A U.S. Department of Energy initiative could refurbish existing supercomputers, turning them into high-performance artificial intelligence machines. WASHINGTON, D.C.--The U.S. Department of Energy (DOE) is planning a major initiative to use artificial intelligence (AI) to speed up scientific discoveries. At a meeting here last week, DOE officials said they will likely ask Congress for between $3 billion and $4 billion over 10 years, roughly the amount the agency is spending to build next-generation "exascale" supercomputers. "That's a good starting point," says Earl Joseph, CEO of Hyperion Research, a high-performance computing analysis firm in St. Paul that tracks AI research funding. He notes, though, that DOE's planned spending is modest compared with the feverish investment in AI by China and industry.

In short, modern AI technology aims to find patterns in big datasets. However, the goal of science is to not only find truths but to articulate the supporting reasons for believing them to be truths. For example, suppose we want to know how many exoplanets (planets that orbit stars other than our sun) might have life. Simple pattern-finding is, at best, a small step in this process. Basic correlations in data don't lead directly to knowledge about causation and certainly don't tell us why they exist or how to understand them.

In short, modern AI technology aims to find patterns in big datasets. However, the goal of science is to not only find truths but to articulate the supporting reasons for believing them to be truths. For example, suppose we want to know how many exoplanets (planets that orbit stars other than our sun) might have life. Simple pattern-finding is, at best, a small step in this process. Basic correlations in data don't lead directly to knowledge about causation and certainly don't tell us why they exist or how to understand them.