“Reservoir computing (RC) is an alternative to Deep Learning Recurrent Neural Networks (RNNs), which are critical to breakthroughs in applications such as natural language processing. Reservoir Computing has the added benefit of greatly reduced computation required in the hidden layers of the network.”
“As an alternative to Deep Learning, Reservoir Computing is transforming artificial intelligence (AI) because it takes less time to train highly accurate models.”
“Artificial Intelligence needs computational breakthroughs to continue a successful trajectory. Demonstrating scalability of large-scale Reservoir Computing models is a game changer in the field”
“In 2018, the IEEE Task Force on Reservoir Computing has been established with the purpose of promoting and stimulating the development of Reservoir Computing research under both theoretical and application perspectives.”
“University of Maryland researchers used a reservoir computing approach for machine learning to improve accuracy. The reservoir computing technique essentially “learns” the dynamics of a chaotic system. The Weather Network describes how they trained their programs to learn the chaotic nature of weather systems. Through repeated analysis and training, the program was able to push the prediction accuracy further than previous attempts.”
“Reservoir computing works by feeding an input signal into a dynamic system of objects that are random and non-linear (the reservoir), which maps the input to a higher-dimensional space before it is read out again on a linear scale.”
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Reservoir computing technology is a game changer for many industries and markets, feeding innovation and growth. For example, reservoir computing is named in the market report “Machine Vision Market Undergoing Revolution” as one of technologies bringing a profound change in the machine vision market.