Topics: Artificial Intelligence, Biology, Computer Science, Materials Science
Researchers in the US have developed a perovskite-based device that could be used to create a high-plasticity architecture for artificial intelligence. The team, led by Shriram Ramanathan at Purdue University, has shown that the material’s electronic properties can be easily reconfigured, allowing the devices to function like artificial neurons and other components. Their results could lead to more flexible artificial-intelligence hardware that could learn much like the brain.
Artificial intelligence systems can be trained to perform a task such as voice recognition using real-world data. Today this is usually done in software, which can adapt when additional training data are provided. However, machine learning systems that are based on hardware are much more efficient and researchers have already created electronic circuits that behave like artificial neurons and synapses.
However, unlike the circuits in our brains, these electronics are not able to reconfigure themselves when presented with new training information. What is needed is a system with high plasticity, which can alter its architecture to respond efficiently to new information.
Device can transform into four components for artificial intelligence systems, Sam Jarman, Physics World