MODERN STAGE OF ARTIFICIAL INTELLIGENCE (AI) DEVELOPMENT AND APPLICATION OF AI METHODS AND SYSTEMS IN POWER ENGINEERING
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
В статье анализируется ряд публикаций на эту тему, а также обобщаются результаты дискуссий на конференции «Знания, онтологии, теории» (Новосибирск, 8-12 ноября 2021 г.) и Круглом столе в ИСЭМ СО РАН «Искусственный интеллект в энергетике» (22 декабря 2021 г.). Рассматриваются понятия: сильный и слабый ИИ, объяснимый ИИ, доверенный ИИ. Анализируются причины «бума» вокруг машинного обучения и его недостатки. Сравниваются облачные технологии и технологии граничных вычислений. Определяется понятие «умный» цифровой двойник, интегрирующий математические, информационные, онтологические модели и технологии ИИ. Рассматриваются этические риски ИИ и перспективы применения методов и технологий ИИ в энергетике. The article analyzes a number of publications on this topic, and also summarizes the results of discussions at the conference "Knowledge, Ontology, Theory" (Novosibirsk, November 8-12, 2021) and the Round Table at the ISEM SB RAS "Artificial Intelligence in Energy" (December 22 2021). The concepts are considered: artificial general intelligence (AGI), strong and narrow AI (NAI), explainable AI, trustworthy AI. The reasons for the "hype" around machine learning and its disadvantages are analyzed. Compares cloud and edge computing technologies. The concept of "smart" digital twin, which integrates mathematical, informational, ontological models and AI technologies, is defined. The ethical risks of AI and the prospects for the application of AI methods and technologies in the energy sector are considered.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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