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.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.Computer Sciences[#item_full_content]