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.

Wikipedia is one of the most popular sources of free data on the Internet and subject to extensive use in numerous areas of research. Wikidata on the other hand, the knowledge base behind Wikipedia, is less popular as a source of data, despite having the "data" already in its name, and despite the fact that many applications in Natural Language Processing in general and Information Extraction in particular benefit immensely from the integration of knowledge bases. In part, this imbalance is owed to the younger age of Wikidata, which launched over a decade after Wikipedia. However, this is also owed to challenges posed by the still evolving properties of Wikidata that make its content more difficult to consume for third parties than is desirable. In this article, we analzye the causes of these challenges from the viewpoint of a data consumer and discuss possible avenues of research and advancement that both the scientific and the Wikidata community can collaborate on to turn the knowledge base into the invaluable asset that it is uniquely positioned to become.

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.

We define On-Average KL-Privacy and present its properties and connections to differential privacy, generalization and information-theoretic quantities including max-information and mutual information. The new definition significantly weakens differential privacy, while preserving its minimalistic design features such as composition over small group and multiple queries as well as closeness to post-processing. Moreover, we show that On-Average KL-Privacy is **equivalent** to generalization for a large class of commonly-used tools in statistics and machine learning that samples from Gibbs distributions---a class of distributions that arises naturally from the maximum entropy principle. In addition, a byproduct of our analysis yields a lower bound for generalization error in terms of mutual information which reveals an interesting interplay with known upper bounds that use the same quantity.

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."

The White House is doing a lot more thinking about the arrival of automated decision-making -- super-intelligent or otherwise. No one in government is yet screaming "Skynet," but in two actions this week the concerns about our artificial intelligence future were sketched out. The big risks of A.I. are well-known (a robot takeover), but the more immediate worries are about the subtle, or not-so-subtle, decisions made by badly coded and designed algorithms. President Barack Obama's administration released a report this week that examines the problem associated with poorly designed systems that, increasingly, are being used in automated decision making. Algorithmic systems can affect employment, education, access to credit -- anything that relies on computer-assisted decisions.

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.

A robot surgeon has been taught to perform a delicate procedure--stitching soft tissue together with a needle and thread--more precisely and reliably than even the best human doctor. The Smart Tissue Autonomous Robot (STAR), developed by researchers at Children's National Health System in Washington, D.C., uses an advanced 3-D imaging system and very precise force sensing to apply stitches with submillimeter precision. The system was designed to copy state-of-the art surgical practice, but in tests involving living pigs, it proved capable of outperforming its teachers. Currently, most surgical robots are controlled remotely, and no automated surgical system has been used to manipulate soft tissue. So the work, described today in the journal Science Translational Medicine, shows the potential for automated surgical tools to improve patient outcomes.

The department of synthetic intelligence known as deep studying has given us new wonders similar to self-driving automobiles and immediate language translation on our telephones. Now it's about to injects smarts into each different object possible. That's as a result of makers of silicon processors from giants similar to Intel INTC 0.50% Corp. and Qualcomm QCOM -0.45% Applied sciences Inc. in addition to a raft of smaller corporations are beginning to embed deep studying software program into their chips, significantly for cellular imaginative and prescient purposes. In pretty quick order, that's prone to result in a lot smarter telephones, drones, robots, cameras, wearables and extra. "Customers will likely be genuinely amazed on the capabilities of those units," says Cormac Brick, vice chairman of machine studying for Movidius Ltd., a maker of imaginative and prescient processor chips in San Mateo, Calif.