Physics Insights from Neural Networks

Machine-learning models based on neural networks are behind many recent technological advances, including high-accuracy translations of text and self-driving cars. They are also increasingly used by researchers to help solve physics problems [1]. Neural networks have identified new phases of matter (see Q&A: A Condensed Matter Theorist Embraces AI) [2], detected interesting outliers in data from high-energy physics experiments [3], and found astronomical objects known as gravitational lenses in maps of the night sky (see Q&A: Paving A Path for AI in Physics Research) [4]. But, while the results obtained by neural networks proliferate, the inner workings of this tool remain elusive, and it is often unclear exactly how the network processes information in order to solve a problem. Now a team at the Swiss Federal Institute of Technology (ETH) in Zurich has demonstrated a way to find this information [5]. Their method could be used by human scientists to see a problem—and a routing to solving it—in an entirely new way.