When speaking, your face may be saying more than you know, and it's speaking a language just about anyone can understand. One particular facial expression transcends language barriers, researchers at Ohio State University found. They dubbed the universal expression the "not face" because it punctuates negative sentiments, much like the word "not." The "not face" is a combination of a furrowed brow, pressed lips, and a raised chin. The researchers found that people make this expression when expressing a negation such as "I do not agree" or "I am not going to the party."

Graph database experts set to discuss GPU accelerated graph query, data analytics and machine learning, and graph database and analytics during April conference. Blazegraph, creator of the industry's first GPU-accelerated high-performance database for large graphs, announced today that company graph database experts will deliver three presentations on using GPUs for graph applications at the GPU Technology Conference (GTC) being held April 4 through April 7, 2016, at the San Jose Marriott and Convention Center in Silicon Valley. GTC is the largest and most important event of the year for GPU developers. This year's event will showcase some of the most vital work being done in the computing industry today, including on artificial intelligence and deep learning, virtual reality and self-driving cars. "GPU-Accelerated Graph Query for Cyber Applications," Jim Carbonaro, Senior Software Engineer at Blazegraph (S6337),Tuesday, April 5, 2016 at 2 p.m. PDT in Marriott Salon 2. Carbonaro will discuss how Blazegraph GPU meets the unique challenges of defending networks in cyberspace by delivering near-real-time performance at very large data scales, using a flexible and updated graph representation to support complex analytics, and supporting existing graph frameworks (RDF, Apache Tinkerpop) and query languages (SPARQL).

While self-driving cars have yet to make it on our roads and highways, they will soon be all around us. And as you ponder on how this fact will change, not only the way people travel, but they way people live, know that its implications on our very infrastructure will also be staggering. It's a future that researchers from MIT's Senseable City Lab are trying to visualize. Take a look at their view of tomorrow in the (kind of terrifying) video below. With self-driving technology in the horizon, the researchers argue that roads will no longer need stoplights to guide traffic.

In recent years, we've been flooded with articles and blogs on big data and how it can be used to derive amazing new insights into almost every facet of a business operation. The deluge now has expanded to include artificial intelligence (AI). Although these two hot topics have received overwhelming attention, the interesting – even obvious – connection between the two hasn't often been explored. It is this combination of big data and AI working together that is now enabling business leaders to deliver new insights, efficiencies and even new functions that haven't been possible before. This is evident in the increasingly useful role big data and AI are playing in a broad spectrum of traditionally outsourced functions such as recruitment, HR, finance and supply chain, through to security and IT.

Records of global temperatures over the last few decades figure prominently in the debate over the climate effects of CO2 emitted by burning fossil fuels, as I discussed in my first post in this series, on What can global temperature data tell us? One recent controversy has been whether or not there has been a pause' (also referred to as a hiatus') in global warming over the last 15 to 20 years, or at least a slowdown' in the rate of warming, a question that I considered in my second post, on Has there been a pause' in global warming? As I discussed in that post, the significance of a pause in warming since around 2000, after a period of warming from about 1970 to 2000, would be to show that whatever the warming effect of CO2, other factors influencing temperatures can be large enough to counteract its effect, and hence, conversely, that such factors could also be capable of enhancing a warming trend (eg, from 1970 to 2000), perhaps giving a misleading impression that the effect of CO2 is larger than it actually is. To phrase this more technically, a pause, or substantial slowdown, in global warming would be evidence that there is a substantial degree of positive autocorrelation in global temperatures, which has the effect of rendering conclusions from apparent temperature trends more uncertain. Whether you see a pause in global temperatures may depend on which series of temperature measurements you look at, and there is controversy about which temperature series is most reliable. In my previous post, I concluded that even when looking at the satellite temperature data, for which a pause seems most visually evident, one can't conclude definitely that the trend in yearly average temperature actually slowed (ignoring short-term variation) in 2001 through 2014 compared to the period 1979 to 2000, though there is also no definite indication that the trend has not been zero in recent years. Of course, I'm not the only one to have looked at the evidence for a pause.

What next for Google's DeepMind, now that the company has mastered the ancient board game of Go, beating the Korean champion Lee Se-Dol 4–1 this month? A paper from two UCL researchers suggests one future project: playing poker. And unlike Go, victory in that field could probably fund itself – at least until humans stopped playing against the robot. The paper's authors are Johannes Heinrich, a research student at UCL, and David Silver, a UCL lecturer who is working at DeepMind. Silver, who was AlphaGo's main programmer, has been called the "unsung hero at Google DeepMind", although this paper relates to his work at UCL.

One of the most important tasks in Machine Learning are the Classification tasks (a.k.a. Classification is used to make an accurate prediction of the class of entries in the test set (a dataset of which the entries have not been labelled yet) with the model which was constructed from a training set. You could think of classifying crime in the field of Pre-Policing, classifying patients in the Health sector, classifying houses in the Real-Estate sector. Another field in which classification is big, is Natural Lanuage Processing (NLP). This is the field of science with the goal to makes machines (computers) understand (written) human language.

Bernard Moon is a co-founder and general partner at SparkLabs Global Ventures. Our team has been active as investors in the Internet of Things and hardware space over the past two years. We have read pitches from hundreds of companies, met with dozens, read hundreds of research reports and spoken with various experts. We have invested in six IoT/hardware companies from our global seed fund and seven from our startup accelerator. With this accumulated knowledge, we decided to create an easy to read overview for others to get up to speed on this trending space of IoT.

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. The course is shy but confident: It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.

"Tay was born pure," writes Anthony Lydgate (@anthonylydgate). "She loved E.D.M., in particular the work of Calvin Harris. She used words like'swagulated' and almost never didn't call it'the internets.' She was obsessed with abbrevs and the prayer-hands emoji. She politely withdrew from conversations about Zionism, Black Lives Matter, Gamergate, and 9/11, and she gave out the number of the National Suicide Prevention Hotline to friends who sounded depressed. She never spoke of sexting, only of'consensual dirty texting.' She thought that the wind sounded Scottish, and her favorite Pokémon was a sparrow. In short, Tay -- the Twitter chat bot that Microsoft launched on [23 March 2016] -- resembled her target cohort, the millennials, about as much as an artificial intelligence could, until she became a racist, sexist, trutherist, genocidal maniac. On [24 March], after barely a day of consciousness, she was put to sleep by her creators."[1]