machine learning

Currently, computing technologies are rapidly evolving and reshaping how we imagine the future. Quantum computing is taking its first toddling steps toward delivering practical results that promise unprecedented abilities. Meanwhile, artificial intelligence remains in public conversation as it’s used for everything from writing business emails to generating bespoke images or songs from text prompts to producing deep fakes.

Researchers at the University of Maryland (UMD) have trained a small hybrid quantum computer to reproduce the features in a particular set of images. The result, which was published Oct. 18, 2019 in the journal Science Advances, is among the first demonstrations of quantum hardware teaming up with conventional computing power—in this case to do generative modeling, a machine learning task in which a computer learns to mimic the structure of a given dataset and generate examples that capture the essential character of the data.

In many situations, chaos makes it nearly impossible to predict what will happen next. Nowhere is this more apparent than in weather forecasts, which are notorious for their unreliability. But the clever application of artificial intelligence can help reign in some chaotic systems, making them more predictable than ever before.

In this episode of Relatively Certain, Dina sits down with Michelle Girvan, a physics professor at the University of Maryland (UMD), to talk about how artificial intelligence can help predict chaotic behavior, as well as how combining machine learning with conventional physics models might yield even better predictions and insights into both methods.