👉 Fluid, often used in the context of artificial intelligence and machine learning, refers to a type of neural network architecture that excels in handling continuous data, such as time series or sensor readings. However, one of the primary concerns with fluid models is their sensitivity to input noise and variability. Because they process data in a continuous manner, small fluctuations or inaccuracies in the input can significantly impact the output, leading to unreliable predictions. Additionally, fluid models can be computationally intensive and require substantial resources for training and inference, making them less practical for real-time applications or resource-constrained environments. This sensitivity to noise and resource demands are key concerns when deploying fluid models in practical scenarios.
