Machine learning came along at just the right time. The world is now awash in more data than ever before, and computer algorithms that can learn and improve as they perform data analysis promise to help scientists handle that information overload.

Yet researchers who think that machine learning by itself can help solve complex problems in science, engineering, and medicine should strive for a more balanced approach, says Roman Grigoriev, part of a School of Physics team with new research suggesting a hybrid approach for conducting science that blends new-era technologies, old-school experimentation, and theoretical analysis. The research suggests faster solutions to complex, data-intensive riddles involving such issues as cancer, earthquakes, weather forecasts, and climate change.

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