Results for "randomness control"

Existential risk from AI systems.

Tendency to gain control/resources.

Restricting distribution of powerful models.

Physical form contributes to computation.

Human or automated process of assigning targets; quality, consistency, and guidelines matter heavily.

Learning where data arrives sequentially and the model updates continuously, often under changing distributions.

Policies and practices for approving, monitoring, auditing, and documenting models in production.

A hidden variable influences both cause and effect, biasing naive estimates of causal impact.

Automated testing and deployment processes for models and data workflows, extending DevOps to ML artifacts.

Central system to store model versions, metadata, approvals, and deployment state.

A broader capability to infer internal system state from telemetry, crucial for AI services and agents.

Generating speech audio from text, with control over prosody, speaker identity, and style.

Optimization problems where any local minimum is global.

Formal framework for sequential decision-making under uncertainty.

Multiple agents interacting cooperatively or competitively.

Coordination arising without explicit programming.

Logged record of model inputs, outputs, and decisions.

Central catalog of deployed and experimental models.

Models time evolution via hidden states.

Identifying abrupt changes in data generation.

Expected causal effect of a treatment.

Probability of treatment assignment given covariates.

Models accessible only via service APIs.

Sample mean converges to expected value.

Sum of independent variables converges to normal distribution.

Optimization under uncertainty.

Willingness of system to accept correction or shutdown.

Ability to inspect and verify AI decisions.

Mechanism to disable AI system.

Limiting inference usage.