It's a strong contender for the geekiest video ever made: a close-up of a smartphone with line upon line of numbers and symbols scrolling down the screen. But when visitors stop by Nicola Marzari's office, which overlooks Lake Geneva, he can hardly wait to show it off. "It's from 2010," he says, "and this is my cellphone calculating the electronic structure of silicon in real time!" Even back then, explains Marzari, a physicist at the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, his now-ancient handset took just 40 seconds to carry out quantum-mechanical calculations that once took many hours on a supercomputer -- a feat that not only shows how far such computational methods have come in the past decade or so, but also demonstrates their potential for transforming the way materials science is done in the future. Instead of continuing to develop new materials the old-fashioned way -- stumbling across them by luck, then painstakingly measuring their properties in the laboratory -- Marzari and like-minded researchers are using computer modelling and machine-learning techniques to generate libraries of candidate materials by the tens of thousands.

It's a strong contender for the geekiest video ever made: a close-up of a smartphone with line upon line of numbers and symbols scrolling down the screen. But when visitors stop by Nicola Marzari's office, which overlooks Lake Geneva, he can hardly wait to show it off. "It's from 2010," he says, "and this is my cellphone calculating the electronic structure of silicon in real time!" Even back then, explains Marzari, a physicist at the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, his now-ancient handset took just 40 seconds to carry out quantum-mechanical calculations that once took many hours on a supercomputer--a feat that not only shows how far such computational methods have come in the past decade or so, but also demonstrates their potential for transforming the way materials science is done in the future. Instead of continuing to develop new materials the old-fashioned way--stumbling across them by luck, then painstakingly measuring their properties in the laboratory--Marzari and like-minded researchers are using computer modelling and machine-learning techniques to generate libraries of candidate materials by the tens of thousands.

Using stock market prediction algorithm to forecast energy stocks: This Energy Stocks forecast is designed for investors and analysts who need predictions of the best-performing stocks for the whole Energy Industry (See Industry Package). Package Name: Energy Stocks Forecast Length: 30 Days (03/29/16 – 04/29/16) I Know First Average: 36.82% Cliffs Natural Resources Inc.(CLF) grew by 88.89% in just 1-month, was the top performing stock in the Energy Stocks forecast for that time period. Another top performing stock was DNR that grew by 71.56%, with an astonishing return of ten out of the ten stocks that increased in accordance with the algorithm's prediction. CDE and VALE also offered strong returns of 48.90% and 37.29%, Within the predicted 30-days it performed very well in the Energy Package.

Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about 1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature) were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN) and false positive (FP) rates.

By 2020, we will all have an Invisible Friend. Whether we call it Siri, Alexa, OK Google, or a chatbot, we are entering a world where an intelligence assistant recognizes our "intent." This could spawn a massive consumer behavior shift, as AI-influenced bots would mean far fewer Google searches by humans. This invisible friend would learn from its mistakes, maintain context, and continue to expand into new areas of expertise through judicious use of Knowledge Management (see below landscape). Although 2020 is our destination, now is a time of heightened activity among the companies that provide the elements of Intelligent Assistance.

Here are 13 books on Machine Learning and Data Mining that are great resources, references, and refreshers for Data Scientists. Ensemble Methods in Data Mining: Improving Accuracy Through Combini..., by Giovanni Seni and John Elder (Older Edition is also available)

The first two, as their names suggest, inform their parent that their operation was a success or a failure. The third means that success or failure is not yet determined, and the node is still running. The node will be ticked again next time the tree is ticked, at which point it will again have the opportunity to succeed, fail or continue running. This functionality is key to the power of behaviour trees, since it allows a node's processing to persist for many ticks of the game. For example a Walk node would offer up the Running status during the time it attempts to calculate a path, as well as the time it takes the character to walk to the specified location. If the pathfinding failed for whatever reason, or some other complication arisen during the walk to stop the character reaching the target location, then the node returns failure to the parent. If at any point the character's current location equals the target location, then it returns success indicating the Walk command executed successfully. This means that this node in isolation has a cast iron contract defined for success and failure, and any tree utilizing this node can be assured of the result it received from this node. These statuses then propagate and define the flow of the tree, to provide a sequence of events and different execution paths down the tree to make sure the AI behaves as desired.

Apple harvested almost 40 million worth of gold from recycled gadgets last year, and is now deploying robots to take iPhones apart in a major environmental push. In its latest annual environmental responsibility report, which was published last week, Apple explained that it gathered 2,204 pounds of recycled gold during its fiscal year 2015. The gold, which weighs more than a ton, is worth 39.6 million. Apple recovered more than 63 million pounds of various materials via its "take-back" recycling initiatives in 2015, according to the company's environmental report. The tech giant gathered over 23 million pounds of steel, making it the most recycled material, and more than 13 million pounds of plastics.

I have not yet read James Joyce's Ulysses. The majority of the population also has not. However, of those who have, it seems that most laud it. It is so universally praised that even people who have never read the book reference it as an archetypal masterpiece. Presumably, reading it doesn't require a special skill set beyond reasonable literacy.

Back in Hannover Messe 2011, Germany announced the Industry 4.0 concept and initiated the world's fourth industrial revolution. Since then, Hannover Messe has become a focal point for Industry 4.0 innovations. As Hannover Messe 2016 closes in, the exhibition will once again be surrounded by various Industry 4.0-related hot topics such as integrated industry, smart manufacturing and more. Coming soon on April 25 to 29, Hannover Messe 2016 will be based on the theme "Integrated Industry – Discover Solutions", which aims to provide an interpretation of the smart manufacturing model of Industry 4.0. As a Taiwanese company with deep expertise in IoT automation, NEXCOM has planned four themed demonstrations that map out a complete solution blueprint for industry 4.0 in the upcoming event. Joe Lin, General Manager of NEXCOM's IoT Automation Solutions Business Group, states, "Early Industry 4.0 solutions focused on the lower layers of factory communication where IoT gateways were used to integrate different industrial protocols, bridging the Industry 4.0 last mile connection to fulfill the'connected' concept.