I built Data Sci­ence: Arti­fi­cial Intel­li­gence, now required of every full-time Hop­kins MBA, on a sim­ple con­vic­tion: future lead­ers should meet AI at its source. We read the foun­da­tion­al papers, train real mod­els, and fol­low the line from a sin­gle neu­ron to the trans­former behind Chat­G­PT. The goal is not flu­en­cy in buzz­words but the judg­ment to tell gen­uine capa­bil­i­ty from false promise.

AI belongs to every­one, not only to com­put­er sci­en­tists. Some of my strongest teams come from his­to­ry, polit­i­cal sci­ence, and lin­guis­tics. In the AI Lab, those teams take on a real health­care prob­lem; one pro­to­typed an at-home, AI-guid­ed cer­vi­cal can­cer screen­ing device that went on to draw nation­al atten­tion. That is the whole idea: the­o­ry you can build with.