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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