Like a collection of 'Pick Up Sticks', this neural network has passed a critical step for developing machine intelligence. For the first time, a physical neural network has successfully been shown to learn and remember 'on the fly', in a way inspired by and similar to how the brain's neurons work.
The result opens a pathway for developing efficient and low-energy machine intelligence for more complex, real-world learning and memory tasks.For the first time, a physical neural network has successfully been shown to learn and remember 'on the fly', in a way inspired by and similar to how the brain's neurons work.
Lead author Ruomin Zhu, a PhD student from the University of Sydney Nano Institute and School of Physics, said:"The findings demonstrate how brain-inspired learning and memory functions using nanowire networks can be harnessed to process dynamic, streaming data." In this study, researchers used the network to recognise and remember sequences of electrical pulses corresponding to images, inspired by the way the human brain processes information.Supervising researcher Professor Zdenka Kuncic said the memory task was similar to remembering a phone number.
"Our novel approach allows the nanowire neural network to learn and remember 'on the fly', sample by sample, extracting data online, thus avoiding heavy memory and energy usage.""If the data is being streamed continuously, such as it would be from a sensor for instance, machine learning that relied on artificial neural networks would need to have the ability to adapt in real-time, which they are currently not optimised for," he said.
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