Super AI, Generative AI, Narrow AI and Chatbots: An Assessment of Artificial Intelligence Technologies for The Public Sector and Public Administration
Bibliographic record
Abstract
Artificial intelligence encompasses a wide range of approaches, methodologies, and techniques aimed at mimicking human intelligence in machines. In recent times, the concepts of Generative Artificial Intelligence (AI), Super AI, and Narrow AI have attracted considerable attention. Undoubtedly, the success of ChatGPT in capturing all attention has played a significant role in this. Artificial intelligence technology has a profound impact on all sectors, and sector representatives are striving to adapt to this technology more quickly. It is projected that artificial intelligence could generate an economic size of 13 trillion American dollars by 2030. Developments in artificial intelligence technologies undoubtedly lead to significant improvements in the functioning of public institutions and access for citizens. Artificial intelligence has the potential to be used in many public services, including security and defense, healthcare services, education, transportation and infrastructure, environmental and natural resource management, law and justice systems, among others. Therefore, evaluating the types of artificial intelligence, Narrow AI applications, and chatbots for public use is seen as highly beneficial from the perspective of public administration and the public sector. In our study, the topics of super artificial intelligence, generative artificial intelligence, narrow artificial intelligence, and chatbots have been extensively evaluated within the context of the public sector and public administration. Utilizing findings from both Turkish and English literature reviews, the importance and potential impacts of artificial intelligence within the public sector, along with current trends, have been comprehensively assessed. This research delves into the concepts of artificial intelligence and its subsets—super AI, generative AI, narrow AI, and chatbots—within the general framework of the public sector. China and the United States are pioneering and leading countries in terms of investment. Although the U.S. stands out in many areas regarding investment, China's integration of artificial intelligence with national strategies and its policies indicate that it may play a more dominant role in the future. There are four main implementation areas of artificial intelligence in the public sector: efficiency and automation, service delivery, data-driven governance, and ethical and regulatory challenges. A review of the literature reveals that the ethical, legal, and social implications of implementing artificial intelligence in the public sector require more careful consideration. The study makes a significant contribution to the field of artificial intelligence discussions in public administration and the public sector, providing a comprehensive assessment of current discussions on artificial intelligence in the literature.
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.
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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".