Training artificial intelligence to enforce even seemingly straightforward rules—like balls and strikes in Major League Baseball (MLB)—is a messy, dynamic process that takes time and careful evaluation of the technology in the wild, according to new Cornell research.
Seven years of testing exposed hidden trade-offs in MLB’s AI strike zone
