👉 The Critics' Fluid, introduced by Andrew Ng in 2015, is a method for evaluating the relative quality of machine learning models by comparing their performance on a diverse set of tasks rather than relying solely on a single metric like accuracy. This approach aims to provide a more balanced and comprehensive assessment of a model's capabilities across various domains, such as image classification, natural language processing, and reinforcement learning. By using a fluid metric that adapts to the distribution of tasks, Critics' Fluid helps identify models that generalize well and perform consistently across different challenges, offering a more nuanced understanding of machine learning performance than traditional evaluation methods.
