Results for "risk"
Empirical Risk Minimization
IntermediateMinimizing average loss on training data; can overfit when data is limited or biased.
Empirical Risk Minimization is like trying to get the best score on a test by practicing with sample questions. When you practice, you want to get as many answers right as possible, which is similar to how a machine learning model learns from its training data. However, if you only focus on those...
Central catalog of deployed and experimental models.
Logged record of model inputs, outputs, and decisions.
Inferring sensitive features of training data.
Incrementally deploying new models to reduce risk.
Average value under a distribution.
Review process before deployment.
Process for managing AI failures.
Governance of model changes.
AI used without governance approval.
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.
Sudden extreme market drop.
Signals indicating dangerous behavior.
Isolating AI systems.
Accelerating safety relative to capabilities.
A formal privacy framework ensuring outputs do not reveal much about any single individual’s data contribution.
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.