Illustrative: layers of units and their weighted connections, an activation front sweeping from input to output

Risk Academy, led by the Nexus Consortium · Course catalogue · Technology and Digital Systems

Artificial Intelligence and Autonomous Systems

Artificial intelligence and machine learning

Artificial intelligence and machine learning, taught twice over: as the working methods of the field and as its frontier since 2024, with labs on the Academy's tools, open data and the Nexus Standards.

Core course Frontier course

The discipline

01Core course · L2 Intermediate

Artificial intelligence and machine learning: foundations and practice

This area covers how AI and machine learning systems are built and deployed, and the reliability, security and societal effects that follow: foundation models and generative AI, agentic systems, AI in science and public services, and AI failure modes.

Level
L2 Intermediate
Study time
about 24 hours (planning estimate)
Status
In authoring
Prerequisites
An L1 course in the area, or an entry review

Why now

Changes compliance timelines and planning assumptions for organisations deploying high-risk AI systems in the EU. Council of the European Union, Artificial intelligence: Council and Parliament agree to simplify and streamline rules, 7 May 2026

Learning outcomes

  1. Define model evaluation and the other key terms of the discipline and use them correctly in a written case
  2. Explain machine learning for risk professionals and foundation models and generative AI with a worked example and its source
  3. Apply the discipline's method to a documented case and explain each result with its source
  4. Analyse a named case against data and training pipelines, stating evidence, date and limits

Modules

  1. Machine learning for risk professionals
    • Machine learning for risk professionals
    • Foundation models and generative AI
    • Data and training pipelines
  2. AI failure modes and robustness
    • AI failure modes and robustness
    • Agentic systems
    • AI security
  3. AI in public services
    • AI in public services
    • AI in science
    • Labour and social effects of AI
  4. Lab: the discipline's method
    • 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.
    • Work the method through a documented case and explain each result
    • Read the method's sources and stated values

Labs with Academy tools

Nexus Standards

Teaches

  • NRS-T1 Artificial intelligence risk
  • NRS-T17 Machine learning lifecycle, data quality and robustness
  • NRS-I7 Intelligence applications: grounded retrieval, reasoning and decision support
  • NRS-H95 Hazard family: AI system failure and misalignment
  • NRS-H99 Hazard family: Algorithmic decision and structural harm
  • NRS-M19 Method fabric: Cyber, AI and frontier technology

Relies on

  • NS-1 The UNI and the artefact catalogue
  • NS-16 UNI application profile (how articles, documents, code, data, APIs and interfaces carry the UNI)
  • NRS-A1 Standards as code: machine-readable standards, requirements and tests
  • NRS-A2 Ontology-native standards: the Nexus risk ontology, vocabularies and shapes
  • NRS-A9 Evaluation suites for AI in risk work
  • NRS-C1 Risk vocabulary and ontology
  • NRS-C2 Risk assessment method
  • NRS-C3 Hazard, exposure, vulnerability and capacity data

Assessment

  • 40%Lab recordWork the discipline's method through a documented case and explain each result with its source and date
  • 40%Case memoApply the discipline's methods to a named place or organisation, stating evidence and limits
  • 20%Key terms checkShort answers on the key terms, marked against the definitions

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Intermediate, 4 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

Open now in this discipline

References

  1. Council of the European Union, Artificial intelligence: Council and Parliament agree to simplify and streamline rules, 7 May 2026
  2. Hendrycks et al., Measuring Massive Multitask Language Understanding, 2021
  3. Mitchell et al., Model Cards for Model Reporting, 2019
  4. Bommasani et al., On the Opportunities and Risks of Foundation Models, 2021
02Live 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

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

03How learning runs

How learning runs, as a process you can run

Select a scenario and press Play: course, pathway, practice, faculty, cells and record.

04What is changing

Frontier course · L3 advanced, programme courses and standards

Artificial intelligence and machine learning: frontier methods

The methods, data, tools and studies that changed artificial intelligence and machine learning between 2024 and 2026, taught by reproducing them on open data.

Level
L3 Advanced
Study time
about 28 hours (planning estimate)
Status
Planned
Prerequisites
Artificial intelligence and machine learning: foundations and practice; Working knowledge of a scripting language (Python or R) for the labs

Why now

Changes compliance timelines and planning assumptions for organisations deploying high-risk AI systems in the EU. Council of the European Union, Artificial intelligence: Council and Parliament agree to simplify and streamline rules, 7 May 2026

Learning outcomes

  1. Explain what changed in artificial intelligence and machine learning since 2024 and cite the studies, data or rules behind each change
  2. Reproduce EU agrees to delay AI Act high-risk obligations on open data and state the uncertainty of the result
  3. Evaluate a 2024 to 2026 study: its question, data, method, limits and what it changes for practice
  4. Design an analysis that combines a frontier method with the faculty's instruments, and justify each choice

Modules

  1. EU agrees to delay AI Act high-risk obligations
    • Council and Parliament reached a provisional deal on the Digital Omnibus on AI, moving high-risk obligations to December 2027 for stand-alone systems and August 2028 for systems embedded in products, and adding a ban on non-consensual intimate content generation.
    • Changes compliance timelines and planning assumptions for organisations deploying high-risk AI systems in the EU.
    • Council of the European Union, 7 May 2026
  2. AI Index 2026 tracks rising adoption and documented AI incidents
    • Stanford HAI reports organisational AI adoption at 88%, agent benchmark success rising sharply but still failing about one in three tasks, and documented AI incidents rising to 362 in 2025 from 233 in 2024.
    • Provides comparable yearly indicators on capability, adoption, incidents and investment that support horizon scanning and exposure estimates.
    • Stanford Institute for Human-Centered Artificial Intelligence, 1 April 2026
  3. International AI Safety Report 2026 updates the shared evidence base
    • The second annual report, chaired by Yoshua Bengio with an advisory panel nominated by over 30 countries and organisations, finds pre-deployment tests do not reliably predict real-world risk and that most frontier risk management remains voluntary.
    • Gives risk teams an independent, government-backed synthesis of AI capabilities, failure modes and safeguard limits to anchor their own assessments.
    • International AI Safety Report, 3 February 2026
  4. EU publishes General-Purpose AI Code of Practice
    • The European Commission published a voluntary code with chapters on transparency, copyright, and safety and security, which providers of general-purpose AI models can use to show compliance with AI Act obligations that applied from August 2025.
    • Sets the first detailed expectations for documenting and managing systemic risks of large models placed on the EU market.
    • European Commission, 10 July 2025
  5. Reproduction lab
    • Extend the discipline's method with one frontier method and compare its results
    • State the uncertainty and the limits of the result
    • Peer review against a shared checklist

Labs with Academy tools

Nexus Standards

Teaches

  • NRS-T1 Artificial intelligence risk
  • NRS-T17 Machine learning lifecycle, data quality and robustness
  • NRS-I7 Intelligence applications: grounded retrieval, reasoning and decision support
  • NRS-H95 Hazard family: AI system failure and misalignment
  • NRS-H99 Hazard family: Algorithmic decision and structural harm
  • NRS-M19 Method fabric: Cyber, AI and frontier technology

Relies on

  • NS-1 The UNI and the artefact catalogue
  • NS-16 UNI application profile (how articles, documents, code, data, APIs and interfaces carry the UNI)
  • NRS-A1 Standards as code: machine-readable standards, requirements and tests
  • NRS-A2 Ontology-native standards: the Nexus risk ontology, vocabularies and shapes
  • NRS-A9 Evaluation suites for AI in risk work
  • NRS-C1 Risk vocabulary and ontology
  • NRS-C2 Risk assessment method
  • NRS-C3 Hazard, exposure, vulnerability and capacity data

Assessment

  • 50%Method reproductionReproduce one frontier method on open data and state the uncertainty of the result
  • 30%Study reviewA critical review of a 2024 to 2026 study: question, data, method, limits, what it changes
  • 20%Peer reviewReview two other learners' reproductions against a shared checklist

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Expert, 5 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

Open now in this discipline

References

  1. Council of the European Union, Artificial intelligence: Council and Parliament agree to simplify and streamline rules, 7 May 2026
  2. Stanford Institute for Human-Centered Artificial Intelligence, The 2026 AI Index Report, 1 April 2026
  3. International AI Safety Report, International AI Safety Report 2026, 3 February 2026
  4. European Commission, The General-Purpose AI Code of Practice, 10 July 2025
  5. Hendrycks et al., Measuring Massive Multitask Language Understanding, 2021
  6. Mitchell et al., Model Cards for Model Reporting, 2019
  7. Bommasani et al., On the Opportunities and Risks of Foundation Models, 2021
05Take part

Take part in the work

Start from who you are.

06Questions

Questions, answers and key terms

Is the course 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 course?
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.

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