Artificial Intelligence and Autonomous Systems
The Global Centre for Risk and Innovation (GCRI)
Machine learning now predicts protein structures, emulates weather and climate models, screens materials and helps analyse vast datasets. It can speed discovery, and it can also produce results that are hard to reproduce, overfit or rest on data leakage. This page sets out how AI is used in science, where it fails, how its results are checked, and what evidence serves the researchers, funders and journals involved.
GCRI · In development · service level R0
AI in science is the use of machine learning to predict, simulate and discover in research, from protein structures to weather and materials. It can speed work, and it can produce irreproducible results through leakage and overfitting, so openness, validation outside the training range and accountability stay central.
Night-time lights and population density, the footprint of electrified, connected activity, framed on East Asia; each layer is a dated NASA product.
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Connected activity
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NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Natural Earth
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Night-time lights and population density, the footprint of electrified, connected activity, framed on East Asia.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
The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.
AI in science · Model registry · Release gate
| M-489 triage assistant | Public service | High | 20 of 24 suites | Blocked |
| M-530 flood nowcast | Science | Medium | 12 of 12 | Released |
| M-079 document search | Internal | Low | 8 of 8 | Released |
| M-881 agent planner | Agentic | High | 11 of 16 | Evaluating |
vs release threshold
Discovery and reproducibility. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Models trained on simulations or observations can emulate expensive calculations quickly. They are reliable within the range of their training data and uncertain outside it.
When information from the test set reaches training, through duplicates, time overlap or preprocessing, reported accuracy overstates real performance. Leakage is a known cause of irreproducible ML science.
Results can be verified only when code, data, models and settings are shared. Proprietary models and data limit independent checks.
AI can suggest hypotheses and draft text, but researchers remain responsible for methods, interpretation and integrity. Journals and funders are setting rules for AI use.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Emulator | A machine learning model trained to approximate a more expensive simulation. |
| Data leakage | Information from test data reaching training or model selection, inflating apparent performance. |
| Out-of-distribution | Inputs that differ from the data a model was trained on. |
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Studies sharing code and data | percent of published ML studies | per review | Publication audits | OBSERVED |
| Leakage issues identified in reviews | count by type | per review | Reproducibility studies | REPORTED |
| Out-of-distribution performance | error on held-out regimes | per evaluation | Model evaluation | OBSERVED |
| Emulator error against reference simulation | error metric | per evaluation | Validation runs | OBSERVED |
| Journals with AI use policies | count | annual | Publisher policies | DECLARED |
| Compute used for scientific model training | floating point operations | per model | Model documentation | ESTIMATED |
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| Protein Data Bank and AlphaFold Protein Structure Database | Experimental and predicted protein structures | Open, CC0 and CC BY 4.0 respectively |
| WeatherBench and related benchmarks | Standard evaluation of data-driven weather models | Open, licence per benchmark (to check) |
| Princeton reproducibility studies on ML-based science | Survey of leakage and reproducibility failures | Licence per publication |
| Materials Project | Computed materials properties for screening | Open, CC BY 4.0 |
| Publisher and funder policies on AI use | Rules on authorship, disclosure and review | Public, terms per publisher |
Artificial Intelligence and Autonomous Systems prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Research teams | Whether an ML result is robust enough to publish | Leakage checklists; validation protocols |
| Journals and reviewers | What evidence to require for ML-based claims | Reporting guidance; reproducibility evidence |
| Research funders | How to fund open models, data and compute for science | Openness and compute evidence |
Machine learning finds patterns in large datasets faster than people can, and surrogate models can emulate expensive simulations, allowing many more scenarios to be explored. In fields with good data and clear validation, such as structural biology, the gains have been substantial.
The risks are equally systematic. Leakage between training and test data has produced inflated results across many disciplines. Models trained on past data may not respect physical laws when extrapolating. Generative systems can fabricate plausible references or data. And when models and data are proprietary, results cannot be checked.
Checklists covering data splits, leakage checks, baselines and uncertainty. Standards help only if journals and reviewers enforce them.
Combining learned components with known physical constraints, using artificial intelligence and machine learning and high-performance, cloud and edge computing. Constraints reduce but do not remove extrapolation risk.
Ensembles and Bayesian methods from probability and statistics to flag predictions the model is unsure of. Uncertainty estimates can themselves be miscalibrated.
Publishing code, data and model weights where rights allow, so others can reproduce and challenge results.
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.