What is being hailed as the world's first commercial regular drone delivery service is beginning drop-offs in Rwanda. The operation uses fixed-wing drones that automatically fly to destinations in the central African nation. They release small packages attached to parachutes without needing to land at the delivery points before returning. The technology promises to make deliveries much faster than had previously been possible by road. Zipline - the US start-up running the project - is made up of engineers who formerly worked at Space X, Google, Lockheed Martin and other tech companies. Its drones will initially be used to deliver blood, plasma, and coagulants to hospitals across rural western Rwanda, helping to cut waiting times from hours to minutes.

In a warehouse outside of Kigali, Rwanda, 15 drones sit waiting to receive a message. When the text comes in, one loads up and zips off into the sky – on a mission to save a life. Today, the government of Rwanda announced an emergency drone delivery service. These drones will make up to 150 trips per day, carrying blood supplies to clinics in need. Rwanda has relatively good infrastructure in some places, but in others it can be unreliable, says Moz Siddiqui at the Global Alliance for Vaccines and Immunization (GAVI), one of the partners in the project, along with UPS and California drone company Zipline.

Mapping forest aboveground biomass (AGB) has become an important task, particularly for the reporting of carbon stocks and changes. AGB can be mapped using synthetic aperture radar data (SAR) or passive optical data. However, these data are insensitive to high AGB levels (\textgreater{}150 Mg/ha, and \textgreater{}300 Mg/ha for P-band), which are commonly found in tropical forests. Studies have mapped the rough variations in AGB by combining optical and environmental data at regional and global scales. Nevertheless, these maps cannot represent local variations in AGB in tropical forests. In this paper, we hypothesize that the problem of misrepresenting local variations in AGB and AGB estimation with good precision occurs because of both methodological limits (signal saturation or dilution bias) and a lack of adequate calibration data in this range of AGB values. We test this hypothesis by developing a calibrated regression model to predict variations in high AGB values (mean \textgreater{}300 Mg/ha) in French Guiana by a methodological approach for spatial extrapolation with data from the optical geoscience laser altimeter system (GLAS), forest inventories, radar, optics, and environmental variables for spatial inter-and extrapolation. Given their higher point count, GLAS data allow a wider coverage of AGB values. We find that the metrics from GLAS footprints are correlated with field AGB estimations (R 2 =0.54, RMSE=48.3 Mg/ha) with no bias for high values. First, predictive models, including remote-sensing, environmental variables and spatial correlation functions, allow us to obtain "wall-to-wall" AGB maps over French Guiana with an RMSE for the in situ AGB estimates of ~51 Mg/ha and R${}^2$=0.48 at a 1-km grid size. We conclude that a calibrated regression model based on GLAS with dependent environmental data can produce good AGB predictions even for high AGB values if the calibration data fit the AGB range. We also demonstrate that small temporal and spatial mismatches between field data and GLAS footprints are not a problem for regional and global calibrated regression models because field data aim to predict large and deep tendencies in AGB variations from environmental gradients and do not aim to represent high but stochastic and temporally limited variations from forest dynamics. Thus, we advocate including a greater variety of data, even if less precise and shifted, to better represent high AGB values in global models and to improve the fitting of these models for high values.

FILE- In this March 1, 2013 file photo, anti-Syrian President Bashar Assad protesters hold the Jabhat al-Nusra flag, as they shout slogans during a demonstration, at Kafranbel town, in Idlib province, northern Syria. Insurgent groups like Hezbollah and the Islamic State group in Syria have learned how to weaponize surveillance drones and use them against each other, adding a new twist to the country's civil war, a U.S. military official and others say. FILE- In this March 1, 2013 file photo, anti-Syrian President Bashar Assad protesters hold the Jabhat al-Nusra flag, as they shout slogans during a demonstration, at Kafranbel town, in Idlib province, northern Syria. Insurgent groups like Hezbollah and the Islamic State group in Syria have learned how to weaponize surveillance drones and use them against each other, adding a new twist to the country's civil war, a U.S. military official and others say. WASHINGTON (AP) -- French and Kurdish forces in northern Iraq were attacked by an exploding drone, the Pentagon said Wednesday, adding a new worry to the wars in Iraq and Syria as militant groups learn to weaponize their store-bought drones.

FILE- In this March 1, 2013 file photo, anti-Syrian President Bashar Assad protesters hold the Jabhat al-Nusra flag, as they shout slogans during a demonstration, at Kafranbel town, in Idlib province, northern Syria. Insurgent groups like Hezbollah and the Islamic State group in Syria have learned how to weaponize surveillance drones and use them against each other, adding a new twist to the country's civil war, a U.S. military official and others say. FILE- In this March 1, 2013 file photo, anti-Syrian President Bashar Assad protesters hold the Jabhat al-Nusra flag, as they shout slogans during a demonstration, at Kafranbel town, in Idlib province, northern Syria. Insurgent groups like Hezbollah and the Islamic State group in Syria have learned how to weaponize surveillance drones and use them against each other, adding a new twist to the country's civil war, a U.S. military official and others say. WASHINGTON (AP) -- Militant groups like Hezbollah and the Islamic State group have learned how to weaponize surveillance drones and use them against each other and coalition forces, adding a new twist to the wars in Iraq and Syria, the Pentagon said Wednesday.

FILE- In this March 1, 2013 file photo, anti-Syrian President Bashar Assad protesters hold the Jabhat al-Nusra flag, as they shout slogans during a demonstration, at Kafranbel town, in Idlib province, northern Syria. Insurgent groups like Hezbollah and the Islamic State group in Syria have learned how to weaponize surveillance drones and use them against each other, adding a new twist to the country's civil war, a U.S. military official and others say. FILE- In this March 1, 2013 file photo, anti-Syrian President Bashar Assad protesters hold the Jabhat al-Nusra flag, as they shout slogans during a demonstration, at Kafranbel town, in Idlib province, northern Syria. Insurgent groups like Hezbollah and the Islamic State group in Syria have learned how to weaponize surveillance drones and use them against each other, adding a new twist to the country's civil war, a U.S. military official and others say. WASHINGTON (AP) -- Militant groups like Hezbollah and the Islamic State group have learned how to weaponize surveillance drones and use them against each other, adding a new twist to Syria's civil war, a U.S. military official and others say.

For the first time ever, researchers have captured footage of wild chimpanzee mothers teaching their offspring to use tools. The videos taken at the Nouabalé-Ndoki National Park in the Republic of Congo shed new light on the evolution of teaching, showing how young chimpanzees learn from their mothers to catch termites with'fishing probes.' Mother chimpanzees were found to bring multiple tools or divide their own in half, allowing them to address the needs of their young without hindering their own ability to gather food. The videos taken at the Nouabalé-Ndoki National Park in the Republic of Congo shed new light on the evolution of teaching, showing how young chimpanzees learn from their mothers to catch termites with'fishing probes' The videos show how young chimpanzees learn from their mothers to catch termites with'fishing probes.' The footage also revealed that the mothers used different strategies to provide their young with tools.

WASHINGTON – Insurgent groups like Hezbollah and the Islamic State group have learned how to weaponize surveillance drones and use them against each other, adding a new twist to Syria's civil war, a U.S. military official and others say. A video belonging to an AL Qaeda offshoot, Jund al-Aqsa, purportedly shows a drone landing on Syrian military barracks. In another video, small explosives purportedly dropped by the Iran-backed Shiite militant group Hezbollah target the Sunni militant group Jabhat Fatah al-Sham, formerly known as the Nusra Front. A U.S. military official, who spoke anonymously because he wasn't authorized to discuss the matter publicly, said the U.S. military is aware of the development. Commanders have warned troops to take cover if they see what they might have once dismissed as a surveillance drone, he said.

FILE- In this March 1, 2013 file photo, anti-Syrian President Bashar Assad protesters hold the Jabhat al-Nusra flag, as they shout slogans during a demonstration, at Kafranbel town, in Idlib province, northern Syria. Insurgent groups like Hezbollah and the Islamic State group in Syria have learned how to weaponize surveillance drones and use them against each other, adding a new twist to the country's civil war, a U.S. military official and others say. FILE- In this March 1, 2013 file photo, anti-Syrian President Bashar Assad protesters hold the Jabhat al-Nusra flag, as they shout slogans during a demonstration, at Kafranbel town, in Idlib province, northern Syria. Insurgent groups like Hezbollah and the Islamic State group in Syria have learned how to weaponize surveillance drones and use them against each other, adding a new twist to the country's civil war, a U.S. military official and others say. WASHINGTON (AP) -- Militant groups like Hezbollah and the Islamic State group have learned how to weaponize surveillance drones and use them against each other, adding a new twist to Syria's civil war, a U.S. military official and others say.

A formal branch of artificial intelligence, machine-learning builds systems that learn directly from the data they are fed and effectively program themselves to analyse that data and make accurate predictions. Having already helped multiple business sectors create new models and drive competitive advantage, now it's the turn of the insurance industry. So just how is machine-learning changing the way insurers do business? "It gives insurers three distinct advantages," explains Max Richter, managing director in Accenture's UK insurance analytics group. "The first is to mine greater volumes of data, the second to scale analytics across the organisation by working smarter and faster, and lastly by answering more complex questions from'will this customer leave me at renewal?' to'what can I do about it?'" As such it is quickly becoming an essential tool for the insurance sector, specifically enabling companies to yield higher predictive accuracy as it can fit more flexible and complex models.