Results for "trial-and-error"

Failure to detect present disease.

Agents have opposing objectives.

Early signals disproportionately influence outcomes.

Sudden jump to superintelligence.

Stored compute or algorithms enabling rapid jumps.

Signals indicating dangerous behavior.

Tendency to gain control/resources.

Intelligence and goals are independent.

Goals useful regardless of final objective.

Methods that learn training procedures or initializations so models can adapt quickly to new tasks with little data.

Minimizing average loss on training data; can overfit when data is limited or biased.

Techniques that discourage overly complex solutions to improve generalization (reduce overfitting).

A robust evaluation technique that trains/evaluates across multiple splits to estimate performance variability.

One complete traversal of the training dataset during training.

Halting training when validation performance stops improving to reduce overfitting.

Stepwise reasoning patterns that can improve multi-step tasks; often handled implicitly or summarized for safety/privacy.

Local surrogate explanation method approximating model behavior near a specific input.

A formal privacy framework ensuring outputs do not reveal much about any single individual’s data contribution.

Samples from the smallest set of tokens whose probabilities sum to p, adapting set size by context.

Hidden behavior activated by specific triggers, causing targeted mispredictions or undesired outputs.

Allows gradients to bypass layers, enabling very deep networks.

Encodes token position explicitly, often via sinusoids.

Routes inputs to subsets of parameters for scalable capacity.

Learning from data generated by a different policy.

Learning only from current policy’s data.

Recovering training data from gradients.

Transformer applied to image patches.

Persistent directional movement over time.

Identifying abrupt changes in data generation.

Trend reversal when data is aggregated improperly.