All art is a form of sampling and remixing; it doesn't start from zero but builds on what has come before.

I think the creative applications of machine learning are fascinating and present opportunities for artists to explore new forms of expression.

I have models that continuously process different bodies of text and music to see what interesting outputs emerge, sometimes resulting in messy garbles and sometimes in intriguing sounds.

To study creativity, researchers should combine lab experiments with real-world observations of artists to find common ground between the two approaches.

Models will get better at understanding and creating images, audio, and especially video, which will dramatically enhance how we interact with data.

Generative models have evolved significantly, moving from basic attempts at generation to real-time applications that can create impressive outputs, such as artistic style transfer.

I believe that having our models out in the wild has provided valuable market feedback, which has shaped our product development significantly.

Artistry begins with exploring how far you can push your interpretation of a piece, rather than staying within a tight sphere of traditional interpretations.

As AI models become more capable, they can handle nuanced issues better, allowing for more artistic freedom while maintaining responsibility.