BEYOND THE HUMAN LENS
Abstract
This work introduces a non-anthropocentric framework for artificial intelligence, challenging the traditional assumption that intelligence should be modeled, aligned, or evaluated through human-centered paradigms. Instead, it proposes a shift toward systems capable of interpreting, interacting with, and learning from non-human forms of cognition, communication, and perception across biological, ecological, and abstract domains.
Drawing on complexity theory, fuzzy logic, dynamical systems, and quantum-inspired computation, the framework redefines intelligence as context-dependent coherence within an environment rather than replication of human behavior. It explores alternative epistemologies grounded in multispecies cognition, decentralized communication systems, and non-human sensory modalities, while addressing the ethical and structural implications of expanding AI beyond anthropocentric objectives.
The work further develops mathematical and computational foundations for these systems, including category-theoretic structures, probabilistic reasoning under ambiguity, and adaptive models of ecological and financial dynamics. Through applications in agriculture, veterinary systems, ecological monitoring, and financial modeling, it demonstrates how non-anthropocentric AI can enable more resilient, adaptive, and ethically grounded decision systems.
Ultimately, the paper positions non-anthropocentric AI not as a niche extension of existing paradigms, but as a fundamental reorientation of artificial intelligence toward a broader, more inclusive understanding of cognition, intelligence, and systemic coexistence.
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