| Title | Artificial Intelligence in the Scientific and Technological Paradigm of Global Economy |
|---|---|
| ID_Doc | 75773 |
| Authors | Lukianenko, D; Simakhova, A |
| Title | Artificial Intelligence in the Scientific and Technological Paradigm of Global Economy |
| Year | 2024 |
| Published | Problemy Ekorozwoju, 19, 2 |
| Abstract | The article examines the impact of artificial intelligence (AI) technologies on global sustainable development. Artificial intelligence affects all three pillars of sustainable development: economic, social, and environmental. Based on the generalization of academic works and authoritative expert assessments, it is shown that this impact is ambiguous. By increasing technological capabilities and enhancing the efficiency of business, public administration, and the provision of administrative and social services, AI creates a number of socio-economic problems, primarily in the labor market, when hundreds of professions are discredited and may disappear. It has been confirmed that almost all sectors of the economy, including education and medicine, are subject to the large-scale impact of AI. AI is able to optimize the use of resources and increase energy efficiency, reducing waste, thus affecting the environmental pillar of sustainable development. The purpose of the article is a systematic study of the intellectual and technological landscape of the global economy with a cross-country analysis of its key indicators using the Kohonen algorithm. The author has positioned artificial intelligence in the technological paradigm of the twenty-first century. If scientific progress in materials science, energy, and mathematical computing led to the digital transformation with the emergence of Industry 4.0, then in synergy with bio- and quantum technologies, AI will form Industry 5.0, i.e., essentially a smart economy, through a technological explosion. To recreate the current global intellectual and technological landscape, the study used the Kohonen algorithm with the Deductor Studio package to analyze 128 countries by 5 indicators. The modeling allowed us to group them into 5 clusters, which makes a real comparative analysis possible. Similar modeling with the implementation of the AI indicator (number of robots per 10,000 population) for 18 countries allowed them to be grouped into 3 clusters according to the level of readiness of governments and society to interact with AI. |
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