Fox News contributor Joey Jones reacts to testimony from Pentagon officials on the Afghanistan withdrawal and slams the New York Times' proposed redesigns of the American flag. An independent Pentagon review has concluded that the U.S. drone strike that killed innocent Kabul civilians and children in the final days of the Afghanistan war was not caused by misconduct or negligence, and it doesn't recommend any disciplinary action. The review, done by Air Force Lt. Gen. Sami Said, found there were breakdowns in communication and in the process of identifying and confirming the target of the bombing. Said concluded that the mistaken strike happened despite prudent measures to prevent civilian deaths. "I found that given the information they had and the analysis that they did -- I understand they reached the wrong conclusion, but ... was it reasonable to conclude what they concluded based on what they had? It just turned out to be incorrect," Said said.

A United States drone attack that killed 10 civilians, including seven children, in the Afghan capital Kabul in August did not violate the laws of war, an internal Pentagon review has concluded. Speaking to reporters at the Pentagon on Wednesday, US Air Force Lieutenant General Sami Said said "execution errors", including "confirmation bias" and "communication breakdowns", contributed to the deadly attack. But Said, who acts as inspector general of the US Air Force, said it was not a violation of the law of war or a result of negligence. "It was an honest mistake," Said said. The drone strike on August 29 came amid the US's chaotic military withdrawal from Kabul, and Said also stressed that it took place as American forces were contending with threats from the Islamic State in Khorasan Province, ISKP (ISIS-K), an affiliate of ISIS (ISIL).

Amazon's squadron of delivery drones was supposed to be in full flight by now. And the fall of 2021 would have been an opportune time to have little automated flying machines delivering packages to customers--what with all the trouble human workers are causing around the country with strikes and labor shortages. Amazon announced an experimental drone delivery service with great fanfare as part of a 60 Minutes feature in 2013. Amazon's promise was quite remarkable: Your packages--containing anything from toothpaste to a new smartphone--would arrive right at your doorstep (or on your lawn) by way of a drone that lands, drops your parcel and flies away. Jeff Bezos, Amazon's then-CEO, said in the televised segment that it would likely take "four to five years" to turn the "R&D project" into a reality.

In rural areas, basic health care can be out of reach. Keller Rinaudo founded Zipline, a delivery company that uses drones to deliver necessary medical supplies within hours, even minutes. Keller Rinaudo is the CEO and co-founder of Zipline, a drone delivery company that delivers life-saving medicine to remote places. The company began by focusing on delivering blood for urgent medical situations. Previously, Rinaudo was also the CEO and a co-founder of Romotive, a former company established in 2011 that made inexpensive small robots that use mobile phones as their computing system, machine vision system, and wireless communication system.

The US Army is planning to demonstrate a 300-kilowatt laser weapon, its most powerful ever, next year. General Atomics Electromagnetic Systems (GA-EMS) and Boeing are building the device, which is the size of a shipping container and mounted on a heavy truck. "The high power, compact laser weapon… will produce a lethal output greater than anything fielded to date," Scott Forney, president of GA-EMS, said in a statement. The US Navy deployed the first high-energy laser weapon, known as LaWS, on the USS Ponce in 2014, with a reported 30 kilowatt output. Most military lasers tend to be in the 30 to 100 kilowatt range, which is mainly useful for shooting down small drones, so the new weapon is a significant increase.

The US Army is planning to demonstrate a 300-kilowatt laser weapon, the most powerful ever built, next year. General Atomics Electromagnetic Systems (GA-EMS) and Boeing are building the device, which is the size of a shipping container and mounted on a heavy truck. "The high power, compact laser weapon… will produce a lethal output greater than anything fielded to date," Scott Forney, president of GA-EMS, said in a statement. The US Navy deployed the first high-energy laser weapon, known as LaWS, on the USS Ponce in 2014, with a reported 30 kilowatt output. Most military lasers tend to be in the 30 to 100 kilowatt range, which is mainly useful for shooting down small drones, so the new weapon is a significant increase.

Grene Robotics, an innovation-led technology company that delivers Robots as a Service (RaaS), has been selected from India to compete with global companies/organisations and teams in the final round of the XPRIZE Rainforest Competition. XPRIZE is a global leader in designing and operating incentive competitions to solve humanity's challenges and started the Rainforest Competition to improve the understanding of the rainforest ecosystem. The five-year XPRIZE Rainforest Competition is a call-to-action initiative to help save rainforests through the development of transformative, scalable, and affordable technology to autonomously survey and monitor biodiversity in near real-time leading to insights that communicate the health, well-being, and value of standing tropical rainforests while ensuring that competing teams co-design and co-create solutions with indigenous people and local communities as key stakeholders. The idea is also to create a business model that can be a beacon in bringing in the technology in conservation. The competing teams will leverage the existing and emerging technologies such as robotics, drone (SWARM and TETHERED), nano drones with sensors (auditory, ultraviolet thermal, air samples), drones designed for sample collection (barks, water, soil, litter, leaves, fecal matter, moths, insects, etc), remote sensing, data analysis, artificial intelligence, DNA sample collection, Genome sequencing, and machine learning to develop new rainforest biodiversity survey tools that will deliver information more quickly, affordably and in unprecedented detail without physical human intrusion into the rainforest.

Many emerging applications involve a robot operating autonomously in an unknown environment; the environment may include hazards, i.e., locations that might disrupt the robot operation, possibly causing it to crash, get stuck, and more generally to fail its mission. Robots are usually capable to perceive hazards that are expected during system development and therefore can be explicitly accounted for when designing the perception subsystem. For example, ground robots can typically perceive and avoid obstacles or uneven ground. In this paper, we study how to provide robots with a different capability: detecting unexpected hazards, potentially very rare, that were not explicitly considered during system design. Because we don't have any model of how these hazards appear, we consider anything that is novel or unusual as a potential hazard to be avoided.

Future Internet involves several emerging technologies such as 5G and beyond 5G networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of Things (IoTs). Moreover, future Internet becomes heterogeneous and decentralized with a large number of involved network entities. Each entity may need to make its local decision to improve the network performance under dynamic and uncertain network environments. Standard learning algorithms such as single-agent Reinforcement Learning (RL) or Deep Reinforcement Learning (DRL) have been recently used to enable each network entity as an agent to learn an optimal decision-making policy adaptively through interacting with the unknown environments. However, such an algorithm fails to model the cooperations or competitions among network entities, and simply treats other entities as a part of the environment that may result in the non-stationarity issue. Multi-agent Reinforcement Learning (MARL) allows each network entity to learn its optimal policy by observing not only the environments, but also other entities' policies. As a result, MARL can significantly improve the learning efficiency of the network entities, and it has been recently used to solve various issues in the emerging networks. In this paper, we thus review the applications of MARL in the emerging networks. In particular, we provide a tutorial of MARL and a comprehensive survey of applications of MARL in next generation Internet. In particular, we first introduce single-agent RL and MARL. Then, we review a number of applications of MARL to solve emerging issues in future Internet. The issues consist of network access, transmit power control, computation offloading, content caching, packet routing, trajectory design for UAV-aided networks, and network security issues.

U.S. officials say they believe Iran was behind the drone attack last week at the military outpost in southern Syria where American troops are based. Officials said Monday the U.S. believes that Iran resourced and encouraged the attack, but that the drones were not launched from Iran. They were Iranian drones, and Iran appears to have facilitated their use, officials said, speaking on condition of anonymity to discuss details that have not been made public. Officials said they believe the attacks involved as many as five drones laden with explosive charges, and that they hit both the U.S. side of al-Tanf garrison and the side where Syrian opposition forces stay. There were no reported injuries or deaths as a result of the attack.