Strategies and prospects for the development of artificial intelligence in the world and in the Republic of Uzbekistan: a comparative analysis
Bibliographic record
Abstract
In this article, the author discusses the issue of the concept of artificial intelligence, strategies for its development in many foreign countries and Uzbekistan. In particular, the conceptual issues of the development of artificial intelligence are discussed by such global participants of artificial intelligence as the United States, China, the European Union, etc. After studying and analysing the steps of artificial intelligence the author came to conclusion that without a well-planned strategic plan, it is impossible to develop artificial intelligence in Uzbekistan. Furthermore, the first steps on the way of introducing artificial intelligence into all spheres and sectors of Uzbekistan are considered, in particular, industry, medicine, science, transport and communications, etc. Based on the analysis of many strategies and plans of the above states, as well as India, the UAE, Canada, Japan, the author presents his own vision and recommendations for the development of artificial intelligence systems in our country.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".