Reservoir computing, a machine learning algorithm that mimics the human brain's workings, is revolutionizing how scientists tackle the most complex data processing challenges, and now researchers have discovered a new technique that can make it up to a million times faster on specific tasks while using far fewer computing resources and requiring far less data input.
The researchers were able to solve a complex computing problem on a desktop computer in less than a second using the next-generation technique, and these overly complex problems, such as forecasting the evolution of dynamic systems like weather that change over time, are exactly why reservoir computing was developed in the early 2000s. The "butterfly effect" is a well-known illustration of how difficult these systems may be to predict. The notion, which is intimately associated with mathematician and meteorologist Edward Lorenz's work, effectively illustrates how a butterfly fluttering its wings might influence weather weeks later. Reservoir computing is well-suited for learning such dynamic systems and can provide accurate forecasts of how they will behave in the future; however, the larger and more complex the system, the more computing resources, an artificial neuron network, and time are required to obtain accurate forecasts. Researchers, on the other hand, only know how reservoir computing works, not what happens inside. Artificial neural networks are built on mathematics in reservoir computing, and it appears that all the system needed to run more efficiently was a simplification. A group of researchers led by Daniel Gauthier, the study's lead author and a professor of physics at The Ohio State University, was able to accomplish this, drastically decreasing the demand for computational resources and saving significant time. According to a study published in the journal Nature Communications, when the notion was put to the test on a forecasting job, it was observed that the next-generation reservoir computing technique was clearly superior to others.
The new method was found to be 33 to 163 times faster, depending on the data. The new model was 1 million times faster when the work aim was adjusted to promote accuracy. The fact that next-generation reservoir computing requires less warmup and training than prior generations allowed for this boost in performance. "There is essentially no warming time required for our next-generation reservoir computing," Gauthier said in a news announcement. "To warm it up, scientists must currently input 1,000 to 10,000 data points or more. And that's all the info that's gone missing, data that isn't required for the job. Only one, two, or three data points are required."
Furthermore, the new technique achieved the same accuracy with only 28 neurons, rather than the 4,000 required by the current-generation model. "What's fascinating about this next generation of reservoir computing is that it takes something that was previously really good and makes it much more efficient," Gauthier said. And it appears that this is just the start. In the future, the researchers want to put the super-efficient neural network to the test against increasingly tough tasks, with the goal of expanding the study to include even more complicated computing challenges like fluid dynamics predictions.
"It's a tremendously difficult challenge to tackle," Gauthier added. "We want to investigate if we can use our simplified reservoir computing model to speed up the process of solving that challenge."
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