Scientists at Stanford University have found a new method in predicting poverty through the use of machine learning and satellite images. The technique could make it easier for organizations to know where across the world their aid is needed most. Also, this could help governments develop a better policy to prevent or fight poverty. Using three data sources namely daytime images, night light images, and survey data, scientists built an algorithm to predict how wealthy or poor an area is. The results of the study have been published in the journal Science. "The idea is that if we train our models right, they help us predict poverty in areas where we don't have the surveys, which will help out aid orgs that are working on this issue," explained Neal Jean, co-author of the study and a doctoral candidate at Stanford.

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Organizations often conduct door-to-door surveys to identify people living in poverty, but the downside is that these surveys are often time-consuming and expensive. Indeed, locating impoverished environments is still a challenging process for researchers, and the availability of accurate information is still lacking. Now, in a new study, scientists from Stanford University propose a more reliable method to map poverty in areas previously void of data -- by combining satellite images and making use of machine learning. Led by Stanford computer science doctoral student Neal Jean, researchers sought to determine whether the combination of high-satellite imagery and machine learning -- the science of designing algorithms that learn from data -- could predict estimates of areas where impoverished people lived. Specifically, they extracted information about poverty from these satellite images, and built upon previous machine learning algorithms to detect impoverished areas across five countries in Africa.

DOHA Medecins Sans Frontieres (MSF) said it was evacuating its staff from six hospitals in northern Yemen on Thursday after a Saudi-led coalition air strike hit a health facility operated by the medical aid group killing 19 people.

Researchers have combined satellite imagery with AI to predict areas of poverty across the world. There's little reliable data on local incomes in developing countries, which hampers efforts to tackle the problem. A team from Stanford University were able to train a computer system to identify impoverished areas from satellite and survey data in five African countries. Neal Jean, Marshall Burke and colleagues say the technique could transform efforts to track and target poverty in developing countries. "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," Dr Burke, assistant professor of Earth system science at Stanford, told the BBC's Science in Action programme.

To make a documentary about the Internet requires nerve. To do so when you can hardly be bothered with a cell phone, however, takes both innocence and bravado, plus a pinch of madness. All of which means that Werner Herzog, now aged seventy-three, is right for the job, and the result is "Lo and Behold: Reveries of the Connected World." The movie is divided into ten parts, none of which could be mistaken for a commandment; Herzog's documentaries have always been fired more by marvelling, and by an explorer's ache to learn, than by any pedagogic urge to tell. If he were struck color-blind tomorrow, he would instantly embark on a film about Matisse.

Welcome to the Applied Materials Earnings Conference Call. During the presentation, all participants will be in a listen-only mode. Afterwards you will be invited to participate in a question-and-answer session. As a reminder, this conference is being recorded. I'd now like to turn the conference over to Michael Sullivan, Vice President of Investor Relations. In a moment, we'll discuss the results for our third quarter which ended on July 31. Joining me are Gary Dickerson, our President and CEO; and Bob Halliday, our Chief Financial Officer. Before we begin, let me remind you that today's call contains forward-looking statements including Applied's current view of its industries, performance, products, share positions, profitability and business outlook. These statements are subject to risks and uncertainties that could cause actual results to differ materially from those expressed or implied by such statements, and are not guarantees of future performance.

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 U.S. 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 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 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.

WHEN it comes to following the money, the authorities have their work cut out. Every year, criminals are thought to launder more than 1.5 trillion worldwide. Which is why Australia's financial intelligence agency is turning to AI for help. In Australia, the scale of the problem could amount to some US 4.5 billion annually. There, the task of cracking down on illegally obtained funds falls to the Australian Transaction Reports and Analysis Centre (AUSTRAC).