Toward aitiopoietic cognition: bridging the evolutionary divide between biological and machine-learned causal systems

Area of Science:

• Cognitive Science
• Artificial Intelligence
• Systems Biology

Background:

• Autopoietic systems (biological organisms) and machine learning systems (MLSs) exhibit functional similarities in behavior and cognition.
• Crucial differences exist in how causality is operationalized, embodied, and grounded in these systems.
• Biological causality is linked to self-maintenance and survival imperatives, unlike MLSs' externally imposed objectives.

Purpose of the Study:

• To compare causal reasoning in autopoietic systems and MLSs.
• To introduce 'aitiopoietic cognition' as a framework for understanding causality.
• To propose a research program for developing artificial systems with intrinsic goals and grounded causal understanding.

Main Methods:

• Comparative analysis of autopoietic and machine learning systems.
• Conceptual framework development ('aitiopoietic cognition').
• Exploration of convergence pathways: evolutionary algorithms, material intelligence, homeostatic regulation, multi-scale integration.

Main Results:

• Autopoietic systems operationalize causality through intrinsic self-maintenance, while MLSs use statistical optimization with external objectives.
• MLSs lack the material self-reorganization crucial for biological causal advancement.
• Aitiopoietic cognition framework links causal understanding to a system's self-constituting processes.

Conclusions:

• Bridging the evolutionary divide between biological and artificial causal understanding is proposed.
• Development of artificial systems with genuine intrinsic goals and materially grounded causal understanding is a potential outcome.
• This research could transform artificial intelligence and deepen the comprehension of biological cognition.