Mapping global dynamics of benchmark creation and saturation in artificial intelligence

Area of Science:

• Artificial intelligence
• Machine learning evaluation
• Computer vision
• Natural language processing

Background:

• Benchmarks are vital for measuring progress in artificial intelligence (AI).
• Concerns exist regarding AI benchmark health, including overfitting, saturation, and dataset centralization.
• Existing methods struggle to capture the global dynamics of AI benchmark creation and utilization.

Purpose of the Study:

• To develop methodologies for mapping the global dynamics of AI benchmark creation and saturation.
• To analyze the health and utilization patterns of AI benchmarks across computer vision and natural language processing.
• To identify factors influencing benchmark popularity and guide future benchmark development.

Main Methods:

• Curated data from 3765 benchmarks across computer vision and natural language processing.
• Developed methodologies for creating condensed maps of benchmark creation and saturation dynamics.
• Analyzed benchmark utilization, saturation trends, and performance gain patterns.

Main Results:

• A significant portion of AI benchmarks rapidly approach saturation.
• Many benchmarks exhibit limited widespread utilization.
• Performance gains in AI tasks show unpredictable burst patterns.
• Benchmark popularity is associated with specific attributes.

Conclusions:

• Future AI benchmarks should prioritize versatility, breadth, and real-world applicability.
• Monitoring benchmark ecosystem health is crucial for sustainable AI progress.
• Understanding benchmark dynamics can mitigate issues like overfitting and saturation.