Analysis of Countries in Terms of Artificial Intelligence Technologies: PROMETHEE and GAIA Method Approach
Why this work is in the frame
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Bibliographic record
Abstract
Artificial intelligence development and research leaders in business, industry, and nations gain a major competitive edge. Additionally, it is clear that nations with a well-established national artificial intelligence policy have an edge over others, both technologically and economically. To further their artificial intelligence capability, nations also seek to develop a strategy, vision, structure, and working environment that encourages collaboration between the public sector, private industry, and educational institutions. Artificial intelligence is thought to be a tool that will help bridge the gap between powerful and developing countries growing in the future. Using data from “The Global AI Index” for 2021, this study aims to examine and analyze the present state of artificial intelligence management in 62 nations in terms of talent, infrastructure, business environment, development and research government policy, and commercial efforts. The research used PROMETHEE, which is widely used in multi-criteria decision-making evaluations, and its geometric representation, the GAIA plane. This study also found that the United States of America is the world leader in artificial intelligence (AI) research and development as well as AI investment. The United Kingdom, China, Israel, Canada, the Netherlands, South Korea, and Germany all rank well. China is rapidly catching up to the USA. At the very bottom of the list are nations such as Armenia, Kenya, Egypt, South Africa, and Pakistan. Turkey’s values are more similar to those of nations towards the bottom of the list than of those in the top half. There is a significant gap between the top three countries and the rest of the world in all parameters included in the survey. Except for the ‘State Strategy’ category, Turkey scores quite low compared to the top-performing countries. Decision makers are expected to address the identified global challenges of the study by creating a more comprehensive national AI strategy, both financially and in terms of content.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it