What are the limits of benchmarking in AI?

What are the limits of benchmarking in AI?

Benchmarking in AI helps evaluate models, but it has limitations like overfitting, lack of real-world scenarios, and bias.

Can you explain how overfitting affects benchmarking?

Overfitting occurs when a model performs well on training data but poorly on unseen data, skewing benchmark results.

What are some common biases in AI benchmarks?

Common biases include dataset bias, where the training data doesn't represent real-world diversity, leading to skewed results.

How can we improve benchmarking practices in AI?

Improving practices involves using diverse datasets, incorporating real-world scenarios, and regularly updating benchmarks.

Are there any alternatives to benchmarking in AI?

Yes, alternatives include continuous evaluation, user feedback, and real-time performance monitoring to assess AI models.