Technology and Digital Systems
The Global Centre for Risk and Innovation (GCRI)
Risk Academy, a Nexortium programmeTechnology and Digital SystemsDepartment of Artificial Intelligence and Autonomous Systems
Foundation models, agentic systems, failure modes and robustness
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.
Back to Department of Artificial Intelligence and Autonomous Systems
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Twin figures for West Cornwall: Goonhilly and the Porthcurno cable landings, counted from its layers at their stated dates.
For Artificial intelligence and machine learning: the faculty's twin of the Lizard and Penwith on the planet map, the globe and in 3D. Pick a generator, switch layers, run the instruments; every layer prints its provider and date.
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 with WebGL once its data and library have been checked.
The map loads from this site as it nears the view (MapLibre 6.9.1, checked by hash).
The discipline teaches machine learning for risk professionals, distribution shift and robustness testing, hallucination and calibration measurement, prompt injection and data poisoning as security failures, agentic systems and the human oversight arrangements they need, under the AI management standard ISO/IEC 42001:2023, its impact assessment companion ISO/IEC 42005:2025, the NIST AI Risk Management Framework 1.0 with its Generative AI Profile and the obligations of Regulation (EU) 2024/1689. The faculty's evaluation ledger records what a model scored on which task with what evidence. A graduate can state a model's intended use, measure its robustness to a named shift, log the result with evidence and write the oversight arrangement a deployment needs.
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. It is taught in the department of Artificial Intelligence and Autonomous Systems, Faculty of Technology and Digital Systems, through courses still to be authored and 4 more that also teach it.
9 subtopics, taught across its courses and labs.
Not yet taught by a course: AI literacy for risk professionals.
Recent developments in artificial intelligence and machine learning, each with its source; sources checked on 16 October 2026. Summaries are short readings of the source; follow the link for the full account.
News and research as they are published, per discipline, with the faculty's other tools.
The newest research, preprints, news and humanitarian reports for Artificial intelligence and machine learning, each with its source and date, load here. Open the research and news hub
From the Academy's risk taxonomy: the discipline's own entries and those of its primary courses, with how many courses teach each.
No course has this as its primary discipline yet. It is listed so the gap stays visible.
Introductory coverage: the only open course that teaches this discipline is a broad technology and data course; deeper courses are in preparation or on the authoring list.
The courses on its authoring list are named below.
These courses count in their own primary discipline and also teach this one; they do not count towards its achievement as primary courses.
Approved for authoringno enrolment and no achievement until authored and reviewed
Cited as places where the subject is practised; the institutions named are sources here, and Risk Academy hosts every course.
| 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 |
The catalogue lists each course with its outcomes, labs, hours and level.