Overview
The creation of artificially produced datasets that mimic the statistical properties of real-world data, used for training AI models while preserving privacy.
More in Artificial Intelligence
F1 Score
Evaluation & MetricsA harmonic mean of precision and recall, providing a single metric that balances both false positives and false negatives.
Sparse Attention
Models & ArchitectureAn attention mechanism that selectively computes relationships between a subset of input tokens rather than all pairs, reducing quadratic complexity in transformer models.
Perplexity
Evaluation & MetricsA measurement of how well a probability model predicts a sample, commonly used to evaluate language model performance.
Few-Shot Learning
Prompting & InteractionA machine learning approach where models learn to perform tasks from only a small number of labelled examples, often achieved through in-context learning in large language models.
AI Safety
Safety & GovernanceThe interdisciplinary field dedicated to making AI systems safe, robust, and beneficial while minimizing risks of unintended consequences.
Artificial General Intelligence
Foundations & TheoryA hypothetical form of AI that possesses the ability to understand, learn, and apply knowledge across any intellectual task a human can perform.
Speculative Decoding
Models & ArchitectureAn inference acceleration technique where a small draft model generates candidate token sequences that are verified in parallel by the larger target model.
Recall
Evaluation & MetricsThe ratio of true positive predictions to all actual positive instances, measuring completeness of positive identification.