Liquid neural nets (LNNs) are an exciting, relatively new direction in AI/ML research that promises more compact and dynamic neural nets for time series prediction. LNNs offer a new approach to tasks like weather prediction, speech recognition, and autonomous driving. The primary benefit LNNs offer is that they continue adapting to new stimuli after training. Additionally, LNNs are robust in noisy conditions and are smaller and more interpretable than their conventional counterparts.
LNNs and similar concepts have been around for a while, but the 2020 paper, Liquid Time Constant Networks catapulted them to the forefront to the AI/ML space. Since then, they’ve cemented themselves as a fascinating direction for time series predictors aimed at increasing individual neurons’ representational capability instead of deriving capability through scale.