💦Transparent interpretation of results and permanent learning in production with non-stop adaptation of neural network to new conditions and data
Liquid NN from MIT for decision making in autonomous driving and medical diagnosis based on nervous system of microscopic nematode with 302 neurons and principles of time series data ananlytics. This ML-model edged out other state-of-the-art time series algorithms by a few percentage points in accurately predicting future values in datasets, ranging from atmospheric chemistry to traffic patterns. Just changing the representation of a neuron with the differential equations, you can deal with small number of highly expressive neurons and peer into the “black box” of the network’s decision making and diagnose why the network made a certain characterization.
https://news.mit.edu/2021/machine-learning-adapts-0128