nexustandards.com/digital-ai · 6 taught
Digital, Cyber and AI
NRS-H95 AI system failure and misalignment; NRS-H99 Algorithmic decision and structural harm
6standards1discipline
Illustrative: layers of units and their weighted connections, an activation front sweeping from input to output
Risk Academy, led by the Nexus Consortium · Technology and Digital Systems · Department of Artificial Intelligence and Autonomous Systems
Foundation models, agentic systems, failure modes and robustness
Artificial intelligence and machine learning
Artificial intelligence and machine learning covers how models are built from data and training pipelines, how they are deployed in science and public services, and the reliability, security and societal effects that follow.
- Courses
- In preparation
- Levels
- In development
- Labs
- GeoAI lab, Network lab, Geo quiz
- Machine learning for risk professionals
- Foundation models and generative AI
- Data and training pipelines
- AI failure modes and robustness
- Agentic systems
- AI security
- AI in public services
- AI in science
- Labour and social effects of AI
Courses in development extend the scope to one further subject: AI literacy for risk professionals.
02Maps
Build a twin of any place
Search any place or open the world; the layers and generators follow artificial intelligence and machine learning on the planet map, the globe and in 3D.
- Terrain 0 to 245 m
- 4 satellite dishes
- 74 communication masts and towers
- 12 telephone exchanges
- 7 data centres and cable landing stations
- 12 aerodromes
- 1 telecom offices
- Communication line 161 km
- 729 known exploited vulnerabilities since 2023-10-17
- Night-time lights, VIIRS day-night band: read live when the 3D twin opens
Drag to move, right drag or Shift with a drag to turn and tilt, Ctrl with the wheel to zoom. Keyboard: arrows move, Shift with arrows turn and tilt, + and – zoom, 0 resets, V steps through the views, Escape clears.
Flat plan of the same data: relief as contours from the inline grid, the mapped ground infrastructure by kind, the mapped cables and the numbers below. The 3D twin opens on devices that can draw it, once its data and code have been checked.
The boundaries, names and designations shown do not imply any judgement on the legal status of any territory, or any endorsement or acceptance of such boundaries. Countries: Natural Earth (public domain).
03Live map
Connected activity in the United Kingdom and Ireland
Night-time lights and population density, the footprint of electrified, connected activity, over the United Kingdom and Ireland.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 2
Connected activity
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
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.
Connected activity, framed on the United Kingdom and Ireland.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONETSources 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
- 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). doi:10.7927/H49C6VHW. CC BY 4.0
- 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
- 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). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. 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.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- NASA GIBS: 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 (3 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
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
04The subject
The subject, methods and measures and debates and bodies
The field and its scope
For analysts, engineers and public servants who must judge where a machine learning system can be relied upon, where it fails, and what its deployment changes for the people and institutions around it.
Quantities and units
Methods and their limits
Each method is listed with what it assumes and where it fails.
What the field debates
Bodies that set practice, and the data they publish
Bodies and standards are named so learners can find and apply their published work; the Academy teaches their use and links to the publisher, and no affiliation is implied.
05The method
From a benchmark score to a deployment call: what a pass rate can and cannot say
A benchmark score is a sample estimate on a fixed item set: the item count sets its sampling interval, contamination of the items by training data inflates it, and only the corrected lower bound can be compared with the threshold a deployment requires.
Sources: Hendrycks et al., Measuring Massive Multitask Language Understanding (2021); Mitchell et al., Model Cards for Model Reporting (2019); Bommasani et al., On the Opportunities and Risks of Foundation Models (2021); NIST, Artificial Intelligence Risk Management Framework (AI RMF 1.0) (2023).
06At a glance
At a glance, with 5 related topics
Artificial intelligence and machine learning at a glance
risksacademy.comtechnology-digitaldiscipline
- Open, 0
- In preparation, 1
- Also taught here, 4
- Subtopics, 9
Nexus Standards it teaches and relies on
The Nexus Standards the discipline teaches toward, grouped by master standard, with the disciplines that teach each.
Nexus Standards at the AcademyThe Academy proposes how each discipline teaches toward the Nexus Standards; the mapping is open for public review.
Research and tools
Research and preprints as they are published, by discipline, with the faculty's other tools.
The hub lists the newest research and preprints for Artificial intelligence and machine learning, each with its source and date. Open the research hub
Hazard families it covers
From the Academy's risk taxonomy: the discipline's own entries and those of its primary courses, with how many courses teach each.
Hazard families
- AI and algorithmic failurescope
What is changing now
Recent developments in artificial intelligence and machine learning, each with its source; sources checked on 16 October 2026.
Courses in this discipline
Courses for this discipline are in development; until they open, study it through the connected courses and open lessons below.
Introductory coverage: the open course that teaches this discipline is a broad technology and data course; deeper courses are in development.
The courses from connected disciplines below also teach this field.
Courses from connected disciplines that also teach this field
Each course has its primary discipline elsewhere and teaches this field as part of its scope.
07Labs
How the subject is practised in labs
| Lab | What you do in it | Courses using it |
|---|---|---|
| GeoAI lab | Apply Earth observation and machine learning to open imagery | 2 |
| Network lab | Build dependency graphs, find critical nodes, trace cascades | 1 |
| Geo quiz | Check spatial reasoning on where hazards and systems meet | 1 |
08Console
How Artificial intelligence and machine learning is taught, as a process you can run
Press Play to run this process from this page's own data.
09Learning
Learning, practice and connections
Achievements in this discipline
Pathways that teach it
Where the subject is practised
- The Global Centre for Risk and Innovation (GCRI Canada)Artificial Intelligence and Autonomous SystemsAgentic systems, AI and public services, AI cyber security, AI in science, Autonomous physical systems, Compute and energy, Labour and social effects, Model evaluation
- Artificial intelligence
Field platforms of the ecosystem where the subject is studied and applied.
The three Foundry systems where it is practised
Disciplines it depends on
- Faculty of Governance, Policy and International AffairsAI and digital governanceThe discipline covers how AI and digital systems are overseen and held accountable: risk-based AI regulation.
- Faculty of Governance, Policy and International AffairsInformation integrity and public trustThe discipline covers the quality of the information ecosystem, the spread of misinformation and disinformation.
Across the ecosystem
10Take part
Take part in Technology and Digital Systems
Start from who you are.
11References
Core references and where to begin
Start with Bommasani, then Bengio, then National Institute of Standards.
12Questions
Questions, answers and key terms
Is study here free?
Yes. Every course and learning material of Risk Academy, led by the Nexus Consortium, is free to study, self-paced or with a cohort. Ordinary participation has no fee; paid work, internships and fellowships are separate arrangements.
What does completing a course give me?
A completion record: the course, its version, the date and what was assessed, private by default and shared by you. It stacks into discipline, department and programme achievements. A record states what was studied and assessed and does not certify competence; it is not a licence, a professional title or an employment status.
Where do the data and the places come from?
Every faculty teaches on a twin of a real place built from open data; each layer carries its provider, licence and date, and every result on the Academy states its source. Maps and figures are indicators for teaching, never an assessment of a place.
How do I take part beyond the courses in artificial intelligence and machine learning?
Form a competence cell of three to seven people, study a shared pathway, take a supervised assignment in the National Working Groups, or bring a cohort from your institution. For organisations.
Key terms
Term
Model evaluation
Testing an AI model's capabilities, reliability and risks against defined tasks and criteria.
Term
Distribution shift
A change between the data a model was trained on and the data it meets in use.
Term
Hallucination
Output from a generative model that is fluent but false or unsupported.
Term
Agentic system
An AI system that plans and takes actions toward goals with limited human direction.
ReportsContribute
Contribute evidence from this page.
Reports is the Consortium's quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
Risk Academy
Related
Around Artificial intelligence and machine learning
Artificial intelligence and machine learning
Start with an open course
The catalogue lists each course with its outcomes, labs, hours and level.