درخت حوزه‌های تخصصی

Background: Throughout history, humans have consistently developed groundbreaking technologies, from fire and the wheel to modern computing, showcasing their ability to innovate and control their creations. The rise of artificial intelligence has sparked renewed concerns about whether machines might eventually surpass human intelligence and autonomy.Aims: This study aims to critically assess AI’s role in human society, particularly addressing concerns that it may surpass human control and agency. It seeks to demonstrate that while AI is a powerful tool, it lacks autonomy, self-augmentation, and intentionality, making it unlikely to replace human decision-making. Additionally, the paper examines historical technological advancements, showing how humans have always adapted and controlled new innovations.Methodology: Employing a historical-comparative methodology, this study traces the evolution of computing technologies from early tally systems to quantum computing. It incorporates philosophical analysis through the works of Hubert Dreyfus, John Searle, and Michael Tomasello, assessing AI’s limitations in replicating human cognition.Findings: The study finds that despite AI’s rapid advancements, it remains fundamentally dependent on human input, lacks true understanding, and is incapable of independent self-enhancement. The historical trajectory of technological progress demonstrates that while new technologies can disrupt societies, humans have consistently adapted and maintained control. Philosophical critiques of AI further reinforce the argument that intelligence is not solely computational but deeply rooted in embodiment, intuition, and shared intentionality—qualities that AI lacks.Conclusions: Concerns about AI overwhelming human agency are largely misplaced. Just as humanity has managed previous technological revolutions—including writing, mechanization, and computing—AI will be integrated and regulated according to human needs and ethical considerations. While vigilance is necessary, the myth of AI autonomy is exaggerated.

Background: The visual turn is one of the most important features of contemporary culture. Therefore, in climate change communication, images can play a crucial role in conveying environmental messages. Environmental advocates use the potential of visuals to deliver part of their messages to their audience. However, only a limited number of studies have focused on the visualization of climate change communication. Given the characteristics of social media, free from the constraints of mainstream media, they can serve as a suitable platform for visualizing climate change messages.Aims: Despite Iran's role in greenhouse gas emissions and the engagement of environmental activists, no research has been conducted on the role of images in conveying climate change messages on Iranian social media. Therefore, the main question of this research is: How do Iranian users on social media platform X frame climate change through images, and within what discourses?Methodology: To answer the question of the research, a combination of quantitative and qualitative visual discourse analysis was applied to 500 images in 2023.Findings: Studies on the visualization of climate change messages have predominantly focused on developed countries. The findings reveal that Iranian users have addressed climate change through four key discourses: scientific, political, ecological, and protest. Furthermore, the results show that visual discourses have been seen globally in Persian X and it has addressed the present tense and the framework of the consequences.Conclusion: In general, the public's attention to climate change in Iranian society reflects the dynamism of the environmental movement in Iran and the growth of environmental literacy among Iranian social media users.

Iran has undergone unique demographic changes in the recent decades. This paper aims to project the natural population growth rate -NPG over the next decade (2024–2034), which would offer a comprehensive perspective into the future of Iran's population dynamics. In this regard, to accomplish the above task, this work deals with the projection of most important demographic measures that characterize the population process, namely the Crude Birth Rate -CBR, the Crude Death Rate -CDR, and the Population Doubling Time- PDT. To this end, a deep neural network modeling approach was developed and applied. Forecasting with deep neural networks is one of the most important and influential techniques used in machine learning and artificial intelligence. The data-driven model, based on data obtained from the Statistical Center of Iran, was subsequently implemented for model development in MATLAB.Results from the paper indicate that the CBR drops from 11.3 per thousand in 2024 to 9.3 per thousand in 2034. On the other hand, the CDR increases from 5.2 per thousand in 2025 to 6.1 per thousand in 2034. With this effect, the NPG decreases from 6.1 per thousand in 2025 to 3.2 per thousand in 2034. Lastly, PDT for the population is forecasted to rise from 114 years in 2025 to 218 years in 2034.This study presents a deep neural network model for describing and forecasting the complex dynamics of population changes in Iran. Constructing this model helps policy-makers and planners use the forecasted population dynamics to design and implement programs and policies with greater precision.