Materials that learn from examples

Learning is usually associated with neural networks. But non-neural systems can also accumulate incremental changes over time and thus respond better to future environments. We show how seemingly `dumb' physical systems like DNA crystals and elastic materials can learn to recognize complex patterns in chemical or mechanical stimuli, much like a neural network. We outline the potential and limits of such `mechanical intelligence' due to physically realizable learning dynamics.