Wi-Fi) and the second/third/fourth generation (2/3/4G) mobile network, the number of mobile phones, which is 7.74 billion, 103.5 per 100 inhabitants all over the world in 2017, is rising dramatically [1]. Nowadays, mobile phone can not only send voice and text messages, but also easily and conveniently access the Internet which has been recognized as the most revolutionary development of Mobile Internet (M-Internet). Meanwhile, worldwide active mobile-broadband subscriptions in 2017 have increased to 4.22 billion, which is 9.21% higher than that in 2016 [1]. Figure 1 shows the numbers of mobile-cellular telephone and active mobile-broadband subscriptions of the world and main districts from 2010 to 2017. The numbers which are up to the bars are the mobile-cellular telephone or active mobile-broadband subscriptions (million) in the world of the year which increase each year. Under the M-Internet, various kinds of content (image, voice, video, etc.) can be sent and received everywhere and the related applications emerge to satisfy people's requirements, including working, study, daily life, entertainment, education, healthcare, etc. In China, mobile applications giants, i.e., Baidu, Alibaba and Tencent, held 78% of M-Internet online time per day in App which was about 2,412 minutes in 2017 [2]. This figure indicates that M-Internet has entered a rapidly growth stage.

Norm Judah is chief technology officer, Microsoft Services. Artificial Intelligence (AI) is already having a transformative impact across every industry. From helping employees at transportation companies predict arrival times or issues that may arise, to predicting toxins in grains of food. It's helping scientists learn how to treat cancer more effectively and farmers are figuring out how to grow more food using fewer natural resources. A 2017 study by PWC calculated global GDP will be 14 percent higher by 2030 as a result of AI adoption, contributing an additional $15.7 trillion to the global economy.

"Space is big," wrote Douglas Adams in The Hitchhiker's Guide to the Galaxy. "You just won't believe how vastly, hugely, mind-bogglingly big it is. I mean, you may think it's a long way down the road to the chemist's, but that's just peanuts to space." Adams's assertion comes repeatedly to mind when reading David Sumpter's Outnumbered, which attempts to reckon with the sheer scale of the systems that manage much of our digital lives. It's easy, when faced with the numbers at hand, to succumb to a kind of vertigo: Facebook has two billion users, who make tens of millions of posts every hour.

Territory Manager, Autodesk Africa, Mr. Simon Bromfield, who was in Nigeria for the Autodesk Future Forum, spoke with Emma Okonji on the need for different sectors of the economy to adopt emerging technologies for business growth. What is your view about Nigeria's manufacturing industry? One of the challenges about Nigeria's manufacturing industry is the issue of doing things in silos instead of through collaboration and integration. Everyone wants to control his or her business and they do not want to share ideas with others. Nigerians need to work together to build better projects and that is the secret of global industries because they share ideas and build on existing ideas that are workable.

This paper addresses planning and control of robot motion under uncertainty that is formulated as a continuous-time, continuous-space stochastic optimal control problem, by developing a topology-guided path integral control method. The path integral control framework, which forms the backbone of the proposed method, re-writes the Hamilton-Jacobi-Bellman equation as a statistical inference problem; the resulting inference problem is solved by a sampling procedure that computes the distribution of controlled trajectories around the trajectory by the passive dynamics. For motion control of robots in a highly cluttered environment, however, this sampling can easily be trapped in a local minimum unless the sample size is very large, since the global optimality of local minima depends on the degree of uncertainty. Thus, a homology-embedded sampling-based planner that identifies many (potentially) local-minimum trajectories in different homology classes is developed to aid the sampling process. In combination with a receding-horizon fashion of the optimal control the proposed method produces a dynamically feasible and collision-free motion plans without being trapped in a local minimum. Numerical examples on a synthetic toy problem and on quadrotor control in a complex obstacle field demonstrate the validity of the proposed method.

Convolutional neural networks have recently achieved significant breakthroughs in various image classification tasks. However, they are computationally expensive,which can make their feasible mplementation on embedded and low-power devices difficult. In this paper convolutional neural network binarization is implemented on GPU-based platforms for real-time inference on resource constrained devices. In binarized networks, all weights and intermediate computations between layers are quantized to +1 and -1, allowing multiplications and additions to be replaced with bit-wise operations between 32-bit words. This representation completely eliminates the need for floating point multiplications and additions and decreases both the computational load and the memory footprint compared to a full-precision network implemented in floating point, making it well-suited for resource-constrained environments. We compare the performance of our implementation with an equivalent floating point implementation on one desktop and two embedded GPU platforms. Our implementation achieves a maximum speed up of 7. 4X with only 4.4% loss in accuracy compared to a reference implementation.

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.