This is normally the time when we start buying candy corn for trick or treaters. But this year is horrifying no matter who comes to the door. After years of investigations, hearings, and the rattling of legal sabers, we finally have a Techlash case: United States of America, et al. v. Google LLC. As I wrote earlier in the week, the government made a direct comparison to the Microsoft case two decades earlier, where it also invoked the trust-busting Sherman Act. In that litigation, the key issue was whether or not Microsoft leveraged its market power to jam its browser down the throats of users.
U.S. Soldiers, assigned to the 1st Squadron, 2d Cavalry Regiment, scan sectors of fire during the AH-64 Apache helicopter gunnery training in Grafenwoehr Training Area, Germany, July 15, 2020. The gunnery training concluded with the squadron's table VIII and IX live-fire certifications. LaShic Patterson) For future battlefield operations, speed is the name of the game. The side that can make decisions faster has the advantage.A new, AI-enabled effort by the U.S. Army can give operators the ability to detect, identify, process and engage targets quicker than ever and at longer ranges than before. The Tactical Intelligence Targeting Access Node, or TITAN, offers frontline forces, as well as headquarter commanders, a resilient tactical ground station capable of rapidly sifting through massive amounts of incoming sensor data to find and track potential threats.
With the addition of artificial intelligence and machine learning, the aim is to make every soldier, regardless of job specialty, capable of identifying and knocking down threatening drones. While much of that mission used to reside mostly in the air defense community, those attacks can strike any infantry squad or tank battalion. The goal is to reduce cognitive burden and operator stress when dealing with an array of aerial threats that now plague units of any size, in any theater. "Everyone is counter-UAS," said Col. Marc Pelini, division chief for capabilities and requirements at the Joint Counter-Unmanned Aircraft Systems Office, or JCO. Army units aren't ready to defeat aerial drones, the study shows.
Last month, an artificial intelligence agent defeated human F-16 pilots in a Defense Advanced Research Projects Agency challenge, reigniting discussions about lethal AI and whether it can be trusted. Allies, non-government organizations, and even the U.S. Defense Department have weighed in on whether AI systems can be trusted. But why is the U.S. military worried about trusting algorithms when it does not even trust its AI developers? Any organization's adoption of AI and machine learning requires three technical tools: usable digital data that machine learning algorithms learn from, computational capabilities to power the learning process, and the development environment that engineers use to code. However, the military's precious few uniformed data scientists, machine learning engineers, and data engineers who create AI-enabled applications are currently hamstrung by a lack of access to these tools.
The US Army is looking to build an autonomous charging system that can support hundreds of drones. It has funded a four-year research project with the ultimate aim of kitting out ground-based vehicles with charging stations that swarms of drones can fly to by themselves. The University of Illinois Chicago landed an $8 million contract from the Combat Capabilities Development Command's Army Research Laboratory. Researchers will work on a system that will enable small drones to determine the location of the closest charging station, travel there and juice up before returning to their mission. The university is working on algorithms to help the drones determine the best route to a charging port.
In 1997, The Simpsons prophesized that for future wars, "most of the actual fighting will be done by small robots" with soldiers only responsible to "build and maintain those robots." Though the cartoon's track record with predictions is debatable, few will argue that robots have played a critical role in combat over the past decade. Whether it is a Predator drone patrolling a No-Fly zone or a Packbot diffusing a bomb, robots have made their presence known on the battlefield. The U.S. military and coalition forces use the base, located in an undisclosed location, to launch airstrikes against ISIL in Iraq and Syria, as well as to distribute cargo and transport troops supporting Operation Inherent Resolve. The Predators at the base are operated and maintained by the 46th Expeditionary Reconnaissance Squadron, currently attached to the 386th Air Expeditionary Wing.
Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. The U.S. military recently conducted a live-fire full combat replication with unmanned-to-unmanned teaming guiding attacks, small reconnaissance drones, satellites sending target coordinates to ground artillery and high-speed, AI-enabled "networked" warfare. This exercise was a part of the Army's Project Convergence 2020, a weapons and platform combat experiment which, service leaders say, represents a massive transformation helping the service pivot its weapons use, tactics and maneuver strategies into a new era. Taking place at Yuma Proving Grounds, Arizona, Project Convergence involved live-fire war experiments aligned in three distinct phases, intended to help the Army cultivate its emerging modern Combined Arms Maneuver strategy.
After weeks of work in the oppressive Arizona desert heat, the U.S. Army carried out a series of live fire engagements Sept. 23 at Yuma Proving Ground to show how artificial intelligence systems can work together to automatically detect threats, deliver targeting data and recommend weapons responses at blazing speeds. Set in the year 2035, the engagements were the culmination of Project Convergence 2020, the first in a series of annual demonstrations utilizing next generation AI, network and software capabilities to show how the Army wants to fight in the future. The Army was able to use a chain of artificial intelligence, software platforms and autonomous systems to take sensor data from all domains, transform it into targeting information, and select the best weapon system to respond to any given threat in just seconds. Army officials claimed that these AI and autonomous capabilities have shorted the sensor to shooter timeline -- the time it takes from when sensor data is collected to when a weapon system is ordered to engaged -- from 20 minutes to 20 seconds, depending on the quality of the network and the number of hops between where it's collected and its destination. "We use artificial intelligence and machine learning in several ways out here," Brigadier General Ross Coffman, director of the Army Futures Command's Next Generation Combat Vehicle Cross-Functional Team, told visiting media.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Imagine this land-war scenario: An enemy fighter is several hundred yards away, another is attacking from one mile while a third fires from a nearby room in a close-quarters urban warfare circumstance, when U.S. Army soldiers apprehend, integrate, and quickly map the locations of multiple targets at once in 3D, all while knowing the range and distance of the enemy forces. How could something like this be possible, one might wonder, given the nuances in perspective, range, navigational circumstances and the limitations of a human eye? These complexities form the conceptual basis upon which the Army is fast-tracking its Integrated Visual Augmentation System, or IVAS, which is a soldier-worn combat goggle engineered with advanced sensors that are able to overcome some of the limitations of human vision and quickly organize target data.
An insect-like machine with six individually powered legs that was intended for the battlefield received millions in US government funding during the 1980s. The project to create a fleet of real-life AT-AT walkers from Star Wars started in 1981 at Ohio State University, as the military searched for ways to traverse rough terrain that wheeled vehicles couldn't manage. Called the Adaptive Suspension Vehicle (ASV), the bizarre vehicle was part of a decade-long project which was eventually scrapped after receiving a reported $1million a year from Darpa between 1981 and 1990. The fate of the ASV is a mystery, with nobody knowing whether it is in storage somewhere or was scrapped decades ago. Professors Robert McGhee and Kenneth Waldron at Ohio State University wrote a scientific paper explaining their project in 1986.