 Today's AI neural networks rely on electrical components called graphical processing units. In these systems, the entire network and all of its interactions must be sequentially loaded from the external memory, which consumes both time and energy. NSF-supported researchers at the University of Michigan have developed the first memristor electrical components that can be tuned to have controllable conductivity, which was shown to reduce AI's energy needs by a factor of 90 compared to traditional GPUs.