SIRTE, Libya Suicide bombings against Libyan forces battling to oust Islamic State from their former North African stronghold of Sirte killed at least 12 fighters and wounded about 60 there on Thursday, a hospital spokesman said.

LONDON (Thomson Reuters Foundation) - 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.

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.

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.