Wetware network-based AI: a chemical approach to embodied cognition for robotics and artificial intelligence.
Frontiers in Robotics and AI January 1, 2025 Luisa Damiano, Antonio Fleres, Andrea Roli et al.
Wetware Network-Based Artificial Intelligence (WNAI) proposes building autonomous cognitive agents from synthetic chemical networks, shifting wetware neuromorphic engineering from disembodied computation and biological mimicry to reticular chemical self-organization. The framework integrates network cybernetics, autopoietic theory, and enaction to treat cognition as a materially grounded, emergent phenomenon. WNAI complements embodied AI and xenobiotics by expanding artificial embodied cognition into fully synthetic domains and exchanges with neural network architectures to advance cross-substrate principles. It offers a roadmap for chemical neural networks and protocellular agents, aiming for robotic systems requiring minimal, adaptive, substrate-sensitive intelligence, thus expanding artificial cognition beyond silicon and biohybrid systems.