Last month, the ScanPyramids project, led by a team of researchers from the University of Cairo's Faculty of Engineering in Egypt and the HIP Institute in France, announced that they'd used muon imaging to discover a large void hidden deep inside the Khufu's Pyramid (also known as the Great Pyramid of Giza, since it's the big one). Nobody knows what's inside, or if there's anything inside at all, or even if maybe that's where the Stargate is stashed. Obviously there's a lot of interest in what may or may not be hiding out in here, and it could help solve mysteries like how and why exactly the pyramids were built. The problem is that (understandably) we're not going to just start blowing holes in the Great Pyramid to see what's going on. In 2002, Egyptologists used a custom exploration robot (made by iRobot, in fact) to explore a small shaft leading out of the Queen's Chamber in the Great Pyramid that was sealed by a door. Rather than try to open the door, likely destroying it in the process, the robot drilled a tiny hole just large enough to poke a camera through in an effort to do the minimum amount of irreversible damage to the only wonder of the ancient world that we've got left.

The U.S. military has carried out 32 airstrikes this year against the Somalia-based al-Shabab and a small but growing presence of fighters linked to the Islamic State group. The Trump administration early this year approved expanded military operations against extremists in the Horn of Africa nation, as the Trump administration puts counterterrorism at the top of its foreign policy agenda for Africa.

Algorithms drive more efficient investment decisions, says co-founder of South Africa's first machine-learning-powered unit trust fund.

Accenture Nigeria said its investments in Artificial Intelligence (AI), Virtual Reality (VR), robotics and blockchain technology capabilities will help businesses across various sectors boost their productivity and efficiency through innovations. The management consulting and professional services giant said technology will continue to evolve. Speaking with reporters after a demo at its Lagos office at the weekend, its Managing Director Mr. Niyi Tayo, said earlier in the year, the firm had predicted that many consumers and enterprise clients will depend on AI to select products. He said: "Early this year, we predicted that in five years, more than half of consumers and enterprise clients will select products and services based on a company's AI, instead of the company's traditional brand. And in seven years, most interfaces will not have a screen and will be integrated into daily tasks. These two predictions alone strongly suggest that companies must act now on developing their AI Journey. "We want businesses in Nigeria – from banking to manufacturing, health, construction, education, retail, security, and other sectors to take advantage of the innovations we have created to improve their businesses.

Artificial intelligence (AI) and machine learning technologies have turned the IT industry on its head, offering enterprises the ability to transform their approach to business strategy and customer insights, and research institutions to pursue humanity's biggest challenges. Once considered an abstract technology that was primarily championed by hyperscale companies (like Google, Microsoft, Baidu etc), it is encouraging to now see startups and larger enterprises alike across a variety of industries explore unique AI applications to solve business problems and scientific challenges. From assisting in healthcare diagnoses, to predicting when things like a jet engine is in need of maintenance, and assisting in crime prevention, the potential for innovation with AI is nearly endless. It's even touching the average consumer's daily life, as well: Facebook's suggested photo tagging feature, for example, uses AI to recognize who's who in your pictures. Of course, AI does have its pain points and a key challenge for businesses today is the ability to differentiate between what's hype and what's reality.

Shares of AeroVironment Inc., a drone manufacturer based in Monrovia, soared Wednesday after the company reported strong second-quarter earnings, boosted by a growth in sales of unmanned aircraft systems. AeroVironment stock was up as much as 34% on Wednesday morning before losing some of its gains. It was up 26% at $54.49 around noon Pacific time. The company held its second-quarter earnings call with analysts Tuesday afternoon and reported revenue of $73.8 million, a 47% increase compared with the same period last year. AeroVironment attributed the gain to increased sales of unmanned aircraft systems, which includes drones, on-board cameras and sensors and ground control stations.

Whether it's logging into a closed system from halfway around the globe, or checking e-mails on your phone, the internet has removed geography as a barrier to communication. Unfortunately, recruitment faces other barriers. Posting job listings to tens or even hundreds of locations takes time, as does reviewing CVs, sorting the highly skilled from the rejection pile, before finally conducting interviews. Fortunately, smart applications, powered by artificial intelligence, are changing this. Sage reports that, in two years, the number of businesses using AI to manage operations is expected to increase sharply, from 38% in 2016 to 62% in 2018.

In this setting, we only have access to the stochastic function f(x; ξ), where the random variable ξ is sampled from an underlying distribution D. The task is to optimize the expected function f(x), which in general may be nonconvex. This framework covers a wide range of problems, including the offline setting where we minimize the empirical loss over a fixed amount of data, and the online setting where data arrives sequentially. One of the most prominent applications of stochastic optimization has been in large-scale statistics and machine learning problems, such as the optimization of deep neural networks. Classical analysis in nonconvex optimization only guarantees convergence to a first-order stationary point (i.e., a point x satisfying ‖ f(x)‖ 0), which can be a local minimum, a local maximum, or a saddle point. This paper goes further, proposing an algorithm that escapes saddle points and converges to a local minimum.

For decades, context-dependent phonemes have been the dominant sub-word unit for conventional acoustic modeling systems. This status quo has begun to be challenged recently by end-to-end models which seek to combine acoustic, pronunciation, and language model components into a single neural network. Such systems, which typically predict graphemes or words, simplify the recognition process since they remove the need for a separate expert-curated pronunciation lexicon to map from phoneme-based units to words. However, there has been little previous work comparing phoneme-based versus grapheme-based sub-word units in the end-to-end modeling framework, to determine whether the gains from such approaches are primarily due to the new probabilistic model, or from the joint learning of the various components with grapheme-based units. In this work, we conduct detailed experiments which are aimed at quantifying the value of phoneme-based pronunciation lexica in the context of end-to-end models. We examine phoneme-based end-to-end models, which are contrasted against grapheme-based ones on a large vocabulary English Voice-search task, where we find that graphemes do indeed outperform phonemes. We also compare grapheme and phoneme-based approaches on a multi-dialect English task, which once again confirm the superiority of graphemes, greatly simplifying the system for recognizing multiple dialects.

Mosquitoes are a major vector for malaria, causing hundreds of thousands of deaths in the developing world each year. Not only is the prevention of mosquito bites of paramount importance to the reduction of malaria transmission cases, but understanding in more forensic detail the interplay between malaria, mosquito vectors, vegetation, standing water and human populations is crucial to the deployment of more effective interventions. Typically the presence and detection of malaria-vectoring mosquitoes is only quantified by hand-operated insect traps or signified by the diagnosis of malaria. If we are to gather timely, large-scale data to improve this situation, we need to automate the process of mosquito detection and classification as much as possible. In this paper, we present a candidate mobile sensing system that acts as both a portable early warning device and an automatic acoustic data acquisition pipeline to help fuel scientific inquiry and policy. The machine learning algorithm that powers the mobile system achieves excellent off-line multi-species detection performance while remaining computationally efficient. Further, we have conducted preliminary live mosquito detection tests using low-cost mobile phones and achieved promising results. The deployment of this system for field usage in Southeast Asia and Africa is planned in the near future. In order to accelerate processing of field recordings and labelling of collected data, we employ a citizen science platform in conjunction with automated methods, the former implemented using the Zooniverse platform, allowing crowdsourcing on a grand scale.