After an intense two-day search, local military forces Friday recovered the body of a U.S. Army commando who was inadvertently left behind after a daylight ambush by militants killed three other Green Berets in a rugged border region in Niger. Pentagon officials had not previously announced that a Green Beret was missing in action after the surprise attack on a joint patrol of U.S. commandos and Nigerien troops Wednesday. Six of the 12 Americans on the patrol were killed or wounded. Officials hoped the missing U.S. Army Special Forces operative might still be hiding in the dense brush, rather than taken captive, and launched a massive search-and-rescue mission with aerial drones and other aircraft, as well as Nigerien ground forces. The death of four Green Berets in remote West Africa marks the worst single loss of U.S. forces under fire since President Trump took office.

Built on NLP and ML, Artificial Intelligence chatbots are based on the human capability of learning and absorbing information, but imbued with more efficiency. These efficient machines can process 1,000 times faster than humans and will come up with more subtle results. Artificial Intelligence chatbots (if they're artificial intelligence powered at all) are superior to any Artificial Intelligence interface. In Uganda, a startup – mFeyti is developing an Artificial intelligence powered platform that enables people to detect counterfeit drugs. The developers collect information from regulators, importers and manufacturers and then use special identification characteristics to identify drugs well.

Some cassava farmers may not be able to tell one plant's debilitating brown streak from another's troubling brown leaf spot--but a smartphone-friendly AI can. Wired reports that researchers have developed a lightweight image-recognition AI that can identify diseases in the cassava plant based on pictures of its leaves. That could be useful, because cassava is one of the most commonly eaten tubers on the planet, but is grown predominantly in developing countries where access to expertise to diagnose unusual crop problems may be limited. In a paper published on the arXiv, the researchers behind the new AI explain how they've used a technique known as transfer learning to retrain an existing image-recognition neural network using just a small number of new images. With just 2,756 pictures of cassava leaves captured from plants in Tanzania, the team was able to build software based on Google's TensorFlow AI library that could reliably identify three crop diseases and two types of pest damage. It could, for instance, discern brown leaf spot with 98 percent accuracy.

Two other Special Forces soldiers were also wounded in the Niger ambush; Lucas Tomlinson has the story for'Special Report.' There was no U.S. surveillance drone overhead at the time of the ambush in Niger which killed three U.S. Army Green Berets and wounded two others Wednesday, multiple officials familiar with the matter tell Fox News. In addition, Fox News has learned the dead and injured soldiers were taken from the firefight by French Puma helicopters. Only one U.S. helicopter was available to pick up the Green Berets. It is not clear why it wasn't used.

Assassin's Creed Origins is due out later this month, taking players to Cleopatra's Egypt and revisiting one of the most fascinating and mysterious times in the history of the human race. Developer Ubisoft, having spent time exploring and researching this period of history realised that there is still much we don't know or understand about it, including the mysteries of one of humanities oldest written languages – hieroglyphics. That's why, with the aid of Google's TensorFlow technology, Ubisoft has set up The Hieroglyphics Initiative. The idea is, with the collaboration with Google, that machine learning could help to look through the imagery and research from history and using object identification to identify all the individual differences in each hieroglyph. To do that, however, Ubisoft and google need help.

If you worked for a startup in the 1990s, chances were you were figuring out how to make money on the brand spankin' new World Wide Web. Leap forward 10 years, and the typical startup was all about apps on your smartphone, to do everything from touching up selfies, to booking flights, to getting your laundry picked up. Now, we're in the decade of the startup launched to remake transportation--the electric cars, ride sharing networks, and personal flying machines that will transform the way people get around. Lyft, formed in 2012, joined Uber and others in persuading people that's it's fine to get into strangers' cars. Zipline started national drone delivery networks, dropping medical supplies in Rwanda and Tanzania. Tesla made electric propulsion, once the province of eco-warriors, fast and cool.

With the final war of the Roman Republic brewing, the period has proven hugely influential in fine art, theatre and film, from Shakespeare to Hollywood. But later this year it may be subject to its most rigorous investigation yet: a video game. Out at the end of October, Assassin's Creed: Origins, follows the story of Bayek, a military officer looking to protect his people as Julius Caesar's Roman army threatens invasion. The game is set to feature a vast open-world recreation of ancient Egypt, featuring several cities as well as stretches of wilderness and ocean. As with all titles in the series, historical events and figures are set to figure, but this time, the gargantuan project isn't just about the game – Ubisoft has more ambitious plans for its rich simulation.

African Business Review: Tell me a little about your role with Econet Wireless Zimbabwe. I have extensive experience in IT networks, telecom services, and the financial technology space, now exploring artificial intelligence to add value to myself and add to my skills, as machine learning is the future of every business and technology of today. I sit in a very key position to oversee the entire changing landscape of the financial IT i.e. fintech services sector. In our previous issue of African Business Review, you discussed the challenge that banking and financial organisations face in trying to keep costs low while ensuring the same high quality of services - is this still the case? Keeping costs low is the prime idea, as this is a low margin business for low value transactions, if the cost at any time starts looking north, customers immediately start running south.

Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large data streams that will make land cover/use classification difficult. Machine learning classifiers can help at this, and many methods are currently available. A popular kernel classifier is the Gaussian process classifier (GPC), since it approaches the classification problem with a solid probabilistic treatment, thus yielding confidence intervals for the predictions as well as very competitive results to state-of-the-art neural networks and support vector machines. However, its computational cost is prohibitive for large scale applications, and constitutes the main obstacle precluding wide adoption. This paper tackles this problem by introducing two novel efficient methodologies for Gaussian Process (GP) classification. We first include the standard random Fourier features approximation into GPC, which largely decreases its computational cost and permits large scale remote sensing image classification. In addition, we propose a model which avoids randomly sampling a number of Fourier frequencies, and alternatively learns the optimal ones within a variational Bayes approach. The performance of the proposed methods is illustrated in complex problems of cloud detection from multispectral imagery and infrared sounding data. Excellent empirical results support the proposal in both computational cost and accuracy.

Listen, you're kinda spooked about the rise of artificial intelligence, and I get that. It's a tremendously powerful technology that promises to transform the very nature of work, inevitably leading to the automation of certain white-collar jobs. But AI also promises to make human labor smarter and more efficient, even something as traditional as small-scale farming. To that end, researchers have developed a smartphone-based program that can automatically detect diseases in the cassava plant--the most widely grown root crop on Earth--with darn near 100 percent accuracy. The most impressive bit about the technology is that the neural network that powers it runs entirely on the smartphone, no cloud computing or hulking processors required, as the researchers detail in a preprint paper to be published in Frontiers in Plant Science.