because additional relevant variables increase model accuracy, which makes feature engineering an essential part of the modeling process. The full white paper may be downloaded at Feature Engineering Tips for Data Scientists.

HUNTING MALWARE in the vast virtual planes of the internet and amid the thousands of files on even the most basic PC is a challenge for the best cyber security boffins. Threat researchers can usually be found at cyber security companies sifting through data, analysing files and tracing the origins of malware down rabbit warrens of scripts, file paths and rogue code hiding behind seemingly harmless executable files. Once malware is found it can be squashed and measures can be put into place to protect other systems from similar attacks. This'threat intelligence' is an important part of protecting individuals and organisations against software vulnerabilities, viruses and hackers. "These services prioritise vulnerabilities and predict threats, enabling security teams to rapidly take action. More advanced services also integrate vulnerability alerting with real-world threat intelligence covering geopolitical and business intelligence," said the UK Computer Emergency Response Team.

During President Obama's 2016 State of the Union Address, Vice President Joe Biden was called to lead a new, national "Moonshot" initiative to eliminate cancer. Last month, the White House announced a new 1 billion initiative to jumpstart this effort. Moonshot's most valuable asset, and greatest challenge, is the volume and variety of "big cancer data". According to Vice President Biden, the biggest challenge impeding the cancer research progress is the siloed nature of clinical data. In response, Tamr has offered a solution to these challenges.

The first four episodes of Minecraft: Story Mode came out in quick succession, giving players a tightly crafted tale before Christmas. But with one episode remaining, developer Telltale suddenly went quiet about when its adventure would be wrapping up. Today, we have a date -- and it's not quite what we were expecting. Episode five will launch on March 29th, followed by a further three episodes later this year. These are being described as "additional escapades" and won't form part of the season pass, so you'll need to pay a little extra if you want the complete story.