mudelinõrkusest

Mudelinõrkus, known in English as model weakness or sometimes model fragility, refers to a vulnerability in artificial intelligence or machine learning models. It describes a situation where a model performs poorly or unpredictably when faced with inputs that are slightly different from the data it was trained on. This can manifest as a sudden drop in accuracy or the generation of nonsensical outputs.

This phenomenon is distinct from general underfitting or overfitting. While overfitting models are too specialized to their training data and struggle with new, unseen data, mudelinõrkus can occur even in models that generalize reasonably well. The weakness often lies in specific, subtle variations or adversarial perturbations that can easily fool the model. These perturbations might be imperceptible to humans but can cause significant misclassifications or errors in the model's predictions.

Identifying and mitigating mudelinõrkus is a significant area of research in AI safety and robustness. Techniques such as adversarial training, data augmentation, and model regularization are employed to make models more resilient to these weaknesses. Understanding mudelinõrkus is crucial for deploying AI systems in real-world applications where unexpected or manipulated inputs are a possibility, ensuring reliable and trustworthy performance.