Intel of California recently announced development of a self-learning, energy-efficient neuromorphic chip called Loihi. The chip mimics functions of the human brain. It has been in development for the last six years. Loihi makes use of 130,000 “neurons” and 130 million “synapses”, which allows it to learn in real time from feedback from the environment. Neuromorphic-based models attempt to mimic how neurons communicate and learn. They use brain pulses and synapses for learning. Therefore, the chip learns over time without needing to be trained in the traditional way. Loihi currently exists as a research test chip that offers flexible on-chip learning and combines training and inference. Intel researchers have demonstrated learning rates that are a 1 million times improvement compared with other typical spiking neural nets. The chip is up to 1,000 times more energy efficient than general-purpose computing required for typical training systems. Early next year, Intel has plans to share Loihi with leading university and research institutions with a focus on advancing artificial intelligence. The goal is to develop and test several algorithms with high efficiency for problems including path planning, constraint satisfaction, sparse coding, dictionary learning, and dynamic pattern learning and adaptation.