Results for "trial-and-error"

AI proposing scientific hypotheses.

Modeling chemical systems computationally.

Combination of cooperation and competition.

Rules governing auctions.

Designing efficient marketplaces.

Inferring the agent’s internal state from noisy sensor data.

A structured collection of examples used to train/evaluate models; quality, bias, and coverage often dominate outcomes.

A parameterized mapping from inputs to outputs; includes architecture + learned parameters.

Harmonic mean of precision and recall; useful when balancing false positives/negatives matters.

Popular optimizer combining momentum and per-parameter adaptive step sizes via first/second moment estimates.

Methods to set starting weights to preserve signal/gradient scales across layers.

Nonlinear functions enabling networks to approximate complex mappings; ReLU variants dominate modern DL.

Converting text into discrete units (tokens) for modeling; subword tokenizers balance vocabulary size and coverage.

A model that assigns probabilities to sequences of tokens; often trained by next-token prediction.

A high-priority instruction layer setting overarching behavior constraints for a chat model.

A high-capacity language model trained on massive corpora, exhibiting broad generalization and emergent behaviors.

Retrieval based on embedding similarity rather than keyword overlap, capturing paraphrases and related concepts.

Ensuring model behavior matches human goals, norms, and constraints, including reducing harmful or deceptive outputs.

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

Stress-testing models for failures, vulnerabilities, policy violations, and harmful behaviors before release.

Tendency to trust automated suggestions even when incorrect; mitigated by UI design, training, and checks.

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

Reduction in uncertainty achieved by observing a variable; used in decision trees and active learning.

Measures divergence between true and predicted probability distributions.

Quantifies shared information between random variables.

Updating beliefs about parameters using observed evidence and prior distributions.

Variability introduced by minibatch sampling during SGD.

Neural networks can approximate any continuous function under certain conditions.

Allows model to attend to information from different subspaces simultaneously.

Stores past attention states to speed up autoregressive decoding.