DOE spends $16m for AI that can make its own scientific discoveries

Brittany A. Roston - Sep 9, 2021, 6:31pm CDT
DOE spends $16m for AI that can make its own scientific discoveries

Technology is getting more complex every year, opening the door to new scientific innovations and discoveries, but with a big downside: there’s more data than ever, and transforming it into something useful gets harder with the more data available. The Department of Energy wants to help address this by funding machine learning and AI research that may ‘automate scientific discovery.’

The funds come from the US Department of Energy (DOE), which says that it will split the money between a total of five research projects developing AI and machine learning algorithms tailored to scientific endeavors. These algorithms will, the goal is, be able to parse through huge amounts of data from various sources and use it to offer insights or even make new scientific discoveries.

The raw data can come from a variety of sources: observational studies, scientific experiments, and even simulations. The AI and ML systems developed through these projects may be able to use the data for, among other things, predicting when an extreme weather event will happen, offer dynamic insights into power grids, form conclusions about space and physics, and more.

The Department of Energy has its own interest in these algorithms; the agency has scientific user facilities that generate tons of data that need to be analyzed. It isn’t reasonable for human scientists to sort through such vast quantities of information in search of breakthroughs, insights, and discoveries.

In a statement about its new funding initiative, the DOE Office of Science’s Associate Director for Advanced Scientific Computing Research Barbara Helland said:

Disruptive technology changes are occurring across science applications, algorithms, architectures, and high-performance computing ecosystems. These projects explore potentially high-impact approaches in AI and machine learning to assist and automate scientific discovery and data analysis for increasingly complex problems.


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