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.

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.

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.

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).

Microsoft's recent purchase of LinkedIn for a reported 26.2 billion may be the biggest acquisition news so far in 2016. But Twitter is betting that its own recent acquisition of Magic Pony Technology – a neural networks/machine learning company – for a mere 150 million will pay big dividends down the stretch. Commenting on the acquisition in a recent Twitter blog, Twitter CEO and co-founder Jack Dorsey said, "Machine learning is increasingly at the core of everything we build at Twitter." Dorsey went on to say that, "Magic Pony's machine learning technology will help us build strength into our deep learning teams with world-class talent, so Twitter can continue to be the best place to see what's happening and why it matters, first. We value deep learning research to help make our world better, and we will keep doing our part to share our work and learnings with the community." Magic Pony Technology is the third machine-learning startup that Twitter has acquired since Madbits in 2014, which begs the question: Why is Twitter so heavily focused on machine learning?

Deep learning has a wide range of applications, from speech recognition, computer vision, to self-driving cars and mastering the game of Go. While the concept is intuitive, the implementation is often heuristic and tedious. We will take a stab at simplifying the process, and make the technology more accessible. We illustrate our approach with the venerable CIFAR-10 dataset. The following code snippet will download the data from its known location to a folder "data/cifar" inside the current workspace.

Researchers at IBM Australia will play a role in building a "cognitive assistant" the IT giant hopes will help ophthalmologists diagnose eye conditions from medical image data. "IBM research is building the next generation cognitive assistant with advanced multi-media capability for early detection and management of diseases that can affect both the eyes and overall health of a person," the firm said in a now closed advertisement. Participating full and part-time interns would apply their clinical knowledge to analyse retinal image data and come up with "novel ideas and insights for cognition on this type of data". Back in June, IBM Australia revealed agreements with organisations including Melanoma Institute Australia to "apply cognitive computing to dermatology images" in the hope of earlier detection and identification of skin cancer.

Researchers at IBM Australia will play a role in building a "cognitive assistant" the IT giant hopes will help ophthalmologists diagnose eye conditions from medical image data. The company recruited a batch of research interns to lend their expertise to the project via the IBM Australia research lab in Melbourne. The interns were slated to begin work last month. "IBM research is building the next generation cognitive assistant with advanced multi-media capability for early detection and management of diseases that can affect both the eyes and overall health of a person," the firm said in a now closed advertisement. "We are building the image-guided informatics system that acts as a filter to extract the essential clinical information ophthalmologists need to know about a patient for diagnosis and treatment planning. "This filtering employs sophisticated medical image processing, pattern recognition and machine learning techniques guided by advanced clinical knowledge.