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Researchers Discover a More Flexible Approach to Machine Learning

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The liquid neural network essentially solves an entire ensemble of linked equations, allowing it to characterize the state of the system at any given moment.

Credit: Kristina Armitage/Quanta Magazine

Artificial intelligence researchers have celebrated a string of successes with neural networks, computer programs that roughly mimic how our brains are organized. But despite rapid progress, neural networks remain relatively inflexible, with little ability to change on the fly or adjust to unfamiliar circumstances.

In 2020, two researchers at the Massachusetts Institute of Technology led a team that introduced a new kind of neural network based on real-life intelligence — but not our own. Instead, they took inspiration from the tiny roundworm, Caenorhabditis elegans, to produce what they called liquid neural networks. After a breakthrough last year, the novel networks may now be versatile enough to supplant their traditional counterparts for certain applications.

Liquid neural networks offer "an elegant and compact alternative," said Ken Goldberg, a roboticist at the University of California, Berkeley. He added that experiments are already showing that these networks can run faster and more accurately than other so-called continuous-time neural networks, which model systems that vary over time.

From Quanta Magazine
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