Prediction meets experiment
Models can help generate hypotheses, identify promising candidates and analyze complex data. But a prediction is not a confirmed scientific result.
Laboratory measurements, replication and peer review remain the mechanisms that turn interesting possibilities into dependable knowledge.
Better tools for scientists
The most effective systems combine domain knowledge with specialized datasets, simulation and human expertise. They are instruments within a research workflow, not replacements for the scientific method.
Data quality and reproducibility are especially important when results could shape medical or industrial decisions.
A broader horizon
Reducing the time to test hypotheses could accelerate progress in many fields. The benefits will depend on access to tools, trustworthy datasets and the capacity to validate findings.
AI changes the pace of discovery most when it strengthens the loop between idea and evidence.