Term tag: reliability

AI Hallucinations

Models hallucinate because training and benchmarks reward guessing over admitting ignorance: a guess sometimes scores, "I don't know" scores zero every time.

AI Healthcare

Where AI is actually deployed in medicine — imaging, triage, ambient notes — and why 'beats doctors' headlines rarely survive contact with a clinic.

Autonomous Vehicle Safety

Autonomous vehicle safety technologies, standards, and testing protocols designed to ensure self-driving cars can operate safely without human intervention.

Calibration

Calibration means a model's confidence matches reality: of everything it calls 80% likely, about 80% should happen — a property separate from raw accuracy.

Concept Drift

Concept drift is when the relationship a deployed model learned changes over time, so accuracy silently decays until you detect the drift and retrain.

Error Handling in AI Systems

Ordinary code fails by raising an exception. A model fails by returning a confident, well-formed, wrong answer that nothing catches. What to do about it.

Guardrails

A guardrail is a deterministic check around a model that can block, rewrite, or force a retry — external code you can test, unlike prompting a model to behave.

Production Systems

A model serving live users under latency and uptime guarantees — judged at the 99th percentile under real load, not by the median on a laptop.

Structured Outputs (Constrained Decoding)

Constrained decoding masks a language model's token distribution against a JSON schema or grammar at each step, so the output is parseable by construction.

Trust

Trust in AI is a person's willingness to rely on a system's output; the aim is calibration — trusting it as much as its real reliability deserves.