Researchers aboard Schmidt Ocean Institute's research vessel Falkor used autonomous underwater robots, along with the Institute's remotely operated vehicle (ROV) SuBastian, to acquire 1.3 million high resolution images of the seafloor at Hydrate Ridge, composing them into the largest known high resolution color 3D model of the seafloor. Using unsupervised clustering algorithms, they identified dynamic biological hotspots in the image data for more detailed surveys and sampling by a remotely operated vehicle. A recent expedition led by Dr. Blair Thornton, holding Associate Professorships at both the University of Southampton and the Institute of Industrial Science, the University of Tokyo, demonstrated how the use of autonomous robotics and artificial intelligence at sea can dramatically accelerate the exploration and study of hard to reach deep sea ecosystems, like intermittently active methane seeps. Thanks to rapid high throughput data analysis at sea, it was possible to identify biological hotspots at the Hydrate Ridge Region off the coast of Oregon, quickly enough to survey and sample them, within days following the Autonomous Underwater Vehicles (AUV) imaging survey. The team aboard research vessel Falkor used a form of Artificial Intelligence, unsupervised clustering, to analyze AUV-acquired seafloor images and identify target areas for more detailed photogrammetric AUV surveys and focused interactive hotspot sampling with ROV SuBastian.