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This is a Quantitative Study study.

October 12, 2025Open Access

Post-Turing Architectures: Mindful Machines and the Energy Efficiency of Knowing

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Mindful Machines achieve functional scalability while significantly lowering energy budgets through structured interaction.
A single train of a foundational model like GPT-3 consumes over 1,200 MWh, indicating high energy costs of current AI paradigms.
The proposed architecture employs physical embodiment and self-regulation, facilitating adaptive knowledge reuse and goal-driven learning.
This approach suggests a paradigm shift toward sustainable AGI based on dynamic constraint satisfaction and semantic grounding.

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This paper proposes a knowledge-centric AI model, enhancing energy efficiency and semantic depth in intelligent architectures.

Rao Mikkilineni (2025) studied this question.

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