Data Science is not just about data. The bare basics are recognizing what all data to keep, identifying how to process it for different results. It does not stop there. Data scientists need to figure out blanks in data and fill them with data that'may' come up in future. Data Science essentially is about connecting dots in businesses and using existing and non-existing data to meet the demands of each business.

Around the world, there are people who need help. But sometimes, populations of poverty-stricken people are difficult to find. That is why researches at Stanford have put together a program to find people who need help, from space. One of the many difficulties in dealing with poverty is simply not knowing where to send aid. In many poor countries, data on which areas need the most help are hard to get.

Attempting to locate and assist people living in impoverished parts of the world could be made easier by using satellite imagery and machine learning algorithms, according to a new study led by researchers at Stanford University and published in the journal Science. Traditionally, international aid group perform door-to-door surveys to record data on local incomes in developing nations, but as study author Marshall Burke of the Stanford Institute for Economic Policy Research explained, these methods can be expensive and time consuming. They believe they've found a more efficient alternative. "If you give a computer enough data it can figure out what to look for. We trained a computer model to find things in imagery that are predictive of poverty," Burke told BBC News.

Detecting anomalies is critical in conducting surveillance, countering credit-card fraud, protecting against network hacking, combating insurance fraud, and many more applications in government, business and healthcare. Sometimes, the analyst has a set of known anomalies, and identifying similar anomalies in the future can be handled as a supervised learning task (a classification model). More often, though, little or no such "training" data are available. In such cases, the goal is to identify cases that are very different from the norm. Some techniques (clustering, nearest neighbors) may be familiar to you, others less so (e.g. based on information theory or spectral techniques).

Even a decade ago, talking to your computer was probably a sign that you'd been working too hard and could do with a lie down. Today, no such stigma applies. That's because chatbots -- the conversational agents capable of simulating intelligent conversations with human users -- have made some massive leaps forward. From changing the way kids learn in schools to picking you out the perfect meal this evening, here are the seven of the most interesting chatbots doing the rounds at the moment. From MOOCs (Massive open online courses) to the use of iPads in schools, there's no doubt that technology is changing the way that we learn.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Machine learning security offers many advantages over signature-based detection, but the technology can be as difficult to explain as it is for malware to beat. During an interview with SearchSecurity, Brett Hansen, executive director of data security solutions at Dell, offered insight into his company's investment in machine learning security and its partnership with advanced threat protection startup Cylance Inc. In part one of the interview, Hansen discussed the problems with traditional antivirus and antimalware programs relying on signature-based detection methods. In part of two of the interview, Hansen talks about the advantages of machine learning for smaller businesses, why it's a struggle to discuss the technology behind it, and how machine learning security serves as a better defense against ransomware attacks and other emerging threats. Here are excerpts from the conversation with Hansen. Is the move to machine learning security more about the shortcomings with signature-based detection and the frustrations people have had with it, or the benefits and value of machine learning?

One of the biggest problems in solving poverty worldwide is the scarcity of reliable data in developing countries. Researchers have sought to address this by combining satellite imagery with artificial intelligence to identify impoverished areas from space, the BBC reports. A team from Stanford University trained a computer system and surveyed information in five African countries. Researchers Neal Jean, Marshall Burke, and their colleagues say this method could go a long way in tracking and targeting poverty in specific countries. Burke, an assistant professor of Earth system science at Stanford, says, "The World Bank, which keeps the poverty data, has for a long time considered anyone who is poor to be someone who lives on below 1 a day."

I've written a few times recently about various projects that are helping develop robots capable of learning by watching how a task is performed. The machines typically observe something like a YouTube video in their attempt to pick up the skill, but the tasks have generally been quite manual in nature, such as the correct handling of kitchen utensils. A recent study from researchers at MIT set out to test whether machines could also use a similar method for picking up something so intuitively human. The researchers were hoping to train robots to be able to instinctively predict how an encounter with a human might unfold. It's the kind of intuition that we develop subconsciously as a result of our lifetime of experience.

Exploration of hydrocarbon resources is a highly complicated and expensive process where various geological, geochemical and geophysical factors are developed then combined together. It is highly significant how to design the seismic data acquisition survey and locate the exploratory wells since incorrect or imprecise locations lead to waste of time and money during the operation. The objective of this study is to locate high-potential oil and gas field in 1: 250,000 sheet of Ahwaz including 20 oil fields to reduce both time and costs in exploration and production processes. In this regard, 17 maps were developed using GIS functions for factors including: minimum and maximum of total organic carbon (TOC), yield potential for hydrocarbons production (PP), Tmax peak, production index (PI), oxygen index (OI), hydrogen index (HI) as well as presence or proximity to high residual Bouguer gravity anomalies, proximity to anticline axis and faults, topography and curvature maps obtained from Asmari Formation subsurface contours. To model and to integrate maps, this study employed artificial neural network and adaptive neuro-fuzzy inference system (ANFIS) methods. The results obtained from model validation demonstrated that the 17x10x5 neural network with R=0.8948, RMS=0.0267, and kappa=0.9079 can be trained better than other models such as ANFIS and predicts the potential areas more accurately. However, this method failed to predict some oil fields and wrongly predict some areas as potential zones.