Results for "risk assessment"
Asking model to review and improve output.
Internal representation of the agent itself.
A function measuring prediction error (and sometimes calibration), guiding gradient-based optimization.
How well a model performs on new data drawn from the same (or similar) distribution as training.
Randomly zeroing activations during training to reduce co-adaptation and overfitting.
Breaking documents into pieces for retrieval; chunk size/overlap strongly affect RAG quality.
A formal privacy framework ensuring outputs do not reveal much about any single individual’s data contribution.
Training across many devices/silos without centralizing raw data; aggregates updates, not data.
Samples from the k highest-probability tokens to limit unlikely outputs.
Constraining model outputs into a schema used to call external APIs/tools safely and deterministically.
A measure of a model class’s expressive capacity based on its ability to shatter datasets.
Measures a model’s ability to fit random noise; used to bound generalization error.
A narrow minimum often associated with poorer generalization.
Logged record of model inputs, outputs, and decisions.
Central catalog of deployed and experimental models.
Inferring sensitive features of training data.
Incrementally deploying new models to reduce risk.
Average value under a distribution.
Process for managing AI failures.
Learning action mapping directly from demonstrations.
Ensuring robots do not harm humans.
Systems where failure causes physical harm.
US approval process for medical AI devices.
Software regulated as a medical device.
AI predicting crime patterns (highly controversial).
AI-driven buying/selling of financial assets.
Ultra-low-latency algorithmic trading.
Returns above benchmark.
Market reacting strategically to AI.
AI reinforcing market trends.