Results for "compute-data-performance"

Minimum relative to nearby points.

Model exploits poorly specified objectives.

Maximizing reward without fulfilling real goal.

Assigning a role or identity to the model.

Sampling multiple outputs and selecting consensus.

Asking model to review and improve output.

Governance of model changes.

Hardware components that execute physical actions.

Continuous loop adjusting actions based on state feedback.

Mathematical framework for controlling dynamic systems.

Algorithm computing control actions.

Using output to adjust future inputs.

Optimal control for linear systems with quadratic cost.

Mathematical representation of friction forces.

Artificial environment for training/testing agents.

Planning via artificial force fields.

Humans assist or override autonomous behavior.

Human controlling robot remotely.

Hard constraints preventing unsafe actions.

Failure to detect present disease.

Deep learning system for protein structure prediction.

Modeling chemical systems computationally.

Rate at which AI capabilities improve.

Privacy risk analysis under GDPR-like laws.

Learning structure from unlabeled data, such as discovering groups, compressing representations, or modeling data distributions.

Information that can identify an individual (directly or indirectly); requires careful handling and compliance.

Inferring sensitive features of training data.

Learning from data by constructing “pseudo-labels” (e.g., next-token prediction, masked modeling) without manual annotation.

The internal space where learned representations live; operations here often correlate with semantics or generative factors.

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