What counts as progress?
We track developments where AI materially contributes to scientific, medical, industrial or societal progress. That contribution must improve a capability, the speed or scale of a process, accuracy, discovery, or a real-world outcome.
A model release or feature announcement does not qualify on its own. A clear application with substantive evidence might.
Five questions for every development.
Evidence
What supports the finding? We distinguish preprints, peer-reviewed studies, independent replication, controlled trials and measured outcomes. Peer review alone does not establish real-world benefit.
Novelty
What changed compared with the previous approach? We look for a new capability or a demonstrated improvement, not a renamed product or an untested announcement.
AI contribution
Did AI materially improve capability, speed, scale, accuracy, discovery or an outcome? Using AI somewhere in a workflow is not enough.
Real-world relevance
Who could benefit, under what conditions, and what obstacles remain? We keep the plausible benefit separate from what has already been measured.
Progress stage
How far has this development reached? Stage describes its relationship to practical use. It is separate from the strength of evidence and our assessment of significance.
From research to measurable impact.
Research
Research published or a model demonstrated.
Validation
Independent or real-world validation.
Human / Field Trial
Clinical trial or real-world field testing.
Deployment
Clinical, industrial or public deployment.
Measurable Impact
A measurable real-world outcome.
This path is a guide, not a promise. Some discoveries remain foundational research. Others skip a stage, are contradicted, or prove unsuitable outside the original setting. Each stage needs evidence of its own.
Sources, uncertainty and corrections.
Each brief links to its primary source and names the institution involved. A press release is context, not independent validation. We look for sample size, study design, baselines, limitations and conflicts of interest before describing a result.
Corrections and retractions can change how a finding should be understood. They matter as much as the original result.
New evidence, not repeated headlines.
Several reports may describe the same study. They belong to one development, with links to the underlying evidence. A follow-up result, independent test or correction can change that record.
Dates refer to the original publication, not the day we found it. Editorial selection reflects relevance and evidence, not an objective ranking of scientific merit. A theme changes when its evidence changes.