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. Stanford researchers combined satellite images and machine learning to predict poverty. Their improved poverty maps could help aid organizations and policymakers distribute funds more efficiently and enact and evaluate policies more effectively.

The newest way to accurately predict poverty comes from satellite images and machine learning. This imaging technique could make it easier for aid organizations to know where and how to spend their money; it may also help governments develop better policy. We already know that the more lit up an area is at night, the richer and more developed it is. Researchers use this method to estimate poverty in places where we don't have exact data. But "night light" estimates are rough and don't tell us much about the wealth differences of the very poor.

Satellites staring down at Earth can see a lot from their posts in space. Powerful eyes in the sky can pick out homes, natural formations, the pyramids and even small cars driving on roads. And now, scientists are using the wealth of data collected by these satellites to solve major problems on Earth. A new study published in the journal Science this week uses machine learning -- a type of artificial intelligence that lets computer algorithms change when given new data -- coupled with satellite imagery to map poverty in Nigeria, Uganda, Tanzania, Rwanda and Malawi. This new technique could help revolutionize the way groups find impoverished areas and eventually get relief to people living in those specific parts of the world.

The South African Jewish computer scientist and educator Seymour Papert, who died on July 31 at age 88, was a long-time fixture at the Massachusetts Institute of Technology. He pioneered artificial intelligence and co-invented the Logo programming language. Yet his work as a social reformer, rather than with machines per se, was a primordial obsession. The human rights activist Janet Levine's memoir "Inside Apartheid" describes how during her childhood in the early 1950s, the Papert family lived not far from her Johannesburg home. Their son Seymour, a university student, was "'in trouble' with the government for his student political activities. My father said that he did not know why someone as talented as Seymour would throw his life away'for the Schwartzes' (a derogatory Yiddish expression for black people)."

Will Artificial Intelligence disrupt business? The once-futuristic predictions about how Artificial Intelligence (AI) will impact the world are becoming reality. Legendary futurist Ray Kurzweil has imagined advanced technology delivering everything from computerised brain chips to near-total automation of industries, and we already see the signs that AI will ultimately change the way we live and work. AI, where computers behave like humans, is no longer the stuff of science fiction. In many respects, AI is like a freight train racing down the tracks.

Machine learning and statistics typically focus on building models that capture the vast majority of the data, possibly ignoring a small subset of data as "noise" or "outliers." By contrast, here we consider the problem of jointly identifying a significant (but perhaps small) segment of a population in which there is a highly sparse linear regression fit, together with the coefficients for the linear fit. We contend that such tasks are of interest both because the models themselves may be able to achieve better predictions in such special cases, but also because they may aid our understanding of the data. We give algorithms for such problems under the sup norm, when this unknown segment of the population is described by a k-DNF condition and the regression fit is s-sparse for constant k and s. For the variants of this problem when the regression fit is not so sparse or using expected error, we also give a preliminary algorithm and highlight the question as a challenge for future work.

Based on the disturbing number of successful data breaches over the past few years, it's pretty evident that organizations are being overwhelmed by the growing number of threats.

This is part of a series on Artificial Intelligence. If you are catching it for the first time I'd recommend that you start here where I introduce the idea and provide some instrumental background. In the last article, I talked about the usefulness of thinking about artificial intelligence in its chapters. True, you could start biting this elephant anywhere and anyhow. The phases approach is just my recommended way of understanding, with better clarity, the goals and ultimate intentions of AI.

GWEN IFILL: Now: developing technology that can better identify your own emotions. At a time when people are concerned about what data can track and how it can be sold, it is an advance that clearly raises concerns. But it may also yield some important benefits. The "NewsHour"'s April Brown takes a look, part of our weekly series on the Leading Edge of science and technology. DAN MCDUFF, Director of Research, Affectiva: You can control the movements of BB-8, the little droid, based on how your facial expressions are changing.

ISLAMABAD – The U.S. drone strike that killed the Islamic State group's commander for Afghanistan and Pakistan was the latest blow to the Middle East-led movement's ambitions to expand into a region where the long-established Taliban remain the dominant Islamist force. The Islamic State group has enticed hundreds, perhaps thousands, of jihadi fighters in Afghanistan and Pakistan to switch loyalty and has held a small swath of territory in the eastern Afghan province of Nangarhar, where leader Hafiz Saeed Khan was killed on July 26 by a U.S. drone, Washington confirmed late Friday. But outside that pocket of territory, security officials and analysts say that the group remains -- for now -- more of a "brand name" than a cohesive militant force in much of the region. "Groups around the world want to jump on that bandwagon and cash in on their popularity and the fear they command," said a Pakistani police official based in Islamabad, on condition of anonymity because he was not authorized to speak to media. Anxiety over the Islamic State group -- also known as ISIS or "Daesh" -- in Afghanistan and Pakistan has been building since the al-Qaida breakaway movement seized portions of territory in Iraq and Syria in 2014 and began promoting itself worldwide.