Dr Sebastian Berns

Queen Mary University of London

Sebastian is a designer and researcher working on use-inspired fundamental research in generative machine learning for creative and artistic applications. Sebastian holds a master’s degree in artificial intelligence and has a background in visual communications. He has worked several years as an independent graphic and type designer with a specialisation in web development. His design work has been awarded national and international design prizes.

A description of Sebastian's research:

"Generative machine learning methods are trained on raw data, modelling the primary patterns that constitute typical examples. They enable the production of high-quality artefacts in very complex domains and provide useful models for generative systems, in particular in the visual arts and video games. However, modelling a training data distribution perfectly is less valuable for applications in art production and video games. In particular, our analysis of the use of generative models in visual art practices motivates the need to increase the output diversity of generative models. 

In my dissertation, I focus on diversity in generative machine learning for visual arts and video games. Our findings benefit the application of generative models in generative systems, quality diversity search, art production and video games. Rather than a ‘ground truth’ that needs to be modelled perfectly, we argue that training datasets are merely a limited snapshot of a complex world with inherent biases. To be useful for applications in visual arts and video games, generative models require higher output diversity. Relatedly, higher generative diversity benefits efforts of equity, diversity and inclusion by reducing harmful biases in generative models."