Wetware network-based AI: a chemical approach to embodied cognition for robotics and artificial intelligence

Area of Science:

• Robotics and Artificial Intelligence
• Synthetic Biology
• Computational Neuroscience

Background:

• Current AI research often focuses on silicon-based computation or biological mimicry.
• Wetware Neuromorphic Engineering explores synthetic chemical networks for cognitive functions.
• Existing approaches in embodied AI and xenobotics can be expanded by synthetic domains.

Purpose of the Study:

• Introduce Wetware Network-Based Artificial Intelligence (WNAI) as a novel approach to robotic cognition.
• Frame cognition as a materially grounded, emergent phenomenon using network cybernetics, autopoietic theory, and enaction.
• Propose a programmatic framework for wetware neuromorphic engineering in AI and robotics.

Main Methods:

• Integrating theoretical insights from network cybernetics, autopoietic theory, and enaction.
• Defining WNAI's heuristic role as complementary to embodied AI, xenobotics, and neural network architectures.
• Outlining a technological roadmap for implementing chemical neural networks and protocellular agents.

Main Results:

• WNAI shifts focus from disembodied computation to reticular chemical self-organization for cognition.
• It expands the design space for artificial embodied cognition into fully synthetic domains.
• WNAI facilitates cross-substrate principles of network-based cognition through exchange with neural networks.

Conclusions:

• WNAI offers a new paradigm for artificial cognition beyond silicon and biohybrid systems.
• Potential applications include robotic systems needing minimal, adaptive, and substrate-sensitive intelligence.
• Wetware neuromorphic engineering provides a framework to expand the capabilities of AI and robotics.