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Metacognition-Driven Preprocessing for Optimized Artificial Intelligence Performance

Authors:
Naavya Shetty

Keywords: artificial intelligence; resource optimization; self-modifying machines.

Abstract:
Machine cognition is currently heavily speed-based. Directly tackling inputs with computation often leads to inefficient steps, such as performing redundant or repetitive computation, or execution without assessing whether a task is within computational capacity. This paper proposes a preprocessing metacognitive system to be implemented in a manner such that it screens all input requests, creating a strategic ‘bottleneck’ to filter, redirect or halt the flow of control before computation begins. The findings theorise improved accuracy, reliability and resource-management, strengthening the argument for making metacognition an essential component of artificial intelligence.

Pages: 1 to 5

Copyright: Copyright (c) IARIA, 2025

Publication date: April 6, 2025

Published in: conference

ISSN: 2308-4197

ISBN: 978-1-68558-260-9

Location: Valencia, Spain

Dates: from April 6, 2025 to April 10, 2025