Results for "allocation systems"

Dynamic resource allocation.

AI that ranks patients by urgency.

Regulating access to large-scale compute.

Maximum system processing rate.

Grouping patients by predicted outcomes.

Agents optimize collective outcomes.

No agent can improve without hurting another.

Designing systems where rational agents behave as desired.

Hardware resources used for training/inference; constrained by memory bandwidth, FLOPs, and parallelism.

Optimization problems where any local minimum is global.

Empirical laws linking model size, data, compute to performance.

Predicting future values from past observations.

Assigning AI costs to business units.

Limiting inference usage.

Quantifying financial risk.

Maximum expected loss under normal conditions.

Rules governing auctions.

Truthful bidding is optimal strategy.

Designing efficient marketplaces.

Systems where failure causes physical harm.

Required human review for high-risk decisions.

AI systems assisting clinicians with diagnosis or treatment decisions.

Forcing predictable formats for downstream systems; reduces parsing errors and supports validation/guardrails.

European regulation classifying AI systems by risk.

Compromising AI systems via libraries, models, or datasets.

AI systems that perceive and act in the physical world through sensors and actuators.

Automated assistance identifying disease indicators.

Existential risk from AI systems.

Isolating AI systems.

Mechanisms for retaining context across turns/sessions: scratchpads, vector memories, structured stores.