Following 11 years of defence cooperation and millions of dollars spent on maintaining military bases, the United States officially pulled its troops out of Niger this week in a surprise divorce that experts are calling a "blow" to Washington's ambitions for influence in the troubled Sahel region of West Africa. Once-close relations between the two countries saw the US establish large, expensive military bases from which it launched surveillance drones in Niger to monitor myriad armed groups linked to al-Qaeda and ISIL (ISIS). However, those ties collapsed in March when Niger's military government, which seized power in July 2023, cancelled a decade-long security agreement and told the US, which was pushing for a transition to civilian rule, to remove its 1,100 military personnel stationed there by September 15. For months, the US has failed to either fully align with or outright oppose the ruling military, analysts say. On the one hand, Washington seemed ready to maintain defence relations with the new ruling power, but on the other, it felt compelled to denounce the coup and pause aid to Niger.

Food insecurity is one of the major challenges facing African countries; therefore, timely and accurate information on agricultural production is essential to feed the growing population on the continent. A synergistic approach comprising a high-resolution multispectral UAV optical dataset and synthetic aperture radar (SAR) can help understand spectral features of target objects, especially with crop type identification. We conducted this work on the experimental plots using high spatial resolution multispectral UAV data (12 cm, re-sampled to 50 cm) in combination with the Sentinel 1C Synthetic Aperture Radar (SAR) dataset. Multiple combinations of the UAV datasets were analysed to assess the impact of canopy height model (CHM) on classification accuracy and to determine the optimum dataset (including spatial resolution) for the land cover classification. We also appraise the impact of variable spatial resolution on classification accuracy.

All human actions are based on anticipated futures. We cannot know the future because it does not exist yet, but we can use our current knowledge to imagine the future and make them happen. The better we understand the present and the history that has created it, the better we can understand the possibilities of the future. To appreciate the opportunities and challenges that artificial intelligence (AI) creates, we need both a good understanding of what AI is today and what the future may bring when AI is widely used in society. AI can enable new ways of learning, teaching, and education, and it may also change society in ways that pose new challenges for educational institutions.

Lee's technical explanations sit alongside Chen's fictional short stories to produce an exploration of the perils and possibilities of AI. This story, translated by Emily Jin, revolves around a Nigerian video producer who is recruited to make an undetectable deepfake. Touching on impending breakthroughs in computer vision, biometrics, and AI security, it imagines a future world marked by cat-and-mouse games between deepfakers and detectors, and between defenders and perpetrators. As the light-rail train inched into Yaba station, Amaka pushed a button next to the door of his carriage. Even before the train came to a complete stop, the doors opened with a whoosh and Amaka hopped off.

Lee's technical explanations sit alongside Chen's fictional short stories to produce an exploration of the perils and possibilities of AI. This story revolves around a Nigerian video producer who is recruited to make an undetectable deepfake. Touching on impending breakthroughs in computer vision, biometrics, and AI security, it imagines a future world marked by cat-and-mouse games between deepfakers and detectors, and between defenders and perpetrators. As the light-rail train inched into Yaba station, Amaka pushed a button next to the door of his carriage. Even before the train came to a complete stop, the doors opened with a whoosh and Amaka hopped off.

DAKAR (Thomson Reuters Foundation) - Data analyst Fabrice Sonzahi enrolled in a course on artificial intelligence (AI) in Dakar, hoping to help struggling farmers improve crop yields in his home country of Ivory Coast. He is part of an inaugural batch of students at a new AI programming school in Senegal, one of the first in West Africa. Its mission is to train local people in using data to solve pressing issues like the impact of climate change on crops. The Dakar Institute of Technology (DIT), which opened in September, is running its first 10-week boot camp with nine students in partnership with French AI school VIVADATA. "I am convinced that by analyzing data we can give (farmers) better solutions," said Sonzahi, 30.

The humanity has been facing a plethora of challenges associated with infectious diseases, which kill more than 6 million people a year. Although continuous efforts have been applied to relieve the potential damages from such misfortunate events, it is unquestionable that there are many persisting challenges yet to overcome. One related issue we particularly address here is the assessment and prediction of such epidemics. In this field of study, traditional and ad-hoc models frequently fail to provide proper predictive situation awareness (PSAW), characterized by understanding the current situations and predicting the future situations. Comprehensive PSAW for infectious disease can support decision making and help to hinder disease spread. In this paper, we develop a computing system platform focusing on collective intelligence causal modeling, in order to support PSAW in the domain of infectious disease. Analyses of global epidemics require integration of multiple different data and models, which can be originated from multiple independent researchers. These models should be integrated to accurately assess and predict the infectious disease in terms of holistic view. The system shall provide three main functions: (1) collaborative causal modeling, (2) causal model integration, and (3) causal model reasoning. These functions are supported by subject-matter expert and artificial intelligence (AI), with uncertainty treatment. Subject-matter experts, as collective intelligence, develop causal models and integrate them as one joint causal model. The integrated causal model shall be used to reason about: (1) the past, regarding how the causal factors have occurred; (2) the present, regarding how the spread is going now; and (3) the future, regarding how it will proceed. Finally, we introduce one use case of predictive situation awareness for the Ebola virus disease.

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.

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.