Poverty could be predicted by reading the satellite images using artificial intelligence. By indicating the areas where the most help is needed, these images could help eradicate global poverty. One can make an idea of a country's wealth by examining how much it shines at night. A comparison between China and South Korea's intense brightness and North Korea's dark mass could be one of the best examples found by the scientists. This kind of information could only be obtained by sending legions of survey-takers in populated rural areas.

Understanding the meaning of words that roll off your tongue as you talk, or your fingertips as you tap out posts is one of the biggest technical challenges facing artificial intelligence researchers. But it is an essential need. Automatic text processing forms a key part of the day-to-day interaction with your computer; it's a critical component of everything from web search and content ranking to spam filtering, and when it works well, it's completely invisible to you. With the growing amount of online data, there is a need for more flexible tools to better understand the content of very large datasets, in order to provide more accurate classification results. To address this need, the Facebook AI Research (FAIR) lab is open-sourcing fastText, a library designed to help build scalable solutions for text representation and classification. Our ongoing commitment to collaboration and sharing with the community extends beyond just delivering code.

London (ANTARA News) - A new technique using artificial intelligence to read satellite images could aid efforts to eradicate global poverty by indicating where help is needed most, a team of U.S. researchers said. The method would assist governments and charities trying to fight poverty but lacking precise and reliable information on where poor people are living and what they need, the researchers based at Stanford University in California said. Eradicating extreme poverty, measured as people living on less than 1.25 U.S. a day, by 2030 is among the sustainable development goals adopted by United Nations member states last year. A team of computer scientists and satellite experts created a self-updating world map to locate poverty, said Marshall Burke, assistant professor in Stanfords Department of Earth System Science. It uses a computer algorithm that recognizes signs of poverty through a process called machine learning, a type of artificial intelligence, he said.

New technique using artificial intelligence to read satellite images could aid efforts to eradicate ...

July 2016 was the first time where a robot was publicly used to kill an armed suspect by Police officers during a Black Lives Matter protest in Dallas, USA. The device was not autonomous, but in the future it could be. And although there are many cases of remote warfare within militaries, such as the case with drones, this was the first occasion where such technology was used in public. There are real concerns around artificial intelligence causing chaos like the scenarios depicted in Hollywood movies such as Terminator, Robocop, Iron Man and iRobot in the future.

LONDON: A new technique using artificial intelligence to read satellite images could aid efforts to eradicate global poverty by indicating where help is needed most, a team of US researchers said on Thursday. The method would assist governments and charities trying to fight poverty but lacking precise and reliable information on where poor people are living and what they need, the researchers based at Stanford University in California said. Eradicating extreme poverty, measured as people living on less than 1.25 US a day, by 2030 is among the sustainable development goals adopted by United Nations member states last year. A team of computer scientists and satellite experts created a self-updating world map to locate poverty, said Marshall Burke, assistant professor in Stanford's Department of Earth System Science. It uses a computer algorithm that recognizes signs of poverty through a process called machine learning, a type of artificial intelligence, he said.

A team of researchers from Stanford University has developed a new algorithm model, which is considered to be better at predicting poverty than all existing methods. The model is more effective than both satellite imagery and household data independently. To eliminate poverty, it is vital to find out the regions that are most affected with it. But the current situation is such that on-the-ground economic measures are sparse. These measures might not be reliable in poorer nations, as they lack resources to collect accurate data. In this situation, satellite data has been considered to be the best solution for the problem.

Everybody from Facebook to Microsoft to President Barack Obama thinks that chat bots -- robots you talk to like humans in apps like Facebook Messenger or Microsoft's Skype -- are the future. As showcased by the relative success of gadgets like the Amazon Echo and digital agents like Apple's Siri or Microsoft's Cortana, we're at the precipice of a new kind of computing, where you can use your natural language skills to get stuff done. The problem is chat bots these days kind of stink out. It's often harder to get stuff done with a chat bot than it is with the suite of apps and websites to which we've become accustomed. Meanwhile, Microsoft's Tay Twitter bot had a high-profile meltdown, showing how far AI still has to go.

Chalenge Masekera BIG data, data science, data mining, machine learning and artificial intelligence are currently buzzwords in the world of technology. So what is the hype all about and what does this mean for business in Zimbabwe? By adopting data science businesses stand a chance to be able to understand and predict customer behaviour and system processes in simple and faster ways. Broadly, data science refers to the use and conversion of data into knowledge and actionable insights. All these industries that embraced ICTs some years ago and record their transactional data daily in computer systems are prime candidates for data science.

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?