Novel memristors to overcome AI’s ‘catastrophic forgetting’
So-called “memristors” consume extremely little power and behave similarly to brain cells. Researchers from Jülich, led by Ilia Valov, have now introduced novel memristive components that offer significant advantages over previous versions: they are more robust, function across a wider voltage range, and can operate in both analog and digital modes. These properties could help address the problem of “catastrophic forgetting,” where artificial neural networks abruptly forget previously learned information.
Over the past few decades, the performance of machine learning models on various real-world tasks has improved significantly. Training and implementing most of these models, however, still requires vast amounts of energy and computational power.
A trio of researchers from the University of California, working with a colleague from the Institute of Computer Science of the Czech Academy of Sciences, has found that it is possible to prevent catastrophic forgetting in AI systems by having such systems mimic human REM sleep.
USC researchers built artificial neurons that replicate real brain processes using ion-based diffusive memristors. These devices emulate how neurons use chemicals to transmit and process signals, offering massive energy and size advantages. The technology may enable brain-like, hardware-based learning systems. It could transform AI into something closer to natural intelligence.
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