ZAMONAVIY RAQAMLI TRANSFORMATSIYADA GENERATIV SUN’IY INTELLEKT VA MA’LUMOTLAR BAZASI TIZIMLARINING O‘RNI
DOI:
https://doi.org/10.65164/fcjh7a10Keywords:
generativ sun'iy intellekt, katta til modellari, vektoral ma'lumotlar bazasi, RAG arxitekturasi, raqamli transformatsiya, ma'lumotlar bazasi tizimlar, LLM, GPT, transformer modeliAbstract
Ushbu tezis zamonaviy raqamli transformatsiya jarayonida generativ sun'iy intellekt (GSI) texnologiyalari va ma'lumotlar bazasi tizimlarining tutgan o'rni, o'zaro integratsiya imkoniyatlari hamda amaliy qo'llanilishini ilmiy jihatdan o'rganadi. Tadqiqot natijasida GSI ning katta til modellari (LLM), vektoral ma'lumotlar bazalari va an'anaviy relyatsion tizimlar bilan birgalikdagi sinergiyasi tahlil qilingan. Bundan tashqari, raqamli ekotizimni takomillashtirish uchun real vaqt rejimida intellektual ma'lumot qayta ishlash arxitekturasi taklif etilgan.
References
1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., &
Polosukhin, I. (2017). Attention is All You Need. Advances in Neural Information Processing
Systems, 30, 5998–6008. [Tezisda nazariy asos sifatida keltirildi]
2. OpenAI. (2023). GPT-4 Technical Report. arXiv preprint arXiv:2303.08774.
https://arxiv.org/abs/2303.08774
3. Mamatov, N. S., Yusupov, M. A., & Toshpulatov, N. O. (2022). O'zbek tilini qayta ishlash
uchun sun'iy intellekt usullari. Axborot texnologiyalari muammolari, 2(18), 45–53. Toshkent:
TATU.
4. Stonebraker, M., & Hellerstein, J. (2021). Readings in Database Systems (5th ed.). MIT Press.
5. Johnson, J., Douze, M., & Jégou, H. (2021). Billion-scale similarity search with GPUs. IEEE
Transactions on Big Data, 7(3), 535–547. https://doi.org/10.1109/TBDATA.2019.2921572
6. Ristić, B. (2023). Redis in Action: Real-time AI Applications and Caching Strategies. Manning
Publications. ISBN 978-1-63343-510-3.
7. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., ... & Kiela, D. (2020).
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. NeurIPS 2020, 33,
9459–9474. arXiv:2005.11401.
8. IBM Institute for Business Value. (2024). The CEO's guide to generative AI: Productivity and
the enterprise data layer. IBM Corporation. https://ibm.com/thought-leadership/institutebusiness-value
9. Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023). QLoRA: Efficient
Finetuning of Quantized LLMs. arXiv:2305.14314. https://arxiv.org/abs/2305.1431460
10. Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., ... & Kaplan, J. (2022).
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human
Feedback. arXiv:2204.05862.
11. McKinsey & Company. (2024). The state of AI in 2024: Generative AI's breakout year.
McKinsey Global Institute. https://mckinsey.com/capabilities/quantumblack/our-insights
12. Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., ... & Amodei, D.
(2020). Scaling Laws for Neural Language Models. arXiv:2001.08361.
13. Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., ... & Lample,
G. (2023). LLaMA: Open and Efficient Foundation Language Models. arXiv:2302.13971.
14. O'zbekiston Respublikasi Prezidentining 2022-yil 2-noyabrdagi PF-287-son Farmoni. "Sun'iy
intellektni rivojlantirish konsepsiyasini tasdiqlash to'g'risida". Toshkent: O'zbekiston
Respublikasi Qonun hujjatlari milliy bazasi.
