Results for "objective design"

A dataset + metric suite for comparing models; can be gamed or misaligned with real-world goals.

Coordinating tools, models, and steps (retrieval, calls, validation) to deliver reliable end-to-end behavior.

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

Measures how much information an observable random variable carries about unknown parameters.

A theoretical framework analyzing what classes of functions can be learned, how efficiently, and with what guarantees.

The shape of the loss function over parameter space.

Limiting gradient magnitude to prevent exploding gradients.

Neural networks can approximate any continuous function under certain conditions.

Early architecture using learned gates for skip connections.

A narrow hidden layer forcing compact representations.

Using same parameters across different parts of a model.

Capabilities that appear only beyond certain model sizes.

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

All possible configurations an agent may encounter.

Set of all actions available to the agent.

Balancing learning new behaviors vs exploiting known rewards.

Separates planning from execution in agent architectures.

System that independently pursues goals over time.

Vectors with zero inner product; implies independence.

Correctly specifying goals.

Required descriptions of model behavior and limits.

Privacy risk analysis under GDPR-like laws.

Mechanism to disable AI system.

Startup latency for services.

Field combining mechanics, control, perception, and AI to build autonomous machines.

The physical system being controlled.

Algorithm computing control actions.

Study of motion without considering forces.

Robots learning via exploration and growth.

Systems where failure causes physical harm.