Policies and strategies for the development of artificial intelligence in the countries of the world: quo vadis? (part 2)
Bibliographic record
Abstract
The organizational and economic and legal aspects of the development and implementation of policies and strategies for the development of artificial intelligence (AI) in the leading countries of the world have been studied. All major economies (more than 60 countries) have developed national policies (strategies) for the development of AI. The following countries are considered advanced in the implementation of national AI strategies: USA, China, Canada, UK, Japan, UAE, France, Germany, South Korea, India and most countries of the European Union (EU). The structure of AI development strategies, priorities, funding models were considered, the main principles of the development and use of AI technologies, priority directions, goals and objectives of the use of AI were analyzed. The problems associated with the use of AI are highlighted: these are issues of data for processing AI, control over the use of AI, tracking AI decisions and responsibility for their adoption, control over confidentiality, ensuring the protection of personal data. Comparing the Ukrainian concept of AI development with the strategies of developed countries, we can conclude that it will not contribute to the effective development of AI, since investments in AI technologies differ hundreds of times, incentive tools and specific actions for the development of AI are not provided. The Institute of Artificial Intelligence Problems of the Ministry of Education and Science of Ukraine and the National Academy of Sciences of Ukraine have developed a project of the Strategy for the Development of Artificial Intelligence in Ukraine for 2022–2030. The Cabinet of Ministers of Ukraine needs to take measures to adopt the Strategy for the Development of Artificial Intelligence in Ukraine. It is concluded that there is a process of formation of two large spaces in the field of AI technologies in the international arena: the first unites the OECD countries with the unconditional financial, technological and value-normative dominance of the USA and the EU. The second is formed around China, in whose orbit countries fall, for which cooperation with the West is complicated due to a wide range of international conflicts (including Russia). Countries that are unable to resist the technological hegemony of China and the United States are faced with the dilemma of choosing between two large technological spaces.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".