Deep learning drives new science


depends on the use case but you can think of two cases where AI is useful. The first case is to solve problems that are hard to solve in a rule-based way, which is a similar domain as you may have outside science for speech recognition or image recognition. ‘Those are an example of problems that

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 modelling, 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. Christoph Angerer, Nvidia’s AI developer

technologies manager for EMEA, Russia and India, breaks down the use of DL into two basic use cases. ‘In general, it

4 Scientific Computing World October/November 2019

are hard to solve with a classic algorithm – people have tried for a long time to sit down and write feature extractors and how to identify faces and so on. That was all manually crafted and that has been changed by using more compute and the availability of neural networks and more data,’ explained Angerer. ‘These are cases that are hard to write in a manual way, and that also exists in science, of course. You could think of the image as satellite data and the analysis could be looking at how to find hurricanes, or to find potential areas of drought,’ Angerer added. ‘You may want to sift through CERN data

to find collisions that may be of particular interest. All those cases where a classical algorithm may be difficult to design, and AI can come to the rescue and help you come up with a solution,’ stated Angerer. In these cases, the objective is to

complete science that could not be done in another way. Here DL is opening up new possibilities for science that were just too complicated to be solved through classical computing techniques. The other example given by Angerer

covers topics which were previously possible, but this came with massive computational barriers or required teams of people to develop algorithms which are orders of magnitude slower than what can be achieved through the use of DL. ‘There is another case in scientific

applications where you use AI as a surrogate model for existing solutions. For example, if you go into weather and climate simulation, you may know the kind of physics you want to model, but often in those cases you do not model the individual air molecules, because that would be too fine grain. Instead, you explore a square kilometre for a weather forecast or in EMCWF-style worldwide forecasts, you may have a grid of 100 x 100km,’ stated Angerer. What you do there transitionally is you

come up with algorithms from physicists that are approximations, they are parametrised models. They come up with

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