Strategies and prospects for the development of artificial intelligence in the world and in the Republic of Uzbekistan: a comparative analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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