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Self-learning electrical network

30.03.2022

Scientists from the University of Pennsylvania have discovered a new approach to machine learning. A conventional electrical circuit learned to recognize flowers by the size of their petals on its own.

A team of scientists led by physicist Samuel Dillavu assembled a small electrical network by randomly connecting 16 resistors. Researchers set the voltage for certain input nodes and read the output nodes. By adjusting the resistors on its own, the network has learned to produce the desired data for a given set of input values.

"The network has been set up to perform a lot of simple AI tasks," Dillavu says. "For example, it can distinguish between three types of iris flowers with over 95% accuracy based on four parameters: petal and sepal length and width."

For AI machine learning, as a rule, artificial neural networks are used. Such networks usually exist only in the computer's memory. A neural network consists of points or nodes, each of which can take on a value from 0 to 1, connected by edges. Each edge has its own weight depending on the values in the nodes. When training such a system, it is necessary to adjust the weight of the edges in order to obtain the desired result.

"This is a complex optimization problem that increases significantly with the size of the network and requires a lot of computing resources," notes Dillavu. "The situation is complicated by the fact that all the edges must be tuned at the same time."

To get around this problem, physicists looked for systems that could tune themselves without external calculations. The scientists built two identical networks on top of each other. In a closed network, they applied voltage and fixed the desired values on the output elements. In an open network, only the voltage across the input resistor was set.

The system adjusted the resistance across the resistors in the two networks depending on the voltage difference between identical nodes in each of them. Over several iterations, these adjustments brought all voltages across all resistors in the two networks into line. The system has learned to produce the correct output for given input values.

"This setup requires little computation," says Dillavu. "The system only needs to compare the voltage drop across the corresponding resistors in the closed and free networks using a comparator. Our work proves the fundamental possibility of a new way of machine learning that does not require large calculations."

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