The United States military's Africa Command has confirmed that its forces began deploying armed drones in Niger earlier this year. The West African country's government granted American forces permission in November 2017 to arm their drones - but neither side had previously confirmed their deployment. "In coordination with the Government of Niger, US Africa Command has armed intelligence, surveillance, and reconnaissance (ISR) aircraft already in Niger to improve our combined ability to respond to threats and other security issues in the region. Armed ISR aircraft began flying in early 2018," Samantha Reho, spokeswoman for US Africa Command told The Associated Press on Monday. The armed drones are currently deployed to Niger's Air Base 101 in the capital, Niamey.

The U.S. military presence in Niger has expanded in recent years to an 800-strong force that accompanies Nigerien troops on intelligence gathering and other missions, reflecting U.S. concerns about rising militancy in West Africa's Sahel region.

Winner of the 2018 digital face-off by digital Africa, hubs dot Ng, has released its in-app assistant: Emily. Emily is an artificial intelligence assistant integrated with the new hubs dot Ng app. Emily delivers a friendly conversation with users on what they want to do with a smooth user experience. Emily also doubles as a customer care agent for review and questions from users. All review and questions are further handled by real customer care representative with a reply sent to respective user.

YAOUNDE – With a flash of green light, a robot sputters and whizzes across the room, obeying the remote control commands 15-year-old Xaviera Nguefo and her team send its way. It is a scene that would not look out of place in a futuristic sci-fi fantasy, but is instead playing out in Yaounde, the dusty capital of Cameroon with its potholed streets and frequent power outages. In a country where 1 in 4 girls do not even learn to read, Xaviera, one of about 20 young Cameroonians studying at the NextGen Technology Center in Yaounde, is picking up the basics of artificial intelligence. "I love doing that because the physics that they teach us (at school) is all applied here," she said. "And it makes me a little bit smarter!"

The underlying factors affecting users' choices of what to watch on TV have for several years been of interest to commercial and academic research. In the midst of a rapidly changing device and multimedia landscape, TVs continue to be at the core of multimedia consumption in the home with scenarios covering, among others, social gatherings and solitary immersive moments. The inherent complexity of viewing situations challenges the creation of experiences that match personal preferences as well as temporal and social contexts. Due to the increased availability of multimedia, research has been focused on improving the users' decision process by reducing large catalogs of content to a few personalized suggestions [1]. Commercial recommender solutions are now considered core to the business of engaging users and thereby preventing abandonment [2]. To do so, recommender systems have explored various features for personalization, such as history of watching, ratings, user/item similarity, and time of the day, the last of which is an example of features characteristic to context-aware recommender systems (CARS) [3]. The main objective of a recommender system is to personalize the experience to the individual, often by studying the user-item matrix. This could be an issue, since an account on a TV is often shared by multiple members of a household that end up diluting the user profile.

Estimation is the computational task of recovering a hidden parameter $x$ associated with a distribution $D_x$, given a measurement $y$ sampled from the distribution. High dimensional estimation problems arise naturally in statistics, machine learning, and complexity theory. Many high dimensional estimation problems can be formulated as systems of polynomial equations and inequalities, and thus give rise to natural probability distributions over polynomial systems. Sum-of-squares proofs provide a powerful framework to reason about polynomial systems, and further there exist efficient algorithms to search for low-degree sum-of-squares proofs. Understanding and characterizing the power of sum-of-squares proofs for estimation problems has been a subject of intense study in recent years. On one hand, there is a growing body of work utilizing sum-of-squares proofs for recovering solutions to polynomial systems when the system is feasible. On the other hand, a general technique referred to as pseudocalibration has been developed towards showing lower bounds on the degree of sum-of-squares proofs. Finally, the existence of sum-of-squares refutations of a polynomial system has been shown to be intimately connected to the existence of spectral algorithms. In this article we survey these developments.

Modern measurement technologies provide the means to measure high density spatial and geometric data in three-dimensional (3D) coordinate systems, referred to as point clouds. Point cloud data analysis has broad applications in advanced manufacturing and metrology for measuring dimensional accuracy and shape analysis, in geographic information systems (GIS) for digital elevation modeling and analysis of terrains, in computer graphics for shape reconstruction, and in medical imaging for volumetric measurement to name a few. The role of point cloud data in manufacturing is now more important than ever, particularly in the field of smart and additive manufacturing processes, where products with complex shape and geometry are manufactured with the help of advanced technologies (Gibson et al., 2010). In these processes, the dimensional and geometric accuracy of manufactured parts are measured in the form of point clouds using modern sensing devices, including touch-probe coordinate measuring machines (CMM) and optical systems, such as laser scanners. Modeling the relationship of the dimensional accuracy, encapsulated in point clouds, with process parameters and machine settings is vital for variation reduction and process optimization.

In January, Amazon opened Amazon Go, a high-tech, cashierless convenience store in Seattle. There are no checkout lines and few employees. The only requirement to shop is downloading an app. Customers just walk in, load up their bags, and go. There's no need to even scan purchases; cameras positioned overhead take note of items in customers' carts and add them to a virtual bill. Amazon Go is both an interesting novelty -- and a profound challenge to the livelihoods of the more than 3.5 million Americans who work as cashiers. Rumors of a coming wave of similar stores and robot-run factories have provoked apocalyptic predictions of mass unemployment among pundits and politicians.

A Saudi-led coalition has launched air raids on Yemen's Hodeidah, in an apparent resumption of military operations on the strategic Red Sea city after Houthi rebels attacked two Saudi oil tankers and one of the United Arab Emirates' (UAE) main airports. The Houthi-run al-Masirah TV said in a series of tweets on Friday that coalition air strikes had targeted a radio station inside the city and a fishing pier. There were no immediate reports of casualties. The latest offensive on the port city of Hodeidah came a day after Houthi rebels claimed responsibility for a drone attack on Abu Dhabi's international airport. According to the Al-Masirah television channel, the Sammad-3 drone launched three attacks on the airport.

Did you know machine learning is behind a vast of technologies that we use today? Muthoni Wanyoike, the team lead at Instadeep Kenya and an Actuarial Science graduate from Dedan Kimathi University of tech is one of the great brains behind the Women in Machine Learning and Data Science community Nairobi chapter, (WiMLDS) Kenya. "When I started the Nairobi chapter of WiMLDS, I was just starting out in Data Science at the ICT Authority's Kenya Open Data Initiative. I was really looking for a community where I would gain new skills and also connect with people with the same interest as well as those already in the industry," says Muthoni Muthoni, however, admits setting up a community was not easy. At first, they organized meetups where they would charge participants to attend sessions on data science and machine learning with an abundance of Faith that attendees would believe in their vision.