Benchmarks (in AI)
Standardized tests or datasets used to compare model performance.
Overview
Benchmarks in AI are standardized tests, datasets, or protocols used to evaluate and compare the performance of different models or algorithms. By providing a common ground for measurement—such as accuracy, speed, or memory consumption—benchmarks enable researchers and practitioners to understand how well new methods perform relative to existing approaches. They also provide a way to systematically track progress in various areas of AI.
Why Benchmarks Matter
They provide a consistent basis for evaluating models, promoting reproducibility and fair comparisons across different techniques.
Common Benchmarks
Popular benchmarks exist for various tasks—image classification (e.g., ImageNet), language understanding (e.g., GLUE), or question-answering (e.g., SQuAD)—allowing the AI community to track progress systematically.