As promising as recent artificial intelligence (AI) advances have been, there are still many areas where it falls short. In those areas where it does well, training is slow and requires an unsustainable amount of energy. This has been measured using video games. Humans are able to play these games well after only a few tries, while AI systems require hundreds of thousands to millions of exposures to achieve the same performance. Here, a team of Northwestern University researchers investigate how an animal's ability to complement experience with the use of something similar to imagination underlies the biological learning advantage. In one phase of the work, first-of-its-kind experiments are done with animals evading an autonomous robot threat while large numbers of brain cells are being monitored to decode how the brain is using imagination during avoidance of the robot. In another phase, state of the art methods in machine learning are used to compare to animal performance, while simultaneously building new AI technology to capture the high efficiency learning the researchers observe in biology. With the anticipated need for electricity to fuel AI significantly outpacing infrastructure to provide it, breakthroughs in closing the gap between the energy efficiency of animal intelligence and inefficiency of machine intelligence will be critical to maintain US technological leadership in the coming decades. Leading reinforcement learning algorithms tackle learning using eith