Reports · GCRI · Established
Artificial intelligence · Data and compute
Reports의 인공지능과 자율 시스템
Trustworthy artificial intelligence, robotics-enabled autonomy, computational methods and social effects. This page shows the series it feeds, the evidence behind it and how to contribute.
- 시리즈
- 2
- 시스템
- 4
- 방법
- 7
What this area brings to the editions.
Trustworthy artificial intelligence, robotics-enabled autonomy, computational methods and social effects.
9 findings of the drafted editions that bear on Artificial Intelligence and Autonomous Systems.
Drawn from the Baseline Edition 2026 drafts through the 시스템 this area reads. Each opens at its anchor in its edition, with its numbered sources.
- ARE아랍에미리트Gas gave 68% of electricity generation in 2025, down from 97% in 2019, while nuclear reached 21% and solar 10%; total generation rose from 138 to 184 TWh.REPORTED · COUNTRY-ARE-ENE-003
- ARG아르헨티나Low-carbon sources supplied 42% of electricity in 2025, and renewables including large hydro covered 40.2% of demand.REPORTED · COUNTRY-ARG-ENE-003
- AU아프리카연합In 2024, 55.1% of people in Sub-Saharan Africa had electricity, up from 46.4% in 2018; about 579 million had none (derived).REPORTED · COUNTRY-AU-ENE-002
- AUS오스트레일리아Wind and solar supplied 33% of electricity in 2025; fossil fuels supplied 61% and coal 43%.REPORTED · COUNTRY-AUS-ENE-003
- BRA브라질Renewables supplied 86.8% of electricity in 2025. Hydropower fell 4.8% to 401.4 TWh while gas generation rose 22.7%, and wind and solar together supplied 26.4%.REPORTED · COUNTRY-BRA-ENE-005
- CHE스위스In 2025 hydropower supplied 55.5% of domestic electricity generation, nuclear power 27.2% and photovoltaics 11.8%, and final electricity consumption was 57,970 GWh.REPORTED · COUNTRY-CHE-ENE-003
- CHN중국Coal supplied 54.8% of electricity in 2025 and 51.4% of total energy consumption, while wind and solar supplied 21.9% of electricity.REPORTED · COUNTRY-CHN-ENE-006
- EU유럽연합Renewables supplied 25.2% of gross final energy consumption in 2024, 17.3 points below the 42.5% target for 2030.REPORTED · COUNTRY-EU-ENE-003
- GS-02글로벌 에너지Global energy demand grew 1.3% (8 EJ) in 2025, down from 2% in 2024, and electricity demand grew nearly 3%, more than twice as fast.REPORTED · SECTOR-ENERGY-GLOBAL-ENE-001
From Foundry evidence to the series.
The evidence, step by step, from the 시스템 this area reads to the series it feeds.
How Artificial Intelligence and Autonomous Systems reaches 보고서: what it reads, how it reads it, the sealed cut and the series it feeds.
| 단계 | 진행 내용 | 출처 |
|---|---|---|
| 01 What it reads | Artificial Intelligence and Autonomous Systems reads NXS-NXQ(소프트웨어, 모델, 역량 수명주기) 와 NXS-LAB(연구, 실험, 시뮬레이션): governed sources with their releases, never a value without its source. | |
| 02 How it reads them | Artificial intelligence and machine learning and AI evaluation and assurance engineering turn the evidence into findings, with assumptions and limits stated beside each value. | |
| 03 The cut is sealed | Each quarter the evidence is sealed in one cut, so OBSERVED, REPORTED and ESTIMATED values stay apart and every edition can be rebuilt. | |
| 04 The series it feeds | The cut feeds Exponential Technology and 랩 Evidence, from the Baseline Edition 2026 into the quarterly series. | |
| 05 Who decides | Decisions stay with the authorities that hold them, under their own rules; an edition informs a decision and never makes it. |
A teaching model of the evidence path, not a forecast and not a record of any run; the systems, methods and series named come from the registries. Sources: Reports의 인공지능과 자율 시스템 (2026); Artificial Intelligence and Autonomous Systems home (2026).
The series it feeds, and what backs each.
| 시리즈 | Foundry systems it draws on | Academy disciplines | Series home |
|---|---|---|---|
| 기하급수 기술 | NXS-NXQ the capability catalogue and fitness assessments of machine capabilities NXS-LAB benchmark comparisons and experiment records for fast-changing technologies | 인공지능과 머신러닝 혁신과 첨단 기술 거버넌스 양자 기술 | Exponential Technology series home |
| 랩 증거 | NXS-LAB what was tried, under which conditions, and what happened, including negative results | 연구, 근거, 과학과 정책의 접점 복잡 시스템 and systems modelling | Labs Evidence series home |
The systems, methods and teaching behind it.
- NXS-NXQNXS-NXQNXS-NXQRooms: Capabilities, Competence.
- NXS-LABNXS-LABNXS-LABRooms: Experiments, Benchmarks.
- NXS-STU(구성, 대시보드, 워크플로)NXS-STUNXS-STURooms: Composer, Populations.
- NXS-NSF(신뢰, 권한 부여, 보증)NXS-NSFNXS-NSFRooms: Authority, Assurance.
- STEM 방법STEM 공유지인공지능과 머신러닝The canonical method page, with its limits.
- STEM 방법STEM 공유지AI 평가 및 보증 공학The canonical method page, with its limits.
- STEM 방법STEM 공유지고성능, 클라우드 및 엣지 컴퓨팅The canonical method page, with its limits.
- STEM 방법STEM 공유지확률과 통계The canonical method page, with its limits.
- STEM 방법STEM 공유지사이버보안 및 암호학The canonical method page, with its limits.
- STEM 방법STEM 공유지사회 및 행동과학The canonical method page, with its limits.
- Academy disciplineRisk Academy인공지능과 머신러닝기술·디지털 시스템 학부
- Academy disciplineRisk AcademyAI 안전과 평가기술·디지털 시스템 학부
- Academy disciplineRisk Academy로봇공학과 자율 시스템기술·디지털 시스템 학부
- 분야GCRI인공지능The field across science, governance and finance.
- 분야GCRI데이터와 컴퓨팅The field across science, governance and finance.
Where the same subject is reported elsewhere.
- Counterpart · GRF보고서Innovation NexusShared: Artificial intelligence.
- Counterpart · GRF보고서AI와 디지털 거버넌스Shared: Artificial intelligence.
- Counterpart · GRA보고서AI와 금융 모델 리스크Shared: Artificial intelligence.
- Standards areaNexus Standards데이터와 데이터셋Data and evidence. Parts NS-3, NS-3b, NS-11, NS-1.
- Standards areaNexus Standards소프트웨어와 공급망Trust, verification and assurance. Parts NS-12, NS-11, NS-1.
- Standards areaNexus StandardsAI, 모델, 에이전트Digital, cyber and AI. Parts NS-12, NS-10, NS-11.
- Standards areaNexus Standards시나리오, 모델, 전략적 예측Systemic risks. Parts NS-12, NS-3b, NS-11.
- Standards areaNexus Standards개인정보와 데이터 보호Data and evidence. Parts NS-10, NS-11, NS-3.
- Standards areaNexus Standards금융 및 결제 데이터Finance, insurance and the economy. Parts NS-9, NS-10, NS-11, NS-14.
Topic pages on Artificial Intelligence and Autonomous Systems.
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