👉 Norm computing is a framework that standardizes the way machine learning models are evaluated and compared, focusing on their performance in real-world scenarios rather than just accuracy on standard datasets. It introduces a set of benchmarks and evaluation metrics designed to assess how well models generalize to new, unseen data, particularly in practical applications. This approach emphasizes robustness, efficiency, and fairness, considering factors like model complexity, computational resources, and ethical implications. By aligning model evaluation with real-world constraints, norm computing aims to produce more reliable and trustworthy AI systems that can perform effectively across diverse environments.
