John Holden is a journalist specializing in science, tech and innovation. His work has appeared mainly in the Irish Times. Cornered at a party by some bore talking about his unimaginative app that will "change the world." All seemingly convincing excuses to leave have already been used by the rest of the group (who were all there just a second ago). The last refuge of the rude scoundrel -- the smartphone -- is in your jacket pocket in the cloakroom all the way across the room. So you just have to suck it up and simulate enthusiasm for this guy's pitch.

At the core of the project is the idea of using machines to identify launcher-shaped objects buried within the staggering amount of digital imagery collected by US spy satellites, manned and unmanned aircraft. A senior official in the Department of Defense explained that it is this vast amount of data that makes manual research inefficient. "What was largely a manual process for intelligence analysts has to become an automated one," he said, cited by Defense One. The ultimate goal is to train computers spot what are called transporter-erector-launchers (TELs). North Korea used these kinds of launchers during missile tests conducted over the last few months.

Public discourse on environmental and health issues has risenon social media. Upon an environmental crisis, various chatterssuch as breaking news, misinformation, and rumor couldaggravate social confusion and proliferate negative publicsentiment. In an effort to study public sentiments on environmentalissues in South Korea, we analyzed 158,964 tweetsgenerated over a 4-year period following the Fukushima accidentin 2011, the largest release of radioactivity to environmentin recent history. This event led to a significant increasein public’s interest on environmental and nuclear issues inKorea. We employed Bayesian network and recursive partitioningto observe the classification regression tree structureof major topics. Topics on health and environment were interlinkedclosely and represented both apprehension and concernabout health threats and pollution. Our methodologyhelps analyze large online discourse efficiently and offers insightto crisis response organizations.

We examine the question of whether we can automatically classify the sentiment of individual tweets in Farsi, to determine their changing sentiments over time toward a number of trending political topics. Examining tweets in Farsi adds challenges such as the lack of a sentiment lexicon and part-of-speech taggers, frequent use of colloquial words, and unique orthography and morphology characteristics. We have collected over 1 million Tweets on political topics in the Farsi language, with an annotated data set of over 3,000 tweets. We find that an SVM classifier with Brown clustering for feature selection yields a median accuracy of 56% and accuracy as high as 70%. We use this classifier to track dynamic sentiment during a key period of Irans negotiations over its nuclear program.

Citigroup is to roll out voice recognition software to its Asian customer base, shrinking its branch network as more customers move to online and mobile banking.

ARTIFICIAL INTELLIGENCE: Can Artificial Intelligence Create the Next Wonder Material? Nicola Nosengo Nature "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." COMPUTING: Why Machine Vision Is Flawed in the Same Way as Human Vision MIT Technology Review "If machine vision and human vision work in similar ways, are they also restricted by the same limitations? Do humans and machines struggle with the same vision-related challenges? Today we get an answer thanks to the work of Saeed Reza Kheradpisheh at the University of Tehran in Iran and a few pals from around the world. These guys have tested humans and machines with the same vision challenges and discovered that they do indeed struggle with the same kind of problems."

After an up-and-down start to the year, Chinese search giant Baidu issued earnings last week that outperformed on a host of key indicators. As we've come to expect from Baidu, revenue growth remained brisk, increasing at a healthy 31% year-over-year pace to total 2.5 billion. In keeping with its recent quarters, increased spending crimped Baidu's operating profits, which grew only 2.6% compared with the first quarter of 2015. Either way, Baidu's earnings exceeded expectations on the top and bottom line. What's more, Baidu's guidance for second-quarter sales proved better than analysts anticipated, sending the company's shares up in after-hours trading the day of the announcement.

After an up-and-down start to the year, Chinese search giant Baidu (NASDAQ:BIDU) issued earnings last week that outperformed on a host of key indicators. As we've come to expect from Baidu, revenue growth remained brisk, increasing at a healthy 31% year-over-year pace to total 2.5 billion. In keeping with its recent quarters, increased spending crimped Baidu's operating profits, which grew only 2.6% compared with the first quarter of 2015. Either way, Baidu's earnings exceeded expectations on the top and bottom line. What's more, Baidu's guidance for second-quarter sales proved better than analysts anticipated, sending the company's shares up in after-hours trading the day of the announcement.

Donald Trump knows many words. He has the best words. He's going to find the best people to help him run the country. He knows many smart people. His presidency will be classy, huge, even.

Guest blog post by Xiu-Shen Wei, originally posted here. Deep Neural Networks, especially Convolutional Neural Networks (CNN), allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-arts in visual object recognition, object detection, text recognition and many other domains such as drug discovery and genomics. In addition, many solid papers have been published in this topic, and some high quality open source CNN software packages have been made available. There are also well-written CNN tutorials or CNN software manuals. However, it might lack a recent and comprehensive summary about the details of how to implement an excellent deep convolutional neural networks from scratch. Thus, we collected and concluded many implementation details for DCNNs. Here we will introduce these extensive implementation details, i.e., tricks or tips, for building and training your own deep networks. We assume you already know the basic knowledge of deep learning, and here we will present the implementation details (tricks or tips) in Deep Neural Networks, especially CNN for image-related tasks, mainly in eight aspects: 1) data augmentation; 2) pre-processing on images; 3) initializations of Networks; 4) some tips during training; 5) selections of activation functions; 6) diverse regularizations; 7)some insights found from figures and finally 8) methods of ensemble multiple deep networks. Additionally, the corresponding slides are available at [slide].