The US Military has long been interested in using the deep machine learning expertise of many tech companies to assist with military deployments of artificial intelligence (AI).

Google has recently stirred some controversy by working with the US military on Project Maven, which is using AI to analyze drone footage. A number of employees have taken a stand, commiting publicly to not assisting with warfare technology.

Now, the US Army Research Laboratory has revealed it is not using AI to produce a new weapon, but as a means of getting human soldiers to learn quicker, making them more lethal on the battlefield. The implementation could help soldiers decipher hints of information and respond faster, such as recognizing threats from a vehicle-borne improvised explosive device, or potential danger zones from aerial warzone images.

Through utilizing cheap and lightweight hardware, the research team was able to implement collaborative filtering, a machine learning technique capable of achieving a 13.3-times speed-up of training compared to a state-of-the-art optimized multicore system and 12.7-times speed-up for optimized GPU systems.

Its lead developer, Dr Rajgopal Kannan, stated that the technique could eventually become part of a suite of tools on board one of the US military’s next-generation combat vehicles. Their research is part of the military’s recent focus on artificial intelligence and machine learning, with the aim of staying one step ahead of its global rivals.

 

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