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Deep learning has seen a huge rise in popularity over the last five years in both enterprise and scientific applications. While the first algorithms were created almost 20 years ago with the development of artificial neural networks in 2000, the technology has come of age due to the massive increases in compute power, development of GPU technologies, and the availability of data to train these systems.
Today the use of this technology is widespread across many scientific disciplines, from earthquake prediction, high-energy particle physics and weather and climate modeling, precision medicine and even the development of clean fusion energy. With so many possible applications, it can be difficult for scientists to figure out if artificial intelligence (AI) or deep learning (DL) can fit into workflow. While some applications, such as speech or image recognition are well documented, other applications are just now coming to light, such as the use of language processing DL frameworks in deciphering protein folding...
Revolution in a neural network
That is not the only example of a huge speedup but there are even more revolutionary effects that come from the implementation of AI.
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Source: insideHPC
