Active Learning (Machine Learning)
A training strategy where the model identifies which unlabeled examples would be most informative to label next, reducing the amount of labeled data needed to reach good performance.
Glossary
Plain-language definitions for AI terms, research methodology jargon, and model names used across the site.
Ensemble Forecasting
Running many slightly different versions of a forecast model to produce a range of possible outcomes rather than a single prediction — the standard approach for quantifying forecast uncertainty in meteorology.
Federated Learning
A machine learning approach where a model is trained across multiple data-holding institutions without the raw data ever leaving each site — particularly important for medical research where patient data cannot be shared.
Graph Neural Network (GNN)
A type of neural network designed to operate on graph-structured data — where entities (nodes) are connected by relationships (edges) — used in molecular modeling, weather forecasting, and materials discovery.
Hallucination (AI)
When an AI model generates text that sounds confident and plausible but is factually incorrect — a fundamental limitation of large language models that matters enormously in research contexts.
Prompt Engineering
The practice of designing inputs to an AI language model to reliably get better, more accurate, or more useful outputs — a practical skill for researchers using LLMs in their work.
Reanalysis Data
A consistent historical record of atmospheric conditions produced by running a modern weather model over decades of past observations — the primary training data for AI weather models like GraphCast.
Retrieval-Augmented Generation (RAG)
An approach that grounds an LLM's responses in retrieved documents — the model searches a document set first, then generates its answer based on what it actually found, reducing hallucination.
Retrosynthesis
A planning strategy in organic chemistry that works backward from a target molecule to identify feasible synthetic routes — AI tools now automate the search for these routes using reaction databases.
Structured Data Extraction
The process of systematically pulling specific, predefined data fields from a set of documents — in research contexts, used to populate tables comparing outcomes, methods, or sample characteristics across papers.
Topic Modeling
An unsupervised machine learning method that discovers recurring themes in a text corpus — useful for exploring large collections of survey responses, interview transcripts, or documents.