The Navy’s been trying for years to improve the slow and plodding process of underwater mine detection. Now, they’re using the military’s affinity for all-things AI to create a mine detection system that minimizes errors through "adaptive learning."
The Navy’s recent mine-hunting efforts have proved imperfect. In 2003, they credited the deployment of dolphins with clearing 100 underwater mines from the Persian Gulf. But the dolphins require lengthy training and a human handler at all times. And then there’s that constant harping from animal advocacy groups. In 2007, Lockheed Martin created a Remote Minehunting System, which got a test run on the Bainbridge destroyer. A 2008 report from the ship's skipper, Commander Stephen Coughlin, cited “growing pains” using the unwieldy ‘bot, which is lifted from the water using a giant hook, precision timing - and luck.
Now, the Navy is funding four projects that may not produce a sleeker system, but hope to produce one that learns from its mistakes. The idea is to program undersea 'bots with sonar tools that are linked to adaptive algorithms. As the 'bot accumulates new input from ocean-floor surveillance, the algorithms will improve accuracy according to correct and incorrect mine identification.
The automated feedback learning mechanisms proposed herein will provide a unique capability to adapt the feature extraction, selection and classification process that can lead to improved false alarm and target identification rates as the system is matured.
The 'bots would be programmed with preliminary data, but they'd continue to train themselves once deployed. And no need to stockpile fish as rewards for a job well done.
But despite the shortcomings of dolphin mine-seekers, the proposed AI alternative has a lot to live up to. In 2003, Navy spokesperson Tom LaPuzza said it was "doubtful anything man-made will ever match the dolphins’ capabilities."