What metrics would you use to evaluate the performance of an AI system?

To evaluate the performance of an AI system, you should consider various metrics depending on the nature of the task. For classification tasks, metrics like accuracy, precision, recall, and F1-score are essential. For regression tasks, mean absolute error (MAE), mean squared error (MSE), and R-squared are commonly used. Additionally, in natural language processing (NLP) systems, perplexity and BLEU scores can be used for evaluating language models. It's also crucial to evaluate user satisfaction and system efficiency metrics such as latency and throughput.