Active Colloidal Particles: The New Frontier of Physical Reservoir Computing

The Significance of Artificial Intelligence and Neural Networks in Predicting Time Series

Artificial intelligence using neural networks performs digital calculations with the help of microelectronic chips. Physicists at Leipzig University have created a type of neural network that works not with electricity but with active colloidal particles.

This type of neural network belongs to the field of physical reservoir computing, which uses the dynamics of physical processes to make calculations. This innovation has been developed with the support of ScaDS.AI, one of Germany’s new AI centres funded since 2019. The research centre with sites in Leipzig and Dresden has been supported by the German government’s AI Strategy and the Federal Ministry of Education and Research and the Free State of Saxony.

In their experiments, the physicists developed tiny units made of plastic and gold nanoparticles, in which one particle rotates around another, driven by a laser. Each of these units can process information, and many units make up the reservoir for the neural network.

The researchers were particularly interested in noise and found that using past states of the reservoir can improve computer performance, allowing smaller reservoirs to be used for certain computations under noisy conditions. This method not only contributes to the field of information processing with active matter but also optimizes reservoir computation by reducing noise.

Overall, this breakthrough in technology has the potential to revolutionize the way artificial intelligence performs calculations, especially in noisy conditions, and has significant implications for the prediction of time series.

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