DAKAR, Senegal – The U.S. military in Africa has taken steps to increase the security of troops on the ground, adding armed drones and armored vehicles and taking a harder look at when American forces go out with local troops, the head of the U.S. Africa Command says. Gen. Thomas D. Waldhauser told reporters on Monday the U.S. also has cut the response time needed for medical evacuations -- the result of a broad review in the wake of last year's ambush in Niger that killed four U.S. soldiers and four of their Niger counterparts. "Since that happened, there were significant things to change and learn," Waldhauser said. "We've done a thorough scrub really on every level, whether it's at a tactical level ... or how we conduct business at AFRICOM." A report is due in mid-August on actions taken in response to the findings, Waldhauser said.

DAKAR – The U.S. military in Africa has taken steps to increase the security of troops on the ground, adding armed drones and armored vehicles and taking a harder look at when American forces go out with local troops, the head of the U.S. Africa Command said Monday. Gen. Thomas D. Waldhauser told reporters the U.S. also has cut the response time needed for medical evacuations -- the result of a broad review in the wake of last year's ambush in Niger that killed four U.S. soldiers and four of their Niger counterparts. "Since that happened, there were significant things to change and learn," Waldhauser said. "We've done a thorough scrub really on every level, whether it's at a tactical level … or how we conduct business at AFRICOM." A report is due in mid-August on actions taken in response to the findings, Waldhauser said.

Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.

Advances in artificial intelligence (AI) will transform modern life by reshaping transportation, health, science, finance, and the military. To adapt public policy, we need to better anticipate these advances. Here we report the results from a large survey of machine learning researchers on their beliefs about progress in AI. Researchers predict AI will outperform humans in many activities in the next ten years, such as translating languages (by 2024), writing high-school essays (by 2026), driving a truck (by 2027), working in retail (by 2031), writing a bestselling book (by 2049), and working as a surgeon (by 2053). Researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years, with Asian respondents expecting these dates much sooner than North Americans. These results will inform discussion amongst researchers and policymakers about anticipating and managing trends in AI. This article is part of the special track on AI and Society.

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.