Machine-learning algorithms are often referred to as a “black box.” Once data are put into an algorithm, it’s not always known exactly how the algorithm arrives at its prediction. This can be particularly frustrating when things go wrong. A new mechanical...
For all that neural networks can accomplish, we still don’t really understand how they operate. Sure, we can program them to learn, but making sense of a machine’s decision-making process remains much like a fancy puzzle with a dizzying, complex pattern where...
Neural networks (NNs) are increasingly being used to predict new materials, the rate and yield of chemical reactions, and drug-target interactions, among others. For these applications, they are orders of magnitude faster than traditional methods such as...
