Do you have the talent to join multi-award winning startup WeFarm? We are a unique social enterprise providing a vital service for the world's 500 million smallholder farmers who live and work without internet access. This pioneering, peer-to-peer platform enables farmers to access crowdsourced information by SMS, creating social impact on a groundbreaking scale and generating a game-changing data feed through the use of cutting edge AI techniques. In just one year WeFarm has scaled to more than 72,000 farmers across Kenya, Uganda and Peru, has facilitated over 11.5 million interactions and featured in the FT, Forbes, Wired.co.uk, as well as winning awards from Google's Impact Challenge, The Venture and the European Commission's Ideas From Europe. With an ambitious goal to reach 1 million farmers in the next 12 months, we are looking for a talented Developer to join the team and support this growth.

There are few bigger challenges than trying to solve world poverty. While there are plenty of initiatives going on in this area, one of the most intriguing is being carried out by researchers at Stanford University. Using a combination of satellite data and machine learning, they've developed a "poverty map" of Africa that could help direct aid to some of the world's most deprived areas. "One part of the problem when it comes to dealing with poverty is that we don't have very good data," Neal Jean, a Ph.D student in Machine Learning at Stanford, told Digital Trends. "If we want to help people, but we don't know exactly where they are, that makes it very difficult to do. Traditionally, the way data is collected on poverty is by going out into the field and having people conduct surveys. Our objective in doing this project was to come up with a cost-effective and scalable way of filling in some of these data gaps."

You can get a pretty good idea of a country's wealth by seeing how much it shines at night -- just compare the intense brightness of China and South Korea to the dark mass of North Korea that's sandwiched between them. But nighttime lights don't tell you which neighborhoods or villages within a large region are merely poor and which are home to people living in abject poverty. That's the level of detail policymakers need when they decide where to deploy their economic development programs. You could get that detail by sending legions of survey-takers into crowded slums and sparsely populated rural areas. But that would be hugely time-consuming and cost tens of millions of dollars or more.

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. Results of the two-year research effort have been published in the journal Science. The system shows an image to a computer, "and the computer's job is to figure what the image is," Burke said.

Reliable data on economic livelihoods remain scarce in the developing world, hampering efforts to study these outcomes and to design policies that improve them. Here we demonstrate an accurate, inexpensive, and scalable method for estimating consumption expenditure and asset wealth from high-resolution satellite imagery. Using survey and satellite data from five African countries--Nigeria, Tanzania, Uganda, Malawi, and Rwanda--we show how a convolutional neural network can be trained to identify image features that can explain up to 75% of the variation in local-level economic outcomes. Our method, which requires only publicly available data, could transform efforts to track and target poverty in developing countries. It also demonstrates how powerful machine learning techniques can be applied in a setting with limited training data, suggesting broad potential application across many scientific domains.

You can get a pretty good idea of a country's wealth by seeing how much it shines at night -- just compare the intense brightness of China and South Korea to the dark mass of North Korea that's sandwiched between them. But nighttime lights don't tell you which neighborhoods or villages within a large region are merely poor and which are home to people living in abject poverty. That's the level of detail policymakers need when they decide where to deploy their economic development programs. You could get that detail by sending legions of survey-takers into crowded slums and sparsely populated rural areas. But that would be hugely time-consuming and cost tens of millions of dollars or more.

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.

"The elimination of poverty worldwide is the first of 17 UN Sustainable Development Goals for the year 2030. To track progress towards this goal, we require more frequent and more reliable data on the distribution of poverty than traditional data collection methods can provide." Those are the opening words on the website of Stanford University's Sustainability and Artificial Intelligence Lab, and its researchers have come up with an unusual -- and effective -- way to map and predict the distribution of poverty; their method combines high-resolution satellite imagery with machine learning. The researchers explain their methodology, which they call "cheap and scalable," in a video. The study, titled "Combining satellite imagery and machine learning to predict poverty," was published in the journal Science.

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 recognises signs of poverty through a process called machine learning, a type of artificial intelligence, he said.

By combining satellite data and sophisticated machine learning, researchers have developed a technique to estimate household consumption and income. Such data is particularly difficult to obtain in poorer countries, yet it is critical for informing research and policy, and for efforts including resource allocation and targeted intervention in these developing nations. The African continent provides a particularly striking example of limited insights into economic wellbeing. According to World Bank data from 2000 to 2010, 39 out of 59 African countries conducted less than two surveys substantial enough to result in poverty measures. Surveys are costly, infrequent, and cannot always reach countries or regions within countries, for instance, due to armed conflict.