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TYURINGMACHINE AND ARTIFICIAL NEURAL NETWORKS

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SCIENCE AND INNOVATIVE DEVELOPMENT

SCIENCE AND INNOVATIVE DEVELOPMENT

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One of the main goals of artificial intelligence is to develop learning algorithms that can be implemented on computers using simulations of the human brain. In this paper, we review methods for solving Turing machine problems using experiment based artificial intelligence algorithms. The paper also presents critical concepts of the NTM method based on a comprehensive review of the research made in this domain. Experimental results of applying the NTM method to memory data and problem solving are being presented. The paper presents scientific discussions ongoing in this domain and solutions to future challenges.

AUTHORS

R.Yusupov

Abdulla Qodiriy nomidagi Jizzax davlat pedagogika universiteti

S.Ergashev

Mirzo Ulug‘bek nomidagi O‘zbekiston Milliy universiteti Jizzax filiali

Tags

# искусственный интеллект# simulation# Машинное обучение# Machine Learning# sun’iy intellekt# artificial intelligence# глубокое обучение# simulyatsiya# Tyuring mashinasi# takrorlanuvchi neyron tarmoq# chuqur o‘qitish# mashinali o‘qitish# kuchaytirilgan o‘qitish# orqaga tarqalish# симуляция# машина Тьюринга# рекуррентная нейронная сеть# усиленное обучение# обратное распространение# Tyuringmachine# Recurrent neural network# Deep learning# reinforced learning# backpropagation

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Article Identifiers

ROI:

https://eroi.uz/11.0094/A0124-0011

DOI:

Not Available

References

Ergashev, S. (2023). Simulyatorlarda oqitish imkoniyatlarining samaradorligini baholash [Evaluating the effectiveness of teaching opportunities in simulators].

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Zaremba, W., & Sutskever, I. (2015). Reinforcement learning neural Tyuringmachines.