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.

One of the biggest challenges in providing relief to people living in poverty is locating them. The availability of accurate and reliable information on the location of impoverished zones is surprisingly lacking for much of the world, particularly on the African continent. Aid groups and other international organizations often fill in the gaps with door-to-door surveys, but these can be expensive and time-consuming to conduct. In the current issue of Science, Stanford researchers propose an accurate way to identify poverty in areas previously void of valuable survey information. The researchers used machine learning - the science of designing computer algorithms that learn from data - to extract information about poverty from high-resolution satellite imagery.

Pop culture's many takes on artificial intelligence

The global healthcare Natural Language Processing (NLP) market is expected to grow from 1.10 billion in 2015 to 2.67 billion by 2020, according to a new report. "Natural Language Processing Market for Health Care and Life Sciences Industry by Type (Rule-Based, Statistical, and Hybrid NLP Solutions) – Worldwide Forecast and Analysis to 2015 – 2020" is published by MarketsandMarkets, The explosive growth in healthcare and life sciences industries, with their vast troves of unstructured clinical data in EHRs, are the main market drivers. As the report describes it, NLP technologies assist machines in understanding the language used by humans to communicate both reading and writing. This form of communication assists the computer in performing various other additional tasks. NLP techniques extract important information from the vast amount of clinical data and analyze it for enhanced processing and analytics.

Pop culture's many takes on artificial intelligence New technique using artificial intelligence to read satellite images could aid efforts to eradicate ...

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.

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 ...

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.