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GCRIArtificial Intelligence and Autonomous SystemsIn development
STEM foundation

STEM foundation of Artificial Intelligence and Autonomous Systems

The science, technology, engineering and mathematics Artificial Intelligence and Autonomous Systems rests on. Each method has a canonical page in the STEM Commons, hosted by GCRI, and a page here on how it is applied in this field.

Open Machine learning behaviour Back to Artificial Intelligence and Autonomous Systems
Publisher
The Global Centre for Risk and Innovation
Field
Artificial intelligence, Data and compute
Canvas · methods feeding this platformDesign, not a record
STEM foundation of Artificial Intelligence and Autonomous Systems: model risk lifecycleEvery model is inventoried and tiered; development is documented; independent validation tests conceptual soundness, outcomes and benchmarks; approval sets limits of use; monitoring and periodic review keep it current until it is retired. Design of the process, not a record of a run.

Fig.The methods this platform draws on and what each one feeds. Design, not a record.

01In brief

The short answer.

The Artificial Intelligence and Autonomous Systems STEM foundation is the set of canonical methods its findings depend on, from artificial intelligence and machine learning to robotics and automation. For the Consortium, Artificial Intelligence and Autonomous Systems stewards artificial intelligence and machine learning as well as AI evaluation and assurance engineering. Each method has a canonical STEM Commons page and a page here on its use in the artificial intelligence field.

02Geography

Connected activity in East Asia.

For STEM foundation of artificial intelligence and autonomous systems, in GCRI's spatial-intelligence reading, the map opens on night lights, true-colour imagery and population density with true colour from NASA GIBS, framed on East Asia; 3 further NASA layers wait in the list.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 3

Connected activity, for this page

Latest available

NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth

Loading the latest NASA imagery.

Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.

For STEM foundation of artificial intelligence and autonomous systems, in GCRI's spatial-intelligence reading, the map opens on night lights, true-colour imagery and population density with true colour from NASA GIBS, framed on East Asia; 3 further NASA layers wait in the list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.

Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · Copernicus Sentinel-2 L2A · NASA HLS S30Sources and licences

  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). CC BY 4.0
  • MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
  • © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). ODbL
  • Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
  • Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.
  • Observed (EO): Copernicus Sentinel-2 L2A (contains modified Copernicus Sentinel data 2026), Microsoft Planetary Computer
  • Observed (EO): NASA HLS S30 (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
  • Optional layers (5 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.

Layers and dates

  • Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
  • True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
  • Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
  • Enhanced vegetation index, rolling 8 days: MODIS Terra EVI, NASA LANCE · 13 Oct 2026
  • Vegetation greenness (NDVI), rolling 8 days: MODIS Terra NDVI, NASA LANCE · 13 Oct 2026

03Methods

The methods behind the field.

  1. Artificial intelligence and machine learningDistribution shift analysis · Fine-tuning for a specific use · Interpretability methodsSteward · Artificial Intelligence and Autonomous SystemsApplied here
    Canonical · STEM Commons
  2. AI evaluation and assurance engineeringAssurance cases for AI systems · Structured red teaming · Post-deployment monitoringSteward · Artificial Intelligence and Autonomous SystemsApplied here
    Canonical · STEM Commons
  3. High-performance, cloud and edge computingCompute access for public research · Energy measurement of workloadsSteward · Data and ComputeApplied here
    Canonical · STEM Commons
  4. Probability and statisticsUncertainty on benchmark scores · Calibration assessment · Audit samplingSteward · Systems Science and ModellingApplied here
    Canonical · STEM Commons
  5. Cybersecurity and cryptographyThreat modelling AI applications · Model supply chain integrity · Injection defence testingSteward · Cybersecurity and Digital ResilienceApplied here
    Canonical · STEM Commons
  6. Social and behavioural scienceHuman oversight studies · Affected community research · Workplace adoption studiesSteward · Society and Human DevelopmentApplied here
    Canonical · STEM Commons
  7. Robotics and automationScenario-based testing · Collaborative robot safety · Operating domain monitoringSteward · Advanced Industry and RoboticsApplied here
    Canonical · STEM Commons
04Method mix

Why AI needs this combination of methods.

AI risk work combines AI and machine learning with AI assurance, computing, probability and statistics, cybersecurity, social science and robotics.

05Why it matters

Grounding makes claims checkable.

When a finding names the methods it depends on, a reader can see its assumptions and limits, and an expert in that method can challenge it.

06Key terms

Terms used on this page.

Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.

TermDefinition
STEM CommonsThe shared reference of science, technology, engineering and mathematics methods for the Nexus network, hosted by GCRI, with one canonical page per method and a named steward platform.
Steward platformThe platform that maintains the canonical description of a method in the STEM Commons and reviews how other platforms apply it.
Artificial intelligence and machine learningArtificial intelligence and machine learning develop algorithms that learn patterns from data to classify, predict or generate outputs.
AI evaluation and assurance engineeringAI evaluation and assurance engineering develops methods to test, monitor and provide justified confidence in AI systems.
07Sources

Where this page's references lead.

The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.

08Pages

Pages in STEM foundation of Artificial Intelligence and Autonomous Systems.

Method

Machine learning behaviour

Method

AI evaluation and assurance

Method

Computing for AI

Method

Statistics for AI evaluation

Method

Cybersecurity for AI systems

Method

Social science of AI

Method

Robotics and autonomy

09Questions

Questions and answers.

What is a steward platform?
The platform that maintains the canonical description of a method and reviews how it is applied across the Consortium.
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Around Artificial Intelligence and Autonomous Systems.

Artificial Intelligence and Autonomous Systems

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