Researchers from the Massachusetts Institute of Technology and the University of California Irvine have developed a new activity-recognition algorithm that has several advantages over its predecessors. One advantage is that the algorithm’s execution time scales linearly with the size of the video file it’s searching. Another advantage is that the algorithm is able to make good estimates about partially completed actions, so it can handle streaming video. Partway through an action, it will issue a probability that the action is of the type that it’s looking for. It may revise that probability as the video continues, but it doesn’t have to wait until the action is complete to assess it. Finally, the amount of memory the algorithm requires is fixed, regardless of how many frames of video it’s already reviewed. That means that, unlike many of its predecessors, it can handle video streams of any length or files of any size. Enabling these advantages is a type of algorithm used in natural language processing.