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.

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.

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."

Pop culture's many takes on artificial intelligence

Stanford scientists have found a low-cost method to map poverty in areas previously devoid of data, by combining satellite images and making use of machine learning. These improved poverty maps could help organisations and policymakers distribute funds more efficiently and evaluate policies, researchers said. 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 in African countries. Aid groups and other international organisations often fill in the gaps with door-to-door surveys, but these can be expensive and time-consuming to conduct.

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