Music and Text Generation "in the style of"

Style modeling is becoming an area of research in itself in Artificial Intelligence, due to the increasing needs for content generation application. I will present some results obtained in the ERC funded Flow-Machines projects, in which we attempt to capture, model, and exploit "style" at various levels of the musical and text creation process. I will show some results in modeling style with Markov chains, and constraining the generation to satisfy various structural properties, necessary to make sequences look natural. The techniques developed exploit interesting connexions between statistical modeling and combinatorial optimization. Some of these results, sometimes striking, also raise new questions about the nature of "interesting" sequences, and the mystery of creativity.