Artificial Intelligence and Autonomous Systems
The Global Centre for Risk and Innovation (GCRI)
The methods Artificial Intelligence and Autonomous Systems relies on, the assumptions they carry and the limits of what they can show.
The Artificial Intelligence and Autonomous Systems methods page sets out the methods the platform relies on, among them artificial intelligence and machine learning, AI evaluation and assurance engineering, and high-performance, cloud and edge computing, with their assumptions, validation and limits. Each analysis lists its assumptions and data, reports ranges and confidence beside central estimates, is checked against observations or other models, and states what it cannot show.
Fig.How a finding is reviewed before it is published. Design, not a record.
For methods and uncertainty, in GCRI's spatial-intelligence reading, the map opens on true-colour imagery, night lights and land cover with true colour from NASA GIBS, framed on East Asia; 3 further NASA layers wait in the list.
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For methods and uncertainty, in GCRI's spatial-intelligence reading, the map opens on true-colour imagery, night lights and land cover 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.
Layers and dates
| Source | Why it matters here | How it is reported |
|---|---|---|
| Benchmark validity | Benchmarks saturate and leak into training data | Several evaluations, held-out tests, dates stated |
| Capability elicitation | Measured capability depends on prompting and tools | Elicitation effort described |
| Deployment context | Risk depends on use, not only the model | Use-specific assessment |
| Incident data | Reporting is voluntary and uneven | Incident sources and coverage stated |
Evaluation results carry the model version, date and setup; AI capability changes quickly and old results expire.
Each analysis lists its assumptions and the data it depends on.
Ranges and confidence are reported alongside central estimates.
Models are checked against observations and, where possible, against other models.
What an analysis cannot show is stated beside what it can.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Uncertainty quantification | Characterising and propagating the uncertainties in a model to state confidence in its outputs. |
| Sensitivity analysis | Repeating an analysis under different assumptions to show how much the result depends on them. |
| Model validation | Comparing model predictions with independent observations to judge fitness for a stated use. |
| Artificial intelligence and machine learning | Artificial intelligence and machine learning develop algorithms that learn patterns from data to classify, predict or generate outputs. |
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.