“Liquid” machine-learning system adapts to changing conditions

Researcher Ramin Hasani, the study’s lead author, coded a neural network that can recognize patterns by analyzing a set of “training” examples.

via MIT

MIT researchers have developed a type of neural network that learns on the job, not just during its training phase. These flexible algorithms, dubbed “liquid” networks, change their underlying equations to continuously adapt to new data inputs. The advance could aid decision making based on data streams that change over time, including those involved in medical diagnosis and autonomous driving.

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