AI's integration into military operations transforms how nations approach conflicts, impacting everything from intelligence gathering to decision-making processes on the battlefield.
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AI processes vast amounts of real-time data, providing military leaders with unparalleled situational awareness. This results in quicker, well-informed decisions during operations, a critical advantage on the battlefield. Autonomous Systems: AI-driven drones and vehicles are reshaping military operations.
The shape of warfare being impacted is not just in strategizing but goes beyond that in changing the battlefield rules. In [1] there is a discussion of dogfights that included an ML algorithm vs a human pilot. The machine learning algorithm developed a head-on strategy that requires such precision that he beat the pilot in the dogfight. The AI calculates this and is able to do it while the human cannot. This changes not just the strategizing but completely changes typical battlefield scenarios. With this example the scenario changes for the pilot (head on fight vs previous attacking by tailing the plane). The nation depending on humans will suffer higher casualty rates because they are engaging in a scenario they cannot win. Since this adversary does not have the capability to do ML training on a plane, the adversary might as well focus on how to poison the ML. Thus the battlefield landscape changes again.
The machine learning algorithm as normally programmed to follow an objective function will not deviate from its objective. Note that single objective learning is not a necessary condition and can take on other parameters, not just winning (as humans do). The problem arises as to what is the objective being given to the ML and if there were other alternatives to consider.