Things Get Strange When AI Starts Training Itself and its How it Should be Regulated

Authors

  • Mardonov Amirzhon Sherzod ugli Tashkent State University of Law, Lecturer of Cyber Law Department

Keywords:

self-training ai, recursive self-improvement, data utilization, pretext tasks, fine-tuning, model autophagy disorder (MAD), ethical ai, ai regulation and data governance

Abstract

The article explores the evolving role of artificial intelligence (AI) as it transitions into self-training systems capable of recursive self-improvement. It begins by showcasing how AI enhances industries like healthcare, finance, and entertainment, demonstrating its integration into modern life. The narrative then shifts to self-training AI, explaining its ability to learn autonomously, optimize performance, and adapt without direct human input. Key concepts like data utilization, pretext tasks, feature learning, and fine-tuning are discussed, illustrating how these mechanisms empower AI to handle complex tasks with minimal manual intervention.

References

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Published

2025-03-28

How to Cite

Sherzod ugli, M. A. (2025). Things Get Strange When AI Starts Training Itself and its How it Should be Regulated. American Journal of Education and Evaluation Studies, 2(3), 285–293. Retrieved from https://semantjournals.org/index.php/AJEES/article/view/1360

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