Fusion Intelligence: A Paradigm for Merging Natural and Artificial Intelligence

Abstract

This review paper presents fusion intelligence (FI), a bio-inspired paradigm that synergistically integrates the intrinsic sensory, cognitive, and locomotive capabilities of intelligent biological organisms with the computational scale, trend analysis, and supervisory control of artificial intelligence (AI)-driven systems. Conventional edge AI nodes deployed across Internet of Things (IoT) networks face severe operational bottlenecks due to high energy consumption, computational constraints, and network latency. Biological entities, in contrast, execute complex sensing, spatial mapping, and physical actuation within milliwatt metabolic power budgets. FI establishes a non-invasive, closed-loop cyber physical framework where edge sensors observe natural intelligence (NI), machine learning models interpret behavioral telemetry, and automated actuators deploy environmental incentives to guide biological entities toward target tasks. We review the taxonomy of bio integrated systems, present the theoretical closed-loop control dynamics, and analyze an applied agricultural case study demonstrating a approximately 50% net improvement in pollination efficacy.