SUN'IY INTELLEKTNING ZAMONAVIY AXBOROT TIZIMLARIGA INTEGRATSIYASI TAMOYILLARI VA AMALIYOTI
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Abstract
Maqolada sun’iy intellekt (SI) texnologiyalarini zamonaviy axborot tizimlariga (AT) integratsiya qilishning tushunchaviy asoslari, arxitektura tamoyillari va amaliy jihatlari tahlil qilingan. Integratsiyaning to‘rt modeli — tayyor modul, API-xizmat, o‘z modelini MLOps asosida ishlab chiqish va generativ modellarga tayanadigan RAG-arxitektura — qiyosiy o‘rganilib, yetti tamoyildan iborat tizim asoslab berilgan. McKinsey va Stanford HAI hisobotlari asosida 2017– 2025-yillarda tashkilotlarda SI dan foydalanish dinamikasi ko‘rib chiqilgan, xalqaro (ES SI qonuni, NIST AI RMF, ISO/IEC 42001) va milliy (PQ-358-son qarori) me’yoriy asoslar solishtirilgan. Adabiyotlar tahlili integratsiya muvaffaqiyati modelning algoritmik sifatidan ko‘ra ma’lumotlar sifati, hayot siklini boshqarish va inson nazorati kabi tashkiliy-texnik omillarga ko‘proq bog‘liqligini ko‘rsatib berdi.
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Maslej N., Fattorini L., Perrault R. va boshq. Artificial Intelligence Index Report 2025. — Stanford, CA: Stanford University, Institute for Human-Centered AI (HAI), 2025. — URL: https://hai.stanford.edu/ai-index/2025-ai-index-report (murojaat: 19.09.2026).
McKinsey & Company. The state of AI in 2025: Agents, innovation, and transformation. — 2025, 5-noyabr. — URL: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai (murojaat: 19.09.2026).
Sculley D., Holt G., Golovin D. et al. Hidden Technical Debt in Machine Learning Systems // Advances in Neural Information Processing Systems 28. — 2015. — P. 2503–2511.
Paleyes A., Urma R.-G., Lawrence N. D. Challenges in Deploying Machine Learning: A Survey of Case Studies // ACM Computing Surveys. — 2022. — Vol. 55, No. 6. — Art. 114.
Russell S., Norvig P. Artificial Intelligence: A Modern Approach. 4th ed. — Hoboken: Pearson, 2021.
Vaswani A., Shazeer N., Parmar N. et al. Attention Is All You Need // Advances in Neural Information Processing Systems 30. — 2017. — P. 5998–6008.
Davenport T. H., Ronanki R. Artificial Intelligence for the Real World // Harvard Business Review. — 2018. — Vol. 96, No. 1. — P. 108–116.
Lewis P., Perez E., Piktus A. et al. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks // Advances in Neural Information Processing Systems 33. — 2020. — P. 9459–9474.
Kreuzberger D., Kühl N., Hirschl S. Machine Learning Operations (MLOps): Overview, Definition, and Architecture // IEEE Access. — 2023. — Vol. 11. — P. 31866–31879.
Amershi S., Begel A., Bird C. et al. Software Engineering for Machine Learning: A Case Study // Proc. 41st ICSE: Software Engineering in Practice (ICSESEIP). — IEEE, 2019. — P. 291–300.