This CSSI project is a multi-university collaboration between Tennessee Technological University, the University of Tennessee, Knoxville, Stony Brook University, and the Illinois Institute of Technology. This project improves how massively parallel computers run large-scale artificial intelligence (AI) applications by enhancing the Message Passing Interface (MPI), a widely used standard for coordinating work across many computers in parallel programs. Currently, the enabling data-transfer software used in AI, for communication between computers enhanced by Graphical Processing Units (GPUs), are often proprietary and/or limited in scope; they cannot be expanded or enhanced by an open community. That situation restricts innovation, making it harder for scientists to collaborate and enhance their science output on limited computer resources, while also creating dependency on a few vendors. By contrast, this project builds on and advances Open MPI, a major open-source implementation of MPI with a long history of broad impact, to make it more efficient, flexible, and better suited for modern AI tasks. In addition to improving the Open MPI implementation, MPI4AI aims at standardizing extensions to MPI so all implementations and users of MPI will benefit from this project's outcome. MPI4AI introduces key improvements to Open MPI, including native support for GPU communication, enhanced collective (group) communication operations including those that are AI-algorithm specific, co