Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products, a new study suggests.
Using machine learning tools to create a digital twin, or a virtual copy, of an electronic circuit that exhibits chaotic behavior, researchers found that they were successful at predicting how it would behave and using that information to control it.
Now, having access to an efficient digital twin is likely to have a sweeping impact on how scientists develop future autonomous technologies, said Robert Kent, lead author of the study and a graduate student in physics at The Ohio State University. Although similarly sized computer chips have been used in devices like smart fridges, according to the study, this novel computing ability makes the new model especially well-equipped to handle dynamic systems such as self-driving vehicles as well as heart monitors, which must be able to quickly adapt to a patient's heartbeat.
"People will find good use out of it just based on how efficient it is," Kent said."You can implement it on pretty much any platform and it's very simple to understand." The algorithm was recently made available to scientists.
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