GCRI · 人工智能与自主系统
科学中的 AI
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.
AI in science: how the risk develops.
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.
Learning from data replaces some simulation
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.
Leakage inflates results
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.
Openness enables checking
Results can be verified only when code, data, models and settings are shared. Proprietary models and data limit independent checks.
Scientists stay accountable
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.
模型登记册。
团队跟踪该主题时会使用的界面：其指标、状态与决策。名称为示例，数值仅作示意。
模型注册库
AI in science · Model registry · Release gate
模型
| M-489 triage assistant | 公共服务 | 高 | 20 of 24 suites | 已阻止 |
| M-530 flood nowcast | 科学 | 中 | 12 / 12 | 已发布 |
| M-079 document search | 内部 | 低 | 8 of 8 | 已发布 |
| M-881 agent planner | 智能体式 | 高 | 11 of 16 | 评估中 |
模型卡
- 预期用途
- 分诊辅助，由人决定
- 不在范围内
- 资格决定
- 已知局限
- 在罕见案例上的准确率较低
- 负责人
- 模型所有者
事件日志
- D-19INC摘要中出现虚构引用
- D-8MIT已增加检索依据
- D-3INC通过上传文件的提示注入
- D-1MIT工具权限已收窄
Evaluation scores
vs release threshold
AI changes how each domain’s science is done.
- 水耦合ML models now rival process models in some prediction tasks.What to watchBenchmarks against process models
- 卫生耦合AI supports protein and pathogen research, raising dual-use questions.What to watchBiosecurity review of tools
- 生物多样性耦合Automated species identification scales monitoring.What to watchIdentification accuracy by region
Studies of AI in science are designed for the 方法与技术系列. Reproducibility requires code, data and model versions, recorded under NS-12 of the proposed Nexus Standards.
- Replications of AI-based results.
- Benchmarks with documented data.
How AI in science is modelled, measured and tested.
模型、其假设、数据和不确定性，以科学家或工程师会核查的形式呈现。结构遵循已发表的方法。所取数值仅为示意，不是研究发现。
模型、方程与假设
The model structure used for AI in science, the assumptions it makes and how each is tested.
Ri = f(Hi, Ei, Vi, Ci)危害、风险暴露、脆弱性和应对能力EAL = ∑k pk · Lk对年概率为 pkxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| 假设 | 为何制作 | 如何检验 |
|---|---|---|
| 在一次事件中，危害与暴露相互独立 | 使损失积分保持可计算 | 与基于联合记录拟合的 copula 进行比较 |
| 耦合在工作点附近是线性的 | 让冲击通过 A 传播 | 在线性范围之外施加扰动并比较 |
| 脆弱性曲线可以在不同地点之间迁移使用 | 很少有本地曲线可用 | 在有观测损失的地方据此进行验证 |
- 输入
- 带溯源信息的版本化数据集
- 成果
- 分布，绝不是单一数字
- 代码
- 连同模型卡和测试一起发布
- 复现
- 相同的数据、代码、环境和随机种子
衡量什么，以及衡量得如何。
每个指标都注明其单位、频率、背后的来源类型以及数值将附带的证据标签。此处不显示数值：数值由计划依据其自身权限提供。
| 指标 | 单位 | 节奏 | 来源类型 | 标签 |
|---|---|---|---|---|
| Studies sharing code and data | percent of published ML studies | 每次审查 | Publication audits | 观测 |
| Leakage issues identified in reviews | count by type | 每次审查 | Reproducibility studies | 已报告 |
| Out-of-distribution performance | error on held-out regimes | per evaluation | 模型评估 | 观测 |
| Emulator error against reference simulation | error metric | per evaluation | Validation runs | 观测 |
| Journals with AI use policies | 计数 | 年度 | Publisher policies | 已声明 |
| Compute used for scientific model training | floating point operations | per model | Model documentation | 估算 |
证据来自何处。
具名公共数据集与机构记录，各自注明许可类别。依协议获得的来源仅按其条款使用，绝不再次发布。
| 来源 | 它提供什么 | 许可类别 |
|---|---|---|
| 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 | 开放，CC BY 4.0 |
| Publisher and funder policies on AI use | Rules on authorship, disclosure and review | Public, terms per publisher |
由谁决定，以及证据服务于什么。
人工智能与自主 系统 prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| 谁来决定 | 问题 | 本页面服务的证据 |
|---|---|---|
| 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 |
| 研究资助方 | How to fund open models, data and compute for science | Openness and compute evidence |
该主题产生的产出
- Leakage and reproducibility checklists for ML studies
- Validation protocols for scientific emulators
- Guidance on reporting AI use in research
- Case studies of AI in specific sciences
What scientists ask about AI in research.
- Does the AI-assisted result hold when tested independently, by experiment or observation?
- Is the model, data and code available so others can reproduce it?
- Has data leakage between training and evaluation inflated reported performance?
- Does the model extrapolate beyond the conditions its training data covered?
How discovery and error both scale.
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 系统 can fabricate plausible references or data. And when models and data are proprietary, results cannot be checked.
Keeping AI-assisted science reproducible.
- 01
Reporting standards
Checklists covering data splits, leakage checks, baselines and uncertainty. Standards help only if journals and reviewers enforce them.
- 02
Physics-informed and hybrid models
Combining learned components with known physical constraints, using 人工智能与机器学习 and high-performance, cloud and edge computing. Constraints reduce but do not remove extrapolation risk.
- 03
Uncertainty estimation
Ensembles and Bayesian methods from 概率与统计 to flag predictions the model is unsure of. Uncertainty estimates can themselves be miscalibrated.
- 04
Open artefacts
Publishing code, data and model weights where rights allow, so others can reproduce and challenge results.
Where this page’s references lead.
阅读本页时所对照的机构和文件。状态和版本会变化；每个链接都指向发布机构。
问题与解答。
What is data leakage in machine learning?
Can AI replace scientific simulation?
How should AI use in research be reported?
Can AI replace experiments?
What is data leakage?
Does the platform review scientific papers?
Are AI-generated hypotheses credited?
从本页贡献证据。
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