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What are AI benchmarks and how do they work?

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Students take standardized tests in school to evaluate their performance relative to their classmates. This is the same principle behind artificial intelligence (AI) benchmarks, which test each model’s abilities and show companies where they need to improve. While there’s no perfect test, benchmarks can help AI companies create safer, more reliable models for personal and business use.

An AI system is a model that uses machine learning to analyze data, generate content, and make judgments. Before companies release AI systems to the public, the models undergo training and testing. Yet, even after rigorous tests, models can provide false information or generate harmful content.

This is where AI benchmarks come into play. A benchmark is a test that evaluates the model’s performance and compares it to other systems or a standardized set of answers. When companies get the results, they can spot areas that need improvement and assess how their models compare to other AI software on the market.

How do AI benchmarks work?

During the test, AI models complete a set of tasks that may include pulling information from a question or image dataset. Common tasks include translation, object recognition, code generation, language comprehension, and reasoning. Afterward, the developers receive a test score that helps them gauge their model’s usefulness.

  • Accuracy: The model’s ability to provide correct responses.
  • Precision: The number of correct vs. incorrect predictions.
  • Recall: The number of true positives the model predicts.
  • F1 score: The model’s precision and recall scores combined.
  • Latency: The model’s response time.

Prominent AI benchmarks and their creators

Some of the most famous AI benchmarks include:

  • ImageNet: Stanford University created ImageNet to test image recognition.
  • GLUE/SuperGLUE: DeepMind, New York University, and the Allen School developed GLUE to evaluate language comprehension. SuperGLUE is the more advanced version.
  • COCO: Sponsored by Facebook, Microsoft, and others, COCO uses a large dataset to test object recognition.
  • SQuAD: Stanford University developed SQuAD as a benchmark for reading comprehension.
  • BLEU: IBM created this classic benchmark in 2002 to evaluate translation quality.
  • LiveBench: Created by Abacus.AI, LiveBench tests models for data contamination.
  • WinoGrande: The University of Washington and the Allen Institute developed WinoGrande as a benchmark for reasoning capabilities.
  • HumanEval: OpenAI built HumanEval to test a model’s code-generation abilities.

What benchmark results can tell us

Benchmark results tell developers how well their AI performs compared to other models. Once they’ve analyzed their model’s strengths and drawbacks, they may release it to the public or continue fine-tuning the software to improve its score. This helps developers set goals and meet their deadlines.

Additionally, benchmarks provide a look at AI’s overall progress. As models pass these exams, developers create more advanced benchmarks that challenge AI to reach new heights. These exams may contain larger datasets, higher standards, increasingly difficult tasks, and more resources for creators.

With this information, businesses can choose the best model for their tasks. Depending on their needs, they may want software that excels in translation, text analysis, image detection, mathematics, predictions, or writing. High benchmark scores may give companies confidence that they’re investing in the right software.