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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.
Symbolic AI , a classical approach using logic and symbols for knowledge representation and manipulation, excels in abstract and structured problems like mathematics and chess but needs help scaling and integrating sensory and motor data.
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