IN 1992, THE poet Anne Carson published a little book called Short Talks. It's a series of micro-essays, ranging in length from a sentence to a paragraph, on seemingly disconnected subjects--orchids, rain, the mythic Andean vicuña. Her "Short Talk on the Sensation of Airplane Takeoff" is what it sounds like. Her "Short Talk on Trout" is mostly about the types of trout that appear in haiku. In what passes for the book's introduction, Carson writes, with dry Canadian relatability, "I will do anything to avoid boredom. It is the task of a lifetime."

In 1992, the poet Anne Carson published a little book called Short Talks. It's a series of micro-essays, ranging in length from a sentence to a paragraph, on seemingly disconnected subjects--orchids, rain, the mythic Andean vicuña. Her "Short Talk on the Sensation of Airplane Takeoff" is what it sounds like. Her "Short Talk on Trout" is mostly about the types of trout that appear in haiku. In what passes for the book's introduction, Carson writes, with dry Canadian relatability, "I will do anything to avoid boredom. It is the task of a lifetime."

Machine co-creativity continues to grow and attract a wider audience to machine learning. Generative models, for example, have enabled new types of media creation across language, images, and music including recent advances such as IMAGEN, Flamingo, and DALL·E2. Machine learning models achieving state-of-the-art in traditional media creation tasks (e.g., image, audio, or video synthesis) that are also being used by the artist community will be showcased. Researchers building the next generation of machine learning models for media creation will be challenged in understanding the needs of artists. Ethical implications, ranging from the use of biased datasets, replicating artistic work, and potential eroding trust in media content.