Michael Coughlin (University of Minnesota - Twin Cities) surveys the present and future of gravitational-wave astronomy, from the first detections through the dawn of multi-messenger discoveries like GW170817. He details how machine learning and low-latency inference pipelines, including Aframe, DeepClean, and AMPLFI, are transforming gravitational-wave detection and parameter estimation, while brokers like BOOM tackle the flood of alerts from surveys such as ZTF and the upcoming Rubin Observatory. The talk closes with a look at near-term prospects for the field, from next-generation detectors to citizen-science efforts like GravitySpy and Kilonova Catcher.