When you've got Egyptian heritage and live in the West, something funny happens when you meet another Egyptian. We get giddy, we smile a lot, we act as if we've known each other forever. After some coffee and a quick tour of his California bungalow, the graphic artist known as Ganzeer hands me a stack of some pages from The Solar Grid. The black-and-white pages are crinkled and dried after being soaked in ink. Each panel looks like it may have taken hours.

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Crisis responders are increasingly using social media, data and other digital sources of information to build a situational understanding of a crisis situation in order to design an effective response. However with the increased availability of such data, the challenge of identifying relevant information from it also increases. This paper presents a successful automatic approach to handling this problem. Messages are filtered for informativeness based on a definition of the concept drawn from prior research and crisis response experts. Informative messages are tagged for actionable data -- for example, people in need, threats to rescue efforts, changes in environment, and so on. In all, eight categories of actionability are identified. The two components -- informativeness and actionability classification -- are packaged together as an openly-available tool called Emina (Emergent Informativeness and Actionability).

We introduce contextual explanation networks (CENs)---a class of models that learn to predict by generating and leveraging intermediate explanations. CENs are deep networks that generate parameters for context-specific probabilistic graphical models which are further used for prediction and play the role of explanations. Contrary to the existing post-hoc model-explanation tools, CENs learn to predict and to explain jointly. Our approach offers two major advantages: (i) for each prediction, valid instance-specific explanations are generated with no computational overhead and (ii) prediction via explanation acts as a regularization and boosts performance in low-resource settings. We prove that local approximations to the decision boundary of our networks are consistent with the generated explanations. Our results on image and text classification and survival analysis tasks demonstrate that CENs are competitive with the state-of-the-art while offering additional insights behind each prediction, valuable for decision support.

Hatem and Mohammed are obsessed with drones and robots. Determined to stop the desert from swallowing up their country, the two Sudanese inventors decide to take part in a television competition for inventors to raise awareness and investment in their dream - Sudan's first and only agricultural drone company. Although isolated by international sanctions and frustrated by a failing economy, the pair succeed in building Sudan's first flying robot farmer. Their drone can plant trees, increase harvests and reduce crop damage. And they are bound by their shared belief that Africa can change its destiny with technology.

What started of as a small Chinese big data startup, soon grew into a full data-intelligence service company listed among the top 10 in China. Data intelligence features Alibaba and other market leaders are offering, are often to expensive and complex for small to medium size businesses. This is exactly what KanKan, a US NASDAQ – listed Remark Holdings ("MARK") – subsidiary, sees as their opportunity. Their offerings are especially targeted towards SMEs, making their AI technology easy to use and very customisable.

What sets humans apart from other species, despite all the biological and genetic similarities, is our brains. But it turns out that the brain of a Homo sapiens who lived about 100,000 years ago was actually differently shaped, compared to ours. The earliest-known specimen of a Homo sapiens is from about 300,000 years ago, and it was discovered at a site in present-day Morocco. The Jebel Irhoud fossil had a modern-looking face that fell within the variations shown by modern humans and was therefore categorized as one. However, even at the time of its discovery's announcement in June 2017, researchers said the shape of its braincase, or cranium, indicated that the human brain had evolved since.

On a visit to one Harvard robotics lab, the sewing machines stand out, while the head of another explains how and why lab members are studying termites in Namibia. Welcome to the new age of machines, in which scientists with seemingly disparate talents are using cutting-edge materials, cheap sensors, 3-D printing, and powerful computers to accelerate advances in robotics. Prior innovations transformed the factory and warehouse, but those robots work best in controlled environments, usually out of public view. For researchers at Harvard and elsewhere, one new target is Main Street. "We were promised these things by sci-fi for 50 years," said Robert Wood, Charles River Professor of Engineering and Applied Sciences.

Enterprise machine learning pilots and deployments are expected to double this year and smartphone adoption will continue to experience a significant increase. This is according to Deloitte Global's 17th edition of the Technology, Media & Telecommunications (TMT) Predictions research. The report predicts that global organisations will double their use of machine learning technology by the end of 2018 and smartphone sales are expected to double, with more than 90% of adults in developed countries expected to have a smartphone by the end of 2023. Enterprise machine learning pilots and deployments are predicted to double this year. TMT predictions highlights some key areas that Deloitte Global believes will unlock more intensive use of machine learning in the enterprise by making it easier, cheaper and faster.

Deloitte's yearly'Technology, Media and Telecommunications (TMT) Predictions' report has forecast major strides in machine learning for enterprises this year. The report predicts a doubling of the number of enterprise machine learning pilots and deployments this year and, subsequently, the use of machine learning technology, underpinned by new chips and better software tools. "We have reached the tipping point where adoption of machine learning in the enterprise is poised to accelerate," said Deloitte Global Media and Entertainment and TMT Africa Leader Mark Casey. Easier, cheaper and faster elements will start moulding the more intensive use of machine learning in the enterprise, with growth emerging from new semiconductor chips that enable applications to use less power and become more responsive, flexible and capable. The TMT Predictions report also highlighted the growth of smartphone adoption, with more than 90% of adults in developed countries expected to own a smartphone by 2023, as well as the rising trend in mobile-only wireless home Internet.