Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI
Sergei Chuprov, Richard D. Lange, Leon Reznik, Paulo Shakarian, Raman Zatsarenko, Dmitrii Korobeinikov

TL;DR
This paper advocates for integrating metacognitive strategies into AI systems to enhance their accuracy, security, and efficiency, supported by a federated learning case study and a new software framework.
Contribution
It introduces the concept of metacognitive AI, discusses design challenges, and provides a practical framework and case study demonstrating improved AI performance.
Findings
Enhanced learning efficiency in federated learning case study
Improved AI security and effectiveness through metacognitive strategies
Development of a software framework for metacognitive AI applications
Abstract
This position paper argues for metacognition as a general design principle for creating more accurate, secure, and efficient AI. The metacognitive solution involves systems monitoring their own states and judiciously allocating resources depending on each problem instance's difficulty or cost of mistakes. Drawing inspiration both from past work on resource-rational AI and from well-documented metacognitive strategies in psychology and cognitive science, we identify specific challenges in embedding these strategies into AI design and highlight open theoretical and implementation problems. We showcase these principles through a tangible example of improved learning efficiency, effectiveness, and security in a Federated Learning (FL) case study. We show how these principles can be translated into practice with a novel software framework developed specifically to allow the community to…
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