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The study, published in Nature Machine Intelligence , proposes a groundbreaking hybrid methodology aimed at refining how AI-based machinery senses, interacts, and reacts to its environment in real-time—critical for autonomous vehicles and precision-action robots.
Despite achieving remarkable results in areas like computer vision and natural language processing , current AI systems are constrained by the quality and quantity of training data, predefined algorithms, and specific optimization objectives.
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