Skip to content

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
01In brief

What this area brings to the editions.

Trustworthy artificial intelligence, robotics-enabled autonomy, computational methods and social effects.

02In the editions

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.

  1. 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
  2. ARG아르헨티나Low-carbon sources supplied 42% of electricity in 2025, and renewables including large hydro covered 40.2% of demand.REPORTED · COUNTRY-ARG-ENE-003
  3. 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
  4. AUS오스트레일리아Wind and solar supplied 33% of electricity in 2025; fossil fuels supplied 61% and coal 43%.REPORTED · COUNTRY-AUS-ENE-003
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
03Evidence path

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.

단계진행 내용출처
01 What it readsArtificial 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 themArtificial 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 sealedEach 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 feedsThe cut feeds Exponential Technology and 랩 Evidence, from the Baseline Edition 2026 into the quarterly series.
05 Who decidesDecisions 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).

04시리즈

The series it feeds, and what backs each.

시리즈Foundry systems it draws onAcademy disciplinesSeries home
기하급수 기술NXS-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 증거NXS-LAB what was tried, under which conditions, and what happened, including negative results연구, 근거, 과학과 정책의 접점
복잡 시스템 and systems modelling
Labs Evidence series home
08기여하기

Contribute on Artificial Intelligence and Autonomous Systems.

Evidence, a case, a correction, a method or profile input on this subject. The composer tags it with this area and its series.

Contribution composer

Write a contribution the editor can act on.

Say what it is, where its record is kept and on which licence. It arrives tagged with this page, its area and its gateway, so it reaches the editor who owns it.

Your email opens with the subject and a template already filled in for GW-GCRI.

  • 증거
  • 정정
  • 사례 연구
  • Analysis or method
  • 동료 검토
  • 번역
  • Profile input

Never send personal data or sensitive infrastructure detail. A contribution is reviewed before use; sending it confers no authorship or editorial rights.

09오픈 사이언스

Open science for Artificial Intelligence and Autonomous Systems.

How a contribution on this subject is deposited, identified, credited, licensed and found, and the metadata for it prefilled from this page.

Artificial Intelligence and Autonomous Systems’s open record

영역
인공지능과 자율 시스템
Feeds
Exponential Technology, Labs Evidence
기록
A UNI for every accepted contribution; cited with its DOI in the edition that uses it
링크
isSupplementTo the edition it informs; isNewVersionOf its earlier version
라이선스
CC BY 4.0 (SPDX CC-BY-4.0) for original material; metadata CC0 1.0

Credit and identity

ORCID
Your iD in the deposit and the contribution record; DataCite can then add the work to your ORCID record
ROR
Your organisation by its ROR identifier, never by a typed name alone
CRediT
Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualisation, Writing, review and editing
계정
None needed to contribute; never a password

Find and follow

Search the open indexes for Artificial Intelligence and Autonomous Systems; software is cited by its SWHID from Software Heritage. Annotation is your choice: Hypothesis opens this page in its own viewer.

Mirrors and harvest

The DOI is canonical. At release each signed bundle is also given an IPFS content identifier and a bloxberg anchor as mirrors; both are listed in the record once they exist. Zenodo exposes every record over OAI-PMH, and reviews and endorsements can arrive through COAR Notify where the repository supports it.

InvenioRDM metadata for a contribution on Artificial Intelligence and Autonomous Systems, prefilled from this page
{
  "metadata": {
    "resource_type": {
      "id": "dataset"
    },
    "title": "Evidence on Artificial Intelligence and Autonomous Systems for Nexus Reports",
    "publication_date": "YYYY-MM-DD",
    "creators": [
      {
        "person_or_org": {
          "type": "personal",
          "family_name": "Your family name",
          "given_name": "Your given name",
          "identifiers": [
            {
              "scheme": "orcid",
              "identifier": "0000-0000-0000-0000"
            }
          ]
        },
        "affiliations": [
          {
            "id": "ROR identifier of your organisation"
          }
        ]
      }
    ],
    "description": "What it is, where its record is kept and how it bears on Artificial Intelligence and Autonomous Systems in Nexus Reports.",
    "rights": [
      {
        "id": "cc-by-4.0"
      }
    ],
    "subjects": [
      {
        "subject": "Nexus Reports"
      },
      {
        "subject": "Artificial Intelligence and Autonomous Systems"
      },
      {
        "subject": "Artificial intelligence"
      },
      {
        "subject": "Data and compute"
      }
    ],
    "related_identifiers": [
      {
        "identifier": "https://therisk.global/nexus-reports/areas/ai-autonomous-systems/",
        "scheme": "url",
        "relation_type": {
          "id": "issupplementto"
        },
        "resource_type": {
          "id": "other"
        }
      }
    ],
    "version": "1.0.0"
  },
  "custom_fields": {},
  "access": {
    "record": "public",
    "files": "public"
  },
  "files": {
    "enabled": true
  },
  "parent": {
    "communities": {
      "default": "nexusreports"
    }
  }
}
10최신

Releases for this area.

Releases tagged to this area appear here once released.

보고서기여하기

Send evidence on Artificial Intelligence and Autonomous Systems.

Reports is the Consortium’s quarterly publication, written by six desks. This page sits with the Global Centre for Risk and Innovation (GCRI) 시스템 증거 담당 부서에 속합니다. 이 주제에 대해 아시는 내용은 다음 판에서 증거, 사례 또는 정정이 될 수 있으며, 귀하의 이름으로 표기되고 직접 작성한 저작물인 경우 CC BY 4.0으로 발행됩니다. 잘못된 부서에 보내더라도 거절되지 않고 전달됩니다.

GCRI 시스템 증거 담당 부서인공지능과 자율 시스템기하급수 기술Labs 근거
Reports · GCRI

Bring what you know to the next edition.

Every quarter closes on a sealed evidence cut. Contributions received before the cut are reviewed for that edition; later ones wait for the next.

CC BY 4.0 Free to read. Text and figures: CC BY 4.0. Data: CC0 1.0. Authors keep copyright. Published by GCRI Canada.