شناسایی و تحلیل پیشرانهای کلیدی توسعه هوش مصنوعی

چکیده مقاله :

این مقاله به شناسایی عوامل کلیدی موثر بر توسعه هوش مصنوعی و تحلیل اثرات متقابل میان آنها می‌پردازد. 19 عامل بر اساس منابع مطالعاتی و نظرات خبرگان شناسایی شد. سپس روابط میان این عوامل بر اساس روش تحلیل اثرات متقابل (CIS) و در نرم افزار میک‌مک مورد تحلیل قرار گرفت. نتایج نشان داد که تنش‌های ژئوپلیتیک و مقررات گذاری افراطی، دو عامل کلیدی هستند که تحت تاثیر سایر عوامل نیستند اما به شدت بر سیستم تاثیر گذارند. علاوه بر این، 8 عامل اعتماد به هوش مصنوعی، شدت الگوریتم، رقابت در سخت‌افزار، کمبود انرژی، کمبود داده، کمبود سرمایه، کمبود نیروی متخصص و ماهر، کمبود تراشه و افزایش قیمت آن متغیرهای استراتژیک و کلیدی سیستم تشخیص داده شدند که علاوه بر آنکه به میزان زیادی بر سایر متغیرها تاثیر گذارند، از متغیرهای دیگر نیز تاثیر می‌پذیرند. عوامل زیست‌محیطی تاثیر چندانی بر سایر عوامل شناسایی شده در این تحقیق نداشتند و تاثیرپذیری کمی نیز از سایر عوامل داشتند و به عنوان متغیرهای مستقل شناسایی شدند که بر سیستم بی تاثیرهستند.

چکیده انگلیسی :

This article identifies key factors affecting the development of artificial intelligence and analyzes the interactions between them. 19 factors were identified based on study sources and expert opinions. Then, the relationships between these factors were analyzed based on Cross Impact Analysis method (CIS) and in the MICMAC software. The results showed that geopolitical tensions and over regulation are two key factors that are not affected by other factors but strongly affect other factors and the system. In addition, 8 factors of trust in artificial intelligence, algorithm intensity, hardware competition, energy shortage, data shortage, capital shortage, shortage of talent and skilled labor, chip shortage and price increase were identified as strategic and key variables of the system, which  while affecting other variables, are affected by other variables. Environmental factors did not have much effect on other factors identified in this study and also had little influence from other factors and were identified as autonomous variables that do not affect the system.

منابع و مأخذ:

D. Acemoglu, “The Simple Macroeconomics of AI”, NBER, 2024, WP.32487

https://www.unep.org/news-and-stories/story/ai-has-environmental-problem-heres-what-world-can-do-about K. M. Pratt, "7 Key Benefits of AI for Business", 2022, [Online]. Available: https://www.techtarget.com/searchenterpriseai/feature/6-key-benefits-of-AI-for-business
Y. Qin, Z. Xu, X. Wang and M. Skare, "Artificial intelligence and economic development: An evolutionary investigation and systematic review". Journal of the Knowledge Economy, 2022, 15(1), 1736-70
A. Widuto, “AI and the energy sector”, European Parliamentary Research Service, Tech. Rep, Jul. 2025
International Energy Agency (IEA), "Energy and AI", special report, 2025: https://www.iea.org/reports/energy-and-ai
McKinsey & Company, Technology Trends Outlook 2025
Ph. Aghion, 2025: https://www.euronews.com/my-europe/2025/12/03/eu-needs-true-single-market-to-stay-competitive-nobel-prize-winner-aghion-tells-euronews
M. Comunale and A. A. Manera , "The Economic Impacts and the Regulation of AI: A Review of the Academic Literature and Policy Actions," IMF Working Papers 2024/065, International Monetary Fund, 2024
C. Alonso C. A. Berg, S. Siddharth Kothari, CH. Papageorgiou, S. Rehman, "Will the AI revolution cause a great divergence?", Journal of Monetary Economics, 2022, Volume 127
A. Nagamatsu, Y. Nagamatsu, CH. Watanabe, "The new paradigm of informal economies under GAI driven innovation", Telecom 2025, 6:39
M. Emami, A.A Haghdoost, V. Yazdi‑Feyzabadi and M. H. Mehrolhassani, "Drivers, uncertainties, and future scenarios of the Iranian health system", BMC Health Services Research, 2022, 22:1402
A. Ahmadzadeh Ghasab, M. Heydari and M. Arab, "Foresight for Internationalization of Medical Sciences Universities in Iran in the 2040 horizon", European Journal of Futures Research, 2025, 13:2
S. Ch. Pandey, N. Virmani, Dimple Bhandari and Sandeep Jagtap, "A sustainability-oriented KPI framework for digital twin adoption in the sugar industry: ISM–MICMAC approach", Journal of Agribusiness in Developing and Emerging Economies, 2025, e-ISSN: 2044-0847
M. Soares, A. do Paço, A. Braga and A. Arantes, "Modelling the Barriers to Reverse Logistics for Sustainable Supply Chains: A Combined ISM and MICMAC Analysis Approach", Sustainability, 2025, 17:9375
A. Agrawal, JS. Gans, A. Goldfarb A, "Exploring the impact of artificial intelligence: Prediction versus judgment",Information Economics and Policy". 2019 Jun 1;47:1-6.
S. Pramono, M. Ulkhaq and Z. Aulia, "Analysing the barriers of reverse logistics implementation: A case study", 2022, IOP Conf. Ser.: Mater. Sci. Eng. 1072 012063
McKinsey & Company, Technology Trends Outlook 2024