Despite decades of progress in semiconductor scaling, computer architecture, and artificial intelligence, our computing technology today still lags biological brains in many respects. While deep artificial neural networks have provided breakthroughs in AI, these gains come with heavy compute and data demands relative to their biological counterparts. Neuromorphic computing aims to narrow this gap by drawing inspiration from the form and function of biological neural circuits. The past several years have seen significant progress in neuromorphic computing research, with chips like Intel’s Loihi demonstrating, for the first time, compelling quantitative gains over a range of workloads—from sensory perception to data efficient learning to combinatorial optimization. This talk surveys recent developments in this endeavor to re-think computing from transistors to software informed by biological principles. It previews a new class of chips that can autonomously process complex data streams, adapt, plan, behave, and learn in real time at extremely low power levels.