Tag: ai

The Air Force Just Tested “Robot Dogs” For Use In Base Security

They look like they were cast straight from an episode of Black Mirror, and eventually, their mission could be similar in some ways, but for now, robot dogs are stretching their legs in the big test exercise environment for the United States Air Force. 

Last week, the U.S. Air Force hosted the second demonstration of its new Advanced Battle Management System (ABMS), a digital battle network system designed to collect, process, and share data among U.S. and allied forces in real-time. The ABMS has already undergone several tests, including a live-fire exercise earlier this year conducted with data and communications provided, in part, by SpaceX Starlink satellites.

The highlight of last week’s demonstration was the use of multiple distributed sensors to detect and shoot down mock Russian cruise missiles. The system involves 5G and 4G networks, cloud computing systems, and AI systems to provide an unprecedented level of situational awareness and course of action decision making. ABMS is a top modernization priority for the Department of the Air Force, which is dedicated $3.3 billion over five years to develop and deploy the architecture and related systems. Senior Air Force leaders cite the system as one of the most pressing capabilities for success in several key theaters of operations.

This latest ABMS demonstration was described as being one of the largest joint experiments in recent history, involving 65 government teams from every service including the Coast Guard, 35 separate military platforms, and 70 different industry partners. The exercise spanned 30 different geographic locations and four national test ranges.

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US Military Robots on Fast Track to Leadership Role

With Covid-19 incapacitating startling numbers of U.S. service members and modern weapons proving increasingly lethal, the American military is relying ever more frequently on intelligent robots to conduct hazardous combat operations. Such devices, known in the military as “autonomous weapons systems,” include robotic sentries, battlefield-surveillance drones and autonomous submarines.

So far, in other words, robotic devices are merely replacing standard weaponry on conventional battlefields. Now, however, in a giant leap of faith, the Pentagon is seeking to take this process to an entirely new level — by replacing not just ordinary soldiers and their weapons, but potentially admirals and generals with robotic systems.

Admittedly, those systems are still in the development stage, but the Pentagon is now rushing their future deployment as a matter of national urgency. Every component of a modern general staff — including battle planning, intelligence-gathering, logistics, communications, and decision-making — is, according to the Pentagon’s latest plans, to be turned over to complex arrangements of sensors, computers, and software.

All these will then be integrated into a “system of systems,” now dubbed the Joint All-Domain Command-and-Control, or JADC2 (since acronyms remain the essence of military life). Eventually, that amalgam of systems may indeed assume most of the functions currently performed by American generals and their senior staff officers.

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MACHINES CAN LEARN UNSUPERVISED ‘AT SPEED OF LIGHT’ AFTER AI BREAKTHROUGH, SCIENTISTS SAY

Researchers have achieved a breakthrough in the development of artificial intelligence by using light instead of electricity to perform computations.

The new approach significantly improves both the speed and efficiency of machine learning neural networks – a form of AI that aims to replicate the functions performed by a human brain in order to teach itself a task without supervision.

Current processors used for machine learning are limited in performing complex operations by the power required to process the data. The more intelligent the task, the more complex the data, and therefore the greater the power demands.

Such networks are also limited by the slow transmission of electronic data between the processor and the memory.

Researchers from George Washington University in the US discovered that using photons within neural network (tensor) processing units (TPUs) could overcome these limitations and create more powerful and power-efficient AI.

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