GCRI · Kecerdasan Buatan dan Sistem Otonom
AI dalam sains
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 in seven parts.
- 01PertanyaannyaAI in science: how the risk develops
- 02Di seluruh domainAI changes how each domain’s science is done
- 03IndikatorApa yang diukur, dan seberapa baik
- 04SumberDari mana bukti berasal
- 05KeputusanSiapa yang memutuskan, dan apa yang dilayani bukti
- 06Pertanyaan intiWhat scientists ask about AI in research
- 07MekanismeHow discovery and error both scale
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.
Registri model.
Layar yang akan digunakan sebuah tim untuk mengikuti subjek ini: ukuran, status, dan keputusannya. Nama adalah contoh dan nilai bersifat ilustratif.
Registri model
AI in science · Model registry · Release gate
Model
| M-489 triage assistant | Layanan publik | Tinggi | 20 of 24 suites | Terhambat |
| M-530 flood nowcast | Sains | Sedang | 12 dari 12 | Dirilis |
| M-079 document search | Internal | Rendah | 8 of 8 | Dirilis |
| M-881 agent planner | Agentik | Tinggi | 11 of 16 | Sedang dievaluasi |
Kartu model
- Penggunaan yang dimaksudkan
- dukungan triase, manusia memutuskan
- Di luar cakupan
- keputusan kelayakan
- Batasan yang diketahui
- akurasi lebih rendah pada kasus langka
- Pemilik
- Pemilik model
Log insiden
- D-19INCKutipan halusinasi dalam ringkasan
- D-8MITLandasan pengambilan data ditambahkan
- D-3INCInjeksi prompt melalui berkas yang diunggah
- D-1MITIzin alat dipersempit
Evaluation scores
vs release threshold
AI changes how each domain’s science is done.
- AirKeterkaitanML models now rival process models in some prediction tasks.What to watchBenchmarks against process models
- KesehatanKeterkaitanAI supports protein and pathogen research, raising dual-use questions.What to watchBiosecurity review of tools
- Keanekaragaman hayatiKeterkaitanAutomated species identification scales monitoring.What to watchIdentification accuracy by region
Studies of AI in science are designed for the seri Metode dan Teknis. 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.
Model, asumsi, data, dan ketidakpastiannya, dalam bentuk yang akan diperiksa ilmuwan atau insinyur. Strukturnya mengikuti metode yang dipublikasikan. Nilai yang digambar bersifat ilustratif, bukan temuan.
Model, persamaan, dan asumsi
The model structure used for AI in science, the assumptions it makes and how each is tested.
Ri = f(Hi, Ei, Vi, Ci)bahaya, keterpaparan, kerentanan, dan kapasitas penanggulanganEAL = ∑k pk · Lkatas peristiwa k dengan probabilitas tahunan pkxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| Asumsi | Mengapa ini dibuat | Bagaimana ini diuji |
|---|---|---|
| Bahaya dan eksposur bersifat independen dalam satu peristiwa | Menjaga integral kerugian tetap dapat dihitung | Bandingkan dengan pencocokan kopula pada catatan gabungan |
| Keterkaitan bersifat linear di dekat titik operasi | Membiarkan guncangan merambat melalui A | Uji di luar rentang linear lalu bandingkan |
| Kurva kerentanan dapat dipindahkan antartempat | Kurva lokal jarang tersedia | Validasi terhadap kerugian teramati jika tersedia |
- Masukan
- dataset berversi dengan asal-usul yang tercatat
- Keluaran
- distribusi, bukan angka tunggal
- Kode
- dirilis bersama kartu model dan pengujiannya
- Reproduksi
- data, kode, lingkungan, dan seed yang sama
Apa yang diukur, dan seberapa baik.
Setiap indikator membawa satuan, frekuensi, jenis sumber di baliknya, dan label bukti yang akan dibawa suatu nilai. Tidak ada nilai yang ditampilkan: sebuah program menyediakannya berdasarkan wewenangnya sendiri.
| Indikator | Unit | Kadensi | Jenis sumber | Label |
|---|---|---|---|---|
| Studies sharing code and data | percent of published ML studies | per tinjauan | Publication audits | TERAMATI |
| Leakage issues identified in reviews | count by type | per tinjauan | Reproducibility studies | DILAPORKAN |
| Out-of-distribution performance | error on held-out regimes | per evaluation | Evaluasi model | TERAMATI |
| Emulator error against reference simulation | error metric | per evaluation | Validation runs | TERAMATI |
| Journals with AI use policies | jumlah | tahunan | Publisher policies | DINYATAKAN |
| Compute used for scientific model training | floating point operations | per model | Model documentation | ESTIMASI |
Asal bukti.
Set data publik bernama dan catatan kelembagaan, masing-masing dengan kelas lisensinya. Sumber berdasarkan perjanjian hanya digunakan sesuai ketentuannya dan tidak pernah diterbitkan ulang.
| Sumber | Apa yang diberikannya | Kelas lisensi |
|---|---|---|
| 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 | Terbuka, CC BY 4.0 |
| Publisher and funder policies on AI use | Rules on authorship, disclosure and review | Public, terms per publisher |
Siapa yang memutuskan, dan apa yang dilayani bukti.
Studio Kecerdasan Buatan dan Sistem prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Siapa yang memutuskan | Pertanyaannya | Bukti yang dilayani halaman ini |
|---|---|---|
| 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 |
| Penyandang dana penelitian | How to fund open models, data and compute for science | Openness and compute evidence |
Keluaran yang dihasilkan topik ini
- 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 sistem 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 kecerdasan buatan dan pembelajaran mesin and high-performance, cloud and edge computing. Constraints reduce but do not remove extrapolation risk.
- 03
Uncertainty estimation
Ensembles and Bayesian methods from probabilitas dan statistika 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.
Cara hal ini dikaji, dan apa yang tidak dapat dinyatakan metodenya.
- 01Kecerdasan buatan dan pembelajaran mesinAnalisis pergeseran distribusi · Fine-tuning untuk penggunaan tertentu · Metode interpretabilitasDiterapkan di sini
- 02Probabilitas dan statistikaKetidakpastian pada skor tolok ukur · Penilaian kalibrasi · Sampel auditDiterapkan di sini
- 03Komputasi berkinerja tinggi, cloud, dan edgeAkses komputasi untuk riset publik · Pengukuran energi beban kerjaDiterapkan di sini
- 04Evaluasi AI dan rekayasa penjaminanKasus penjaminan untuk sistem AI · Red teaming terstruktur · Pemantauan pascapenerapanDiterapkan di sini
Where this page’s references lead.
Badan dan dokumen yang menjadi acuan halaman ini. Status dan edisi berubah; setiap tautan menuju badan penerbitnya.
Tanya jawab.
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?
Kontribusikan bukti dari halaman ini.
Laporan 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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Bawa metode, dataset, atau pertanyaan.
Informasi dan pertanyaan sudah terbuka hari ini. Sebuah pertanyaan menerima nomor referensi dan menyebutkan langkah berikutnya; lamaran, pembayaran, alokasi komputasi, dan pendaftaran hanya dibuka ketika tim, ketentuan, dan kapasitas tinjauannya tersedia.
Dokumen hukum.
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