A NEW technique using artificial intelligence (AI), to read satellite images could aid efforts to eradicate global poverty by indicating where help is needed most, a team of US researchers says. The method would assist governments and charities that are trying to fight poverty but that lack precise and reliable information on where poor people are living and what they need, the researchers based at Stanford University in California say. Eradicating extreme poverty, measured as people living on less than 1.25 a day, by 2030 is among the sustainable development goals adopted by UN member states in 2015. A team of computer scientists and satellite experts created a self-updating world map to locate poverty, says 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 says.

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.

Iraq built an armed robotic vehicle, according to Baghdad Post, and it could be used to take back a town occupied by ISIS. Based on Defense One's translation of the story, the robot is car-sized and tank-like, equipped with an automatic machine gun and a rocket launcher. It also has four cameras to be able to show operators its field of view, since it's controlled using a laptop from a kilometer away. That distance means its operators still have to be on the battlefield, but at least they can stay hidden and safe while the machine does its job. It's not exactly clear who built this vehicle nicknamed "Alrobot" (that's Arabic for robot).

Logistical problems in identifying impoverished communities may become relics of the past, as researchers are now combining satellite data with advanced computer algorithms to bypass traditional hurdles. In a study published Friday in the journal Science, Stanford University researchers proposed a way to use machine learning -- the science of designing computer algorithms that learn from data -- to interpret data acquired from high-resolution satellite imagery. The availability of accurate and reliable information on the location of impoverished zones is sorely lacking, which forces aid groups and other international organizations to conduct door-to-door surveys to supplement existing data -- an expensive and time-consuming process. Using earlier machine-learning methods, the team found pockets of poverty across five African nations which have previously been void of valuable survey information. "We have a limited number of surveys conducted in scattered villages across the African continent, but otherwise we have very little local-level information on poverty," said study co-author Marshall Burke.

Satellites are best known for helping smartphones map driving routes or televisions deliver programs. But now, data from some of the thousands of satellites orbiting Earth are helping track things like crop conditions on rural farms, illegal deforestation, and increasingly, poverty in the hard-to-reach places around the globe. As much as that data has the potential to provide invaluable information to humanitarian organizations, watchdog groups, and policymakers, there is too much of it to sift through in order to draw insights that could influence important decisions. A team of researchers from Stanford University, however, says it has developed an efficient way. By creating a deep-learning algorithm that can recognize signs of poverty in satellite images – such as condition of roads – the team sorted through a million images to accurately identify economic conditions in five African countries, reported the scientists in the journal Science on Thursday.

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 need more frequent and more reliable data on the distribution of poverty than traditional data collection methods can provide. In this project, we propose an approach that combines machine learning with high-resolution satellite imagery to provide new data on socioeconomic indicators of poverty and wealth. For more information, check out... Our recently published Science paper: http://science.sciencemag.org/content... A project website featuring poverty maps of Nigeria, Tanzania, Uganda, Malawi, and Rwanda: http://sustain.stanford.edu/predictin...

Japanese Prime Minister Shinzo Abe, dressed as Super Mario, holds a red ball during the closing ceremony of the Rio 2016 Olympic Games in Rio de Janeiro. Japanese PM Shinzo Abe's show-stopping appearance at today's closing ceremony in Rio, dressed as iconic game character Super Mario, already sets the tone for what lies in store. Japan is known internationally for its technological innovations, so Tokyo 2020 organizers are aiming to launch ambitious tech projects that will boost the economy and wow crowds. Tourists staying next to the Olympic Village in Tokyo's Odaiba neighborhood can choose, for example, to hang out with robot helpers of all sizes and sorts that offer up tips on the best transport, food and entertainment options in Tokyo. And that won't be the only place they'll encounter their robotic counterparts.

Uber launches self-driving cars in a trial in Pittsburgh this week. I was in an Uber returning from the airport when this was reported on the BBC by EV subscriber, Rory Cellan Jones. My Uber driver heard the headline and leant forward to increase the volume, ears pricking up. This is the sharp end of automation. Even if you hold the reasonable belief that work isn't going to go anywhere soon, and that we'll continuously reinvent things for humans to do, the question is not about the statistics in aggregate.

Researchers with Stanford University have used machine learning to extract information about poverty from satellite imagery of areas where survey information from sources on the ground is previously unavailable. "We have a limited number of surveys conducted in scattered villages across the African continent, but otherwise we have very little local-level information on poverty," said Marshall Burke, an assistant professor of earth system science at Stanford and co-author of a study in the current issue of journal Science. "At the same time, we collect all sorts of other data in these areas -- like satellite imagery -- constantly." In trying to understand whether high-resolution satellite imagery, an unconventional but readily available data source, could inform estimates of where impoverished people live, the researchers based their solution on an assumption that areas that are brighter at night are usually more developed, therefore used the "nightlight" data to identify features in the higher-resolution daytime imagery that are correlated with economic development. However, while machine learning, the science of designing computer algorithms that learn from data, works best when it can access vast amounts of data, there was little data on poverty to start with for the researchers.

Alan Turing, the British mathematician, is widely recognized as being one of the first people to come up with the idea of artificial intelligence in 1950. However the idea of a thinking machine existed as early as 2500 B.C., when the Egyptians sought mystical advice from talking statues. In the Cairo Museum, there is a bust of, Re-Harmakis, an Egyptian God, whose neck reveals the secret of his genius: an opening at the nape just big enough to hold a priest. Automata, the predecessors of today's robots, date back to ancient Egyptian figurines with movable limbs like those found in Tutankhamen's tomb. It took the invention of the Analytical Engine by Charles Babbage in 1833 to make artificial intelligence a real possibility.