Results for "dynamics learning"

Alternative formulation providing bounds.

Model optimizes objectives misaligned with human values.

Learned subsystem that optimizes its own objective.

Task instruction without examples.

One example included to guide output.

Multiple examples included in prompt.

Explicit output constraints (format, tone).

Sampling multiple outputs and selecting consensus.

Probabilities do not reflect true correctness.

Required descriptions of model behavior and limits.

Centralized AI expertise group.

Coordinating models, tools, and logic.

Dynamic resource allocation.

Devices measuring physical quantities (vision, lidar, force, IMU, etc.).

Software pipeline converting raw sensor data into structured representations.

Using output to adjust future inputs.

Optimizing continuous action sequences.

Imagined future trajectories.

Perceived actions an environment allows.

Interpreting human gestures.

Intelligence emerges from interaction with the physical world.

Closed loop linking sensing and acting.

AI systems assisting clinicians with diagnosis or treatment decisions.

AI applied to X-rays, CT, MRI, ultrasound, pathology slides.

Ability to correctly detect disease.

Failure to detect present disease.

Grouping patients by predicted outcomes.

Predicting disease progression or survival.

Differences between training and deployed patient populations.

Unequal performance across demographic groups.