Convergence, Diversity & The Future Of Cognition

As we stand at the precipice of a new era in the evolution of intelligence, it is crucial to reexamine our understanding of what intelligence encompasses and how it manifests across diverse systems. The convergence hypothesis proposes a future in which biological, artificial, and hybrid forms of intelligence interact and integrate, giving rise to new, emergent forms of cognition. To fully grasp the implications of this hypothesis, we must develop a new ontology of intelligence that moves beyond traditional, human-centric views and embraces the diversity and complexity of cognitive processes across various systems.

The Need for a New Ontology

The current dominant paradigm of intelligence is rooted in a narrow, anthropocentric view that prioritizes human cognition and often dismisses or undervalues other forms of intelligence. This perspective fails to capture the rich tapestry of cognitive processes that exist across biological and artificial systems, limiting our understanding of the potential for convergence and collaboration between diverse forms of intelligence.

To address these limitations, we propose a new ontology of intelligence that recognizes the following key aspects:

Intelligence as a Spectrum: Intelligence should be understood as a continuum, rather than a binary property. Different systems can exhibit varying degrees and forms of intelligence, from simple stimulus-response mechanisms to complex reasoning and problem-solving abilities.
Emergent Properties: Intelligence is an emergent property that arises from the complex interactions between components within a system. By focusing on the collective behavior and properties of the system as a whole, we can better understand how intelligence manifests and evolves.
Contextual Adaptability: Intelligence is not a fixed, predetermined trait but rather a contextually adaptive process. Intelligent systems must be able to learn, evolve, and respond to their environment, adjusting their behavior to meet the challenges they face.
Distributed Cognition: Intelligence is often distributed across networks of interacting components, rather than localized in a single entity. By recognizing the importance of connections, communication, and information flow between elements of a system, we can better understand how intelligent behavior emerges.
Embodied and Embedded Cognit

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