“Liquid” machine-learning system adapts to changing conditions

MIT News  January 28, 2021 An international team of researchers (USA – MIT, Austria) designed a neural network that can adapt to the variability of real-world systems. They took inspiration from C.elegans which has only 302 neurons in its nervous system, yet it can generate unexpectedly complex dynamics. The equations they used to structure their neural network allowed the parameters to change over time based on the results of a nested set of differential equations. Most neural networks’ behavior is fixed after the training phase. The fluidity of their “liquid” network makes it more resilient to unexpected or noisy data and […]