Artificial Intelligence.
What AI systems measurably do, and how we disclose our own use of them.
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AI has moved from research demo to editorial infrastructure faster than the evaluation methods have. We cover what these systems measurably do, disclose where we use them, and treat vendor benchmarks as marketing until independently reproduced.
What this field actually measures
- Benchmark score
Performance on a standard evaluation set.
ReferenceUseful only when the test set is verifiably absent from training data.
LimitContamination has invalidated more than one headline result.
- Hallucination rate
Share of confidently stated outputs that are false.
ReferenceNon-zero in every deployed system, and highly task-dependent.
- Human evaluation agreement
Consistency between independent human raters of output quality.
ReferenceLow inter-rater agreement invalidates the headline score above it.
Evidence & metrics
The published work behind the numbers above, and the public datasets you can open to check us. If a claim can’t be traced here, we don’t print it.
Citations
Vaswani et al. — NeurIPS · 2017
Introduced the transformer architecture underlying essentially every current large language model.
On the dangers of stochastic parrots
Bender, Gebru, McMillan-Major & Shmitchell — FAccT · 2021
Documents how scale alone does not produce understanding and how training corpora encode measurable bias.
Datasets
Papers with Code benchmark leaderboards
Papers with Code · free
Reported model results with linked code across standard benchmarks.
Stanford HAI · free
Annual measured trends in compute, cost, capability, and deployment.
Still open
- Which capability claims survive independent replication?
- How should AI assistance be disclosed in editorial work? (We publish ours.)
- What evaluation exists for reasoning that is not contaminated by the training set?
What we won’t say
We will not report a vendor benchmark as a finding. A self-graded exam is not evidence.
- VO-1Evidence before wit
- VO-2Punch at claims, never at people
- VO-3Say the uncertainty out loud
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